Crowd-Based Scores for Locations from Measurements of Affective Response

[0001] This Application is a Continuation-In-Part of U.S. application Ser. No. 14/833,035, filed Aug. 21, 2015, which claims the benefit of U.S. Provisional Patent Application Ser. No. 62/040,345, filed Aug. 21, 2014, and U.S. Provisional Patent Application Ser. No. 62/040,358, filed Aug. 21, 2014. This Application is also a Continuation-In-Part of U.S. application Ser. No. 15/010,412, filed Jan. 29, 2016, which claims the benefit of U.S. Provisional Patent Application Ser. No. 62/109,456, filed Jan. 29, 2015, and U.S. Provisional Patent Application Ser. No. 62/185,304, filed Jun. 26, 2015.

[0002] This Application is related to U.S. application Ser. No. 15/051,892, filed concurrently herewith on Feb. 24, 2016, hereby incorporated herein by reference in its entirety.

[0003] Wearable and mobile computing devices are popular and widely available these days. These devices now include a wide array of sensors that can be used to measure the environment as well as the people who use the devices. This enables collection of large amounts of data about the users, which may include measurements of their affective response (e.g., physiological signals and behavioral cues). These measurements may be taken throughout the day while having many they are at various locations and having various experiences. Measurements of affective response of a person can be interpreted to determine how the person feels (i.e., determine the person's emotional response). While logging this data is becoming ever more prevalent (e.g., through life-logging), leveraging this data for useful applications is not widely done.

[0004] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that enable computation of various types of crowd-based results regarding experiences users may have in their day-to-day life. Some examples of experiences involve visiting various locations (e.g., in the physical world or virtual locations). Some of the types of results that may be generated by embodiments described herein include scores for locations, rankings of locations, alerts based on scores for the locations, and various functions that describe how affective response to being in a location is expected to change with respect to various parameters (e.g., the duration of the stay, the period in which one visits the location, a condition of the environment at the location, and more).

[0005] Various embodiments described herein involve experiences in which a user is at a location. In one example, a location is a travel destination (e.g., New York). Other examples of locations that are travel destinations may include one or more of the following: continents, countries, counties, cities, resorts, neighborhoods, hotels, nature reserves, and parks. In another example, a location may be an entertainment establishment (e.g., a club, a pub, a movie theater, etc.) In yet another example, a location may be a place of business (e.g., a store, a shopping mall, a restaurant, etc.) A location may also refer to a region of a larger location, such as a certain seat in a vehicle or a certain room in a hotel. In still another example, a location may be a virtual environment such as a virtual world and/or a virtual store (e.g., an online retailer).

[0006] Some aspects of this disclosure involve obtaining measurements of affective response of users and utilizing the measurements to generate crowd-based results. In some embodiments, the measurements of affective response of the users are collected with one or more sensors coupled to the users. A sensor may be coupled to the body of a user in various ways. For example, a sensor may be a device that is implanted in the user's body, attached to the user's body, embedded in an item carried and/or worn by the user (e.g., a sensor may be embedded in a smartphone, smartwatch, and/or clothing), and/or remote from the user (e.g., a camera taking images of the user). In one example, a sensor coupled to a user may be used to obtain a value that is indicative of a physiological signal of the user (e.g., a heart rate, skin temperature, or brainwave activity). In another example, a sensor coupled to a user may be used to obtain a value indicative of a behavioral cue of the user (e.g., a facial expression, body language, or the level of stress in the user's voice). In some embodiments, measurements of affective response of a user may be used to determine how the user feels while they are at a certain location. In one example, the measurements may be indicative of the extent the users feel one or more of the following emotions: pain, anxiety, annoyance, stress, aggression, aggravation, fear, sadness, drowsiness, apathy, anger, happiness, contentment, calmness, attentiveness, affection, and excitement.

[0007] Various embodiments described herein utilize systems whose architecture includes a plurality of sensors and a plurality of user interfaces. This architecture supports various forms of crowd-based recommendation systems in which users may receive information, such as scores for locations, suggestions for locations and/or alerts regarding locations, which are determined based on measurements of affective response of users who are at the locations. In some embodiments, being crowd-based means that the measurements of affective response are taken from a plurality of users, such as at least three, ten, one hundred, or more users. In such embodiments, it is possible that the recipients of information generated from the measurements may not be the same people from whom the measurements were taken.

[0166] A measurement of affective response of a user is obtained by measuring a physiological signal of the user and/or a behavioral cue of the user. A measurement of affective response may include one or more raw values and/or processed values (e.g., resulting from filtration, calibration, and/or feature extraction). Measuring affective response may be done utilizing various existing, and/or yet to be invented, measurement devices such as sensors. Optionally, any device that takes a measurement of a physiological signal of a user and/or of a behavioral cue of a user may be considered a sensor. A sensor may be coupled to the body of a user in various ways. For example, a sensor may be a device that is implanted in the user's body, attached to the user's body, embedded in an item carried and/or worn by the user (e.g., a sensor may be embedded in a smartphone, smartwatch, and/or clothing), and/or remote from the user (e.g., a camera taking images of the user). Additional information regarding sensors may be found in this disclosure at least in section 1—Sensors.

[0167] Herein, “affect” and “affective response” refer to physiological and/or behavioral manifestation of an entity's emotional state. The manifestation of an entity's emotional state may be referred to herein as an “emotional response”, and may be used interchangeably with the term “affective response”. Affective response typically refers to values obtained from measurements and/or observations of an entity, while emotional states are typically predicted from models and/or reported by the entity feeling the emotions. For example, according to how terms are typically used herein, one might say that a person's emotional state may be determined based on measurements of the person's affective response. In addition, the terms “state” and “response”, when used in phrases such as “emotional state” or “emotional response”, may be used herein interchangeably. However, in the way the terms are typically used, the term “state” is used to designate a condition which a user is in, and the term “response” is used to describe an expression of the user due to the condition the user is in and/or due to a change in the condition the user is in.

[0168] It is to be noted that as used herein in this disclosure, a “measurement of affective response” may comprise one or more values describing a physiological signal and/or behavioral cue of a user which were obtained utilizing a sensor. Optionally, this data may be also referred to as a “raw” measurement of affective response. Thus, for example, a measurement of affective response may be represented by any type of value returned by a sensor, such as a heart rate, a brainwave pattern, an image of a facial expression, etc.

[0169] Additionally, as used herein, a “measurement of affective response” may refer to a product of processing of the one or more values describing a physiological signal and/or behavioral cue of a user (i.e., a product of the processing of the raw measurements data). The processing of the one or more values may involve one or more of the following operations: normalization, filtering, feature extraction, image processing, compression, encryption, and/or any other techniques described further in the disclosure and/or that are known in the art and may be applied to measurement data. Optionally, a measurement of affective response may be a value that describes an extent and/or quality of an affective response (e.g., a value indicating positive or negative affective response such as a level of happiness on a scale of 1 to 10, and/or any other value that may be derived from processing of the one or more values).

[0170] It is to be noted that since both raw data and processed data may be considered measurements of affective response, it is possible to derive a measurement of affective response (e.g., a result of processing raw measurement data) from another measurement of affective response (e.g., a raw value obtained from a sensor). Similarly, in some embodiments, a measurement of affective response may be derived from multiple measurements of affective response. For example, the measurement may be a result of processing of the multiple measurements.

[0171] In some embodiments, a measurement of affective response may be referred to as an “affective value” which, as used in this disclosure, is a value generated utilizing a module, function, estimator, and/or predictor based on an input comprising the one or more values describing a physiological signal and/or behavioral cue of a user, which are in either a raw or processed form, as described above. As such, in some embodiments, an affective value may be a value representing one or more measurements of affective response. Optionally, an affective value represents multiple measurements of affective response of a user taken over a period of time. An affective value may represent how the user felt while utilizing a product (e.g., based on multiple measurements taken over a period of an hour while using the product), or how the user felt during a vacation (e.g., the affective value is based on multiple measurements of affective response of the user taken over a week-long period during which the user was on the vacation).

[0172] In some embodiments, measurements of affective response of a user are primarily unsolicited, i.e., the user is not explicitly requested to initiate and/or participate in the process of measuring. Thus, measurements of affective response of a user may be considered passive in the sense that it is possible that the user will not be notified when the measurements are taken, and/or the user may not be aware that measurements are being taken. Additional discussion regarding measurements of affective response and affective values may be found in this disclosure at least in section 2—Measurements of Affective Response.

[0173] Embodiments described herein may involve computing values based on measurements of affective response of users, which are referred to as “crowd-based” results. One example of a crowd-based result is a score for an experience, which is a representative value from a plurality of measurements of affective response of one or more users who had the experience. Such a value may be referred to herein as “a score for an experience”, an “experience score”, or simply a “score” for short.

[0174] In some embodiments described herein, the experience may be related to one or more locations. For example, the experience involves being at a certain location and the measurements are taken while the users are at the certain location (or shortly after that). For example, a score indicative of the quality of a stay at a hotel may be computed based on measurements of affective response of guests taken while they stayed at the hotel.

[0175] When a score is computed for a certain user or a certain group of users, such that different users or different groups of users may receive scores with different values, the score may be referred to as a “personalized score”, “personal score”, and the like. In a similar fashion, in some embodiments, experiences and/or locations corresponding to the experiences, may be ranked and/or compared based on a plurality of measurements of affective response of users who had the experiences. A form of comparison of experiences, such as an ordering of experiences (or a partial ordering of the experiences), may be referred to herein as a “ranking” of the experiences. Optionally, a ranking is computed for a certain user or a certain group of users, such that different users or different groups of users may receive different rankings, the ranking be referred to as a “personalized ranking”, “personal ranking”, and the like.

[0176] Additionally, a score and/or ranking computed based on measurements of affective response that involve a certain type of experience may be referred to based on the type of experience. For example, a score for a location may be referred to as a “location score”, a ranking of hotels may be referred to as a “hotel ranking”, etc. Also when the score, ranking, and/or function parameters that are computed based on measurements refer to a certain type of affective response, the score, ranking, and/or function parameter may be referred to according to the type of affective response. For example, a score may be referred to as a “satisfaction score” or “comfort score”. In another example, a function that describes satisfaction from a vacation may be referred to as “a satisfaction function” or “satisfaction curve”.

[0177] Herein, when it is stated that a score, ranking, and/or function parameters are computed based on measurements of affective response, it means that the score, ranking, and/or function parameters have their value set based on the measurements and possibly other measurements of affective response and/or other types of data. For example, a score computed based on a measurement of affective response may also be computed based on other data that is used to set the value of the score (e.g., a manual rating, data derived from semantic analysis of a communication, and/or a demographic statistic of a user). Additionally, computing the score may be based on a value computed from a previous measurement of the user (e.g., a baseline affective response value described further below).

[0178] Some of the experiences described in this disclosure involve something that happens to a user and/or that the user does, which may affect the physiological and/or emotional state of the user in a manner that may be detected by measuring the affective response of the user. Optionally, experiences may belong to different groups and/or types such as discussed in further detail at least in sections 3—Experiences.

[0179] In some embodiments, an experience is something a user actively chooses and is aware of; for example, the user chooses to take a vacation. While in other embodiments, an experience may be something that happens to the user, of which the user may not be aware. A user may have the same experience multiple times during different periods. For example, the experience of being at school may happen to certain users every weekday except for holidays. Each time a user has an experience, this may be considered an “event”. Each event has a corresponding experience and a corresponding user (who had the corresponding experience). Additionally, an event may be referred to as being an “instantiation” of an experience and the time during which an instantiation of an event takes place may be referred to herein as the “instantiation period” of the event. That is, the instantiation period of an event is the period of time during which the user corresponding to the event had the experience corresponding to the event. Optionally, an event may have a corresponding measurement of affective response, which is a measurement of the corresponding user to having the corresponding experience (during the instantiation of the event or shortly after it). For example, a measurement of affective response of a user that corresponds to an experience of being at a location may be taken while the user is at the location and/or shortly after that time. Further details regarding experiences and events may be found at least in sections 4—Events and 5—Identifying Events.

[0180] Various embodiments described herein involve experiences in which a user is at a location. Herein, a discussion regarding experiences in general, e.g., scoring experiences, ranking experiences, and/or taking measurements of affective to experiences, is also applicable to certain types of experiences, such as experiences involving locations.

[0181] In some embodiments, a location may refer to a place in the physical world. A location in the physical world may occupy various areas in, and/or volumes of, the physical world. Following are examples of location, which may be considered locations for crowd-based results are computed in embodiments described herein. The examples below are not intended to be limiting, other types of places that are not mentioned below may be considered a “location” in this disclosure. Additionally, the examples are not meant to be a classification of locations, the same physical location may correspond to multiple types of locations mentioned below.

[0182] In one example, a location is a travel destination (e.g., New York). Other examples of locations that are travel destinations may include one or more of the following: continents, countries, counties, cities, resorts, neighborhoods, hotels, nature reserves, and parks.

[0183] In another example, a location may be an entertainment establishment that is one or more of the following: a club, a pub, a movie theater, a theater, a casino, a stadium, and a certain concert venue.

[0184] In yet another example, a location may be a place of business that is one or more of the following: a store, a booth, a shopping mall, a shopping center, a market, a supermarket, a beauty salon, a spa, a hospital, a clinic, a laundromat, a bank, a courier service office, and a restaurant.

[0185] In still another example, a location may be a certain region of a larger location. For example, the location may be one or more of the following: a certain wing of a hotel, a certain floor of a hotel, a certain room in a hotel, a certain room in a resort, a certain cabin in a ship, a certain seat in a vehicle, a certain class of seats in a vehicle, a certain type of seating location in a vehicle.

[0186] FIG. 1 illustrates some examples of various types of locations, which include among them locations in the physical world. For example, the figure illustrates location 503a which is a certain business place (e.g., a hotel), location 503b which is a certain city, and location 503c which is a certain seat in an aircraft.

[0187] In other embodiments, a location may refer to a virtual environment such as a virtual world and/or a virtual store (e.g., an online retailer), with at least one instantiation of the virtual environment stored in a memory of a computer. Optionally, a user is considered to be in the virtual environment by virtue of having a value stored in the memory indicating a presence of a representation of the user in the virtual environment. Optionally, different locations in virtual environment correspond to different logical spaces in the virtual environment. For example, different rooms in an inn in a virtual world may be considered different locations. In another example, different continents in a virtual world may be considered different locations. In yet another example, different sections of a virtual store and/or different stores in a virtual mall may be considered different locations.

[0188] In one embodiment, a user interacts with a graphical user interface in order to participate in activities within a virtual environment. In some examples, a user may be represented in the virtual environment as an avatar. Optionally, the avatar of the user may represent the presence of the user at a certain location in the virtual environment. Furthermore, by seeing where the avatar is, other users may determine what location the user is in, in the virtual environment. FIG. 1 illustrates location 503d which is a certain region in a virtual world (e.g., viewed by the user in virtual reality).

[0189] Various embodiments described herein utilize systems whose architecture includes a plurality of sensors and a plurality of user interfaces. This architecture supports various forms of crowd-based recommendation systems in which users may receive information, such as suggestions and/or alerts, which are determined based on measurements of affective response to experiences involving locations. In some embodiments, being crowd-based means that the measurements of affective response are taken from a plurality of users, such as at least three, ten, one hundred, or more users. In such embodiments, it is possible that the recipients of information generated from the measurements may not be the same users from whom the measurements were taken.

[0190] FIG. 2a illustrates a system architecture that includes sensors and user interfaces, as described above. The architecture illustrates systems in which measurements 501 of affective response of a crowd 500 of users at one or more locations may be utilized to generate crowd-based result 502.

[0191] A plurality of sensors may be used, in various embodiments described herein, to take the measurements 501 of affective response of users belonging to the crowd 500. Each sensor of the plurality of sensors may be a sensor that captures a physiological signal and/or a behavioral cue of a user. Additional details about the sensors may be found in this disclosure at least in section 1—Sensors.

[0192] In one embodiment, the measurements 501 of affective response are transmitted via a network 112. Optionally, the measurements 501 are sent to one or more servers that host modules belonging to one or more of the systems described in various embodiments in this disclosure (e.g., systems that compute scores for experiences, rank experiences, generate alerts for experiences, and/or learn parameters of functions that describe affective response).

[0193] Depending on the embodiment being considered, the crowd-based result 502 may be one or more of various types of values that may be computed by systems described in this disclosure based on measurements of affective response. For example, the crowd-based result 502 may refer to a score for a location (e.g., location score 507), a notification about affective response to location (e.g., various alerts described herein), a recommendations regarding a location, and/or a rankings of locations (e.g., ranking 580). Additionally or alternatively, the crowd-based result 502 may include, and/or be derived from, parameters of various functions learned from measurements (e.g., function parameters and/or aftereffect scores).

[0194] Additionally, it is to be noted that all location scores and various types of location scores mentioned in this disclosure (e.g., hotel scores, seat scores, restaurant scores, etc.) are types of scores for experiences. Thus various properties of scores for experiences described in this disclosure (e.g., in sections 3—Experiences and 10—Scoring) are applicable to the various types of location scores discussed herein.

[0195] A more comprehensive discussion of the architecture in FIG. 2a may be found in this disclosure at least in section 8—Crowd-Based Applications, e.g., in the discussion regarding FIG. 60. The discussion regarding FIG. 60 involves measurements 110 of affective response of a crowd 100 of users and generation of crowd-based results 115. The measurements 110 and results 115 involve experiences in general, which comprise location-related experiences. Thus, the teachings in this disclosure regarding measurements 110 and/or results 115 are applicable to measurements related to specific types of experiences (e.g., measurements 501) and crowd-based results (e.g., the crowd-based result 502).

[0196] FIG. 2b illustrates steps involved in one embodiment of a method for reporting a crowd-based result such as the crowd-based result 502. In one example, the crowd-based result that is reported is the location score 507. The steps illustrated in FIG. 2b may be used, in some embodiments, by systems modeled according to FIG. 2a. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations of the method.

[0197] In one embodiment, the method for reporting the location score for the certain location, which is computed based on measurements of affective response, includes at least the following steps:

[0198] In step 506a, taking measurements of affective response of users with sensors. Optionally, the measurements include measurements of affective response of at least ten users, taken at most ten minutes after the users left the certain location. Optionally, the measurements are taken while the users are at the certain location.

[0199] In step 506b, receiving data describing the location score; the location score is computed based on the measurements of the at least ten users and represents an affective response of the at least ten users to being at the certain location.

[0200] And in step 506c, reporting the location score via user interfaces, e.g., as a recommendation (as described in more detail further below).

[0201] In one embodiment, the method described above may include an optional step of receiving a profile of a certain user and profiles of at least some of the at least ten users, computing similarities between the profile of the certain user and a profile of each of the at least some of the at least ten users, weighting the measurements of the at least ten users based on the similarities, and utilizing the weights for computing the score.

[0202] In one embodiment, the method described above may include an optional step of receiving baseline affective response value for at least some of the at least ten users, measurements of affective response of the at least some of the at least ten users, and normalizing the measurements of the at least some of the at least ten users with respect to the baseline affective response values.

[0203] In one embodiment, the method described above includes an optional step of computing the score for the certain experience. Optionally, the score for the certain experience is computed utilizing the scoring module 150.

[0204] It is to be noted that in a similar fashion, the method described above may be utilized, mutatis mutandis, to report other types of crowd-based results described in this disclosure, which may be reported via user interfaces, and which are based on measurements of affective response of user who were at locations. For example, similar steps to the method described above may be utilized to report the ranking 580.

[0205] FIG. 3a illustrates a system configured to compute scores for experiences involving locations, which may also be referred to herein as “location scores”. The system that computes a location score includes at least a collection module (e.g., collection module 120) and a scoring module (e.g., scoring module 150). Optionally, such a system may also include additional modules such as the personalization module 130, score-significance module 165, location verifier module 505, map-displaying module 240, and/or recommender module 178. The illustrated system includes modules that may optionally be found in other embodiments described in this disclosure. This system, like other systems described in this disclosure, includes at least a memory 402 and a processor 401. The memory 402 stores computer executable modules described below, and the processor 401 executes the computer executable modules stored in the memory 402.

[0206] In some embodiments, the collection module 120 is configured to receive the measurements 501. Optionally, the measurement 501 comprise measurements of at least ten users who were at a certain location.

[0207] In one embodiment, the measurements of the at least ten users are taken in temporal proximity to when the at least ten users were in the certain location and represent an affective response of those users to being in the certain location. Herein “temporal proximity” means nearness in time. For example, at least some of the measurements 501 are taken while users are in the certain location and/or shortly after being there. Additional discussion of what constitutes “temporal proximity” may be found at least in section 2—Measurements of Affective Response.

[0208] It is to be noted that references to the “certain location” with respect to FIG. 3a and/or the modules described therein may refer to any type of location described in this disclosure (in the physical world and/or a virtual location). Some examples of locations are illustrated in FIG. 1.

[0209] In some embodiments, each measurement from among the measurements 501 is a measurement of affective response of a user, taken utilizing a sensor coupled to the user, and comprises at least one of the following: a value representing a physiological signal of the user and a value representing a behavioral cue of the user. Optionally, a measurement of affective response, which corresponds to an event involving being at the certain location and/or having an experience at the certain location, is based on values acquired by measuring the user corresponding to the event with the sensor during at least three different non-overlapping periods while the user was at the location corresponding to the event.

[0210] In some embodiments, the system may optionally include location verifier module 505, which is configured to determine when the user is in the location. Optionally, a measurement of affective response of a user, from among the at least ten users, is based on values obtained during periods for which the location verifier module 505 indicated that the user was at the certain location. Optionally, the location verifier module 505 may receive indications regarding the location of the user from devices carried by the user (e.g., a wearable electronic device), from a software agent operating on behalf of the user, and/or from a third party (e.g., a party which monitors the user).

[0211] The collection module 120 is also configured, in some embodiments, to forward at least some of the measurements 501 to the scoring module 150. Optionally, at least some of the measurements 501 undergo processing before they are received by the scoring module 150. Optionally, at least some of the processing is performed via programs that may be considered software agents operating on behalf of the users who provided the measurements 501. Additional information regarding the collection module 120 may be found in this disclosure at least in section 8—Crowd-Based Applications and 9—Collecting Measurements. It is to be noted that these sections, and other portions of this disclosure, describe measurements 110 of affective response to experiences (in general). The measurements 501, which are measurements of affective response involving experiences involving being in locations, may be considered a subset of the measurements 110. Thus, the teachings regarding the measurements 110 are also applicable to the measurements 501. In particular, the measurements 501 may be provided to baseline normalizer 124 and for normalization with respect to a baseline. Additionally or alternatively, the measurements 501 may be provided to Emotional State Estimator (ESE) 121, for example, in order to compute an affective value representing an emotional state of a user based on a measurement of affective response of the user.

[0212] In addition to the measurements 501, in some embodiments, the scoring module 150 may receive weights for the measurements 501 of affective response and to utilize the weights to compute the location score 507. Optionally, the weights for the measurements 501 are not all the same, such that the weights comprise first and second weights for first and second measurements from among the measurements 501 and the first weight is different from the second weight. Weighting measurements may be done for various reasons, such as normalizing the contribution of various users, computing personalized scores, and/or normalizing measurements based on the time they were taken, as described elsewhere in this disclosure.

[0213] In one embodiment, the scoring module 150 is configured to receive the measurements of affective response of the at least ten users. The scoring module 150 is also configured to compute, based on the measurements of affective response of the at least ten users, a location score 507 that represents an affective response of the users to being at the certain location and/or to having an experience at the certain location.

[0214] A scoring module, such as scoring module 150, may utilize one or more types of scoring approaches that may optionally involve one more other modules. In one example, the scoring module 150 utilizes modules that perform statistical tests on measurements in order to compute the location score 507, such as statistical test module 152 and/or statistical test module 158. In another example, the scoring module 150 utilizes arithmetic scorer 162 to compute the location score 507. Additional information regarding how the location score 507 may be computed may be found in this disclosure at least in sections 8—Crowd-Based Applications and 10—Scoring. It is to be noted that these sections, and other portions of this disclosure, describe scores for experiences (in general) such as score 164. The score 507, which is a score for an experience that involves being at a location, may be considered a specific example of the score 164. Thus, the teachings regarding the score 164 are also applicable to the score 164.

[0215] A location score, such as the location score 507, may include and/or represent various types of values. In one example, the location score comprises a value representing a quality of the location to which the location score corresponds. In another example, the location score 507 comprises a value that is at least one of the following types: a physiological signal, a behavioral cue, an emotional state, and an affective value. Optionally, the location score comprises a value that is a function of measurements of at least ten users.

[0216] In one embodiment, a location score, such as the location score 507, may be indicative of significance of a hypothesis that users who contributed measurements of affective response to the computation of the location score had a certain affective response. Optionally, experiencing the certain affective response causes changes to values of at least one of measurements of physiological signals and measurements of behavioral cues, and wherein the changes to values correspond to an increase, of at least a certain extent, in level of at least one of the following emotions: pain, anxiety, annoyance, stress, aggression, aggravation, fear, sadness, drowsiness, apathy, anger, happiness, contentment, calmness, attentiveness, affection, and excitement. Optionally, detecting the increase, of at least the certain extent, in level of at least one of the emotions is done utilizing an ESE.

[0217] As discussed in further detail in section 3—Experiences, an experience, such as an experience that involves being at the certain location, may be considered to comprise a combination of characteristics.

[0218] In some embodiments, being at the certain location, and/or having an experience at the certain location, may involve engaging in a certain activity at the certain location. Thus, for example, at least some of the measurements from among the measurements 501 used to compute the location score 507 are measurements that correspond to events in which the users engaged in the certain activity at the certain location. Examples of such scores may include a location score 507 that represents affective response of people exercising at Central Park. In this example, the “certain location” is Central Park and the activity is exercising. Thus, the location score 507 is computed based on measurements of users who were exercising at Central Park, and based to a lesser extent (or not at all) on measurements of users who engaged in other activities, such as reading, sightseeing, or picnicking. In another example, the location score 507 may be computed based on measurements of affective response of users at a virtual store that bought items at the store, and based to a lesser extent (or none at all) on measurements of users who just browsed and did not buy items at the virtual store.

[0219] In other embodiments, being at the certain location, and/or having an experience at the certain location, may involve being in the certain location during a certain period of time. Thus, for example, at least some of the measurements from among the measurements 501 used to compute the location score 507 are measurements that correspond to events in which the users were at the certain location during a certain period of time. For example, the certain period of time may be a recurring period of time that includes at least one of the following periods: a certain hour during the thy, a certain day of the week, a certain thy of the month, and a certain holiday, a certain a season of the year, and a certain month of the year. Thus, for example, the location score 507 may be used to compute a score for visiting the downtown of a city during the thy (as opposed to visiting it at night), or visiting it on the weekend (as opposed to visiting it during the week). In another example, the location score 507 may represent a score for a virtual world at a certain time of day, reflecting factors such as the type of people and/or server load at that time of day.

[0220] In still other embodiments, being at the certain location, and/or having an experience at the certain location, may involve being in the certain location for a certain duration. Optionally, the certain duration corresponds to a certain length of time (e.g., one to five minutes, one hour to four hours, or one thy to one week). Thus, for example, at least some of the measurements from among the measurements 501 used to compute the location score 507 are measurements that correspond to events in which the users were at the certain location for the certain duration. Examples of such scores may include a location score for a resort based on measurements of users who spent at least a week at the resort. In another example, a location score may be computed based on measurements of users who were at a city for less than 6 hours, representing a location score for a day-trip, as opposed to a location score for the city that represents affective response to a longer stay.

[0221] In still other embodiments, being at the certain location, and/or having an experience at the certain location, may involve being in the certain location while a certain environmental condition persists. Optionally, the certain environmental condition is characterized by an environmental parameter being in a certain range. Optionally, the environmental parameter describes at least one of the following: a temperature in the environment, a level of precipitation in the environment, a level of illumination in the environment (e.g., as measured in lux), a degree of air pollution in the environment, wind speed in the environment, an extent at which the environment is overcast, a degree to which the environment is crowded with people, and a noise level at the environment. Thus, for example, at least some of the measurements from among the measurements 501 used to compute the location score 507 are measurements that correspond to events in which the users were at the certain location while there were certain environmental conditions. Examples of such scores may include a location scores for places like a beach or a park for different weather conditions (e.g., a score for a cloudy day vs. a score for a sunny day). In another example, there may be a first location score for a city for when the air in the city is of good quality and a second location score for the city for when the air in the city is of poor quality.

[0222] Location scores may be computed for a specific group of people by utilizing measurements of affective response of users belonging to the specific group. There may be various criteria that may be used to compute a group-specific score such as demographic characteristics (e.g., age, gender, income, religion, occupation, etc.) Optionally, obtaining the measurements of the group-specific location score may be done utilizing the personalization module 130 and/or modules that may be included in it such as drill-down module 142, as discussed in further detail in this disclosure at least in section 11—Personalization.

[0223] Systems modeled according to FIG. 3a may optionally include various modules, as discussed in more detail in Section 8—Crowd-Based Applications. Following are examples of such modules.

[0224] In one embodiment, a score, such as the location score 507, may be a score personalized for a certain user. In one example, the personalization module 130 is configured to receive a profile of the certain user and profiles of other users, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the other users. Additionally, in this example, the scoring module 150 is configured to compute the location score 507 for the certain user based on the measurements and the output. Computing personalized location scores involves computing different location scores for at least some users. Thus, for example, for at least a certain first user and a certain second user, who have different profiles, the scoring module 150 computes respective first and second location scores that are different. Additional information regarding the personalization module may be found in this disclosure at least in section 11—Personalization and in the discussion involving FIG. 4.

[0225] In another embodiment, map-displaying module 240 may be utilized to present on a display: a map comprising a description of an environment that comprises a certain location, and an annotation overlaid on the map, which indicates at least one of: the location score 507, and the certain location.

[0226] In yet another embodiment, the location score 507 may be provided to the recommender module 178, which may utilize the location score 507 to generate recommendation 508, which may be provided to a user (e.g., by presenting an indication regarding the certain location on a user interface used by the user, such as map-displaying module 240). Optionally, the recommender module 178 is configured to recommend the certain location to which the location score 507 corresponds, based on the value of the location score 507, in a manner that belongs to a set comprising first and second manners, as described in section 8—Crowd-Based Applications. Optionally, when the location score 507 reaches a threshold, the certain location is recommended in the first manner, and when the location score 507 does not reach the threshold, the certain location is recommended in the second manner, which involves a weaker recommendation than a recommendation given when recommending in the first manner.

[0227] In still another embodiment, significance of a score, such as the location score 507, may be computed by the score-significance module 165. Optionally, significance of a score, such as the significance 509 of the location score 507, may represent various types of values derived from statistical tests, such as p-values, q-values, and false discovery rates (FDRs). Additionally or alternatively, significance may be expressed as ranges, error-bars, and/or confidence intervals. As explained in more detail in section 8—Crowd-Based Applications with reference to significance 176 and in section 16—Determining Significance of Results, computing the significance 509 may be done in various ways. In one example, the significance 509 may be based on the number of users who contributed to measurements used to compute a result such as the location score 507. In another example, determining the significance 509 by the score-significance module 165 may be done based on distribution parameters of scores, which are derived from previously observed scores. In yet another example, the significance 509 may be computed utilizing statistical test (e.g., a t-test or a non-parametric test) that may be used to compute a p-value for the location score 507.

[0228] FIG. 3b illustrates steps involved in one embodiment of a method for computing the location score for the certain location based on measurements of affective response. The steps illustrated in FIG. 3b may be, in some embodiments, performed by systems modeled according to FIG. 3a. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[0229] In one embodiment, the method for computing the location score for the certain location based on measurements of affective response, comprising:

[0230] In step 510b, receiving, by a system comprising a processor and memory, measurements of affective response of at least ten users; each measurement of a user is taken at most ten minutes after leaving the certain location. Optionally, at least 25% of the measurements are collected by the system within a period of one hour.

[0231] And in step 510c, computing, by the system, the location score based on the measurements of affective response of at least ten users. Optionally, the location score represents an affective response of the at least ten users to visiting the certain location.

[0232] In one embodiment, the method described above may optionally include step 510a, which comprises taking the measurements of the at least ten users with sensors; each sensor is coupled to a user, and a measurement of a sensor coupled to a user comprises at least one of the following: a measurement of a physiological signal of the user and a measurement of a behavioral cue of the user.

[0233] In one embodiment, the method described above may optionally include step 510d, which comprises recommending, based on the location score, the certain location to a user in a manner that belongs to a set comprising first and second manners. Optionally, recommending the certain location in the first manner involves a stronger recommendation for the certain location, compared to a recommendation for the certain location that is involved when recommending in the second manner.

[0234] The crowd-based results generated in some embodiments described in this disclosure may be personalized results. That is, the same set of measurements of affective response may be used to generate, for different users, scores, rankings, alerts, and/or function parameters that are different. The personalization module 130 is utilized in order to generate personalized crowd-based results in some embodiments described in this disclosure. Depending on the embodiment, personalization module 130 may have different components and/or different types of interactions with other system modules, such as scoring modules, ranking modules, function learning modules, etc.

[0235] In one embodiment, a system, such as illustrated in FIG. 3a, is configured to utilize profiles of users to compute personalized location scores based on measurements of affective response of the users. The system includes at least the following computer executable modules: the collection module 120, the personalization module 130, and the scoring module 150. Optionally, the system also includes recommender module 178. Optionally, the location may be any one of the locations in the physical world and/or virtual locations mentioned in this disclosure.

[0236] The collection module 120 is configured to receive measurements of affective response 501, which in this embodiment comprise measurements of at least ten users; each measurement of a user corresponds to an event in which the user is at a location. Optionally, a measurement of affective response of a user, taken utilizing a sensor coupled to the user, comprises at least one of the following: a value representing a physiological signal of the user, and a value representing a behavioral cue of the user. Optionally, a measurement of affective response corresponding to an event is based on values acquired by measuring the user corresponding to the event with the sensor during at least three different non-overlapping periods while the user was at the location.

[0237] The personalization module 130 is configured, in one embodiment, to receive a profile of a certain user and profiles of the at least ten users, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the at least ten users. The scoring module 150 is configured to compute a location score for the certain user based on the measurements and the output.

[0238] The location scores that are computed are not necessarily the same for all users. By providing the scoring module 150 with outputs indicative of different selections and/or weightings of measurements from among the measurements 501, it is possible that the scoring module 150 may compute different scores corresponding to the different selections and/or weightings of the measurements 501.

[0239] That is, for at least a certain first user and a certain second user, who have different profiles, the scoring module 150 computes respective first and second location scores that are different. Optionally, the first location score is computed based on at least one measurement that is not utilized for computing the second location score. Optionally, a measurement utilized to compute both the first and second location scores has a first weight when utilized to compute the first location score and the measurement has a second weight, different from the first weight, when utilized to compute the second location score.

[0240] In one embodiment, the system described above may include the recommender module 178 and responsive to the first location score being greater than the second location score, the location is recommended to the certain first user in a first manner and the location is recommended to the certain second user in a second manner. Optionally, a recommendation provided by the recommender module 178 in the first manner is stronger than a recommendation provided in the second manner, as explained in more detail in section 8—Crowd-Based Applications.

[0241] It is to be noted that profiles of users belonging to the crowd 500 are typically designated by the reference numeral 504. This is not intended to mean that in all embodiments all the profiles of the users belonging to the crowd 500 are the same, rather, that the profiles 504 are profiles of users from the crowd 500, and hence may include any information described in this disclosure as possibly being included in a profile. Thus, using the reference numeral 504 for profiles signals that these profiles are for users who have a location-related experience which may involve any location described in this disclosure. The profiles 504 may be assumed to be a subset of profiles 128 discussed in more detail in section 11—Personalization. Thus, all teachings in this disclosure related to the profiles 128 are also applicable to the profiles 504 (and vice versa).

[0242] A profile of a user may include various forms of information regarding the user. In one example, the profile includes demographic data about the user, such as age, gender, income, address, occupation, religious affiliation, political affiliation, hobbies, memberships in clubs and/or associations, and/or other attributes of the like. In another example, indications of experiences the user had (such as locations the user has visited). In yet another example, a profile of a user may include medical information about the user. The medical information may include data about properties such as age, weight, diagnosed medical conditions, and/or genetic information about a user. And in yet another example, a profile of a user may include information derived from content the user consumed and/or produced (e.g., movies, games, and/or communications). A more comprehensive discussion about profiles, what they may contain, and how they may be compared may be found in section 11—Personalization.

[0243] There are various implementations that may be utilized in embodiments described herein for the personalization module 130. Following is a brief overview of different implementations for the personalization module 130. Personalization is discussed in further detail at least in section 11—Personalization in this disclosure, were various possibilities for personalizing results are discussed. The examples of personalization in that section are given by describing an exemplary system for computing personalized scores for experiences. However, the teachings regarding how the different types of components of the personalization module 130 operate and influence the generation of crowd-based results are applicable to other modules, systems, and embodiments described in this disclosure. And in particular, those teachings are relevant to generating crowd-based results for experiences involving locations.

[0244] In one embodiment, the personalization module 130 may utilize profile-based personalizer 132, which is implemented utilizing profile comparator 133 and weighting module 135. Optionally, the profile comparator module 133 is configured to compute a value indicative of an extent of a similarity between a pair of profiles of users. Optionally, the weighting module 135 is configured to receive a profile of a certain user and the at least some of the profiles 504, which comprise profiles of the at least ten users, and to generate, utilizing the profile comparator 133, an output that is indicative of weights for the measurements of the at least ten users. Optionally, the weight for a measurement of a user, from among the at least ten users, is proportional to a similarity computed by the profile comparator module 133 between a pair of profiles that includes the profile of the user and the profile of the certain user, such that a weight generated for a measurement of a user whose profile is more similar to the profile of the certain user is higher than a weight generated for a measurement of a user whose profile is less similar to the profile of the certain user. Additional information regarding profile-based personalizer 132, and how it may be utilized to compute personalized location scores, is given in the discussion regarding FIG. 69.

[0245] In another embodiment, the personalization module 130 may utilize clustering-based personalizer 138, which is implemented utilizing clustering module 139 and selector module 141. Optionally, the clustering module 139 is configured to receive the profiles 504 of the at least ten users, and to cluster the at least ten users into clusters based on profile similarity, with each cluster comprising a single user or multiple users with similar profiles. Optionally, the clustering module 139 may utilize the profile comparator 133 in order to determine the similarity between profiles. Optionally, the selector module 141 is configured to receive a profile of a certain user, and based on the profile, to select a subset comprising at most half of the clusters of users. Optionally, the selection of the subset is such that, on average, the profile of the certain user is more similar to a profile of a user who is a member of a cluster in the subset, than it is to a profile of a user, from among the at least ten users, who is not a member of any of the clusters in the subset. Additionally, the selector module 141 may also be configured to select at least eight users from among the users belonging to clusters in the subset. Optionally, the selector module 141 generates an output that is indicative of a selection of the at least eight users. Additional information regarding clustering-based personalizer 138, and how it may be utilized to compute personalized location scores, is given in the discussion regarding FIG. 70.

[0246] In still another embodiment, the personalization module 130 may utilize, the drill-down module 142, which may serve as a filtering layer that may be part of the collection module 120 or situated after it. Optionally, the drill-down module 142 receives an attribute and/or a profile of a certain user, and filters and/or weights the measurements of the at least ten users according to the attribute and/or the profile in different ways. In one example, an output produced by the drill-down module 142 includes information indicative of a selection of measurements of affective response from among the measurements 501 and/or a selection of users from among the user belonging to the crowd 500. Optionally, the selection includes information indicative of at least four users whose measurements may be used by the scoring module 150 to compute the location score. Additional information regarding the drill-down module 142, and how it may be utilized to compute personalized location scores, is given in the discussion regarding FIG. 71.

[0247] FIG. 4 illustrates a system in which users with different profiles may receive different location scores. The system is modeled according to the system illustrated in FIG. 3a, and may optionally include other modules discussed with reference to FIG. 3a, such as recommender module 178 and/or score-significance module 165, which are not depicted in FIG. 4. In this embodiment, the users (denoted crowd 500) are in a location 512. The location 512 may be any of the locations in the physical world and/or virtual locations described in this disclosure. The users in the crowd 500 contribute the measurements 501 of affective response corresponding to being in the location 512.

[0248] It is to be noted that while it is possible, in some embodiments, for more than one of the users from crowd 500, or even all of the users from the crowd 500 to simultaneously be in the location 512, this is not necessarily the case in all embodiments. In other embodiments, each of the users from the crowd 500 might have been in at the location 512 at a different time.

[0249] Generation of personalized results in this embodiment means that for at least a first user 513a and a second user 513b, who have different profiles 514a and 514b, respectively, the system computes different location scores based on the same set of measurements 501 received by the collection module 120. In this embodiment, the location score 515a computed for the first user 513a is different from the location score 515b computed for the second user 513b. The system is able to compute different location scores by having the personalization module 130 receive different profiles (514a and 514b), and compares them to the profiles 504 utilizing one of the personalization mechanisms described above (e.g., utilizing the profile-based personalizer 132, the clustering-based personalizer 138, and/or the drill-down module 142).

[0250] As discussed above, when personalization is introduced, having different profiles can lead to it that users receive different crowd-based results computed for them, based on the same measurements of affective response. This process is illustrated in FIG. 5, which describes how steps carried out for computing personalized crowd-based results can lead to different users receiving the different location scores. The steps illustrated in FIG. 5 may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 4. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[0251] In one embodiment, a method for utilizing profiles of users for computing personalized scores for a location, based on measurements of affective response of the users, includes the following steps:

[0252] In step 517b, receiving, by a system comprising a processor and memory, measurements of affective response of at least ten users who were at the location. Optionally, the location may be any of the locations in the physical world and/or virtual locations mentioned in this disclosure.

[0253] In step 517c, receiving a profile of a certain first user (e.g., the profile 514a of the user 513a).

[0254] In step 517d, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least ten users.

[0255] In step 517e, computing, based on the measurements received in Step 517b and the first output, a first location score for the location. Optionally, the first location score is computed by the scoring module 150.

[0256] In step 517g, receiving a profile of a certain second user (e.g., the profile 514b of the user 513b).

[0257] In step 517h, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least ten users. Optionally, the second output is different from the first output.

[0258] And in step 517i, computing, based on the measurements received in Step 517b and the second output, a second location score for the location. Optionally, the second location score is computed by the scoring module 150. Optionally, the first location score is different from the second location score. For example, there is at least a 10% difference in the values of the first and second location scores. Optionally, computing the first location score for the location involves utilizing at least one measurement that is not utilized for computing the second location score for the location.

[0259] In one embodiment, the method described above may optionally include an additional step 517a that involves utilizing sensors for taking the measurements of the at least ten users. Optionally, each sensor is coupled to a user, and a measurement of a sensor coupled to a user comprises at least one of the following: a value representing a physiological signal of the user, and a value representing a behavioral cue of the user.

[0260] In one embodiment, the method described above may optionally include additional steps such as step 517f that involves forwarding the first location score to the certain first user and/or step 517j that involves forwarding the second location score to the certain second user.

[0261] In some embodiments, forwarding a score, such as a location score that is forwarded to a user, may involve sending the user a message that contains an indication of the score (e.g., the score itself and/or content such as a recommendation that is based on the score). Optionally, sending the message may be done by providing information that may be accessed by the user via a user interface (e.g., reading a message or receiving an indication on a screen). Optionally, sending the message may involve providing information indicative of the score to a software agent operating on behalf of the user.

[0262] In one embodiment, computing the first and second location scores involves weighting of the measurements of the at least ten users. Optionally, the method described above involves a step of weighting a measurement utilized to compute both the first and second location scores with a first weight when utilized to compute the first location score and with a second weight, different from the first weight, when utilized to compute the second location score.

[0263] Generating the first and second outputs may be done in various ways, as described above. The different personalization methods may involve different steps that are to be performed in the method described above, as described in the following examples.

[0264] In one example, generating the first output in Step 517d comprises the following steps: computing a first set of similarities between the profile of the certain first user and the profiles of the at least ten users, and computing, based on the first set of similarities, a first set of weights for the measurements of the at least ten users. Optionally, each weight for a measurement of a user is proportional to the extent of a similarity between the profile of the certain first user and the profile of the user, such that a weight generated for a measurement of a user whose profile is more similar to the profile of the certain first user is higher than a weight generated for a measurement of a user whose profile is less similar to the profile of the certain first user. In this example, the first output may be indicative of the values of the first set of weights.

[0265] In another embodiment, generating the first output in Step 517d comprises the following steps: (i) clustering the at least ten users into clusters based on similarities between the profiles of the at least ten users, with each cluster comprising a single user or multiple users with similar profiles; (ii) selecting, based on the profile of the certain first user, a subset of clusters comprising at least one cluster and at most half of the clusters; where, on average, the profile of the certain first user is more similar to a profile of a user who is a member of a cluster in the subset, than it is to a profile of a user, from among the at least ten users, who is not a member of any of the clusters in the subset; and (iii) selecting at least eight users from among the users belonging to clusters in the subset. In this example, the first output may be indicative of the identities of the at least eight users. It is to be noted that instead of selecting at least eight users, a different minimal number of users may be selected such as at least five, at least ten, and/or at least fifty different users.

[0266] The values of the first and second location scores can lead to different behaviors regarding how their values are treated. In one embodiment, the first location score may be greater than the second location score, and the method described above may optionally include steps involving recommending a location, for which the location scores are computed, differently to different users based on the values of the first and second location scores. For example, the method may include steps comprising recommending the location to the certain first user in a first manner and recommending the location to the certain second user in a second manner. Optionally, recommending a location in the first manner comprises providing stronger recommendation for the location, compared to a recommendation provided when recommending the location in the second manner.

[0267] In one embodiment, the first location score reaches a certain threshold, while the second location score does not reach the certain threshold. Responsive to the first location score reaching the certain threshold, the location is recommended to the certain first user (e.g., by providing an indication on a user interface of the certain first user). Additionally, responsive to the second location score not reaching the certain threshold, the location is not recommended to the certain second user (e.g., by not providing, on a user interface of the certain second user, a similar indication to the one on the user interface of the certain first user). Optionally, the recommendation for the certain first user is done utilizing the map-displaying module 240, and comprises providing an indication of the first score near a representation of the location 512. Further details regarding the difference in manners of recommendation may be found in the discussion regarding recommender module 178 in section 8—Crowd-Based Applications.

[0268] The discussion above regarding FIG. 4 and FIG. 5 involved users from a crowd 500, who were at a certain location (e.g., the location 512). The principles of personalizing scores for different users with different profiles, which were described above, are applicable for embodiments in which the users 500 were in a specific type of location. Following are some examples of such embodiments in which personalized location scores that are computed for different users.

[0269] FIG. 6a describes different locations in a vehicle for which, in some embodiments, personalized location scores may be computed, as described above. The figure illustrates locations corresponding to seats in a vehicle that is an airplane. However, the use of an airplane is just for exemplary purposes and is not intended to be limiting. In a similar fashion, locations may involve seats on other types of vehicles that may be used to transport people. For example, the vehicles may be at least one of the following: a two-wheel vehicle, a three-wheel vehicle, a car, a bus, a train, a ship, an aircraft, and a space shuttle. Additionally, herein a “seat” in a vehicle refers to any area or object that a user may sit in, lay in, and/or occupy in another way while traveling in the vehicle. In embodiments in which a location represents a seat in a vehicle, the location score may be referred to as a “seat score”, “a score for a seat”, and/or simply a “score” (when the context is understood).

[0270] A location illustrated in FIG. 6a may correspond to a single seat in the vehicle. For example, reference numeral 518d corresponds to a specific seat 23E (a middle seat in the middle isle in economy) and reference numeral 518e corresponds to a specific seat 1A (a window seat alone in business class). Additionally or alternatively, a location illustrated in FIG. 6a may correspond to multiple seats in the vehicle sharing a similar characteristic. For example, 518a represents seats in the economy class of the airplane, 518b represents seats in economy plus, and 518c represents seats in business. Locations may represent other groups of seats. In one example, a location in the vehicle may represent window seats (or window seats in a certain class), while another location may represent seats near the isle, and yet another location may represent seats near a toilet.

[0271] FIG. 6b illustrates how different users may have different profiles, which could lead to different personalized seat scores being computed for the users. For example, user 519a illustrated FIG. 6b is a tall 60 year old male. User 519a's profile may include various other aspects which may be important in determining which other users are likely to feel like user 519a regarding different seats. Some of these aspects may include physical dimensions (e.g., height and weight), age, occupations, etc. Profile 520a is a profile of user 519a; it lists some examples of data that may be in a profile of a user that is utilized to compute similarities of profiles which may be relevant to computing a seat score (e.g., the data may be indicative of the following attributes: age, height, weight, occupation, income, and hobbies). In other embodiments, other data may be included in profiles of users. Another example of a user and a corresponding profile of the user is given by user 519b and her profile 520b, which are illustrated in FIG. 6b.

[0272] User 519b, a 22 year old female student, is different in certain aspects from the user 519a as indicated in the profile 520b. Consequently, a seat score computed for user 519a may be quite different than a seat score computed for user 519b, if properties of their respective profiles may influence the computation of the seat scores. In particular, when a score computed for a user is based on measurements of users who are similar to the user, then having different profiles can lead to different seat scores for different users, even when staring off with the same pool of measurements of affective response. This difference is illustrated in FIG. 6c, which illustrates a scenario in which different seats on a certain airplane receive different personalized seat scores for the two users (the seat scores 521a for the user 519a and the seat scores 521b for the user 519b). In this example, seat scores are given in the form of a 1 to 5 star rating, but in other embodiments, other forms of scores may be utilized (e.g., numerical values, “like” or “dislike”, etc.)

[0273] FIG. 6c illustrates an expected trend, in which a seat score for a seat representing a higher class of seats receives a score that is higher than a seat representing a lower class of seats (e.g., seat scores for business class are higher than seat scores for economy class). User 519a has a lower seat score for economy class (2 stars), and in particular being stuck in the middle of a row (the seat 23A referred to by 518d) is expected to be very uncomfortable for the user 519a, as indicated by the 1-star score that seat is given when a seat score for that seat is computed for user 519a. The score may be low because measurements of other users similar to user 519a (e.g., tall males) who occupied that seat may indicate that they had a really bad time (e.g., due to limited leg and/or elbow room). User 519b, on the other hand, who is physically smaller, is not expected to have as bad a time in that seat, as indicated by the 2.5-star score she receives. The seat score for the business class seats computed for the user 519a for business class is higher than the seat score computed for the user 519b (5 stars vs. 4 stars), possibly due to the fact that users similar to user 519b who were measured in business class seats were more slightly uncomfortable despite the additional leg room and complementary alcoholic beverage. FIG. 6c also illustrates that in some cases, different users with different profiles may have the same seat score computed for them for a certain seat (e.g., the seat score for both users for economy plus seats is 4 stars).

[0274] Following are exemplary embodiments of systems and methods that may be used to generate personalized seat scores for users, as illustrated in FIG. 6c. In some embodiments, the user 519a may be considered the certain first user mentioned below, and the user 519b may be considered the certain second user mentioned below.

[0275] In one embodiment, a system, such as illustrated in FIG. 3a, is configured to compute a personalized score for a seat in a vehicle utilizing measurements of affective response of users. The system includes at least the following computer executable modules: the collection module 120, the personalization module 130 and the scoring module 150. Optionally, the system also includes recommender module 178 and/or location verifier module 505. Optionally, the seat may be any one of the seats (or groups of seats) mentioned above, and the vehicle may be any of the vehicles mentioned above.

[0276] The collection module 120 is configured, in one embodiment, to receive measurements of affective response 501, which in this embodiment comprise measurements of at least five users; where each user occupied the seat for at least five minutes (e.g., by sitting in it and/or laying in it), and a measurement of the user is taken, utilizing a sensor coupled to the user, while the user is in the seat. Optionally, a measurement of affective response of a user, taken utilizing a sensor coupled to the user, comprises at least one of the following: a value representing a physiological signal of the user, and a value representing a behavioral cue of the user. Optionally, a measurement of affective response of each user is based on values acquired by measuring the user with the sensor during at least three different non-overlapping periods while the user was in the seat. Optionally, the collection module 120 receives measurements of a larger number of users, such as at least ten users.

[0277] Depending on the embodiment, the at least five users may have all occupied the same type of vehicle (e.g., an airplane or a bus), in the same model of a vehicle (e.g., a Boeing 737), in the same model operated by the same company (Boeing 777 operated by Delta), or the same exact vehicle.

[0278] In some embodiments, the measurements of the at least five users were taken while the at least five users were in similar conditions. For example, the at least five users all occupied the seat for a similar duration (e.g., up to 2 hours, 2 to 5 hours, or more than 5 hours). Thus, a personalized seat score may correspond to a certain duration. For example, different scores may be computed for short and long flights; a certain seat may be comfortable enough for a certain user when the certain user needs to sit in it only for a couple of hours, but sitting in the same seat may be excruciating in the case of a long twelve hour flight. In another example, the at least five users all traveled the same route when their measurements were collected (e.g., the same flight number, same bus line, etc.)

[0279] The system may optionally include, in some embodiments, the location verifier module 505, which is configured to determine whether the user is likely in the seat or not (or is likely in the seat). In one embodiment, the location verifier module 505 is configured to determine whether the user is in a certain seat by receiving signals from the vehicle, e.g., an output generated by an entertainment system in the vehicle indicating to what seat a device of the user is paired. In another embodiment, location verifier module 505 is configured to determine, by receiving wireless transmissions (e.g., by identifying a network and/or using triangulation of wireless signals), in what seat or region of the vehicle the user is sitting.

[0280] The location verifier module 505 may be configured, in some embodiments, to determine whether the user is likely sitting in a seat. In one example, the location verifier module 505 may receive indications of whether the user is stationary or not (e.g., from a pedometer and/or an accelerometer is a device carried by a user, such as a smart phone). In another example, the location verifier module 505 may receive information indicating that the vehicle is ascending and/or descending at a pace consistent with times the user is required to be seated (e.g., after takeoff and/or before landing of an aircraft).

[0281] The personalization module 130 is configured, in one embodiment, to receive a profile of a certain user (e.g., the user 519a or the user 519b) and profiles of the at least five users, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the at least five users. Optionally, the output is generated using one or more of the following modules: the profile-based personalizer 132, the clustering-based personalizer 138, and/or the drill-down module 142. Optionally, the scoring module 150 may utilize the output to compute a seat score based on the measurements of the at least five users, which is personalized for the certain user based on the output, as explained in more detail at least in section 11—Personalization.

[0282] In one embodiment, a profile of a user may include information that describes one or more of the following: the age of the user, the gender of the user, the height of the user, the weight of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, and a feature value derived from semantic analysis of a communication of the user. Optionally, the profile of a user may include information regarding travel habits of the user. For example, the profile may include itineraries of the user indicating to travel destinations, such as countries and/or cities the user visited. Optionally, the profile may include information regarding the type of trips the user took (e.g., business or leisure), what hotels the user stayed at, the cost, and/or the duration of stay. Optionally, the profile may include information regarding seats the user occupied in vehicles when traveling.

[0283] The seat scores that are computed are not necessarily the same for all users. By providing the scoring module 150 with outputs indicative of different selections and/or weightings of measurements from among the measurements 501, it is possible that the scoring module 150 may compute different scores corresponding to the different selections and/or weightings of the measurements 501. That is, for at least a certain first user and a certain second user (e.g., the users 519a and 519b, respectively), who have different profiles (e.g., 520a and 520b, respectively), the scoring module 150 computes first and second seat scores that are different (e.g., 521a and 521b, respectively). Optionally, the first seat score is computed based on at least one measurement that is not utilized for computing the second seat score. Optionally, a measurement utilized to compute both the first and second seat scores has a first weight when utilized to compute the first seat score and the measurement has a second weight, different from the first weight, when utilized to compute the second seat score.

[0284] In one embodiment, each measurement of affective response of a user is based on, and/or comprises, multiple values collected throughout the period of time during which the user was in the seat and/or expected to be in the seat. For example, a seat score for a seat in an airplane may be computed based on multiple values taken continuously or periodically while the airplane was in the air.

[0285] A seat score may express various values in different embodiments. In one embodiment, a seat score may express the average mood of users sitting in the seat and/or average stress level of users sitting in the seat. Optionally, the measurements of affective response used to compute a seat score are normalized with respect to baseline values of the users of whom the measurements were taken in order to compute a relative value indicating the expected mood change and/or change to stress that is expected due to sitting in the seat. In another embodiment, a seat score may express an expected quality of sleep and/or rest, and/or an expected duration of sleep, as a computed based on measurements of affective response of users in the seat.

[0286] In some embodiments, a seat score may have multiple components, each of which may optionally be considered a separate seat score. Optionally, each component corresponds to a certain type of activity conducted while in the seat. For example, a seat score may include an eating component (e.g., based on measurements taken while users ate in the seat), a sleeping component (e.g., based on measurements taken while users slept in the seat), and/or working/playing component (e.g., based on measurements taken while users were interacting with a computer and/or gaming system while in the seat). Optionally, the components are reported separately (as different types of seat scores for the seat). Additionally or alternatively, the components are combined into a single value used as the seat score (e.g., by attributing a certain weight to each component). Additional discussion regarding how a score may be comprised of components is given in this disclosure at least in section 10—Scoring.

[0287] In one embodiment, a method for computing a personalized score for a seat in a vehicle based on measurements of affective response of users includes at least the following steps:

[0288] In step 1, receiving, by a system comprising a processor and memory, measurements of affective response of at least five users; each user occupied the seat for at least five minutes, and a measurement of the user is taken, utilizing a sensor coupled to the user, while the user is in the seat.

[0289] In step 2, receiving a profile of a certain first user (e.g., profile 520a of user 519a), a profile of a certain second user (e.g., profile 520b of user 519b), and profiles of the at least five users (e.g., profiles from among the profiles 504). In this embodiment, the profile of the certain first user is different from the profile of the certain second user.

[0290] In step 3, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least five users.

[0291] In step 4, computing, based on the measurements and the first output, a first seat score for the seat. Optionally, this step may include forwarding the first seat score to the certain first user.

[0292] In step 5, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least five users; here the second output is different from the first output.

[0293] And in step 6, computing, based on the measurements and the second output, a second seat score for the seat, with the first seat score being different from the second seat score. Optionally, this step may include forwarding the second seat score to the certain second user. Optionally, computing the first seat score is done based on at least one measurement that is not utilized for computing the second seat score.

[0294] In one embodiment, computing the first and second seat scores involves weighting of the measurements of the at least five users. Optionally, the method described above involves a step of weighting a measurement utilized to compute both the first and second seat scores with a first weight when utilized to compute the first seat score and with a second weight, different from the first weight, when utilized to compute the second seat score.

[0295] The values of the first and second seat scores can lead to different behaviors regarding how their values are treated. In one embodiment, the first seat score may be greater than the second seat score, and the method described above may optionally include steps involving recommending the seat differently to different users based on the values of the first and second seat scores. For example, the method may include steps comprising recommending the seat to the certain first user in a first manner and recommending the seat to the certain second user in a second manner. Optionally, recommending the seat in the first manner comprises providing stronger recommendation for the seat, compared to a recommendation provided when recommending the seat in the second manner. Further details regarding the difference in manners of recommendation may be found in the discussion regarding recommender module 178.

[0296] Staying at a hotel is an experience that many users have, often many times a year. Given the expenses that are typically involved in the stay, and the importance of a quality experience (e.g., a bad experience may be detrimental to one's mood and/or to the ability to work the next day), being able to choose an appropriate hotel for a person is important and beneficial.

[0297] Herein, a hotel may be any lodging that provides a person with a room in which the user may sleep. Thus, a hotel may be an establishment that offers multiple rooms (to multiple guests) and/or has a single room to offer (e.g., a room offered on an online service such as Airbnb). Additionally, a hotel need not be a building on the land; a cruise ship and/or a space station may be considered hotels in embodiments described in this disclosure.

[0298] Since different users may have different characteristics, personalities, and preferences, they are likely to react differently to staying at different hotels. Thus, it may be beneficial to be able to compute, for different users, personalized scores indicative of the quality of a stay at a hotel. Herein, such a score may be referred to as a “hotel score”, a “score for the hotel”, and/or simply “score” (when the context is understood).

[0299] FIG. 7 illustrates how different users, who have different profiles, receive different personalized hotel scores. For example, user 525a illustrated FIG. 7 is a tall 45 year old female sales manager. User 525a's profile may include various other aspects which may be important in determining which other users are likely to feel like user 525a regarding a hotel. Profile 526a is a profile of user 525a, and lists some examples of data that may be in a profile of a user that may be useful for computing similarities between profiles (e.g., the profile may include the following information: age, occupation, information indicative of traveling of the user, and/or information indicative of spending habits of the user). In other embodiments, other data may be included in profiles of users. Another example of a user and a corresponding profile of the user is user 525b and his profile 526b, which are illustrated in FIG. 7.

[0300] User 525b, a 30 year old male creative director, is different in certain aspects from the user 525a as indicated in the profile 525b. Consequently, a hotel score computed for user 525a may be quite different than a hotel score computed for user 525b, if properties of their respective profiles may influence the computation of the hotel scores. In particular, when a score computed for a user is based on measurements of users who are similar to the user, then having different profiles can lead to different hotel scores for different users, even when staring off with the same pool of measurements of affective response. This difference in hotel score is illustrated in FIG. 7, which illustrates a scenario in which different users have different hotel scores computed for them (the hotel scores 527a for the user 525a and the hotel score 527b for the user 527b). In this example, hotel scores are given in the form of a numerical rating, but other scoring systems may be used, such as a 1 to 5 star rating.

[0301] There may be various reasons behind the difference in the hotel scores computed for the users 525a and 525b. In one example, the hotel may be business oriented, thus users who are frequent travelers may find it more acceptable that users who are infrequent travelers, and consequently the measurements of affective response of the frequent travelers are likely to be more positive than the measurements of affective response of the infrequent travelers. Since user 525a is a frequent traveler and user 525b, when computing the score 527a, it is likely that a higher weight was given to positive measurements of frequent traveler, compared to the weight given to those measurements when computing the score 527b. In another example, the decor and atmosphere in the hotel may influence different types of users in different ways. Thus, the outdated décor (e.g., furniture, uniforms, lighting, etc.) may not influence users like user 525a, but users like user 526b may be determinedly affected by these things, making their stay less enjoyable.

[0302] Following are exemplary embodiments of systems and methods that may be used to generate personalized hotels scores for users, as illustrated in FIG. 7. In some embodiments, the user 525a may be considered the certain first user mentioned below, and the user 525b may be considered the certain second user mentioned below.

[0303] The collection module 120 is configured, in one embodiment, to receive measurements of affective response 501, which in this embodiment, comprise measurements of at least five users who stayed at the hotel for at least twelve hours. A measurement of affective response of each user, from among the at least five users, is collected using one or more sensors coupled to the user. Examples of sensors that may be used are given at least in section 1—Sensors. Optionally, a measurement of affective response of a user comprises at least one of the following: a value representing a physiological signal of the user, and a value representing a behavioral cue of the user.

[0304] In some embodiments, each of the at least five users stayed at the hotel for a certain duration (which may be longer than 12 hours). Optionally, the length of the certain duration is within one of the following ranges: up to 24 hours, 24 to 48 hours, three days to one week, and more than one week. Thus, a score computed based on the measurements may reflect a quality of staying at the hotel for a specific duration.

[0305] In some embodiments, a measurement of affective response of a user is based on multiple values acquired while measuring the user with a sensor during different periods of time while the user was at the hotel. For example, the measurement may be based on values acquired by measuring the user during at least three different non-overlapping periods while the user was at the hotel. Optionally, the measurement is based on values acquired continuously or periodically during the user's stay at the other.

[0306] In one embodiment, the measurements of affective response of the at least five users are all taken during a certain period (e.g., during the same day or during the same week). Thus, a score computed based on the measurements may reflect on the quality of staying at the hotel during the certain period.

[0307] In one embodiment, the hotel offers more than one type of room to guests. For example, rooms may have different features, such as different sizes, be located on different floors, have different views, and/or include different amenities (e.g., a balcony, a Jacuzzi, etc.) Optionally, rooms with different features may be considered rooms of different types. Optionally, the at least five users all stayed in the same type of room in the hotel, thus, the score for the hotel may be considered a score for the certain type of room at the hotel.

[0308] The system may optionally include the location verifier module 505, which in one embodiment may be configured to identify when the at least five users were at the hotel. Optionally, the measurements of affective response of the at least five users are based on values acquired during periods for which the location verifier module 505 indicated that the users were at the hotel. Verifying that users are at the hotel may be done in various ways. In one example, a device of the user may indicate the location of the user (e.g., via GPS and/or joining a local network at the hotel). In another example, a billing and/or management system of the hotel may receive indication of transactions conducted by the user at the hotel (e.g., ordering room service) and/or receive indication from a room management system that the user is in his/her room in the hotel (e.g., by noting when the room's door is opened and/or locked). In yet another example, a security system of the hotel may identify when the user walks in or out of the hotel (e.g., via image analysis of video feeds obtained from security cameras).

[0309] The personalization module 130 is configured, in one embodiment, to receive a profile of a certain user (e.g., the user 525a or the user 525b) and profiles of the at least five users (e.g., profiles from among the profiles 504), and to generate an output indicative of similarities between the profile of the certain user and the profiles of the at least five users. Optionally, the output is generated using one or more of the following modules: the profile-based personalizer 132, the clustering-based personalizer 138, and/or the drill-down module 142. Optionally, the scoring module 150 may utilize the output to compute a hotel score based on the measurements of the at least five users, which is personalized for the certain user based on the output, as explained in more detail at least in section 11—Personalization.

[0310] In one embodiment, a profile of a user, such as a profile from among the profiles 504, may include information that describes one or more of the following: the age of the user, the gender of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, information indicative of spending and/or traveling habits of the user, and/or a feature value derived from semantic analysis of a communication of the user. Optionally, the profile of a user may include information regarding travel habits of the user. For example, the profile may include itineraries of the user indicating to travel destinations, such as countries and/or cities the user visited. Optionally, the profile may include information regarding the type of trips the user took (e.g., business or leisure), what hotels the user stayed at, the cost, and/or the duration of stay.

[0311] The hotel scores that are computed are not necessarily the same for all users. By providing the scoring module 150 with outputs indicative of different selections and/or weightings of measurements from among the measurements 501, it is possible that the scoring module 150 may compute different scores corresponding to the different selections and/or weightings of the measurements 501. That is, for at least a certain first user and a certain second user (e.g., the users 525a and 525b, respectively), who have different profiles (e.g., 526a and 526b, respectively), the scoring module 150 computes first and second hotel scores that are different (e.g., 527a and 527b, respectively). Optionally, the first hotel score 527a is computed based on at least one measurement that is not utilized for computing the second hotel score 527b. Optionally, a measurement utilized to compute both the first hotel score 527a and the second hotel score 527b has a first weight when utilized to compute the first hotel score 527a and the measurement has a second weight, different from the first weight, when utilized to compute the second hotel score 527b.

[0312] A hotel score may express various values in different embodiments. In one embodiment, a hotel score may express the average mood of users while staying at the hotel. Optionally, this value may be indicative of a level of an emotion such as a level of happiness. In another embodiment, a hotel score may express an expected level of relaxation and/or stress when staying at the hotel. In still another embodiment, a hotel score may express an expected quality of sleep and/or rest, and/or an expected duration of sleep, as a computed based on measurements of affective response of users that stayed at the hotel.

[0313] In some embodiments, the measurements of affective response used to compute a hotel score are normalized with respect to baseline values of the users of whom the measurements were taken. Optionally, such normalization may enable computation of a relative value indicating the expected mood change and/or change to stress and/relaxation, expected during the stay at the hotel.

[0314] In some embodiments, a hotel score may have multiple components, each of which may optionally be considered a separate hotel score. Optionally, each component corresponds to a certain type of activity conducted while at the hotel. For example, a hotel score may include a dining component (e.g., based on measurements taken while users dined at the hotel), a sleeping component (e.g., based on measurements taken while users slept at the hotel), and/or an activity component (e.g., based on measurements taken while users were not eating or sleeping). In another example, different regions of the hotel (the guest rooms, the restaurants, the gym, the pool, conference halls, etc.) may have separate score computed for it based on measurements acquired while users were in those regions. Optionally, the components may be reported separately (as different types of hotel scores for the hotel). Additionally or alternatively, the components may be combined into a single value used as the hotel score (e.g., by attributing a certain weight to each component). Additional discussion regarding how a score may be comprised of components is given in this disclosure at least in section 10—Scoring.

[0315] In one embodiment, a method for computing a personalized score for a hotel utilizing measurements of affective response of users who stayed at the hotel includes at least the following steps:

[0316] In step 1, receiving, by a system comprising a processor and memory, measurements of affective response of at least five users who stayed at the hotel for at least twelve hours. Optionally, a measurement of affective response of each user is based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the hotel.

[0317] In step 2, receiving a profile of a certain first user (e.g., profile 526a of user 525a), a profile of a certain second user (e.g., profile 526b of user 525b), and profiles of the at least five users (e.g., profiles from among the profiles 504); here the profile of the certain first user is different from the profile of the certain second user.

[0318] In step 3, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least five users.

[0319] In step 4, computing, based on the measurements and the first output, a first score for the hotel.

[0320] In step 5, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least five users; here the second output is different from the first output.

[0321] And in step 6, computing, based on the measurements and the second output, a second score for the hotel, with the first score being different from the second score. Optionally, this step may include forwarding the first score to the certain first user and/or forwarding the second score to the certain second user. Optionally, computing the first score is done based on at least one measurement that is not utilized for computing the second score.

[0322] In one embodiment, computing the first and second scores described above involves weighting of the measurements of the at least five users. Optionally, the method described above involves a step of weighting a measurement utilized to compute both the first and second scores with a first weight when utilized to compute the first score and with a second weight, different from the first weight, when utilized to compute the second score.

[0323] The values of the first and second scores can lead to different behaviors regarding how their values are treated. In one embodiment, the first score may be greater than the second score, and the method described above may optionally include steps involving recommending the hotel differently to different users based on the values of the first and second scores. For example, the method may include steps comprising recommending the hotel to the certain first user in a first manner and recommending the seat to the certain second user in a second manner. Optionally, recommending the hotel in the first manner comprises providing stronger recommendation for the hotel, compared to a recommendation provided when recommending the hotel in the second manner. Further details regarding the difference in manners of recommendation may be found in the discussion regarding recommender module 178.

[0324] Dining at a restaurant is a common experience, which some people may have on a daily basis. In urban areas, there are often many choices when it comes to restaurants, so being able to choose an appropriate restaurant that suits one's preferences may be difficult.

[0325] Since different users may have different characteristics, personalities, and preferences, they are likely to react differently to dining at different restaurants. Thus, it may be beneficial to be able to compute, for different users, personalized scores indicative of the quality of the dining experience at a restaurant. Herein, such a score may be referred to as a “restaurant score”, a “score for the restaurant”, and/or simply “score” (when the context is understood).

[0326] FIG. 8 illustrates how different users, who have different profiles, receive different personalized sores for a restaurant. For example, user 531a illustrated FIG. 8 is a female doctor. User 531a's profile may include various other aspects which may be important in determining which other users are likely to feel like user 531a regarding a restaurant. Profile 532a is a profile of user 531a, and lists some examples of data that may be in a profile of a user that is may be useful for computing similarities between profiles (e.g., the profile may be indicative of the following information: occupation, is the user a vegetarian, income, frequency of eating in Asian restaurants, and a favorite pizza topping). In other embodiments, other data may be included in profiles of users. Another example of a user and a corresponding profile of the user is user 531b and her profile 532b, which are illustrated in FIG. 8.

[0327] Based on measurements of affective response of users who have profiles that are similar to the profile 532a, the user 531a receives “thumbs up” score 533a for the restaurant 530 (which is a sushi restaurant). That means that people who have a profile that is similar to the profile 532a, of the user 531a, who dined at the restaurant 530 generally enjoyed the experience (according to their corresponding measurements of affective response). In contrast, based on measurements of affective response of users who have profiles that are similar to the profile 532b, the user 531b receives “thumbs down” score 533b for the restaurant 530. That means that people who have a profile that is similar to the profile 532b, of the user 531b, who dined at the restaurant 530 generally did not enjoy the experience very much (according to their corresponding measurements of affective response).

[0328] There may be various reasons behind the different scores computed for users 531a and 531b (from the same original set of measurements of affective response 501). In one example, the restaurant 530 may not specialize in vegetarian dishes, thus people who only eat vegetarian food may enjoy the experience less than people who select from the complete menu. In another example, the restaurant 530 may serve sophisticated and/or expensive dishes. Such dishes may be appreciated by people who frequently eat at Asian restaurants, such as user 531a, but are often not appreciated as much by people who go to Asian restaurants much less frequently, such as user 531b (as indicated in the profile 532b of the user 531b). In many examples, the reason behind the different personalized scores computed for users may stem from a combination of factors in the profiles (possibly due to complex and/or not immediately apparent correlations).

[0329] Herein, a restaurant may be any establishment that provides food and/or beverages. Optionally, a restaurant may offer people an area in which they may consume the food and/or beverages. In some embodiments, a reference made to “a restaurant” and/or “the restaurant” refers to a distinct location in the physical world (e.g., a certain address). In other embodiments, a reference made to “a restaurant” and/or “the restaurant” refers to a location of a certain type, such as any location of a certain chain restaurant. In such embodiments, measurements of affective response of users who ate at “the restaurant” may include measurements taken at different locations, such as different restaurants of the same franchise. Herein, a “diner at restaurant” may be any person who ate food prepared at the restaurant. Optionally, a diner may eat the food at the restaurant. Alternatively, the diner may eat the food prepared at the restaurant at some other location. Thus, in some embodiments, a score for a restaurant may be a “franchise score”, and alert for a restaurant may be an alert for the franchise, a ranking of restaurants may be a ranking of different franchises, etc.

[0330] Following are exemplary embodiments of systems and methods that may be used to generate personalized restaurant scores for users, as illustrated in FIG. 8. In one embodiment, a system, such as illustrated in FIG. 3a, is configured to compute a personalized score for a restaurant utilizing measurements of affective response of users. The system includes at least the following computer-executable modules: the collection module 120, the personalization module 130 and the scoring module 150. Optionally, the system also includes recommender module 178 and/or location verifier module 505. In some embodiments, the user 531a may be considered the certain first user mentioned below, and the user 531b may be considered the certain second user mentioned below.

[0331] The collection module 120 is configured, in one embodiment, to receive measurements of affective response 501, which in this embodiment comprise measurements of at least five users who dined at the restaurant. A measurement of affective response of each user, from among the at least five users, is collected using one or more sensors coupled to the user. Examples of sensors that may be used are given at least in section 1—Sensors. Optionally, a measurement of affective response of a user comprises at least one of the following: a value representing a physiological signal of the user, and a value representing a behavioral cue of the user.

[0332] In one embodiment, a measurement of affective response of a user from among the measurements 501 may be a based on values measured with the sensor while the user was at the restaurant. Optionally, these values may reflect how the user felt about various aspects of the restaurant, such as the ambiance, the decor, the service, and/or food and beverages that were served to the user (or the user's surroundings). Additionally or alternatively, a measurement of affective response of a user from among the measurements 501 may be a based on values measured with the sensor after the user left the restaurant (e.g., a during a period that ends at most one hour, four hours, or at most twelve hours after the user left). Optionally, such values may represent how the user's body reacted to the food from the restaurant.

[0333] In some embodiments, a measurement of affective response of a user is based on multiple values acquired while measuring the user with a sensor during different periods of time while the user was dining and/or during a period after dining (as described above). For example, the measurement may be based on values acquired by measuring the user during at least three different non-overlapping periods while the user was dining. Optionally, the measurement is based on values acquired continuously or periodically during the user's dining at the restaurant. In another example, the measurement may be based on values acquired by measuring the user during at least five different non-overlapping periods spread over a period of at least thirty minutes of dining (i.e., sitting in the restaurant), with each period during which the user was measured being at least thirty seconds long.

[0334] In one embodiment, the measurements of affective response of the at least five users are all taken during a certain period (e.g., during the same day or during the same week). Thus, a score computed based on the measurements may reflect on the quality of the restaurant during the certain period. For example, separate scores may be computed for lunch and dinner and/or separate scores for weekdays and weekends.

[0335] In one embodiment, different scores may be computed for the restaurant 530, based on characteristics of the dining experience of the at least five users. In one example, the at least five users may have all ordered from a certain section of the menu at the restaurant (e.g., the “business meal” or “blue plate” specials). Thus, the score may represent the experience of having a meal at the restaurant 530 which involves ordering from that portion of the menu. In another example, the at least five users may have all dined at a certain section of a restaurant (e.g., the bar, the patio, or the main dining hall), and thus, a score computed based on the measurements of the at least five users may reflect the experience of dining in the certain section.

[0336] In one embodiment, the at least five users do not all have the same exact meal. For example, the measurements of at least five users who dined at the restaurant include a measurement of a first user who ate a first item while dining in the restaurant and a measurement of a second user who did not eat the food item while dining in the restaurant.

[0337] The system may optionally include location verifier module 505, which in one embodiment may be configured to identify when the at least five users were at the restaurant. Optionally, the measurements of affective response of the at least five users are based on values acquired during periods for which the location verifier module 505 indicated that the users were at the restaurant. Verifying that users are at the restaurant may be done in various ways. In one example, a device of the user may indicate the location of the user (e.g., via GPS and/or joining a local network at the hotel). In another example, a billing information may indicate the time the meal at a restaurant 530 essentially ended for a user (and thus provide a window of time during which a user likely dined at the restaurant 530).

[0338] The personalization module 130 is configured, in one embodiment, to receive a profile of a certain user (e.g., profile 532a of the user 531a or profile 532b of the user 531b) and profiles of the at least five users (e.g., profiles from among the profiles 504), and to generate an output indicative of similarities between the profile of the certain user and the profiles of the at least five users. Optionally, the output is generated using one or more of the following modules: the profile-based personalizer 132, the clustering-based personalizer 138, and/or the drill-down module 142. Optionally, the scoring module 150 may utilize the output to compute a score for the restaurant based on the measurements of the at least five users, which is personalized for the certain user based on the output, as explained in more detail at least in section 11—Personalization.

[0339] In one embodiment, a profile of a user, such as a profile from among the profiles 504, may include information that describes one or more of the following: the age of the user, the gender of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, information indicative of spending and/or traveling habits of the user, and/or a feature value derived from semantic analysis of a communication of the user. Optionally, the profile of a user may include information regarding culinary and/or dieting habits of the user. For example, the profile may include dietary restrictions, information about sensitivities to certain substances, and/or allergies the user may have. In another example, the profile may include various preference information such as favorite cuisine and/or dishes, preferences regarding consumptions of animal source products and/or organic food, and/or preferences regarding a type and/or location of seating at a restaurant. In yet another example, the profile may include data derived from monitoring food and beverages the user consumed. Such information may come from various sources, such as billing transactions and/or a camera-based system that utilizes image processing to identify food and drinks the user consumes from images taken by a camera mounted on the user and/or in the vicinity of the user.

[0340] The restaurant scores that are computed are not necessarily the same for all users. By providing the scoring module 150 with outputs indicative of different selections and/or weightings of measurements from among the measurements 501, it is possible that the scoring module 150 may compute different scores corresponding to the different selections and/or weightings of the measurements 501. That is, for at least a certain first user and a certain second user (e.g., the users 531a and 531b, respectively), who have different profiles (e.g., 532a and 532b, respectively), the scoring module 150 computes first and second scores for the restaurant that are different (e.g., 533a and 533b, respectively). Optionally, the first score 533a is computed based on at least one measurement that is not utilized for computing the second score 533b. Optionally, a measurement utilized to compute both the first score 533a and the second score 533b has a first weight when utilized to compute the first score 533a and the measurement has a second weight, different from the first weight, when utilized to compute the second score 533b.

[0341] A score for a restaurant may express various values in different embodiments. In one embodiment, such a score may express the average mood of users while dining at the restaurant. Optionally, this value may be indicative of a level of an emotion such as a level of happiness. In another embodiment, the score may express an expected level of relaxation and/or stress when dining at the restaurant. In still another embodiment, the score may express an expected quality of the digestion of the food consumed at the restaurant (e.g., based on measurements taken during and after having a meal at the restaurant).

[0342] In some embodiments, measurements of affective response used to compute a score for a restaurant are normalized with respect to baseline values of the users of whom the measurements were taken. Optionally, such normalization may enable computation of a relative value indicating the expected mood change and/or change to stress and/relaxation, as a result of dining at the restaurant.

[0343] In some embodiments, a score for a restaurant may have multiple components, each of which may optionally be considered a separate type of score for the restaurant. For example, one component of the score may relate to the ambiance in the restaurant, the other to the service, and another to the food. Optionally, each component is computed based on measurements of affective response taken during an appropriate period of the dining experience. For example, measurements related to the ambiance may be taken when a user enters the restaurant and/or between courses. In another example, measurements related to the service may be taken during interactions the user has with service personnel and/or devices (e.g., service robots). In another example, measurements related to the food may be taken while chewing and/or drinking, and/or after the meal (e.g., to reflect the effect of the meal on the user's body). Optionally, the components may be reported separately (as different types of scores for the restaurant). Additionally or alternatively, the components may be combined into a single value used as the score for the restaurant (e.g., by attributing a certain weight to each component).

[0344] In one embodiment, method for computing a personalized score for a restaurant (e.g., the restaurant 530) utilizing measurements of affective response of users who dined at the restaurant includes at least the following steps:

[0345] In step 1, receiving, by a system comprising a processor and memory, measurements of affective response of at least five users who dined at the restaurant. Optionally, each measurement of affective response of a user is based on values acquired by measuring the user, with a sensor coupled to the user, while the user was at the restaurant. Optionally, each measurement of affective response of a user comprises at least one of the following: a value representing a physiological signal of the user, and a value representing a behavioral cue of the user

[0346] In step 2, receiving a profile of a certain first user (e.g., profile 532a of user 531a), a profile of a certain second user (e.g., profile 532b of user 531b), and profiles of the at least five users (e.g., profiles from among the profiles 504). In this embodiment, the profile of the certain first user is different from the profile of the certain second user.

[0347] In step 3, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least five users.

[0348] In step 4, computing, based on the measurements and the first output, a first score for the restaurant.

[0349] In step 5, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least five users; here the second output is different from the first output.

[0350] And in step 6, computing, based on the measurements and the second output, a second score for the restaurant, with the first score being different from the second score. Optionally, this step may include forwarding the first score to the certain first user and/or forwarding the second score to the certain second user. Optionally, computing the first score is done based on at least one measurement that is not utilized for computing the second seat score.

[0351] In one embodiment, computing the first and second scores described above involves weighting of the measurements of the at least five users. Optionally, the method described above involves a step of weighting a measurement utilized to compute both the first and second scores with a first weight when utilized to compute the first score and with a second weight, different from the first weight, when utilized to compute the second score.

[0352] In one embodiment, computing the first and second scores described above is done based on additional measurements of affective response of the at least five users, which were taken after the at least five users left the restaurant. Optionally, the additional measurements may reflect upon the influence of the meal eaten at the restaurant of the bodies of the users.

[0353] The values of the first and second scores can lead to different behaviors regarding how their values are treated. In one embodiment, the first score may be greater than the second score, and the method described above may optionally include steps involving recommending the restaurant differently to different users based on the values of the first and second scores. For example, the method may include steps comprising recommending the restaurant to the certain first user in a first manner and recommending the restaurant to the certain second user in a second manner. Optionally, recommending the restaurant in the first manner comprises providing stronger recommendation for the restaurant, compared to a recommendation provided when recommending the restaurant in the second manner. Further details regarding the difference in manners of recommendation may be found in the discussion regarding recommender module 178.

[0354] In some embodiments, scores computed for an experience involving a location may be dynamic, i.e., they may change over time. It may be of interest to determine when a score reaches a threshold and/or passes (e.g., by exceeding the threshold or falling below the threshold), since that may signify a certain meaning and/or require taking a certain action, such as issuing a notification about the score. Issuing a notification about a value of a score reaching and/or exceeding a threshold may be referred to herein as “alerting” and/or “dynamically alerting”.

[0355] Various aspects of systems, methods, and/or computer-readable media that involve generating notifications about changes to scores and/or scores reaching thresholds (also referred to as “issuing alerts”), are described in more detail at least in section 12—Alerts. That section discusses teachings regarding alerts based on scores for experiences in general, which include experiences involving locations (with alerts being based on scores for locations). Thus, the teachings of section 12—Alerts are also applicable to embodiments described below that explicitly involve locations. Following is a discussion regarding some aspects of systems, methods, and/or computer-readable media that may be utilized to generate such alerts that involve various types of locations.

[0356] FIG. 9 illustrates a system configured to alert about affective response to being at a location. The system includes at least the collection module 120, the dynamic scoring module 180, and an alert module 184. Optionally, the system may include additional modules such as the personalization module 130 and/or location verifier module 505.

[0357] The collection module 120 is configured to receive measurements 501 of affective response of users (denoted crowd 500). In this embodiment, the measurements 501 comprise measurements of affective response of at least some of the users from the crowd 500 to being at the location 512, which may be any of the locations in the physical world and/or virtual locations described in this disclosure.

[0358] There are various types of locations in the physical world and/or virtual locations that are mentioned in this disclosure and to which the location 512 may refer. In one embodiment, the location 512 is a place that provides entertainment such as a club, a bar, a movie theater, a theater, a casino, a stadium, and/or a concert venue. In another embodiment, the location 512 is a place of business such as a store, a restaurant, a booth, a shopping mall, a shopping center, a market, a supermarket, a beauty salon, a spa, a clinic, and/or a hospital. In yet another embodiment, the location 512 is a travel destination, such as a certain continent, a certain country, a certain county, a certain city, a certain resort, and/or a certain neighborhood. And in still another embodiment, the location 512 is a virtual location, such as a virtual world and/or a certain server that hosts a virtual world.

[0359] The collection module 120 is configured, in one embodiment, to provide measurements of at least some of the users from the crowd 500 to other modules such as the dynamic scoring module 180. Optionally, the measurement of affective response of the user is based on at least one of the following values: (i) a value acquired by measuring the user, with a sensor coupled to the user, while the user was at the location 512, and (ii) a value acquired by measuring the user with the sensor up to one hour after the user had left the location 512. Optionally, the measurement of affective response comprises at least one of the following: a value representing a physiological signal of the user and a value representing a behavioral cue of the user. Examples of sensors that may be used are given at least in section 1—Sensors.

[0360] Herein, a measurement of affective response of a user to being at a location (e.g., the location 512) is a measurement corresponding to an event that involves the user having an experience at the location and/or an event in which the user is simply at the location (possibly having one or more of various experiences). In some embodiments, the measurements of at least some of the users from the crowd 500 to being at the location 512 include measurements of the at least some of the users while they were at the location each having—what could be characterized as—different experiences while at the location 512. For example, the location 512 may be a mall, and some of the users might have been shopping, while others were eating, etc. Nonetheless, the measurements of affective response collected from those users may be utilized to compute a score to the experience of “being at the mall”. As explained in further detail in section 3—Experiences, in different embodiments, experiences may have different scopes in hierarchies. Thus, in one embodiment, measurements may be considered “measurements of affective response of users to eating at the mall”, while in another embodiment, the same measurements may be considered “measurements of affective response of users to being at the mall”.

[0361] In one embodiment, the dynamic scoring module 180 is configured to compute scores 535 for the location 512 based on the measurements 501. Optionally, the dynamic scoring module 180 may utilize similar modules to the ones utilized by scoring module 150. For example, the dynamic scoring module may utilize the statistical test module 152, the statistical test module 158, and/or the arithmetic scorer 162. In one embodiment, a score computed by the dynamic scoring module 180, such as one of the scores 535, is computed based on measurements of at least five users taken at a time that is after a first period before the time t to which the score corresponds, but not after that time t. Optionally, the score corresponding to t is also computed based on measurements taken earlier than the first period before t. Additional details regarding computation of multiple scores that correspond to different times, such as the scores 535, is given in the discussion regarding the dynamic scoring module 180 in section 12—Alerts. That section discusses scores for experiences in general, which include experiences involving locations, and is thus relevant to embodiments modeled according to FIG. 9.

[0362] Embodiments of the system illustrated in FIG. 9 may optionally include location verifier 505. Optionally, measurements used by the dynamic scoring module 180 are based on values obtained during periods for which the location verifier module 505 indicated that the user was at the location 512.

[0363] In one embodiment, the alert module 184 evaluates the scores 535 in order to determine whether to issue an alert, e.g., in the form of notification 537. If a score for the location 512, from among the scores 535, which corresponds to a certain time, reaches a threshold 536, the alert module 184 may forward the notification 537. The notification 537 is indicative of the score for the location 512 reaching the threshold, and is forwarded by the alert module 184 no later than a second period after the certain time. Optionally, both the first and the second periods are shorter than twelve hours. In one example, the first period is shorter than four hours and the second period is shorter than two hours. In another example, both the first and the second periods are shorter than one hour. Optionally, the dynamic nature of the scores 535 is such that for at least a certain first time t₁ and a certain second time t₂, a score corresponding to t₁ does not reach the threshold 536 and a score corresponding to t₂ reaches the threshold 536; here t₂>t₁, and the score corresponding to t₂ is computed based on at least one measurement taken after t₁.

[0364] Forwarding a notification, such as the notification 537, may be done in various ways. Optionally, forwarding a notification is done by providing a user a recommendation, such as by utilizing recommender module 178. Further discussion regarding notifications is given at least in section 12—Alerts.

[0365] The notification 537 may be forwarded to multiple users. When the location 512 represents a location in the physical world, in some embodiments, a decision on whether to forward the notification 537 to a certain user, from among the multiple users, may depend on the distance between the certain user and the location 512 and/or on the expected time it would take the certain user to reach the location 512. For example, if the notification 537 indicates that people at a certain nightclub (the location 512) are having a good time, it may not be beneficial to forward the notification 537 to a certain user that is at a different city that is a three hour drive away from the location 512. By the time that certain user would reach the location 512, the notification 537 may not be relevant, e.g., the party might have moved on. Thus, in some embodiments, the location of a user and/or the distance of a user from locations may be a factor that is to be considered by a module that issues notifications (e.g., the alert module 184 and/or the recommender module 178) and/or an entity that controls which notification to present the user (e.g., a software agent operating on behalf of the user). In one embodiment, the notification 537 may be forwarded to a first recipient whose distance from the location is below a distance-threshold, and the notification is not forwarded to a second recipient whose distance from the location is above the distance-threshold. Optionally, the distance-threshold is received by the alert module 184 and is utilized by the alert module 184 to determine who to send the notification 537. Optionally, different users may have different distance-thresholds according to which it may be determined whether they shall receive notifications regarding the location 512.

[0366] In one embodiment, the alert module 184 may issue notifications that may cancel alerts. For example, the alert module 184 may be configured to determine whether, after a score corresponding to a certain time reaches the threshold 536, a second score corresponding to a later time occurring after the certain time falls below the threshold 536. Responsive to the second score falling below the threshold 536, the alert module 184 may forward, no later than the second period after the later time, a notification indicative of the score falling below the threshold 536.

[0367] In one embodiment, the notification 537 sent by the alert module 184 is indicative of the location 512. For example, the notification specifies the location 512, presents an image depicting the location 512, and/or provides instructions on how to reach the location 512. Optionally, the map-displaying module 240 is utilized to present the notification 537 by presenting on a display: a map comprising a description of an environment that comprises the location 512, and an annotation overlaid on the map, which indicates at least one of: the score corresponding to a certain time, the certain time, and the location 512. In one example, location 512 may be a location in the physical world such as a park, and the map includes a description of a city in which the park is situated. In this example, the notification may involve placing an icon, on a screen of a device of a user that depicts the map, at a location corresponding to the park (e.g., at the location of the park and/or nearby it). The icon may convey to the user that a score corresponding to the park reaches a certain level (e.g., people at the park are having a good time).

[0368] Notifications issued by the alert module 184 are not necessarily the same for all users. In one example, different users may receive different alerts because the scores 535 computed for each of the different users based on the measurements 501 may be different. Such a scenario may arise if the scores 535 are computed utilizing an output of the personalization module 130. The personalization module 130 may receive a profile of a certain user and the profiles 504 of users belonging to the crowd 500. Based on similarities between the profile of the certain user and the profiles 504, the personalization module may generate an output indicative of a certain weighting and/or selection of at least some of the measurements 501. Since different users will have different outputs generated for them, the scores 535 computed for the different users may be different. Thus, for the same time t, a score corresponding to t for a first user may reach the threshold 536, while a score computed for a second user, corresponding to the same time t, might not reach the threshold 536. Such an approach to personalization of alerts is illustrated in FIG. 76a, which describes personalization of alerts regarding experiences in general, which also include experiences involving the location 512, as is the case in this example (thus, the discussion regarding FIG. 76a is applicable to the aforementioned example).

[0369] In another embodiment, the alert module 184 may receive different thresholds 536 for different users. Thus, a score corresponding to the time t may reach one user's threshold, but not another user's threshold. Consequently, the system may behave differently, with the different users, as far as the forwarding of notifications is concerned. This approach for personalization of alerts is illustrated in FIG. 77a.

[0370] FIG. 10 illustrates steps involved in one embodiment of a method for alerting about affective response to being at a location, such as the location 512. The steps illustrated in FIG. 10 may be used, in some embodiments, by systems modeled according to FIG. 9. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations of the method.

[0371] In one embodiment, the method for alerting about affective response corresponding to being in the location includes at least the following steps:

[0372] In step 539a, receiving, by a system comprising a processor and memory, measurements of affective response of users to being at the location. For example, the users may belong to the crowd 500, and the measurements may be the measurements 501. Optionally, each of the measurements comprises at least one of the following: a value representing a physiological signal of the user and a value representing a behavioral cue of the user.

[0373] In step 539b, computing a score for the location. The score corresponds to a time t, and is computed based on measurements of at least five of the users taken at a time that is after a first period before t, but not after t (i.e., the measurements of the at least five users were taken at a time that falls between t minus the first period and t). Optionally, measurements taken earlier than the first period before the time t are not utilized for computing the score corresponding to t.

[0374] In step 539c, determining whether the score reaches a threshold. Following the “No” branch, in different embodiments, different behaviors may occur. In one embodiment, the method may return to step 539a to receive more measurements, and proceeds to compute an additional score for the location, corresponding to a time t′>t. In another embodiment, the method may return to step 539b and compute a new score for a time t′>t. Optionally, the score corresponding to t′ is computed using a different selection and/or weighting of measurements, compared to a weighting and/or selection used to compute the score corresponding to the time t. And in still another embodiment, the method may terminate its execution.

[0375] And in step 539d, responsive to the score reaching the threshold, forwarding, no later than a second period after t, a notification indicative of the score reaching the threshold. That is, the notification is forwarded at a time that falls between t and t plus the second period.

[0376] In one embodiment, both the first and second periods are shorter than twelve hours. Additionally, for at least a first time t₁ and a second time t₂, a score corresponding to t₁ does not reach the threshold and a score corresponding to t₂ reaches the threshold. In this case t₂>t₁, and the score corresponding to t₂ is computed based on at least one measurement taken after t₁.

[0377] Given that the alert module 184 does not necessarily forward notifications corresponding to each score computed, one embodiment of the method described above includes performing at least the following steps:

[0378] In step 1, receiving measurements of affective response of users to being at the location.

[0379] In step 2, computing a first score for the location, corresponding to t₁, based on measurements of at least five of the users taken at a time that is after a first period before t₁, but not after t₁. Optionally, the first period is shorter than twelve hours.

[0380] In step 3, determining that the first score does not reach the threshold.

[0381] In step 4, computing a second score for the location, corresponding to t₂, based on measurements of at least five of the users taken at a time that is after the first period before t₂, but not after t₂. Optionally, the second score is computed based on at least one measurement taken after t₁.

[0382] In step 5, determining that the second score reaches the threshold.

[0383] And in step 6, responsive to the second score reaching the threshold, forwarding, no later than the second period after t₂, a notification indicative of the second score for the location reaching the threshold.

[0384] In one embodiment, the method illustrated in FIG. 10 involves a step of assigning weights to measurements used to compute the score corresponding to the time t, such that an average of weights assigned to measurements taken earlier than the first period before t is lower than an average of weights assigned to measurements taken later than the first period before t. Additionally, the weights may be utilized for computing the score corresponding to t.

[0385] The embodiments discussed above, which may be illustrated in FIG. 9 and/or FIG. 10, relate to embodiments in which alerts are generated based on scores computed for locations in general. Following is a description of some embodiments, which may be considered specific examples of the embodiments described above, in which an alert of a certain kind is generated for specific location. These examples include embodiments for generating the following alert: an alert about an exciting sale at a store, an alert about dissatisfied customers at a location in which the customers are provided a service, an alert about sickness after eating at a restaurant, and an alert about negative affective response of users logged into a server hosting a virtual environment.

[0386] Shopping is an activity that users engage in quite often; it typically mixes between necessity and recreation. There are often many locations one might go to purchase a certain item, such as various “brick and mortar” stores and/or virtual stores (e.g., virtual malls). Since stores are aware that shoppers have many options, the stores often try to entice users by having sales and/or other promotions to lure shoppers to their business. However, not all sales and/or promotions are equal; some may excite shoppers, while others may disappoint them. For example, some sales may involve false advertisement, e.g., promising merchandise and/or discounts that are not really available. In another example, a certain sale may start off well, but due to its popularity, desirable items may be sold out by the time a user has an opportunity to explore a store. In still another example, a store may offer desirable merchandise and/or have competitive pricing, however the location of the store and/or ambiance at the store may be such that the shopping experience at the store is not a positive one.

[0387] Given the large number of alternatives stores, a user may have when shopping (e.g., end of season sales), there is a need to evaluate different stores in order to determine where and/or when it is worthwhile to go. Evaluating the shopping experience in each of the stores may be difficult for users. For example, it may be a time-exhausting experience and require visits to many locations. Additionally, such an evaluation may be time-critical, since, for example, items that are on sale may be snatched from a store's shelfs. Therefore, waiting until a large number of alternative stores are evaluated may result in a loss of attractive shopping opportunities. In addition, a person might not be aware of attractive shopping opportunities, and/or not want to waste time on being on the lookout for such opportunities. Thus, there is a need for a way to receive an indication regarding the quality of sales, promotions, and/or the shopping experience in general at various stores.

[0388] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that enable generation of alerts about the shopping experience at one or more stores. For example, such an alert may indicate whether a sale at a store is truly exciting for shoppers at the store. The alerts generated in the embodiments described herein are generated based on scores that are computed for stores based on measurements of affective response of shoppers who are at the stores. Additionally, an alert may be time sensitive, since it may be derived from a score computed based on measurements of affective response taken during a certain time-frame, and therefore may represent affective response of shoppers during the certain time-frame. Thus, for example, if a certain store's sale becomes unattractive after a while (e.g., because desirable items are sold out), this may be reflected in the affective response of the shoppers, which will lead to a lower score for the store.

[0389] Embodiments described herein may refer to a “store”. As used herein, a “store” is a business that allows users (also referred to as “shoppers”) to buy goods and/or services. The goods may be tangible (e.g., clothes, shoes, automobiles, etc.) and/or virtual goods such as items that may be accessed in a virtual world and/or digital content (e.g., movies, music, and/or games). Services may involve service received in the physical world (e.g., from a teller at a bank) and/or services by a software agent. Buying items may be done utilizing transactions involving one or more forms of currency, including, but not limited to: cash, credit cards, e-wallet transactions, cryptocurrencies, and/or credits and/or points that may be utilized to receive goods and/or services. Herein, the user of the term “shopper” refers to a user who is at a store. Referring to a user as a shopper is not meant to imply that the user purchased something at the store.

[0390] In one embodiment, a “store” may refer to a business in the physical world that has a single location (e.g., a certain street address). A shopper may be considered at the store if the shopper is physically in the store, such as physically being in a building and/or room which houses the store.

[0391] In another embodiment, a “store” may refer to a business in the physical world that has multiple locations. Optionally, each location is located at a different address and/or is housed in a different room and/or building. Thus, the store may refer to any one of the locations of a certain chain of stores (e.g., a certain chain of fashion outlets). Optionally, a shopper may be considered at the store if the shopper is physically in one of the multiple locations.

[0392] In yet another embodiment, a “store” may refer to a business that has an on line and/or virtual storefront. In one example, the store may be a virtual store, which corresponds to a certain location in a virtual world. In this example, a shopper may be considered at the store if a representation of the user (e.g., an avatar) is present in the certain location in the virtual world, and/or if the shopper receives images describing the certain location in the virtual world. A large virtual store (with many areas) or a collection of virtual stores in the same area in a virtual world may be referred to as a virtual mall.

[0393] In some embodiments, an alert generated for a store may indicate whether a sale at the store is exciting at a certain time. Additionally or alternatively, the alert may relate to the ambiance and/or shopping experience at the store at the certain time (irrespective of whether there is a sale at the time). Herein, a “sale” refers to any promotion, discount, and/or product offering that may be presented to at least some of the shoppers as having limited availability and/or being offered for a limited time. A sale may involve only some of the goods and/or services offered at a store, and/or may be relevant to only a portion of the shoppers. Nonetheless, the store may be considered to be having a sale. In one example, a store that most of the time has at least one item in its stock that it offers at a discount may be considered having a sale constantly.

[0394] In some embodiments, scores, upon which alerts corresponding to a certain store may be based, are computed based on measurements of affective response of shoppers taken during a certain period while the shoppers were at the certain store. Thus, at different times, the certain store may have different scores computed for it, depending on the shoppers that were there and their measurements at that time. A decision to generate an alert, e.g., by issuing a notification indicative about a score computed for the store, is dependent on the score reaching a threshold. Therefore, an alert may be generated (or canceled) at a certain time depending on the value of a score corresponding to the certain time.

[0395] Such a behavior of alerts for stores is illustrated in FIG. 11a and FIG. 11b. These figures illustrate scores computed for different stores during different times of the day. The scores in the figures represent a level of excitement of shoppers at a store as determined based on measurements taken during a certain period of time. FIG. 11a illustrates various scores 542, computed for a certain store 540. Each dot on the graph represents a certain score from among the scores 542, which corresponds to a certain time t, based on the position of the dot on the horizontal time line. The height of the dot in the plot is indicative of the level of excitement of shoppers during a certain period of time leading up to the time t. Each of the scores 542, which corresponds to a certain time t, is computed based on measurements of at least five of the shoppers that were taken while they were at the store 540 at a time that was after a first period before t, and not later than t. For example, if the first period may be an hour, then the score corresponding to the time 12 PM is computed based on measurements of at least five shoppers that were taken sometime between 11 AM and 12 PM. Note, each of the at least five shoppers was present in the store at some time during that period (but not necessarily at the same time). In a similar fashion to FIG. 11a, FIG. 11b illustrates scores 545 that are computed for a different store 544.

[0396] FIG. 11a and FIG. 11b illustrate how an alert for a sale at a store is generated when the score reaches a threshold 541. In FIG. 11a, early in the day, the score for the store was low, below the threshold 541. There may be various reasons for the low scores. For example, early in the day, not all the items that were to go on sale were displayed, perhaps the ambiance in the store was not exciting (e.g., an empty store), and/or the first customers were simply grumpy. However, by 11 AM the score climbed reaching (and then exceeding) the threshold 541. Thus, after that time, an alert may be generated, e.g., by issuing a notification to users that the shopping experience at the store is exciting (there is a good sale over there). This high score level continues for a few hours, until the score for the store 540 starts to drop and falls below the threshold 541 after 3 PM. There may be various reasons for the declines in the stores, such as the store running out of items that were on sale, and/or the ambiance in the store becoming unpleasant (e.g., the store is too crowded and/or there are long waiting times at the cashier).

[0397] Alerts might have been issued at various times between 11 AM to 3 PM, since at those times the scores for the store were above the threshold 541. However, once the scores for the store fall below the threshold 541, no new alerts will be generated. Furthermore, previously issued alerts might be canceled, e.g., by issuing new notifications to users indicating that the previous alerts are no longer relevant (the sales are not hot any more).

[0398] FIG. 11b, illustrated a different scenario, in which throughout the day, none of the scores 545 for the store 544 reach the threshold 541. Thus, no alerts regarding a sale at the store 544 are generated that day. There are various reasons why the scores 545 are lower than the scores 542 and do not lead to generating alert. One reason may be that the sale at the store 544 (10% off) is simply not as good as the sale at the store 540 (which offers 50% off). Another reason may be the location and/or décor of the store 544 which is simply not as nice as the store 540; consequently, shopping at the store 544 is less exciting than shopping at the store 540, which may be in a nicer mall and/or have a trendier design.

[0399] Following are exemplary embodiments of systems and methods that may be used to compute scores and generate alerts, as illustrated in FIG. 11a and FIG. 11b. In one example, the store 540 may be considered the store for which the scores described below are generated. Additionally, in the description below, the threshold may be the threshold 541 mentioned above and/or the scores computed for the store may be scores 542 mentioned above.

[0400] It is to be noted that the exemplary embodiments described below may be considered embodiments of systems modeled according to FIG. 9 and/or embodiments of methods modeled according to FIG. 10, which are discussed above. FIG. 9 and FIG. 10 pertain to embodiments in which alerts are generated for locations in general, while the embodiments described below involve a specific type of location (a store) and a specific type of alert (an alert related to excitement from a sale). Thus, the teachings provided above, with respect to embodiments modeled according to FIG. 9 and/or FIG. 10, are to be considered applicable, mutatis mutandis, to the embodiments discussed below.

[0401] In one embodiment, a system, such as illustrated in FIG. 9, is configured to alert about an exciting sale at a store (e.g., the store 540). The system includes at least the collection module 120, the dynamic scoring module 180, and an alert module 184. Optionally, the system may include additional modules such as the personalization module 130 and/or location verifier module 505.

[0402] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response, which in this embodiment comprise measurements of shoppers who were at the store. Optionally, the measurements 501 of the shoppers are taken utilizing sensors coupled to the shoppers. In one example, each measurement of affective response of a shopper is taken utilizing a sensor coupled to the shopper, and the measurement comprises at least one of the following: a value representing a physiological signal of the shopper, and a value representing a behavioral cue of the shopper. Optionally, each measurement of affective response of a shopper is based on values acquired by measuring the shopper during at least three different non-overlapping periods while the shopper was at the store. Additional information regarding sensors and how measurements of affective response of shoppers may be collected may be found at least in sections 1—Sensors and 2—Measurements of Affective Response.

[0403] In one embodiment, the dynamic scoring module 180 is configured to compute the scores for the store (e.g., the scores 542) based on the measurements 501. Optionally, in this embodiment, the scores are indicative of a level of excitement to being in the store. Additionally or alternatively, the scores may express other values related to shopping at the store, such as an average mood of shoppers at the store. Optionally, each of the scores corresponds to a time t and is computed based on measurements of at least five shoppers taken at a time that is after a first period before the time t to which the score corresponds, but not after that time t. In one example, the first period may be one hour. In other examples, the first period may be shorter, e.g., thirty minutes long, or longer, such as four, twelve, or twenty four hours.

[0404] Measurements received by the collection module 120 may be utilized in various ways in order to compute the score corresponding to the time t. In one example, measurements taken earlier than the first period before the time t are not utilized by the dynamic scoring module 180 to compute the score corresponding to t. In another example, the dynamic scoring module 180 is configured to assign weights to measurements used to compute the score corresponding to the time t, such that an average of weights assigned to measurements taken earlier than the first period before t is lower than an average of weights assigned to measurements taken later than the first period before t. Optionally, these weights are taken into account by the dynamic scoring module 180 when computing the score corresponding to t.

[0405] In one embodiment, the alert module 184 evaluates the scores for the store in order to determine whether to issue an alert, e.g., in the form of the notification 537. If a score corresponding to a certain time reaches a threshold (e.g., the threshold 541), a notification is forwarded by the alert module 184 no later than a second period after the certain time. Optionally, in this embodiment, the notification is indicative of an excitement level of shoppers at the store and/or expresses a level of another emotional state (e.g., happiness). Optionally, in this embodiment, both the first and the second periods are shorter than twelve hours. In one example, the first period is shorter than four hours and the second period is shorter than two hours. In another example, both the first and the second periods equal one hour, or less. Optionally, the dynamic nature of the scores computed for the store is such that for at least a certain first time t₁ and a certain second time t₂, a score corresponding to t₁ does not reach the threshold and a score corresponding to t₂ reaches the threshold; here t₂>t₁, and the score corresponding to t₂ is computed based on at least one measurement taken after t₁.

[0406] In one example, reaching the threshold means that the store has an exciting sale, and the notification includes a coupon related to the sale. Optionally, a user that receives the coupon is not one of the shoppers whose measurements of affective response were used by the alert module 184 to determine that the score corresponding to the certain time reaches the threshold. In another example, a notification forwarded to a user includes an image taken by at least one of the shoppers whose measurements of affective response were used by the alert module to determine that the score corresponding to a certain time reaches the threshold.

[0407] In one embodiment, the store exists in a space in the physical world, and each of the at least five shoppers whose measurements were used to compute the score corresponding to the time t was in the store at some time between the first period before t and the time t. Optionally, the location verifier module 505 is utilized to determine when a shopper is in the store. Optionally, when a score for the store reaches the threshold, a notification is forwarded to a first recipient whose distance from the store is below a distance-threshold, and the notification is not forwarded to a second recipient whose distance from the store is above the distance-threshold. For example, the distance-threshold may be a distance of fifteen miles.

[0408] In another embodiment, the store is a virtual store (e.g., a store in a virtual mall). Optionally, the store is hosted on at least one server, and each of the at least five shoppers whose measurements were used to compute the score corresponding to the time t accessed, at some time between the first period before t and the time t, data that originated from the at least one server.

[0409] In one embodiment, the map-displaying module 240 may be utilized to present on a display: a map comprising a description of an environment that includes the store, and an annotation overlaid on the map and indicating at least one of: the score corresponding to the certain time, the certain time, and the store.

[0410] As discussed in more detail in section 12—Alerts, e.g., with regards to FIG. 76a and FIG. 76b, there are various ways in which alerts regarding exciting sales at a store may be personalized.

[0411] In one example, each user may choose to set his/her own value for a threshold that a score for a store needs to reach in order for the alert module 184 to issue a notification to the user. Optionally, setting a user's threshold is done by a software agent operating on behalf of the user. Thus, the same score value may reach one user's threshold (that user will receive a notification), while the score does not reach another user's threshold (that user will not receive a notification). Optionally, the value of the threshold of a user is proportional to the distance of the user from the store. For example, the value of the threshold increases as the distance of the user from the store increases. This may reflect the fact that a user is inclined to travel a long distance only if a sale at the store is very exciting, but will consider going to a store with a less exciting sale, if that store is nearby.

[0412] In another example, personalization module 130 may be utilized to generate scores for the store, which are personalized for a certain user based on similarities of a profile of the certain user to profiles of at least some of the shoppers. Optionally, a profile of a user (e.g., the profile of the certain user or of one of the shoppers) may include various information related to shopping habits of the user. For example, the profile may include information about stores frequented by the user and/or various items purchased by the user. Additional information that may be included in the profile is described at least in section 11—Personalization.

[0413] In one embodiment, a method for alerting about an exciting sale at a store (e.g., the store 540) includes at least the following steps:

[0414] In step 1, receiving, by a system comprising a processor and memory, measurements of affective response of shoppers who are at the store.

[0415] In step 2, computing a score for the store, which corresponds to a time t, based on measurements of at least five of the shoppers, which were taken at a time that is after a first period before t, but not after t.

[0416] In step 3, determining whether the score reaches a threshold.

[0417] And in step 4, responsive to the score reaching the threshold, forwarding, no later than a second period after t, a notification indicative of the score reaching the threshold. Optionally, the notification is forwarded to a first recipient whose distance from the store is below a distance-threshold, and the notification is not forwarded to a second recipient whose distance from the store is above the distance-threshold.

[0418] In one embodiment, both the first and second periods are shorter than twelve hours. Additionally, for at least a first time t₁ and a second time t₂, a score corresponding to t₁ does not reach the threshold and a score corresponding to t₂ reaches the threshold. In this case t₂>t₁, and the score corresponding to t₂ is computed based on at least one measurement taken after t₁.

[0419] In one embodiment, the method described above includes an additional step of presenting on a display: a map comprising a description of an environment that comprises the store, and an annotation overlaid on the map and indicating at least one of: the score corresponding to the certain time, the certain time, and the location of the store.

[0420] In one embodiment, the method described above includes additional steps comprising: receiving a profile of a certain user and profiles of the shoppers, and generating an output indicative of similarities between the profile of the certain user and the profiles of the shoppers, and computing scores for the store for a certain user based on the output and the measurements of at least five of the users taken at a time that is at most a first period before t, and not later than t. In this embodiment, for at least a certain first user and a certain second user, who have different profiles, there are different respective first and second scores computed, which correspond to the same certain time. Additionally, the first score (for the first certain user) reaches the threshold, while the second score (for the certain second user) does not reach the threshold.

[0421] In one embodiment, the method described above includes additional steps comprising: determining whether, after a first score corresponding to a certain time reaches the threshold, a second score corresponding to a later time occurring after the certain time falls below the threshold, and responsive to the second score falling below the threshold, forwarding, no later than the second period after the later time, a notification indicative of the second score falling below the threshold.

[0422] In day-to-day life, there are often scenarios in which users are customers who are provided service by a business at a location. For example, a user may be a guest at an amusement park, and is provided with entertainment services. In this example, the guest may be entertained by simply being in the park and/or by interacting with workers and/or park attractions. In another example, a user may be a patient in a health care facility, receiving service from staff who work at the facility. It is often important for such businesses to provide good service to their customers. Thus, it may be important for businesses to be able to monitor their customers' satisfaction. In particular, it may be beneficial for such businesses to be able to identify, as soon as possible, if their customers are dissatisfied. Receiving a prompt notification regarding possible problems in the experience provided to customers can enable the businesses to identify the causes and/or find a way to improve the experience the customers are having. Without such a prompt notification, the businesses may not be aware of problems in the customers' experience and/or they may be late to address the problems, such that the customers may end up having a bad experience.

[0423] Conventional methods for monitoring customer satisfaction may be suboptimal in many cases. For example, a business may accept and even solicit customer feedback (e.g., customers approaching employees, a suggestion box, comments on a website, or a customer service phone number). However, such feedback requires customers to take action, which many are reluctant to do, and is often done after the fact, when it is already too late to fix the experience for those customers. In contrast, a prompt notification of customer dissatisfaction can help businesses provide better service to their customers by being able to address problems in real time. For example, monitoring guest in a casino may yield an indication that at a certain area (e.g., the bar area) people's satisfaction has greatly decreased. In this example, there may be various reasons for the dissatisfaction: slow service, the area may be crowded (too many people at the bar), or the music may be inappropriate for the crowd. If the casino staff become aware of the problem within a short time, they may take steps to improve the customers' experience, such as send more service providers (or free drinks), open another room where people can sit and drink, or change the music to improve the ambiance. However, if they were to wait until receiving customer feedback it may be too late; many of the guests might have already left the casino in frustration, to seek a better experience elsewhere.

[0424] Thus, there is a need for businesses to be able to monitor customer satisfaction in an automatic way that does not require the customers to take a specific action. Having such prompt feedback can help the businesses to address problems that arise quickly in order to improve the experience at their place of business.

[0425] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that enable generation of alerts about customer satisfaction at a location. The alerts may be generated if satisfaction levels of customers at the location fall below a certain satisfaction-threshold. In such a case, one or more entities may be notified (e.g., human manager and/or software that manages an experience at the location) in order for them to be able to take steps to improve the experience the customers are having at the location.

[0426] Alerts that are described in some embodiments herein are generated based on scores that are computed for locations based on measurements of affective response of customers who are at the locations. Additionally, an alert may be time sensitive, since it may be derived from a score computed based on measurements of affective response taken during a certain time-frame, and therefore, may represent affective response of customers during the certain time-frame.

[0427] In one embodiment, a location for which alerts are generated is a location that provides a recreational service and/or an entertainment service to customers. For example, the location may involve one or more of the following places: an amusement park, a water park, a casino, a restaurant, a resort, and a bar. It is to be noted that the location may be the business itself, or a region within a larger location, such as an area involving a certain attraction in an amusement park or a certain dining room of a restaurant. Optionally, when a score indicates that satisfaction of customers at the location falls below a threshold, entities operating on behalf of the location may seek to improve the customers' satisfaction. For example, this may involve improving the service (e.g., by adding service personnel to the location), providing the customers with a reward (e.g., free drinks at a casino), and/or diverting some of the customers to alternative locations (e.g., suggesting to customers to visit another area of a resort they are at). The size of the location may vary between different embodiments, from a portion of a room, to a whole building (e.g., a casino or club), to even an area of more than a few square miles (e.g., a resort). In one example, the location that provides a service that involves entertainment includes at least 800 square feet of floor space. In other examples, the area of the location may be different, such as less than 400 square feet, more than 2000 square feet, or more than ten acres.

[0428] In another embodiment, a location for which alerts are generated is a location at which health treatments and/or healthcare services are provided to customers. For example, the location may be an area in one or more of the following facilities: a clinic, a hospital, and an elderly care facility. Optionally, the location may correspond to a certain room, floor, wing, and/or department in a facility that provides health related services. Optionally, when a score indicates that satisfaction of customers at the location falls below a threshold, entities operating on behalf of the location may seek to improve the customers' satisfaction. For example, the entities may add service personnel and/or better trained service personnel to the location. In another example, the entities may conduct an inspection to determine the cause of the decline in the satisfaction of the customers. In one example, the location that provides a service that involves a health treatment and/or healthcare includes at least 400 square feet of floor space. In other examples, the area of the location may be different, such as less than 400 square feet, more than 2000 square feet, or more than an acre.

[0429] In another embodiment, a location for which alerts are generated is a location at which customers are provided with sleeping accommodations. For example, the location may be a room, an apartment, a floor of a hotel, a wing of a hotel, a hotel, and/or a resort. Optionally, a “hotel” may be any structure that holds one or more rooms and/or a collection of rooms in the same vicinity. For example, a cruise ship may be considered a hotel. Optionally, when a score indicates that satisfaction of customers at the location falls below a threshold, entities operating on behalf of the location may seek to improve the customers' satisfaction. For example, the entities may investigate the cause of the lower satisfaction, e.g., unclean rooms, noise, problems with the air-conditioning, etc.

[0430] Satisfaction of customers may be interpreted in different ways in different embodiments. However, typically, a score representing a higher satisfaction level is indicative of a more positive affective response of users who contributed measurements to the score, compared to measurements of affective response of users used to compute another score representing a lower satisfaction level. In one example, when a first score is indicative of a higher satisfaction level than a second score, it means that the users who contributed measurements to the first score were, on average, happier than the users who contributed measurements to the second score. Additionally or alternatively, it may mean that the users who contributed measurements to the first score were, on average, calmer, more relaxed, and/or less stressed than the users who contributed measurements to the second score.

[0431] Herein, a customer at a location at which a service is provided may be any person at the location. In some embodiments, a person may be considered a customer even if that person does not pay for any service received at the location. For example, a customer at a park may be a person that simply visits the park, even if that person did not pay an admittance fee to the park, or any other fee while at the park. In other embodiments, a customer at the location is a person who pays for a service that is provided at location, such as a guest at a hotel, a patient at a hospital, etc. It is to be noted that in the embodiments below, each customer may be considered a “user” as the term is used in this disclosure, such as the user 101a, 101b, or 101c. The term “customer” is used to emphasize that the user receives a service of some sort from a business at a location, and may therefore be considered a customer of the business.

[0432] In some embodiments, scores, upon which alerts corresponding to a certain location may be based, are computed based on measurements of affective response of customers, taken during a certain period while the customers were at the certain location. Thus, at different times, the certain location may have different scores computed for it, depending on the customers that were there and their measurements at that time. A decision to generate an alert, e.g., by issuing a notification indicative about a score computed for the location, is dependent on the score falling below a satisfaction-threshold. Therefore, an alert may be generated (or canceled) at a certain time depending on the value of a score corresponding to the certain time.

[0433] Such a behavior of alerts for locations is illustrated in FIG. 12. This figure illustrates scores 548 computed during different times of the day for a location 546, which is a certain area in an amusement park. The scores 546 represent levels of satisfaction of customers that are in the certain area of the amusement park, as determined based on measurements taken during a certain period of time. Each dot on the graph represents a certain score from among the scores 548, which corresponds to a certain time t, based on the position of the dot on the horizontal time line. The height of the dot in the plot is indicative of the level of satisfaction of customers during a certain period of time leading up to the time t. Each of the scores 548, which corresponds to a certain time t, is computed based on measurements of at least five of the customers that were taken while they were at the location 546 at a time that was after a first period before t, and not later than t. For example, if the first period is an hour, then the score corresponding to the time 12 PM is computed based on measurements of at least five users that were taken sometime between 11 AM and 12 PM. Note that each of the at least five customers was present in the location at some time during that period (but not necessarily at the same time).

[0434] FIG. 12 illustrates how an alert for the location 546 is generated when the score falls below the satisfaction-threshold 547. In FIG. 12, early in the day, the score for the location is relatively high, and above, the satisfaction-threshold 547. However, as the day progresses, the scores tend to be lower and lower, until 12 PM, when the scores fall below the satisfaction-threshold 547. There may be various reasons for the low scores. For example, earlier in the day lines to attractions were shorter and/or weather conditions were pleasant. However, as the hours passed, the lines became longer and the weather became less pleasant, causing the customers to be less satisfied.

[0435] When a score from among the scores 548 falls below the satisfaction-threshold 547, an alert may be generated by issuing a notification to managers of the location 546. In response to the notification, the managers may take various actions, as described in FIG. 12. Following an alert generated after 12 PM, the operators sent clowns to entertain the customers, sometime around 1 PM, but the satisfaction level remained below the satisfaction-threshold 547. Then, sometime after 2 PM, the managers distributed free beer to the customers, which led to an immediate improvement in the mood and customer satisfaction.

[0436] Following are exemplary embodiments of systems and methods that may be used to compute scores and generate alerts, as illustrated in FIG. 12. In one example, the location 546 may be considered the location for which the scores described below are generated. Additionally, in the description below, the satisfaction-threshold may be the satisfaction-threshold 547 mentioned above and/or the scores computed for the location may be scores 548 mentioned above.

[0437] It is to be noted that the exemplary embodiments described below may be considered embodiments of systems modeled according to FIG. 9 and/or embodiments of methods modeled according to FIG. 10, which are discussed above. FIG. 9 and FIG. 10 pertain to embodiments in which alerts are generated for locations in general, while the embodiments described below involve a specific type of location (one in which a service is provided) and a specific type of alert (an alert related to satisfaction of customers). Thus, the teachings provided above, with respect to embodiments modeled according to FIG. 9 and/or FIG. 10, are to be considered applicable, mutatis mutandis, to the embodiments discussed below.

[0438] In one embodiment, a system, such as illustrated in FIG. 9, is configured to alert about unsatisfied customers at a location at which a service is provided (e.g., the location 546). The system includes at least the collection module 120, the dynamic scoring module 180, and an alert module 184. Optionally, the system may include additional modules such as the personalization module 130 and/or location verifier module 505.

[0439] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response, which in this embodiment comprise measurements of customers who were at the location. Optionally, the measurements 501 of the customers are taken utilizing sensors coupled to the customers. In one example, each measurement of affective response of a customer is taken utilizing a sensor coupled to the customer, and the measurement comprises at least one of the following: a value representing a physiological signal of the customer, and a value representing a behavioral cue of the customer. Optionally, each measurement of affective response of a customer is based on values acquired by measuring the customer during at least three different non-overlapping periods while the customer was at the store. Additional information regarding sensors and how measurements of affective response of customers may be collected may be found at least in sections 1—Sensors and 2—Measurements of Affective Response.

[0440] In one embodiment, measurements of affective response of the customers at the location are taken utilizing an image capturing device that captures images describing at least one of the following: facial expressions of the customers, body language of the customers. For example, the image capturing device may be a CCTV camera or other form of video camera that is placed in the location. In another example, the image capturing device may be a camera of a device (e.g., a head-mounted display) of a customer that captures images of one or more other customers.

[0441] In one embodiment, the dynamic scoring module 180 is configured to compute the scores for the location (e.g., the scores 548) based on the measurements 501. Optionally, in this embodiment, the scores are indicative of a level of satisfaction of the customers while at the location. Additionally or alternatively, the scores may express other values related to being at the location, such as an average mood of customers at the store. Optionally, each of the scores corresponds to a time t and is computed based on measurements of at least five customers taken at a time that is after a first period before the time t to which the score corresponds, but not after that time t.

[0442] Measurements received by the collection module 120 may be utilized in various ways in order to compute the score corresponding to the time t. In one example, measurements taken earlier than the first period before the time t are not utilized by the dynamic scoring module 180 to compute the score corresponding to t. In another example, the dynamic scoring module 180 is configured to assign weights to measurements used to compute the score corresponding to the time t, such that an average of weights assigned to measurements taken earlier than the first period before t is lower than an average of weights assigned to measurements taken later than the first period before t. Optionally, these weights are taken into account by the dynamic scoring module 180 when computing the score corresponding to t.

[0443] In one embodiment, the alert module 184 evaluates the scores for the store in order to determine whether to issue an alert, e.g., in the form of the notification 537. If a score corresponding to a certain time falls below a satisfaction-threshold (e.g., the satisfaction-threshold 547), a notification is forwarded by the alert module 184. Optionally, the notification is forwarded no later than a second period after the certain time. Optionally, in this embodiment, the notification is indicative of a level of satisfaction of customers at the location and/or expresses a level of another emotional state (e.g., happiness, calmness, and/or mental stress). Optionally, in this embodiment, both the first and the second periods are shorter than twelve hours. In one example, the first period is shorter than four hours and the second period is shorter than two hours. In another example, both the first and the second periods are shorter than one hour. Optionally, the dynamic nature of the scores computed for the store is such that for at least a certain first time t₁ and a certain second time t₂, a score corresponding to t₁ does not fall below the satisfaction-threshold and a score corresponding to t₂ falls below the satisfaction-threshold; here t₂>t₁, and the score corresponding to t₂ is computed based on at least one measurement taken after t₁.

[0444] The alert module 184 may be configured, in some embodiments, to determine whether to cancel an alert. For example, the alert module 184 may be configured to determine whether, after a score corresponding to a certain time t falls below the satisfaction-threshold, a second score, corresponding to a later time occurring after the certain time t, is above the satisfaction-threshold. Responsive to the second score being above the satisfaction-threshold, the alert module 184 may forward a notification indicative of the second score being above the satisfaction-threshold (thus a recipient may understand that the previously indicated customer dissatisfaction has passed).

[0445] In one embodiment, the location for which alerts are generated exists in a space in the physical world, and each of the at least five customers whose measurements were used to compute the score corresponding to the time t was at the location at some time between the first period before t and the time t. Optionally, the location verifier module 505 is utilized to determine when a customer is at the location. Optionally, when a score for the location falls below the satisfaction-threshold, a notification is forwarded to a first recipient whose distance from the location is below a distance-threshold, and the notification is not forwarded to a second recipient whose distance from the location is above the distance-threshold. For example, the distance-threshold may be a distance of fifteen miles.

[0446] In another embodiment, the location for which alerts are generated is in a virtual environment (e.g., a store in a virtual mall). Optionally, the virtual environment is hosted on at least one server, and each of the at least five customers whose measurements were used to compute the score corresponding to the time t accessed, at some time between the first period before t and the time t, data that originated from the at least one server.

[0447] In one embodiment, the map-displaying module 240 may be utilized to present on a display: a map comprising a description of an environment that includes the location for which alerts are generated, and an annotation overlaid on the map and indicating at least one of: the score corresponding to the certain time, the certain time, and the location.

[0448] In one embodiment, a method for alerting about unsatisfied customers at a location at which a service is provided (e.g., the location 546) includes at least the following steps:

[0449] In step 1, receiving, by a system comprising a processor and memory, measurements of affective response of customers who are at the location at which the service is provided.

[0450] In step 2, computing a score for the store, which corresponds to a time t, based on measurements of at least five of the customers, which were taken at a time that is after a first period before t, but not after t. Optionally, the score is indicative of the satisfaction level of the at least five of the customers.

[0451] In step 3, determining whether the score falls below a satisfaction-threshold.

[0452] And in step 4, responsive to the score falling below the satisfaction-threshold, forwarding a notification indicative of the score falling below the satisfaction-threshold. Optionally, the notification is forwarded no later than a second period after t. Optionally, the notification is forwarded to a first recipient whose distance from the location is below a distance-threshold, and the notification is not forwarded to a second recipient whose distance from the location is above the distance-threshold.

[0453] In one embodiment, both the first and second periods are shorter than twelve hours. In one embodiment, for at least a first time t₁ and a second time t₂, a score corresponding to t₁ does not fall below the satisfaction-threshold and a score corresponding to t₂ falls below the satisfaction-threshold. In this case t₂>t₁, and the score corresponding to t₂ is computed based on at least one measurement taken after t₁.

[0454] In one embodiment, the method described above includes an additional step of presenting on a display: a map comprising a description of an environment that comprises the location, and an annotation overlaid on the map and indicating at least one of: the score corresponding to the certain time, the certain time, and the location.

[0455] In one embodiment, the method described above includes additional steps that comprises determining whether, after a first score corresponding to a certain time falls below the satisfaction-threshold, a second score, corresponding to a later time occurring after the certain time, is above the satisfaction-threshold. Responsive to the second score being above the satisfaction-threshold, the method includes a step of forwarding a notification indicative of the second score being above the satisfaction-threshold.

[0456] Many people frequently eat at restaurants. Eating food from restaurants is often a fun experience, enabling people to try different types of cuisines, without needing to possess the required expertise, facilities, and/or time that are often necessary to prepare the food. However, there is a risk that in some cases, food prepared at a certain restaurant and/or facility may adversely affect the diners' health. There are various reasons why food from a restaurant may end up being harmful. In one example, the restaurant may use tainted food and/or improperly store or prepare the food, which may cause diners to suffer from food poisoning. In another example, sanitary conditions at the restaurant, e.g., due to improperly maintained personal hygiene of staff at the restaurant, may put diners at risk of becoming ill due to various pathogens.

[0457] Being able to alert about restaurant that may adversely affect diners' health is important, both on a personal level for individual users, and from a public safety perspective. At the personal level, each user would appreciate being warned about a restaurant that may cause the user to be sick, in order to avoid that location. From a public safety perspective, being able to identify restaurants that adversely affect the health of diners can help public health officials quickly address the problem (e.g., by closing a dangerous restaurant). Promptly taking an action may help avert wide-spread food-related epidemics, such as cases where a large portion of the occupants of a cruise ship become ill due to food poisoning. Thus, there is a need for ways to quickly discover when eating food from, and/or at, a certain restaurant has an immediate adverse effect on the health of diners.

[0458] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that enable generation of alerts about how eating at a restaurant may have an adverse effect on the health of diners. The alerts may be generated if a score computed based on measurements of affective response of diners that ate the restaurant falls below a certain wellness-threshold. In such a case, a notification may be sent to various entities, such as users who may consider dining at the restaurant and/or public health officials. In some embodiments, an alert is time sensitive, since it may be derived from a score computed based on measurements of affective response taken during a certain time-frame, and therefore, may represent affective response of diners during the certain time-frame. Thus, if the time-frame is not too large, changes in affective response of multiple diners, who ate at the restaurant more or less at the same time, may help identify when a restaurant has an adverse effect on the diners' health. Since an alert is based on scores computed from multiple diners (in this case, an alert is a crowd-based result), it is likely to reflect a property of the restaurant and not a condition of a single diner (e.g., when a diner has a negative affective response due to being sick with the flu).

[0459] Herein, a restaurant may be any establishment that provides food and/or beverages. Optionally, a restaurant may offer people an area in which they may consume the food and/or beverages. In some embodiments, a reference made to “a restaurant” and/or “the restaurant” refers to a distinct location in the physical world (e.g., a certain address). In other embodiments, a reference made to “a restaurant” and/or “the restaurant” refers to a location of a certain type, such as any location of a certain chain restaurant. In such embodiments, measurements of affective response of users who ate at “the restaurant” may include measurements taken at different locations, such as different restaurants of the same franchise. Herein, a “diner at restaurant” may be any person who ate food prepared at the restaurant. Optionally, a diner may eat the food at the restaurant. Alternatively, the diner may eat the food prepared at the restaurant at some other location. Thus, in some embodiments, a score for a restaurant may be a “franchise score”, and alert for a restaurant may be an alert for the franchise, a ranking of restaurants may be a ranking of different franchises, etc.

[0460] In some embodiments, a score computed based on measurements of affective response of diners is indicative of the state of health and/or a change to the state of health of the diners. Optionally, the score may be based on measurements of one or more sensors that measure physiological signals such as heart rate, heart rate variability, skin conductance, skin temperature, and/or brainwave activity. Optionally, the score may be based on measurements of one or more sensors that measure behavioral cues such as shivering, vomiting, and/or body language indicative of discomfort. In some embodiments, a score indicative of the state of health of diners (and/or a change to the state of health) may be indicative of the extent to which, on average, the diners suffer from food poisoning and/or other illnesses with similar symptoms.

[0461] Symptoms from the most common types of food poisoning will often start within two to six hours of eating tainted food. Some of the symptoms of food poisoning include nausea, vomiting, diarrhea, abdominal pain and cramps, and fever. That time may be longer or shorter, depending on the cause of the food poisoning. Thus, measurements of affective response of diners taken up to twelve hours after eating food at restaurant are likely to reflect symptoms of food poisoning if the food had such an adverse effect on the diners.

[0462] The symptoms mentioned above are likely to cause changes to physiological signals and/or behavioral cues of users. In some embodiments, computing a score indicative of the state of diners utilizes a model trained on data comprising measurements of affective response of users taken while the users were sick (e.g., suffering from the flu, food poisoning, and/or other sicknesses with similar symptoms). Optionally, the measurements used to train the models include various physiological signals, as described in more detail in section 1—Sensors. Additionally or alternatively, the measurements used to train the models include values indicative of various behavioral cues that may be helpful in identifying sickness and/or its intensity, such as detecting shivering, nausea, vomiting, frequency of visiting the bathroom, drowsiness, and/or a general mood (which tends to be more negative when people are sick). Additional details regarding how a model may be used to compute a score indicative of a condition of user (e.g., the health state of the diners) is given in this disclosure at least in section 10—Scoring.

[0463] In some embodiments, scores, upon which alerts corresponding to a certain restaurant may be based, are computed based on measurements of affective response of diners, taken up to twelve hours after they ate food from the restaurant. Thus, at different times, the certain restaurant may have different scores computed for it, depending on the diners that were there and their measurements at that time. A decision to generate an alert, e.g., by issuing a notification indicative about a score computed for the restaurant, is dependent on the score falling below a wellness-threshold. Therefore, an alert may be generated (or canceled) at a certain time depending on the value of a score corresponding to the certain time.

[0464] Such a behavior of alerts for restaurants is illustrated in FIG. 13. This figure illustrates scores 553 (represented by dots on the graph), which are computed for a restaurant 550, for different times during a couple of days of the week. The scores 553 represent a state of health of the diners (e.g., how much the display characteristics of being healthy), as determined based on measurements taken during a certain period of time following their eating at the restaurant. Each dot on the graph represents a certain score from among the scores 553, which corresponds to a certain time t, based on the position of the dot on the horizontal time line. The height of the dot in the plot is indicative of the state of health of the diners during a certain period of time leading up to the time t. Each of the scores 553, which corresponds to a certain time t, is computed based on measurements of at least five of the diners, which were taken up to twelve hours after they ate at the restaurant 553. Optionally, each of the measurements of affective response used to compute a score corresponding to a time t, was taken at a time that was after a first period before t, and not later than t. In one example, the first period is less than 24 hours, therefore a score corresponding to a time t may be based on measurements of diners who ate at the restaurant 550 up to 36 hours before t. In another example, the first period is 6 hours, such that a score corresponding to the time t is based on measurements of diners that ate at the restaurant 550 at most 18 hours before t.

[0465] FIG. 13 illustrates how an alert for the restaurant 550 is generated when a score falls below the wellness-threshold 552. The figure illustrates the scores 553, which were computed for the restaurant 550 over a period of two days (Sunday and Monday). Up to about 11 PM on Sunday, the scores were generally positive (above neutral), indicating that diners who ate food from the restaurant felt alright. However, after 11 PM the scores fall dramatically indicating that diners who ate at the restaurant earlier that day (e.g., six or twelve hours earlier) have become sick. The cause may be some contamination in the food served to the lunch and/or early dinner crowds at the restaurant.

[0466] Scores corresponding to Sunday 2 AM and later fall below the wellness-threshold 552, and thus, may lead to the generation of an alert by issuing a notification to one or more parties, such as managers of the restaurant 550, public health officials, and/or users who may consider eating at the restaurant 550. Note that even if an immediate action is taken as a result of the notification, such as closing the restaurant, additional scores may still be negative and below the wellness-threshold 552. This may happen because of the time it takes symptoms to manifest themselves. However, a prompt response (even after a few hours) can help limit the number of people who become sick.

[0467] Following are exemplary embodiments of systems and methods that may be used to compute scores and/or generate alerts for a restaurant, as illustrated in FIG. 13. In one example, the restaurant 550 may be considered the restaurant for which the scores described below are computed. Additionally, in the description below, the wellness-threshold may be the wellness-threshold 552 mentioned above and/or the scores computed for the restaurant may be scores 553 mentioned above.

[0468] It is to be noted that the exemplary embodiments described below may be considered embodiments of systems modeled according to FIG. 9 and/or embodiments of methods modeled according to FIG. 10, which are discussed above. FIG. 9 and FIG. 10 pertain to embodiments in which alerts are generated for locations in general, while the embodiments described below involve a specific type of location (a restaurant) and a specific type of alert (an alert related to a state of health of diners). Thus, the teachings provided above, with respect to embodiments modeled according to FIG. 9 and/or FIG. 10, are to be considered applicable, mutatis mutandis, to the embodiments discussed below.

[0469] In one embodiment, a system, such as illustrated in FIG. 9, is configured to alert about sickness after eating at a restaurant (e.g., the restaurant 550). The system includes at least the collection module 120, the dynamic scoring module 180, and an alert module 184. Optionally, the system may include additional modules such as the personalization module 130 and/or location verifier module 505.

[0470] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response, which in this embodiment comprise measurements of diners who ate at the restaurant. Optionally, the measurements 501 of the diners are taken utilizing sensors coupled to the diners. In one example, each measurement of affective response of a diner is taken up to twelve hours after the diner ate at the restaurant, utilizing a sensor coupled to the diner, and the measurement comprises at least one of the following: a value representing a physiological signal of the diner, and a value representing a behavioral cue of the diner. Optionally, each measurement of affective response of a diner is based on values acquired by measuring the diner during at least three different non-overlapping periods occurring up to twelve hours after the diner ate at the restaurant. Additional information regarding sensors and how measurements of affective response of diners may be collected may be found at least in sections 1—Sensors and 2—Measurements of Affective Response.

[0471] In one embodiment, the dynamic scoring module 180 is configured to compute the scores for the restaurant (e.g., the scores 550) based on the measurements 501. Optionally, in this embodiment, the scores are indicative of a state of the health of diners who ate at the restaurant. Additionally or alternatively, the scores may express other values related to health, such as an average mood of diners who ate at the restaurant. Optionally, each of the scores corresponds to a time t and is computed based on measurements of at least five diners taken at a time that is after a first period before the time t to which the score corresponds, but not after that time t.

[0472] Measurements received by the collection module 120 may be utilized in various ways in order to compute the score corresponding to the time t. In one example, measurements taken earlier than the first period before the time t are not utilized by the dynamic scoring module 180 to compute the score corresponding to t. In another example, the dynamic scoring module 180 is configured to assign weights to measurements used to compute the score corresponding to the time t, such that an average of weights assigned to measurements taken earlier than the first period before t is lower than an average of weights assigned to measurements taken later than the first period before t. Optionally, these weights are taken into account by the dynamic scoring module 180 when computing the score corresponding to t. In one example, the first period may be more than 24 hours. In another example, the first period may be shorter than 24 hours, such as between 12 hours and 24 hours. In yet another example, the first period is between 6 and 12 hours. And in still another example, the first period is less than 6 hours.

[0473] In one embodiment, the alert module 184 evaluates the scores for the restaurant in order to determine whether to issue an alert, e.g., in the form of the notification 537. If a score corresponding to a certain time falls below a wellness-threshold (e.g., the wellness-threshold 552), a notification is forwarded by the alert module 184. Optionally, the notification is forwarded no later than a second period after the certain time. Optionally, in this embodiment, the notification is indicative of a state of the health of diners at the restaurant and/or expresses a level of another emotional state (e.g., happiness, calmness, and/or mental stress). Optionally, in this embodiment, both the first and the second periods are shorter than twelve hours. In one example, the first period is shorter than four hours and the second period is shorter than two hours. In another example, both the first and the second periods are shorter than one hour. Optionally, the dynamic nature of the scores computed for the restaurant is such that for at least a certain first time t₁ and a certain second time t₂, a score corresponding to t₁ does not fall below the wellness-threshold and a score corresponding to t₂ falls below the wellness-threshold; here t₂>t₁, and the score corresponding to t₂ is computed based on at least one measurement taken after t₁.

[0474] The alert module 184 may be configured, in some embodiments, to determine whether to cancel an alert. For example, the alert module 184 may be configured to determine whether, after a score corresponding to a certain time t falls below the wellness-threshold, a second score, corresponding to a later time occurring after the certain time t, is above the wellness-threshold. Responsive to the second score being above the wellness-threshold, the alert module 184 may forward a notification indicative of the second score being above the wellness-threshold (thus a recipient may understand that the previously indicated threat has passed).

[0475] In one embodiment, the wellness-threshold represents an affective response that an average user would have when experiencing a mild case of the flu. Optionally, the wellness-threshold represents a change, to a certain extent, to one or more physiological signals. In one example, the wellness-threshold may correspond to an increase of at least 2° F. in the body temperature. In another example, the wellness-threshold may correspond to an increase of at least 10 beats-per-minute to the heart rate. Additionally or alternatively, the wellness-threshold may represent a certain affective value, such as a value on a scale of 1 to 10 indicating how well a person feels. Optionally, the wellness-threshold is below the average value and/or baseline value users have when they are not sick. For example, if on average measurements of affective response, and/or self-reporting of users, correspond to a score of 6, the wellness-threshold is set to a lower value, such as 3. Optionally, the lower value is a value corresponding to a state of sickness, as determined based on measurements of affective response, and/or self-reports of users.

[0476] In one embodiment, the map-displaying module 240 may be utilized to present on a display: a map comprising a description of an environment that includes the restaurant for which alerts are generated, and an annotation overlaid on the map and indicating at least one of: the score corresponding to the certain time, the certain time, and the restaurant. Optionally, when an alert is generated for a restaurant it is removed from the map, so a user viewing the map does not see it.

[0477] In one embodiment, the location verifier module 505 is utilized to determine when a diner ate at the restaurant. In one example, the location verifier module 505 may utilize billing information (e.g., a credit card transaction, a digital wallet transaction, or the like), in order to determine when the user ate at the restaurant. Optionally, the billing information indicates at least one of the following data: the identity of the restaurant, the identity of the user, and the time of a transaction in which the user paid the restaurant.

[0478] In one embodiment, when a score for the restaurant falls below the wellness-threshold, a notification is forwarded to a first recipient whose distance from the restaurant is below a distance-threshold, and the notification is not forwarded to a second recipient whose distance from the restaurant is above the distance-threshold. For example, the distance-threshold may be a distance of fifteen miles. Optionally, a software agent operating on behalf of a user receives the notification issued by the alert module 184 and forwards it to the user if the distance between the user and the restaurant falls below the distance-threshold.

[0479] In one embodiment, a method for alerting about sickness after eating food at a restaurant (e.g., the restaurant 550) includes at least the following steps:

[0480] In step 1, receiving, by a system comprising a processor and memory, measurements of affective response of diners who ate at the restaurant. Optionally, each measurement of a diner is taken with a sensor coupled to the diner at most twelve hours after the diner ate at the restaurant. Optionally, each measurement of affective response of a diner is based on values acquired by measuring the diner with the sensor during at least three different non-overlapping periods that end at most twelve hours after the diner left the restaurant.

[0481] In step 2, computing a score for the restaurant, which is indicative of a state of the health of diners who ate at the restaurant. Optionally, the score corresponds to a time t and is based on measurements of at least five of the diners, taken at a time that is after a first period before t, but not after t. Optionally, the first period is shorter than 24 hours.

[0482] In step 3, determining whether the score falls below a wellness-threshold (e.g., the wellness-threshold 552).

[0483] And in step 4, responsive to the score falling below the wellness-threshold, forwarding a notification indicative of the score falling below the wellness-threshold. Optionally, the notification is forwarded no later than a second period after t. Optionally, the notification is forwarded to a first recipient whose distance from the restaurant is below a distance-threshold, and the notification is not forwarded to a second recipient whose distance from the restaurant is above the distance-threshold.

[0484] In one embodiment, both the first and second periods are shorter than twelve hours. In one embodiment, for at least a first time t₁ and a second time t₂, a score corresponding to t₁ does not fall below the wellness-threshold and a score corresponding to t₂ falls below the wellness-threshold. In this case t₂>t₁, and the score corresponding to t₂ is computed based on at least one measurement taken after t₁.

[0485] In one embodiment, the method described above includes an additional step of presenting on a display: a map comprising a description of an environment that comprises the restaurant, and an annotation overlaid on the map and indicating at least one of: the score corresponding to the certain time, the certain time, and the restaurant.

[0486] In one embodiment, the method described above includes additional steps that comprises determining whether, after a first score corresponding to a certain time falls below the wellness-threshold, a second score, corresponding to a later time occurring after the certain time, is above the wellness-threshold. Responsive to the second score being above the wellness-threshold, the method includes a step of forwarding a notification indicative of the second score being above the wellness-threshold.

[0487] In one embodiment, the method described above includes an additional step of determining when a diner ate at the restaurant based on billing information of the diner. Optionally, a measurement of affective response of the diner is based on values acquired up to twelve hours after the determined time, by a sensor coupled to the diner.

[0488] Virtual environments, such as environments involving massively multiplayer online role-playing games (MMORPGs) and/or virtual worlds (e.g., Second Life) have become very popular options for recreational activities. With the improvements in virtual reality, graphics, and network latency and capacity, virtual environments have also become a place where users meet for business and/or social interactions. Being able to visit and/or interact in a virtual environment typically involves logging into a server that hosts the virtual environment. The server may be used to perform various computations required for the virtual environment, as well as serve as a hub through which users may communicate and interact. Some virtual environments may allow large numbers of users to connect to them (each possibly connecting to a different instantiation of the virtual environment). Thus, a virtual environment may be hosted on multiple servers.

[0489] Depending on which server a user is logged into, the user may be provided with a different quality of experience. The quality of the experience that a user logged into a server has may be influenced by various factors. In one example, the quality of the experience is influenced by technical factors, such as the quality of connection with the server (e.g., network latency) and/or the load on the server, which may be proportional to the number of users connected to it. In another example, the quality of the experience may depend on the identity and/or behavior of the users connected to the server (e.g., are the pleasant people to interact with). And in still another example, the quality of the experience may be influenced by the characteristics of the instantiation of the virtual world that is presented to users on the server, such as how interesting is the area of the virtual world hosted on the server, or whether user logged into the server have an exciting mission to complete at the time.

[0490] The factors that can influence the quality of the experience, which a user logged into a server hosting a virtual environment may have, are often quite dynamic and may change in a short time. In one example, a positive experience provided by a server can quickly deteriorate due to communication problems with the server and/or a sudden extensive computational load on the server. In another example, the fact that the composition of users changed (e.g., due to logging on or off of certain users), can also dramatically change the quality of the experience. Thus, there is a need to be able to identify and alert when such a change in the quality of an experience deteriorates. Having such an alert may help users avoid a bad experience and/or help the system improve its quality of service.

[0491] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that enable generation of alerts regarding a determination in the quality of experience associated with a certain server. The alerts may be generated if a score computed based on measurements of affective response of users that are logged into the server falls below a certain threshold. In such a case, a notification may be sent to various entities, such as users who may consider logging into the server, users who are currently logged into the server, and/or a system administrator.

[0492] In some embodiments, an alert is time sensitive, since it may be derived from a score computed based on measurements of affective response taken during a certain time-frame, and therefore, may represent affective response of users during the certain time-frame. Additionally, since an alert is based on scores computed from multiple users (in this case an alert is a crowd-based result), it is likely to reflect a property of the server and not a condition specific to a single user.

[0493] Herein, a virtual environment, is an environment such as a virtual world, which may have one or more instantiations, with each instantiation of the virtual environment being stored in a memory of a computer (e.g., the memory 402 of the computer 400). Herein, a virtual environment may be represented by a server hosting it. Thus, in some embodiments, users connected to different servers are considered to be in different virtual environments (even if the servers host the same world and/or game).

[0494] It is to be noted that herein, a server may refer to a single computer or to multiple computers connected via a network. In some embodiments, a server that involves multiple computers may be considered a single logical unit (e.g., an instance of a virtual machine), such that as far as users logged in to the server are concerned, the experience they have is essentially the same to the experience they would have had the server been a single (larger) machine. For example, a server involving multiple computers may represent a certain region of a virtual world. In another example, a server involving multiple computers may host a certain group of users, allowing the group of users to interact with each other (e.g., play the same game together). In some embodiments, a server may be a public server, allowing any user who desires to log into it. While in other embodiments, a server may be restricted and/or private, allowing only certain authorized users to log into it.

[0495] Connecting to a server may also be referred to herein as “logging into” the server. Herein, being logged into a server may be considered equivalent as being in a virtual environment hosted by the server. In one embodiment, connecting to a server may be done manually and/or explicitly by a user. In one example, a user may specify a certain name, icon, IP address and/or port representing a server. In another example, the user may select a server from a list or a ranking of servers (i.e., a list of servers ordered according to a certain quality).

[0496] In another embodiment, connecting to a server may be done automatically without a user explicitly selecting a server and/or initiating a connection to the server. In one example, such an automatic login of a user into a server may be done by a software agent operating on behalf of the user. In another example, when a user turns on a device that enables interaction in a virtual world (e.g., turning on a gaming platform) and/or wears a device that enables interaction in a virtual world (e.g., by wearing a virtual reality headset) the user may be logged in automatically to a certain server that hosts a virtual environment. Optionally, the server may be preselected (e.g., it was selected by the user and/or was the last server the user was logged into) and/or selected by a software agent operating on behalf of the user.

[0497] In some embodiments, logging into a server may involve providing information representing the user. In one example, such information may include one or more of the following: a user name, an account identification, a password, an encryption key, and/or other values that represent the user and does not typically represent other users. In another example, logging into a server may require a user to provide biometric information that may be used to identify the user. Examples of biometric information may include, but are not limited to, the following: an image of the face of the user, an image of an eye of the user, information derived from monitoring cardiac activity of the user, and/or information derived from recording of brainwave activity of the user.

[0498] In some embodiments, logging in and logging out of a server (i.e., disconnecting) may involve establishing and/or terminating communications with a server. In other embodiments, a communication with a server may be maintained, but a user may be considered to log in and log out of a server simply by virtue of engaging with a user interface that enables communication with the server. In one example, wearing a virtual reality headset may be considered logging on to a server, while removing the headset may be considered logging out. In this example, the device itself may be continually in communication with the server; however, the act of putting on and taking of the headset, from the perspective of the user, may be considered establishing and terminating connection with the server, respectively.

[0499] It is to be noted that in this disclosure when a server hosts a virtual environment which is a location the user may enter (e.g., a virtual world in a game or a virtual meeting place), logging into a server, in the various ways described above may be considered entering the location. Similarly, logging out of the server may be considered leaving the location.

[0500] Hosting a virtual environment may involve storing information representing a state of an instantiation of the virtual environment. In some embodiments, the state of the instantiation of a virtual environment may describe: (i) objects and/or characters controlled by the virtual environment (e.g., by an AI entity operating on behalf of the virtual environment), and/or (ii) objects and/or characters controlled by a user logged into a server hosting the virtual environment and/or by a software agent operating on behalf of the user. In some embodiments, a server hosting a virtual environment may manage at least some of the aspects concerning user interactions in the virtual environment, such as receiving actions a user performs and/or forwarding to a user reactions of other entities in the environment (e.g., entities controlled by an AI or other users). In some embodiments, hosting a virtual environment may involve performing at least some of the computations involved in presenting the virtual environment to users (e.g., rendering of images, sounds, and/or haptic feedback).

[0501] There may be different relationships between servers and virtual environments hosted on the servers in embodiments described herein. In one embodiment, each virtual environment is hosted on a certain server (so being in the virtual environment involves logging into the certain server). Thus, different servers may each correspond to a different virtual environment (e.g., each server represents a certain game, store in a mall, or virtual world). In another embodiment, different instantiations of a virtual environment may be hosted on different servers. In one example, a server may host an instantiation that enables a certain set of users to interact. In another example, a server may host a portion of a virtual world. In still another embodiment, different instantiations of a virtual world may be hosted on the same machine; however, since they involve different instantiations they may be considered to be hosted on separate servers (e.g., each server being run in a different virtual environment running on the same physical machine).

[0502] In some embodiments, different servers may be located in different physical locations and may host users from various locations around the world. The different servers may be implemented with different hardware and/or have different communication constraints (e.g., latency and/or throughput limitations). This may lead to a phenomenon in which different servers may provide a different experience (despite hosting the same game and/or virtual world).

[0503] In other embodiments, different servers may be used for specific purposes, such as hosting a specific activity, a specific game, and/or a specific mission to be completed in a virtual world. Thus, users who want to play a certain game, complete a certain mission, etc., need to log into a specific server dedicated for that purpose.

[0504] As discussed above, in some embodiments, different servers may host different regions of a virtual world. In one example, a server may host region of a virtual world may be a certain realm in World of Warcraft; thus, user s who wish to enter the realm must connect to that server. In another example, a server may be a simulator of a certain region in Second Life. Thus, users who want to enter the region and interact with other users in that region must connect to that server. In still another example, a server may host certain areas of a virtual mall.

[0505] In some embodiments, a user may be in a virtual environment, interact with a virtual environment, and/or interact with other users in a virtual environment. Optionally, a user is considered to be in a virtual environment by virtue of having a value stored in a memory that hosts the virtual environment, which indicates a presence of a representation of the user in the virtual environment. Optionally, different locations in virtual environment correspond to different logical spaces in the virtual environment. For example, different rooms in an inn in a virtual world may be considered different locations. In another example, different continents in a virtual world may be considered different locations. In yet another example, different stores in a virtual mall may be considered different locations.

[0506] When logged into a server hosting a virtual environment, there may be various ways in which a user may view occurrences in the virtual environment and/or interact in (or with) the virtual environment. In one embodiment, logging into a server hosting a virtual environment (or a region of a virtual environment) may enable the user to view things that are happening in the virtual environment (e.g., content generated by the virtual environment and/or actions of other users in the virtual environment). In another embodiment, a user may interact with a graphical user interface in order to participate in activities within a virtual environment. In some embodiments, a user may be represented in a virtual environment as an avatar. Optionally, the avatar of the user may represent the presence of the user at a certain location in the virtual environment. Furthermore, by seeing where the avatar is in the virtual environment, other users may determine the location of the user in the virtual environment.

[0507] The quality of experience users have when they are logged into a certain server may change over time. There may be various factors upon the quality of the experience may depend.

[0508] In one example, the quality of the experience a user has when the user is logged into a server may depend on technical factors such as the distance of the user from the server, the load on the server, and/or network throughput bottlenecks. Thus, if the user is too far away, the server is too heavily burdened with computations, and/or the network throughput is too low, the experience may be suboptimal. For example, the user may experience unsmooth graphics and/or sluggish system responses. This can lead to frustration and a negative experience in general.

[0509] In another example, the quality of the experience a user has when the user is logged into a server may depend on the identity and/or behavior of other players on the server. For example, if the user is a novice and there are many expert users on the server, the user may be frustrated from the difference in skills (or the other users may be frustrated from the user). In another example, some users may behave inappropriately (e.g., be aggressive and/or behave in a way unbefitting and/or unsupportive of a mission). Interacting with such users may negatively affect the experience a user has.

[0510] In still another example, the quality of the experience a user has when the user is logged into a server may depend on the condition of the instantiation virtual world hosted by the server. In one example, a server may host a region that is undeveloped, e.g., lacking few interesting features (e.g., with few entities to interact with and/or uninspiring scenery). In another example, the server might have hosted an interesting mission that had already been completed, thus at the present time there is not much happening on the server that may hold a user's interest.

[0511] In some embodiments, a score computed based on measurements of affective response of users that are logged into a server hosting a virtual environment is indicative of an emotional state of users who are logged into the server. In one example, the score may be indicative of the average mood of the users who are logged in. In another example, the score may be indicative of the average level of enjoyment, happiness, excitement, and/or degree of engagement of the user. Optionally, the score may be based on measurements of one or more sensors that measure physiological signals such as heart rate, heart rate variability, skin conductance, skin temperature, and/or brainwave activity. Optionally, the score may be based on measurements of one or more sensors that measure behavioral cues such as yawning, smiling, and/or frowning.

[0512] In some embodiments, scores, upon which alerts corresponding to a server may be based, are computed based on measurements of affective response of users logged into the server, taken while the users were logged in. Thus, at different times, the server may have different scores computed for it, depending on the users who are logged in, performance characteristics of the server and/or network, and/or the state of the instantiation of the virtual world at that time. A decision to generate an alert, e.g., by issuing a notification indicative about a score computed for the server, is dependent on the score falling below a threshold. Therefore, an alert may be generated (or canceled) at a certain time depending on the value of a score corresponding to the certain time.

[0513] Such a behavior of alerts for server is illustrated in FIG. 14. This figure illustrates a crowd 500, which in the illustrated embodiment includes users logged into a server 555. The figure illustrates scores 557 (represented by dots on the illustrated graph), which are computed for the server 555, for different times during a certain period of almost 24 hours. The scores 557 represent an emotional state of the users, such as their average level of enjoyment on a scale of 0 to 10. This average level of enjoyment is determined based on measurements of affective response of the users while they were logged in to the server 555. Each dot on the graph represents a certain score from among the scores 557, which corresponds to a certain time t, based on the position of the dot on the horizontal time line. The height of the dot in the plot is indicative of the state of the level of enjoyment of the users during a certain period of time leading up to the time t. Each of the scores 557, which corresponds to a certain time t, is computed based on measurements of at least five of the users, which were taken while they were logged into the server 555. Optionally, each of the measurements of affective response used to compute a score corresponding to a time t, was taken at a time that was after a first period before t, and not later than t. In one example, the first period is less than one hour.

[0514] FIG. 14 illustrates how an alert for the server 555 is generated when a score falls below the threshold 556. The figure illustrates the scores 557, which were computed for the server 555 over a period of almost 24 hours. Up to 12 AM, the scores were generally positive (above the threshold 556), indicating that users who were logged in were sufficiently enjoying themselves. However, a little after 12 AM the scores fall dramatically indicating that users who were logged in started to have a less enjoyable experience. In this example, the cause for the deterioration in the enjoyment may be any of the reasons mentioned above (e.g., hardware and/or networking problems, unfriendly users logging in, and/or the instantiation of the virtual world hosted on the server became boring). Falling below the threshold 556 may have led to the generation of an alert by issuing a notification to one or more parties, such users logged in to the server 555, users that may consider logging into the server 555, and/or to a system administrator (e.g., a human and/or a software agent).

[0515] Following are exemplary embodiments of systems and methods that may be used to compute scores and/or generate alerts for a server, as illustrated in FIG. 14. In one example, the server 555 may be considered the server for which the scores described below are computed. Additionally, in the description below, the threshold may be the threshold 556 mentioned above and/or the scores computed for the server may be scores 557 mentioned above.

[0516] It is to be noted that the exemplary embodiments described below may be considered embodiments of systems modeled according to FIG. 9 and/or embodiments of methods modeled according to FIG. 10, which are discussed above. FIG. 9 and FIG. 10 pertain to embodiments in which alerts are generated for locations in general, while the embodiments described below involve a specific type of location (a virtual location hosted on a server) and a specific type of alert (an alert related to the emotional state of users logged in). Thus, the teachings provided above, with respect to embodiments modeled according to FIG. 9 and/or FIG. 10, are to be considered applicable, mutatis mutandis, to the embodiments discussed below.

[0517] In one embodiment, a system, such as illustrated in FIG. 9, is configured to alert about negative affective response of users logged into a server that hosts a virtual environment (e.g., the server 555). The system includes at least the collection module 120, the dynamic scoring module 180, and an alert module 184. Optionally, the system may include additional modules such as the personalization module 130.

[0518] In one embodiment, the server comprises one computer or multiple computers connected via a network. Optionally, users logged into the server are able to interact with each other, such as converse with each other, and/or play a game with (or against) each other. In one example, the server may host a certain portion of a virtual world (e.g., corresponding to a certain geographical region of the virtual world).

[0519] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response, which in this embodiment comprise measurements of users who were logged into the server. Optionally, the measurements 501 of the users are taken utilizing sensors coupled to the users. In one example, each measurement of affective response of a user comprises at least one of the following: a value representing a physiological signal of the user, and a value representing a behavioral cue of the user. Optionally, each measurement of affective response of a user is based on values acquired by measuring the user during at least three different non-overlapping periods while the user was logged into the server. Additional information regarding sensors and how measurements of affective response of users may be collected may be found at least in sections 1—Sensors and 2—Measurements of Affective Response.

[0520] In one embodiment, the dynamic scoring module 180 is configured to compute the scores for the server (e.g., the scores 557) based on the measurements 501. Optionally, in this embodiment, the scores are indicative of an emotional state of users who were logged into the server. For example, the scores may express values such as an average mood of users, an average level of enjoyment, and/or a level of engagement. Optionally, each of the scores corresponds to a time t and is computed based on measurements of at least five users taken at a time that is after a first period before the time t to which the score corresponds, but not after that time t. That is, the score corresponding to the time t is based on measurements of affective response of users taken at some point in time that falls between the first period before t and the time t (i.e., a time that is between t minus the first period and t). In one example, the first period may be longer than four hours. In another example, the first period may be shorter than four hours, such as between one hour and four hours. In yet another example, the first period is between ten minutes and one hour. And in still another example, the first period is shorter than ten minutes.

[0521] In one embodiment, the alert module 184 evaluates the scores for the server in order to determine whether to issue an alert, e.g., in the form of the notification 537. If a score corresponding to a certain time falls below a threshold (e.g., the threshold 556), a notification is forwarded by the alert module 184. Optionally, the notification is forwarded no later than a second period after the certain time. Optionally, in this embodiment, the notification is indicative of an emotional state of the users logged into the server (e.g., a level of enjoyment happiness, and/or calmness). Optionally, in this embodiment, both the first and the second periods are shorter than one hour. In another embodiment, the first period is shorter than thirty minutes and the second period is shorter than fifteen minutes. And in another embodiment, both the first and the second periods are shorter than ten minutes. Optionally, the dynamic nature of the scores computed for the server is such that for at least a certain first time t₁ and a certain second time t₂, a score corresponding to t₁ does not fall below the wellness-threshold and a score corresponding to t₂ falls below the wellness-threshold; here t₂>t₁, and the score corresponding to t₂ is computed based on at least one measurement taken after t₁.

[0522] The alert module 184 may be configured, in some embodiments, to determine whether to cancel an alert. For example, the alert module 184 may be configured to determine whether, after a score corresponding to a certain time t falls below the threshold, a second score, corresponding to a later time occurring after the certain time t, is above the threshold. Responsive to the second score being above the threshold, the alert module 184 may forward a notification indicative of the second score being above the threshold (thus a recipient may understand that the previously indicated user dissatisfaction has passed).

[0523] As discussed in more detail in section 12—Alerts, e.g., with regards to FIG. 76a and FIG. 76b, there are various ways in which alerts regarding an emotional state of users logged into a server may be personalized.

[0524] In one example, each user may choose to set his/her own value for a threshold that a score for a server needs to fall below in order for the alert module 184 to issue a notification to the user. Optionally, setting a user's threshold is done by a software agent operating on behalf of the user. Thus, the same score value may fall below one user's threshold (that user will receive a notification), while the score does not fall below another user's threshold (that user will not receive a notification).

[0525] In another example, personalization module 130 may be utilized to generate scores for the server, which are personalized for a certain user based on similarities of a profile of the certain user to profiles of at least some of the shoppers. Optionally, a profile of a user (e.g., the profile of the certain user or of one of the users logged in to the server) may include various demographic information (e.g., age, gender, occupation, spoken languages, and/or place of residence). Additionally, the profile may include information about experiences the user had in the virtual environment (e.g., level of expertise, behavioral patterns, and/or accomplishments such as completed missions, levels, etc.). Additional information that may be included in the profile is described at least in section 11—Personalization.

[0526] In one embodiment, a method for alerting about negative affective response of users logged into a server that hosts a virtual environment (e.g., the server 555) includes at least the following steps:

[0527] In step 1, receiving, by a system comprising a processor and memory, measurements of affective response of users who are logged into the server. Optionally, each measurement of affective response of a user is taken with a sensor coupled to the user while the user is logged into a server. Optionally, each measurement of affective response of a user is based on values acquired by measuring the user with the sensor during at least three different non-overlapping periods while the user was logged into the server.

[0528] In step 2, computing a score for the server, which is indicative of an emotional state of users who are logged into the server. Optionally, the score corresponds to a time t and is based on measurements of at least five of the users, taken at a time that is after a first period before t, but not after t. Optionally, the first period is shorter than one hour.

[0529] In step 3, determining whether the score falls below a threshold (e.g., the threshold 556).

[0530] And in step 4, responsive to the score falling below the threshold, forwarding a notification indicative of the score falling below the threshold. Optionally, the notification is forwarded no later than a second period after t. Optionally, the notification is forwarded to one of the users who are logged into the server (e.g., to prompt them to leave).

[0531] In one embodiment, both the first and the second periods are shorter than one hour. In one embodiment, for at least a first time t₁ and a second time t₂, a score corresponding to t₁ does not fall below the threshold and a score corresponding to t₂ falls below the threshold. In this case, t₂>t₁ and the score corresponding to t₂ is computed based on at least one measurement taken after t₁.

[0532] In one embodiment, the method described above includes additional steps comprising: determining whether, after a score corresponding to a certain time falls below the threshold, a second score, corresponding to a later time occurring after the certain time, is above the threshold, and responsive to the second score being above the threshold, forwarding a notification indicative of the second score being above the threshold.

[0533] In one embodiment, the method described above includes additional steps comprising: receiving a profile of a certain user and profiles of the users, and generating an output indicative of similarities between the profile of the certain user and the profiles of the users, computing scores for the server for a certain user based on the output and the measurements of at least five of the users taken at a time that is at most a first period before t, and not later than t.

[0534] In one embodiment, the method described above includes additional steps comprising: receiving the threshold from at least one of the following entities: a certain user, and a software agent operating on behalf of the certain user. Responsive to the score corresponding to the certain time reaching the threshold, the notification is forwarded to the certain user, no later than the second period after the certain time.

[0535] Some of the embodiments mentioned above relate to alerts that are generated when a score for a location reaches a threshold. Thus, the notification issued by the alert module is typically forwarded after the score reaches the threshold. However, in many cases, it would be beneficial to receive the alert earlier, which indicates an expectation that a score for the location is intended to reach the threshold in a future time. In order to be able to generate such an alert, which corresponds to a future time, some embodiments involve projections of scores for locations corresponding to future times, based on scores for the locations that correspond to earlier times. In some embodiments, projecting a score for a location, which corresponds to a future time, is based on a trend learned from scores for the location, which correspond to earlier times, and which are computed based on measurements that have already been taken.

[0536] Following are various embodiments that involve systems, methods, and/or computer program products that may be utilized to generate alerts and/or make recommendations for locations based on trends learned from scores computed based on measurements of affective response. Optionally, the dynamic scoring module 180 is utilized to compute scores that are utilized to make projections regarding values of scores for an experience. Such scores corresponding to future times may be referred to herein as “projected scores”, “future scores”, and the like.

[0537] Various aspects of systems, methods, and/or computer-readable media, which involve generating notifications (also referred to as “issuing alerts”) about projected scores and/or scores reaching thresholds at future times, are described in more detail at least in section 13—Projecting Scores. That section discusses teachings regarding alerts based on projected scores for experiences in general, which include experiences involving locations (with alerts based on projected scores for locations). Thus, the teachings of section 13—Projecting Scores are also applicable to embodiments described below that explicitly involve locations. Following is a discussion regarding some aspects of systems, methods, and/or computer-readable media that may be utilized to generate such alerts that involve various types of locations.

[0538] FIG. 15 illustrates a system configured to alert about projected affective response to being at a location. The system includes at least the collection module 120, the dynamic scoring module 180, score projector module 200, and alert module 208. The system may optionally include additional modules such as the personalization module 130 and/or the location verifier module 505.

[0539] The collection module 120 is configured to receive measurements 501 of affective response of users (denoted crowd 500). In this embodiment, the measurements 501 comprise measurements of affective response of at least some of the users from the crowd 500 to being at the location 512, which may be any of the locations in the physical world and/or virtual locations described in this disclosure. In one example, the location 512. Optionally, a measurement of affective response of a user who was at the location 512 is based on at least one of the following values: (i) a value acquired by measuring the user, with a sensor coupled to the user, while the user was at the location, and (ii) a value acquired by measuring the user with the sensor up to one hour after the user had left the location. The collection module 120 is also configured, in one embodiment, to provide measurements of at least some of the users from the crowd 500 to other modules, such as the dynamic scoring module 180.

[0540] The dynamic scoring module is configured, in one embodiment, to compute scores 560 for the location 512 based on the measurements received from the collection module 120. Optionally, each score corresponds to a time t and is computed based on measurements of at least ten of the users taken at a time that is after a certain period before t, but not after t. That is, each of the measurements is taken at a time that is not earlier than the time that is t minus the certain period, and not after the time t. Depending on the embodiment, the certain period may have different lengths. Optionally, the certain period is shorter than at least one of the following durations: one minute, ten minutes, one hour, four hours, twelve hours, one day, one week, one month, and one year. The scores 560 include at least scores S₁ and S₂, which correspond to times t₁ and t₂, respectively. The time t₂ is after t₁, and S₂>S₁. Additionally, S₂ is below threshold 563. Optionally, S₂ is computed based on at least one measurement that was taken after t₁. Optionally, S₂ is not computed based measurements that were taken before t₁.

[0541] Embodiments of the system illustrated in FIG. 15 may optionally include location verifier 505. Optionally, measurements used by the dynamic scoring module 180 are based on values obtained during periods for which the location verifier module 505 indicated that the user was at the location 512.

[0542] The score projector module 200 is configured, in one embodiment, to compute projected scores 562 corresponding to future times, based on the scores 560. In one example, the score projector module 200 computes a projected score S₃ corresponding to a time t₃>t₂, based on S₁ and S₂ (and possibly other scores from among the scores 203 corresponding to a time that is earlier than the certain time before the certain future time). Optionally, the score projector module 200 computes a trend based on S₁ and S₂ (and possibly other scores) and utilizes the trend to compute the score S₃. Optionally, the score S₃ represents an expected score for the time t₃, which is an estimation of what the score corresponding to the time t₃ will be. As such, the score S₃ may be considered indicative of expected values of measurements of affective response of users that will be at the location 512 around the time t₃, such as at a time that is after the certain period before t₃, but is not after t₃. Additional details related to projection of scores by the score projector module 200, including projecting scores based on trends, may be found in section 13—Projecting Scores. That section discusses scores for experiences in general, which include experiences involving locations, and is thus relevant to embodiments modeled according to FIG. 15.

[0543] The alert module 208 is configured to determine whether a projected score reaches a threshold, and responsive to the projected score reaching the threshold, to forward, a notification indicative of the projected score reaching the threshold. In one embodiment, the alert module 208 evaluates the scores 562, and the notification 564 is indicative of times when the projected score is to reach the threshold 563. In one example, responsive to S₃ reaching the threshold 563, the alert module 208 forwards, at a time prior to the time t₃, notification 564 which is indicative of S₃ reaching the threshold 563. Additionally, in this example, the alert module 208 may refrain from forwarding a notification indicative of a score S₄ reaching the threshold 563, where S₄ is computed based on S₁ and S₂, and corresponds to a time t₄, where t₂<t₄<t₃. In this example, the score S₄ may be below the threshold 563, and thus, at the time t₂, based on scores computed at that time, it is not expected that a score corresponding to the time t₄ will reach the threshold 563. It may be the case, that until t₂, the scores had not been increasing in a sufficient pace for the scores to reach the threshold 563 by the time t₄. However, given more time (e.g., until t₃>t₄), it is expected that the scores reach the threshold 563.

[0544] Depending on the value of the threshold 563 and/or the type of values it represents, reaching the threshold 563 may mean different things. In one example, S₃ reaching the threshold 563 is indicative that, on average, at the time t₃, users will have a positive affective response to being at the location 512. In another example, S₃ reaching the threshold 563 may be indicative of the opposite, i.e., that on average, at the time t₃, users are expected to have a negative affective response to being at the location 512.

[0545] The threshold 563 may be a fixed value and/or a value that may change over time. In one example, the threshold 563 is received from a user and/or software agent operating on behalf of the user. Thus, in some embodiments, different users may have different thresholds, and consequently receive notifications forwarded by the alert module 208 at different times and/or under different circumstances. In particular, in one example, a first user may receive the notification 564 because S₃ reaches that user's threshold, but a second user may not receive the notification 564 before t₃ because S₃ does not reach that user's threshold.

[0546] Forwarding a notification, such as the notification 564, may be done in various ways. Optionally, forwarding a notification is done by providing a user a recommendation, such as by utilizing recommender module 178. The notification 564 sent by the alert module 208 may convey various types of information. In some embodiments, the notification 564 may be indicative of the location 512 and/or of a time at which a score computed for the location 512 is expected to reach the threshold 512. In one example, the notification 564 may specify the location 512, present an image depicting the location 512, and/or provides instructions on how to reach the location 512. In another example, the notification 564 may specify a certain time, or a range of times, during which it is recommended to be at the location 512. Further discussion regarding notifications is given at least in section 12—Alerts.

[0547] The notification 564 may be forwarded to multiple users. When the location 512 represents a location in the physical world, in some embodiments, a decision on whether to forward the notification 564 to a certain user, from among the multiple users, may depend on the distance between the certain user and the location 512 and/or on the expected time it would take the certain user to reach the location 512. For example, if the notification 564 indicates that people at a certain nightclub (the location 512) are having a good time, it may not be beneficial to forward the notification 564 to a certain user that is at a different city that is a three hour drive away from the location 512. By the time that certain user would reach the location 512, the notification 564 may not be relevant, e.g., the party might have moved on.

[0548] In one embodiment, a notification is forwarded by the alert module 208 to users who are expected to be able to reach the location 512 by a certain time that is relative to the time to which the notification corresponds. In one example, the notification 564 is forwarded to one or more users who are expected to reach the location 512 by the time t₃ (to which the notification 564 corresponds). These one or more users may be at most at a certain distance from the location 512 and/or have means of transportation that allow them to reach the location 512.

[0549] In one embodiment, the notification 564 may be forwarded to a first recipient whose distance from the location is below a distance-threshold, and the notification is not forwarded to a second recipient whose distance from the location is above the distance-threshold. Optionally, the distance-threshold is received by the alert module 208 and is utilized by the alert module 208 to determine who to send the notification 564. Optionally, different users may have different distance-thresholds according to which it may be determined whether they shall receive notifications regarding the location 512.

[0550] In different embodiments, there may be different time frames that are used to make score projections (i.e., the certain period mentioned above may be longer or shorter in different embodiments). Additionally, the distance of projections into the future of the projected scores (e.g., represented by the duration t₃−t₂) may also vary between different embodiments. The choice of how long the certain period should be, and/or how large t₃−t₂ may be, can depend on various factors. In one example, the length of those periods is influenced by the time users spend at the location 512 for which projected scores are computed. In another example, the length of those periods is influenced by how far other users (who may receive the notification 564) are from the location 512. Following are some examples of the location 512 and possible values for the parameters mentioned above.

[0551] In one embodiment, the location 512 is a place in which entertainment may be provided to users from among the crowd 500. For example, the location 512 may include one or more of the following establishments: a club (e.g., a nightclub), a bar, a movie theater, a theater, a casino, a stadium, and a concert venue. In this embodiment, projections of scores may be done for periods of various lengths of time into the future. In one example, t₃ is at least 10 minutes after t₂. In another example, t₃ is at least 30 minutes after t₂. And in yet another example, t₃ is at least one hour after t₂. Optionally, when the score S₃ reaches the threshold 563 it may mean that a recipient of the notification 564 is expected to be interested in receiving the notification 564 about the place that provides entertainment. For example, the score S₃ reaching the threshold 563 may indicate that people at the location 512 are having fun, and thus, the recipient of the notification 564 will probably also have fun, so he should go there. However, when S₃ does not reach the threshold 563, it may mean that a recipient is not expected to be interested in receiving a notification corresponding to S₃. For example, not reaching the threshold 563 may indicate that the location 512 is not lively, and people are not having enough fun there.

[0552] In one embodiment, the location 512 is a place of business. For example, the location 512 may include one or more of the following establishments: a store, a restaurant, a booth, a shopping mall, a shopping center, a market, a supermarket, a beauty salon, a spa, and a hospital clinic. In this embodiment, projections of scores may be done for periods of various lengths of time into the future. In one example, t₃ is at least 20 minutes after t₂. In another example, t₃ is at least one hour after t₂. And in yet another example, t₃ is at least two hours after t₂. Optionally, when the score S₃ reaches the threshold 563 it may mean that a recipient of the notification 564 is expected to be interested in receiving the notification 564 about the place of business. For example, the score S₃ reaching the threshold 563 may indicate that the atmosphere at the location 512 is positive, and thus, the recipient of the notification 564 should consider going there. However, when S₃ does not reach the threshold 563, it may mean that a recipient is not expected to be interested in receiving a notification corresponding to S₃. For example, not reaching the threshold 563 may indicate that the atmosphere at the location 512 is not nice enough.

[0553] In yet another embodiment, the location 512 is a vacation destination. For example, the location 512 may be a continent, a country, a county, a city, a resort, and/or a neighborhood. In this embodiment, projections of scores may be done for periods of various lengths of time into the future. In one example, t₃ is at least one hour after t₂. In another example, t₃ is at one day after t₂. And in yet another example, t₃ is at least one week after t₂. Optionally, when the score S₃ reaches the threshold 563 it may mean that a recipient of the notification 564 is expected to be interested in receiving the notification 564 about the place of business. For example, the score S₃ reaching the threshold 563 may indicate that people staying at the location 512 are enjoying themselves, and thus, the recipient of the notification 564 may also want to consider taking a vacation at the location 512. However, when S₃ does not reach the threshold 563, it may mean that a recipient is not expected to be interested in receiving a notification corresponding to S₃. For example, not reaching the threshold 563 may indicate that presently the location 512 is not a good place to vacation at, as evident by measurements of people at the location 512 who are not sufficiently enjoying themselves.

[0554] In one embodiment, the alert module 208 is also configured to determine whether a trend for scores corresponding to the location 512 changes, and thus, whether certain alerts that have been issued (e.g., through forwarding a notification) should be altered or canceled based on fresher projections. For example, the alert module 208 may determine that a score S₅ corresponding to a time t₅>t₃ falls below the threshold 563, and responsive to S₅ falling below the threshold 563, forward, prior to the time t₅, a notification indicative of S₅ falling below the threshold 563.

[0555] The map-displaying module 240 may be utilized to present the notification 564. For example, the map-displaying module 240 may, in some embodiments, present on a display one or more of the following: a map comprising a description of an environment that comprises the location 512, and an annotation overlaid on the map, which indicates at least one of: the score corresponding to a certain time, the certain time, and the location 512. In one example, location 512 may be a location in the physical world such as a park, and the map includes a description of a city in which the park is situated. In this example, the notification may involve placing an icon, on a screen of a device of a user that depicts the map, at a location corresponding to the park (e.g., at the location of the park and/or nearby it). The icon may convey to the user that a score corresponding to the park reaches a certain level (e.g., people at the park are having a good time).

[0556] Notifications issued by the alert module 208 do not necessarily need to be the same for all users. In one example, different users may receive different alerts because the scores 560, computed for each of the different users based on the measurements 501, may be different. Such a scenario may arise if the scores 560 are computed utilizing an output of the personalization module 130. The personalization module 130 may receive a profile of a certain user and the profiles 504 of users belonging to the crowd 500. Based on similarities between the profile of the certain user and the profiles 504, the personalization module may generate an output indicative of a certain weighting and/or selection of at least some of the measurements 501. Since different users will have different outputs generated for them, the scores 560 computed for the different users may be different. Consequently, the projected scores 562 computed for the different users may also be different. Thus, for the same future time t, a projected score corresponding to t for a first user may reach the threshold 563, while a projected score computed for a second user, corresponding to the same time t, might not reach the threshold 563.

[0557] In another embodiment, the alert module 208 may receive different thresholds 563 for different users. Thus, a projected score corresponding to the time t may reach one user's threshold, but not another user's threshold. Consequently, the system may behave differently, with the different users, as far as the forwarding of notifications is concerned.

[0558] FIG. 16 illustrates steps involved in one embodiment of a method for alerting about projected affective response to being at a location. The steps illustrated in FIG. 16 may be used, in some embodiments, by systems modeled according to FIG. 15. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations of the method.

[0559] In one embodiment, the method for alerting about projected affective response being the location comprises the following steps:

[0560] In step 566a, receiving, by a system comprising a processor and memory, measurements of affective response of users to being at the location. In one example, the location is the location 512, the users may belong to the crowd 500, and the measurements of affective response may be the measurements 501. Optionally, each of the measurements comprises at least one of the following: a value representing a physiological signal of the user and a value representing a behavioral cue of the user.

[0561] In step 566b, computing a first score, denoted S₁, for the location. The first score corresponds to a first time t₁, and is computed based on measurements of at least ten of the users, taken at a time that is after a certain period before t₁, but not after t₁ (i.e., the measurements of the at least ten users were taken at a time that falls between t₁ minus the first certain and t₁). Optionally, measurements taken earlier than the certain period before the time t₁ are not utilized for computing S₁. Optionally, the certain period is shorter than at least one of the following durations: one minute, ten minutes, one hour, four hours, twelve hours, one day, one week, one month, and one year.

[0562] In step 566c, computing a second score, denoted S₂, for the location. The second score corresponds to a second time t₂, and is computed based on measurements of at least ten of the users, taken at a time that is after the certain period before t₂, but not after t₂ (i.e., the measurements of the at least ten users were taken at a time that falls between t₂ minus the certain period and t₂). Optionally, measurements taken earlier than the certain period before the time t₂ are not utilized for computing S₂. Optionally, measurements taken before t₁ are not utilized for computing S₂.

[0563] In step 566d, computing a projected score S₃ for the location, which corresponds to a future time t₃ that is after t₂. Optionally, the score S₃ is a based on S₁ and S₂. For example, S₃ may be computed based on a trend that describes one or more extrapolated values, for times greater than t₂, which are based on values comprising S₁ and S₂, and the times to which they correspond, t₁ and t₂, respectively. Optionally, computing S₃ involves assigning weights to S₁ and S₂ such that a higher weight is assigned to S₂ compared to the weight assigned to S₁, and utilizing the weights to for computing S₃ (e.g., by giving S₂ more influence on the value of S₃ compared to the influence of S₁).

[0564] In step 566e, determining whether S₃ reaches a threshold. Following the “No” branch, in different embodiments, different behaviors may occur. In one embodiment, the method may return to step 566a to receive more measurements, and proceeds to compute an additional score for the location, which corresponds to a time t′>t. In another embodiment, the method may return to steps 566b and/or 566c to compute a new score corresponding to a time t′>t. Optionally, the score corresponding to t′ is computed using a different selection and/or weighting of measurements, compared to a weighting and/or selection used to compute the score corresponding to the time t. And in still another embodiment, the method may terminate its execution.

[0565] And in step 566f, responsive to the score S₃ reaching the threshold, forwarding, no later than t₃, a notification indicative of S₃ reaching the threshold. That is, the notification is forwarded at a time that falls between t₂ and t₃. Optionally, the notification that is forwarded is the notification 564 mentioned above. Optionally, no notification indicative of a score S₄ reaching the threshold is forwarded prior to t₃; where the score S₄ corresponds to a time t₄, such that t₂<t₄<t₃.

[0566] In one embodiment, the notification is forwarded to a first recipient whose distance from the location is below a distance-threshold, but is not forwarded to a second recipient whose distance from the location is above the distance-threshold. For example, a notification about a lively nightclub may be forwarded to recipients in the same city as the nightclub, but not to recipients that are in a city three hour's driving away.

[0567] In one embodiment, the method illustrated in FIG. 16 involves a step of assigning weights to measurements used to compute the score corresponding to the time t, such that an average of weights assigned to measurements taken earlier than the first period before t is lower than an average of weights assigned to measurements taken later than the first period before t. Additionally, the weights may be utilized for computing the score corresponding to t.

[0568] In one embodiment, the method illustrated in FIG. 16 may include a step of presenting on a display: a map comprising a description of an environment that comprises the location, and an annotation overlaid on the map indicating at least one of: S₃, t₃, and the location.

[0569] In one embodiment, the method illustrated in FIG. 16 involves a step of determining whether a score S₅ corresponding to a time t₅>t₃ falls below the threshold, and responsive to S₅ falling below the threshold, forwarding, prior to the time t₅, a notification indicative of S₅ falling below the threshold.

[0570] Certain steps of the method illustrated in FIG. 16 may involve personalization for a certain user. Such personalization may lead to different users receiving different notifications and/or receiving notifications at different times.

[0571] In one embodiment, steps 566b and/or 566c may involve computing scores S₁ and/or S₂, which are personalized for the certain user. In order to compute such personalized scores, the method may include the following additional steps involved in computing a score corresponding to a certain time (e.g., the times t₁ and/or t₂): receiving a profile of a certain user and profiles of the users, and generating an output indicative of similarities between the profile of the certain user and the profiles of the users; and computing a score for the certain user, which corresponds to the certain time, based on a subset of measurements and the output. Optionally, for at least a certain first user and a certain second user, who have different profiles, respective certain first and certain second scores are computed, which correspond to the certain time, and which are different.

[0572] In another embodiment, step 566e may involve receiving a threshold for the certain user. In order to compute such personalized scores, the method may include the following additional step: receiving the threshold from at least one of: a certain user, and a software agent operating on behalf of the certain user. Optionally, responsive to a projected score corresponding to a future time reaches the threshold, forwarding the notification to the certain user, no later than the future time.

[0573] The embodiments discussed above, which may be illustrated in FIG. 15 and/or FIG. 16, relate to embodiments in which alerts are generated based on projected scores computed for locations in general. That is, the location 512 mentioned in the embodiments above may be any of the locations described in this disclosure, be they locations in the physical world (e.g., a country, hotel, nightclub, etc.) or virtual locations (e.g., a virtual world). Thus, the embodiments described above of systems, methods, and/or computer-readable media that may be utilized to alert about projected affective response to being at a location, may serve as a blueprint for one skilled in the art to implement systems, methods, and/or computer-readable media that may be utilized to alert about projected affective response to being in a specific type of location.

[0574] Since projected scores used for alerts and/or recommendations often extrapolate values (e.g., they rely on a trend), there may be different recommendations depending on how far ahead a time the projected scores correspond. In one example, there may be a first location and a second location for which scores are computed based on measurements of affective response (e.g., the measurements 501), utilizing the dynamic scoring module 180. A recommendation is to be made that involves one of the two locations. Typically, the location with the higher score would be recommended. However, when the recommendation is based on a projected score and is made for a certain future time, the recommendation may change depending on how far ahead the certain future time is. This is because such recommendations can take into accounts trends of scores; thus, a score that is currently high may become lower in the near future, and vice versa. Therefore, when a location is to be recommended to a user to have in a future time, the recommendation should be based on scores projected for the future time, and should not necessarily be based on the scores observed at the time at which the recommendation is made time.

[0575] For an example of such a scenario, consider two night clubs to which a user may go out in the evening. The first club is full early on in the evening, but as the evening progresses, the attendance at that club dwindles and the atmosphere there becomes less exciting. The second club starts off with a low key atmosphere, but as the evening progresses things seem to pick up there, and the atmosphere becomes more exciting. Consider scores computed for the clubs based on measurements of affective response of people who are at the clubs. For example, the scores may be values on a scale from 1 to 10, and may indicate how much fun people are having at each club. Because initially there was a nice atmosphere at the first club, the score at 10 PM at that club might have been 9, but as the evening progressed the scores dropped, such that by 11:30 PM the score was 7. And because the second club started off slow, the score for that club at 10 PM might have been 4, but the scores improved as the evening progressed, such that by 11:30 PM the score was 6.5. Now, if at 11:30 PM, a recommendation is to be made regarding which club to visit at 12:30 AM, which club should be recommended? Based on trends of the scores, it is likely that despite the first club having a higher score at the time the recommendation is made (11:30 PM), the second club is likely to have a higher score when the experience is to be had (12:30 AM). Thus, it is likely, that in this example, the second club would be recommended. This type of situation is illustrated in FIG. 17b, and is discussed in more detail below.

[0576] FIG. 17a illustrates a system configured to recommend a location at which to be at a future time. The embodiment described below exhibits a similar logic, to the one outlined above, when it comes to making recommendations based on projected scores, to the logic described above in the example of the night clubs. The system includes at least the collection module 120, the dynamic scoring module 180, the score projection module 200, and recommender module 214.

[0577] In the illustrated embodiment, the collection module 120 is configured to receive the measurements 501, which in this embodiment comprise measurements corresponding to events in which users were at a first location or a second location. The dynamic scoring module 180 computes scores 569a for the first location and scores 569b for the second location. When computing a score for a certain location from among the first and second locations, the dynamic scoring module 180 utilizes a subset of the measurements 501 comprising measurements of users who were at the certain location, and the measurements in the subset are taken at a time that is after a certain period before a time t, but is not after the time t. Such a score may be referred to as “corresponding to the time t and to the certain location”. Optionally, the certain period is shorter than at least one of the following durations: one minute, ten minutes, one hour, four hours, twelve hours, one day, one week, one month, and one year.

[0578] In one embodiment, the dynamic scoring module 180 computes at least the following scores:

[0579] a score S₁ corresponding to a time t₁ and to the first location;

[0580] a score S₂ corresponding to a time t₂ and to the second location;

[0581] a score S₃ corresponding to a time t₃ and to the first location; and

[0582] a score S₄ corresponding to a time t₄ and to the second location.

[0583] Where t₃>t₁, t₄>t₁, t₃>t₂, t₄>t₂, S₃>S₁, S₂>S₄, and S₄>S₃. Note that these scores and corresponding times need not necessarily be the same scores and corresponding times described with reference to figure FIG. 16. Additionally, though illustrated as different times, in some examples, t₁=t₂ and/or t₃=t₄.

[0584] The scores S₁ to S₄ from the present embodiment (possibly with other data) may be utilized by the score projector module 200 to project scores for future times and/or learn trends of scores indicative of the affective response to the first and second locations. FIG. 17b illustrates the scores mentioned above and the trends that may be learned from them.

[0585] In one embodiment, the score projector module 200 is configured to compute projected scores 570a and 570b based on the scores 569a and 569b, respectively. The projected scores 570a include one or more scores corresponding to the first location and to a time t that is greater than t₃ (the time corresponding to S₃). Similarly, the projected scores 570b include one or more scores corresponding to the second location and to a time t that is greater than t₄ (the time corresponding to S₄). In one embodiment illustrated in FIG. 17b, the projected scores 570a include a score S₅ corresponding to the first location and a time t₅ that is after both t₃ and t₄. Additionally, in that figure, the projected scores 570b include a score S₆ which corresponds to the second location and also to the time t₅. Alternatively, the score S₆ may correspond to a time t₆ which is after t₄ but before t₅.

[0586] In another embodiment, the score projector module 200 is configured to compute trends 571a and 571b, based on the scores 569a and 569b, respectively. Optionally, the trend 571a describes expected values of projected scores corresponding to the first location and to times after t₃. Optionally, the trend 571b describes expected values of projected scores corresponding to the second locations and to times after t₄.

[0587] The recommender module 214 is configured to receive information from the score projector module 200 and also to receive a future time at which to be at a location. The recommender module 214 utilizes the information to recommend a location at which to be at the future time, from among the first and second locations. Optionally, the information received from the score projector module 200 may include values indicative of one or more of the following: the projected scores 570a, the projected scores 570b, parameters describing the 571a, and parameters describing the trend 571b. Optionally, information describing a projected score includes both the value of the score and the time to which the score corresponds.

[0588] The information received from the score projector module 200, by the recommender module 214, may be used by the recommender module 214 in various ways in order to determine which location to recommend. In one embodiment, the recommender module 214 receives information regarding projected scores, such as information that includes the scores S₅ and S₆ illustrated in FIG. 17b (e.g., the projected scores 570a and 570b) and optionally the times to which the projected scores correspond. In one example, the recommender module 214 may determine that for times that are after t₅, it will recommend the first location. In one example, this decision may be made based on the facts that (i) the projected score S₅, which corresponds to the first location is greater than the projected score S₆, which corresponds to the second location, and (ii) prior to the times corresponding to S₅ and S₆, the case was the opposite (i.e., scores for the second location were higher than scores for the first location). Thus, the fact that S₅>S₆ may serve as evidence that in future times after t₅, the scores for the first location are expected to remain higher than the scores for the second location (at least for a certain time). Such a speculation may be based on the fact that the previous scores for those locations indicate that, during the period of time being examined (which includes t₁, . . . , t₄), the scores for the first location increase with the progression of time, while the scores for the second location decrease with the progression of time (see for example, the trends 571a and 571b in FIG. 80b). Thus, in this example, the time t₅ may be the first time for which there is evidence that the scores for the first location are expected to increase above of the scores for the second location, so for times that are after t₅, the recommender module 214 may recommend the first location. For times that are not after t₅, there may be various options. For example, the recommender module 214 may recommend the second location, or recommend both locations the same. It is to be noted that in some embodiments, the time t₅ may serve as the threshold-time t′ mentioned below.

[0589] In another embodiment, the recommender module 214 receives information regarding trends of projected scores for the first and second locations, (e.g., the trends 571a and 571b). Optionally, the information includes parameters that define the trends 571a and/or 571b (e.g., function parameters) and/or values computed based on the trends (e.g., projected scores for different times in the future). In one example, the recommender module 214 may utilize the information in order to determine a certain point in time (in the future) which may serve as a threshold-time t′, after which the recommendations change. Before the threshold-time t′, the recommender module 214 recommends one location, from among the first and second locations, for which the projected scores are higher. After the threshold-time t′, the recommender module 214 recommends the other location, for which the projected scores have become higher.

[0590] This situation is illustrated in FIG. 17b. Before the time t′, the projected scores 570b for the second location are higher; thus, when tasked with recommending a location at which to be a time t<t′, the recommender module 214 would recommend to be at the second location. However, after the time t′, the projected scores 570a for the first location are higher; thus, when tasked with recommending a location at which to be at a time t>t′ the recommender module 214 would recommend to be at the first location. Optionally, when tasked with recommending a location at to be at the time t′, the recommender module 214 may make an arbitrary choice (e.g., always recommend one location or the other), make a random choice (i.e., randomly select one of the location), or recommend both locations the same.

[0591] There are various ways in which the threshold-time t′ may be determined. In one example, t′ may be a point corresponding to an intersection of the trends 571a and/or 571b that is found using various numerical and/or analytical methods known in the art. In one example, the trends 571a and 571b are represented by parameters of polynomials, and t′ is found by computing intersections for the polynomials, and selecting a certain intersection as the time t′.

[0592] When the recommender module 214 makes a recommendation, in some embodiments, it may take into account the expected duration of time that is to be spent at a recommended location. In one example, the recommendation may be made such that for most of the time a user is to spend at the recommended location, the recommended location is the location, from among the first and second locations, for which the projected scores are higher. For example, the average projected score for the recommended location, during an expected duration of the stay, is higher than the average projected score for the other location, during the same expected duration of stay.

[0593] In some embodiments, the future time t for which a location is recommended represents the arrival time at the recommended location. In other embodiments, the time t may represent a time at which to leave the recommended location. And in yet other embodiments, the future time t may represent some time in the middle of the stay at the recommended location. Thus, recommendation boundaries (e.g., regions defined relative to the time t′) may be adjusted in different embodiments, to account for the length of the expected stay and/or to account for the exact meaning of what the future time t represents in a certain embodiment.

[0594] In some embodiments, the recommender module 214 is configured to recommend a location to a user to be at, at a certain time in the future in a manner that belongs to a set comprising first and second manners. Optionally, when recommending the location in the first manner, the recommender module 214 provides a stronger recommendation for the location, compared to a recommendation for the location that the recommender module 214 provides when recommending in the second manner. With reference to the discussion above (e.g., as illustrated in FIG. 17b), in one example involving a future time t, such that t>t₅ and/or t>t′, the recommender module 214 recommends the first location in the first manner and does not recommend the second location in the first manner. Optionally, for that time t, the recommender module 214 recommends the second location in the second manner. It is to be noted that what may be involved in making a recommendation in the first or second manners is discussed in further detail above (e.g., with regards to the recommender module 178).

[0595] In one embodiment, map-displaying module 240 is utilized to present on a display: a map comprising a description of an environment that comprises the first and second locations, and an annotation overlaid on the map indicating at least one of: S₅, S₆, and an indication of a time that S₅>S₆ and/or of the threshold-time t′. Optionally, the description of the environment comprises one or more of the following: a two-dimensional image representing the environment, a three-dimensional image representing the environment, an augmented reality representation of the environment, and a virtual reality representation of the environment. Optionally, the annotation comprises at least one of: images representing the first and second locations, and text identifying the first and second locations.

[0596] FIG. 18 illustrates steps involved in one embodiment of a method for recommending a location at which to be at a future time. The steps illustrated in FIG. 18 may be used, in some embodiments, by systems modeled according to FIG. 17a. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations of the method.

[0597] In one embodiment, the method for recommending a location to visit at a future time includes at least the following steps:

[0598] In Step 575a, receiving, by a system comprising a processor and memory, measurements of affective response of users (e.g., the measurements 501). Optionally, each measurement of a user corresponds to an event in which the user is at a first location or a second location. The first and second locations be any of the various types of locations mentioned in this disclosure (e.g., locations in the physical world and/or in a virtual world).

[0599] In step 575b, computing scores based on the measurements. Optionally, each score corresponds to a time t and to a location from among the first and second locations. Additionally, each score is computed based on a subset of the measurements 501 comprising measurements of users who were at the location, and the measurements in the subset are taken at a time that is after a certain period before the time t, but is not after t. For example, if the length of the certain period is denoted Δ, each of the measurements in the subset was taken at a time that is between t−Δ and t. Optionally, each score is computed based on measurements of at least five different users. Optionally, a different minimal number of measurements of different users may be used to compute each score, such as computing each score based on measurements of at least ten different users.

[0600] In one embodiment, when computing a score corresponding to a time t, measurements taken earlier than the certain period before the time t (i.e., taken before t−Δ), are not utilized to compute the score corresponding to the time t. In another embodiment, measurements are weighted according to how long before the time t they were taken. Thus, the method may optionally include the following steps: assigning weights to measurements used to compute a score corresponding to the time t, such that an average of weights assigned to measurements taken earlier than the certain period before the time t is lower than an average of weights assigned to measurements taken after the certain period before the time t; and utilizing the weights to compute the score corresponding to the time t. For example, the score corresponding to the time t may be a weighted average of the measurements, and the more recent the measurements (i.e., they are taken at a time close to t), the more they influence the value of the score.

[0601] The scores computed in Step 575b may include scores corresponding to various times. In one example, the scores that are computed include at least the following scores: a score S₁ corresponding to a time t₁ and to the first location, a score S₂ corresponding to a time t₂ and to the second location, a score S₃ corresponding to a time t₃ and to the first location, and a score S₄ corresponding to a time t₄ and to the second location. Optionally, t₃>t₁, t₄>t₁, t₃>t₂, t₄>t₂, S₃>S₁, S₂>S₄, and S₄>S₃. Optionally, t₁=t₂ and/or t₃=t₄.

[0602] In step 575c, computing, based on the scores S₁, S₂, S₃, and S₄ at least one of the following sets of values: (i) projected scores for the first and second locations, and (ii) trends of projected scores for the first and second locations.

[0603] In step 575d, identifying a threshold-time t′ based on the set of values, where t′ is selected such that t′>t₄ and t′>t₃. Additionally, t′ is selected such that projected scores corresponding to a time that is before t′ and to the first location are lower than projected scores corresponding to the same time and to the second location.

[0604] In Step 575e, receiving a time t for which a location from among the first and second locations is to be recommended. Optionally, t>t₃ and t>t₄.

[0605] In Step 575f, determining whether the time t is after the threshold-time t′.

[0606] In Step 575g, responsive to t being after t′, following the “Yes” branch and recommending the first location for the time t.

[0607] And in Step 575h, responsive to t not being after t′, following the “No” branch and recommending the second location for the time t.

[0608] In one embodiment, Step 575c may involve computing a set of values comprising: (i) a projected score S₅, corresponding to the first location and to a time t₅>t₃, based on S₁ and S₃, and (ii) a projected score S₆, corresponding to the second location and to a time t₆>t₄, based on S₂ and S₄. Optionally, in this embodiment S₅>S₆, and t′≧t₅.

[0609] In another embodiment, Step 575c may involve computing a set of values comprising parameters describing trends of projected scores for the first and second locations (e.g., the trends 571a and 571b). Optionally, the threshold-time t′ is a time corresponding to an intersection of the trends of the projected scores for the first and second locations.

[0610] In one embodiment, Step 575g and/or Step 575h may optionally involve recommending the respective location to a user to have at the future time in a manner that belongs to a set comprising first and second manners. Optionally, recommending a location in the first manner involves providing a stronger recommendation for the location, compared to a recommendation for the location that is provided when recommending in the second manner.

[0611] In one example, responsive to the future time being after t′ recommending the first location in Step 575g is done in the first manner, while the second location is not recommended in the first manner. Optionally, in this example, the second location is recommended in the second manner. In another example, responsive to the future time not being after the threshold-time t′, recommending the second location in Step 575h is done in the first manner, while the first location is not recommended in the first manner. Optionally, in this example, the first location is recommended in the second manner.

[0612] The embodiments discussed above relate to embodiments in which recommendations for a location at which to be at a future time are made for locations in general. That is, the locations mentioned in the embodiments above may be any of the locations described in this disclosure, be they locations in the physical world (e.g., a country, hotel, nightclub, etc.) or virtual locations (e.g., a virtual world). Thus, the embodiments described above of systems, methods, and/or computer-readable media that may be utilized to alert about projected affective response to being at a location, may serve as a blueprint for one skilled in the art to implement systems, methods, and/or computer-readable media that may be utilized to alert about projected affective response to being in a specific type of location.

[0613] One type of crowd-based result that is generated in various embodiments in this disclosure involves ranking of experiences. In particular, some embodiments involve ranking of experiences that involve being in locations and/or engaging in activities at locations. Ranking such experiences that are related to locations may be referred to as ranking the locations. The results obtained from ranking locations may be referred to as a “ranking of the locations”. The ranking is an ordering of at least some of the locations, which is indicative of preferences of the users towards those locations and/or is indicative of the extent of emotional response of the users to those locations.

[0614] Various aspects of systems, methods, and/or computer-readable media that involve ranking experiences are described in more detail at least in section 14—Ranking Experiences. That section discusses teachings regarding ranking of experiences in general, which include experiences involving locations (e.g., experiences involving being in locations and/or engaging in certain activities at the locations). Thus, the teachings of section 14—Ranking Experiences are also applicable to embodiments described below that explicitly involve locations. Following is a discussion regarding some aspects of systems, methods, and/or computer-readable media that may be utilized to rank locations.

[0615] FIG. 19 illustrates a system configured to rank locations based on measurements of affective response of users. The system includes at least the collection module 120 and the ranking module 220. This system, like other systems described in this disclosure, may be realized via a computer, such as the computer 400, which includes at least a memory 402 and a processor 401. The memory 402 stores computer executable modules described below, and the processor 401 executes the computer executable modules stored in the memory 402.

[0616] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response of users belonging to the crowd 500. Optionally, the measurements 501 include measurements of affective response of users who were at the locations. A measurement of affective response of a user who was at a location may also be referred to herein as a “measurement of affective response of a user to being at the location”. Optionally, determining when a user was at a location from among the locations may be done utilizing the location verifier 505.

[0617] In one embodiment, each measurement is a measurement of affective response of a user who was at a location, from among the locations, is based on at least one of the following values: (i) a value acquired by measuring the user, with a sensor coupled to the user, while the user was at the location and (ii) a value acquired by measuring the user, with a sensor coupled to the user, at most one hour after the user had left the location.

[0618] In one embodiment, each measurement is a measurement of affective response of a user who was at a location, from among the locations, is based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the location.

[0619] The collection module 120 is also configured to forward at least some of the measurements 501 to the ranking module 220. Optionally, at least some of the measurements 501 undergo processing before they are received by the ranking module 220. Optionally, at least some of the processing is performed via programs that may be considered software agents operating on behalf of the users who provided the measurements 501.

[0620] In one embodiment, measurements received by the ranking module 220 include measurements of affective response of users who were at the locations being ranked. Optionally, for each location the measurements received by the ranking module 220 include measurements of affective response of at least five users who were at the location. Optionally, for each location, the measurements received by the ranking module 220 may include measurements of a different minimal number of users, such as measurements of at least eight, at least ten, or at least one hundred users. The ranking module 220 is configured to generate the ranking 580 of the locations based on the received measurements. Optionally, in the ranking 580, a first location from among the locations is ranked higher than a second location from among the locations.

[0621] When the first location is ranked higher than the second location, it may mean different things in different embodiments. In some embodiments, the ranking of the locations is based on a positive trait, such as ranking based on how much people enjoy being at the locations, how good the locations make them feel, how relaxed they are at the locations, etc. Thus, on average, the measurements of the at least five users who were at the first location are expected to be more positive than the measurements of the at least five users who were at the second location. However, in other embodiments, the ranking may be based on a negative trait; thus, on average, the measurements of the at least five users who were at the first location are expected to be more negative than the measurements of the at least five users who were at the second location.

[0622] In some embodiments, measurements utilized by the ranking module to generate a ranking, such as the ranking 580, may all be taken during a certain period of time. Depending on the embodiment, the certain period of time may span different lengths of time. For example, the certain period may be less than one day long, between one day and one week long, between one week and one month long, between one month and one year long, or more than a year long. When a ranking of locations is generated based on measurements that were all taken during a certain period, it may be considered to correspond to a certain period. Thus, for example, a ranking of hotels may be a “ranking of hotels for the first week of July”, a ranking of restaurants may be a “ranking of the best restaurants for 2016”, and a ranking of virtual malls may be a “ranking of the best virtual malls for Black Friday”.

[0623] In some embodiments, in the ranking 580, each location from among the locations has its own rank, i.e., there are no two locations that share the same rank. In other embodiments, at least some of the locations may be tied in the ranking 580. In particular, there may be third and fourth locations, from among the locations, that are given the same rank by the ranking module 220. It is to be noted that the third location in the example above may be the same location as the first location or the second location mentioned above.

[0624] It is to be noted that while it is possible, in some embodiments, for the measurements received by modules, such as the ranking module 220, to include, for each user from among the users who contributed to the measurements, at least one measurement of affective response of the user to being in each location from among the locations, this is not the case in all embodiments. In some embodiments, some users may contribute measurements corresponding to a proper subset of the locations (e.g., those users may not have been at some of the locations), and thus, the measurements 501 may be lacking measurements of some users to some of the locations. In some embodiments, some users might have been only to one location from among the locations being ranked.

[0625] There are different approaches to ranking locations, which may be utilized in embodiments described herein. In some embodiments, locations may be ranked based on scores computed for the locations. In such embodiments, the ranking module 220 may include the scoring module 150 and a score-based rank determining module 225. In other embodiments, locations may be ranked based on preferences generated from measurements of affective response. In such embodiments, an alternative embodiment of the ranking module 220 includes preference generator module 228 and preference-based rank determining module 230. The different approaches that may be utilized for ranking locations are discussed in more detail in section 14—Ranking Experiences, e.g., in the discussion related to FIG. 85 and FIG. 86.

[0626] In some embodiments, the personalization module 130 may be utilized in order to personalize rankings of locations for certain users. Optionally, this may be done utilizing the output generated by the personalization module 130 after being given a profile of a certain user and profiles of at least some of the users who provided measurements that are used to rank the locations (e.g., profiles from among the profiles 504). Optionally, when generating personalized rankings for locations, there are at least a certain first user and a certain second user, who have different profiles, for which the ranking module 220 ranks locations differently. For example, for the certain first user, the first location may be ranked above the second location, and for the certain second user, the second location is ranked above the first location. The way in which, in the different approaches to ranking, an output from the personalization module 130 may be utilized to generate personalized rankings for different users, is discussed in more detail in section 14—Ranking Experiences.

[0627] In some embodiments, the recommender module 235 is utilized to recommend a location to a user, from among the locations ranked by the ranking module 220, in a manner that belongs to a set comprising first and second manners. Optionally, when recommending a location in the first manner, the recommender module 235 provides a stronger recommendation for the location, compared to a recommendation for the location that the recommender module 235 would provide when recommending in the second manner. Optionally, the recommender module 235 determines the manner in which to recommend a location, from among the locations, based on the rank of the location in the ranking 580. In one example, if the location is ranked at a certain rank it is recommended in the first manner. Optionally, if the location is ranked at least at the certain rank (i.e., it is ranked at the certain rank or higher), it is recommended in the first manner). Optionally, if the location is ranked lower than the certain rank, it is recommended in the second manner. In different embodiments, the certain rank may refer to different values. Optionally, the certain rank is one of the following: the first rank (i.e., the location is the top-ranked location), the second rank, or the third rank. Optionally, the certain rank equals at most half of the number of locations being ranked. Additional discussion regarding recommendations in the first and second manners may be found at least in the discussion about recommender module 178 in section 8—Crowd-Based Applications; recommender module 235 may employ first and second manners of recommendation in a similar way to how the recommender module 178 recommends in those manners.

[0628] In some embodiments, map-displaying module 240 may be utilized to present to a user the ranking 580 of the locations and/or a recommendation based on the ranking 580. Optionally, the map may display an image describing the locations and annotations describing at least some of the locations and their respective ranks.

[0629] It is to be noted that references to the “locations” that are being ranked, e.g., with respect to FIG. 19 and/or other figures, may refer to any type of location described in this disclosure (be it in the physical world and/or in a virtual location). Following are some examples of the types of locations that may be ranked in different embodiments.

[0630] In one embodiment, at least some of the locations are establishments in which entertainment is provided. Optionally, such an establishment may be one or more of the following: a club, a bar, a movie theater, a theater, a casino, a stadium, and a concert venue.

[0631] In another embodiment, at least some of the locations are vacation destinations. Optionally, a vacation destination may be one or more of the following: a continent, a country, a county, a city, a resort, and a neighborhood.

[0632] In still another embodiment, at least some of the locations are regions of a larger location. Optionally, a region of a larger location may be one or more of the following: a certain wing of a hotel, a certain floor of a hotel, a certain room in a hotel, a certain room in a resort, a certain cabin in a ship, a certain seat in a vehicle, a certain class of seats in a vehicle, a certain type of seating location in a vehicle.

[0633] In yet another embodiment, at least some of the locations are virtual environments in a virtual world, with at least one instantiation of each virtual environment stored in a memory of a computer. Optionally, a user may be considered to be in a virtual environment by virtue of having a value stored in the memory of the computer indicating the presence of a representation of the user in the virtual environment.

[0634] In embodiments described herein, a reference to “locations” generally refers to a plurality of different locations (e.g., each location is a different place). In particular, when a ranking of locations, such as the ranking 580, includes a first location that is ranked higher than a second location, it implies that the first location is different from the second location. Depending on the embodiment, locations may be considered different from each other for different reasons. In one example, the first location and second location have different addresses (e.g., street addresses), and are thus considered different. In another example, the first location and the second location occupy different regions on a map that includes multiple locations. In still another example, if a user cannot simultaneously be both at the first location and at the second location, they may be considered different locations. In yet another example, virtual locations may be considered different if they are hosted on different servers.

[0635] Section 3—Experiences describes how different experiences, including experiences that involve locations, may be characterized by a combination of attributes. Examples of such combinations of attributes include the following characterizations that may be used to characterize an experience that involves a certain location: (i) an experience that takes place at the certain location and involves having a certain activity at the certain location, (ii) an experience that takes place at the certain location during a certain period of time, and (iii) an experience that takes place at a certain location and lasts for a certain duration.

[0636] Thus, in some embodiments, when ranking locations, the locations being ranking may involve different combinations. For example, a ranking may indicate which is better: to spend a week in London or a weekend in New York (ranking combinations of a certain location and a certain duration). In another example, a ranking may indicate to which of the following users have a more positive affective response: a picnic at the park or shopping at the mall (ranking combinations of a location and an activity that takes place at the location).

[0637] Following are examples of embodiments in which locations are characterized as a combination of different attributes. The locations described in the following embodiments may represent a “locations” in any of the embodiments in this disclosure that involve generating a crowd-based result for a location. For example, ranking locations in any of the embodiments of systems modeled according to FIG. 19, and/or other embodiments of systems involving dynamic and/or personalized rankings (e.g., FIG. 87a, FIG. 89a, and/or FIG. 91a), may involve ranking of locations that are characterized by combinations of attributes described in the embodiments below.

[0638] In some embodiments, a ranking of locations, such as the ranking 580, involves locations that may be characterized by an activity that user engage in while they are at the locations. Additionally, the ranking includes a first location that is ranked higher than a second location. In this embodiment, when at the first location, users engage in a first activity, and when at the second location, users engage in a second activity. Optionally, the first activity is different from the second activity and the first location is different from the second location. Optionally, the first activity and the second activity involve one or more of the following types of activities: exercising, recreational activities, shopping related activities, dining related activities, resting, playing games, visiting a location in the physical world, interacting in a virtual environment, and receiving services. Optionally, the first location and the second location are of one or more of the following types of locations: countries of the world, cities in the world, neighborhoods in cities, private houses, parks, beaches, stadiums, hotels, restaurants, theaters, night clubs, bars, shopping malls, stores, amusement parks, museums, zoos, spas, health clubs, exercise clubs, clinics, and hospitals. In one example, being at the first location involves exercising at a certain park, and being in the second location involves drinking at a certain bar. In another example, being at the first location involves playing games at a certain person's house, and being at the second location involves dancing at a certain night club.

[0639] In other embodiments, a ranking of locations, such as the ranking 580, involves locations that may be characterized by a period during which users visit the locations. Additionally, the ranking includes a first location that is ranked higher than a second location. In this embodiment, users are at the first location during a first period of time, and are at the second location during a second period of time. Optionally, the first location is different from the second location and the first period is different from the second period. In one example, the first period does not overlap with the second period. In another example, there is less than a 50% overlap between the first period and the second period.

[0640] In one embodiment, the first location and the second location are each locations that may be of one or more of the following types of locations: cities, neighborhoods, parks, beaches, restaurants, theaters, night clubs, bars, shopping malls, stores, amusement parks, museums, zoos, spas, health clubs, exercise clubs, clinics, and hospitals. In this embodiment, the first and second periods may each be a different recurring period of time that corresponds to at least one of: a certain hour during the thy, a certain day during the week, a certain day of the month, and a holiday. In one example, the first location is a zoo visited during the week, and the second location is an amusement park visited on the weekend. In another example, the first location is a spa visited in the morning from 8 to 12 and the second location is a beach visited in the afternoon from 3 to 5.

[0641] In another embodiment, the first location and the second location are each locations that may be of one or more of the following types of locations: continents, countries, cities, parks, beaches, amusement parks, museums, and zoos In this embodiment, the first and second periods may each be a different recurring period of time that corresponds to at least one of: a season of the year, a month of the year, and a certain holiday. In one example, the first location is New York when visited in July and the second location is Los Angeles when visited in July. In another example, the first location is Disneyland when visited on Labor Day and the second location is Universal Studios when visited in the summer.

[0642] In still other embodiments, a ranking of locations, such as the ranking 580, involves locations that may be characterized by a duration spent by users at the locations. Additionally, the ranking includes a first location that is ranked higher than a second location. In this embodiment, users are at the first location for a first duration, and users are at the second location for a second duration. Optionally, the first location is different from the second location and the first duration is different from the second duration. Optionally, the first and second durations correspond to first and second ranges of lengths of time, such that the first and second ranges do not overlap or the overlap between the first and second ranges comprises less than 50% of either of the first and second ranges. Optionally, the first duration is at least 50% longer than the second duration.

[0643] In one embodiment, the first location and the second location are each locations that may be of one or more of the following types of locations: cities, neighborhoods, parks, beaches, restaurants, theaters, night clubs, bars, shopping malls, stores, amusement parks, museums, zoos, spas, health clubs, and exercise clubs. In this embodiment, the maximum of the first and second durations is longer than 5 minutes and shorter than a week. In one example, being at the first location involves spending two hours at a certain zoo, and being at the second location involves spending four hours at a certain amusement park.

[0644] In one embodiment, the first location and the second location are each locations that may be of one or more of the following types of locations: continents, countries, cities, parks, hotels, cruise ships, and resorts. In this embodiment, the maximum of the first and second durations is between an hour and two months. In one example, being in the first location involves spending four days in Denmark, and being in the second locations involves spending 6 to 12 days in Australia.

[0645] FIG. 20 illustrates steps involved in one embodiment of a method for ranking locations based on measurements of affective response of users. The steps illustrated in FIG. 20 may be used, in some embodiments, by systems modeled according to FIG. 19. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations of the method.

[0646] In one embodiment, the method for ranking locations based on measurements of affective response of users includes at least the following steps:

[0647] In Step 585b, receiving, by a system comprising a processor and memory, the measurements of affective response of the users. Optionally, for each location from among the locations, the measurements include measurements of affective response of at least five users who were at the location.

[0648] And in Step 585c, ranking the locations based on the measurements, such that, a first location from among the locations is ranked higher than a second location from among the locations.

[0649] In one embodiment, the method optionally includes Step 585a that involves utilizing a sensor coupled to a user who was at a location, from among the locations being ranked, to obtain a measurement of affective response of the user who was at the location. Optionally, the measurement of affective response of the user is based on at least one of the following values: (i) a value acquired by measuring the user with the sensor while the user was at the location, and (ii) a value acquired by measuring the user with the sensor up to one hour after the user had left the location.

[0650] In one embodiment, the method optionally includes Step 585d that involves recommending the first location to a user in a first manner, and not recommending the second location to the user in the first manner. Optionally, the Step 585d may further involve recommending the second location to the user in a second manner. As mentioned above, e.g., with reference to recommender module 235, recommending a location in the first manner may involve providing a stronger recommendation for the location, compared to a recommendation for the location that is provided when recommending it in the second manner.

[0651] Ranking locations utilizing measurements of affective response may be done in different embodiments, in different ways. In particular, in some embodiments, ranking may be score-based ranking (e.g., performed utilizing the scoring module 150 and the score-based rank determining module 225), while in other embodiments, ranking may be preference-based ranking (e.g., utilizing the preference generator module 228 and the preference-based rank determining module 230). Therefore, in different embodiments, Step 585c may involve performing different operations.

[0652] In one embodiment, ranking the locations based on the measurements in Step 585c includes performing the following operations: for each location from among the locations, computing a score based on the measurements of the at least five users who were at the location, and ranking the locations based on the magnitudes of the scores. Optionally, two locations in this embodiment may be considered tied if a significance of a difference between scores computed for the two locations is below a threshold. Optionally, determining the significance is done utilizing a statistical test involving the measurements of the users who were at the two locations (e.g., utilizing the score-difference evaluator module 260).

[0653] In another embodiment, ranking the locations based on the measurements in Step 585c includes performing the following operations: generating a plurality of preference rankings for the locations, and ranking the locations based on the plurality of the preference rankings utilizing a method that satisfies the Condorcet criterion. Optionally, each preference ranking is generated based on a subset of the measurements and comprises a ranking of at least two of the locations, such that one of the at least two locations is ranked ahead of another location from among the at least two locations. In this embodiment, ties between locations may be handled in various ways, as described in section 14—Ranking Experiences.

[0654] A ranking of locations generated by a method illustrated in FIG. 20 may be personalized for a certain user. In such a case, the method may include the following steps: (i) receiving a profile of a certain user and profiles of at least some of the users (who contributed measurements used for ranking the locations); (ii) generating an output indicative of similarities between the profile of the certain user and the profiles; and (iii) ranking the locations based on the measurements and the output. Optionally, the output is generated utilizing the personalization module 130. Depending on the type of personalization approach used and/or the type of ranking approach used, the output may be utilized in various ways to perform a ranking of the locations, as discussed elsewhere herein. Optionally, for at least a certain first user and a certain second user, who have different profiles, third and fourth locations, from among the locations, are ranked differently, such that for the certain first user, the third location is ranked above the fourth location, and for the certain second user, the fourth location is ranked above the third location. It is to be noted that the third and fourth locations mentioned here may be the first and second locations mentioned above with reference to FIG. 19, respectively.

[0655] Personalization of rankings of locations, e.g., utilizing the personalization module 130, as described above, can lead to the generation of different rankings of locations for users who have different profiles. Obtaining different rankings for different users may involve performing the steps illustrated in FIG. 21, which illustrates steps involved in one embodiment of a method for utilizing profiles of users to compute personalized rankings of locations based on measurements of affective response of the users. The steps illustrated in FIG. 21 may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 19. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[0656] In one embodiment, the method for utilizing profiles of users to compute personalized rankings of locations based on measurements of affective response of the users includes the following steps:

[0657] In Step 586b, receiving, by a system comprising a processor and memory, measurements of affective response of the users to being at locations. That is, each measurement of affective response to being at a location, from among the locations, is a measurement of affective response of a user who was at the location, taken while the user was at the location, or shortly after that time (e.g., up to one hour after that time). Optionally, for each location from among the locations, the measurements comprise measurements of affective response of at least eight users who were at the location. Optionally, for each location from among the locations, the measurements comprise measurements of affective response of at least some other minimal number of users who were at the location, such as measurements of at least five, at least ten, and/or at least fifty different users.

[0658] In Step 586c, receiving profiles of at least some of the users who contributed measurements in Step 586b.

[0659] In Step 586d, receiving a profile of a certain first user.

[0660] In Step 586e, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least some of the users.

[0661] In Step 586f, computing, based on the measurements received in Step 586b and the first output, a first ranking of the locations.

[0662] In Step 586h, receiving a profile of a certain second user, which is different from the profile of the certain first user.

[0663] In Step 586i, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least some of the users. Here, the second output is different from the first output.

[0664] And in Step 586j, computing, based on the measurements received in Step 586b and the second output, a second ranking of the locations. Optionally, the first and second rankings are different, such that in the first ranking a first location is ranked above a second location, and in the second ranking, the second location is ranked above the first location.

[0665] In one embodiment, the method optionally includes Step 586a that involves utilizing a sensor coupled to a user who was at a location, from among the locations being ranked, to obtain a measurement of affective response of the user. Optionally, the measurement of affective response of the user is based on at least one of the following values: (i) a value acquired by measuring the user with the sensor while the user was at the location, and (ii) a value acquired by measuring the user with the sensor up to one hour after the user had left the location.

[0666] In one embodiment, the method may optionally include steps that involve reporting a result based on the ranking of the locations to a user. In one example, the method may include Step 586g, which involves forwarding to the certain first user a result derived from the first ranking of the locations. In this example, the result may be a recommendation to go to the first location (which for the certain first user is ranked higher than the second location). In another example, the method may include Step 586k, which involves forwarding to the certain second user a result derived from the second ranking of the locations. In this example, the result may be a recommendation for the certain second user to go to the second location (which for the certain second user is ranked higher than the first location).

[0667] In one embodiment, generating the first output and/or the second output may involve computing weights based on profile similarity. For example, generating the first output in Step 586e may involve performing the following steps: (i) computing a first set of similarities between the profile of the certain first user and the profiles of the at least ten users; and (ii) computing, based on the first set of similarities, a first set of weights for the measurements of the at least ten users. Optionally, each weight for a measurement of a user is proportional to the extent of a similarity between the profile of the certain first user and the profile of the user (e.g., as determined by the profile comparator 133), such that a weight generated for a measurement of a user whose profile is more similar to the profile of the certain first user is higher than a weight generated for a measurement of a user whose profile is less similar to the profile of the certain first user. Generating the second output in Step 586i may involve similar steps, mutatis mutandis, to the ones described above.

[0668] In another embodiment, the first output and/or the second output may involve clustering of profiles. For example, generating the first output in Step 586e may involve performing the following steps: (i) clustering the at least some of the users into clusters based on similarities between the profiles of the at least some of users, with each cluster comprising a single user or multiple users with similar profiles; (ii) selecting, based on the profile of the certain first user, a subset of clusters comprising at least one cluster and at most half of the clusters, on average, the profile of the certain first user is more similar to a profile of a user who is a member of a cluster in the subset, than it is to a profile of a user, from among the at least ten users, who is not a member of any of the clusters in the subset; and (iii) selecting at least eight users from among the users belonging to clusters in the subset. It is to be noted that instead of selecting at least eight users, a different minimal number of users may be selected such as at least five, at least ten, and/or at least fifty different users. Here, the first output is indicative of the identities of the at least eight users. Generating the second output in Step 586i may involve similar steps, mutatis mutandis, to the ones described above.

[0669] In some embodiments, the method may optionally include steps involving recommending one or more of the locations being ranked to users. Optionally, the type of recommendation given for a location is based on the rank of the location. For example, given that in the first ranking, the rank of the first location is higher than the rank of the second location, the method may optionally include a step of recommending the first location to the certain first user in a first manner, and not recommending the second location to the certain first user in first manner. Optionally, the method includes a step of recommending the second location to the certain first user in a second manner. Optionally, recommending a location in the first manner involves providing a stronger recommendation for the location, compared to a recommendation for the location that is provided when recommending it in the second manner. The nature of the first and second manners is discussed in more detail with respect to the recommender module 178, which may also provide recommendations in first and second manners.

[0670] In some embodiments, rankings computed for locations may be dynamic, i.e., they may change over time. In one example, rankings may be computed utilizing a “sliding window” approach, and use measurements of affective response that were taken during a certain period of time. In another example, measurements of affective response may be weighted according to the time that has elapsed since they were taken. Dynamic ranking typically involves computing rankings that correspond to a certain time based on measurements of affective response taken during a certain window around the certain time (or which ends at the certain time). Some of the embodiments described above, e.g., embodiments modeled according to FIG. 19 may be used to generate dynamic rankings by providing measurements of affective response to the dynamic ranking module 250 instead of to the ranking module 220.

[0671] Section 14—Ranking Experiences discusses dynamic ranking in more detail, e.g., in the discussion related to FIG. 89a, FIG. 89b, FIG. 91a, and FIG. 91b. The discussion in that section involves dynamic ranking of experiences in general, which include location-related experiences described below; thus, the teachings regarding dynamic ranking of experiences are applicable to the embodiments below. Following are descriptions of some embodiments of systems, methods, and/or computer-readable media that may be utilized in order to generate dynamic rankings of locations and/or personalized dynamic rankings of locations.

[0672] In one embodiment, a system configured to dynamically rank locations based on measurements of affective response of users includes at least the collection module 120 and the dynamic ranking module 250. In this embodiment, the collection module 120 is configured to receive the measurements 501 of affective response of users belonging to the crowd 500. For each location from among the locations, the measurements 501 include measurements of affective response of at least ten users who were at the location.

[0673] In this embodiment, the dynamic ranking module 250 is configured to generate rankings of the locations. Each ranking corresponds to a time t and is generated based on a subset of the measurements of the users that includes measurements of at least five users, with each measurement taken at a time that is not earlier than a certain period before t and is not after t. For example, if the length of the certain period is denoted Δ, each of the measurements in the subset was taken at a time that is between t−Δ and t. Optionally, for each location from among the locations, the subset includes measurements of at least five different users who were at the location. Optionally, measurements taken earlier than the certain period before a time t are not utilized by the dynamic ranking module 250 to generate a ranking corresponding to t. Additionally or alternatively, the dynamic ranking module 250 may be configured to assign weights to measurements used to compute a ranking corresponding to a time t, such that an average of weights assigned to measurements taken earlier than the certain period before t is lower than an average of weights assigned to measurements taken later than the certain period before t. The dynamic ranking module may be further configured to utilize the weights to compute the ranking corresponding to t.

[0674] In one embodiment, the rankings generated by the dynamic ranking module 250 include at least a first ranking corresponding to a time t₁ and a second ranking corresponding to a time t₂ that is after t₁. In the first ranking corresponding to the time t₁, a first location from among the locations is ranked above a second location from among the locations. However, in the second ranking corresponding to the time t₂, the second location is ranked above the first location. In this embodiment, the second ranking is computed based on at least one measurement taken after t₁.

[0675] Since dynamic rankings may change over time, this may change the nature of recommendations given to users at different times. In one embodiment, the recommender module 235 is configured to recommend a location to a user in a manner that belongs to a set comprising first and second manners. When recommending a location in the first manner, the recommender module 235 provides a stronger recommendation for the location, compared to a recommendation for the location that the recommender module 235 provides when recommending it in the second manner. With reference to the embodiment described above, which includes the first and second rankings corresponding to t₁ and t₂, respectively, the recommender module 235 is also configured to: recommend the first location to a user during a period that ends before t₂ in the first manner, and not to recommend to the user the second location in the first manner. Optionally, during that period, the recommender module 235 recommends the second location in the second manner. After t₂, the behavior of the recommender module 235 may change, and it may recommend to the user the second location in the first manner, and not recommend the first location in the first manner. Optionally, after t₂, the recommender module 235 may recommend the first location in the second manner.

[0676] Following is a description of steps that may be performed in a method for dynamically ranking locations based on affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to dynamically rank locations based on measurements of affective response of users. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for dynamically ranking locations based on measurements of affective response of users includes at least the following steps:

[0677] In Step 1, receiving, by a system comprising a processor and memory, a first set of measurements of affective response of users. Each measurement belonging to the first set was taken at a time that is not earlier than a certain period before a time t₁ and is not after t₁. Additionally, for each location from among the locations, the first set of measurements comprises measurements of affective response of at least five users who were at the location.

[0678] In Step 2, generating, based on the first set of measurements, a first ranking of the locations. In the first ranking, a first location from among the locations is ranked ahead of a second location from among the locations.

[0679] In Step 3, receiving a second set of measurements of affective response of users. Each measurement belonging to the second set was taken at a time that is not earlier than the certain period before a time t₂ and is not after t₂. Additionally, for each location from among the locations, the second set of measurements comprises measurements of affective response of at least five users who were at the location.

[0680] And in Step 4, generating, based on the second set of measurements, a second ranking of the locations. In the second ranking, the second location is ranked ahead of the first location. Additionally, t₂>t₁ and the second set of measurements comprises at least one measurement of affective response of a user taken after t₁.

[0681] The method described above illustrates the dynamic nature of the rankings, including how the rankings may change over time. Sometimes the changes may be due to different compositions of measurements and/or weights for measurements that are used to compute rankings corresponding to different times. In one example, measurements taken earlier than the certain period before a time t are not utilized for generating a ranking corresponding to t. In another example, weights are assigned to measurements used to compute a ranking corresponding to a time t based on how long before t the measurements were taken. Typically, the older the measurements, the lower their assigned weight, such that an average of weights assigned to measurements taken earlier than the certain period before t is lower than an average of weights assigned to measurements taken later than the certain period before t. These weights may be utilizing for generating the ranking corresponding to t.

[0682] The method described above may optionally include a step that involves recommending a location from among the locations to a user. The nature of such a recommendation may depend on the ranking of the locations, and as such may change over time. Optionally, recommending a location may be done in a first manner or in a second manner; recommending a location in the first manner involves providing a stronger recommendation for the location, compared to a recommendation for the location provided when recommending it in the second manner. In one example, at a time that is before t₂, the first location may be recommended to a user in the first manner, and the second location may be recommended to the user in the second manner. However, at a time that is after t₂, the first location may be recommended to the user in the second manner, and the second location is recommended to the user in the first manner.

[0683] In some embodiments, personalization module 130 may be utilized to generate personalized dynamic rankings of locations, e.g., as illustrated in FIG. 91a and FIG. 91b, which involve personalized rankings of experiences, and as such are relevant to personalized dynamic rankings of locations (since being at a location is a specific type of experience).

[0684] In one embodiment, a system configured to dynamically generate personalized rankings of locations based on affective response of users includes at least the collection module 120, the personalization module 130, and the dynamic ranking module 250. In this embodiment, the collection module 120 is configured to receive the measurements of affective response of the users. For each location from among the locations, the measurements comprise measurements of affective response of at least ten users who were at the location. The personalization module 130 is configured, in one embodiment, to receive a profile of a certain user and profiles of the users, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the users. The dynamic ranking module 250 is configured to generate, for the certain user, rankings of the locations. Each ranking corresponds to a time t and is generated based on the output and a subset of the measurements comprising measurements of at least five users; each measurement in the subset is taken at a time that is not earlier than a certain period before t and is not after t.

[0685] By utilizing the personalization module 130, it is possible that different users may receive different dynamic rankings, at different times. In particular, in one embodiment, rankings generated by the system described above are such that for at least a certain first user and a certain second user, who have different profiles, the dynamic ranking module 250 generates the following rankings: (i) a ranking corresponding to a time t₁ for the certain first user, in which a first location is ranked ahead of a second location; (ii) a ranking corresponding to the time t₁ for the certain second user in which the second location is ranked ahead of the first location; (iii) a ranking corresponding to a time t₂>t₁ for the certain first user, in which the first location is ranked ahead of the second location; and (iv) a ranking corresponding to the time t₂ for the certain second user in which the first location is ranked ahead of the second location. Additionally, the rankings corresponding to t₂ are generated based on at least one measurement of affective response taken after t₁.

[0686] Following is a description of steps that may be performed in a method for dynamically generating personalized rankings of locations based on affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to dynamically generate personalized rankings of locations based on affective response of users. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for dynamically generating personalized rankings of locations based on affective response of users includes at least the following steps:

[0687] In Step 1, receiving, by a system comprising a processor and memory, a profile of a certain first user and a profile of a certain second user. In this embodiment, the profile of the certain first user is different from the profile of the certain second user.

[0688] In Step 2, receiving first measurements of affective response of a first set of users who were at the locations. For each location from among the locations, the first measurements comprise measurements of affective response of at least five users who were at the location, and which were taken between a time t₁−Δ and t₁. Here Δ represents a certain period of time; examples for Δ include one hour, one day, one week, one month, one year, and some other period of time between ten minutes and five years.

[0689] In step 3, receiving a first set of profiles comprising profiles of at least some of the users belonging to the first set of users.

[0690] In step 4, generating a first output indicative of similarities between the profile of the certain first user and profiles belonging to the first set of profiles. Optionally, the first output is generated utilizing the personalization module 130.

[0691] In step 5, computing, based on the first measurements and the first output, a first ranking of the locations. In the first ranking, a first location from among the locations is ranked above the second location from among the locations.

[0692] In Step 6, generating a second output indicative of similarities between the profile of the certain second user and profiles belonging to the first set of profiles. The second output is different from the first output. Optionally, the second output is generated utilizing the personalization module 130.

[0693] In Step 7, computing, based on the first measurements and the second output, a second ranking of the locations. In the second ranking, the second location is ranked above the first location.

[0694] In Step 8, receiving second measurements of affective response of a second set of users who were at the locations. For each location from among the locations, the second measurements comprise measurements of affective response of at least five users who were at the location, and which were taken between a time t₂−Δ and t₂. Additionally, t₂>t₁.

[0695] In Step 9, receiving a second set of profiles comprising profiles of at least some of the users belonging to the second set of users.

[0696] In Step 10, generating a third output indicative of similarities between the profile of the certain second user and profiles belonging to the second set of profiles. Optionally, the third output is generated utilizing the personalization module 130.

[0697] And in Step 11, computing, based on the measurements and the third output, a third ranking of the locations. In the third ranking, the first location is ranked above the second location. Additionally, the third ranking is computed based on at least one measurement taken after t₁.

[0698] In one embodiment, the method described above may optionally include the following steps:

[0699] In Step 12, generating a fourth output indicative of similarities between the profile of the certain first user and profiles belonging to the second set of profiles. Optionally, the fourth output is different from the third output. Optionally, the fourth output is generated utilizing the personalization module 130.

[0700] And in Step 13, computing, based on the second measurements and the fourth output, a fourth ranking of the locations. In the fourth ranking, the first location is ranked above the second location. Additionally, the fourth ranking is computed based on at least one measurement taken after t₁.

[0701] Many people frequently eat at restaurants. Eating food from restaurants is often a fun experience, enabling people to try different types of cuisines, without needing to possess the required expertise, facilities, and/or time that are often necessary to prepare the food. There are often many restaurants to choose from, and in some cities the number of restaurants may be so large that a user may not have eaten, nor may even know of someone who has eaten, in most of the restaurants that are available. Given the large number of dining options typically available, and the large disparity observed between restaurants in terms of cost, quality, and the overall dining experience, there is a need for users to be able to receive information that may assist them in determining which restaurants are worthwhile to dine at.

[0702] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that enable generation of rankings restaurants based on measurements of affective response of users who dined at the restaurants. Such rankings can help a user decide which restaurants are worthwhile to visit, and which should be avoided. A ranking of restaurants is an ordering of at least some of the restaurants, which is indicative of preferences of users towards those restaurants and/or is indicative of the extent of emotional response of the users to those restaurants. Typically, a ranking of restaurants that is generated in embodiments described herein will include at least a first restaurant and a second restaurant, such that the first restaurant is ranked ahead of the second restaurant. When the first restaurant is ranked ahead of the second restaurant, this typically means that, based on the measurements of affective response of the users, the first restaurant is preferred by the users over the second restaurant.

[0703] Herein, a restaurant may be any establishment that provides food and/or beverages. Optionally, a restaurant may offer people an area in which they may consume the food and/or beverages. In some embodiments, a reference made to “a restaurant” and/or “the restaurant” refers to a distinct location in the physical world (e.g., a certain address). In other embodiments, a reference made to “a restaurant” and/or “the restaurant” refers to a location of a certain type, such as any location of a certain chain restaurant. In such embodiments, measurements of affective response of users who ate at “the restaurant” may include measurements taken at different locations, such as different restaurants of the same franchise. Herein, a user who ate at a restaurant may also be referred to as a “diner at restaurant” may be any person who ate food prepared at the restaurant. Optionally, a diner may eat the food at the restaurant. Alternatively, the diner may eat the food prepared at the restaurant at some other location. Thus, in some embodiments, a score for a restaurant may be a “franchise score”, and alert for a restaurant may be an alert for the franchise, a ranking of restaurants may be a ranking of different franchises, etc.

[0704] Some aspects of this disclosure involve collecting measurements of affective response of users who dined at restaurants. In embodiments described herein, a measurement of affective response of a user is typically collected with one or more sensors coupled to the user, which are used to obtain a value that is indicative of a physiological signal of the user (e.g., a heart rate, skin temperature, or brainwave activity) and/or indicative of a behavioral cue of the user (e.g., a facial expression, body language, or the level of stress in the user's voice). Additionally or alternatively, a measurement of affective response of a user may also include indications of biochemical activity in a user's body, e.g., by indicating concentrations of one or more chemicals in the user's body (e.g., levels of various electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity).

[0705] Differences between users can naturally lead to it that they will have different tastes and different preferences when it comes to restaurants they may dine at. Thus, a ranking of restaurants may represent, in some embodiments, an average of the experience the users had when dining at different restaurants, which may reflect an average of the taste of various users. However, for some users, such a ranking of restaurants may not be suitable, since those users, or their taste in restaurants, may be different from the average. In such cases, users may benefit from a ranking of restaurants that is better suited for them. To this end, some aspects of this disclosure involve systems, methods and/or computer-readable media for generating personalized rankings of restaurants based on measurements of affective response of users. Some of these embodiments may utilize a personalization module that weights and/or selects measurements of affective response of users based on similarities between a profile of a certain user (for whom a ranking is personalized) and the profiles of the users (of whom the measurements are taken). An output indicative of these similarities may then be utilized to compute a personalized ranking of restaurants that is suitable for the certain user. Optionally, computing the personalized ranking is done by giving a larger influence, on the ranking, to measurements of users whose profiles are more similar to the profile of the certain user.

[0706] Some aspects of this disclosure involve generating rankings of restaurants based on measurements of affective response of users collected over long periods of time. For example, different measurements used to generate a ranking may be taken during a period of hours, days, weeks, months, and in some embodiments, even years. Naturally, over a stretch of time, the quality of experiences may change. In one example, a restaurant that was busy at a certain time may become less busy; on a larger timescale, staff may be replaced or trained over a period of time which may also change the quality of an experience involving dining at a restaurant. Due to the dynamic nature of the experience of eating at a restaurant, some aspects of this disclosure involve generating rankings of restaurants that correspond to a certain time. Optionally, a ranking of restaurants corresponding to a time t may be based on a certain number of measurements of affective response (e.g., measurements of affective response of at least five different users), taken within a certain window of time before t. For example, a ranking of restaurants corresponding to a time t may be based on measurements taken at some time between t−Δ and t; where Δ may have different values in different embodiments, such as being equal to one day, one week, one month, one year, or some other length of time. Thus, as time progresses, different measurements are included in the window of time between t−Δ and t, thus enabling a ranking computed for the restaurants to reflect the dynamic nature of the experiences that involve staying at the restaurants.

[0707] Following are exemplary embodiments of systems, methods, and computer-readable media that may be used to generate rankings of restaurants, some of which are illustrated in FIG. 22 and FIG. 23. Since herein restaurants are to be considered a certain type of location, the exemplary embodiments described below may be considered embodiments of systems, methods, and/or computer-readable media that may be utilized to generate rankings for locations (of the certain type), as illustrated in FIG. 19, FIG. 20, and FIG. 21. Therefore, the teachings in this disclosure regarding various embodiments, in which rankings for locations are generated, are applicable to embodiments in which rankings for restaurants are generated (i.e., rankings of locations of the certain type). In a similar manner, additional teachings relevant to embodiments described below, which involve generation of rankings for restaurants, may be found at least in section 14—Ranking Experiences, which describes various embodiments in which rankings are generated for experiences in general (and dining at a restaurant is a certain type of experience).

[0708] In one embodiment, a system modeled according to FIG. 19 is configured to generate a ranking of restaurants based on measurements of affective response of users. The system includes at least the collection module 120 and the ranking module 220. The system may optionally include additional modules such as the recommender module 235, the map-displaying module 240, the personalization module 130, and/or the location verifier module 505, to name a few.

[0709] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response of users belonging to the crowd 500, which in this embodiment, include measurements of affective response of users who dined at the restaurants that are being ranked. The collection module 120 is also configured to forward at least some of the measurements 501 to the ranking module 220.

[0710] In one embodiment, each measurement of affective response of a user who dined at a restaurant, from among the restaurants being ranked, is based on at least one of the following values: (i) a value acquired by measuring the user, with a sensor coupled to the user, while the user was at the restaurant and (ii) a value acquired by measuring the user, with a sensor coupled to the user, at most six hours after the user had left the restaurant. Optionally, a period of up to six hours is sufficient, in some embodiments, to detect the effects of food poisoning. Additionally or alternatively, each measurement of affective response of a user who dined at a restaurant, from among the restaurants being ranked, may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the restaurant. Examples of various types of sensors that may be utilized to measure a user are given at least in section 1—Sensors of this disclosure.

[0711] It is to be noted that a measurement of affective response of a user who dined at a restaurant may reflect at least two types of responses of the user to the dining experience. In one example, the measurement may reflect the quality of the experience the user had while dining. In this example, the measurement may reflect factors such as the ambiance, the quality of the service, and/or and the extent to which the food consumed at the restaurant was tasty. In another example, the measurement may reflect how the influence of the food and/or beverages consumed at the restaurant on the user, such as how the user's body reacted to the food (e.g., the extent to which the user became euphoric, lethargic) and/or whether the user became ill (e.g., due to food poisoning). Optionally, determining the effect of the food and/or beverages consumed at a restaurant may be done based on values obtained after the user finished dining at the restaurant.

[0712] In one embodiment, determining when a user dined at a restaurant may be done utilizing the location verifier 505. For example, location verifier may determine from a device of a user (e.g., via GPS, Bluetooth, and/or Wi-Fi signals) when the user was at the restaurant. In another example, the location verifier module 505 may determine from billing information (e.g., credit card transactions and/or a digital wallet transaction) when the user paid for the meal and deduce from that a window of time during which the user was at the restaurant.

[0713] In one embodiment, each of the restaurants being ranked is an establishment that serves at least one of the following items: a food item, and a beverage. Optionally, each of the restaurants offers a space in which users may consume an item they purchase.

[0714] In one embodiment, each restaurant from among the restaurants being ranked offers at least two food items and the users whose measurements are utilized to generate the ranking of the restaurants, and who dined at the restaurant, all consumed the same food item from among the at least two food items. Thus, the ranking of the restaurants may indicate what food item is suggested to be eaten at each restaurant.

[0715] In one embodiment, measurements received by the ranking module 220 include, for each restaurant from among the restaurants being ranked, measurements of affective response of at least five users who dined at the restaurant. Optionally, for each restaurant, the measurements received by the ranking module 220 may include measurements of a different minimal number of users who dined at the restaurant, such as measurements of at least eight, at least ten, or at least one hundred users. The ranking module 220 is configured to generate a ranking of the restaurants based on the received measurements. Optionally, in the generated ranking, a first restaurant is ranked higher than a second restaurant. Optionally, when the first restaurant is ranked ahead of the second restaurant, this generally means that the users who dined at the first restaurant were more satisfied from the dining experience than the users who dined at the second restaurant.

[0716] In some embodiments, in a ranking of restaurants, such as a ranking generated by the ranking module 220, each restaurant has a unique rank, i.e., there are no two restaurants that share the same rank. In other embodiments, at least some of the restaurants may be tied in the ranking. In one example, there may be third and fourth restaurants that are given the same rank by the ranking module 220. It is to be noted that the third restaurant in the example above may be the same restaurant as the first restaurant or the second restaurant mentioned above.

[0717] FIG. 22 illustrates an example of a ranking of restaurants that may be generated utilizing the ranking module 220, as described above. In the illustration, the restaurants being ranked include at least three restaurants, denoted by the reference numerals 587a, 587b, and 587c. Measurements 501 of affective response, which in this example include measurements of users who dined at the restaurants being ranked are transmitted via the network 112 and used to generate the ranking 589. In the ranking 589, the top three restaurants are: Sushi Fun House (587c), which is ranked first, Burritos and Dreams (587b), which is ranked second, and La Petite Entrecôte (587a), which is ranked third. Optionally, this means that in this example, on average, the measurements of users who ate at Sushi Fun House were more positive than the measurements of users who ate at La Petite Entrecôte; thus, indicating that the users, on average, had a better time at the Sushi Fun House than they did at La Petite Entrecôte. There may be various reasons why the users preferred Sushi Fun House over La Petite Entrecôte. In one example, the food at Sushi Fun House might have been tastier to those users. In another example, the ambiance at Sushi Fun House (e.g., lively shots and Karaoke night) might have been much nicer than La Petite Entrecôte. In yet another example, the heavy sauces at La Petite Entrecôte might have caused some of the users to become lethargic and/or morose after the meal. With Burritos and Dreams being ranked second, this example, also illustrates that generally, a burrito is a solid unpretentious choice for a meal.

[0718] It is to be noted that while it is possible, in some embodiments, for the measurements received by modules, such as the ranking module 220, to include, for each user from among the users belonging to the crowd 500 who contributed to the measurements, at least one measurement of affective response of the user to being in each restaurant being ranked, this is not the case in all embodiments. In some embodiments, some users may contribute measurements corresponding to a proper subset of the restaurants (e.g., those users may not have dined at some of the restaurants being ranked), and thus, the measurements 501 may be lacking measurements of some users to some of the restaurants. In some embodiments, some users may have dined only at one of the restaurants being ranked.

[0719] There may be different approaches to ranking that can be used to generate rankings of restaurants, which may be utilized in embodiments described herein. In some embodiments, restaurants may be ranked based on scores computed for the restaurants. In such embodiments, the ranking module 220 may include the scoring module 150 or the dynamic scoring module 180, and a score-based rank determining module 225. In other embodiments, restaurants may be ranked based on preferences generated from measurements. In such embodiments, an alternative embodiment of the ranking module 220 includes preference generator module 228 and preference-based rank determining module 230. The different approaches that may be utilized for ranking restaurants are discussed in more detail in section 14—Ranking Experiences, e.g., in the discussion related to FIG. 85 and FIG. 86.

[0720] In some embodiments, the recommender module 235 is utilized to recommend to a user a restaurant, from among the restaurants ranked by the ranking module 220, in a manner that belongs to a set comprising first and second manners. Optionally, when recommending a restaurant in the first manner, the recommender module 235 provides a stronger recommendation for the restaurant, compared to a recommendation for the restaurant that the recommender module 235 would provide when recommending in the second manner. Optionally, the recommender module 235 determines the manner in which to recommend a restaurant based on the rank of the restaurant in a ranking (e.g., a ranking generated by the ranking module 220). In one example, if a restaurant is ranked at least at a certain rank (e.g., at least in the top 5), it is recommended in the first manner, which may involve providing a promotion for the restaurant to the user (e.g., a coupon) and/or the restaurant is displayed more prominently on a list (e.g., a larger font, at the top of the list, or on the first screen of suggested restaurants) or on a map (e.g., using a picture or icon representing the restaurant). In this example, if a restaurant is not ranked high enough, then it is recommended in the second manner, which may involve no promotion, a smaller font on a listing of restaurants, the hotel may appear on a page that is not the first page of suggested restaurants, the restaurant may have a smaller icon representing it on a map (or no icon at all), etc.

[0721] In some embodiments, map-displaying module 240 may be utilized to present to a user the ranking of the restaurants. Optionally, the map may display an image describing an area in which the restaurants are located and annotations describing at least some of the restaurants and their respective ranks and/or scores computed for the restaurants. Optionally, higher ranked restaurants are displayed more prominently on the map than lower ranked restaurants.

[0722] Following is a description of steps that may be performed in a method for ranking restaurants based on measurements of affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to rank restaurants based on measurements of affective response of users. The steps below may be considered a special case of an embodiment of a method illustrated FIG. 20, which illustrates steps involved in one embodiment of a method for ranking locations based on measurements of affective response of users (because restaurants are a specific type of location being ranked). In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for ranking restaurants based on measurements of affective response of users includes at least the following steps:

[0723] In Step 1, receiving, by a system comprising a processor and memory, the measurements of affective response of the users. For each restaurant from among the restaurants being ranked, the measurements comprise measurements of affective response of at least five users who dined at the restaurant.

[0724] And in Step 2, ranking the restaurants based on the measurements, such that, a first restaurant from among the restaurants is ranked higher than a second restaurant from among the restaurants. Optionally, the ranking of the restaurants may involve performing different operations, as discussed in the description of embodiments whose steps are described in FIG. 20.

[0725] In one embodiment, the method described above may optionally include a step that involves utilizing a sensor coupled to a user who dined at a restaurant, from among the restaurants being ranked, to obtain a measurement of affective response of the user who dined at the restaurant. Optionally, the measurement of affective response of the user is based on at least one of the following values: (i) a value acquired by measuring the user with the sensor while the user was at the restaurant, and (ii) a value acquired by measuring the user with the sensor up to six hours after the user had left the restaurant.

[0726] In one embodiment, the method described above may optionally include a step that involves recommending the first restaurant to a user in a first manner, and not recommending the second restaurant to the user in the first manner. Optionally, the step may further involve recommending the second restaurant to the user in a second manner. As mentioned above, e.g., with reference to recommender module 235, recommending a restaurant in the first manner may involve providing a stronger recommendation for the restaurant, compared to a recommendation for the restaurant that is provided when recommending it in the second manner.

[0727] Since different users may have different backgrounds, tastes, and/or preferences, in some embodiments, the same ranking of restaurants may not be the best suited for all users. Thus, in some embodiments, rankings of restaurants may be personalized for some of the users (also referred to as a “personalized ranking” of restaurants). Optionally, the personalization module 130 may be utilized in order to generate such personalized rankings of restaurants. In one example, generating the personalized rankings is done utilizing an output generated by the personalization module 130 after being given a profile of a certain user and profiles of at least some of the users who provided measurements that are used to rank the restaurants (e.g., profiles of diners from among the profiles 504). The output is indicative of similarities between the profile of the certain user and the profiles of the at least some of the users. When computing a ranking of restaurants based on the output, more influence may be given to measurements of users whose profiles indicate that they are similar to the certain user. Thus, the resulting ranking may be considered personalized for the certain user. Since different certain users are likely to have different profiles, the output generated for them may be different, and consequently, the personalized rankings of the restaurants that are generated for them may be different. For example, in some embodiments, when generating personalized rankings for restaurants, there are at least a certain first user and a certain second user, who have different profiles, for which the ranking module 220 may rank restaurants differently. For example, for the certain first user, a first restaurant may be ranked above a second restaurant, and for the certain second user, the second restaurant is ranked above the first restaurant. The way in which, in the different approaches to ranking, an output from the personalization module 130 may be utilized to generate personalized rankings for different users, is discussed in more detail in section 14—Ranking Experiences.

[0728] In one embodiment, a profile of a user who dined at a restaurant, such as a profile from among the profiles 504, may include information that describes one or more of the following: the age of the user, the gender of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, information indicative of spending and/or traveling habits of the user, and/or a feature value derived from semantic analysis of a communication of the user. Optionally, the profile of a user may include information regarding culinary and/or dieting habits of the user. For example, the profile may include dietary restrictions, information about sensitivities to certain substances, and/or allergies the user may have. In another example, the profile may include various preference information such as favorite cuisine and/or dishes, preferences regarding consumptions of animal source products and/or organic food, and/or preferences regarding a type and/or location of seating at a restaurant. In yet another example, the profile may include data derived from monitoring food and beverages the user consumed. Such information may come from various sources, such as billing transactions and/or a camera-based system that utilizes image processing to identify food and drinks the user consumes from images taken by a camera mounted on the user and/or in the vicinity of the user.

[0729] FIG. 23 illustrates a system configured to generate personalized rankings of restaurants. In the illustrated embodiment, the crowd 500 includes users who dined at the restaurants, and from whom measurements 501 of affective response were taken while they were at the restaurants and/or up to six hours after that time, as described above. FIG. 23 illustrates two different users, denoted 592a and 592b, who have different profiles 591a and 591b, respectively. In one embodiment, the profiles 591a and 591b are provided to the personalization module 130 which generates, based on the provided profiles, first and second outputs, respectively. As described above, these outputs are used by the ranking module 220 to generate different rankings of the restaurants: ranking 593a for user 592a, and ranking 593b for user 592b. The different order of the restaurants in the rankings 593a and 593b, may have resulted from the fact that, on average, users more similar to user 592a had more positive measurements of affective to dining at La Petite Entrecôte, compared to their measurements of affective response when dining at Sushi Fun House. With users more similar to user 592b, it might have been the opposite; they had more positive measurements measured when dining Sushi Fun House, compared to measurements measured for such users when they dined at La Petite Entrecôte.

[0730] Generating rankings of restaurants that are personalized for different users may involve execution of certain steps. Following is a more detailed discussion of steps that may be involved in a method for generating personalized rankings of restaurants. These steps may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 19 and/or steps of a method modeled according to FIG. 21. The aforementioned figures illustrate embodiments that involve generation of personalized rankings of locations. Since restaurants are a specific type of location, the teachings of those embodiments are relevant to the steps of the method described below. In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[0731] In one embodiment, the method for utilizing profiles of users to compute personalized rankings of restaurants based on measurements of affective response of the users includes at least the following steps:

[0732] In Step 1, receiving, by a system comprising a processor and memory, measurements of affective response of the users who dined at the restaurants being ranked. For each restaurant from among the restaurants being ranked, the measurements comprise measurements of affective response of at least eight users who dined at the restaurant. Optionally, for each restaurant from among the restaurants being ranked, the measurements comprise measurements of affective response of at least some other minimal number of users who dined at the restaurant, such as measurements of at least five, at least ten, and/or at least fifty different users.

[0733] In Step 2, receiving profiles of at least some of the users who contributed measurements in Step 1. Optionally, the received profiles are some of the profiles 504.

[0734] In Step 3, receiving a profile of a certain first user.

[0735] In Step 4, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least some of the users. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586e in FIG. 21.

[0736] In Step 5, computing, based on the measurements and the first output, a first ranking of the restaurants.

[0737] In Step 6, receiving a profile of a certain second user, which is different from the profile of the certain first user.

[0738] In Step 7, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least some of the users. Here, the second output is different from the first output. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586i in FIG. 21.

[0739] And in 8, computing, based on the measurements and the second output, a second ranking of the restaurants. Optionally, the first and second rankings are different, such that in the first ranking, a first restaurant is ranked above a second restaurant, and in the second ranking, the second restaurant is ranked above the first restaurant.

[0740] In one embodiment, the method optionally includes a step that involves utilizing a sensor coupled to a user who dined at a restaurant, from among the restaurants being ranked, for obtaining a measurement of affective response of the user. Optionally, the measurement of affective response of the user is based on at least one of the following values: (i) a value acquired by measuring the user with the sensor while the user dined at the restaurant, and (ii) a value acquired by measuring the user with the sensor up to six hour after the user had left the restaurant. Optionally, obtaining a measurement of affective response of a user who dined at a restaurant is done by measuring the user with the sensor during at least three different non-overlapping periods while the user was at the restaurant.

[0741] In one embodiment, the method may optionally include steps that involve reporting to a certain user a result based on a ranking of the restaurants personalized for the certain user. In one example, the method may include a step that involves forwarding to the certain first user a result derived from the first ranking of the restaurants. In this example, the result may be a recommendation to go to the first restaurant (which for the certain first user is ranked higher than the second restaurant). In another example, the method may include a step that involves forwarding to the certain second user a result derived from the second ranking of the restaurants. In this example, the result may be a recommendation for the certain second user to go to the second restaurant (which for the certain second user is ranked higher than the first restaurant).

[0742] In some embodiments, the method may optionally include steps involving recommending one or more of the restaurants being ranked to users. Optionally, the type of recommendation given for a restaurant is based on the rank of the restaurant. For example, given that in the first ranking, the rank of the first restaurant is higher than the rank of the second restaurant, the method may optionally include a step of recommending the first restaurant to the certain first user in a first manner, and not recommending the second restaurant to the certain first user in first manner. Optionally, the method may include a step of recommending the second restaurant to the certain first user in a second manner. Optionally, recommending a restaurant in the first manner involves providing a stronger recommendation for the restaurant, compared to a recommendation for the restaurant that is provided when recommending it in the second manner. The nature of the first and second manners is discussed in more detail with respect to the recommender module 178, which may also provide recommendations in first and second manners.

[0743] The quality of dining at certain restaurants may change over time. Thus, in some embodiments, rankings generated for restaurants may dynamic rankings. For example, a ranking of restaurants may correspond to a time t, and be based on measurements of affective response taken in temporal proximity to t (e.g., in a certain window of time Δ preceding t). Thus, given that over time, the values of measurements in the window that are used to compute a ranking of restaurants may change, the computed rankings of the restaurants may also change over time. Some of the embodiments described above, e.g., embodiments for ranking restaurants modeled according to FIG. 19 may be used to generate dynamic rankings of restaurants by providing measurements of affective response to the dynamic ranking module 250 instead of to the ranking module 220. A more detailed discussion of dynamic ranking may be found in this disclosure at least in section 14—Ranking Experiences.

[0744] In one embodiment, a system configured to dynamically rank restaurants based on measurements of affective response of users includes at least the collection module 120 and the dynamic ranking module 250. In this embodiment, the collection module 120 is configured to receive the measurements 501 of affective response of users belonging to the crowd 500, which include measurements of users who dined at the restaurants being ranked. Optionally, for each restaurant from among the restaurants being ranked, the measurements 501 include measurements of affective response of at least ten users dined at the restaurant. In this embodiment, the dynamic ranking module 250 is configured to generate rankings of the restaurants. Each ranking corresponds to a time t and is generated based on a subset of the measurements of the users that includes measurements of at least five users; where each measurement is taken at a time that is not earlier than a certain period before t and is not after t. For example, if the length of the certain period is denoted Δ, each of the measurements in the subset was taken at a time that is between t−Δ and t. Optionally, for each restaurant from among the restaurants, the subset includes measurements of at least five different users who were at the restaurant. Optionally, measurements taken earlier than the certain period before a time t are not utilized by the dynamic ranking module 250 to generate a ranking corresponding to t. Optionally, the dynamic ranking module 250 may be configured to assign weights to measurements used to compute a ranking corresponding to a time t, such that an average of weights assigned to measurements taken earlier than the certain period before t is lower than an average of weights assigned to measurements taken later than the certain period before t. The dynamic ranking module 250 may be further configured to utilize the weights to compute the ranking corresponding to t.

[0745] In one embodiment, the rankings generated by the dynamic ranking module 250 include at least a first ranking corresponding to a time t₁ and a second ranking corresponding to a time t₂, which is after t₁. In the first ranking corresponding to the time t₁, a first restaurant is ranked above a second restaurant. However, in the second ranking corresponding to the time t₂, the second restaurant is ranked above the first restaurant. In this embodiment, the second ranking is computed based on at least one measurement taken after t₁.

[0746] Since dynamic rankings of restaurants may change over time, this may change the nature of recommendations of restaurants that are given to users at different times. In one embodiment, the recommender module 235 is configured to recommend a restaurant to a user in a manner that belongs to a set comprising first and second manners. When recommending a restaurant in the first manner, the recommender module 235 provides a stronger recommendation for the restaurant, compared to a recommendation for the restaurant that the recommender module 235 provides when recommending it in the second manner. With reference to the embodiment described above, which includes the first and second rankings corresponding to t₁ and t₂, respectively, the recommender module 235 may be configured to: recommend the first restaurant to a user during a period that ends before t₂ in the first manner, and not to recommend to the user the second restaurant in the first manner during that period. Optionally, during that period, the recommender module 235 recommends the second restaurant in the second manner. After t₂, the behavior of the recommender module 235 may change, and it may recommend to the user the second restaurant in the first manner, and not recommend the first restaurant in the first manner. Optionally, after t₂, the recommender module 235 may recommend the first restaurant in the second manner.

[0747] Following is a description of steps that may be performed in a method for dynamically ranking restaurants based on measurements of affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to dynamically rank restaurants based on measurements of affective response of users. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for dynamically ranking restaurants based on measurements of affective response of users includes at least the following steps:

[0748] In Step 1, receiving, by a system comprising a processor and memory, a first set of measurements of affective response of users. Each measurement belonging to the first set was taken at a time that is not earlier than a certain period before a time t₁ and is not after t₁. Additionally, for each restaurant from among the restaurants being ranked, the first set of measurements comprises measurements of affective response of at least five users who were at the restaurant.

[0749] In Step 2, generating, based on the first set of measurements, a first ranking of the restaurants. In the first ranking, a first restaurant is ranked ahead of a second restaurant.

[0750] In Step 3, receiving a second set of measurements of affective response of users. Each measurement belonging to the second set was taken at a time that is not earlier than the certain period before a time t₂ and is not after t₂. Additionally, for each restaurant from among the restaurants, the second set of measurements comprises measurements of affective response of at least five users who were at the restaurant.

[0751] And in Step 4, generating, based on the second set of measurements, a second ranking of the restaurants. In the second ranking, the second restaurant is ranked ahead of the first restaurant. Additionally, t₂>t₁ and the second set of measurements comprises at least one measurement of affective response of a user taken after t₁.

[0752] The method described above may optionally include a step that involves recommending to a user a restaurant from among the restaurants being ranked. The nature of such a recommendation may depend on the ranking of the restaurants, and as such, may change over time. Optionally, recommending a restaurant may be done in a first manner or in a second manner; recommending a restaurant in the first manner may involve providing a stronger recommendation for the restaurant, compared to a recommendation for the restaurant provided when recommending it in the second manner. In one example, at a time that is before t₂, the first restaurant may be recommended to a user in the first manner, and the second restaurant may be recommended to the user in the second manner. However, at a time that is after t₂, the first restaurant may be recommended to the user in the second manner, and the second restaurant is recommended to the user in the first manner.

[0753] In a similar manner to the personalization of rankings of restaurants described above, in some embodiments, dynamic rankings of restaurants may also be personalized for different users. Optionally, this is done utilizing the personalization module 130, which may be utilized to generate personalized dynamic rankings of restaurants, e.g., as illustrated in FIG. 91a and FIG. 91b, which involve personalized rankings of experiences, and as such are relevant to personalized dynamic rankings of restaurants (since dining at a restaurant is a specific type of experience).

[0754] In one embodiment, a system configured to dynamically generate personalized rankings of restaurants based on measurements of affective response of users includes at least the collection module 120, the personalization module 130, and the dynamic ranking module 250. In this embodiment, the collection module 120 is configured to receive the measurements of affective response of the users that include, for each restaurant from among the restaurants, measurements of affective response of at least ten users who dined at the restaurant. The personalization module 130 is configured, in one embodiment, to receive a profile of a certain user and profiles of the users, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the users. The dynamic ranking module 250 is configured to generate, for the certain user, rankings of the restaurants. Each ranking of the restaurants corresponds to a time t and is generated based on the output and a subset of the measurements comprising, for each restaurant in the ranking, measurements of at least five users who dined at the restaurant. Additionally, each measurement in the subset is taken at a time that is not earlier than a certain period before t and is not after t.

[0755] By utilizing the personalization module 130, it is possible that different users may receive different dynamic rankings, at different times. In particular, in one embodiment, rankings generated by the system described above are such that for at least a certain first user and a certain second user, who have different profiles, the dynamic ranking module 250 generates the following rankings: (i) a ranking corresponding to a time t₁ for the certain first user, in which a first restaurant is ranked ahead of a second restaurant; (ii) a ranking corresponding to the time t₁ for the certain second user in which the second restaurant is ranked ahead of the first restaurant; (iii) a ranking corresponding to a time t₂>t₁ for the certain first user, in which the first restaurant is ranked ahead of the second restaurant; and (iv) a ranking corresponding to the time t₂ for the certain second user in which the first restaurant is ranked ahead of the second restaurant. Additionally, the rankings corresponding to t₂ are generated based on at least one measurement of affective response taken after t₁.

[0756] Following is a description of steps that may be performed in a method for dynamically generating personalized rankings of restaurants based on measurements of affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to dynamically generate personalized rankings of restaurants based on measurements of affective response of users. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for dynamically generating personalized rankings of restaurants based on measurements of affective response of users includes at least the following steps:

[0757] In Step 1, receiving, by a system comprising a processor and memory, a profile of a certain first user and a profile of a certain second user. In this embodiment, the profile of the certain first user is different from the profile of the certain second user.

[0758] In Step 2, receiving first measurements of affective response of a first set of users who dined at the restaurants. For each restaurant from among the restaurants, the first measurements comprise measurements of affective response of at least five users who were at the restaurant, and which were taken between a time t₁−Δ and t₁. Here Δ represents a certain period of time; examples of values Δ may include one hour, one day, one week, one month, one year, and some other period of time between ten minutes and five years.

[0759] In step 3, receiving a first set of profiles comprising profiles of at least some of the users belonging to the first set of users.

[0760] In step 4, generating a first output indicative of similarities between the profile of the certain first user and profiles belonging to the first set of profiles. Optionally, the first output is generated utilizing the personalization module 130.

[0761] In step 5, computing, based on the first measurements and the first output, a first ranking of the restaurants. In the first ranking, a first restaurant is ranked above a second restaurant.

[0762] In Step 6, generating a second output indicative of similarities between the profile of the certain second user and profiles belonging to the first set of profiles. The second output is different from the first output. Optionally, the second output is generated utilizing the personalization module 130.

[0763] In Step 7, computing, based on the first measurements and the second output, a second ranking of the restaurants. In the second ranking, the second restaurant is ranked above the first restaurant.

[0764] In Step 8, receiving second measurements of affective response of a second set of users who were at the restaurants. For each restaurant from among the restaurants, the second measurements comprise measurements of affective response of at least five users who were at the restaurant, and which were taken between a time t₂−Δ and t₂. Additionally, t₂>t₁.

[0765] In Step 9, receiving a second set of profiles comprising profiles of at least some of the users belonging to the second set of users.

[0766] In Step 10, generating a third output indicative of similarities between the profile of the certain second user and profiles belonging to the second set of profiles. Optionally, the third output is generated utilizing the personalization module 130.

[0767] And in Step 11, computing, based on the measurements and the third output, a third ranking of the restaurants. In the third ranking, the first restaurant is ranked above the second restaurant. Additionally, the third ranking is computed based on at least one measurement taken after t₁.

[0768] In one embodiment, the method described above may optionally include the following steps:

[0769] In Step 12, generating a fourth output indicative of similarities between the profile of the certain first user and profiles belonging to the second set of profiles. Optionally, the fourth output is different from the third output. Optionally, the fourth output is generated utilizing the personalization module 130.

[0770] And in Step 13, computing, based on the second measurements and the fourth output, a fourth ranking of the restaurants. In the fourth ranking, the first restaurant is ranked above the second restaurant. Additionally, the fourth ranking is computed based on at least one measurement taken after t₁.

[0771] Staying at a hotel is an experience that many users have, often many times a year. Given the expenses that are typically involved in the stay, and the importance of a quality experience (e.g., a bad experience may be detrimental to one's mood and/or to the ability to work the next day), being able to choose an appropriate hotel for a person is important and beneficial.

[0772] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that enable generation of rankings of hotels based on measurements of affective response of users who stayed at the hotels. Such rankings can help a user decide which hotels are worthwhile to stay at, and which should be avoided. A ranking of hotels is an ordering of at least some of the hotels, which is indicative of preferences of users towards those hotels and/or is indicative of the extent of emotional response of the users to those hotels. Typically, a ranking of hotels that is generated in embodiments described herein will include at least a first hotel and a second hotel, such that the first hotel is ranked ahead of the second hotel. When the first hotel is ranked ahead of the second hotel, this typically means that, based on the measurements of affective response of the users, the first hotel is preferred by the users over the second hotel.

[0773] Herein, a hotel may be any lodging that provides a person with a room in which the user may sleep. Thus, a hotel may be an establishment that offers multiple rooms (to multiple guests) and/or has a single room to offer (e.g., a room offered on an online service such as Airbnb). Additionally, a hotel need not be a building on the land; a cruise ship and/or a space station may be considered hotels in embodiments described in this disclosure.

[0774] Some aspects of this disclosure involve collecting measurements of affective response of users who stayed at hotels. In embodiments described herein, a measurement of affective response of a user is typically collected with one or more sensors coupled to the user, which are used to obtain a value that is indicative of a physiological signal of the user (e.g., a heart rate, skin temperature, or brainwave activity) and/or indicative of a behavioral cue of the user (e.g., a facial expression, body language, or the level of stress in the user's voice). Additionally or alternatively, a measurement of affective response of a user may also include indications of biochemical activity in a user's body, e.g., by indicating concentrations of one or more chemicals in the user's body (e.g., levels of various electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity).

[0775] Differences between users can naturally lead to it that they will have different tastes and different preferences when it comes to hotels they may stay at. Thus, a ranking of hotels may represent, in some embodiments, an average of the experience the users had when staying at different hotels, which may reflect an average of the taste of various users. However, for some users, such a ranking of hotels may not be suitable, since those users, or their preferences with regards to hotels, may be different from the average. In such cases, users may benefit from a ranking of hotels that is better suited for them. To this end, some aspects of this disclosure involve systems, methods and/or computer-readable media for generating personalized rankings of hotels based on measurements of affective response of users. Some of these embodiments may utilize a personalization module that weights and/or selects measurements of affective response of users based on similarities between a profile of a certain user (for whom a ranking is personalized) and the profiles of the users (of whom the measurements are taken). An output indicative of these similarities may then be utilized to compute a personalized ranking of hotels, which is suitable for the certain user. Optionally, computing the personalized ranking is done by giving a larger influence, on the ranking, to measurements of users whose profiles are more similar to the profile of the certain user.

[0776] Some aspects of this disclosure involve generating rankings of hotels based on measurements of affective response of users collected over long periods of time. For example, different measurements used to generate a ranking may be taken during a period of hours, days, weeks, months, and in some embodiments, even years. Naturally, over a stretch of time, the quality of experiences may change. In one example, renovations at a hotel, which inconvenienced its guests, might end, and the improved hotel may offer a much more exciting experience. In another example, a hotel may be very busy during a convention, or understaffed (e.g., due to a flu epidemic), which may temporarily change the quality of an experience involving staying at the hotel. Due to the dynamic nature that an experience involving staying at a hotel may have, some aspects of this disclosure involve generating rankings of hotels that correspond to a certain time. Optionally, a ranking of hotels corresponding to a time t may be based on a certain number of measurements of affective response (e.g., measurements of affective response of at least five different users), taken within a certain window of time before t. For example, a ranking of hotels corresponding to a time t may be based on measurements taken at some time between t−Δ and t; where Δ may have different values in different embodiments, such as being equal to one day, one week, one month, one year, or some other length of time. Thus, as time progresses, different measurements are included in the window of time between t−Δ and t, thus enabling a ranking computed for the hotels to reflect the dynamic nature of experiences that involve staying at the hotels.

[0777] In some embodiments described herein, instead of generating rankings of hotels, some systems, methods, and/or computer-readable media may generate rankings of hotel facilities that users may utilize at hotels. For example, various hotels may include one or more of hotel facilities, such as a reception desk, a pool, a restaurant, a gym, a bar, a club, a store, a movie theatre, a beach, and a golf course. A ranking of the hotel facilities may be generated based on measurements of affective response of users who utilized the hotel facilities, and be indicative of how much the users enjoyed utilizing the hotel facilities. In one embodiment, a ranking of hotel facilities may include multiple hotel facilities that belong to a certain hotel (e.g., the ranking may involve a bar, a pool, and a restaurant, all in the same hotel). In another embodiment, a ranking of hotel facilities may include the same type of hotel facility at multiple hotels (e.g., the ranking may involve different bars at different hotels).

[0778] Following are exemplary embodiments of systems, methods, and computer-readable media that may be used to generate rankings of hotels, some of which are illustrated in FIG. 24 and FIG. 25. Since herein hotels are to be considered a certain type of location, the exemplary embodiments described below may be considered embodiments of systems, methods, and/or computer-readable media that may be utilized to generate rankings for locations (of the certain type), as illustrated in FIG. 19, FIG. 20, and FIG. 21. Therefore, the teachings in this disclosure regarding various embodiments, in which rankings for locations are generated, are applicable to embodiments in which rankings of hotels are generated (i.e., rankings for locations of the certain type). In a similar manner, additional teachings relevant to embodiments described below, which involve generation of rankings of hotels, may be found at least in section 14—Ranking Experiences, which describes various embodiments in which rankings are generated for experiences in general (and staying at a hotel is a certain type of experience).

[0779] In one embodiment, a system modeled according to FIG. 19 is configured to generate a ranking of hotels based on measurements of affective response of users. The system includes at least the collection module 120 and the ranking module 220. The system may optionally include additional modules such as the recommender module 235, the map-displaying module 240, the personalization module 130, and/or the location verifier module 505, to name a few.

[0780] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response of users belonging to the crowd 500, which in this embodiment, include measurements of affective response of users who stayed at the hotels being ranked. The collection module 120 is also configured to forward at least some of the measurements 501 to the ranking module 220.

[0781] In one embodiment, each measurement of affective response of a user who stayed at a hotel, from among the hotels being ranked, is based on a value obtained by measuring the user, with a sensor coupled to the user, while the user was at the hotel. Optionally, the measurement may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the hotel. Examples of various types of sensors that may be utilized to measure a user are given at least in section 1—Sensors of this disclosure.

[0782] In one embodiment, determining when a user stayed at a hotel may be done utilizing the location verifier 505. For example, location verifier may determine from a device of a user (e.g., via GPS, Bluetooth, and/or Wi-Fi signals) when the user was at the hotel. In another example, the location verifier module 505 may determine from billing information (e.g., credit card transactions and/or a digital wallet transaction) when the user paid for the hotel, and deduce from that a window of time during which the user was at the hotel. In yet another example, the location verifier module 505 may receive information from one or more of the following software systems indicating when the user was at the hotel: a room ordering system, a room management system (e.g., a “smart” room controller), and a security system of the hotel (e.g., a system that includes cameras and face recognition).

[0783] In one embodiment, measurements received by the ranking module 220 include, for each hotel from among the hotels being ranked, measurements of affective response of at least five users who stayed at the hotel for at least one hour. Optionally, for each hotel, the measurements received by the ranking module 220 may include measurements of a different minimal number of users who stayed at the hotel, such as measurements of at least eight, at least ten, or at least one hundred users. Optionally, each of the users whose measurements are used to compute the ranking might have stayed at the hotel for a longer duration, such as at least four hours, at least a day, at least a weekend, at least a week, or at least a month. The ranking module 220 is also configured, in one embodiment, to generate a ranking of the hotels based on the received measurements. Optionally, in the generated ranking, a first hotel is ranked higher than a second hotel. Optionally, when the first hotel is ranked ahead of the second hotel, this generally means that the users who stayed at the first hotel were more satisfied from their hotel than the users who stayed at the second hotel were of their hotel. For example, during their stay, the average level of enjoyment measured for the at least five users who stayed at the first hotel was higher than the average level of the enjoyment measured for the at least five users who stayed at the second hotel.

[0784] In some embodiments, in a ranking of hotels, such as a ranking generated by the ranking module 220, each hotel has a unique rank, i.e., there are no two hotels that share the same rank. In other embodiments, at least some of the hotels may be tied in the ranking. In one example, there may be third and fourth hotels that are given the same rank by the ranking module 220. It is to be noted that the third hotel in the example above may be the same hotel as the first hotel or the second hotel mentioned above.

[0785] In some embodiments, measurements used to generate a ranking of hotels share a similar characteristic. In one example, the measurements are of users who stayed in a certain type of room at the different hotels (e.g., the penthouse), thus the, ranking may represent a ranking of penthouse rooms at the different hotels. In another example, the measurements are all taken during a certain period, e.g., during the summer vacation. In still another example, the measurements all involved users who spent a certain duration at the hotels, such as a duration of at least a week.

[0786] FIG. 24 illustrates an example of a ranking of hotels that may be generated utilizing the ranking module 220, as described above. In the illustration, the hotels being ranked include at least three hotels. Measurements 501 of affective response, which in this example include measurements of users who stayed at the hotels are transmitted via the network 112 and used to generate the ranking 595. In FIG. 24 the ranking 595 is illustrated as a screen a user may view (e.g., on an online hotel booking site), which describes the hotels, their ranks, and other information (e.g., price range for a room, and the number of measurements obtained of users who stayed at the hotel). In this illustration, the top ranked hotel is recommended a stronger manner compared to the other hotels (e.g., it is denoted as being the “best choice” and there is a promotional offer for booking it).

[0787] It is to be noted that while it is possible, in some embodiments, for the measurements received by modules, such as the ranking module 220, to include, for each user from among the users belonging to the crowd 500 who contributed to the measurements, at least one measurement of affective response of the user to being in each hotel being ranked, this is not the case in all embodiments. In some embodiments, some users may contribute measurements corresponding to a proper subset of the hotels (e.g., those users may not have stayed at some of the hotels being ranked), and thus, the measurements 501 may be lacking measurements of some users to some of the hotels. In some embodiments, some users may have stayed only at one of the hotels being ranked.

[0788] There may be different approaches to ranking that can be used to generate rankings of hotels, which may be utilized in embodiments described herein. In some embodiments, hotels may be ranked based on scores computed for the hotels. In such embodiments, the ranking module 220 may include the scoring module 150 or the dynamic scoring module 180, and a score-based rank determining module 225. In other embodiments, hotels may be ranked based on preferences generated from measurements. In such embodiments, an alternative embodiment of the ranking module 220 includes preference generator module 228 and preference-based rank determining module 230. The different approaches that may be utilized for ranking hotels are discussed in more detail in section 14—Ranking Experiences, e.g., in the discussion related to FIG. 85 and FIG. 86.

[0789] In some embodiments, the recommender module 235 is utilized to recommend to a user a hotel, from among the hotels ranked by the ranking module 220, in a manner that belongs to a set comprising first and second manners. Optionally, when recommending a hotel in the first manner, the recommender module 235 provides a stronger recommendation for the hotel, compared to a recommendation for the hotel that the recommender module 235 would provide when recommending in the second manner. Optionally, the recommender module 235 determines the manner in which to recommend a hotel based on the rank of the hotel in a ranking (e.g., a ranking generated by the ranking module 220). In one example, if a hotel is ranked at least at a certain rank (e.g., at least in the top 5), it is recommended in the first manner, which may involve providing a promotion for the hotel to the user (e.g., a coupon) and/or the hotel is displayed more prominently on a list (e.g., a larger font, at the top of the list, or on the first screen of suggested hotels) or on a map (e.g., using a picture or icon representing the hotel). In this example, if a hotel is not ranked high enough, then it is recommended in the second manner, which may involve no promotion, a smaller font on a listing of hotels, the hotel may appear on a page that is not the first page of suggested hotels, or the hotel may have a smaller icon representing it on a map (or no icon at all), etc.

[0790] In some embodiments, map-displaying module 240 may be utilized to present to a user the ranking of the hotels. Optionally, the map may display an image describing an area in which the hotels are located and annotations describing at least some of the hotels and their respective ranks and/or scores computed for the hotels. Optionally, higher ranked hotels are displayed more prominently on the map than lower ranked hotels.

[0791] Following is a description of steps that may be performed in a method for ranking hotels based on measurements of affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to rank hotels based on measurements of affective response of users. The steps below may be considered a special case of an embodiment of a method illustrated FIG. 20, which illustrates steps involved in one embodiment of a method for ranking locations based on measurements of affective response of users (because hotels are a specific type of location being ranked). In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for ranking hotels based on measurements of affective response of users includes at least the following steps:

[0792] In Step 1, receiving, by a system comprising a processor and memory, the measurements of affective response of the users. For each hotel from among the hotels being ranked, the measurements comprise measurements of affective response of at least five users who stayed at the hotel for at least four hours. Optionally, each of the users staying at a hotel stayed for a longer period, such as at least twelve hours, at least one thy, at least one week, or at least one month.

[0793] And in Step 2, ranking the hotels based on the measurements, such that, a first hotel is ranked higher than a second hotel. Optionally, the ranking of the hotels may involve performing different operations, as discussed in the description of embodiments whose steps are described in FIG. 20.

[0794] In one embodiment, the method described above may optionally include a step that involves utilizing a sensor coupled to a user who stayed at a hotel, from among the hotels being ranked, to obtain a measurement of affective response of the user. Optionally, the measurement may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the hotel.

[0795] In one embodiment, the method described above may optionally include a step that involves recommending the first hotel to a user in a first manner, and not recommending the second hotel to the user in the first manner. Optionally, the step may further involve recommending the second hotel to the user in a second manner. As mentioned above, e.g., with reference to recommender module 235, recommending a hotel in the first manner may involve providing a stronger recommendation for the hotel, compared to a recommendation for the hotel that is provided when recommending it in the second manner.

[0796] Since different users may have different backgrounds, tastes, and/or preferences, in some embodiments, the same ranking of hotels may not be the best suited for all users. Thus, in some embodiments, rankings of hotels may be personalized for some of the users (also referred to as a “personalized ranking” of hotels). Optionally, the personalization module 130 may be utilized in order to generate such personalized rankings of hotels. In one example, generating the personalized rankings is done utilizing an output generated by the personalization module 130 after being given a profile of a certain user and profiles of at least some of the users who provided measurements that are used to rank the hotels (e.g., profiles of users from among the profiles 504). The output is indicative of similarities between the profile of the certain user and the profiles of the at least some of the users. When computing a ranking of hotels based on the output, more influence may be given to measurements of users whose profiles indicate that they are similar to the certain user. Thus, the resulting ranking may be considered personalized for the certain user. Since different certain users are likely to have different profiles, the output generated for them may be different, and consequently, the personalized rankings of the hotels that are generated for them may be different. For example, in some embodiments, when generating personalized rankings of hotels, there are at least a certain first user and a certain second user, who have different profiles, for which the ranking module 220 may rank hotels differently. For example, for the certain first user, a first hotel may be ranked above a second hotel, and for the certain second user, the second hotel is ranked above the first hotel. The way in which, in the different approaches to ranking, an output from the personalization module 130 may be utilized to generate personalized rankings for different users, is discussed in more detail in section 14—Ranking Experiences.

[0797] In one embodiment, a profile of a user who stayed at a hotel, such as a profile from among the profiles 504, may include information that describes one or more of the following: the age of the user, the gender of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, information indicative of spending and/or traveling habits of the user, and/or a feature value derived from semantic analysis of a communication of the user. Optionally, the profile of a user may include information regarding travel habits of the user. For example, the profile may include itineraries of the user indicating to travel destinations, such as countries and/or cities the user visited. Optionally, the profile may include information regarding the type of trips the user took (e.g., business or leisure), what hotels the user stayed at, the cost, and/or the duration of stay.

[0798] FIG. 25 illustrates a system configured to generate personalized rankings of hotels. In the illustrated embodiment, the crowd 500 includes users who stayed at the hotels, and from whom measurements 501 of affective response were taken while they were at the hotels, as described above. FIG. 25 illustrates two different users, denoted 598a and 598b, who have different profiles 597a and 597b, respectively. In one embodiment, the profiles 597a and 597b are provided to the personalization module 130 which generates, based on the provided profiles, first and second outputs, respectively. As described above, these outputs are used by the ranking module 220 to generate different rankings of the hotels: ranking 599a for user 598a, and ranking 599b for user 598b. As the illustration shows, the ranking 599a is different from the ranking 599b; each of the rankings includes the same three hotels in the top-three positions, however, the hotel that is ranked first in ranking 599a is ranked second in ranking 599b, and vice versa. Additionally, the illustration shows that a hotel that is ranked first is presented more prominently than a hotel with a lower ranking (i.e., the hotel ranked first receives a stronger recommendation). In the illustration, the more prominent representation involves a larger image of the hotel that is ranked first.

[0799] Generating rankings of hotels that are personalized for different users may involve execution of certain steps. Following is a more detailed discussion of steps that may be involved in a method for generating personalized rankings of hotels. These steps may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 19 and/or steps of a method modeled according to FIG. 21. The aforementioned figures illustrate embodiments that involve generation of personalized rankings of locations. Since hotels are a specific type of location, the teachings of those embodiments are relevant to the steps of the method described below. In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[0800] In one embodiment, the method for utilizing profiles of users to compute personalized rankings of hotels based on measurements of affective response of the users includes at least the following steps:

[0801] In Step 1, receiving, by a system comprising a processor and memory, measurements of affective response of the users who stayed at the hotels being ranked. For each hotel from among the hotels being ranked, the measurements comprise measurements of affective response of at least eight users who stayed at the hotel for at least an hour. Optionally, each of the users whose measurements are used to compute the ranking might have stayed at the hotel for a longer duration, such as at least four hours, at least a thy, at least a weekend, at least a week, or at least a month. Optionally, for each hotel from among the hotels being ranked, the measurements comprise measurements of affective response of at least some other minimal number of users who stayed at the hotel, such as measurements of at least five, at least ten, and/or at least fifty different users.

[0802] In Step 2, receiving profiles of at least some of the users who contributed measurements in Step 1. Optionally, the received profiles are some of the profiles 504.

[0803] In Step 3, receiving a profile of a certain first user.

[0804] In Step 4, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least some of the users. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586e in FIG. 21.

[0805] In Step 5, computing, based on the measurements and the first output, a first ranking of the hotels.

[0806] In Step 6, receiving a profile of a certain second user, which is different from the profile of the certain first user.

[0807] In Step 7, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least some of the users. Here, the second output is different from the first output. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586i in FIG. 21.

[0808] And in 8, computing, based on the measurements and the second output, a second ranking of the hotels. Optionally, the first and second rankings are different, such that in the first ranking, a first hotel is ranked above a second hotel, and in the second ranking, the second hotel is ranked above the first hotel.

[0809] In one embodiment, the method optionally includes a step that involves utilizing a sensor coupled to a user who stayed at a hotel, from among the hotel being ranked, for obtaining a measurement of affective response of the user. Optionally, the measurement of affective response of the user is based on a value obtained by measuring the user with the sensor while the user was at the hotel. Optionally, the measurement may be based on values acquired by measuring the user with the sensor during at least three different non-overlapping periods while the user was at the hotel. Examples of various types of sensors that may be utilized to measure a user are given at least in section 1—Sensors of this disclosure.

[0810] In one embodiment, the method may optionally include steps that involve reporting to a certain user a result based on a ranking of the hotels personalized for the certain user. In one example, the method may include a step that involves forwarding to the certain first user a result derived from the first ranking of the hotels. In this example, the result may be a recommendation to stay at the first hotel (which for the certain first user is ranked higher than the second hotel). In another example, the method may include a step that involves forwarding to the certain second user a result derived from the second ranking of the hotels. In this example, the result may be a recommendation for the certain second user to stay at the second hotel (which for the certain second user is ranked higher than the first hotel).

[0811] In some embodiments, the method may optionally include steps involving recommending one or more of the hotels being ranked to users. Optionally, the type of recommendation given for a hotel is based on the rank of the hotel. For example, given that in the first ranking, the rank of the first hotel is higher than the rank of the second hotel, the method may optionally include a step of recommending the first hotel to the certain first user in a first manner, and not recommending the second hotel to the certain first user in first manner. Optionally, the method may include a step of recommending the second hotel to the certain first user in a second manner. Optionally, recommending a hotel in the first manner involves providing a stronger recommendation for the hotel, compared to a recommendation for the hotel that is provided when recommending it in the second manner. The nature of the first and second manners is discussed in more detail with respect to the recommender module 178, which may also provide recommendations in first and second manners.

[0812] The quality of an experience involving staying at certain hotels may change over time. Thus, in some embodiments, rankings generated for hotels may be considered dynamic rankings. For example, a ranking of hotels may correspond to a time t, and be based on measurements of affective response taken in temporal proximity to t (e.g., in a certain window of time Δ preceding t). Thus, given that over time, the values of measurements in the window that are used to compute a ranking of the hotels may change, the computed rankings of the hotels may also change over time. Some of the embodiments described above, e.g., embodiments for ranking hotels modeled according to FIG. 19 may be used to generate dynamic rankings of hotels by providing measurements of affective response to the dynamic ranking module 250 instead of to the ranking module 220. A more detailed discussion of dynamic ranking may be found in this disclosure at least in section 14—Ranking Experiences.

[0813] In one embodiment, a system configured to dynamically rank hotels based on measurements affective response of users includes at least the collection module 120 and the dynamic ranking module 250. In this embodiment, the collection module 120 is configured to receive the measurements 501 of affective response of users belonging to the crowd 500, which include measurements of users who stayed at the hotels being ranked. Optionally, for each hotel from among the hotels being ranked, the measurements 501 include measurements of affective response of at least ten users who stayed at the hotel. In this embodiment, the dynamic ranking module 250 is configured to generate rankings of the hotels. Each ranking corresponds to a time t and is generated based on a subset of the measurements of the users that includes measurements of at least five users; where each measurement is taken at a time that is not earlier than a certain period before t and is not after t. For example, if the length of the certain period is denoted Δ, each of the measurements in the subset was taken at a time that is between t−Δ and t. Optionally, for each hotel from among the hotels, the subset includes measurements of at least five different users who were at the hotel. Optionally, measurements taken earlier than the certain period before a time t are not utilized by the dynamic ranking module 250 to generate a ranking corresponding to t. Optionally, the dynamic ranking module 250 may be configured to assign weights to measurements used to compute a ranking corresponding to a time t, such that an average of weights assigned to measurements taken earlier than the certain period before t is lower than an average of weights assigned to measurements taken later than the certain period before t. The dynamic ranking module 250 may be further configured to utilize the weights to compute the ranking corresponding to t.

[0814] In one embodiment, the rankings generated by the dynamic ranking module 250 include at least a first ranking corresponding to a time t₁ and a second ranking corresponding to a time t₂, which is after t₁. In the first ranking corresponding to the time t₁, a first hotel is ranked above a second hotel. However, in the second ranking corresponding to the time t₂, the second hotel is ranked above the first hotel. In this embodiment, the second ranking is computed based on at least one measurement taken after t₁.

[0815] Since dynamic rankings of hotels may change over time, this may change the nature of recommendations of hotels that are given to users at different times. In one embodiment, the recommender module 235 is configured to recommend a hotel to a user in a manner that belongs to a set comprising first and second manners. When recommending a hotel in the first manner, the recommender module 235 provides a stronger recommendation for the hotel, compared to a recommendation for the hotel that the recommender module 235 provides when recommending it in the second manner. With reference to the embodiment described above, which includes the first and second rankings corresponding to t₁ and t₂, respectively, the recommender module 235 may be configured to: recommend the first hotel to a user during a period that ends before t₂ in the first manner, and not to recommend to the user the second hotel in the first manner during that period. Optionally, during that period, the recommender module 235 recommends the second hotel in the second manner. After t₂, the behavior of the recommender module 235 may change, and it may recommend to the user the second hotel in the first manner, and not recommend the first hotel in the first manner. Optionally, after t₂, the recommender module 235 may recommend the first hotel in the second manner.

[0816] Following is a description of steps that may be performed in a method for dynamically ranking hotels based on measurements of affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to dynamically rank hotels based on measurements of affective response of users. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for dynamically ranking hotels based on measurements of affective response of users includes at least the following steps:

[0817] In Step 1, receiving, by a system comprising a processor and memory, a first set of measurements of affective response of users. Each measurement belonging to the first set was taken at a time that is not earlier than a certain period before a time t₁ and is not after t₁. Additionally, for each hotel from among the hotels being ranked, the first set of measurements comprises measurements of affective response of at least five users who stayed at the hotel.

[0818] In Step 2, generating, based on the first set of measurements, a first ranking of the hotels. In the first ranking, a first hotel is ranked ahead of a second hotel.

[0819] In Step 3, receiving a second set of measurements of affective response of users. Each measurement belonging to the second set was taken at a time that is not earlier than the certain period before a time t₂ and is not after t₂. Additionally, for each hotel from among the hotels, the second set of measurements comprises measurements of affective response of at least five users who stayed at the hotel.

[0820] And in Step 4, generating, based on the second set of measurements, a second ranking of the hotels. In the second ranking, the second hotel is ranked ahead of the first hotel. Additionally, t₂>t₁ and the second set of measurements comprises at least one measurement of affective response of a user taken after t₁.

[0821] The method described above may optionally include a step that involves recommending to a user a hotel from among the hotels being ranked. The nature of such a recommendation may depend on the ranking of the hotels, and as such, may change over time. Optionally, recommending a hotel may be done in a first manner or in a second manner; recommending a hotel in the first manner may involve providing a stronger recommendation for the hotel, compared to a recommendation for the hotel provided when recommending it in the second manner. In one example, at a time that is before t₂, the first hotel may be recommended to a user in the first manner, and the second hotel may be recommended to the user in the second manner. However, at a time that is after t₂, the first hotel may be recommended to the user in the second manner, and the second hotel is recommended to the user in the first manner.

[0822] In a similar manner to the personalization of rankings of hotels described above, in some embodiments, dynamic rankings of hotels may also be personalized for different users. Optionally, this is done utilizing the personalization module 130, which may be utilized to generate personalized dynamic rankings of hotels, e.g., as illustrated in FIG. 91a and FIG. 91b, which involve personalized rankings of experiences, and as such are relevant to personalized dynamic rankings of hotels (since staying at a hotel is a specific type of experience).

[0823] In one embodiment, a system configured to dynamically generate personalized rankings of hotels based on measurements of affective response of users includes at least the collection module 120, the personalization module 130, and the dynamic ranking module 250. In this embodiment, the collection module 120 is configured to receive the measurements of affective response of the users that include, for each hotel from among the hotels, measurements of affective response of at least ten users who stayed at the hotel. The personalization module 130 is configured, in one embodiment, to receive a profile of a certain user and profiles of the users, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the users. The dynamic ranking module 250 is configured to generate, for the certain user, rankings of the hotels. Each ranking of hotels corresponds to a time t and is generated based on the output and a subset of the measurements comprising, for each hotel in the ranking, measurements of at least five users who stayed at the hotel. Additionally, each measurement in the subset is taken at a time that is not earlier than a certain period before t and is not after t.

[0824] By utilizing the personalization module 130, it is possible that different users may receive different dynamic rankings, at different times. In particular, in one embodiment, rankings generated by the system described above are such that for at least a certain first user and a certain second user, who have different profiles, the dynamic ranking module 250 generates the following rankings: (i) a ranking corresponding to a time t₁ for the certain first user, in which a first hotel is ranked ahead of a second hotel; (ii) a ranking corresponding to the time t₁ for the certain second user in which the second hotel is ranked ahead of the first hotel; (iii) a ranking corresponding to a time t₂>t₁ for the certain first user, in which the first hotel is ranked ahead of the second hotel; and (iv) a ranking corresponding to the time t₂ for the certain second user in which the first hotel is ranked ahead of the second hotel. Additionally, the rankings corresponding to t₂ are generated based on at least one measurement of affective response taken after t₁.

[0825] Following is a description of steps that may be performed in a method for dynamically generating personalized rankings of hotels based on measurements of affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to dynamically generate personalized rankings of hotels based on measurements of affective response of users. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for dynamically generating personalized rankings of hotels based on measurements of affective response of users includes at least the following steps:

[0826] In Step 1, receiving, by a system comprising a processor and memory, a profile of a certain first user and a profile of a certain second user. In this embodiment, the profile of the certain first user is different from the profile of the certain second user.

[0827] In Step 2, receiving first measurements of affective response of a first set of users who stayed at the hotels. For each hotel from among the hotels being ranked, the first measurements comprise measurements of affective response of at least five users who stayed at the hotel, and which were taken between a time t₁−Δ and t₁. Here Δ represents a certain period of time; examples of values Δ may include one hour, one day, one week, one month, one year, and some other period of time between ten minutes and five years.

[0828] In step 3, receiving a first set of profiles comprising profiles of at least some of the users belonging to the first set of users.

[0829] In step 4, generating a first output indicative of similarities between the profile of the certain first user and profiles belonging to the first set of profiles. Optionally, the first output is generated utilizing the personalization module 130.

[0830] In step 5, computing, based on the first measurements and the first output, a first ranking of the hotels. In the first ranking, a first hotel is ranked above a second hotel.

[0831] In Step 6, generating a second output indicative of similarities between the profile of the certain second user and profiles belonging to the first set of profiles. The second output is different from the first output. Optionally, the second output is generated utilizing the personalization module 130.

[0832] In Step 7, computing, based on the first measurements and the second output, a second ranking of the hotels. In the second ranking, the second hotel is ranked above the first hotel.

[0833] In Step 8, receiving second measurements of affective response of a second set of users who stayed at the hotels. For each hotel from among the hotels, the second measurements comprise measurements of affective response of at least five users who stayed at the hotel, and which were taken between a time t₂−Δ and t₂. Additionally, t₂>t₁.

[0834] In Step 9, receiving a second set of profiles comprising profiles of at least some of the users belonging to the second set of users.

[0835] In Step 10, generating a third output indicative of similarities between the profile of the certain second user and profiles belonging to the second set of profiles. Optionally, the third output is generated utilizing the personalization module 130.

[0836] And in Step 11, computing, based on the measurements and the third output, a third ranking of the hotels. In the third ranking, the first hotel is ranked above the second hotel. Additionally, the third ranking is computed based on at least one measurement taken after t₁.

[0837] In one embodiment, the method described above may optionally include the following steps:

[0838] In Step 12, generating a fourth output indicative of similarities between the profile of the certain first user and profiles belonging to the second set of profiles. Optionally, the fourth output is different from the third output. Optionally, the fourth output is generated utilizing the personalization module 130.

[0839] And in Step 13, computing, based on the second measurements and the fourth output, a fourth ranking of the hotels. In the fourth ranking, the first hotel is ranked above the second hotel. Additionally, the fourth ranking is computed based on at least one measurement taken after t₁.

[0840] Systems, methods, and/or computer-readable media described above for generating various rankings of hotels may be adapted, in some embodiments, to generate rankings of hotel facilities that users may utilize at hotels. Herein, a hotel facility is a certain area in a hotel in which a user may receive a service. Some examples of hotel facilities include the following: a reception desk, a pool, a restaurant, a gym, a bar, a club, a store, a movie theatre, a beach, and a golf course.

[0841] Similarly to a ranking of hotels, a ranking of the hotel facilities may also be generated based on measurements of affective response of users who utilized the hotel facilities. Such a ranking may be indicative of how much the users enjoyed utilizing the hotel facilities, and thus, may be useful for suggesting to users which hotel has good hotel facilities and/or what hotel facilities are recommended in different hotels. In one embodiment, a ranking of hotel facilities may include multiple hotel facilities that belong to a certain hotel (e.g., the ranking may involve a bar, a pool, and a restaurant, all in the same hotel). In another embodiment, a ranking of hotel facilities may include the same type of hotel facility at multiple hotels (e.g., the ranking may involve different bars at different hotels).

[0842] FIG. 26 illustrates a system configured to generate a ranking of hotel facilities based on measurements of affective response of users. The system includes at least the collection module 120 and the ranking module 220. The system may optionally include additional modules such as the recommender module 235, the map-displaying module 240, the personalization module 130, and/or the location verifier module 505, to name a few. It is to be noted that a system modeled according to FIG. 26 may also be considered to be a system that generates ranks for locations (because a hotel facility is a certain type of location), and as such, the system is also an embodiment of the system illustrated in FIG. 19; therefore, the teachings given with respect to systems modeled according to FIG. 19 are relevant to embodiments described below.

[0843] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response of users belonging to the crowd 500, which in this embodiment, include measurements of affective response of users who utilized the hotel facilities being ranked. The collection module 120 is also configured to forward at least some of the measurements 501 to the ranking module 220.

[0844] In one embodiment, each measurement of affective response of a user who utilized a hotel facility is based on a value obtained by measuring the user, with a sensor coupled to the user, while the user was utilizing the hotel facility. Optionally, the measurement may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was utilizing the hotel facility. Examples of various types of sensors that may be utilized to measure a user are given at least in section 1—Sensors of this disclosure.

[0845] In one embodiment, determining when a user utilized a certain hotel facility may be done utilizing the location verifier 505. For example, location verifier may determine from a device of a user (e.g., via GPS, Bluetooth, and/or Wi-Fi signals) when the user was at the certain hotel facility. In another example, the location verifier module 505 may determine from billing information (e.g., credit card transactions and/or a digital wallet transaction) when the user paid for the hotel facility and/or when the user was at the hotel facility. In yet another example, the location verifier module 505 may receive information indicating when the user was at the certain hotel facility from a security system of the hotel (e.g., a system that includes cameras and face recognition).

[0846] In one embodiment, measurements received by the ranking module 220 include for each hotel facility from among the hotel facilities being ranked, measurements of affective response of at least five users who utilized the hotel facility. Optionally, each of the at least five users utilized the hotel facility for at least a certain period of time, such as at least one minute, at least five minutes, at least thirty minutes, or at least one hour. Optionally, for each hotel facility, the measurements received by the ranking module 220 may include measurements of a different minimal number of users who utilized the hotel facility, such as measurements of at least eight, at least ten, or at least one hundred users. The ranking module 220 is also configured, in one embodiment, to generate a ranking of the hotel facilities based on the received measurements. Optionally, in the generated ranking, a first hotel facility is ranked higher than a second hotel facility. Optionally, when the first hotel facility is ranked ahead of the second hotel facility, it means that, on average, the measurements of the at least five users who utilized the first hotel facility are more positive than the measurements of the at least five users who utilized the second hotel facility. Thus, for example, it may be assumed, in some embodiments, that the users who utilized the first hotel facility were more satisfied than the users who used the second facility.

[0847] In one embodiment, the first hotel facility and the second hotel facility are in the same hotel. For example, the first hotel facility is a restaurant in a certain hotel and the second hotel facility is a bar in the certain hotel. In another embodiment, the first hotel facility and the second hotel facility are the same type of facility, but in different hotels. For example, the first hotel facility is a reception desk at first hotel and the second hotel facility is a reception desk at a second hotel.

[0848] In some embodiments, in a ranking of hotel facilities, such as a ranking generated by the ranking module 220, each hotel facility has a unique rank, i.e., there are no two hotel facilities that share the same rank. In other embodiments, at least some of the hotel facilities may be tied in the ranking. In one example, there may be third and fourth hotel facilities that are given the same rank by the ranking module 220. It is to be noted that the third hotel facility in the example above may be the same hotel facility as the first hotel facility or the second hotel facility mentioned above.

[0849] In one embodiment, the hotel facilities being ranked include one or more of the following hotel facilities: a facility of a first type at a first hotel, a facility of a second type at the first hotel, a facility of the first type at a second hotel, and a facility of the second type at the second hotel. Additionally, the facility of the first type at the first hotel is ranked above the facility of the first type at the second hotel, and the facility of the second type at the second hotel is ranked above the facility of the second type at the first hotel. For example, a ranking of hotel facilities may include a pool and a bar at a first hotel and a pool and a bar at the second hotel. In this example, the bar at the first hotel is ranked ahead of the bar in the second hotel, but the pool in the second hotel is ranked ahead of the pool in the first hotel.

[0850] FIG. 26 illustrates an example of a ranking of hotels that may be generated utilizing the ranking module 220, as described above. In the illustration, the hotel facilities being ranked include at least four facilities. Measurements 501 of affective response, which in this example include measurements of users who the hotel facilities being ranked are used to generate the ranking 596.

[0851] It is to be noted that while it is possible, in some embodiments, for the measurements received by modules, such as the ranking module 220, to include, for each user from among the users belonging to the crowd 500 who contributed to the measurements, at least one measurement of affective response of the user taken while utilizing each of the hotel facilities being ranked, this is not the case in all embodiments. In some embodiments, some users may contribute measurements corresponding to a proper subset of the hotel facilities (e.g., those users may not have utilized at least some of the hotel facilities being ranked), and thus, the measurements 501 may be lacking measurements of some users to some of the hotel facilities. In some embodiments, some users may have utilized only one of the hotel facilities being ranked.

[0852] There may be different approaches to ranking that can be used to generate rankings of hotel facilities, which may be utilized in embodiments described herein. In some embodiments, hotel facilities may be ranked based on scores computed for the hotel facilities. In such embodiments, the ranking module 220 may include the scoring module 150 or the dynamic scoring module 180, and a score-based rank determining module 225. In other embodiments, hotels may be ranked based on preferences generated from measurements. In such embodiments, an alternative embodiment of the ranking module 220 includes preference generator module 228 and preference-based rank determining module 230. The different approaches that may be utilized for ranking hotels are discussed in more detail in section 14—Ranking Experiences, e.g., in the discussion related to FIG. 85 and FIG. 86.

[0853] In some embodiments, the recommender module 235 is utilized to recommend to a user a hotel facility, from among the hotel facilities ranked by the ranking module 220, in a manner that belongs to a set comprising first and second manners. Optionally, when recommending a hotel facility in the first manner, the recommender module 235 provides a stronger recommendation for the hotel facility, compared to a recommendation for the hotel facility that the recommender module 235 would provide when recommending in the second manner. Optionally, the recommender module 235 determines the manner in which to recommend a hotel facility based on the rank of the hotel facility in a ranking. In one example, if a hotel facility is ranked at least at a certain rank (e.g., at least in the top 5), it is recommended in the first manner, which may involve providing a promotion for the hotel facility to the user (e.g., a coupon) and/or the hotel facility is displayed more prominently on a list (e.g., a larger font, at the top of the list, or on the first screen of suggested hotel facilities) or on a map (e.g., using a picture or icon representing the hotel facility). In this example, if a hotel facility is not ranked high enough, then it is recommended in the second manner, which may involve no promotion, a smaller font on a listing of hotel facilities, the hotel facility may appear on a page that is not the first page of suggested hotel facilities, the hotel facility may have a smaller icon representing it on a map (or no icon at all), etc.

[0854] In some embodiments, map-displaying module 240 may be utilized to present to a user a ranking of the hotel facilities. Optionally, the map may display an image describing an area in which the hotels, to which the hotel facilities belong, are located and annotations describing at least some of the hotels and/or their hotel facilities, and respective ranks of the hotel facilities and/or scores computed for the hotel facilities. Optionally, higher ranked hotel facilities are displayed more prominently on the map than lower ranked hotel facilities

[0855] Following is a description of steps that may be performed in a method for ranking hotel facilities based on measurements of affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to rank hotel facilities based on measurements of affective response of users. The steps below may be considered a special case of an embodiment of a method illustrated FIG. 20, which illustrates steps involved in one embodiment of a method for ranking locations based on measurements of affective response of users (because hotel facilities are a specific type of location being ranked). In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for ranking hotel facilities based on measurements of affective response of users includes at least the following steps:

[0856] In Step 1, receiving, by a system comprising a processor and memory, measurements of affective response of at least ten users. Each measurement of a user is taken by a sensor coupled to the user while the user utilizes a hotel facility from among the hotel facilities, and is based on values acquired by measuring the user with the sensor during at least three different non-overlapping periods while the user utilized the hotel facility. Optionally, for each hotel facility from among the hotel facilities, the measurements comprise measurements of at least five users who utilized the hotel facility.

[0857] And in Step 2, ranking the hotel facilities based on the measurements, such that, at least a first hotel facility is ranked higher than a second hotel facility. Optionally, when the first hotel facility is ranked ahead of the second hotel facility, it means that, on average, the measurements of the at least five users who utilized the first hotel facility are more positive than the measurements of the at least five users who utilized the second hotel facility. Optionally, the ranking of the hotel facilities may involve performing different operations, as discussed in the description of embodiments whose steps are described in FIG. 20.

[0858] In one embodiment, the method described above may optionally include a step that involves recommending the first hotel facility to a user in a first manner, and not recommending the second hotel facility to the user in the first manner. Optionally, the step may further involve recommending the second hotel facility to the user in a second manner. As mentioned above, e.g., with reference to recommender module 235, recommending a hotel facility in the first manner may involve providing a stronger recommendation for the restaurant, compared to a recommendation for the hotel facility that is provided when recommending it in the second manner.

[0859] Not all seats in a vehicle are the same, some may be more comfortable than others. When planning a trip, e.g., on an airplane, train, bus, etc., it may be possible for a user to choose where to sit when reserving a seat. However, a user may not be familiar with the vehicle and/or with the particular characteristics of different seats and/or different regions in the vehicle, which may make a choice of seat difficult for the user. Additionally, the user may always be at risk of making a suboptimal choice due the ignorance about the actual comfort and/or suitability of different seats. Thus, there is a need to be able to help a user to determine which seats in the vehicle to choose.

[0860] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that enable generation of rankings of seats in a vehicle based on measurements of affective response of users who occupied the seats (e.g., by sitting in the seats and/or laying in them). Such rankings can help a user decide which seats to choose, and which seat should be avoided. A ranking of seats is an ordering of at least some of the seat, which is indicative of preferences of users towards those seats and/or is indicative of the extent to which those seats were found to be comfortable by the users. Typically, a ranking of seats that is generated in embodiments described herein will include at least a first seat and a second seat, such that the first seat is ranked ahead of the second seat. When the first seat is ranked ahead of the second seat, this typically means that, based on the measurements of affective response of the users, the first seat is preferred by the users over the second seat (e.g., due to it being more comfortable, in a better location, etc.)

[0861] Some aspects of this disclosure involve collecting measurements of affective response of users who occupied seats in a vehicle. In embodiments described herein, a measurement of affective response of a user is typically collected with one or more sensors coupled to the user, which are used to obtain a value that is indicative of a physiological signal of the user (e.g., a heart rate, skin temperature, or brainwave activity) and/or indicative of a behavioral cue of the user (e.g., a facial expression, body language, or the level of stress in the user's voice). Additionally or alternatively, a measurement of affective response of a user may also include indications of biochemical activity in a user's body, e.g., by indicating concentrations of one or more chemicals in the user's body (e.g., levels of various electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity).

[0862] Differences between users can naturally lead to it that they will have different tastes and different preferences when it comes to seats they may occupy in a vehicle. For example, different physical characteristics (e.g., age, weight, or height) can lead to it that a certain seat may be comfortable for one user but extremely uncomfortable for another user. Thus, a ranking of seats may represent, in some embodiments, an average of the experience the users had when sitting in different seats. However, for some users, such a ranking of seats may not be suitable, since those users may be quite different from average users when it comes to their seating needs. In such cases, users may benefit from a ranking of seats that is better suited for them. To this end, some aspects of this disclosure involve systems, methods and/or computer-readable media for generating personalized rankings of seats based on measurements of affective response of users. Some of these embodiments may utilize a personalization module that weights and/or selects measurements of affective response of users based on similarities between a profile of a certain user (for whom a ranking is personalized) and the profiles of the users (of whom the measurements are taken). An output indicative of these similarities may then be utilized to compute a personalized ranking of seats that is suitable for the certain user. Optionally, computing the personalized ranking is done by giving a larger influence, on the ranking, to measurements of users whose profiles are more similar to the profile of the certain user.

[0863] Following are exemplary embodiments of systems, methods, and computer-readable media that may be used to generate rankings of seats, some of which are illustrated in FIG. 27 and FIG. 28. Herein, a reference to a ranking of seats may be considered a ranking of locations of seats. In one example, a location of a seat may correspond to a specific seat in a vehicle such as seat 43B. In another example, a location of a seat may correspond to a certain region of a vehicle, such as the upper deck of a ship. Since seats may be considered a certain type of location, the exemplary embodiments described below may be considered embodiments of systems, methods, and/or computer-readable media that may be utilized to generate rankings for locations (of the certain type), as illustrated in FIG. 19, FIG. 20, and FIG. 21. Therefore, the teachings in this disclosure regarding various embodiments, in which rankings for locations are generated, are applicable to embodiments in which rankings for seats are generated (i.e., ranking of locations of the certain type). In a similar manner, additional teachings relevant to embodiments described below, which involve generation of rankings of seats, may be found at least in section 14—Ranking Experiences, which describes various embodiments in which rankings are generated for experiences in general (and sitting in a seat is a certain type of experience).

[0864] FIG. 6a describes different locations in a vehicle for which, in some embodiments, personalized rankings may be generated, as described above. The figure illustrates locations corresponding to seats in a vehicle that is an airplane. However, the use of an airplane is just for exemplary purposes and is not intended to be limiting. In a similar fashion, locations may involve seats on other types of vehicles that may be used to transport people. For example, the vehicles may be at least one of the following: a two-wheel vehicle, a three-wheel vehicle, a car, a bus, a train, a ship, an aircraft, and a space shuttle. Additionally, herein a “seat” in a vehicle refers to any area or object that a user may sit in, lay in, and/or occupy in another way while traveling in the vehicle.

[0865] A location illustrated in FIG. 6a may correspond to a single seat in the vehicle. For example, reference numeral 518d corresponds to a specific seat 23E (a middle seat in the middle isle in economy) and reference numeral 518e corresponds to a specific seat 1A (a window seat alone in business class). Additionally or alternatively, a location illustrated in FIG. 6a may correspond to multiple seats in the vehicle sharing a similar characteristic. For example, 518a represents seats in the economy class of the airplane, 518b represents seats in economy plus, and 518c represents seats in business class. Locations may represent other groups of seats. In one example, a location in the vehicle may represent window seats (or window seats in a certain class), while another location may represent seats near the isle, and yet another location may represent seats near a toilet.

[0866] In one embodiment, a system modeled according to FIG. 19 is configured to generate a ranking of seats in a vehicle based on measurements of affective response of users. The system includes at least the collection module 120 and the ranking module 220. The system may optionally include additional modules such as the recommender module 235, the personalization module 130, and/or the location verifier module 505, to name a few.

[0867] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response of users belonging to the crowd 500, which in this embodiment, include measurements of affective response of users who occupied the seats being ranked (e.g., by sitting in them and/or laying in them). The collection module 120 is also configured to forward at least some of the measurements 501 to the ranking module 220.

[0868] In one embodiment, each measurement of affective response of a user who occupied a seat, from among the seats being ranked, is based on a value obtained by measuring the user, with a sensor coupled to the user, while the user occupied the seat. Optionally, the measurement may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user occupied the seat. Examples of various types of sensors that may be utilized to measure a user are given at least in section 1—Sensors of this disclosure.

[0869] In one embodiment, measurements received by the ranking module 220 include, for each seat from among the seats being ranked, measurements of affective response of at least five users who occupied the seat for at least five minutes. Optionally, each of the at least five users occupied the seat for a longer minimal duration, such as at least thirty minutes, at least two hours, or some other duration of time that is greater than five minutes. Optionally, for each seat, the measurements received by the ranking module 220 may include measurements of a different minimal number of users who occupied the seat, such as measurements of at least eight, at least ten, or at least one hundred users. The ranking module 220 is also configured, in one embodiment, to generate a ranking of the seats based on the received measurements. Optionally, in the generated ranking, a first seat is ranked higher than a second seat. Optionally, when the first seat is ranked ahead of the second seat, it means that the first seat is considered more comfortable than the second seat, as far as the users who provided measurements to computing the ranking may be concerned.

[0870] In some embodiments, in a ranking of seats, such as a ranking generated by the ranking module 220, each seat has a unique rank, i.e., there are no two seats that share the same rank. In other embodiments, at least some of the seats may be tied in the ranking. In one example, there may be third and fourth seats that are given the same rank by the ranking module 220. It is to be noted that the third seat in the example above may be the same seat as the first seat or the second seat mentioned above.

[0871] Depending on the embodiment, a reference to a seat may mean different things. In some embodiments, a seat may refer to one or more of the following: a location in a vehicle in general, a location in a certain type of vehicle, and a location in a specific vehicle. Thus, for example, the at least five users may have all sat at a location in the same type of vehicle (e.g., an airplane or a bus), in the same model of a vehicle (e.g., a Boeing 737), in the same model operated by the same company (Boeing 777 operated by Delta), or the same exact vehicle.

[0872] In some embodiments, the measurements of the at least five users who occupied a seat were taken while the at least five users were in similar conditions. For example, the at least five users all occupied the seat for a similar duration (e.g., up to 2 hours, 2 to 5 hours, or more than 5 hours). Thus, a ranking of seats may correspond to a certain duration. For example, different rankings may be computed for short and long flights; a certain seat may be comfortable enough for the duration of a short flight, but sitting in that same seat may be excruciating in the case of a long twelve hour flight. In another example, the at least five users who occupied a seat all traveled the same route when their measurements were collected (e.g., the same flight number, same bus line, etc.)

[0873] The system may optionally include, in some embodiments, the location verifier module 505, which is configured to determine whether the user is in a seat or not (or is likely in the seat). In one embodiment, the location verifier module 505 is configured to determine whether the user is in a certain seat by receiving signals from the vehicle, e.g., an output generated by an entertainment system in the vehicle indicating to what seat a device of the user is paired. In another embodiment, location verifier module 505 is configured to determine, by receiving wireless transmissions (e.g., by identifying a network and/or using triangulation of wireless signals), in what seat or region of the vehicle the user is sitting.

[0874] The location verifier module 505 may be configured, in some embodiments, to determine whether the user is likely sitting in a seat. In one example, the location verifier module 505 may receive indications of whether the user is stationary or not (e.g., from a pedometer and/or an accelerometer is a device carried by a user, such as a smart phone). In another example, the location verifier module 505 may receive information indicating that the vehicle is ascending and/or descending at a pace consistent with times the user is required to be seated (e.g., after takeoff and/or before landing of an aircraft).

[0875] FIG. 27 illustrates an example of a ranking 602 of seats that may be generated utilizing the ranking module 220, as described above. In the illustration, the seats being ranked include at least four seats that are available in an aircraft. In FIG. 27 the ranking 602 includes both the location of the four seats, and comfort scores computed for each seat based on the measurements of affective response of the users who occupied those seats. Generally, in the example, seats that were roomier, and/or near an aisle or a window, were found to be more comfortable (and ranked higher) than other seats, such as seats in the middle of a row.

[0876] It is to be noted that while it is possible, in some embodiments, for the measurements received by modules, such as the ranking module 220, to include, for each user from among the users who contributed to the measurements, at least one measurement of affective response of the user taken while sitting in each of the seats being ranked, this is not the case in all embodiments. In some embodiments, some users may contribute measurements corresponding to a proper subset of the seats (e.g., those users may not have occupied some of the seat locations being ranked), and thus, the measurements may be lacking measurements of some users to some of the seats. In some embodiments, some users may have sat only on one of the seats being ranked.

[0877] There may be different approaches to ranking that can be used to generate rankings of seats, which may be utilized in embodiments described herein. In some embodiments, seats may be ranked based on scores computed for the seats. In such embodiments, the ranking module 220 may include the scoring module 150 or the dynamic scoring module 180, and a score-based rank determining module 225. In other embodiments, seats may be ranked based on preferences generated from measurements. In such embodiments, an alternative embodiment of the ranking module 220 includes preference generator module 228 and preference-based rank determining module 230. The different approaches that may be utilized for ranking seats are discussed in more detail in section 14—Ranking Experiences, e.g., in the discussion related to FIG. 85 and FIG. 86.

[0878] In some embodiments, the recommender module 235 may be utilized to recommend to a user a seat, from among the seats ranked by the ranking module 220, in a manner that belongs to a set comprising first and second manners. Optionally, when recommending a seat in the first manner, the recommender module 235 provides a stronger recommendation for the seat, compared to a recommendation for the seat that the recommender module 235 would provide when recommending it in the second manner. Optionally, the recommender module 235 determines the manner in which to recommend a seat based on the rank of the seat in a ranking (e.g., a ranking generated by the ranking module 220). In one example, if a seat is ranked at least at a certain rank (e.g., at least in the top 5), it is recommended in the first manner, which may involve providing a promotion for the seat to the user (e.g., a coupon) and/or the seat is displayed more prominently on a list (e.g., a larger font, at the top of the list, or on the first screen of suggested seats) or on a map of available seating locations. In this example, if a seat is not ranked high enough, then it is recommended in the second manner, which may involve no promotion, a smaller font on a listing of seats, the seat may appear on a page that is not the first page of suggested seats, the seat may have a smaller icon representing it on a map seating locations (or no icon at all), etc.

[0879] Following is a description of steps that may be performed in a method for ranking seats in a vehicle based on measurements of affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to rank seats in a vehicle based on measurements of affective response of users. The steps below may be considered a special case of an embodiment of a method illustrated FIG. 20, which illustrates steps involved in one embodiment of a method for ranking locations based on measurements of affective response of users (because seats in a vehicle are a specific type of location being ranked). In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for ranking seats in a vehicle based on measurements of affective response of users includes at least the following steps:

[0880] In Step 1, receiving, by a system comprising a processor and memory, the measurements of affective response of the users. For each seat from among the seats being ranked, the measurements comprise measurements of affective response of at least five users who occupied the seat for at least five minutes. Optionally, each of the users occupied the seat for a longer period, such as at least thirty minutes, at least two hours, or at least six hours.

[0881] And in Step 2, ranking the seats based on the measurements, such that, a first seat is ranked higher than a second seat. Optionally, the ranking of the seats may involve performing different operations, as discussed in the description of embodiments whose steps are described in FIG. 20.

[0882] In one embodiment, the method described above may optionally include a step that involves utilizing a sensor coupled to a user who occupied a seat, from among the seats being ranked, to obtain a measurement of affective response of the user. Optionally, the measurement may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user occupied the seat.

[0883] In one embodiment, the method described above may optionally include a step that involves recommending the first seat to a user in a first manner, and not recommending the second seat to the user in the first manner. Optionally, the step may further involve recommending the second seat to the user in a second manner. As mentioned above, e.g., with reference to recommender module 235, recommending a seat in the first manner may involve providing a stronger recommendation for the seat, compared to a recommendation for the seat that is provided when recommending it in the second manner.

[0884] Since different users may have different characteristics and/or preferences, in some embodiments, the same ranking of seat may not be the best suited for all users. Thus, in some embodiments, rankings of seats in a vehicle may be personalized for some of the users (also referred to as a “personalized ranking” of seats). Optionally, the personalization module 130 may be utilized in order to generate such personalized rankings of seats. In one example, generating the personalized rankings is done utilizing an output generated by the personalization module 130 after being given a profile of a certain user and profiles of at least some of the users who provided measurements that are used to rank the seats (e.g., profiles from among the profiles 504). The output is indicative of similarities between the profile of the certain user and the profiles of the at least some of the users. When computing a ranking of seats based on the output, more influence may be given to measurements of users whose profiles indicate that they are similar to the certain user. Thus, the resulting ranking may be considered personalized for the certain user. Since different certain users are likely to have different profiles, the output generated for them may be different, and consequently, the personalized rankings of the seats that are generated for them may be different. For example, in some embodiments, when generating personalized rankings of seats, there are at least a certain first user and a certain second user, who have different profiles, for which the ranking module 220 may rank seats differently. For example, for the certain first user, a first seat may be ranked above a second seat, and for the certain second user, the second seat is ranked above the first seat. The way in which, in the different approaches to ranking, an output from the personalization module 130 may be utilized to generate personalized rankings for different users, is discussed in more detail in section 14—Ranking Experiences.

[0885] In one embodiment, a profile of a user, such as a profile from among the profiles 504, may include information that describes one or more of the following: the age of the user, the gender of the user, the height of the user, the weight of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, and a feature value derived from semantic analysis of a communication of the user. Optionally, the profile of a user may include information regarding travel habits of the user. For example, the profile may include itineraries of the user indicating to travel destinations, such as countries and/or cities the user visited. Optionally, the profile may include information regarding the type of trips the user took (e.g., business or leisure), what hotels the user stayed at, the cost, and/or the duration of stay. Optionally, the profile may include information regarding seats the user occupied in vehicles when traveling.

[0886] FIG. 28 illustrates one examples of different personalized rankings of seats that are generated for users with different profiles. User 519a is a tall 60 year old male. User 519a's profile may include various other aspects which may be important in determining which other users are likely to feel like user 519a regarding different seats. Some of these aspects may include physical dimensions (e.g., height and weight), age, occupations, etc. Profile 520a is a profile of user 519a, and lists some examples of data that may be in a profile of a user utilized to compute similarities of profiles which may be relevant to computing a seat score (e.g., the profile may be indicative of the following attributes: age, height, weight, occupation, income, and hobbies). Another example of a user and a corresponding profile of the user is given by user 519b and her profile 520b. User 519b, a 22 year old female student, is different in certain aspects from the user 519a as indicated in the profile 520b. Consequently, a ranking 604a of seats generated for user 519a may be different than a ranking 604b of seats generated for user 519b. For example, ranking 604a indicates that user 519a will probably find higher-class and aisle seats more preferable, while seats in the middle of a row are much less preferred. Ranking 604b indicates the user 519b is probably less likely to be negatively affected by being in the middle of a row (possibly due to her smaller build).

[0887] Generating rankings of seats in a vehicle, which are personalized for different users may involve execution of certain steps. Following is a more detailed discussion of steps that may be involved in a method for generating personalized rankings of seats in a vehicle based on measurements of affective response. These steps may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 19 and/or steps of a method modeled according to FIG. 21. The aforementioned figures illustrate embodiments that involve generation of personalized rankings of locations. Since locations of seats in a vehicle are a specific type of location, the teachings involving those embodiments are relevant to the steps of the method described below. In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[0888] In one embodiment, the method for utilizing profiles of users to compute personalized rankings of seats in a vehicle based on measurements of affective response of the users includes at least the following steps:

[0889] In Step 1, receiving, by a system comprising a processor and memory, measurements of affective response of the users. For each seat from among the seats being ranked, the measurements comprise measurements of affective response of at least five users who occupied the seat for at least five minutes. Additionally, each measurement of a user is taken with a sensor coupled to the user while the user is in the seat. Optionally, each of the users whose measurements are used to compute the ranking might have occupied the seat for a longer duration, such as at least thirty minutes, at least two hours, or more than six hours.

[0890] In Step 2, receiving profiles of at least some of the users who contributed measurements in Step 1. Optionally, the received profiles are some of the profiles 504.

[0891] In Step 3, receiving a profile of a certain first user.

[0892] In Step 4, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least some of the users. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586e in FIG. 21.

[0893] In Step 5, computing, based on the measurements and the first output, a first ranking of the seats.

[0894] In Step 6, receiving a profile of a certain second user, which is different from the profile of the certain first user.

[0895] In Step 7, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least some of the users. Here, the second output is different from the first output. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586i in FIG. 21.

[0896] And in 8, computing, based on the measurements and the second output, a second ranking of the seats. Optionally, the first and second rankings are different, such that in the first ranking, a first seat is ranked above a second seat, and in the second ranking, the second seat is ranked above the first seat.

[0897] In one embodiment, the method optionally includes a step that involves utilizing a sensor coupled to a user who stayed who occupied a seat, from among the seats being ranked, for obtaining a measurement of affective response of the user. Optionally, the measurement of affective response of the user is based on a value obtained by measuring the user with the sensor while the user was in the seat. Optionally, the measurement may be based on values acquired by measuring the user with the sensor during at least three different non-overlapping periods while the user was in the seat. Examples of various types of sensors that may be utilized to measure a user are given at least in section 1—Sensors of this disclosure.

[0898] In one embodiment, the method may optionally include steps that involve reporting to a certain user a result based on a ranking of the seats personalized for the certain user. In one example, the method may include a step that involves forwarding to the certain first user a result derived from the first ranking of the seats. In this example, the result may be a recommendation to reserve the first seat (which for the certain first user is ranked higher than the second seat). In another example, the method may include a step that involves forwarding to the certain second user a result derived from the second ranking of the seats. In this example, the result may be a recommendation for the certain second user to reserve the second seat (which for the certain second user is ranked higher than the first seat).

[0899] In some embodiments, the method may optionally include steps involving recommending one or more of the seats being ranked to users. Optionally, the type of recommendation given for a seat is based on the rank of the seat. For example, given that in the first ranking, the rank of the first seat is higher than the rank of the second seat, the method may optionally include a step of recommending the first seat to the certain first user in a first manner, and not recommending the second seat to the certain first user in first manner. Optionally, the method may include a step of recommending the second seat to the certain first user in a second manner. Optionally, recommending a seat in the first manner involves providing a stronger recommendation for the seat, compared to a recommendation for the seat that is provided when recommending it in the second manner. The nature of the first and second manners is discussed in more detail with respect to the recommender module 178, which may also provide recommendations in first and second manners.

[0900] In day-to-day life, there many scenarios in which users are customers who are provided services by a businesses at various locations. For example, a user may be a guest at an amusement park, and is provided with entertainment services. In this example, the guest may be entertained by simply being in the park and/or by interacting with workers and/or park attractions. In another example, a user may be a patient in a health care facility, receiving service from staff who work at the facility. Often there are multiple locations at which a user can be a customer (e.g., different parks, various stores, different restaurants, various banks, etc.) Knowing which business location is worthwhile to frequent may be hard. Thus, there is a need for a way to rate (rank) various locations at which services are provided.

[0901] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that enable generation of rankings of locations at which service is provided based on measurements of affective response of customers who were provided services at the locations. Such rankings can help a user decide which locations (businesses) are worthy of the user's patronage, and which should be avoided. A ranking of locations is an ordering of at least some of the locations, which is indicative of preferences of users towards those locations and/or is indicative of the extent of emotional response of the users to those locations. Typically, a ranking of locations at which service is provided, which is generated in embodiments described herein, will include at least a first location and a second location, such that the first location is ranked ahead of the second location. When the first location is ranked ahead of the second location, this typically means that, based on the measurements of affective response of the customers, customers who were provided a service at the first location were more satisfied from their experience than customers who were provided a service at the second location.

[0902] In one embodiment, a location at which a service is provided is a location that provides a recreational service and/or an entertainment service to customers. For example, the location may involve one or more of the following places: an amusement park, a water park, a casino, a restaurant, a resort, and a bar.

[0903] In another embodiment, a location at which a service is provided is a place of business in which a customer may interact with a service representative. Optionally, the service repetitive may be a human. Alternatively, the service representative may be a robot. For example, the location may be an area in one or more of the following types of business: stores, booths, shopping malls, shopping centers, markets, supermarkets, beauty salons, spas, laundromats, banks, automobile dealerships, and a courier service offices.

[0904] In still another embodiment, a location at which a service is provided is a location at which health treatments and/or healthcare services are provided to customers. For example, the location may be an area in one or more of the following facilities: a clinic, a hospital, and an elderly care facility. Optionally, the location may correspond to a certain room, floor, wing, and/or department in a facility that provides health related services.

[0905] In yet another embodiment, a location at which a service is provided is a location at which customers are provided with sleeping accommodations. For example, the location may be a room, an apartment, a floor of a hotel, a wing of a hotel, a hotel, and/or a resort. Optionally, a “hotel” may be any structure that holds one or more rooms and/or a collection of rooms in the same vicinity. For example, a cruise ship may be considered a hotel.

[0906] Satisfaction of customers may be interpreted in different ways in different embodiments. However, typically, a higher satisfaction level of customers at a certain location is indicative of a more positive affective response of those customers. In particular, if a first group of customers is said to be more satisfied than another group of customers, this may mean that, on average, users in the first group are calmer, more relaxed, and/or less stressed than the users in the second group.

[0907] Herein, a customer at a location at which a service is provided may be any person at the location. In some embodiments, a person may be considered a customer even if that person does not pay for any service received at the location. For example, a customer at a park may be a person that simply visits the park, even if that person did not pay an admittance fee to the park, or any other fee while at the park. In other embodiments, a customer at the location is a person who pays for a service that is provided at location, such as a guest at a hotel, a patient at a hospital, etc. It is to be noted that in the embodiments below, each customer may be considered a “user” as the term is used in this disclosure, such as the user 101a, 101b, or 101c. The term “customer” is used to emphasize that the user receives a service of some sort from a business at a location, and may therefore be considered a customer of the business.

[0908] Some aspects of this disclosure involve collecting measurements of affective response of customers who received a service at a location. In embodiments described herein, a measurement of affective response of a customer is typically collected with one or more sensors coupled to the customer, which are used to obtain a value that is indicative of a physiological signal of the customer (e.g., a heart rate, skin temperature, or brainwave activity) and/or indicative of a behavioral cue of the customer (e.g., a facial expression, body language, or the level of stress in the user's voice). Additionally or alternatively, a measurement of affective response of a user may also include indications of biochemical activity in a customer's body, e.g., by indicating concentrations of one or more chemicals in the body (e.g., levels of various electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity).

[0909] Not all customers and users have the same tastes and preferences. Therefore, the same ranking may not represent the preferences of all users; some users may benefit from a ranking of locations that is tailored for them. To this end, some aspects of this disclosure involve systems, methods and/or computer-readable media for generating personalized rankings of locations at which services are provided based on measurements of affective response of customers. Some of these embodiments may utilize a personalization module that weights and/or selects measurements of affective response of customers based on similarities between a profile of a certain user (for whom a ranking is personalized) and the profiles of the customers (of whom the measurements are taken). An output indicative of these similarities may then be utilized to compute a personalized ranking of the locations, which is suitable for the certain user. Optionally, computing the personalized ranking is done by giving a larger influence, on the ranking, to measurements of customers whose profiles are more similar to the profile of the certain user.

[0910] Some aspects of this disclosure involve generating rankings of locations at which services are provided based on measurements of affective response of customers collected over long periods of time. For example, different measurements used to generate a ranking may be taken during a period of hours, days, weeks, months, and in some embodiments, even years. Naturally, over a stretch of time, the quality of experiences may change. In one example, the size of the crowd at a location may dictate how satisfied individual customers at the location will be. In another example, at certain days of the week a business may be low-staffed, and at other days, well-staffed; thus, the quality of service customers receive at the business may change over time. To account for this dynamic nature of quality of service, some aspects of this disclosure involve generating rankings of locations that correspond to a certain time. Optionally, a ranking of locations corresponding to a time t may be based on a certain number of measurements of affective response (e.g., measurements of affective response of at least five different customers), taken within a certain window of time before t. For example, a ranking of locations corresponding to a time t may be based on measurements taken at some time between t−Δ and t; where Δ may have different values in different embodiments, such as being equal to one hour, one day, one week, one month, one year, or some other length of time. Thus, as time progresses, different measurements of customers are included in the window of time between t−Δ and t, enabling a ranking computed for the locations to reflect the dynamic nature of experiences that may change over time.

[0911] Following are exemplary embodiments of systems, methods, and computer-readable media that may be used to generate rankings of locations at which service is provided based on customer satisfaction, some of which are illustrated in FIG. 30. Since herein locations at which service is provided are to be considered a certain type of location, the exemplary embodiments described below may be considered embodiments of systems, methods, and/or computer-readable media that may be utilized to generate rankings for locations (of the certain type), as illustrated in FIG. 19, FIG. 20, and FIG. 21.

[0912] The collection module 120 is configured, in one embodiment, to receive measurements of affective response of customers who were at the locations at which services were provided to them. Optionally, each measurement of affective response of a customer who was at a location is based on values acquired by measuring the customer with the sensor during at least three different non-overlapping periods while the customer was at the location. Optionally, each customer was at the location for at least a certain time, such as at least five minutes, at least thirty minutes, at least one hour, at least four hours, at least one day, at least one week, or some other period of time that is greater than one minute. The collection module 120 is also configured to forward at least some of the measurements to the ranking module 220.

[0913] In one embodiment, measurements received by the ranking module 220 include measurements of affective response of users who were at the locations being ranked. Optionally, for each location from among the locations being ranked, the measurements comprise measurements of affective response of at least five customers who were at the location. Optionally, for each location, the measurements received by the ranking module 220 may include measurements of a different minimal number of users, such as measurements of at least eight, at least ten, or at least one hundred users. The ranking module 220 is configured to generate a ranking of the locations based on the received measurements. Optionally, in the ranking, a first location is ranked higher than a second location. Optionally, the higher rank is indicative that, on average, the at least five customers who were at the first location were more satisfied than the at least five customers who were at the second location.

[0914] In embodiments described in this disclosure, references to “locations at which service is provided” may be directed to different types of locations. Following are some examples of different types of locations that the “locations at which service is provided” may be.

[0915] In one embodiment, at least some of the locations at which service is provided (including the first and second locations mentioned above) are businesses or areas in a business. In one example, at least some of the locations are a place of business that is one or more of the following: a store, a booth, a shopping mall, a shopping center, a market, a supermarket, a beauty salon, a spa, a laundromat, a bank, an automobile dealership, and a courier service offices. In another example, at least some of the locations are within some other business, such as resort, a water park, a casino, a restaurant, or a bar. FIG. 29 illustrates an example where the locations being ranked correspond to regions of different rides at an amusement park, and the ranking 608 describes an order of the rides based on how satisfying the users found them.

[0916] In another embodiment, at least some of the locations at which service is provided (including the first and second locations mentioned above) offer sleeping accommodations for the customers. For example, these locations may include rooms for rent, such as rooms of hotels, resorts, or apartments for rent.

[0917] In yet another embodiment, the locations at which service is provided (including the first and second locations mentioned above) are businesses, or areas in a businesses, in which health-related services are provided. In one example, at least some of the locations are in a health-care facility such as a clinic, a hospital wing, or an elderly care facility.

[0918] It is to be noted that a location at which service is provided may be part of a larger location at which service is provided. In one example, the larger location may be a business and the location may be a certain region in the business (e.g., a certain department in a store, a certain wing of a hotel, an area involving a certain attraction in an amusement park, or a certain dining room of a restaurant). Additionally, depending on the embodiment, locations at which service is provided may occupy various spaces (e.g., represented as areas of floor space). Areas occupied by locations may vary from a few square feet (e.g., a stall or a booth), to hundreds, thousands and even tens of thousands of square feet (stores and supermarkets), to even acres or more (e.g., malls or resorts). In one example, a location at which a health care is provided includes an area of at least 400 square feet of floor space. In another example, a location at which entertainment is provided (e.g., an amusement park, a water park, a casino, a restaurant, or a bar), includes an area of at least 800 square feet.

[0919] Following is a description of steps that may be performed in a method for ranking locations at which service is provided based on customer satisfaction. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to rank locations at which service is provided based on customer satisfaction. The steps below may be considered a special case of an embodiment of a method illustrated FIG. 20, which illustrates steps involved in one embodiment of a method for ranking locations based on measurements of affective response of users (because locations at which service is provided are a specific type of location being ranked). In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for ranking locations at which service is provided based on customer satisfaction includes at least the following steps:

[0920] In Step 1, receiving, by a system comprising a processor and memory, the measurements of affective response of the customers. For each location from among the locations, the measurements comprise measurements of affective response of at least five customers who were at the location. Optionally, each measurement of affective response of a customer who was at a location is taken with a sensor coupled to the customer while the customer was at the location. Optionally, each measurement may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the location. Optionally, each customer was at the location for at least a certain time, such as at least five minutes, at least thirty minutes, at least one hour, at least four hours, at least one day, at least one week, or some other period of time that is greater than one minute.

[0921] And in Step 2, ranking the locations based on the measurements, such that, a first location is ranked higher than a second location. Optionally, the higher rank of the first location is indicative that, on average, the at least five customers who were at the first location were more satisfied than the at least five customers who were at the second location. Optionally, the ranking of the locations may involve performing different operations, as discussed in the description of embodiments whose steps are described in FIG. 20.

[0922] Since different users may have different backgrounds, tastes, and/or preferences, in some embodiments, the same ranking of locations at which service is provided may not be the best suited for all users. Thus, in some embodiments, rankings of locations may be personalized for some of the users (also referred to as a “personalized ranking” of locations). Optionally, the personalization module 130 may be utilized in order to generate such personalized rankings. In one example, generating the personalized rankings is done utilizing an output generated by the personalization module 130 after being given a profile of a certain user and profiles of at least some of the users who provided measurements that are used to generate rankings (e.g., profiles from among the profiles 504). The output is indicative of similarities between the profile of the certain user and the profiles of the at least some of the users. When computing a ranking of locations based on the output, more influence may be given to measurements of users whose profiles indicate that they are similar to the certain user. Thus, the resulting ranking may be considered personalized for the certain user. The way in which, in the different approaches to ranking, an output from the personalization module 130 may be utilized to generate personalized rankings for different users, is discussed in more detail in section 14—Ranking Experiences.

[0923] In one embodiment, a profile of a user, such as a profile from among the profiles 504, may include information that describes one or more of the following: the age of the user, the gender of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, information indicative of spending and/or traveling habits of the user, and/or a feature value derived from semantic analysis of a communication of the user. It is to be noted that a profile of a customer may be considered to have the same characteristics as profiles of users, and in particular, the profiles 504 may include the profiles of the customers whose measurements are utilized to generate rankings in embodiments described herein.

[0924] FIG. 30 illustrates a system configured to utilize profiles of customers to compute personalized rankings of locations, in which a service is provided, based on customer satisfaction. In the illustrated embodiment, the crowd 500 includes customers who provided services at the locations being ranked, and from whom measurements 501 of affective response were taken while they were at the locations, as described above. In the illustrated embodiment, the customers were at an amusement park, and the different locations correspond to different regions of the park that have different attractions. FIG. 30 illustrates two different users, denoted 610a and 610b, who have different profiles 609a and 609b, respectively. In one embodiment, the profiles 609a and 609b are provided to the personalization module 130 which generates, based on the provided profiles, first and second outputs, respectively. As described above, these outputs are used by the ranking module 220 to generate different rankings of the locations: ranking 611a for user 610a, and ranking 611b for user 610b. As the illustration shows, the ranking 611a is different from the ranking 611b. The profile 609a may be similar to profiles of thrill-seeking customers, and thus, more weight may be given to such customers' measurements when computing the ranking 611a. Consequently, the ranking 611a places attractions that involve movement and speed at higher ranks than more stationary attractions. In contrast, the profile 609b may be similar to profiles of less thrill-seeking customers, and thus, more weight may be given to such customers' measurements when computing the ranking 611b. Consequently, the ranking 611b places stationary attractions at higher ranks than attractions that involve a lot of movement and speed.

[0925] Generating rankings of locations at which services are provided, which are personalized for different users may involve execution of certain steps. Following is a more detailed discussion of steps that may be involved in a method for utilizing profiles of customers to compute personalized rankings of locations in which a service is provided based on customer satisfaction. These steps may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 19 and/or steps of a method modeled according to FIG. 21. The aforementioned figures illustrate embodiments that involve generation of personalized rankings of locations (of which locations at which services are provided are a certain type). In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[0926] In one embodiment, the method for utilizing profiles of customers to compute personalized rankings of locations in which a service is provided based on customer satisfaction includes at least the following steps:

[0927] In Step 1, receiving, by a system comprising a processor and memory, measurements of affective response of the customers. For each location from among the locations being ranked, the measurements comprise measurements of affective response of at least eight customers who were at the location. Additionally, each measurement of affective response of a customer who was at a location is taken with a sensor coupled to the customer while the customer was at the location. Optionally, each measurement may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the location. Optionally, each customer was at the location for at least a certain time, such as at least five minutes, at least thirty minutes, at least one hour, at least four hours, at least one day, at least one week, or some other period of time that is greater than one minute.

[0928] In Step 2, receiving profiles of the customers who contributed measurements in Step 1. Optionally, the received profiles are some of the profiles 504.

[0929] In Step 3, receiving a profile of a certain first user.

[0930] In Step 4, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the customers. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586e in FIG. 21.

[0931] In Step 5, computing, based on the measurements and the first output, a first ranking of the locations.

[0932] In Step 6, receiving a profile of a certain second user, which is different from the profile of the certain first user.

[0933] In Step 7, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the customers. Here, the second output is different from the first output. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586i in FIG. 21.

[0934] And in 8, computing, based on the measurements and the second output, a second ranking of the locations. Optionally, the first and second rankings are different, such that in the first ranking, a first location is ranked above a second location, and in the second ranking, the second location is ranked above the first location. Optionally, when one location is ranked higher than another location, this is indicative that, on average, the at least five customers who were at the one location were more satisfied than the at least five customers who were at the other location.

[0935] In one embodiment, the method described above may optionally include steps that involve reporting to a certain user a result based on a ranking of the locations personalized for the certain user. In one example, the method may include a step that involves forwarding to the certain first user a result derived from the first ranking of the locations. In this example, the result may be a recommendation to go to the first location (which for the certain first user is ranked higher than the second location). In another example, the method may include a step that involves forwarding to the certain second user a result derived from the second ranking of the locations. In this example, the result may be a recommendation for the certain second user to go to the second location (which for the certain second user is ranked higher than the first location).

[0936] The quality of service provided at certain locations may change over time. Thus, in some embodiments, rankings generated for locations at which service is provided may be considered dynamic rankings. For example, a ranking of such locations may correspond to a time t, and be based on measurements of affective response taken in temporal proximity to t (e.g., in a certain window of time Δ preceding t). Thus, given that over time, the values of measurements in the window that are used to compute a ranking of the locations may change, the computed rankings may also change over time. Some of the embodiments described above, e.g., embodiments for ranking locations modeled according to FIG. 19 may be used to generate dynamic rankings of locations at which service is provided by providing measurements of affective response to the dynamic ranking module 250 instead of to the ranking module 220. A more detailed discussion of dynamic ranking may be found in this disclosure at least in section 14—Ranking Experiences.

[0937] FIG. 31 illustrates dynamic rankings of locations in the amusement park illustrated in FIG. 29. The illustration in FIG. 31 shows how over the course of a day, the ranking of different locations, which correspond to different attractions, changes over time (e.g., see changes to ranks given to some of the locations in the rankings 613a, 613b, 613c, and 613d). These changes may be due to various factors, such as the size of the crowd at each location, environmental conditions (e.g., some locations may be in direct sun light and some may be shaded), and/or the composition of visitors at the amusement park at different hours.

[0938] In one embodiment, a system configured to dynamically rank locations at which service is provided based on customer satisfactions includes at least the collection module 120 and the dynamic ranking module 250. In this embodiment, the collection module 120 is configured to receive the measurements 501 of affective response of customers who were at the locations being ranked. Optionally, for each location from among the locations being ranked, the measurements comprise measurements of affective response of at least ten customers who were at the location. Additionally, each measurement of affective response of a customer who was at a location was taken with a sensor coupled to the customer, while the customer was at the location. In this embodiment, the dynamic ranking module 250 is configured to generate rankings of the locations. Each ranking corresponds to a time t and is generated based on a subset of the measurements of the customers that includes measurements of at least five users; where each measurement is taken at a time that is not earlier than a certain period before t and is not after t. For example, if the length of the certain period is denoted Δ, each of the measurements in the subset was taken at a time that is between t−Δ and t. Optionally, for each location, the subset includes measurements of at least five different customers who were at the location. Optionally, measurements taken earlier than the certain period before a time t are not utilized by the dynamic ranking module 250 to generate a ranking corresponding to t. Optionally, the dynamic ranking module 250 may be configured to assign weights to measurements used to compute a ranking corresponding to a time t, such that an average of weights assigned to measurements taken earlier than the certain period before t is lower than an average of weights assigned to measurements taken later than the certain period before t. The dynamic ranking module 250 may be further configured to utilize the weights to compute the ranking corresponding to t.

[0939] In one embodiment, the rankings generated by the dynamic ranking module 250 include at least a first ranking corresponding to a time t₁ and a second ranking corresponding to a time t₂, which is after t₁. In the first ranking corresponding to the time t₁, a first location is ranked above a second location. However, in the second ranking corresponding to the time t₂, the second location is ranked above the first location. In this embodiment, the second ranking is computed based on at least one measurement taken after t₁. Optionally, when one location is ranked higher than another location, this is indicative that, on average, the at least five customers who were at the one location were more satisfied than the at least five customers who were at the other location.

[0940] Since dynamic rankings of locations may change over time, this may change the nature of recommendations of location at which service is provided that are given to users at different times, e.g., by the recommender module 235 as described in section 14—Ranking Experiences. With reference to the embodiment described above, which includes the first and second rankings corresponding to t₁ and t₂, respectively, the recommender module 235 may be configured to: recommend the first location to a user during a period that ends before t₂ in the first manner, and not to recommend to the user the second location in the first manner during that period. Optionally, during that period, the recommender module 235 recommends the second location in the second manner. After t₂, the behavior of the recommender module 235 may change, and it may recommend to the user the second location in the first manner, and not recommend the first location in the first manner. Optionally, after t₂, the recommender module 235 may recommend the first location in the second manner.

[0941] Following is a description of steps that may be performed in a method for dynamically ranking locations at which service is provided based on customer satisfaction. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to dynamically rank locations at which service is provided based on customer satisfaction. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for dynamically ranking locations at which service is provided based on customer satisfaction includes at least the following steps:

[0942] In Step 1, receiving, by a system comprising a processor and memory, a first set of measurements of affective response of users. Each measurement belonging to the first set was taken at a time that is not earlier than a certain period before a time t₁, and is not after t₁. Additionally, for each location from among the locations being ranked, the measurements comprise measurements of affective response of at least eight customers who were at the location. Optionally, each measurement of affective response of a customer who was at a location is taken with a sensor coupled to the customer while the customer was at the location. Optionally, each measurement of affective response of the customer who was at the location may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the location. Optionally, each customer was at the location for at least a certain time, such as at least five minutes, at least thirty minutes, at least one hour, at least four hours, at least one day, at least one week, or some other period of time that is greater than one minute.

[0943] In Step 2, generating, based on the first set of measurements, a first ranking of the locations. In the first ranking, a first location is ranked ahead of a second location.

[0944] In Step 3, receiving a second set of measurements of affective response of users. Each measurement belonging to the second set was taken at a time that is not earlier than the certain period before a time t₂ and is not after t₂. Additionally, for each location from among the locations being ranked, the measurements comprise measurements of affective response of at least eight customers who were at the location.

[0945] And in Step 4, generating, based on the second set of measurements, a second ranking of the locations. In the second ranking, the second location is ranked ahead of the first location. Additionally, t₂>t₁ and the second set of measurements comprises at least one measurement of affective response of a user taken after t₁. Optionally, when one location is ranked higher than another location, this is indicative that, on average, the at least five customers who were at the one location were more satisfied than the at least five customers who were at the other location.

[0946] The method described above may optionally include a step that involves recommending to a user a location from among the locations being ranked. The nature of such a recommendation may depend on the ranking of the locations, and as such, may change over time. Optionally, recommending a location may be done in a first manner or in a second manner; recommending a location in the first manner may involve providing a stronger recommendation for the location, compared to a recommendation for the location provided when recommending it in the second manner. In one example, at a time that is before t₂, the first location may be recommended to a user in the first manner, and the second location may be recommended to the user in the second manner. However, at a time that is after t₂, the first location may be recommended to the user in the second manner, and the second location is recommended to the user in the first manner.

[0947] In a similar manner to the personalization of rankings of locations at which service is provided, which is described above (e.g., as illustrated in FIG. 30), in some embodiments, dynamic rankings of locations at which service is provided may also be personalized for different users. Optionally, this is done utilizing the personalization module 130, which may be utilized to generate personalized dynamic rankings of locations at which service is provided, e.g., as illustrated in FIG. 91a and FIG. 91b, which involve personalized rankings of experiences, and as such are relevant to personalized dynamic rankings of locations at which service is provided (since being in a location at which service is provided is a specific type of experience).

[0948] Virtual environments, such as environments involving massively multiplayer online role-playing games (MMORPGs) and/or virtual worlds (e.g., Second Life) have become very popular options for recreational activities. With the improvements in virtual reality, graphics, and network latency and capacity, virtual environments have also become a place where users meet for business and/or social interactions. Being able to visit and/or interact in a virtual environment typically involves logging into a server that hosts the virtual environment. The server may be used to perform various computations required for the virtual environment, as well as serve as a hub through which users may communicate and interact. Some virtual environments may allow large numbers of users to connect to them (each possibly connecting to a different instantiation of the virtual environment). Thus, a virtual environment may be hosted on multiple servers.

[0949] Depending on which server a user is logged into, the user may be provided with a different quality of experience. The quality of the experience that a user logged into a server has may be influenced by various factors. In one example, the quality of the experience is influenced by technical factors, such as the quality of connection with the server (e.g., network latency) and/or the load on the server, which may be proportional to the number of users connected to it. In another example, the quality of the experience may depend on the identity and/or behavior of the users connected to the server (e.g., are the pleasant people to interact with). And in still another example, the quality of the experience may be influenced by the characteristics of the instantiation of the virtual world that is presented to users on the server, such as how interesting is the area of the virtual world hosted on the server, or whether users logged into the server have an exciting mission to complete at the time.

[0950] There is a large number of virtual environments available to users, such as different games, virtual worlds, business meetings places, and/or virtual stores; each of the virtual environments may have one or more servers that a user may log into in order to view what is happening in the virtual world and/or to participate in interactions in the virtual world. Given the large number of options, determining to which server to log into may be a difficult task for users. Thus, there is a need for a way to estimate a quality of experience users may have when logging into different servers that host virtual world(s) in order to help the users select a server that will provide them with an enjoyable experience.

[0951] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that enable generation of rankings of servers based on measurements of affective response of users who were logged into the servers. Such rankings can help a user decide which servers are worthwhile to log into, and which should be avoided. A ranking of servers is an ordering of at least some of the servers, which is indicative of preferences of users towards those servers and/or is indicative of the quality of the experience users had when they were logged into the servers (and viewed and/or interacted in the virtual worlds hosted on the servers). Typically, a ranking of servers, generated in embodiments described herein, will include at least a first server and a second server, such that the first server is ranked ahead of the second server. When the first server is ranked ahead of the second server, this typically means that, based on the measurements of affective response of the users who were logged into the servers, the first server is preferred by the users over the second server. For example, measurements of affective response of users who were logged into the first server indicate that they had a better time (e.g., they were happier and/or more satisfied) compared to the sentiment indicated by measurements of affective response of users who were logged into the second server.

[0952] Some aspects of embodiments described herein involve rankings of servers the host virtual environments. In one embodiment, each server from among the servers being ranked hosts a different virtual environment. For example, different virtual environments may involve different virtual world, different MMORPGs, or different virtual malls. In another embodiment, each server from among the servers being ranked may host a different instantiation of a virtual environment. In one example, each instantiation may represent a different region of a virtual environment (e.g., a different area in a virtual world). In another example, each instantiation may involve a different set of users that may interact in the virtual environment.

[0953] Some aspects of this disclosure involve collecting measurements of affective response of users who were logged into servers. In embodiments described herein, a measurement of affective response of a user is typically collected with one or more sensors coupled to the user, which are used to obtain a value that is indicative of a physiological signal of the user (e.g., a heart rate, skin temperature, or brainwave activity) and/or indicative of a behavioral cue of the user (e.g., a facial expression, body language, or the level of stress in the user's voice). Additionally or alternatively, a measurement of affective response of a user may also include indications of biochemical activity in a user's body, e.g., by indicating concentrations of one or more chemicals in the user's body (e.g., levels of various electrolytes, metabolites, steroids, hormones, neurotransmitters, and/or products of enzymatic activity).

[0954] Differences between users can naturally lead to it that they will have different tastes and different preferences, which may lead to it that they can have a different quality of experience in different virtual environments. Thus, a ranking of servers that host virtual environments may represent, in some embodiments, an average of the experience the users had when logged into different servers, which may reflect an average of the taste of various users. However, for some users, such a ranking of servers may not be suitable, since those users, and/or their preferences with regards to virtual environments, may be different from the average. In such cases, users may benefit from a ranking of servers that is better suited for them. To this end, some aspects of this disclosure involve systems, methods and/or computer-readable media for generating personalized rankings of servers based on measurements of affective response of users who were logged into the servers. Some of these embodiments may utilize a personalization module that weights and/or selects measurements of affective response of users based on similarities between a profile of a certain user (for whom a ranking is personalized) and the profiles of the users (of whom the measurements are taken). An output indicative of these similarities may then be utilized to compute a personalized ranking of servers, which is suitable for the certain user. Optionally, computing the personalized ranking is done by giving a larger influence, on the ranking, to measurements of users whose profiles are more similar to the profile of the certain user.

[0955] Some aspects of this disclosure involve generating rankings of servers based on measurements of affective response of users who were logged into the servers. In some embodiments, the measurements may be collected over long periods of time. For example, a set of different measurements used to generate a ranking may be taken during a period of hours, days, weeks, months, and in some embodiments, even years. Naturally, over a stretch of time, the quality of experiences in virtual environments may change.

[0956] In one example, the quality of the experience a user has when the user is logged into a server may depend on technical factors such as the distance of the user from the server, the load on the server, and/or network throughput bottlenecks. Thus, if the user is too far away, the server is too heavily burdened with computations, and/or the network throughput is too low, the experience may be suboptimal. For example, the user may experience unsmooth graphics and/or sluggish system responses. This can lead to frustration and a negative experience in general.

[0957] In another example, the quality of the experience a user has when the user is logged into a server may depend on the identity and/or behavior of other players on the server. For example, if the user is a novice and there are many expert users on the server, the user may be frustrated from the difference in skills (or the other users may be frustrated from the user). In another example, some users may behave inappropriately (e.g., be aggressive and/or behave in a way unbefitting and/or unsupportive of a mission). Interacting with such users may negatively affect the experience a user has.

[0958] In still another example, the quality of the experience a user has when the user is logged into a server may depend on the condition of the instantiation virtual world hosted by the server. In one example, a server may host a region that is undeveloped, e.g., lacking few interesting features (e.g., with few entities to interact with and/or uninspiring scenery). In another example, the server might have hosted an interesting mission that had already been completed, thus at the present time there is not much happening on the server that may hold a user's interest.

[0959] Due to the dynamic nature of an experience involving logging into a server that hosts a virtual environment, some aspects of this disclosure involve generating rankings of servers that correspond to a certain time. Optionally, a ranking of servers corresponding to a time t may be based on a certain number of measurements of affective response (e.g., measurements of affective response of at least five different users), taken within a certain window of time before t. For example, a ranking of servers corresponding to a time t may be based on measurements taken at some time between t−Δ and t; where Δ may have different values in different embodiments, such as being equal to one day, one week, one month, one year, or some other length of time. Thus, as time progresses, different measurements are included in the window of time between t−Δ and t, thus enabling a ranking computed for the servers to reflect the dynamic nature of experiences that involve the virtual environments hosted on the servers.

[0960] Following are exemplary embodiments of systems, methods, and computer-readable media that may be used to generate rankings of servers, some of which are illustrated in FIG. 32 and FIG. 33. Since herein servers are to be considered representing a certain type of location (e.g., a virtual environment), the exemplary embodiments described below may be considered embodiments of systems, methods, and/or computer-readable media that may be utilized to generate rankings for locations (of the certain type), as illustrated in FIG. 19, FIG. 20, and FIG. 21. Therefore, the teachings in this disclosure regarding various embodiments, in which rankings for locations are generated, are applicable to embodiments in which rankings for servers are generated. In a similar manner, additional teachings relevant to embodiments described below, which involve generation of rankings of servers, may be found at least in section 14—Ranking Experiences, which describes various embodiments in which rankings are generated for experiences in general (and being in virtual environment hosted on a server is a certain type of experience).

[0961] It is to be noted that in some embodiments, a ranking of servers may be considered to correspond to a ranking of the virtual environments hosted by the servers. For example, based on a certain server being highly ranked, a user may consider a virtual environment hosted on the server to be highly ranked. Optionally, in these embodiments, servers are the objects being ranked, rather than the virtual environments hosted on the servers, because the servers are gateways through which users may enter into the hosted virtual environments. Thus, for example, a user desiring to enter a certain virtual world may choose one of multiple ways in to the virtual world, each involving logging into a different server.

[0962] In one embodiment, a system modeled according to FIG. 19 is configured to generate a ranking of servers based on measurements of affective response of users who were logged into the servers. The system includes at least the collection module 120 and the ranking module 220. The system may optionally include additional modules such as the recommender module 235, the map-displaying module 240, and/or the personalization module 130, to name a few.

[0963] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response of users belonging to the crowd 500, which in this embodiment, include measurements of affective response of users who were logged into a server, from among the servers being ranked, for at least five minutes. The collection module 120 is also configured to forward at least some of the measurements 501 to the ranking module 220.

[0964] In one embodiment, each measurement of affective response of a user who was logged into a server, from among the servers being ranked, is based on a value obtained by measuring the user, with a sensor coupled to the user, while the user was logged into the server. Optionally, the measurement may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was logged into the server. Examples of various types of sensors that may be utilized to measure a user are given at least in section 1—Sensors of this disclosure.

[0965] In one embodiment, measurements received by the ranking module 220 include, for each server from among the servers being ranked, measurements of affective response of at least five users who were logged into the server for at least five minutes. Optionally, for each server, the measurements received by the ranking module 220 may include measurements of a different minimal number of users who were logged into the server, such as measurements of at least eight, at least ten, or at least one hundred users. Optionally, each of the users whose measurements are used to compute the ranking might have been logged into the server for a longer duration, such as at least fifteen minutes, at least an hour, at least six hours, or more than twelve hours.

[0966] The ranking module 220 is also configured, in one embodiment, to generate a ranking of the servers based on the received measurements. Optionally, in the generated ranking, a first server is ranked higher than a second server. Optionally, when the first server is ranked ahead of the second server, this generally means that the users who were logged into the first server had a better time than the users who were logged into the second server. Optionally, each of the servers being ranked hosts a different game and/or virtual world. Alternatively, each of the servers being ranked may host a certain portion of a certain virtual world (e.g., a different area on a map of the virtual world, a different meeting room, or a different virtual store in a virtual mall).

[0967] What constitutes “having a better time” may differ between embodiments. In one embodiment, the servers being ranked may host a game and/or a virtual world in which games are played. In this embodiment, “having a better time” may mean being happier, more content, and/or more relaxed. In another embodiment, the servers being ranked may host an action/horror themed virtual world (e.g., a shooter game or zombie-themed adventure). In this embodiment, “having a better time” may mean being more excited, thrilled, and/or scared. In some embodiments, an Emotional State Estimator (ESE) may be used

[0968] In some embodiments, in a ranking of server, such as a ranking generated by the ranking module 220, each server has a unique rank, i.e., there are no two servers that share the same rank. In other embodiments, at least some of the server may be tied in the ranking. In one example, there may be third and fourth servers that are given the same rank by the ranking module 220. It is to be noted that the third server in the example above may be the same server as the first server or the second server mentioned above.

[0969] FIG. 32 illustrates an example of a ranking of servers that may be generated utilizing the ranking module 220, as described above. In the illustration, a user 614 may log into a server from among servers 615 (there are four servers illustrated in the figure). The fours servers host the following different virtual worlds from different genres of computer games: Fantasy World, Dragon World, Urban Development, and Alien Invasion. In this example, Fantasy World may be considered a world in which users can explore various fantasy realms, Urban Development is a world in which user build cities and run communities (a simulator world), and Alien Invasion and Dragon World involve action-related game play. In order to assist user 614 in the selection of a virtual world to log into, the user 614 may rely on the ranking 616 that provides an ordering of the virtual worlds along with a score indicating how much users who were logged into them enjoyed their time in each virtual world.

[0970] It is to be noted that while it is possible, in some embodiments, for the measurements received by modules, such as the ranking module 220, to include, for each user from among the users who contributed to the measurements, at least one measurement of affective response of the user taken while being logged into each server being ranked, this is not the case in all embodiments. In some embodiments, some users may contribute measurements corresponding to a proper subset of the servers (e.g., those users may not have logged into some servers being ranked. In some embodiments, some users may have logged into only one of the servers being ranked.

[0971] There may be different approaches to ranking that can be used to generate rankings of servers, which may be utilized in embodiments described herein. In some embodiments, servers may be ranked based on scores computed for the servers. In such embodiments, the ranking module 220 may include the scoring module 150 or the dynamic scoring module 180, and a score-based rank determining module 225. In other embodiments, servers may be ranked based on preferences generated from measurements. In such embodiments, an alternative embodiment of the ranking module 220 includes preference generator module 228 and preference-based rank determining module 230. The different approaches that may be utilized for ranking servers are discussed in more detail in section 14—Ranking Experiences, e.g., in the discussion related to FIG. 85 and FIG. 86.

[0972] In some embodiments, the recommender module 235 is utilized to recommend to a user a server, from among the servers being ranked by the ranking module 220, in a manner that belongs to a set comprising first and second manners. Herein, a recommendation of a server may be considered a recommendation to log into the server in order to enter a virtual environment hosted on the server. Optionally, when recommending a server in the first manner, the recommender module 235 provides a stronger recommendation for the server, compared to a recommendation for the server that the recommender module 235 would provide when recommending in the second manner. Optionally, the recommender module 235 determines the manner in which to recommend a server based on the rank of the server in a ranking (e.g., a ranking generated by the ranking module 220). In one example, if a server is ranked at least at a certain rank (e.g., at least in the top three), it is recommended in the first manner, which may involve providing a promotion for the server to the user (e.g., a coupon) and/or the server is displayed more prominently on a list (e.g., a larger font, at the top of the list, or on the first screen of suggested servers) or on a map (e.g., using a picture or icon representing a virtual environment hosted by the server). In this example, if a server is not ranked high enough, then it is recommended in the second manner, which may involve no promotion, a smaller font on a listing of servers, the server may appear on a page that is not the first page of suggested servers, a virtual environment hosted on the server may have a smaller icon representing it on a map (or no icon at all), etc.

[0973] In some embodiments, different servers may host different virtual environments and/or regions of virtual environments that correspond to locations on a map. Optionally, map-displaying module 240 may be utilized to present to a user the ranking of the server by displaying an image describing an area in which the environments hosted by the servers are located and annotations describing at least some of the environments hosted by the servers and their respective ranks and/or scores computed for the severs hosting them. Optionally, virtual environments hosted by higher ranked servers are displayed more prominently on the map than environments hosted by lower ranked servers. In one embodiment, in an annotation presented on the map, a value related to the first server is displayed more prominently on the map than a value related to the second server.

[0974] Following is a description of steps that may be performed in a method for ranking servers based on measurements of affective response of users who were logged into the servers. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to rank servers based on measurements of affective response of users who were logged into the servers. The steps below may be considered a special case of an embodiment of a method illustrated FIG. 20, which illustrates steps involved in one embodiment of a method for ranking locations based on measurements of affective response of users (because servers virtual environments that are a specific type of location being ranked). In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for ranking servers based on measurements of affective response of users who were logged into the servers includes at least the following steps:

[0975] In Step 1, receiving, by a system comprising a processor and memory, the measurements of affective response of the users. For each server from among the servers, the measurements comprise measurements of affective response of at least five users who were logged into the server for a period of at least five minutes, and each measurement of a user is taken with a sensor coupled to the user while the user is logged into the server. Optionally, each of the users was logged into the server for a longer minimal period of time, such as at least fifteen minutes, at least an hour, at least six hours, or more than twelve hours. Optionally, each measurement of affective response of a user who is logged into a server is obtained by measuring the user with the sensor during at least three different non-overlapping periods while the user is logged into the server.

[0976] And in Step 2, ranking the servers based on the measurements, such that, a first server is ranked higher than a second server. Optionally, the higher rank of the first server is indicative that, on average, the at least five users who were logged into the first server had a better time than the at least five users who were at the second server. Optionally, the ranking of the servers may involve performing different operations, as discussed in the description of embodiments whose steps are described in FIG. 20.

[0977] In one embodiment, the method described above may optionally include a step that involves recommending the first server to a user in a first manner, and not recommending the second server to the user in the first manner. Optionally, the step may further involve recommending the second server to the user in a second manner. As mentioned above, e.g., with reference to recommender module 235, recommending a server in the first manner may involve providing a stronger recommendation for the server, compared to a recommendation for the server that is provided when recommending it in the second manner.

[0978] Since different users may have different backgrounds, tastes, and/or preferences, in some embodiments, the same ranking of servers may not be the best suited for all users. Thus, in some embodiments, rankings of servers may be personalized for some of the users (also referred to as a “personalized ranking” of servers). Optionally, the personalization module 130 may be utilized in order to generate such personalized rankings of servers. In one example, generating the personalized rankings is done utilizing an output generated by the personalization module 130 after being given a profile of a certain user and profiles of at least some of the users who provided measurements that are used to rank the servers (e.g., profiles of users from among the profiles 504). The output is indicative of similarities between the profile of the certain user and the profiles of the at least some of the users. When computing a ranking of servers based on the output, more influence may be given to measurements of users whose profiles indicate that they are similar to the certain user. Thus, the resulting ranking may be considered personalized for the certain user. Since different certain users are likely to have different profiles, the output generated for them may be different, and consequently, the personalized rankings of the servers that are generated for them may be different. For example, in some embodiments, when generating personalized rankings of servers, there are at least a certain first user and a certain second user, who have different profiles, for which the ranking module 220 may rank servers differently. For example, for the certain first user, a first server may be ranked above a second server, and for the certain second user, the second server is ranked above the first server. The way in which, in the different approaches to ranking, an output from the personalization module 130 may be utilized to generate personalized rankings for different users, is discussed in more detail in section 14—Ranking Experiences.

[0979] In one embodiment, a profile of a user, such as a profile from among the profiles 504, may include information that describes one or more of the following: the age of the user, the gender of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, information indicative of spending habits of the user, and/or a feature value derived from semantic analysis of a communication of the user. Additionally, the profile may include information about experiences the user had in virtual environments (e.g., level of expertise in certain environments, behavioral patterns, and/or accomplishments such as completed missions, levels, etc.) Additional information that may be included in the profile is described at least in section 11—Personalization.

[0980] FIG. 33 illustrates a system configured to generate personalized rankings of servers based on measurements of affective response and profiles of users. In the illustrated embodiment, the crowd 500 includes users who were logged into virtual environments (e.g., the virtual worlds 615 in FIG. 32). Measurements 501 of affective response were taken from those users while they were logged into the servers, as described above. FIG. 33 illustrates two different users, denoted 618a and 618b, who have different profiles 619a and 619b, respectively. In one embodiment, the profiles 619a and 619b are provided to the personalization module 130 which generates, based on the provided profiles, first and second outputs, respectively. As described above, these outputs are used by the ranking module 220 to generate different rankings of the servers ranking 620a for user 618a, and ranking 620b for user 618b. As the illustration shows, the ranking 620a is different from the ranking 620b; each of the rankings includes the same four virtual world (hosted on different servers). Ranking 620a indicates that users with profiles similar to the profile 619a of the user 618a had a better time when they were in imaginative worlds such as Fantasy World and Urban Development. While ranking 620b indicates the users with profiles similar to the profile 619b of the user 618b had a better time when they were in action-packed worlds such as Alien Invasion or Dragon World. In the illustration, the more prominent representation of a server hosting a virtual world involves a larger image of the hosted virtual world that is ranked first.

[0981] Generating rankings of servers that are personalized for different users may involve execution of certain steps. Following is a more detailed discussion of steps that may be involved in a method for generating personalized rankings of servers based on measurements of affective response and profiles of users. These steps may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 19 and/or steps of a method modeled according to FIG. 21. The aforementioned figures illustrate embodiments that involve generation of personalized rankings of locations. Since virtual environments hosted on servers are a specific type of location, the teachings of those embodiments are relevant to the steps of the method described below. In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[0982] In one embodiment, the method for utilizing profiles of users to compute personalized rankings of servers based on measurements of affective response of the users includes at least the following steps:

[0983] In Step 1, receiving, by a system comprising a processor and memory, measurements of affective response of the users. For each server from among the servers being ranked, the measurements comprise measurements of affective response of at least eight users who were logged into the server for at least five minutes. Optionally, each of the users whose measurements are used to compute the ranking might have been logged into the server for a longer duration, such as at least fifteen minutes, at least an hour, at least six hours, or more than twelve hours. Optionally, for each server from among the servers being ranked, the measurements comprise measurements of affective response of at least some other minimal number of users who were logged into the server, such as measurements of at least five, at least ten, and/or at least fifty different users.

[0984] In Step 2, receiving profiles of at least some of the users who contributed measurements in Step 1. Optionally, the received profiles are some of the profiles 504.

[0985] In Step 3, receiving a profile of a certain first user.

[0986] In Step 4, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least some of the users. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586e in FIG. 21.

[0987] In Step 5, computing, based on the measurements and the first output, a first ranking of the servers.

[0988] In Step 6 receiving a profile of a certain second user, which is different from the profile of the certain first user.

[0989] In Step 7, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least some of the users. Here, the second output is different from the first output. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586i in FIG. 21.

[0990] And in 8, computing, based on the measurements and the second output, a second ranking of the servers. Optionally, the first and second rankings are different, such that in the first ranking, a first server is ranked above a second server, and in the second ranking, the second server is ranked above the first server. Optionally, in the generated ranking, a first server is ranked higher than a second server. Optionally, when the first server is ranked ahead of the second server, this generally means that the users who were logged into the first server had a better time than the users who were logged into the second server. Optionally, each of the servers being ranked hosts a different game and/or virtual world. Alternatively, each of the servers being ranked may host a certain portion of a certain virtual world (e.g., a different area on a map of the virtual world, a different meeting room, or a different virtual store in a virtual mall).

[0991] In one embodiment, the method optionally includes a step that involves utilizing a sensor coupled to a user who was logged into a server, from among the servers being ranked, for obtaining a measurement of affective response of the user. Optionally, the measurement of affective response of the user is based on a value obtained by measuring the user with the sensor while the user was logged into the server. Optionally, the measurement may be based on values acquired by measuring the user with the sensor during at least three different non-overlapping periods while the user was logged into the server. Examples of various types of sensors that may be utilized to measure a user are given at least in section 1—Sensors of this disclosure.

[0992] In one embodiment, the method may optionally include steps that involve reporting to a certain user a result based on a ranking of the servers personalized for the certain user. In one example, the method may include a step that involves forwarding to the certain first user a result derived from the first ranking of the servers. In this example, the result may be a recommendation to log into the first server (which for the certain first user is ranked higher than the second server). In another example, the method may include a step that involves forwarding to the certain second user a result derived from the second ranking of the servers. In this example, the result may be a recommendation for the certain second user to log into the second server (which for the certain second user is ranked higher than the first server).

[0993] In some embodiments, the method may optionally include steps involving recommending one or more of the servers being ranked to users. Optionally, the type of recommendation given for a server is based on the rank of the server. For example, given that in the first ranking, the rank of the first server is higher than the rank of the second server, the method may optionally include a step of recommending the first server to the certain first user in a first manner, and not recommending the second server to the certain first user in first manner. Optionally, the method may include a step of recommending the second server to the certain first user in a second manner. Optionally, recommending a server in the first manner involves providing a stronger recommendation for the server, compared to a recommendation for the server that is provided when recommending it in the second manner. The nature of the first and second manners is discussed in more detail with respect to the recommender module 178, which may also provide recommendations in first and second manners.

[0994] The quality of an experience involving being in a virtual environment hosted on a server may change over time. Thus, in some embodiments, rankings generated for servers may be considered dynamic rankings. For example, a ranking of servers may correspond to a time t, and be based on measurements of affective response taken in temporal proximity to t (e.g., in a certain window of time Δ preceding t). Thus, given that over time, the values of measurements in the window that are used to compute a ranking of the servers may change, the computed rankings of servers may also change over time. Some of the embodiments described above, e.g., embodiments for ranking servers according to FIG. 19 may be used to generate dynamic rankings of servers by providing measurements of affective response to the dynamic ranking module 250 instead of to the ranking module 220. A more detailed discussion of dynamic ranking may be found in this disclosure at least in section 14—Ranking Experiences.

[0995] In one embodiment, a system configured to dynamically rank servers based on measurements affective response of users includes at least the collection module 120 and the dynamic ranking module 250. In this embodiment, the collection module 120 is configured to receive the measurements 501 of affective response of users belonging to the crowd 500, which include measurements of users who were logged into the servers being ranked. Optionally, for each server from among the servers being ranked, the measurements 501 include measurements of affective response of at least ten users who were logged into the server for at least five minutes. In this embodiment, the dynamic ranking module 250 is configured to generate rankings of the servers. Each ranking corresponds to a time t and is generated based on a subset of the measurements of the users that includes measurements of at least five users; where each measurement is taken at a time that is not earlier than a certain period before t and is not after t. For example, if the length of the certain period is denoted Δ, each of the measurements in the subset was taken at a time that is between t−Δ and t. Optionally, for each server from among the servers being ranked, the subset includes measurements of at least five different users who were logged into the server. Optionally, measurements taken earlier than the certain period before a time t are not utilized by the dynamic ranking module 250 to generate a ranking corresponding to t. Optionally, the dynamic ranking module 250 may be configured to assign weights to measurements used to compute a ranking corresponding to a time t, such that an average of weights assigned to measurements taken earlier than the certain period before t is lower than an average of weights assigned to measurements taken later than the certain period before t. The dynamic ranking module 250 may be further configured to utilize the weights to compute the ranking corresponding to t.

[0996] In one embodiment, the rankings generated by the dynamic ranking module 250 include at least a first ranking corresponding to a time t₁ and a second ranking corresponding to a time t₂, which is after t₁. In the first ranking corresponding to the time t₁, a first server is ranked above a second server. However, in the second ranking corresponding to the time t₂, the second server is ranked above the first server. In this embodiment, the second ranking is computed based on at least one measurement taken after t₁.

[0997] Since dynamic rankings of servers may change over time, this may change the nature of recommendations of servers that are given to users at different times. In one embodiment, the recommender module 235 is configured to recommend a server to a user in a manner that belongs to a set comprising first and second manners. When recommending a server in the first manner, the recommender module 235 provides a stronger recommendation for the server, compared to a recommendation for the server that the recommender module 235 provides when recommending it in the second manner. With reference to the embodiment described above, which includes the first and second rankings corresponding to t₁ and t₂, respectively, the recommender module 235 may be configured to: recommend the first server to a user during a period that ends before t₂ in the first manner, and not to recommend to the user the second server in the first manner during that period. Optionally, during that period, the recommender module 235 recommends the second server in the second manner. After t₂, the behavior of the recommender module 235 may change, and it may recommend to the user the second server in the first manner, and not recommend the first server in the first manner. Optionally, after t₂, the recommender module 235 may recommend the first server in the second manner.

[0998] FIG. 34 illustrates dynamic rankings of servers hosting the virtual worlds described in FIG. 32. The illustration in FIG. 34 shows how over the course of a day, the ranking of different servers, which host different virtual worlds, changes over time (e.g., see changes to ranks given to some of the servers in the rankings 620a, 620b, 620c, and 620d). These changes may be due to various factors, such as the size of a different composition of users who log into the virtual world at different hours. For example, users who prefer action-based experiences are more likely to log in during the late hours of the night and the early hours of the morning.

[0999] Following is a description of steps that may be performed in a method for dynamically ranking servers based on measurements of affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to dynamically rank servers based on measurements of affective response of users. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for dynamically ranking servers based on measurements of affective response of users includes at least the following steps:

[1000] In Step 1, receiving, by a system comprising a processor and memory, a first set of measurements of affective response of users. Each measurement belonging to the first set was taken at a time that is not earlier than a certain period before a time t₁ and is not after t₁. Additionally, for each server from among the servers being ranked, the first set of measurements comprises measurements of affective response of at least five users who were logged into the server for at least five minutes.

[1001] In Step 2, generating, based on the first set of measurements, a first ranking of the servers. In the first ranking, a first server is ranked ahead of a second server.

[1002] In Step 3, receiving a second set of measurements of affective response of users. Each measurement belonging to the second set was taken at a time that is not earlier than the certain period before a time t₂ and is not after t₂. Additionally, for each server from among the servers being ranked, the second set of measurements comprises measurements of affective response of at least five users who were logged into the server for at least five minutes.

[1003] And in Step 4, generating, based on the second set of measurements, a second ranking of the servers. In the second ranking, the second server is ranked ahead of the first server. Additionally, t₂>t₁ and the second set of measurements comprises at least one measurement of affective response of a user taken after t₁.

[1004] The method described above may optionally include a step that involves recommending to a user a server from among the servers being ranked. The nature of such a recommendation may depend on the ranking of the servers, and as such, may change over time. Optionally, recommending a server may be done in a first manner or in a second manner; recommending a server in the first manner may involve providing a stronger recommendation for the server, compared to a recommendation for the server provided when recommending it in the second manner. In one example, at a time that is before t₂, the first server may be recommended to a user in the first manner, and the second server may be recommended to the user in the second manner. However, at a time that is after t₂, the first server may be recommended to the user in the second manner, and the second server is recommended to the user in the first manner.

[1005] In a similar manner to the personalization of rankings of server described above (e.g., with reference to FIG. 33), in some embodiments, dynamic rankings of servers may also be personalized for different users. Optionally, this is done utilizing the personalization module 130, which may be utilized to generate personalized dynamic rankings of servers, e.g., as illustrated in FIG. 91a and FIG. 91b, which involve personalized rankings of experiences, and as such are relevant to personalized dynamic rankings of server (since staying logged into a server and being in the virtual environment it hosts is a specific type of experience).

[1006] In one embodiment, a system configured to dynamically generate personalized rankings of servers based on measurements of affective response of users includes at least the collection module 120, the personalization module 130, and the dynamic ranking module 250. In this embodiment, the collection module 120 is configured to receive the measurements of affective response of the users that include, for each server from among the servers being ranked, measurements of affective response of at least ten users who were logged into the server for at least five minutes. The personalization module 130 is configured, in one embodiment, to receive a profile of a certain user and profiles of the users, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the users. The dynamic ranking module 250 is configured to generate, for the certain user, rankings of the servers. Each ranking of servers corresponds to a time t and is generated based on the output and a subset of the measurements comprising, for each server in the ranking, measurements of at least five users who were logged into the server for at least five minutes. Additionally, each measurement in the subset is taken at a time that is not earlier than a certain period before t and is not after t.

[1007] By utilizing the personalization module 130, it is possible that different users may receive different dynamic rankings, at different times. In particular, in one embodiment, rankings generated by the system described above are such that for at least a certain first user and a certain second user, who have different profiles, the dynamic ranking module 250 generates the following rankings: (i) a ranking corresponding to a time t₁ for the certain first user, in which a first server is ranked ahead of a second server; (ii) a ranking corresponding to the time t₁ for the certain second user in which the second server is ranked ahead of the first server; (iii) a ranking corresponding to a time t₂>t₁ for the certain first user, in which the first server is ranked ahead of the second server; and (iv) a ranking corresponding to the time t₂ for the certain second user in which the first server is ranked ahead of the second server. Additionally, the rankings corresponding to t₂ are generated based on at least one measurement of affective response taken after t₁.

[1008] Following is a description of steps that may be performed in a method for dynamically generating personalized rankings of servers based on measurements of affective response of users. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is configured to dynamically generate personalized rankings of servers based on measurements of affective response of users. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for dynamically generating personalized rankings of servers based on measurements of affective response of users includes at least the following steps:

[1009] In Step 1, receiving, by a system comprising a processor and memory, a profile of a certain first user and a profile of a certain second user. In this embodiment, the profile of the certain first user is different from the profile of the certain second user.

[1010] In Step 2, receiving first measurements of affective response of a first set of users who were logged into the servers for at least five minutes. For each server from among the servers being ranked, the first measurements comprise measurements of affective response of at least five users who were logged into the server, and which were taken between a time t₁−Δ and t₁. Here Δ represents a certain period of time; examples of values Δ may include five minutes, fifteen minutes, one hour, one day, one week, one month, and some other period of time between ten minutes and one year.

[1011] In step 3, receiving a first set of profiles comprising profiles of at least some of the users belonging to the first set of users.

[1012] In step 4, generating a first output indicative of similarities between the profile of the certain first user and profiles belonging to the first set of profiles. Optionally, the first output is generated utilizing the personalization module 130.

[1013] In step 5, computing, based on the first measurements and the first output, a first ranking of the servers. In the first ranking, a first server is ranked above a second server.

[1014] In Step 6, generating a second output indicative of similarities between the profile of the certain second user and profiles belonging to the first set of profiles. The second output is different from the first output. Optionally, the second output is generated utilizing the personalization module 130.

[1015] In Step 7, computing, based on the first measurements and the second output, a second ranking of the servers. In the second ranking, the second server is ranked above the first server.

[1016] In Step 8, receiving second measurements of affective response of a second set of users who were logged into the servers for at least five minutes. For each server from among the servers, the second measurements comprise measurements of affective response of at least five users who were logged into the server, and which were taken between a time t₂−Δ and t₂. Additionally, t₂>t₁.

[1017] In Step 9, receiving a second set of profiles comprising profiles of at least some of the users belonging to the second set of users.

[1018] In Step 10, generating a third output indicative of similarities between the profile of the certain second user and profiles belonging to the second set of profiles. Optionally, the third output is generated utilizing the personalization module 130.

[1019] And in Step 11, computing, based on the measurements and the third output, a third ranking of the servers. In the third ranking, the first server is ranked above the second server. Additionally, the third ranking is computed based on at least one measurement taken after t₁.

[1020] In one embodiment, the method described above may optionally include the following steps:

[1021] In Step 12, generating a fourth output indicative of similarities between the profile of the certain first user and profiles belonging to the second set of profiles. Optionally, the fourth output is different from the third output. Optionally, the fourth output is generated utilizing the personalization module 130.

[1022] And in Step 13, computing, based on the second measurements and the fourth output, a fourth ranking of the servers. In the fourth ranking, the first server is ranked above the second server. Additionally, the fourth ranking is computed based on at least one measurement taken after t₁.

[1023] Various embodiments described herein involve presenting crowd-based results that may involve locations. For example, the results may include scores or rankings of experiences that take place at certain locations and/or scores or rankings of the locations themselves (i.e., the experiences may involve simply being at the locations). In some embodiments, map-displaying module 240 may be utilized to display such crowd-based results on a display. Examples of such embodiments are illustrated at least in FIG. 19, FIG. 82, and FIG. 98.

[1024] Referring to FIG. 19, in one embodiment, a system configured to present a ranking of locations on a map includes at least the collection module 120, the ranking module 220, and the map-displaying module 240. The system may include additional optional modules such as personalization module 130, location verifier module 505 and/or the recommender module 235. It is to be noted that instead of using the ranking module 220 in the system, in one embodiment, dynamic ranking module 250 may be used in order to display on the map dynamic rankings (i.e., rankings that correspond to certain times). Furthermore, in another embodiment, instead of using the ranking module 220 in the system, aftereffect ranking module 300 may be utilized if the rankings refer to aftereffects of having experiences at different locations.

[1025] The collection module 120 is configured, in one embodiment, to receive measurements of affective response of users who were at the locations being ranked. The locations being ranked include first and second locations, and for each of the locations, the measurements include measurements of affective response of at least five users who were at the location. Each location may be any of the locations described in this disclosure, such as locations in the physical world (e.g., business, hotels, restaurants, rooms, etc.) and/or locations that are virtual environments. Optionally, each measurement of affective response of a user who was at a location is based on at least one of the following values: (i) a value acquired by measuring the user, with a sensor coupled to the user, while the user is at the location, and (ii) a value acquired by measuring the user with the sensor up to one hour after the user was at the location. Optionally, each measurement of affective response of a user who was at a location is based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the location.

[1026] The system also includes a ranking module. In one embodiment, the ranking module is the ranking module 220, which is configured to rank the locations based on the measurements received from the collection module 120, such that the first location is ranked higher than the second location. In another embodiment, the ranking module may be the dynamic ranking module 250, which is configured to generate a ranking of the locations based on the measurements received from the collection module 120, which corresponds to a time t. Optionally, in the ranking corresponding to t, the first location is ranked higher than the second location. In yet another embodiment, the ranking module may be the aftereffect ranking module 300, which is configured to generate a ranking of the locations based on aftereffects computed based on the measurements received from the collection module 120. Ranking the locations may involve various approaches to ranking as explained in more detail in section 14—Ranking Experiences.

[1027] The map-displaying module 240 is configured, in one embodiment, to present on a display: a map comprising a description of an environment that comprises the first and second locations, and an annotation overlaid on the map. The annotation is based on results obtained from the ranking module (e.g., a ranking of the locations), and indicates at least one of the following: a first score computed for the first location, a second score computed for the second location, a rank of the first location, and a rank of the second location. Optionally, the annotation comprises at least one of the following: images representing the first and/or second locations, and text identifying the first and/or second locations. Optionally, the scores and/or ranks of the first and/or second locations may be the scores and/or ranks of the first and second experiences (which involve those locations).

[1028] In some embodiments, the first location is presented on the display (e.g., via a corresponding descriptor that is part of the annotation) in the first manner and the second location is presented on the display in the second manner. Optionally, presenting a location in the first manner draws more attention to the location and/or emphasizes it to a greater extent, compared to when the location is presented in the second manner. Optionally, presenting the first location in the first manner and the second location in the second manner implies to a user who views the map that the first location is to be preferred over the second location and/or that the first location was preferred by users over the second location.

[1029] Additional details are given below regarding the first and second manners. Optionally, when presenting a location in the second manner, no descriptor corresponding to the second location is comprised in the annotation that is overlaid on the map.

[1030] In some embodiments, when the annotation is overlaid on the map, it means that the annotation is in the vicinity of the map and/or placed (at least partly on it). Thus, when an annotation is overlaid on a map, a user that looks at the map is also expected to see at least part of the annotation. In one embodiment, the annotation may occlude a certain portion of the map (e.g., by covering certain areas of the map with images). In another embodiment, both the map and the annotation may be visible, at least part of the time, in their entirety. For example, the map and/or annotation may include transparent and/or semi-transparent elements that enable viewing both portion of the map that has a descriptor overlaid on it. In another example, the annotation and/or map may include moving and/or disappearing elements, which do not occlude each other for the whole duration they are visible. In one embodiment, an annotation may include overlaid images, video, and/or holograms that appear in front and/or above the map (e.g., the annotation may include elements presented in augmented or virtual reality).

[1031] It is to be noted that when the annotation is based on a ranking corresponding to a time t the annotation and/or the map may also be considered to correspond to the time t. Thus, in some embodiments, a map and/or annotations presented by the map-displaying module may be considered dynamic maps and/or dynamic annotations, and change over time, e.g., based on the dynamic results generated by modules such as the dynamic scoring module 180 and/or the dynamic ranking module 250.

[1032] In some embodiments, the map-displaying module 240 may be considered part of a user interface and/or it is implemented using a user interface. As such, the map-displaying module 240 may interact with various components that may be characterized as being hardware, firmware, and/or software. In one embodiment, the map-displaying module 240 generates content (e.g., by rendering the map and/or annotation) that is to be presented on a display. In another embodiment, the map-displaying module 240 includes firmware that is utilized for presenting content on a display. In yet another embodiment, the map-displaying module 240 may also include hardware such as a display (in addition to computer executable components as illustrated for example in FIG. 19).

[1033] Different types of displays may be utilized in order to present the map and the overlaid annotation. In one example, the display may include a screen of computing device, such as a monitor of a computer, a screen of a tablet, a screen of a smartphone, a screen of wrist worn wearable device (e.g., a smartwatch), and/or another form of screen that may present information to a user. In another example, the display may be part of headgear worn by the user, such as augmented reality, virtual reality, and/or mixed-reality displays (e.g., glasses or a helmet).

[1034] A map as used herein is a description of an environment that includes locations. In one example, the description may be realistic, e.g., a map drawn to scale of an urban environment. In another example, the description may represent locations in a real environment but not with 100% fidelity, e.g., a map may be cartoonish and/or not drawn to scale. In yet another example, the description may be virtual and represent places that do not exist in the real world (e.g., a map that includes virtual locations).

[1035] There may be various forms of descriptions of an environment that may constitute a “map” as the term is used herein. In one example, a map may be a two-dimensional image representing an environment. In another example, a map may be a three-dimensional image representing the environment. In still another example, a map may include an image (two-dimensional or three-dimensional) that is presented as a layer upon other images in augmented reality. And in yet another example, a map may include an image (two-dimensional or three-dimensional) that is presented in a virtual reality representation. It is to be noted that some maps may include single static images, while other maps may include sequences of images and thus, for example, be presented as video (e.g., two- or three-dimensional video images).

[1036] In some embodiments, the annotation that is overlaid on a map presented to a user comprises one or more descriptors, each presented at a position on the map. Optionally, each descriptor, from among the one or more descriptors, corresponds to a location, and may be indicative of at least one of the following:

[1037] Position of the location—The position indicates the relative place of the location (in a larger environment). For example, the descriptor may indicate where a building is on a map of a city (e.g., using an icon or text indicating that position). In another example, the position may be indicated utilizing an image presented in augmented reality on top of an image.

[1038] Experience at the location—A descriptor may include some indication of a type of experience that a user may have at the location. For example, an icon representing an activity that may be had at a park such as biking, walking, or picnicking may be placed on the map near the location of the park in order to represent the park.

[1039] Name or Image of the location—The name of a location may be explicitly presented on the map in order to represent it and inform a user that the location is there. Similarly, an image of a location (e.g., a photo or animation) may be placed on the map near the position of the location in order for a user to recognize the location.

[1040] Cost associated with the location—The cost of having an experience at the location may also be a descriptor. For example, the average price of staying a night at a hotel may be overlaid near the location of a hotel. In another example, the price of a meal may be overlaid near a location of a restaurant on a map. Optionally, costs that are likely to be more attractive to a user are emphasized (e.g., presented in an eye-catching way).

[1041] Score and/or Rank of the location—A descriptor may include information derived from a ranking of locations and/or information used for computing the ranking of the locations. For example, a score for a location and/or a rank of a location may be conveyed via a descriptor that is placed on a map at the position of the location on the map. The score and/or rank may be expressed as a numerical value (e.g., “rank #1” or “score 7.8”), text (“the best place!”), and/or some other way to convey such information (e.g., animation of Fonzie giving thumbs up near a highly ranked location). Optionally, when a descriptor conveys a score computed for a location, the score may be a score computed as part of the ranking process (e.g., a score used by the score-based rank determining module 225). Alternatively, the score may be a score that was not utilized for the purpose of ranking the locations, e.g., it might have been computed at a previous time by the scoring module 150 or the dynamic scoring module 180.

[1042] The positions of descriptors overlaid on the map are typically near the positions on the map of the locations to which the descriptors correspond. In one embodiment, a descriptor corresponding to the first location is located on the map at a position that is closer to a position on the map that corresponds to the first location than it is to a position on the map that corresponds to the second location. Additionally or alternatively, a descriptor corresponding to the first location is visibly linked to a position on the map that corresponds to the first location. For example, the descriptor may be connected via a line, dots, and/or other forms of visual imagery that can establish with a viewer a connection between a descriptor and a location.

[1043] In one embodiment, the map-displaying module 240 is configured to present on the display a location in a manner belonging to a set comprising at least a first manner and a second manner. Optionally, presenting the location is done by presenting on the map a descriptor that corresponds to the location. Optionally, the descriptor is positioned closer to the location than it is to most of the other locations that appear on the map.

[1044] Generally, as in the case of various recommender modules described in this disclosure (e.g., the recommender module 178 or the recommender module 235), presenting a location in a first manner involves drawing more attention to it than when presenting it in the second manner. There are various ways in which this may be achieved. In some embodiments, presenting a location in the first manner comprises one or more of the following: (i) utilizing a larger descriptor to represent the location, compared to a descriptor utilized when presenting the location in the second manner; (ii) presenting a descriptor representing the location for a longer duration on the display, compared to the duration during which a descriptor representing the location is presented when presenting the location in the second manner; (iii) utilizing a certain visual effect when presenting a descriptor representing the location, which is not utilized when presenting a descriptor that represents the location when presenting the location in the second manner; and (iv) utilizing a descriptor that comprises certain information related to the location, which is not comprised in a descriptor that represents the location when presenting the location in the second manner.

[1045] In one embodiment, whether or not a descriptor related to a certain location appears on a map (is overlaid on the map) may depend on how closely a user is looking at the location on the map. Optionally, determining where the user is looking (and from that the distance and/or angle relative to a location) is done using an eye-tracking system. In one example, the eye-tracking system may be embedded in a device that presents the map to the user, such as a camera embedded near a screen of a smartphone, tablet, or smartwatch on which the map is displayed. In another example, the eye-tracking system may be part of headgear used to present the map, such as an eye-tracking system that is part of an augmented reality headset or a virtual reality headset.

[1046] In one embodiment, when a location is presented in the first manner, its descriptors appear overlaid on the map even if the user is not looking directly at the location. However, when the location is presented in the second manner, the user needs to be looking in the vicinity of the location in order for its descriptors to appear. In one embodiment, a descriptor related to a location may appear on a map if the angle between the line of sight and the position of the location on a map is smaller than a certain angle and/or if the distance between the position of the user's focus and the location is smaller than a certain distance. Optionally, in this embodiment, when the user is presented a location in the first manner, the relevant threshold (e.g., minimal distance between the focus point and the location) is larger than the threshold used when the location is presented in the second manner. Optionally, the threshold is infinity when presenting a location in the first manner, i.e., descriptors related to location are always presented when the map is presented.

[1047] In some embodiments, a map viewed by a user may be an image of a world as seen from the point of view of the user through an augmented reality, a mixed reality, or a virtual reality headset, which is worn by the user. Optionally, the map itself may be an unprocessed view of the world (e.g., as seen by simple passage of light through lessees), a minimally processed view (e.g., as seen through video obtained with a camera), and/or a rendered image of the physical, a virtual world, or a mixture of both. In these embodiments, an annotation on a map may include various descriptors which may be elements presented on the map (e.g., numbers, names, and/or images) that are presented as a layer (e.g., an augmented reality layer) on an image of the world as seen by the user. For example, the map may be an augmented reality map in which a descriptor may dynamically appear in a user's view, next to the physical location it refers to, when the user is in the vicinity of the location and/or is looking in the direction of the location.

[1048] FIG. 35 illustrates steps involved in one embodiment of a method for presenting a ranking of locations on a map. The steps illustrated in FIG. 35 may be used, in some embodiments, by systems modeled according to FIG. 19, such as an embodiment of the system configured to present a ranking of locations on a map, described above. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations of the method.

[1049] In one embodiment, the method presenting a ranking of locations on a map includes at least the following steps:

[1050] In Step 635b, receiving, by a system comprising a processor and memory, the measurements of affective response of users who were at locations. The locations comprise first and second locations, and for each location from among the locations, the measurements comprise measurements of affective response of at least five users who were at the location.

[1051] In Step 635c, ranking the locations based on the measurements, such that the first location is ranked higher than the second location. Optionally, the ranking of the restaurants may involve performing different operations, as discussed in the description of embodiments whose steps are described in FIG. 20.

[1052] And in Step 635d, presenting on a display: a map comprising a description of an environment that comprises the first and second locations, and an annotation, overlaid on the map, indicating at least one of the following: a first score computed for the first location, a second score computed for the second location, a rank of the first location, and a rank of the second location. Optionally, presenting the annotation comprises presenting one or more descriptors on the display. Optionally, each of the one or more descriptors corresponds to a location from among the locations, and is indicative of at least one of the following: a type of activity to have at the location, a rank of the location, a cost associated with the location, and a score computed for the location. Optionally, each of the one or more descriptors comprises at least one of the following: text, an image, a visual effect, a video sequence, an animation, and a hologram.

[1053] In one embodiment, the method optionally includes Step 635a that involves utilizing a sensor coupled to a user who was at a location, from among the locations being ranked, to obtain a measurement of affective response of the user who was at the location. Optionally, the measurement of affective response of the user is based on at least one of the following values: (i) a value acquired by measuring the user with the sensor while the user was at the location, and (ii) a value acquired by measuring the user with the sensor up to one hour after the user had left the location.

[1054] In some embodiments, the personalization module 130 may be utilized in order to personalize rankings of locations for certain users. Different personalized rankings may lead to it that different maps and/or different annotations are presented to different users by the map-displaying device 240. Optionally, personalized rankings of locations may be obtained utilizing the personalization module 130, as explained in the discussion regarding embodiments illustrated in FIG. 19.

[1055] In one embodiment, a system modeled according to FIG. 19, which is configured to present personalized rankings of locations on maps, includes at least the collection module 120, the personalization module 130, a ranking module (e.g., the ranking module 220, the dynamic ranking module 250, or the aftereffect ranking module 300), and the map-displaying module 240. In this embodiment, the collection module 120 is configured to receive measurements of affective response of users who were at the locations. The locations comprise first and second locations, and for each location from among the locations, the measurements comprise measurements of affective response of at least five users who were at the location. The personalization module 130 is configured, in one embodiment, to receive a profile of a certain user and profiles of the users, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the users. Additional information regarding the personalization module 130, profiles of users, and how the output is generated may be found at least in section 11—Personalization. The ranking module is configured, in this embodiment, to rank the locations utilizing the output and the measurements. For at least a certain first user and a certain second user, who have different profiles, the ranking module ranks first and second locations differently, such that for the certain first user the first location is ranked above the second location, and for the certain second user the second location is ranked above the first location. The map-displaying module 240 is configured to present on a display: a map comprising a description of an environment that comprises the first and second locations, and an annotation, overlaid on the map, indicating a ranking of the first and second locations.

[1056] Because the personalized rankings for the certain first user and the certain second user are different, the annotations presented on displays of the certain first user and the certain second user may be different. In one example, on the display of the certain first user, the map-displaying module 240 presents an annotation that indicates that the first location is ranked above the second location, and on the display of the certain second user, the map-displaying module 240 presents an annotation that indicates that the second location is ranked above the first location.

[1057] There may be various ways in which an annotation that is overlaid on a map may indicate that one location is ranked above another location. In one embodiment, the annotation may include one or more values that explicitly show the fact that one location is ranked above another location; such values may be rank numbers and/or scores placed next to positions corresponding to the locations on the map. In one example, placing the number “2” next to the first location and the number “5” next to the second location may directly convey that the first location is ranked ahead of the second location. In another example, the annotation may include scores for the locations, so if the first location has four smiley faces next to it, and the second location has only two smiley faces next to it, that may directly convey that the first location is ranked ahead of the second location. In yet another example, when the first location have a text label next to it that says “The Best!!!” while the second location does not have such a label, that may directly convey the preference of the first location over the second location.

[1058] In another embodiment, the annotation may present one location in the first manner and the other location in the second manner, as discussed above, in order to convey that the first location is ranked ahead of the second location. In one example, having a larger image associated with the first location displayed on the map (representation in the first manner), and a smaller image associated with the second location displayed on the map (representation in the second manner) may convey that the first location is ranked ahead of the second location. In another example, emphasizing a representation of the first location displayed on the map (e.g., by having the representation be bright or employ some other visual effect), when a representation of the second location displayed on the map is not as emphasized, may convey that the first location is ranked ahead of the second location.

[1059] In some embodiments, personalized rankings may lead to it that the same location is presented on displays of different users in different manners. In particular, in one embodiment, the first location is presented on a display of the certain first user in the first manner, and the second location is presented on the display of the certain first user in the second manner. Optionally, for the certain second user, the presentation is the other way around, i.e., the first location is presented on a display of the certain second user in the second manner, and the second location is presented on the display of the certain second user in the first manner.

[1060] Presenting maps and annotations based on personalized rankings of locations, e.g., done utilizing the personalization module 130 as described above, may involve performing the steps illustrated in FIG. 36, which illustrates steps involved in one embodiment of a method for presenting annotations on a map indicative of personalized ranking of locations. The steps illustrated in FIG. 36 may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 19, as described above. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[1061] In one embodiment, the method for presenting annotations on a map indicative of personalized ranking of locations includes at least the following steps:

[1062] In Step 636b, receiving, by a system comprising a processor and memory, measurements of affective response of the users to being at locations. Optionally, each measurement of affective response to being at a location, from among the locations, is a measurement of affective response of a user who was at the location, taken while the user was at the location, or shortly after that time (e.g., up to one hour after that time). Optionally, for each location from among the locations, the measurements comprise measurements of affective response of at least eight users who were at the location. Optionally, for each location from among the locations, the measurements comprise measurements of affective response of at least some other minimal number of users who were at the location, such as measurements of at least five, at least ten, and/or at least fifty different users.

[1063] In Step 636c, receiving profiles of at least some of the users who contributed measurements in Step 636b. Optionally, the profiles of at least some of the users are from among the profiles 504.

[1064] In Step 636d, receiving a profile of a certain first user.

[1065] In Step 636e, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least some of the users. Optionally, the output is generated utilizing the personalization module 130, and/or may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586e in FIG. 21.

[1066] In Step 636f, computing, based on the measurements and the first output, a first ranking of the locations, in which a first location is ranked ahead of a second location.

[1067] In Step 636g, presenting on a first display: a first map comprising a description of a first environment that comprises the first and second locations, and a first annotation overlaid on the first map, which is determined based on the first ranking and indicates that the first location is ranked above the second location. Optionally, presenting the first map and the first annotation is done by the map-displaying module 240 and/or may involve presenting various types of descriptors, as described above. In particular, the first annotation may involve presentation of the first location in the first manner and presentation of the second location in the second manner, which is less eye-catching than the first manner (as described above). Optionally, the first display belongs to a device utilized by the certain first user.

[1068] In Step 636h, receiving a profile of a certain second user, which is different from the profile of the certain first user.

[1069] In Step 636i, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least some of the users. Here, the second output is different from the first output. Optionally, generating the second output is done utilizing the personalization module 130, and/or may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586i in FIG. 21.

[1070] In Step 636j, computing, based on the measurements and the second output, a second ranking of the locations. Optionally, the first and second rankings are different, such in the second ranking, the second location is ranked above the first location.

[1071] And in Step 636k, presenting on a second display: a second map comprising a description of a second environment that comprises the first and second locations, and a second annotation overlaid on the second map, which is determined based on the second ranking and indicates that the second location is ranked above the first location. Optionally, presenting the second map and the second annotation is done by the map-displaying module 240 and/or may involve presenting various types of descriptors, as described above. In particular, the second annotation may involve presentation of the second location in the first manner and presentation of the first location in the second manner. Optionally, the second display belongs to a device utilized by the certain second user.

[1072] In one embodiment of the method described above, the first environment and the second environment are the same environment. Additionally, the first map and the second map may be the same map. Thus, the difference between what is displayed on the first display and what is displayed on the second display may be essentially due to differences between the first and second annotations. In another embodiment, the first map presented on the first display may be different than the second map presented on the second display. In one example, such a scenario may arise if a map is laid out in such a way that a certain higher-ranked (or highest-ranked) location appears in the center of the map. Thus, since the first map may have the first location in its center, and the second may have the second location in its centers, as a result, the first map may be different than the second map (e.g., they may have different boundaries and/or include some different locations).

[1073] In one embodiment, the method optionally includes Step 636a that involves utilizing a sensor coupled to a user who was at a location, from among the locations being ranked, to obtain a measurement of affective response of the user. Optionally, the measurement of affective response of the user is based on at least one of the following values: (i) a value acquired by measuring the user with the sensor while the user was at the location, and (ii) a value acquired by measuring the user with the sensor up to one hour after the user had left the location.

[1074] Following are some example embodiments of systems, methods, and/or computer-readable media, modeled according to embodiments described above, which may be utilized to present a ranking of locations of a specific type on a map and/or present annotations on a map indicative of personalized ranking of specific types of locations. These examples involve maps that present annotations related to rankings of restaurants (e.g., as illustrated in FIG. 37 and FIG. 38), maps that present annotations related to rankings of hotels (e.g., as illustrated in FIG. 39 and FIG. 40), and maps that present annotations related to rankings of locations at which service is provided to customers (e.g., as illustrated in FIG. 41 and FIG. 42).

[1075] FIG. 37 illustrates a system configured to present a ranking of restaurants on a map. The illustrated embodiment includes at least the collection module 120, the ranking module 220, and the map-displaying module 240. The system may optionally include additional modules such as personalization module 130, location verifier module 505 and/or recommender module 235. It is to be noted that instead of using the ranking module 220 in the system, in one embodiment, dynamic ranking module 250 may be used in order to display on the map dynamic rankings (i.e., rankings that correspond to certain times). Furthermore, in another embodiment, instead of using the ranking module 220 in the system, aftereffect ranking module 300 may be utilized, e.g., order to rank restaurants based on how well users feel after eating at the restaurants.

[1076] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response of users belonging to the crowd 500, who in this embodiment, were at restaurants. Optionally, determining when a user was at a restaurant is done utilizing the location verifier 505.

[1077] The restaurants at which the users dined include first and second restaurants. For each of the restaurants, the measurements 501 include measurements of affective response of at least five users who were dined at the restaurant. Optionally, each measurement of affective response of a user who dined at a restaurant, from among the restaurants being ranked, is based on at least one of the following values: (i) a value acquired by measuring the user, with a sensor coupled to the user, while the user was at the restaurant and (ii) a value acquired by measuring the user, with a sensor coupled to the user, at most six hours after the user had left the restaurant.

[1078] The system also includes a ranking module. In one embodiment, the ranking module is the ranking module 220, which is configured to rank the restaurants based on the measurements received from the collection module 120, such that the first restaurant is ranked higher than the second restaurant. In another embodiment, the ranking module may be the dynamic ranking module 250, which is configured to generate a ranking of the restaurants based on the measurements received from the collection module 120, which corresponds to a time t. Optionally, in the ranking corresponding to t, the first restaurant is ranked higher than the second restaurant. In yet another embodiment, the ranking module may be the aftereffect ranking module 300, which is configured to generate a ranking of the restaurants based on aftereffects computed based on the measurements received from the collection module 120. In this embodiment, an aftereffect computed for a restaurant may reflect how well users felt after dining at the restaurant (e.g., were they content afterwards, did they suffer from food poisoning, heartburn, etc.) Ranking the restaurants may involve various approaches to ranking as explained in more detail in section 14—Ranking Experiences and in the discussion regarding FIG. 19.

[1079] The map-displaying module 240 is configured, in one embodiment, to present on a display: map 622, which includes a description of an environment that comprises the first and second restaurants, and an annotation overlaid on the map 622. The annotation is based on results obtained from the ranking module (e.g., the ranking 589 of the restaurants), and indicates at least one of the following: a first score computed for the first restaurant, a second score computed for the second restaurant, a rank of the first restaurant, and a rank of the second restaurant. In one example, the map 622 may be presented on a screen of a device of a user (e.g., a screen of a smartphone, tablet, or smartwatch). In another example, the map 622 may be presented as a map displayed on eyewear such as virtual and/or augmented reality glasses. In still another example, a descriptor that is part of an annotation overlaid on the map (e.g., a number, a name, and/or an image representing a restaurant) may be presented as an augmented reality layer on an image of an environment that includes one or more of the restaurants. For example, the map 622 may be an augmented reality map in which a descriptor may dynamically appear in a user's field of view, next to the physical location it refers to, when the user is in the vicinity of the location and/or is looking in the direction of the location.

[1080] In one embodiment, the annotation overlaid on the map 622 comprises one or more descriptors, each presented at a position on the map; each descriptor, from among the one or more descriptors, corresponds to a restaurant from among the restaurants being ranked, and is indicative of at least one of the following: a type of food to eat at the restaurant, a rank of the restaurant, a cost associated with the restaurant (e.g., average meal price), and a score computed for the restaurant (e.g., representing a level of satisfaction). Optionally, each of the descriptors comprises at least one of the following elements: text, an image, a visual effect, a video sequence, an animation, and a hologram.

[1081] In one embodiment, the map-displaying module 240 is also configured to present on the display a restaurant in a manner belonging to a set comprising at least a first manner and a second manner. Presenting a restaurant in the first manner may involve one or more of the following: (i) utilizing a larger descriptor to represent the restaurant, compared to a descriptor utilized when presenting the restaurant in the second manner; (ii) presenting a descriptor representing the restaurant for a longer duration on the display, compared to the duration during which a descriptor representing the restaurant is presented when presenting the restaurant in the second manner; (iii) utilizing a certain visual effect when presenting a descriptor representing the restaurant, which is not utilized when presenting a descriptor that represents the restaurant when presenting the restaurant in the second manner; and (iv) utilizing a descriptor that comprises certain information related to the restaurant, which is not comprised in a descriptor that represents the restaurant when presenting the restaurant in the second manner. Optionally, the first restaurant is presented in the first manner and the second restaurant is presented in the second manner. Optionally, when presenting a restaurant in the second manner, no descriptor corresponding to the second restaurant is comprised in the annotation.

[1082] FIG. 37 illustrates how restaurants may be presented in first and second manners, as discussed above. In one example, restaurants ranked 1-3 (Sushi Fun House, Burritos and Dreams, and La Petite Entrecôte) may be considered to be presented in a first manner, while the restaurants ranked 4-7 are presented in the second manner. In this example, presenting a restaurant in the first manner involves presenting descriptors that include its name, rank, and possibly an image of a dish served at the restaurant; while presenting a restaurant in the second manner may involve only placing a number representing the restaurant's rank at the location of the restaurant on the map 622. Optionally, when a user looks at the number representing a restaurant or selects it (e.g., by touching the restaurant's location on a screen or pointing in the direction of the location of the restaurant on the map 622), the additional descriptors related to the restaurant, which are associated with the first manner, are presented on the map 622.

[1083] Following is a description of steps that may be performed in a method for presenting a ranking of restaurants on a map. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is illustrated in FIG. 37. The steps below may be considered a special case of an embodiment of a method illustrated FIG. 35, which illustrates steps involved in one embodiment of a method for presenting a ranking of locations on a map (because restaurants are a specific type of location being ranked). In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[1084] In one embodiment, the method presenting a ranking of restaurants on a map includes at least the following steps:

[1085] In Step 1, receiving, by a system comprising a processor and memory, the measurements of affective response of users who dined at restaurants. The restaurants include at least first and second restaurants, and for each restaurant from among the restaurants being ranked, the measurements include measurements of affective response of at least five users who dined at the restaurant.

[1086] In Step 2, ranking the restaurants based on the measurements, such that the first restaurant is ranked higher than the second restaurant. Optionally, the ranking of the restaurants may involve performing different operations, as discussed in the description of embodiments whose steps are described in FIG. 20.

[1087] And in Step 3, presenting on a display: a map comprising a description of an environment that comprises the first and second restaurants, and an annotation, overlaid on the map, indicating at least one of the following: a first score computed for the first restaurant, a second score computed for the second restaurant, a rank of the first restaurant, and a rank of the second restaurant. Optionally, presenting the annotation comprises presenting one or more descriptors on the display. Optionally, presenting the map and/or the annotation is done utilizing the map-displaying module 240.

[1088] In one embodiment, the method described above may optionally include a step that involves utilizing a sensor coupled to a user who dined at a restaurant, from among the restaurants being ranked, to obtain a measurement of affective response of the user who dined at the restaurant. Optionally, the measurement of affective response of the user is based on at least one of the following values: (i) a value acquired by measuring the user with the sensor while the user was at the restaurant, and (ii) a value acquired by measuring the user with the sensor up to six hours after the user had left the restaurant.

[1089] As mentioned in the discussion regarding FIG. 23, since different users may have different backgrounds, tastes, and/or preferences, in some embodiments, the same ranking of restaurants may not be the best suited for all users; various personalization approaches may be used in order to generate rankings of restaurants that are personalized for certain users. Optionally, such personalization of rankings of restaurants may be done utilizing the personalization module 130. FIG. 38 illustrates a system that is configured to present personalized rankings of restaurants on maps. Aspects of this system are similar to the system illustrated in FIG. 37; however, in this system, the personalization module 130 is utilized to generate personalized rankings of restaurants for different users.

[1090] In one embodiment, the system configured to present personalized rankings of restaurants on maps includes at least the collection module 120, the personalization module 130, a ranking module, and the map-displaying module 240. The system may optionally include additional modules such as personalization module 130, location verifier module 505 and/or the recommender module 235. It is to be noted that instead of using the ranking module 220 in the system, in one embodiment, dynamic ranking module 250 may be used in order to display on the map dynamic rankings (i.e., rankings that correspond to certain times). Furthermore, in another embodiment, instead of using the ranking module 220 in the system, aftereffect ranking module 300 may be utilized, e.g., order to rank restaurants based on how well users feel after eating at the restaurants.

[1091] The collection module 120 is configured, in this embodiment, to receive measurements 501 of affective response of users belonging to the crowd 500, who in this embodiment, were at restaurants. The restaurants include first and second restaurants. For each of the restaurants, the measurements 501 include measurements of affective response of at least five users who were dined at the restaurant. Optionally, each measurement of affective response of a user who dined at a restaurant, from among the restaurants being ranked, is based on at least one of the following values: (i) a value acquired by measuring the user, with a sensor coupled to the user, while the user was at the restaurant and (ii) a value acquired by measuring the user, with a sensor coupled to the user, at most six hours after the user had left the restaurant. The personalization module 130 is configured, in this embodiment, to receive a profile of a certain user and profiles of the users, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the users. Optionally, the profiles of the users are selected from among the profiles 504. The ranking module is configured, in this embodiment, to rank the restaurants utilizing the output and the measurements.

[1092] In one embodiment, a profile of a user who dined at a restaurant, such as a profile from among the profiles 504, may include information that describes one or more of the following: the age of the user, the gender of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, information indicative of spending and/or traveling habits of the user, and/or a feature value derived from semantic analysis of a communication of the user. Optionally, the profile of a user may include information regarding culinary and/or dieting habits of the user. For example, the profile may include dietary restrictions, information about sensitivities to certain substances, and/or allergies the user may have. In another example, the profile may include various preference information such as favorite cuisine and/or dishes, preferences regarding consumptions of animal source products and/or organic food, and/or preferences regarding a type and/or location of seating at a restaurant. In yet another example, the profile may include data derived from monitoring food and beverages the user consumed. Such information may come from various sources, such as billing transactions and/or a camera-based system that utilizes image processing to identify food and drinks the user consumes from images taken by a camera mounted on the user and/or in the vicinity of the user.

[1093] In one embodiment, for at least a certain first user and a certain second user, who have different profiles, the ranking module ranks the first and second restaurants differently, such that for the certain first user the first restaurant is ranked above the second restaurant, and for the certain second user the second restaurant is ranked above the first restaurant. Because the personalized rankings for the certain first user and the certain second user are different, in this embodiment, maps and/or annotations presented, using the map-displaying module 240, on displays of the certain first user and the certain second user, may be different. In one example, on the display of the certain first user, the map-displaying module 240 presents an annotation that indicates that the first restaurant is ranked above the second restaurant, and on the display of the certain second user, the map-displaying module 240 presents an annotation that indicates that the second restaurant is ranked above the first restaurant.

[1094] As described above, presenting a location on a display, such as a restaurant, may be done, in some embodiments, in the first manner or the second manner; where in the first manner involves utilizing a more eye-catching descriptor than the second manner (e.g., using larger image, displaying descriptors for a longer duration, using visual effects, and/or presenting more information). In some embodiments, personalized rankings may lead to it that the same restaurant is presented on displays of different users in different manners. In particular, in one embodiment, the first restaurant is presented on a display of the certain first user in the first manner, and the second restaurant is presented on the display of the certain first user in the second manner. Optionally, for the certain second user, the presentation is the other way around, i.e., the first restaurant is presented on a display of the certain second user in the second manner, and the second restaurant is presented on the display of the certain second user in the first manner.

[1095] Referring to FIG. 38, this figure illustrates a system configured to present personalized rankings of restaurants on maps. The figure illustrates how the two different users, the first user 592a and the second user 592b, are presented with different rankings of restaurants on maps 623a and 623b, respectively. The different rankings are generated because the personalization module 130 receives a different profile for each user (the profile 591a for the user 592a and the profile 591b for the user 592b). For each of the different profiles, the personalization module 130 generates a different output, which is utilized by the ranking module (ranking module 220 in the illustration) in order to generate a different ranking. For example, as the maps 623a and 623b illustrate that a restaurant ranked first in the ranking of the user 592a (a wine bar) is ranked second in the ranking of the user 592b, while the restaurant ranked first in the ranking of user 592b (a sushi restaurant) is ranked fifth in the ranking of user 592a.

[1096] Presenting maps and annotations based on personalized rankings of restaurants may involve execution of certain steps. Following is a more detailed discussion of steps that may be involved in a method for presenting annotations on a map indicative of personalized ranking of restaurants. These steps may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 38 and/or steps of a method modeled according to FIG. 36. The latter figure illustrates embodiments that involve presenting annotations on a map indicative of personalized ranking of locations. Since restaurants are a specific type of location, the teachings of those embodiments are relevant to the steps of the method described below. In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[1097] In one embodiment, the method for presenting annotations on a map indicative of personalized ranking of restaurants includes at least the following steps:

[1098] In Step 1, receiving, by a system comprising a processor and memory, measurements of affective response of the users who dined at the restaurants being ranked. For each restaurant from among the restaurants being ranked, the measurements comprise measurements of affective response of at least eight users who dined at the restaurant. Optionally, for each restaurant from among the restaurants being ranked, the measurements comprise measurements of affective response of at least some other minimal number of users who dined at the restaurant, such as measurements of at least five, at least ten, and/or at least fifty different users.

[1099] In Step 2, receiving profiles of at least some of the users who contributed measurements in Step 1. Optionally, the received profiles are some of the profiles 504.

[1100] In Step 3, receiving a profile of a certain first user.

[1101] In Step 4, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least some of the users. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586e in FIG. 21.

[1102] In Step 5, computing, based on the measurements and the first output, a first ranking of the restaurants, in which a first restaurant is ranked ahead of a second restaurant.

[1103] In Step 6, presenting on a first display: a first map comprising a description of a first environment that comprises the first and second restaurants, and a first annotation overlaid on the first map, which is determined based on the first ranking and indicates that the first restaurant is ranked above the second restaurant. Optionally, presenting the first map and the first annotation is done by the map-displaying module 240 and/or may involve presenting one or more descriptors. In particular, the first annotation may involve presentation of the first restaurant in the first manner and presentation of the second restaurant in the second manner, which is less eye-catching than the first manner (as described above). Optionally, the first display belongs to a device utilized by the certain first user.

[1104] In Step 7, receiving a profile of a certain second user, which is different from the profile of the certain first user.

[1105] In Step 8, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least some of the users. Here, the second output is different from the first output. Optionally, generating the second output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586i in FIG. 21.

[1106] In Step 9, computing, based on the measurements and the second output, a second ranking of the restaurants. Optionally, the first and second rankings are different, such that in the second ranking, the second restaurant is ranked above the first restaurant.

[1107] And in Step 10, presenting on a second display: a second map comprising a description of a second environment that comprises the first and second restaurants, and a second annotation overlaid on the second map, which is determined based on the second ranking and indicates that the second restaurant is ranked above the first restaurant. Optionally, presenting the second map and the second annotation is done by the map-displaying module 240 and/or may involve presenting one or more descriptors. In particular, the second annotation may involve presentation of the second restaurant in the first manner and presentation of the first restaurant in the second manner. Optionally, the second display belongs to a device utilized by the certain second user.

[1108] In one embodiment of the method described above, the first environment and the second environment may be the same environment. Additionally, the first map and the second map may be the same map. Thus, the difference between what is displayed on the first display and what is displayed on the second display may be essentially due to differences between the first and second annotations. In another embodiment, the first map presented on the first display may be different than the second map presented on the second display, as explained in the discussion regarding FIG. 36.

[1109] In one embodiment, the method described above may optionally include a step that involves utilizing a sensor coupled to a user who dined at a restaurant, from among the restaurants being ranked, for obtaining a measurement of affective response of the user. Optionally, the measurement of affective response of the user is based on at least one of the following values: (i) a value acquired by measuring the user with the sensor while the user dined at the restaurant, and (ii) a value acquired by measuring the user with the sensor up to six hour after the user had left the restaurant. Optionally, obtaining a measurement of affective response of a user who dined at a restaurant is done by measuring the user with the sensor during at least three different non-overlapping periods while the user was at the restaurant.

[1110] FIG. 39 illustrates a system configured to present a ranking of hotels on a map. The illustrated embodiment includes at least the collection module 120, the ranking module 220, and the map-displaying module 240. The system may optionally include additional modules such as personalization module 130, location verifier module 505 and/or recommender module 235. It is to be noted that instead of using the ranking module 220 in the system, in one embodiment, dynamic ranking module 250 may be used in order to display on the map dynamic rankings (i.e., rankings that correspond to certain times). Furthermore, in another embodiment, instead of using the ranking module 220 in the system, aftereffect ranking module 300 may be utilized, e.g., in order to rank hotels based on how invigorating was the stay at each of the hotels.

[1111] The collection module 120 is configured to receive measurements 501 of affective response of users belonging to the crowd 500, who in this embodiment, stayed at hotels. Optionally, determining when a user was at a hotel is done utilizing the location verifier 505. Optionally, each user who stayed at a hotel was in the hotel for a period of at least six hours. Optionally, each user who stayed at a hotel was in the hotel for a longer period of time such as at least twelve hours, at least one day, at least one week, or at least one month.

[1112] The hotels at which the users stayed include first and second hotels. For each of the hotels, the measurements 501 include measurements of affective response of at least five users who stayed at the hotel. Optionally, a measurement of affective response of a user who stayed at a hotel is obtained by measuring the user with a sensor coupled to the user while the user is at the hotel. Optionally, the measurement is based on values acquired by measuring the user with the sensor during at least three different non-overlapping periods while the user was at the hotel.

[1113] The system also includes a ranking module. In one embodiment, the ranking module is the ranking module 220, which is configured to rank the hotels based on the measurements received from the collection module 120, such that the first hotel is ranked higher than the second hotel. In another embodiment, the ranking module may be the dynamic ranking module 250, which is configured to generate a ranking of the hotels based on the measurements received from the collection module 120, which corresponds to a time t. Optionally, in the ranking corresponding to t, the first hotel is ranked higher than the second hotel. In yet another embodiment, the ranking module may be the aftereffect ranking module 300, which is configured to generate a ranking of the hotels based on aftereffects computed based on the measurements received from the collection module 120. In this embodiment, an aftereffect computed for a hotel may reflect how well users felt after staying at the hotel (e.g., to what extent they were relaxed, and/or invigorated in the days after staying at the hotel). Ranking the hotels may involve various approaches to ranking as explained in more detail in section 14—Ranking Experiences and in the discussion regarding FIG. 19.

[1114] The map-displaying module 240 is configured, in one embodiment, to present on a display: map 625, which includes a description of an environment that comprises the first and second hotels, and an annotation overlaid on the map 625. The annotation is based on results obtained from the ranking module (e.g., the ranking 595 of the hotels), and indicates at least one of the following: a first score computed for the first hotel, a second score computed for the second hotel, a rank of the first hotel, and a rank of the second hotel. In one example, the map 625 may be presented on a screen of a device of a user (e.g., a screen of a smartphone, tablet, or smartwatch). In another example, the map 625 may be presented as a map displayed on eyewear such as virtual and/or augmented reality glasses. In still another example, a descriptor that is part of an annotation overlaid on the map (e.g., a number, a name, and/or an image representing a hotel) may be presented as an augmented reality layer on an image of an environment that includes one or more of the hotels. For example, the map 625 may be an augmented reality map in which a descriptor may dynamically appear in a user's field of view, next to the physical location it refers to, when the user is in the vicinity of the location and/or is looking in the direction of the location.

[1115] In one embodiment, the annotation overlaid on the map 625 comprises one or more descriptors, each presented at a position on the map; each descriptor, from among the one or more descriptors, corresponds to a hotel from among the hotels being ranked, and is indicative of at least one of the following: an amenity at the hotel, a rank of the hotel, a cost associated with the hotel (e.g., average rate of a day), and a score computed for the hotel (e.g., representing a level of satisfaction of guests who stayed at the hotel). Optionally, each of the descriptors comprises at least one of the following elements: text, an image, a visual effect, a video sequence, an animation, and a hologram.

[1116] In one embodiment, the map-displaying module 240 is also configured to present on the display a hotel in a manner belonging to a set comprising at least a first manner and a second manner. Presenting a hotel in the first manner may involve one or more of the following: (i) utilizing a larger descriptor to represent the hotel, compared to a descriptor utilized when presenting the hotel in the second manner; (ii) presenting a descriptor representing the hotel for a longer duration on the display, compared to the duration during which a descriptor representing the hotel is presented when presenting the hotel in the second manner; (iii) utilizing a certain visual effect when presenting a descriptor representing the hotel, which is not utilized when presenting a descriptor that represents the hotel when presenting the hotel in the second manner; and (iv) utilizing a descriptor that comprises certain information related to the hotel, which is not comprised in a descriptor that represents the hotel when presenting the hotel in the second manner. Optionally, the first hotel is presented in the first manner and the second hotel is presented in the second manner. Optionally, when presenting a hotel in the second manner, no descriptor corresponding to the second hotel is comprised in the annotation.

[1117] FIG. 39 illustrates how hotels may be presented in first and second manners, as discussed above. In one example, hotels ranked 1-3 may be considered to be presented in a first manner, while the hotels ranked 4-7 are presented in the second manner. In this example, presenting a hotel in the first manner involves presenting descriptors that include an image of the hotel; while presenting a hotel in the second manner may involve only placing a number representing the hotel's rank at the location of the hotel on the map 625. In another example, the hotel ranked first may be considered presented in the first manner, while the other hotels are not presented in the first manner, since the name of the hotel (“The Grand”) is provided on the map for the first hotel, but not for the others. Optionally, when a user looks at the number representing a hotel or selects it (e.g., by touching the hotel's location on a screen or pointing in the direction of the location of the hotel on the map 625), additional descriptors related to the hotel, which are associated with the first manner, are presented on the map 625.

[1118] Following is a description of steps that may be performed in a method for presenting a ranking of hotels on a map. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is illustrated in FIG. 39. The steps below may be considered a special case of an embodiment of a method illustrated FIG. 35, which illustrates steps involved in one embodiment of a method for presenting a ranking of locations on a map (because hotels are a specific type of location being ranked). In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[1119] In one embodiment, the method presenting a ranking of hotels on a map includes at least the following steps:

[1120] In Step 1, receiving, by a system comprising a processor and memory, measurements of affective response of users who stayed at the hotels. The hotels include at least first and second hotels, and for each hotel from among the hotels being ranked, the measurements include measurements of affective response of at least five users who stayed at the hotel for at least four hours. Optionally, each of the users staying at a hotel stayed for a longer period, such as at least twelve hours, at least one thy, at least one week, or at least one month.

[1121] In Step 2, ranking the hotels based on the measurements, such that, the first hotel is ranked higher than the second hotel. Optionally, the ranking of the hotels may involve performing different operations, as discussed in the description of embodiments whose steps are described in FIG. 20.

[1122] And in Step 3, presenting on a display: a map comprising a description of an environment that comprises the first and second hotels, and an annotation, overlaid on the map, indicating at least one of the following: a first score computed for the first hotel, a second score computed for the second hotel, a rank of the first hotel, and a rank of the second hotel. Optionally, presenting the annotation comprises presenting one or more descriptors on the display. Optionally, presenting the map and/or the annotation is done utilizing the map-displaying module 240.

[1123] In one embodiment, the method described above may optionally include a step that involves utilizing a sensor coupled to a user who stayed at a hotel, from among the hotels being ranked, to obtain a measurement of affective response of the user. Optionally, the measurement may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the hotel.

[1124] As mentioned in the discussion regarding FIG. 25, since different users may have different backgrounds, tastes, and/or preferences, in some embodiments, the same ranking of hotels may not be the best suited for all users; various personalization approaches may be used in order to generate rankings of hotels that are personalized for certain users. Optionally, such personalization of rankings of hotels may be done utilizing the personalization module 130. FIG. 40 illustrates a system that is configured to present personalized rankings of hotels on maps. Aspects of this system are similar to the system illustrated in FIG. 39; however, in this system, the personalization module 130 is utilized to generate personalized rankings of hotels for different users.

[1125] In one embodiment, the system configured to present personalized rankings of hotels on maps includes at least the collection module 120, the personalization module 130, a ranking module, and the map-displaying module 240. The system may optionally include additional modules such as personalization module 130, location verifier module 505 and/or the recommender module 235. It is to be noted that instead of using the ranking module 220 in the system, in one embodiment, dynamic ranking module 250 may be used in order to display on the map dynamic rankings (i.e., rankings that correspond to certain times). Furthermore, in another embodiment, instead of using the ranking module 220 in the system, aftereffect ranking module 300 may be utilized, e.g., in order to rank hotels based on how invigorating was the stay at each of the hotels.

[1126] The collection module 120 is configured to receive measurements 501 of affective response of users belonging to the crowd 500, who in this embodiment, stayed at hotels. Optionally, determining when a user was at a hotel is done utilizing the location verifier 505. Optionally, each user who stayed at a hotel was in the hotel for a period of at least six hours. Optionally, each user who stayed at a hotel was in the hotel for a longer period of time such as at least twelve hours, at least one thy, at least one week, or at least one month.

[1127] The personalization module 130 is configured, in this embodiment, to receive a profile of a certain user and profiles of the users who stayed at the hotels, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the users. Optionally, the profiles of the users are selected from among the profiles 504. The ranking module is configured, in this embodiment, to rank the hotels utilizing the output and the measurements.

[1128] In one embodiment, a profile of a user who stayed at a hotel, such as a profile from among the profiles 504, may include information that describes one or more of the following: the age of the user, the gender of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, information indicative of spending and/or traveling habits of the user, and/or a feature value derived from semantic analysis of a communication of the user. Optionally, the profile of a user may include information regarding travel habits of the user. For example, the profile may include itineraries of the user indicating to travel destinations, such as countries and/or cities the user visited. Optionally, the profile may include information regarding the type of trips the user took (e.g., business or leisure), what hotels the user stayed at, the cost, and/or the duration of stay.

[1129] In one embodiment, for at least a certain first user and a certain second user, who have different profiles, the ranking module ranks the first and second hotels differently, such that for the certain first user the first hotel is ranked above the second hotel, and for the certain second user the second hotel is ranked above the first hotel. Because the personalized rankings for the certain first user and the certain second user are different, in this embodiment, maps and/or annotations presented, using the map-displaying module 240, on displays of the certain first user and the certain second user, may be different. In one example, on the display of the certain first user, the map-displaying module 240 presents an annotation that indicates that the first hotel is ranked above the second hotel, and on the display of the certain second user, the map-displaying module 240 presents an annotation that indicates that the second hotel is ranked above the first hotel.

[1130] As described above, presenting a location on a display, such as a hotel, may be done, in some embodiments, in the first manner or the second manner. Optionally, the first manner involves utilizing a more eye-catching descriptor than the second manner (e.g., using larger image, displaying descriptors for a longer duration, using visual effects, and/or presenting more information). In some embodiments, personalized rankings may lead to it that the same hotel is presented on displays of different users in different manners. In particular, in one embodiment, the first hotel is presented on a display of the certain first user in the first manner, and the second hotel is presented on the display of the certain first user in the second manner. Optionally, for the certain second user, the presentation is the other way around, i.e., the first hotel is presented on a display of the certain second user in the second manner, and the second hotel is presented on the display of the certain second user in the first manner.

[1131] Referring to FIG. 40, this figure illustrates a system configured to present personalized rankings of hotels on maps. The figure illustrates how the two different users, the first user 598a and the second user 598b, are presented with different rankings of hotels on maps 627a and 627b, respectively. The different rankings are generated because the personalization module 130 receives a different profile for each of the users (the profile 597a for the user 598a and the profile 597b for the user 598b). For each of the different profiles, the personalization module 130 generates a different output, which is utilized by the ranking module (ranking module 220 in the illustration) in order to generate a different ranking of the hotels. For example, as the maps 627a and 627b illustrate that the hotel ranked first in the ranking of the user 598a is ranked fourth in the ranking of the user 598b. Additionally, the illustration shows that the hotel ranked first in the ranking of user 598b is not in the top five hotels presented to the user 598a on the map 627a.

[1132] Presenting maps and annotations based on personalized rankings of hotels may involve execution of certain steps. Following is a more detailed discussion of steps that may be involved in a method for presenting annotations on a map indicative of personalized ranking of hotels. These steps may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 40 and/or steps of a method modeled according to FIG. 36. The latter figure illustrates embodiments that involve presenting annotations on a map indicative of personalized ranking of locations. Since hotels are a specific type of location, the teachings of those embodiments are relevant to the steps of the method described below. In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[1133] In one embodiment, the method for presenting annotations on a map indicative of personalized ranking of hotels includes at least the following steps:

[1134] In Step 1, receiving, by a system comprising a processor and memory, the measurements of affective response of the users. For each hotel from among the hotels being ranked, the measurements include measurements of affective response of at least eight users who stayed at the hotel for at least four hours. Optionally, each of the users staying at a hotel stayed for a longer period, such as at least twelve hours, at least one day, at least one week, or at least one month. Optionally, for each hotel from among the hotels being ranked, the measurements comprise measurements of affective response of at least some other minimal number of users who stayed at the hotel, such as measurements of at least five, at least ten, and/or at least fifty different users.

[1135] In Step 2, receiving profiles of at least some of the users who contributed measurements in Step 1. Optionally, the received profiles are some of the profiles 504.

[1136] In Step 3, receiving a profile of a certain first user.

[1137] In Step 4, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least some of the users. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586e in FIG. 21.

[1138] In Step 5, computing, based on the measurements and the first output, a first ranking of the hotels, in which a first hotel is ranked ahead of a second hotel.

[1139] In Step 6, presenting on a first display: a first map comprising a description of a first environment that comprises the first and second hotels, and a first annotation overlaid on the first map, which is determined based on the first ranking and indicates that the first hotel is ranked above the second hotel. Optionally, presenting the first map and the first annotation is done by the map-displaying module 240 and/or may involve presenting one or more descriptors. In particular, the first annotation may involve presentation of the first hotel in the first manner and presentation of the second hotel in the second manner, which is less eye-catching than the first manner (as described above). Optionally, the first display belongs to a device utilized by the certain first user.

[1140] In Step 7, receiving a profile of a certain second user, which is different from the profile of the certain first user.

[1141] In Step 8, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least some of the users. Here, the second output is different from the first output. Optionally, generating the second output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586i in FIG. 21.

[1142] In Step 9, computing, based on the measurements and the second output, a second ranking of the hotels, in which the second hotel is ranked ahead of the first hotel.

[1143] And in Step 10, presenting on a second display: a second map comprising a description of a second environment that comprises the first and second hotels, and a second annotation overlaid on the second map, which is determined based on the second ranking and indicates that the second hotel is ranked above the first hotel. Optionally, presenting the second map and the second annotation is done by the map-displaying module 240 and/or may involve presenting one or more descriptors. In particular, the second annotation may involve presentation of the second hotel in the first manner and presentation of the first hotel in the second manner. Optionally, the second display belongs to a device utilized by the certain second user.

[1144] In one embodiment of the method described above, the first environment and the second environment may be the same environment. Additionally, the first map and the second map may be the same map. Thus, the difference between what is displayed on the first display and what is displayed on the second display may be essentially due to differences between the first and second annotations. In another embodiment, the first map presented on the first display may be different than the second map presented on the second display, as explained in the discussion regarding FIG. 36.

[1145] In one embodiment, the method described above may optionally include a step that involves utilizing a sensor coupled to a user who stayed at a hotel, from among the hotels being ranked, to obtain a measurement of affective response of the user. Optionally, the measurement may be based on values acquired by measuring the user with a sensor coupled to the user during at least three different non-overlapping periods while the user was at the hotel.

[1146] FIG. 41 illustrates a system configured to present a ranking of locations at which service is provided to customers on a map. Some examples or locations at which service is provided to customers include: (i) locations at which recreational services and/or entertainment services are provided to customers (e.g., amusement parks, water parks, casinos, restaurants, resorts, and bars); (ii) locations at which health treatments and/or healthcare services are provided to customers (e.g., clinics, hospitals, and elderly care facilities); (iii) various businesses (or areas in businesses), such as booths, shopping malls, shopping centers, markets, supermarkets, beauty salons, spas, laundromats, banks, automobile dealerships, and a courier service offices; and (iv) hotels and/or other facilities that provide sleeping accommodations to guests.

[1147] The illustrated embodiment includes at least the collection module 120, the ranking module 220, and the map-displaying module 240. The system may optionally include additional modules such as personalization module 130, location verifier module 505 and/or recommender module 235. It is to be noted that instead of using the ranking module 220 in the system, in one embodiment, dynamic ranking module 250 may be used in order to display on the map dynamic rankings (i.e., rankings that correspond to certain times).

[1148] The collection module 120 is configured to receive measurements 501 of affective response of customers belonging to the crowd 500, who in this embodiment, were at locations at which service was provided to them. Optionally, determining when a customer was at a location at which service was provided to the customer is done utilizing the location verifier 505. Optionally, each measurement of affective response of a customer who was at a location is obtained by measuring the customer with a sensor coupled to the customer in order to obtain a value indicative of a physiological signal and/or a behavioral cue of the customer. Optionally, each measurement of affective response is based on values acquired by measuring the customer with the sensor during at least three different non-overlapping periods while the customer was at the location. Optionally, each customer was at the location for at least a certain time, such as at least five minutes, at least thirty minutes, at least one hour, at least four hours, at least one day, at least one week, or some other period of time that is greater than one minute.

[1149] The system also includes a ranking module that is configured to rank the locations at which service is provided based on measurements of affective response of customers who were at the locations, which were received from the collection module 120. Optionally, for each location from among the locations being ranked, the measurements received by the ranking module include measurements of affective response of at least five customers who were provided service at the location. Optionally, for each location, the measurements received by the ranking module may include measurements of a different minimal number of customers, such as measurements of at least eight, at least ten, or at least one hundred customers.

[1150] In one embodiment, the ranking module is the ranking module 220, which is configured to rank the locations at which service is provided based on the measurements received from the collection module 120, such that a first location is ranked higher than a second location. In another embodiment, the ranking module may be the dynamic ranking module 250, which is configured to generate a ranking of the locations based on the measurements received from the collection module 120, which corresponds to a time t. Optionally, in the ranking corresponding to t, the first location is ranked higher than the second location. Ranking the locations may involve various approaches to ranking as explained in more detail in section 14—Ranking Experiences and in the discussion regarding FIG. 19. Optionally, when a first location has a higher rank than a second location, it is indicative that, on average, customers who were at the first location were more satisfied than customers who were at the second location.

[1151] The map-displaying module 240 is configured, in one embodiment, to present on a display: map 629, which includes a description of an environment that comprises the first and second locations at which service is provided, and an annotation overlaid on the map 629. The annotation is based on results obtained from the ranking of the locations computed by the ranking module, and indicates at least one of the following: a first score computed for the first location, a second score computed for the second location, a rank of the first location, and a rank of the second location. In one example, the map 629 may be presented on a screen of a device of a user (e.g., a screen of a smartphone, tablet, or smartwatch). In another example, the map 629 may be presented as a map displayed on eyewear such as virtual and/or augmented reality glasses. In still another example, a descriptor that is part of an annotation overlaid on the map (e.g., a number, a name, and/or an image representing a location) may be presented as an augmented reality layer on an image of an environment that includes one or more of the locations. For example, the map 629 may be an augmented reality map in which a descriptor may dynamically appear in a user's field of view, next to the physical location it refers to, when the user is in the vicinity of the location and/or is looking in the direction of the location.

[1152] In one embodiment, the annotation overlaid on the map 629 comprises one or more descriptors, each presented at a position on the map; each descriptor, from among the one or more descriptors, corresponds to a location from among the locations being ranked, and is indicative of at least one of the following: a service provided at the location, a rank of the location, a cost associated with the location, and a score computed for the location (e.g., the score computed for the location may be a level of satisfaction of customers who received service at the location). Optionally, each of the descriptors comprises at least one of the following elements: text, an image, a visual effect, a video sequence, an animation, and a hologram.

[1153] In one embodiment, the map-displaying module 240 is also configured to present on the display a location, from among the location being ranked, in a manner belonging to a set comprising at least a first manner and a second manner. Presenting the location in the first manner may involve one or more of the following: (i) utilizing a larger descriptor to represent the location, compared to a descriptor utilized when presenting the location in the second manner; (ii) presenting a descriptor representing the location for a longer duration on the display, compared to the duration during which a descriptor representing the location is presented when presenting the location in the second manner; (iii) utilizing a certain visual effect when presenting a descriptor representing the location, which is not utilized when presenting a descriptor that represents the location when presenting the location in the second manner; and (iv) utilizing a descriptor that comprises certain information related to the location, which is not comprised in a descriptor that represents the location when presenting the location in the second manner. Optionally, the first location is presented in the first manner and the second location is presented in the second manner. Optionally, when presenting a location in the second manner, no descriptor corresponding to the second location is comprised in the annotation.

[1154] FIG. 41 illustrates how locations at which service is provided may be presented in first and second manners, as discussed above. In the illustration, the locations correspond to different stores at the No Tengo Dinero Mall. In one example, stores ranked 1-3 may be considered to be presented in a first manner, while the stores ranked 4-7 are presented in the second manner. In this example, presenting a location in the first manner involves presenting descriptors that include an image of a product that may be purchased at the location; while presenting a location in the second manner may involve only placing a number representing the location's rank at a position on the map 629 corresponding to the location. In another example, the location ranked first may be considered presented in the first manner, while the other locations are not presented in the first manner, since the name of the store (“Gary's Shoes”) is provided on the map for the first location, but not for the others. Optionally, when a user looks at the number representing a location or selects it (e.g., by touching the vicinity of the representation of the location on a screen or pointing in the direction of the location on the map 629), additional descriptors related to the location, which are associated with the first manner, are presented on the map 629.

[1155] In embodiments described in this disclosure, references to “locations at which service is provided” may be directed to different types of locations. Following are some examples of different types of locations that the “locations at which service is provided” may be.

[1156] In one embodiment, at least some of the locations at which service is provided (including the first and second locations mentioned above) are businesses or areas in a business. In one example, at least some of the locations are a place of business that is one or more of the following: a store, a booth, a shopping mall, a shopping center, a market, a supermarket, a beauty salon, a spa, a laundromat, a bank, an automobile dealership, and a courier service offices. In another example, at least some of the locations are within some other business, such as resort, a water park, a casino, a restaurant, or a bar. FIG. 29 illustrates an example where the locations being ranked correspond to regions of different rides at an amusement park, and the ranking 608 describes an order of the rides based on how satisfying the customers found them.

[1157] In another embodiment, at least some of the locations at which service is provided (including the first and second locations mentioned above) offer sleeping accommodations for the customers. For example, these locations may include rooms for rent, such as rooms of hotels, resorts, or apartments for rent.

[1158] In yet another embodiment, the locations at which service is provided (including the first and second locations mentioned above) are businesses, or areas in a businesses, in which health-related services are provided. In one example, at least some of the locations are in a health-care facility such as a clinic, a hospital wing, or an elderly care facility.

[1159] It is to be noted that a location at which service is provided may be part of a larger location at which service is provided. In one example, the larger location may be a business and the location may be a certain region in the business (e.g., a certain department in a store, a certain wing of a hotel, an area involving a certain attraction in an amusement park, or a certain dining room of a restaurant). Additionally, depending on the embodiment, locations at which service is provided may occupy various spaces (e.g., represented as areas of floor space). Areas occupied by locations may vary from a few square feet (e.g., a stall or a booth), to hundreds, thousands and even tens of thousands of square feet (stores and supermarkets), to even acres or more (e.g., malls or resorts). In one example, a location at which a health care is provided includes an area of at least 400 square feet of floor space. In another example, a location at which entertainment is provided (e.g., an amusement park, a water park, a casino, a restaurant, or a bar), includes an area of at least 800 square feet.

[1160] Following is a description of steps that may be performed in a method for presenting a ranking of locations at which service is provided on a map. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above, which is illustrated in FIG. 41. The steps below may be considered a special case of an embodiment of a method illustrated FIG. 35, which illustrates steps involved in one embodiment of a method for presenting a ranking of locations on a map. In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[1161] In one embodiment, the method presenting a ranking of locations at which service is provided on a map includes at least the following steps:

[1162] In Step 1, receiving, by a system comprising a processor and memory, measurements of affective response of customers. The locations include at least first and second locations, and for each location from among the locations being ranked, the measurements include measurements of affective response of at least five customers who were at the location and received service there. Optionally, each measurement of affective response of a customer who was at a location is obtained by measuring the customer with a sensor coupled to the customer in order to obtain a value indicative of a physiological signal and/or a behavioral cue of the customer. Optionally, each measurement of affective response is based on values acquired by measuring the customer with the sensor during at least three different non-overlapping periods while the customer was at the location. Optionally, each customer was at the location for at least a certain time, such as at least five minutes, at least thirty minutes, at least one hour, at least four hours, at least one thy, at least one week, or some other period of time that is greater than one minute.

[1163] In Step 2, ranking the locations based on the measurements, such that, the location is ranked higher than the second location. Optionally, the ranking of the locations may involve performing different operations, as discussed in the description of embodiments whose steps are described in FIG. 20.

[1164] And in Step 3, presenting on a display: a map comprising a description of an environment that comprises the first and second locations, and an annotation, overlaid on the map, indicating at least one of the following: a first score computed for the first location, a second score computed for the second location, a rank of the first location, and a rank of the second location. Optionally, presenting the annotation comprises presenting one or more descriptors on the display. Optionally, presenting the map and/or the annotation is done utilizing the map-displaying module 240.

[1165] As mentioned in the discussion regarding FIG. 30, since different users may have different backgrounds, tastes, and/or preferences, in some embodiments, the same ranking of locations at which service is provided may not be the best suited for all users; various personalization approaches may be used in order to generate rankings of the locations that are personalized for certain users. Optionally, such personalization of rankings of the locations may be done utilizing the personalization module 130. FIG. 42 illustrates a system that is configured to present on maps personalized rankings of locations at which service is provided. Aspects of this system are similar to the system illustrated in FIG. 41; however, in this system, the personalization module 130 is utilized to generate personalized rankings of the locations for different users.

[1166] In one embodiment, the system configured to present on maps personalized rankings of locations at which service is provided includes at least the collection module 120, the personalization module 130, a ranking module, and the map-displaying module 240. The system may optionally include additional modules such as personalization module 130, location verifier module 505 and/or the recommender module 235. It is to be noted that instead of using the ranking module 220 in the system, in one embodiment, dynamic ranking module 250 may be used in order to display on the map dynamic rankings (i.e., rankings that correspond to certain times). Furthermore, in another embodiment, instead of using the ranking module 220 in the system, aftereffect ranking module 300 may be utilized, e.g., in order to rank locations based on how invigorating and/or relaxed customers were after receiving service at the locations.

[1167] The collection module 120 is configured to receive measurements 501 of affective response of customers belonging to the crowd 500, who in this embodiment, were at the locations and received service over there. Optionally, determining when a customer was at a location at which service is provided is done utilizing the location verifier 505.

[1168] The personalization module 130 is configured, in this embodiment, to receive a profile of a certain user and profiles of the customers, and to generate an output indicative of similarities between the profile of the certain user and the profiles of the customers. Optionally, the profiles of the customers are selected from among the profiles 504. The ranking module is configured, in this embodiment, to rank the locations at which service is provided utilizing the output and the measurements.

[1169] In one embodiment, a profile of a user, such as a profile from among the profiles 504, may include information that describes one or more of the following: the age of the user, the gender of the user, a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, information indicative of spending and/or traveling habits of the user, and/or a feature value derived from semantic analysis of a communication of the user. It is to be noted that a profile of a customer may be considered to have the same characteristics as profiles of users, and in particular, the profiles 504 may include the profiles of the customers whose measurements are utilized to generate rankings in embodiments described herein.

[1170] In one embodiment, for at least a certain first user and a certain second user, who have different profiles, the ranking module ranks the first and second locations differently, such that for the certain first user the first location is ranked above the second location, and for the certain second user the second location is ranked above the first location. Because the personalized rankings for the certain first user and the certain second user are different, in this embodiment, maps and/or annotations presented, using the map-displaying module 240, on displays of the certain first user and the certain second user, may be different. In one example, on the display of the certain first user, the map-displaying module 240 presents an annotation that indicates that the first location is ranked above the second location, and on the display of the certain second user, the map-displaying module 240 presents an annotation that indicates that the second location is ranked above the first location.

[1171] As described above, presenting a location on a display, such as a location at which service is provided, may be done, in some embodiments, in the first manner or the second manner; where in the first manner involves utilizing a more eye-catching descriptor than the second manner (e.g., using larger image, displaying descriptors for a longer duration, using visual effects, and/or presenting more information). In some embodiments, personalized rankings may lead to it that the same location at which service is provided is presented on displays of different users in different manners. In particular, in one embodiment, the first location is presented on a display of the certain first user in the first manner, and the second location is presented on the display of the certain first user in the second manner. Optionally, for the certain second user, the presentation is the other way around, i.e., the first location is presented on a display of the certain second user in the second manner, and the second location is presented on the display of the certain second user in the first manner.

[1172] Referring to FIG. 42, this figure illustrates a system configured to present one maps personalized rankings of locations at which service is provided. The figure illustrates how the two different users, a first user 631a and a second user 631b, are presented with different rankings of locations at which service is provided, on maps 633a and 633b, respectively. The different rankings are generated because the personalization module 130 receives a different profile for each of the user (profile 632a for the user 631a and profile 632b for the user 631b). For each of the different profiles, the personalization module 130 generates a different output, which is utilized by the ranking module (ranking module 220 in the illustration) in order to generate a different ranking of the locations at which service is provided. For example, as the maps 633a and 633b illustrate that a location ranked first in the ranking of the user 331a is not one of the top five locations in the ranking of the user 631b. Additionally, the illustration shows that the location ranked first in the ranking of user 631b is ranked third in the ranking of the locations presented to the user 631a.

[1173] Presenting maps and annotations based on personalized rankings of locations at which service is provided may involve execution of certain steps. Following is a more detailed discussion of steps that may be involved in a method for presenting on a map annotations indicative of personalized ranking of locations at which service is provided. These steps may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 42 and/or steps of a method modeled according to FIG. 36. The latter figure illustrates embodiments that involve presenting annotations on a map indicative of personalized ranking of locations in general, which include the locations at which service is provided; as such, the teachings of those embodiments are relevant to the steps of the method described below. In some embodiments, instructions for implementing the method described below may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[1174] In one embodiment, the method for presenting on a map annotations indicative of personalized ranking of locations at which service is provided includes at least the following steps:

[1175] In Step 1, receiving, by a system comprising a processor and memory, the measurements of affective response of the customers. For each location from among the locations being ranked, the measurements include measurements of affective response of at least eight customers who were at the location.

[1176] In Step 2, receiving profiles of at least some of the customers who contributed measurements in Step 1. Optionally, the received profiles are some of the profiles 504.

[1177] In Step 3, receiving a profile of a certain first user.

[1178] In Step 4, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least some of the customers. Optionally, generating the first output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586e in FIG. 21.

[1179] In Step 5, computing, based on the measurements and the first output, a first ranking of the locations at which service is provided, in which a first location is ranked ahead of a second location.

[1180] In Step 6, presenting on a first display: a first map comprising a description of a first environment that comprises the first and second locations, and a first annotation overlaid on the first map, which is determined based on the first ranking and indicates that the first location is ranked above the second location. Optionally, presenting the first map and the first annotation is done by the map-displaying module 240 and/or may involve presenting one or more descriptors. In particular, the first annotation may involve presentation of the first location in the first manner and presentation of the second location in the second manner, which is less eye-catching than the first manner (as described above). Optionally, the first display belongs to a device utilized by the certain first user.

[1181] In Step 7, receiving a profile of a certain second user, which is different from the profile of the certain first user.

[1182] In Step 8, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least some of the customers. Here, the second output is different from the first output. Optionally, generating the second output may involve various steps such as computing weights based on profile similarity and/or clustering profiles, as discussed in an explanation of Step 586i in FIG. 21.

[1183] In Step 9, computing, based on the measurements and the second output, a second ranking of the locations at which service is provided, in which the second location is ranked ahead of the first location.

[1184] And in Step 10, presenting on a second display: a second map comprising a description of a second environment that comprises the first and second locations, and a second annotation overlaid on the second map, which is determined based on the second ranking and indicates that the second location is ranked above the first location. Optionally, presenting the second map and the second annotation is done by the map-displaying module 240 and/or may involve presenting one or more descriptors. In particular, the second annotation may involve presentation of the second location in the first manner and presentation of the first location in the second manner. Optionally, the second display belongs to a device utilized by the certain second user.

[1185] In one embodiment of the method described above, the first environment and the second environment may be the same environment. Additionally, the first map and the second map may be the same map. Thus, the difference between what is displayed on the first display and what is displayed on the second display may be essentially due to differences between the first and second annotations. In another embodiment, the first map presented on the first display may be different than the second map presented on the second display, as explained in the discussion regarding FIG. 36.

[1186] In one embodiment, the method described above may optionally include a step that involves utilizing a sensor coupled to a customer who was at a location from among the locations at which is provided, to obtain a measurement of affective response of the customer. Optionally, each measurement of affective response of a customer who was at a location is obtained by measuring the customer with a sensor coupled to the customer in order to obtain a value indicative of a physiological signal and/or a behavioral cue of the customer. Optionally, each measurement of affective response is based on values acquired by measuring the customer with the sensor during at least three different non-overlapping periods while the customer was at the location. Optionally, each customer was at the location for at least a certain time, such as at least five minutes, at least thirty minutes, at least one hour, at least four hours, at least one day, at least one week, or some other period of time that is greater than one minute.

[1187] Affective response to an experience that involves spending time at a location may happen while a user has the experience and possibly after it. Such “post-experience” affective response after having an experience may last a certain period of time after the experience, which may span hours, days, and even longer. For example, going on a vacation to a certain destination may influence how a user feels days and even weeks after coming back from the vacation. Thus, different locations may have different effects on users who spent time at the locations. For example, some locations may be more relaxing and enable users to “recharge their batteries” better than other locations. In another example, spending time in nature may help a person be relaxed throughout the rest of the day. However, not all parks may be considered to have the same effect; a stroll in one park may have a better influence on a user than another park.

[1188] Since different locations may have different post-visit influences on users who visit at the locations, it may be desirable to be able to determine which locations have a better post-visit influence on users than others. Having such information may assist users in selecting what location to visit when considering various locations to visit such as vacation destinations (e.g., hotels, parks, and resorts), and even what virtual environments are better for a user to spend time at in order to feel better when the user is not in a virtual environment.

[1189] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that may be used to rank locations a user may visit based on how the locations are expected to influence the user after the visit. The post-experience influence of an experience is referred to herein as an “aftereffect”. When an experience takes place at a location, and/or the experience involves simply being in the location, the aftereffect of the experience represents the residual influence that the visit to the location has on a user. Such a residual influence may be referred to herein using expressions such as “an aftereffect of the location” and/or the “aftereffect of being at the location”. Examples of aftereffects to locations include the relaxation and/or happiness felt even days after returning from a vacation, calmness felt hours after taking nice mid-day stroll in a park and/or after visiting a in a virtual environment for ten minutes.

[1190] One aspect of this disclosure involves ranking locations based on their aftereffects (i.e., a residual affective response from visiting the locations). In some embodiments, a collection module receives measurements of affective response of users who were at the locations. An aftereffect ranking module is used to rank the locations based on their corresponding aftereffects, which are determined from the measurements. The measurements of affective response are typically taken by sensors coupled to the users (e.g., sensors in wearable devices and/or sensors implanted in the users). One way in which aftereffects may be determined is by measuring users before and after they leave a location, in order to assess how being at the location changed their affective response. Such measurements are referred to as prior and subsequent measurements. Optionally, a prior measurement may be taken before arriving at the location (e.g., before leaving to go on a vacation) and a subsequent measurement is taken after leaving the location (e.g., after returning from the vacation). Typically, a difference between a subsequent measurement and a prior measurement, of a user who was at a location, is indicative of an aftereffect being at the location had on the user. In the example with the vacation, the aftereffect may indicate how relaxing the vacation was for the user. In some cases, the prior measurement may be taken while the user is at the location.

[1191] FIG. 44 illustrates a system configured to rank locations based on aftereffects determined from measurements of affective response of users. The system includes at least the collection module 120 and an aftereffect ranking module 300. The system may optionally include other modules such as the personalization module 130, the location verifier 505, recommender module 235, and/or map-displaying module 240.

[1192] The collection module 120 is configured, in one embodiment, to receive the measurements 501 of affective response of users who were at the locations. In this embodiment, the measurements 501 of affective response comprise, for each location from among the locations, prior and subsequent measurements of at least five users who were at the location. Optionally, each prior measurement and/or subsequent measurement of a user comprises at least one of the following: a value representing a physiological signal of the user, and a value representing a behavioral cue of the user. Optionally, for each location, prior and subsequent measurements of a different minimal number of users are received, such as at least eight, at least ten, or at least fifty different users.

[1193] A prior measurement of a user who was at a location is taken before the user leaves the location, and a subsequent measurement of the user who was at location is taken after the user leaves the location. In one example, the subsequent measurement may be taken at the moment a user leaves the location (e.g., exits a building or leaves a virtual environment). In another example, the subsequent measurement is taken a certain period after leaving the location, such as at least ten minutes after the user left the location. Optionally, the prior measurement is taken before the user arrives at the location. Optionally, the subsequent measurement is taken less than one day after the user left the location, and before the user arrives at the same location again or arrives at an additional location of the same type. For example, a subsequent measurement of a user who was at a certain vacation destination is taken before the user goes on another vacation.

[1194] The prior and subsequent measurements of affective response of users may be taken with sensors coupled to the users. Optionally, each prior measurement of affective response of a user who was at a location is based on values acquired by measuring the user, with a sensor coupled to the user, during at least three different non-overlapping periods before the user left the location and/or during at least three different non-overlapping periods before the user arrived at the location. Optionally, each subsequent measurement of affective response of a user who was at a location is based on values acquired by measuring the user, with a sensor coupled to the user, during at least three different non-overlapping periods, the earliest of which starts upon leaving the location, or at least a certain period after leaving the locations, such as at least ten minutes after the user left the location.

[1195] In some embodiments, the location verifier module 505 is utilized to determine when to take a prior measurement and/or a subsequent measurement of affective response of a user who was at a location. For example, based on the location verifier module 505 the system may determine when the user arrives and/or leaves a location, and thus, may derive a prior measurement from values obtained with a sensor coupled to the user before the user left the location and/or before the user arrived at the location. Additionally or alternatively, the subsequent measurement of the user may be based on values obtained with the sensor at a time that is after a time at which the location verifier module 505 indicates that the user is no longer at the location.

[1196] The aftereffect ranking module 300 is configured to generate a ranking 640 of the locations based on prior and subsequent measurements received from the collection module 120. Optionally, the ranking 640 does not rank all of the locations the same. In particular, the ranking 640 includes at least first and second locations for which the aftereffect of the first location is greater than the aftereffect of the second location; consequently, the first location is ranked above the second location in the ranking 640.

[1197] In one embodiment, having the first location being ranked above the second location is indicative that, on average, a difference between the subsequent measurements and the prior measurements of the at least five users who were the first location is greater than a difference between the subsequent and the prior measurements of the at least five users who were at the second location. In one example, the greater difference is indicative that the at least five users who were at the first location had a greater change in the level of one or more of the following emotions: happiness, satisfaction, alertness, and/or contentment, compared to the change in the level of the one or more of the emotions in the at least five users who were at the second location.

[1198] In another embodiment, having the first location being ranked above the second location is indicative that a first aftereffect score computed based on the prior and subsequent measurements of the at least five users who were at the first location is greater than a second aftereffect score computed based on the prior and subsequent measurements of the at least five users who were at the second location. Optionally, an aftereffect score of a location may be indicative of an increase to the level of one or more of the following emotions in users who were at the location: happiness, satisfaction, alertness, and/or contentment.

[1199] In some embodiments, measurements utilized by the aftereffect ranking module 300 to generate a ranking of locations, such as the ranking 640, may all be taken during a certain period of time. Depending on the embodiment, the certain period of time may span different lengths of time. For example, the certain period may be less than one day long, between one day and one week long, between one week and one month long, between one month and one year long, or more than a year long. Additionally or alternatively, the measurements utilized by the aftereffect ranking module 300 to generate a ranking of the locations may involve users who visited the locations for similar durations. For example, a ranking of vacation destinations based on aftereffects may be based on prior and subsequent measurements of users who stayed at a vacation destination for a certain period (e.g., one week) or for a period that is in a certain range of time (e.g., three to seven days). Additionally or alternatively, the measurements utilized by the aftereffect ranking module 300 to generate the ranking of the locations may involve prior and subsequent measurements of affective response taken under similar conditions. For example, the prior measurements for all users are taken right before arriving at a location (e.g., not earlier than 10 minutes before), and the subsequent measurements are taken a certain time after leaving the location (e.g., between 45 and 90 minutes after leaving the location).

[1200] It is to be noted that while it is possible, in some embodiments, for the measurements received by modules, such as the aftereffect ranking module 300, to include, for each user from among the users who contributed to the measurements, at least one pair of prior and subsequent measurements of affective response of the user corresponding to each location from among the locations being ranked, this is not necessarily the case in all embodiments. In some embodiments, some users may contribute measurements corresponding to a proper subset of the locations (e.g., those users may not have visited some of the locations), and thus, the measurements 501 may be lacking some prior and subsequent measurements of some users with respect to some of the locations. In some embodiments, some users may have visited only one of the locations being ranked.

[1201] The aftereffect ranking module 300, similar to the ranking module 220 and other ranking modules described in this disclosure, may utilize various approaches in order to generate a ranking of experiences. For example, the different approaches to ranking experiences may include score-based ranking and preference-based ranking, which are described in more detail in the description of the ranking module 220, e.g., with respect to FIG. 19 and in section 14—Ranking Experiences. That section discusses teachings regarding ranking of experiences in general, which include experiences involving locations (e.g., experiences involving being in locations and/or engaging in certain activities at the locations). Thus, the teachings of section 14—Ranking Experiences are also applicable to embodiments described below that explicitly involve locations. Thus, different implementations of the aftereffect ranking module 300 may comprise different modules to accommodate the different ranking approaches.

[1202] In one embodiment, the aftereffect ranking module 300 is configured to rank locations using a score-based approach. In this embodiment, the aftereffect ranking module 300 comprises aftereffect scoring module 302, which in this embodiment, is configured to compute aftereffect scores for the locations. An aftereffect score for a location is computed based on prior and subsequent measurements of the at least five users who were at the location.

[1203] It is to be noted that the aftereffect scoring module 302 is a scoring module such as other scoring module in this disclosure (e.g., the scoring module 150). The use of the reference numeral 302 is intended to indicate that scores computed by the aftereffect scoring module 302 represent aftereffects (which may optionally be considered a certain type of emotional response to an experience). However, in some embodiments, the aftereffect scoring module 302 may comprise the same modules as the scoring module 150, and use similar approaches to scoring locations. In one example, the aftereffect scoring module 302 utilizes modules that perform statistical tests on measurements in order to compute aftereffect scores, such as statistical test module 152 and/or statistical test module 158. In another example, the aftereffect scoring module 302 may utilize arithmetic scorer 162 to compute the aftereffect scores.

[1204] In some embodiments, in order to compute an aftereffect score, the aftereffect scoring module 302 may utilize prior measurements of affective response in order to normalize subsequent measurements of affective response. Optionally, a subsequent measurement of affective response of a user (taken after leaving a location) may be normalized by treating a corresponding prior measurement of affective response the user as a baseline value (the prior measurement being taken before leaving the location or before arriving at it). Optionally, a score computed by such normalization of subsequent measurements represents a change in the emotional response due to being at the location to which the prior and subsequent measurements correspond. Optionally, normalization of a subsequent measurement with respect to a prior measurement may be performed by the baseline normalizer 124 or a different module that operates in a similar fashion.

[1205] In one embodiment, an aftereffect score for a location is indicative of an extent of feeling at least one of the following emotions after leaving the location: pain, anxiety, annoyance, stress, aggression, aggravation, fear, sadness, drowsiness, apathy, anger, happiness, contentment, calmness, attentiveness, affection, and excitement. Optionally, the aftereffect score is indicative of a magnitude of a change in the level of the at least one of the emotions due to being at the location.

[1206] When the aftereffect ranking module 300 includes the aftereffect scoring module 302, it may also include the score-based rank determining module 225, which in this embodiment, is configured to rank the locations based on their respective aftereffect scores, such that a location with a higher aftereffect score is not ranked lower than a location with a lower aftereffect score, and the first location (mentioned above) has a higher corresponding aftereffect score than the second location (mentioned above).

[1207] In one embodiment, the aftereffect ranking module 300 is configured to rank locations using a preference-based approach. In this embodiment, the aftereffect ranking module 300 comprises a preference generator module 304 that is configured to generate a plurality of preference rankings. Each preference ranking is indicative of ranks of at least two of the locations, such that one location, of the at least two location, is ranked above another location of the at least two locations. Additionally, each preference ranking is determined based on a subset comprising at least a pair of prior and subsequent measurements of a first user who was at the one location, and at least a pair of prior and subsequent measurements of a second user who was at the other location. Optionally, the first and second users are the same user. Optionally, a majority of the measurements comprised in each subset of the measurements that is used to generate a preference ranking are prior and subsequent measurements of a single user. Optionally, all of the measurements comprised in each subset of the measurements that is used to generate a preference ranking are prior and subsequent measurements of a single user. Optionally, a majority of the measurements comprised in each subset of the measurements that is used to generate a preference ranking are prior and subsequent measurements of similar users as determined based on an output of the profile comparator 133.

[1208] It is to be noted that the preference generator 304 operates in a similar fashion to other preference generator modules in this disclosure (e.g., the preference generator 228). The use of the reference numeral 304 is intended to indicate that a preference ranking of experiences (e.g., involving being in locations) is generated based on prior and subsequent measurements. However, in some embodiments, the preference generator 304 generates preference rankings similar to the way they are generated by the preference generator 228. In particular, in some embodiments, a pair of measurements (e.g., a prior and subsequent measurement of the same user taken before and after being in a location, respectively), may be used generate a normalized value, as explained above with reference to aftereffect scoring module 302. Thus, in some embodiments, the preference generator 304 may operate similarly to preference generator 228, with the addition of a step involving generating normalized values (representing the aftereffect of being at the location) based on prior and subsequent measurements.

[1209] In one embodiment, if in a preference ranking, one location is ranked ahead of another location, this means that based on a first pair comprising prior and subsequent measurements taken with respect to the one location, and a second pair comprising prior and subsequent measurements taken with respect to the other location, the difference between the subsequent and prior measurement of the first pair is greater than the difference between the subsequent and prior measurement of the second pair. Thus, for example, if the first and second pairs consist measurements of the same user, the preference ranking reflects the fact that being in the one location had a more positive effect on the emotional state of the user than being in the other location had.

[1210] When the aftereffect ranking module 300 includes the preference generator module 304, it may also include the preference-based rank determining module 230, which is configured to rank the locations based on the plurality of the preference rankings utilizing a method that satisfies the Condorcet criterion. The ranking of location by the preference-based rank determining module 230 is such that a certain location, which in a pair-wise comparison with other location is preferred over each of the other locations, is not ranked below any of the other locations. Optionally, the certain location is ranked above at least one of the other locations. Optionally, the certain location is ranked above each of the other locations.

[1211] In one embodiment, the recommender module 235 may utilize the ranking 640 to make recommendation 642 in which the first location is recommended in a first manner (which involves a stronger recommendation than a recommendation made by the recommender module 235 when making a recommendation in a second manner). Thus, based on the fact that the aftereffect associated with the first location is greater than the aftereffect associated with the second location, the recommender module 235 provides a stronger recommendation for the first location than it does to the second location. There are various ways in which the stronger recommendation may be realized; additional discussion regarding recommendations in the first and second manners may be found at least in the discussion about recommender module 178 in section 8—Crowd-Based Applications; recommender module 235 may employ first and second manners of recommendation in a similar way to how the recommender module 178 recommends in those manners.

[1212] In one embodiment, the map-displaying module 240 is configured to present a result obtained from the ranking 640 on a map. Optionally, the map-displaying module 240 is configured to present on a display: a map comprising a description of an environment that comprises the first and second locations, and an annotation overlaid on the map. The annotation is based on ranking 640 and indicates at least one of the following: an aftereffect score computed for the first location, an aftereffect score computed for the second location, a rank of the first location, and a rank of the second location, and an indication that the first location has a higher aftereffect score than the second location. Optionally, the annotation comprises at least one of the following: images representing the first and/or second locations, and text identifying the first and/or second locations.

[1213] It is to be noted that references to the “locations” that are being ranked based on aftereffects, e.g., with respect to FIG. 44 and/or other figures, may refer to any type of location described in this disclosure (be it in the physical world and/or in a virtual location). Following are some examples of the types of locations that may be ranked in different embodiments.

[1214] In one embodiment, at least some of the locations are establishments in which entertainment is provided. Optionally, such an establishment may be one or more of the following: a club, a bar, a movie theater, a theater, a casino, a stadium, and a concert venue. Optionally, the ranking 640 indicates which establishments leave users who visit them more satisfied, relaxed, and/or content in the hours after their visit.

[1215] In another embodiment, at least some of the locations are vacation destinations. Optionally, a vacation destination may be one or more of the following: a continent, a country, a county, a city, a resort, and a neighborhood. Optionally, the ranking 640 indicates which vacation destinations help users “recharge” the most, such that their levels of happiness, attention, and/or contentedness are the highest in the days after returning from the vacation.

[1216] In yet another embodiment, at least some of the locations are virtual environments in a virtual world, with at least one instantiation of each virtual environment stored in a memory of a computer. Optionally, a user may be considered to be in a virtual environment by virtue of having a value stored in the memory of the computer indicating a presence of a representation of the user in the virtual environment. Optionally, the ranking 640 indicates which virtual environments have the most positive effect on users, such that in the hours after leaving the virtual environment, their level of happiness, attention, and/or contentedness are the highest and/or the levels of stress and anxiety are the lowest.

[1217] In some embodiments, the personalization module 130 may be utilized in order to generate personalized rankings of locations based on their aftereffects. Utilization of the personalization module 130 in these embodiments may be similar to how it is utilized for generating personalized rankings of locations, which is discussed in greater detail with respect to the ranking module 220. For example, personalization module 130 may be utilized to generate an output that is indicative of a weighting and/or selection of the prior and subsequent measurements based on profile similarity.

[1218] FIG. 46 illustrates how the output generated by the personalization module, when it receives profiles of certain users, can enable the system illustrated in FIG. 44 to produce different rankings of locations for different users. A certain first user 647a and a certain second user 647b have corresponding profiles 648a and 648b, which are different from each other. The personalization module 130 produces different outputs based on the profiles 648a and 648b. Consequently, the aftereffect ranking module 300 generates different rankings 649a and 649b for the certain first user 647a and the certain second user 647b, respectively. Optionally, in the ranking 649a, location A has a higher aftereffect than location B, and in the ranking 649b, it is the other way around (location B has a higher aftereffect than location A).

[1219] In one example, the locations being ranked according to their aftereffects are vacations destinations that include Ibiza and London. The profile 648a of the certain first user 647a indicates that the certain first user 647a is 22 years old, enjoys parties, techno music, and online gaming. The profile 648b of the certain second user 647b indicates that the certain second user 647b is 50 years old, enjoys classical music and visiting museums. Thus, in this example, in the ranking 649a generated utilizing a first output of the personalization module 130, which gives higher weights to prior and subsequent measurements of users with profiles similar to the profile 648a, it is likely that Ibiza is ranked ahead of London. This is because the residual effect of going to Ibiza on users similar to user 647a is more positive than the effect of going to London. For example, going to dance parties on the beach for a week leaves these users with a better feeling, even days after returning from the vacation, compared to how these users feel when coming back from a week-long vacation in drizzly London. However, in the ranking 649b, it is likely that London is ranked ahead of Ibiza. This may be because users with profiles similar to the profile 648b of the user 647b are not likely to be relaxed and content after returning from a week of noisy parties on the beach. They are much more likely to be reinvigorated by a week of touring the many museums in London, going on shopping, etc.

[1220] FIG. 45 illustrates steps involved in one embodiment of a method for ranking locations based on aftereffects determined from measurements of affective response of users. The steps illustrated in FIG. 45 may be used, in some embodiments, by systems modeled according to FIG. 44. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations of the method.

[1221] In one embodiment, the method for ranking locations based on aftereffects determined from measurements of affective response of users includes at least the following steps:

[1222] In Step 645b, receiving, by a system comprising a processor and memory, the measurements of affective response of the users who were at the location being ranked. Optionally, for each location, the measurements include prior and subsequent measurements of at least five users who were at the location. Optionally, each prior measurement and/or subsequent measurement of a user comprises at least one of the following: a value representing a physiological signal of the user, and a value representing a behavioral cue of the user. Optionally, the measurements received in Step 645b are received by the collection module 120.

[1223] And in Step 645c, ranking the locations based on the measurements received in Step 645b. Optionally, ranking the locations is performed by the aftereffect ranking module 300. Optionally, ranking the locations involves generating a ranking that includes at least first and second locations; the aftereffect of the first location is greater than the aftereffect of the second location, and consequently, in the ranking, the first location is ranked above the second location.

[1224] In one embodiment, the method optionally includes Step 645a that involves utilizing a sensor coupled to a user who was at a location, from among the locations being ranked, to obtain a prior measurement of affective response of the user and/or a subsequent measurement of affective response of the user who had the user.

[1225] In one embodiment, the method optionally includes Step 645d that involves recommending the first location to a user in a first manner, and not recommending the second location to the user in the first manner. Optionally, the Step 645d may further involve recommending the second location to the user in a second manner. As mentioned above, e.g., with reference to recommender module 235, recommending a location in the first manner may involve providing a stronger recommendation for the location, compared to a recommendation for the location that is provided when recommending it in the second manner.

[1226] As discussed in more detail above, ranking locations utilizing measurements of affective response may be done in different embodiments, in different ways. In particular, in some embodiments, ranking may be score-based ranking (e.g., performed utilizing the aftereffect scoring module 302 and the score-based rank determining module 225), while in other embodiments, ranking may be preference-based ranking (e.g., utilizing the preference generator module 304 and the preference-based rank determining module 230). Therefore, in different embodiments, Step 645c may involve performing different operations.

[1227] In one embodiment, ranking the locations in Step 645c includes performing the following operations: for each location from among the locations being ranked, computing an aftereffect score based on prior and subsequent measurements of the at least five users who were at the location, and ranking the locations based on the magnitudes of the aftereffect scores. Optionally, two locations in this embodiment may be considered tied if a significance of a difference between aftereffect scores computed for the two locations is below a threshold. Optionally, determining the significance is done utilizing a statistical test involving the measurements of the users who were at the two locations (e.g., utilizing the score-difference evaluator module 260).

[1228] In another embodiment, ranking the locations based on the measurements in Step 645c includes performing the following operations: generating a plurality of preference rankings for the locations based on prior and subsequent measurements (as explained above), and ranking the locations based on the plurality of the preference rankings utilizing a method that satisfies the Condorcet criterion. Optionally, each preference ranking is generated based on a subset comprising prior and subsequent measurements, and comprises a ranking of at least two of the locations, such that one of the at least two locations is ranked ahead of another locations from among the at least two locations. Optionally, the preference rankings are generated utilizing preference generator module 304, as explained above. Optionally, two locations in this embodiment may be considered tied if a significance of differences between subsequent and prior measurements of affective response related to each of the location is below a threshold. Optionally, determining the significance is done utilizing a statistical test involving the measurements of the users who were at the two locations (e.g., utilizing the difference-significance evaluator module 270).

[1229] A ranking of locations generated by a method illustrated in FIG. 45 may be personalized for a certain user. In such a case, the method may include the following steps: (i) receiving a profile of a certain user and profiles of at least some of the users (who contributed measurements used for ranking the locations); (ii) generating an output indicative of similarities between the profile of the certain user and the profiles; and (iii) ranking the locations based on the measurements received in Step 645b and the output. Optionally, the output is generated utilizing the personalization module 130. Depending on the type of personalization approach used and/or the type of ranking approach used, the output may be utilized in various ways to perform a ranking of the locations, as discussed in further detail above. Optionally, for at least a certain first user and a certain second user, who have different profiles, third and fourth locations, from among the locations, are ranked differently, such that for the certain first user, the third location is ranked above the fourth location, and for the certain second user, the fourth location is ranked above the third location.

[1230] Personalization of rankings of locations based on aftereffects, as described above, can lead to the generation of different rankings for users who have different profiles, as illustrated in FIG. 46. Obtaining different rankings for different users may involve performing the steps illustrated in FIG. 47, which describes how steps carried out when computing crowd-based rankings can lead to different users receiving the different rankings. The steps illustrated in FIG. 47 may, in some embodiments, be part of the steps performed by systems modeled according to FIG. 44 and/or FIG. 46. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[1231] In one embodiment, a method for utilizing profiles of users for computing personalized rankings of locations, based on aftereffects determined from measurements of affective response of the users, includes the following steps:

[1232] In Step 650b, receiving, by a system comprising a processor and memory, measurements of affective response of the users who were at the locations being ranked. The measurements received in this step include, for each location from among the locations, prior and subsequent measurements of at least eight users who were at the location. Optionally, a prior measurement of a user is taken before the user leaves the location, and a subsequent measurement of the user is taken after the user leaves the location (e.g., at least ten minutes after the user left the location). Optionally, for each location from among the location being ranked, the measurements received in this step comprise prior and subsequent measurements of affective response of at least some other minimal number of users, such as measurements of at least five, at least ten, and/or at least fifty different users.

[1233] In Step 650c, receiving profiles of at least some of the users who contributed measurements in Step 650b. Optionally, profiles received in this step are from among the profiles 504.

[1234] In Step 650d, receiving a profile of a certain first user.

[1235] In Step 650e, generating a first output indicative of similarities between the profile of the certain first user and the profiles of the at least some of the users. Optionally, the first output is generated by the personalization module 130.

[1236] In Step 650f, computing, based on the measurements and the first output, a first ranking of the locations. Optionally, the first ranking reflects aftereffects of being at the locations. In one example, in the first ranking, a first location is ranked ahead of a second location. Optionally, the ranking of the first location ahead of the second location indicates that for the certain first user, an expected aftereffect of being the first location is greater than an expected aftereffect of being at the second location. Optionally, computing the first ranking in this step is done by the aftereffect ranking module 300.

[1237] In Step 650h, receiving a profile of a certain second user, which is different from the profile of the certain first user.

[1238] In Step 650i, generating a second output indicative of similarities between the profile of the certain second user and the profiles of the at least some of the users. Here, the second output is different from the first output. Optionally, the second output is generated by the personalization module 130.

[1239] And in Step 650j, computing, based on the measurements and the second output, a second ranking of the locations. Optionally, the second ranking reflects aftereffects to being at the locations. In one example, the first and second rankings are different, such that in the second ranking, the second location is ranked above the first location. Optionally, the ranking of the second location ahead of the first location indicates that for the certain second user, an expected aftereffect of to being at the second location is greater than an expected aftereffect to being at the first location. Optionally, computing the second ranking in this step is done by the aftereffect ranking module 300.

[1240] In one embodiment, the method optionally includes Step 650a that involves utilizing a sensor coupled to a user who was at a location, from among the locations being ranked, to obtain a prior measurement of affective response of the user and/or a subsequent measurement of affective response of the user. Optionally, obtaining a prior measurement of affective response of a user who was at a location is done by measuring the user with the sensor during at least three different non-overlapping periods before the leaves the location (and in some embodiments before the user arrives at the location). Optionally, obtaining the subsequent measurement of affective response of a user who was at the location is done by measuring the user with the sensor during at least three different non-overlapping periods after the user left the location (e.g., at least ten minutes after the user left the location).

[1241] In one embodiment, the method may optionally include steps that involve reporting a result based on the ranking of the locations to a user. In one example, the method may include Step 650g, which involves forwarding to the certain first user a result derived from the first ranking of the locations. In this example, the result may be a recommendation to visit the first location (which for the certain first user is ranked higher than the second locations). In another example, the method may include Step 650k, which involves forwarding to the certain second user a result derived from the second ranking of the locations. In this example, the result may be a recommendation for the certain second user to visit the second location (which for the certain second user is ranked higher than the first location).

[1242] In one embodiment, generating the first output and/or the second output may involve computing weights based on profile similarity. For example, generating the first output in Step 650e may involve performing the following steps: (i) computing a first set of similarities between the profile of the certain first user and the profiles of the at least eight users; and (ii) computing, based on the first set of similarities, a first set of weights for the measurements of the at least eight users. Optionally, each weight for a measurement of a user is proportional to the extent of a similarity between the profile of the certain first user and the profile of the user (e.g., as determined by the profile comparator 133), such that a weight generated for a measurement of a user whose profile is more similar to the profile of the certain first user is higher than a weight generated for a measurement of a user whose profile is less similar to the profile of the certain first user. Generating the second output in Step 650i may involve similar steps, mutatis mutandis, to the ones described above.

[1243] In another embodiment, the first output and/or the second output may involve clustering of profiles. For example, generating the first output in Step 650e may involve performing the following steps: (i) clustering the at least some of the users into clusters based on similarities between the profiles of the at least some of users, with each cluster comprising a single user or multiple users with similar profiles; (ii) selecting, based on the profile of the certain first user, a subset of clusters comprising at least one cluster and at most half of the clusters, on average, the profile of the certain first user is more similar to a profile of a user who is a member of a cluster in the subset, than it is to a profile of a user, from among the at least eight users, who is not a member of any of the clusters in the subset; and (iii) selecting at least eight users from among the users belonging to clusters in the subset. Here, the first output is indicative of the identities of the at least eight users. Generating the second output in Step 650i may involve similar steps, mutatis mutandis, to the ones described above.

[1244] In some embodiments, the method may optionally include steps involving recommending one or more of the locations being ranked to users. Optionally, the type of recommendation given for a location is based on the rank of the location. For example, given that in the first ranking, the rank of the first location is higher than the rank of the second location, the method may optionally include a step of recommending the first location to the certain first user in a first manner, and not recommending the second location to the certain first user in first manner. Optionally, the method includes a step of recommending the second location to the certain first user in a second manner. Optionally, recommending a location in the first manner involves providing a stronger recommendation for the location, compared to a recommendation for the location that is provided when recommending it in the second manner. The nature of the first and second manners is discussed in more detail with respect to the recommender module 178, which may also provide recommendations in first and second manners.

[1245] In typical, real-world, scenarios the quality of an experience at a location that a user has may involve various uncontrollable factors (e.g., environmental factors and/or influence of other users). Thus, the quality of the visit to the location may be different at different times. However, in some cases, it may be possible to anticipate these changes to the quality of a visit to the location, since the changes may have a periodic temporal nature. For example, a certain restaurant may be busy on certain days (e.g., during the weekend) and relatively empty during other times (e.g., weekdays). Thus, it may be desirable to be able to determine when it is (typically) a good time to visit a location.

[1246] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that may be used to determine when to visit a location. In some embodiments, different times during a periodic unit of time are evaluated in order to assess the quality of an experience, when it is experienced at the different times. A periodic unit of time is a unit of time that repeats itself regularly. An example of periodic unit of time is a thy (a period of 24 hours that repeats itself), a week (a periodic of 7 days that repeats itself, and a year (a period of twelve months that repeats itself). A ranking of the times to have an experience indicates at least one portion of the periodic unit of time that is preferred over another portion of the periodic unit of time during which to have the experience. For example, the ranking may indicate what thy of the week is preferable for dining at a restaurant, or what season of the year is preferable for visiting a certain vacation destination.

[1247] As discussed in Section 3—Experiences, different experiences may be characterized by a combination of attributes. In particular, the time a user has an experience can be an attribute that characterizes the experience. Thus, in some embodiments, doing the same thing at different times (e.g., being at a location at different times), may be considered different experiences. In particular, in some embodiments, different times at which a location is visited may be evaluated, scored, and/or ranked. This can enable generation of suggestions to users of when to visit a certain experience. For example, going on a vacation during a holiday weekend may be less relaxing than going during the week. In another example, a certain area of town may be more pleasant to visit in the evening compared to visiting it in the morning.

[1248] In some embodiments, measurements of affective response may be utilized to learn when it is best to visit locations. This may involve ranking different times at which a location may be visited. FIG. 43a illustrates a system that may be utilized for this task. The system is configured to rank times at which to visit a location based on measurements of affective response. Optionally, each of the times being ranked corresponds to a certain portion of a periodic unit of time, as explained below. The location users visit at the different times being ranked may be of any of the different types of locations mentioned in this disclosure (see examples below). The system includes at least the collection module 120 and a ranking module 333, and may optionally include additional modules such as the personalization module 130, the recommender module 343, and/or the location verifier module 505.

[1249] The collection module 120 receives measurements 501 of affective response. In this embodiment, the measurements 501 include measurements of affective response of at least ten users, where each user visits the location at some time during a periodic unit of time, and a measurement of the user is taken by a sensor coupled to the user while the user is at the location. Optionally, each measurement of affective response of a user who was at the location is based on values acquired by measuring the user with the sensor during at least three different non-overlapping periods while the user was at the location. Optionally, a measurement of a user is taken during a period indicated by the location verifier module 505 as being a time during which the user was at the location. Additional information regarding sensors that may be used to collect measurements of affective response and/or ways in which the measurements may be taken is given at least in section 1—Sensors and section 2—Measurements of Affective Response.

[1250] Herein, a periodic unit of time is a unit of time that repeats itself regularly. In one example, the periodic unit of time is a thy, and each of the at least ten users visits the location during a certain hour of the day. In another example, the periodic unit of time is a week, and each of the at least ten users visits the location during a certain day of the week. In still another example, the periodic unit of time is a year, and each of the at least ten users visits the location during a time that is at least one of the following: a certain month of the year, and a certain annual holiday. A periodic unit of time may also be referred to herein as a “recurring unit of time”.

[1251] The ranking module 333 is configured, in one embodiment, to generate ranking 636 of times to visit the location based on based on measurements from among the measurements 501, which are received from the collection module 120. Optionally, the ranking 636 is such that it indicates that visiting the location during a first portion of the periodic unit of time is ranked above visiting the location during a second portion of the periodic unit of time. Furthermore, in this embodiment, the measurements received by the ranking module 333 include measurements of at least five users who visited the location during the first portion, and measurements of at least five users who visited the location during the second portion.

[1252] In some embodiments, when visiting the location during the first portion of the periodic unit of time is ranked above visiting the location during the second portion of the periodic unit of time, it typically means that, on average, the measurements of the at least five users who visited the location during the first portion are more positive than measurements of the at least five users who visited the location during the second portion. Additionally or alternatively, when visiting the location during the first portion of the periodic unit of time is ranked above visiting the location during the second portion of the periodic unit of time, that may indicate that a first score computed based on measurements of the at least five users who visited the location during the first portion is greater than a second score computed based on the measurements of the at least five users who visited the location during the second portion.

[1253] In one example, the periodic unit of time is a week, and the first portion corresponds to one of the days of the week (e.g., Tuesday), while the second portion corresponds to another of the days of the week (e.g., Sunday). In this example, the location may be an amusement park, so when the first portion is ranked above the second portion, this means that based on measurements of affective response of users who visited the amusement park, it is better to visit the amusement park on Tuesday, compared to visiting it on Sunday. In another example, the periodic unit of time is a day, the first portion corresponds to midday hours (e.g., 11 AM to 3 PM) and the second portion corresponds to the evening hours (e.g., 5 PM to 10 PM). In this example, the location may be a certain restaurant, so when the first portion is ranked above having the second portion, this means that based on measurements of affective response of users having lunch at the restaurant is preferred to having dinner at the restaurant.

[1254] In some embodiments, the portions of the periodic unit of time that include the times being ranked are of essentially equal length. In one example, each of the portions corresponds to a day of the week (so the ranking of times may amount to ranking days of the week to visit a certain location). In some embodiments, the portions of the periodic unit of time that include the times being ranked may not necessarily have an equal length. For example, one portion may include times that fall within weekdays, while another portion may include times that fall on the weekend. Optionally, in embodiments in which the first and second portions of the periodic unit of time are not of the equal length, the first portion is not longer than the second portion. Optionally, in such a case, the overlap between the first portion and the second portion is less than 50% (i.e., most of the first portion and most of the second portion do not correspond to the same times). Furthermore, in some embodiments, there may be no overlap between the first and second portions of the periodic unit of time.

[1255] In embodiments described herein, not all the measurements utilized by the ranking module 333 to generate the ranking 636 are necessarily collected during the same instance of the periodic unit of time. In some embodiments, the measurements utilized by the ranking module 333 to generate the ranking 636 include at least a first measurement and a second measurement such that the first measurement was taken during one instance of the periodic unit of time and the second measurement was taken during a different instance of the periodic unit of time. For example, if the periodic unit of time is a week, then the first measurement might have been taken during one week (e.g., the first week of August 2016) and the second measurement might have been taken during the following week (e.g., the second week of August 2016). Optionally, the difference between the time the first and second measurements were taken is at least the periodic unit of time.

[1256] It is to be noted that the ranking module 333 is configured to rank different times at which to visit the location; with each time being ranked corresponding to a different portion of a periodic unit of time (and where being at the location may be considered a certain type of experience). Since some experiences may be characterized as occurring at a certain period of time (as explained in more detail above), the ranking module 333 may be considered a module that ranks different experiences of a certain type (e.g., involving engaging in the same activity and/or being in the same location, but at different times). Thus, the teachings in this disclosure regarding the ranking module 220 may be relevant, in some embodiments, to the ranking module 333. The use of the different reference numeral (333) is intended to indicate that rankings in these embodiments involve a ranking of different times at which to have an experience.

[1257] Ranking module 333, like the ranking module 220 and other ranking modules described in this disclosure, may utilize various approaches to ranking, such as score-based ranking and/or preference-based ranking, as described below.

[1258] In one embodiment, the ranking module 333 is configured to rank the times at which to visit the location using a score-based ranking approach, and comprises the scoring module 150, which computes scores for the location, with each score corresponding to a certain portion of the periodic unit of time. The score corresponding to a certain portion of the periodic unit of time is computed based on the measurements of the at least five users who were at the location during the certain portion of the periodic unit of time. Additionally, in this embodiment, the ranking module 333 comprises score-based rank determining module 336, which is configured to rank portions of the periodic unit of time in which to visit the location based on their respective scores, such that a period with a higher score is ranked ahead of a period with a lower score. In some embodiments, the score-based rank determining module 336 is implemented similarly to the score-based rank determining module 225, which generates a ranking of experiences from scores for any of the various types of experiences described herein (which includes experiences that are characterized as involving being at the location during a certain portion of a periodic unit of time).

[1259] In another embodiment, the ranking module 333 is configured to rank the times at which to visit the location using a preference-based ranking approach. In this embodiment, the ranking module 333 comprises the preference generator module 228 which is configured to generate a plurality of preference rankings, with each preference ranking being indicative of ranks of at least two portions of the periodic unit of time during which to visit the location. For each preference ranking, at least one portion, of the at least two portions, is ranked above another portion of the at least two portions. Additionally, each preference ranking is determined based on a subset of the measurements 501 comprising a measurement of a first user who was at the location during the one portion of the periodic unit of time, and a measurement of a second user who was at the location during the other portion of the periodic unit of time. Optionally, the first user and the second user are the same user; thus, the preference ranking is based on measurements of the same user taken while the user was at the location at two different times. Optionally, the first user and the second user have similar profiles, as determined based on a comparison performed by the profile comparator 133. Additionally, in this embodiment, the ranking module 333 includes preference-based rank determining module 340 which is configured to rank times at which to visit the location based on the plurality of the preference rankings utilizing a method that satisfies the Condorcet criterion. The ranking of portions of the periodic unit of time generated by the preference-based rank determining module 340 is such that a certain portion, which in a pair-wise comparison with other portions of the periodic unit of time is preferred over each of the other portions, is not ranked below any of the other portions. Optionally, the certain portion is ranked above each of the other portions. In some embodiments, the preference-based rank determining module 340 is implemented similarly to the preference-based rank determining module 230, which generates a ranking of experiences from preference rankings for any of the various types of experiences described herein (which includes experiences that are characterized by their involving being in the location during a certain portion of a periodic unit of time).

[1260] In one embodiment, the system illustrated in FIG. 43a includes the personalization module 130 which is configured to receive a profile of a certain user and profiles of users belonging to a set comprising at least five users who were at the location during the first portion of the periodic unit of time, and at least five users who were at the location during the second portion of the periodic unit of time. Optionally, the profiles of the users belonging to the set are profiles from among the profiles 504. The personalization module 130 is also configured to generate an output indicative of similarities between the profile of the certain user and the profiles of the users from the set of users. In this embodiment, the ranking module 333 is also configured to rank the portions of the periodic unit of time during which to visit the location based on the output.

[1261] When generating personalized rankings of times to visit the location (which belong to different portions of the periodic unit of time), not all users have the same ranking generated for them. For at least a certain first user and a certain second user, who have different profiles, the ranking module 333 ranks times to visit the location differently, such that for the certain first user, having visiting the location during the first portion of the periodic unit of time is ranked above visiting the location during the second portion of the periodic unit of time, and for the certain second user, visiting the location during the second portion of the periodic unit of time is ranked above visiting the location during the first portion of the periodic unit of time. As described elsewhere herein, the output may be indicative of a weighting and/or of a selection of measurements of users that may be utilized to generate a personalized ranking of the times at which to visit the location.

[1262] In one embodiment, the ranking 636 is provided to recommender module 343 that forwards a recommendation to a user to visit the location during the first portion of the periodic unit of time. FIG. 43b illustrates a user interface that displays the ranking 636 and a recommendation 637 based on the ranking 636. In this illustration, the periodic unit of time is a year, and portions of the periodic unit of time correspond to months in the year. The location is the city Paris, and the recommendation 637 that is illustrated is to visit Paris in April. Thus, based on measurements of tourists who visited Paris during different times of year, the best time to visit Paris is April. Optionally, the recommender module 343 recommends the first portion of the periodic unit of time in a first manner and the second portion of the periodic unit of time in a second manner, which involves a recommendation that is not as strong. For example, the first portion is recommended with an indication that it is the “best” while the second portion is not recommended that way. Additional discussion regarding different ways in which recommendation may be made by recommender module 343 are described in the discussion involving recommender module 178.

[1263] As mentioned above, embodiments of a system illustrated in FIG. 43a may involve different types of locations. Following are some examples of locations and periodic units of times that may be involved in implementation of the illustrated system in different embodiments.

[1264] In one embodiment, the location is an establishment in which entertainment is provided that is one or more of the following establishments: a club, a bar, a movie theater, a theater, a casino, a stadium, and a concert venue. Optionally, in this embodiment, the periodic unit of time is a week, and the ranking 636 indicates what day is best to frequent the establishment.

[1265] In another embodiment, the location is a place of business that is one or more of the following places of business: a store, a restaurant, a booth, a shopping mall, a shopping center, a market, a supermarket, a beauty salon, a spa, and a hospital clinic. Optionally, in this embodiment, the periodic unit of time is a day, and the ranking 636 indicates what hour of the thy is best to visit the business.

[1266] In yet another embodiment, the location is a vacation destination that is one or more of the following: a continent, a country, a county, a city, a resort, a neighborhood, and a hotel. Optionally, in this embodiment, the periodic unit of time is a year, and the ranking 636 indicates what season of the year is recommended for visiting the vacation destination.

[1267] In still another embodiment, the location is a virtual environment in a virtual world, with at least one instantiation of the virtual environment stored in a memory of a computer. Optionally, a user is considered to be in the virtual environment by virtue of having a value stored in the memory of the computer indicating a presence of a representation of the user in the virtual environment. Optionally, in this embodiment, the periodic unit of time is a day, and the ranking 636 indicates what time of the day is best to login to the virtual environment.

[1268] Following is a description of steps that may be performed in a method for ranking times during which to visit a location based on measurements of affective response. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above (illustrated in FIG. 43a), which is configured to rank times during which to visit a location based on measurements of affective response. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method. In one embodiment, the method for ranking times during which to visit the location based on measurements of affective response includes at least the following steps:

[1269] In Step 1, receiving, by a system comprising a processor and memory, measurements of affective response of at least ten users. Optionally, each user of the at least ten users, is at the location at some time during a periodic unit of time, and a measurement of the user is taken with sensor coupled to the user while the user is at the location. Optionally, each measurement of affective response of a user is based on values acquired by measuring the user with the user during at least three different non-overlapping periods while the user is at the location.

[1270] And in Step 2, ranking times to visit the location based on the measurements, such that, visiting the location during a first portion of the periodic unit of time is ranked above visiting the location during a second portion of the periodic unit of time. Furthermore, the measurements upon which the times for visiting the location are ranked include the following the measurements: measurements of at least five users who were at the location during the first portion of the periodic unit of time, and measurements of at least five users who were at the location during the second portion of the periodic unit of time.

[1271] In one embodiment, ranking the times to visit the location in Step 2 involves the following: (i) computing scores for the location corresponding to portions of the periodic unit of time (each score corresponding to a certain portion of the periodic unit of time is computed based on the measurements of the at least five users who were at the location during the certain portion of the periodic unit of time); and (ii) ranking the times to visit the location based on their respective scores.

[1272] In one embodiment, ranking the times to visit the location in Step 2 involves the following: (i) generating a plurality of preference rankings (each preference ranking is indicative of ranks of at least two portions of the periodic unit of time during which to visit the location, such that one portion, of the at least two portions, is ranked above another portion of the at least two portions, and the preference ranking is determined based on a subset of the measurements comprising a measurement of a first user who was at the location during the one portion and a measurement of a second user who was at the location during the other portion; and (ii) ranking the times to visit the location based on the plurality of the preference rankings utilizing a method that satisfies the Condorcet criterion. Optionally, for at least some of the preference rankings mentioned above, if not for all of the preference rankings, the first and second users are the same user.

[1273] In one embodiment, the method described above may include the following steps involved in generating personalized rankings of times during which to visit the location: (i) receiving a profile of a certain user and profiles of at least some of the users who were at the location; (ii) generating an output indicative of similarities between the profile of the certain user and the profiles of the users; and (iii) ranking the times to visit the location based on the output and the measurements. In this embodiment, not all users necessarily have the same ranking of times generated for them. That is, for at least a certain first user and a certain second user, who have different profiles, times for visiting the location are ranked differently, such that for the certain first user, visiting the location during the first portion of the periodic unit of time is ranked above visiting the location during the second portion of the periodic unit of time, and for the certain second user, visiting the location during the second portion of the periodic unit of time is ranked above visiting the location during the first portion of the periodic unit of time.

[1274] Since in typical real-world scenarios the quality of an experience at a location that a user has may involve various uncontrollable factors (e.g., environmental factors and/or influence of other users); thus, the quality of the visit to the location may be different at different times. However, in some cases, it may be possible to anticipate these changes to the quality of a visit to the location, since the changes may have a periodic temporal nature. For example, a certain restaurant may be busy on certain days (e.g., during the weekend) and relatively empty during other times (e.g., weekdays).

[1275] The quality of an experience may correspond to how the user feels while having an experience. Additionally, the quality of an experience may correspond to how a user feels after having the experience. For example, when an experience involves going to a certain vacation destination, the experience may be evaluated both in terms of how much fun a user has while at the destination and/or in terms of how relaxed, invigorated, and/or happy the user was after returning from the vacation.

[1276] Thus, it may be desirable to be able to determine when it is (typically) a good time to visit a location. Additionally, it may be desirable to determine when it is a good time to visit the location in terms of how a user is expected to feel after visiting the location (when the user visits the location at a certain time).

[1277] Some aspects of embodiments described herein involve systems, methods, and/or computer-readable media that may be used to determine when to visit a location. In some embodiments, different times during a periodic unit of time are evaluated in order to assess the quality of an experience when it is had at the different times. A periodic unit of time is a unit of time that repeats itself regularly. An example of periodic unit of time is a thy (a period of 24 hours that repeats itself), a week (a periodic of 7 days that repeats itself, and a year (a period of twelve months that repeats itself). A ranking of the times to have an experience indicates at least one portion of the periodic unit of time that is preferred over another portion of the periodic unit of time during which to have the experience. For example, the ranking may indicate what day of the week is preferable for dining at a restaurant, or what season of the year is preferable for visiting a certain vacation destination.

[1278] Some aspects of this disclosure involve ranking times to visit a location based on aftereffects associated with visiting the location at the different times. Herein, an aftereffect of visiting a location at a certain time may be considered a residual affective response a user may have from visiting the location at the certain time. In some embodiments, a collection module receives measurements of affective response of users who were at the location at different times. A ranking module is used to rank the times based on their corresponding aftereffects, which are determined from the measurements. The measurements of affective response are typically taken by sensors coupled to the users (e.g., sensors in wearable devices and/or sensors implanted in the users). One way in which aftereffects may be determined is by measuring users before and after they leave a location, in order to assess how being at the location changed their affective response. Such measurements are referred to as prior and subsequent measurements. Optionally, a prior measurement may be taken before arriving at the location (e.g., before leaving to go on a vacation) and a subsequent measurement is taken after leaving the location (e.g., after returning from the vacation). Typically, a difference between a subsequent measurement and a prior measurement, of a user who was at a location, is indicative of an aftereffect being at the location had on the user. For example, the value of an aftereffect may indicate how relaxing a vacation was for the user.

[1279] As discussed in Section 3—Experiences, different experiences may be characterized by a combination of attributes. In particular, the time a user has an experience can be an attribute that characterizes the experience. Thus, in some embodiments, doing the same thing at different times (e.g., being at a location at different times), may be considered different experiences. In particular, in some embodiments, different times at which a location is visited may be evaluated, scored, and/or ranked. This can enable generation of suggestions to users of when to visit a certain location. For example, going on a vacation during a holiday weekend may be less relaxing than going during the week. In another example, a certain area of town may be more pleasant to visit in the evening compared to visiting it in the morning.

[1280] In some embodiments, measurements of affective response may be utilized to learn when it is best to visit locations. This may involve ranking different times at which a location may be visited. In some embodiments, the ranking of the different times may be based on how the users felt while they were at the location (e.g., based on measurements taken while at the location, as illustrated in FIG. 43a). In other embodiments, the ranking of the different times may be based on how the users felt after visiting the location (when visiting it at the different times). This approach is illustrated in FIG. 48 below.

[1281] FIG. 48 illustrates a system configured to rank periods to visit a location based on expected aftereffect values. The system includes at least the collection module 120 and aftereffect ranking module 334. The system may optionally include other modules such as the personalization module 130, the location verifier module 505 and/or recommender module 343.

[1282] The collection module 120 is configured to receive, in one embodiment, measurements 501 of affective response. In this embodiment, the measurements 501 include prior and subsequent measurements of affective response of at least ten users, where each user was at the location at some time during a periodic unit of time. A prior measurement is taken before the user leaves the location, and a subsequent measurement of the user is taken after the user leaves the location (e.g., at least ten minutes after the user leaves). Optionally, the prior measurement is taken before the user arrives at the location. Optionally, a difference between a subsequent measurement and a prior measurement of a user who was at the location is indicative of an aftereffect of being at the location has on the user. In this embodiment, measurements 501 comprise prior and subsequent measurements of at least five users who were at the location during a first portion of the periodic unit of time and prior and subsequent measurements of at least five users who were at the location during a second portion of the periodic unit of time that is different from the first period.

[1283] In one example, the periodic unit of time is a thy, and each of the at least ten users are at the location during certain hours of the day (e.g., the first portion may correspond to the morning hours and the second portion may correspond to the afternoon hours). In another example, the periodic unit of time is a week, and each of the at least ten users visits the location during a certain day of the week. In still another example, the periodic unit of time is a year, and each of the at least ten users visits the location during a time that is at least one of the following: a certain month of the user, and a certain annual holiday.

[1284] The aftereffect ranking module 334 is configured, in one embodiment, to generate ranking 652 of periods of time to visit the location based on aftereffects indicated by the measurements 501. The ranking 652 does not necessarily rank different times to visit the location. In particular, in some embodiments, the ranking 652 involves ranking different times to visit the location differently, such that visiting the location during the first portion of the periodic unit of time is ranked above visiting the location during the second portion of the periodic unit of time.

[1285] In one embodiment, when visiting the location during the first portion of the periodic unit of time is ranked above visiting the location during the second portion of the periodic unit of time, that is indicative that, on average, a difference between the subsequent measurements and the prior measurements of the at least five users who were at the location during the first portion is greater than a difference between the subsequent and the prior measurements of the at least five users who were at the location during the second portion. Additionally or alternatively, when visiting the location during the first portion of the periodic unit of time is ranked above visiting the location during the second portion of the periodic unit of time, that is indicative that a first aftereffect score computed based on the prior and subsequent measurements of the at least five users who were at the location during the first portion is greater than a second aftereffect score computed based on the prior and subsequent measurements of the at least five users who were at the location during the second portion.

[1286] In one example, the periodic unit of time is a week, and the first portion corresponds to one of the days of the week (e.g., Tuesday), while the second portion corresponds to another of the days of the week (e.g., Sunday). In this example, the location may be a park, so when the first portion is ranked above the second portion, this means that based on aftereffects determined from prior and subsequent measurements of affective response of users who visited the park, it is better to visit the amusement park on Tuesday, compared to visiting it on Sunday. This might be because on Tuesday the park is not as crowded as it is on Sunday, so visiting the park has a more calming influences for the rest of the thy than visiting the park on Sunday.

[1287] In another example, the periodic unit of time is a year, the first portion corresponds to the months of summer and the second portion corresponds to the months of winter. In this example, the location may be a certain city, so when the first portion is ranked above having the second portion, this means that based on aftereffects determined from prior and subsequent measurements of affective response of users who were at the city, visiting the certain city in the summer has a more positive residual effect on a user's emotional state than visiting the certain city in the winter. For example, after a visit in the summer users are relatively more calm and invigorated compared to their state after a visit to the certain city in the winter.

[1288] In embodiments described herein, not all the measurements utilized by the aftereffect ranking module 334 to generate the ranking 652 are necessarily collected during the same instance of the periodic unit of time. In some embodiments, the measurements utilized by the aftereffect ranking module 334 to generate the ranking 652 include at least a first prior measurement and a second prior measurement such that the first prior measurement was taken during one instance of the periodic unit of time, and the second prior measurement was taken during a different instance of the periodic unit of time. For example, if the periodic unit of time is a week, then the first prior measurement might have been taken during one week (e.g., the first week of August 2016) and the second prior measurement might have been taken during the following week (e.g., the second week of August 2016). Optionally, the difference between the time the first and second prior measurements were taken is at least the periodic unit of time. In a similar fashion, in some embodiments, the measurements utilized by the aftereffect ranking module 334 to generate the ranking 652 include at least a first subsequent measurement and a second subsequent measurement such that the first subsequent measurement was taken during one instance of the periodic unit of time, and the second subsequent measurement was taken during a different instance of the periodic unit of time.

[1289] It is to be noted that the aftereffect ranking module 334 is configured to rank different times at which to visit the location based on aftereffects; with each time being ranked corresponding to a different portion of a periodic unit of time (and where being at the location may be considered a certain type of experience). Since some experiences may be characterized as occurring at a certain period of time (as explained in more detail above), the aftereffect ranking module 334 may be considered a module that ranks different experiences of a certain type (e.g., involving engaging in the same activity and/or being in the same location, but at different times). Thus, the teachings in this disclosure regarding the ranking module 220 may be relevant, in some embodiments, to the aftereffect ranking module 334. Additionally, the teachings related to the aftereffect ranking module 300 (which involves ranking location based on aftereffects), may also be applicable to embodiments involving the aftereffect ranking module 334. The use of the different reference numeral (334) is intended to indicate that rankings in these embodiments involve a ranking of different times at which to have an experience based on an aftereffects.

[1290] The aftereffect ranking module 334, like the ranking module 220 or the aftereffect ranking module 300 and other ranking modules described in this disclosure, may utilize various approaches in order to generate a ranking of times to visit the location. Optionally, each of the times to visit the location is represented by a portion of the periodic unit of time. For example, the different approaches to ranking may include score-based ranking and preference-based ranking, which are described in more detail in at least in section 14—Ranking Experiences. Thus, different implementations of the aftereffect ranking module 334 may comprise different modules to implement the different ranking approaches, as discussed below.

[1291] In one embodiment, the aftereffect ranking module 334 is configured to rank times to visit the location using a score-based approach and comprises the aftereffect scoring module 302, which is configured to compute aftereffect scores for the location, with each score corresponding to a portion of the periodic unit of time. Optionally, each aftereffect score corresponding to a certain portion of the periodic unit of time is computed based on prior and subsequent measurements of the at least five users who were at the location during the certain portion of the periodic unit of time.

[1292] When the aftereffect ranking module 334 includes the aftereffect scoring module 302, it may also include the score-based rank determining module 336, which, in one embodiment, is configured to rank times to visit the location based on their respective aftereffect scores. Optionally, the ranking by the score-based rank determining module 336 is such that a portion of the periodic unit of time with a higher aftereffect score associated with it is not ranked lower than a portion of the periodic unit of time with a lower aftereffect score associated with it. Furthermore, in the discussion above, the first portion of the periodic unit of time has a higher aftereffect score associated with it, compared to the aftereffect score associated with the second portion of the periodic unit of time. It is to be noted that the score-based rank determining module 336 operates in a similar fashion to score-based rank determining module 225, and the use of the reference numeral 336 is done to indicate that the scores according to which ranks are determined correspond to aftereffects associated with visiting the location during different portions of the periodic unit of time.

[1293] In another embodiment, the aftereffect ranking module 334 is configured to rank the times to visit the location using a preference-based approach. In this embodiment, the aftereffect ranking module 334 comprises a preference generator module 338 that is configured to generate a plurality of preference rankings. Each preference ranking is indicative of ranks of at least two portions of the periodic unit of time during which to visit the location, such that one portion, of the at least two portions, is ranked above another portion of the at least two portions. The preference ranking is determined based on a subset comprising at least a pair comprising a prior and a subsequent measurement of a first user who was at the location during the one portion, and at least a pair comprising a prior and a subsequent measurement of a user who was at the location during the other portion. Optionally, the first user and the second user are the same user. Optionally, all of the measurements comprised in each subset of the measurements that is used to generate a preference ranking are prior and subsequent measurements of a single user. Optionally, a majority of the measurements comprised in each subset of the measurements that is used to generate a preference ranking are prior and subsequent measurements of similar users as determined based on the profile comparator 133.

[1294] When the aftereffect ranking module 334 includes the preference generator module 338, it may also include the preference-based rank determining module 340, which, in one embodiment, is configured to rank the times to visit the location based on the plurality of the preference rankings utilizing a method that satisfies the Condorcet criterion. The ranking of the portions of the periodic unit of time by the preference-based rank determining module 340 is such that a certain portion of the periodic unit of time, which in a pair-wise comparison with other portions of the periodic unit of time is preferred over each of the other portions, is not ranked below any of the other portions. Optionally, the certain portion of the periodic unit of time is ranked above at least one of the other portions. Optionally, the certain portion of the periodic unit of time is ranked above each of the other portions.

[1295] In some embodiments, the personalization module 130 may be utilized in order to generate personalized rankings of times to visit the location based on aftereffects of visiting the location at different times. Optionally, the aftereffect ranking module 334 is configured to rank the times to visit the location based on an output generated by the personalization module 130. For at least some of the users, personalized rankings generated based on their profiles are different. In particular, for at least a certain first user and a certain second user, who have different profiles, the aftereffect ranking module 334 ranks times to visit the location differently, such that for the certain first user, visiting the location during the first portion of the periodic unit of time is ranked above visiting the location during the second portion of the periodic unit of time. For the certain second user it is the other way around; visiting the location during the second portion of the periodic unit of time is ranked above visiting the location during the first portion of the periodic unit of time.

[1296] In one embodiment, the recommender module 343 utilizes the ranking 652 to make recommendation 654 in which visiting the location during the first portion of the periodic unit of time is recommended in a first manner (which involves a stronger recommendation than a recommendation made by the recommender module 343 when making a recommendation in the second manner). Optionally, visiting the location during the second portion of the periodic unit of time is recommended in the second manner. Additional discussion regarding recommendations in the first and second manners may be found at least in the discussion about recommender module 178; recommender module 343 may employ first and second manners of recommendation for times to visit the location in a similar manner to the way the recommender module 178 does so when recommending different experiences to have.

[1297] As mentioned above, embodiments of a system illustrated in FIG. 48 may involve different types of locations. Following are some examples of locations and periodic units of times that may be involved in implementation of the illustrated system in different embodiments.

[1298] In one embodiment, the location is an establishment in which entertainment is provided that is one or more of the following establishments: a club, a bar, a movie theater, a theater, a casino, a stadium, and a concert venue. Optionally, in this embodiment, the periodic unit of time is a week, and the ranking 652 indicates what day is best to frequent the establishment in order to increase a positive aftereffect (e.g., level of satisfaction a user is expected to feel in the hours after visiting the establishment).

[1299] In another embodiment, the location is a place of business that is one or more of the following places of business: a store, a restaurant, a booth, a shopping mall, a shopping center, a market, a supermarket, a beauty salon, a spa, and a hospital clinic. Optionally, in this embodiment, the periodic unit of time is a day, and the ranking 652 indicates what hour of the thy is best to visit the business in order for a user to minimize a negative aftereffect of the visit (e.g., reduce the amount of tension measured in the user in the hours after the visiting the business).

[1300] In yet another embodiment, the location is a vacation destination that is one or more of the following: a continent, a country, a county, a city, a resort, a neighborhood, and a hotel. Optionally, in this embodiment, the periodic unit of time is a year, and the ranking 652 indicates what season of the year is recommended for visiting the vacation destination in order to increase a positive aftereffect (e.g., average level of happiness expected to be felt in the days after returning from the vacation destination).

[1301] In still another embodiment, the location is a virtual environment in a virtual world, with at least one instantiation of the virtual environment stored in a memory of a computer. Optionally, a user is considered to be in the virtual environment by virtue of having a value stored in the memory of the computer indicating a presence of a representation of the user in the virtual environment. Optionally, in this embodiment, the periodic unit of time is a day, and the ranking 652 indicates what time of the day is best to login to the virtual environment in order to increase the level of calmness the user is expected to feel in the hours after the user logs out (i.e., leaves) the virtual environment.

[1302] Following is a description of steps that may be performed in a method for ranking times during which to visit a location based on aftereffects computed from measurements of affective response of users who were at the location at the different times. The steps described below may, in one embodiment, be part of the steps performed by an embodiment of the system described above (illustrated in FIG. 48), which is configured to rank times to visit a location based on aftereffects. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations that are part of the method.

[1303] In one embodiment, the method for ranking times during which to visit a location based on aftereffects includes at least the following steps:

[1304] In Step 1, receiving, by a system comprising a processor and memory, prior and subsequent measurements of affective response of at least ten users. Optionally, each user visits the location at some time during a periodic unit of time, a prior measurement is taken before the user leaves the location, and a subsequent measurement taken after the user leaves the location (e.g., at least ten minutes after the user leaves). Optionally, the received measurements comprise prior and subsequent measurements of at least five users who were at the location during a first portion of the periodic unit of time, and prior and subsequent measurements of at least five users who were at the location during a second portion of the periodic unit of time, which is different from the first period. Optionally, the at least five users who were at the location during the first portion do not also visit the location during the second portion. In some embodiments, the measurements received by the system in Step 1 are received by the collection module 120.

[1305] And in Step 2, ranking times to visit the location based on aftereffects indicated by the measurements. Optionally, the ranking is such that it indicates that visiting the location during the first portion of the periodic unit of time is ranked above visiting the location during the second portion of the periodic unit of time. Optionally, the ranking in Step 2 is performed by the aftereffect ranking module 334.

[1306] In one embodiment, ranking the times to visit the location in Step 2 involves the following: (i) computing aftereffect scores for the visiting the location, each score corresponding to a portion of the periodic unit of time (each aftereffect score corresponding to a certain portion of the periodic unit of time is computed based on prior and subsequent measurements of the at least five users who were at the location during the certain portion of the periodic unit of time); and (ii) ranking the times to visit the location based on their respective aftereffect scores. Optionally, the aftereffect scores are computed utilizing the aftereffect scoring module 302 and/or ranking the times is done utilizing score-based rank determining module 336.

[1307] In one embodiment, ranking the times to visit the location in Step 2 involves the following: (i) generating a plurality of preference rankings (each preference ranking is indicative of ranks of at least two portions of the periodic unit of time during which to visit the location, such that one portion, of the at least two portions, is ranked above another portion of the at least two portions, and the preference ranking is determined based on a subset of the measurements comprising a prior and subsequent measurement of a first user who was at the location during the one portion, and a prior and subsequent measurement of a second user who was at the location during the other portion; and (ii) ranking the times to visit the location based on the plurality of the preference rankings utilizing a method that satisfies the Condorcet criterion. Optionally, generating the plurality of preference rankings is done utilizing the preference generator 338 and/or ranking the times utilizing the preference rankings is done utilizing the preference-based rank determining module 340. Optionally, for at least some of the preference rankings mentioned above, if not for all of the preference rankings, the first and second users are the same user.

[1308] In one embodiment, the method described above may include the following steps involved in generating personalized rankings of times during which to visit the location: (i) receiving a profile of a certain user and profiles of at least some of the users who were at the location (optionally, the profiles of the users are from among the profiles 504); (ii) generating an output indicative of similarities between the profile of the certain user and the profiles of the users; and (iii) ranking the times to visit the location based on the output and the prior and subsequent measurements received in Step 2. In this embodiment, not all users necessarily have the same ranking of times generated for them. That is, for at least a certain first user and a certain second user, who have different profiles, times for visiting the location are ranked differently, such that for the certain first user, visiting the location during the first portion of the periodic unit of time is ranked above visiting the location during the second portion of the periodic unit of time. For the certain second user, visiting the location during the second portion of the periodic unit of time is ranked above visiting the location during the first portion of the periodic unit of time.

[1309] When a user has an experience, such as spending time at a certain location, the experience may have an immediate impact on the affective response of the user. However, in some cases, having the experience may also have a delayed and/or residual impact on the affective response of the user. For example, going on a vacation can influence how a user feels after returning from the vacation. After having a nice, relaxing vacation a user may feel invigorated and relaxed, even days after returning from the vacation. However, if the vacation was not enjoyable, the user may be tense, tired, and/or edgy in the days after returning. Having knowledge about the nature of the residual and/or delayed influence associated with a location can help to determine whether a user should be at the location. Thus, there is a need to be able to evaluate locations to determine not only their immediate impact on a user's affective response (e.g., the affective response while the user is at a location), but also their delayed and/or residual impact.

[1310] Some aspects of this disclosure involve learning functions that represent the aftereffect of a location at different times after leaving the location. Herein, an aftereffect of a location may be considered a residual affective response a user may have from visiting the location. In some embodiments, determining the aftereffect is done based on measurements of affective response of users who were at the location (e.g., these may include measurements of at least five users). The measurements of affective response are typically taken with sensors coupled to the users (e.g., sensors in wearable devices and/or sensors implanted in the users). One way in which aftereffects may be determined is by measuring users before and after they leave the location. Having these measurements may enable assessment of how being at the location changed the users' affective response. Such measurements may be referred to herein as “prior” and “subsequent” measurements. A prior measurement may be taken before leaving the location (or even before having arrived at the location) and a subsequent measurement is taken after having leaving the location. Typically, the difference between a subsequent measurement and a prior measurement, of a user who was at a location, is indicative of an aftereffect of the location. For example, the value of an aftereffect may indicate how relaxing a vacation was for the user.

[1311] In some embodiments, a function that describes an aftereffect of a location may be considered to behave like a function of the form ƒ(Δt)=v, where Δt represents a duration that has elapsed since leaving the location and v represents the values of the aftereffect corresponding to the time Δt. In one example, v may be a value indicative of the extent the user is expected to have a certain emotional response, such as being happy, relaxed, and/or excited at a time that is Δt after leaving the location.

[1312] Various approaches may be utilized, in embodiments described herein, to learn parameters of the function mentioned above from the measurements of affective response. In some embodiments, the parameters of the aftereffect function may be learned utilizing an algorithm for training a predictor. For example, the algorithm may be one of various known machine learning-based training algorithms that may be used to create a model for a machine learning-based predictor that may be used to predict target values of the function (e.g., v mentioned above) for different domain values of the function (e.g., Δt mentioned above). Some examples of algorithmic approaches that may be used involve predictors that use regression models, neural networks, nearest neighbor predictors, support vector machines for regression, and/or decision trees. In other embodiments, the parameters of the aftereffect function may be learned using a binning-based approach. For example, the measurements (or values derived from the measurements) may be placed in bins based on their corresponding domain values. Thus, for example, each training sample of the form (Δt,v), the value of Δt is used to determine in which bin to place the sample. After the training data is placed in bins, a representative value is computed for each bin; this value is computed from the v values of the samples in the bin, and typically represents some form of aftereffect score for the location.

[1313] Some aspects of this disclosure involve learning personalized aftereffect functions for different users utilizing profiles of the different users. Given a profile of a certain user, similarities between the profile of the certain user and profiles of other users are used to select and/or weight measurements of affective response of other users, from which an aftereffect function is learned. Thus, different users may have different aftereffect functions created for them, which are learned from the same set of measurements of affective response.

[1314] FIG. 49a illustrates a system configured to learn a function of an aftereffect of a location, which may be considered the residual affective response resulting from being at the location. The function learned by the system (also referred to as an “aftereffect function”), describes the extent of the aftereffect at different times since leaving the location. The system includes at least collection module 120 and function learning module 280. The system may optionally include additional modules, such as the personalization module 130, function comparator 284, and/or the display 252.

[1315] It is to be noted that references to “the location” with respect to an embodiment corresponding to FIG. 49a, modules described in the figure, and/or steps of methods related to figure, may refer to any type of location described in this disclosure (in the physical world and/or a virtual location). Some examples of locations are illustrated in FIG. 1.

[1316] The collection module 120 is configured, in one embodiment, to receive measurements 501 of affective response of users belonging to the crowd 500. The measurements 501 are taken utilizing sensors coupled to the users (as discussed in more detail at least in section 1—Sensors and section 2—Measurements of Affective Response). In this embodiment, the measurements 501 include prior and subsequent measurements of at least ten users who were at the location (denoted with reference numerals 656 and 657, respectively). A prior measurement of a user, from among the prior measurements 656, is taken before the user leaves the location. Optionally, the prior measurement of the user is taken before the user arrives at the location. A subsequent measurement of the user, from among the subsequent measurements 657, is taken after the user leaves the location (e.g., after the elapsing of a duration of at least ten minutes from the time the user leaves the location). Optionally, the subsequent measurements 657 comprise multiple subsequent measurements of a user who was at the location, taken at different times after the user left the location. Optionally, a difference between a subsequent measurement and a prior measurement of a user who was at the location is indicative of an aftereffect of the location (on the user).

[1317] In some embodiments, the prior measurements 656 and/or the subsequent measurements 657 are taken with respect to experiences involving spending a certain length of time in the location. In one example, each user of whom a prior measurement and subsequent measurement are taken, spends a duration at the location that falls within a certain window. In one example, the certain window may be five minutes to two hours (e.g., if the location is a store). In another example the certain window may be one day to one week (e.g., when the location is a vacation destination).

[1318] In some embodiments, the subsequent measurements 657 include measurements taken after different durations had elapsed since leaving the location. In one example, the subsequent measurements 657 include a subsequent measurement of a first user, taken after a first duration had elapsed since the first user left the location. Additionally, in this example, the subsequent measurements 657 include a subsequent measurement of a second user, taken after a second duration had elapsed since the second user left the location. In this example, the second duration is significantly greater than the first duration. Optionally, by “significantly greater” it may mean that the second duration is at least 25% longer than the first duration. In some cases, being “significantly greater” may mean that the second duration is at least double the first duration (or even longer than that).

[1319] The function learning module 280 is configured to receive the prior measurements 656 and the subsequent measurements 657, and to utilize them in order to learn an aftereffect function. Optionally, the aftereffect function describes values of expected affective response after different durations since leaving the location (the function may be represented by model comprising function parameters 658 and/or aftereffect scores 659, which are described below). FIG. 49b illustrates an example of an aftereffect function learned by the function learning module 280. The function is depicted as a graph 658′ of the function whose parameters 658 are learned by the function learning module 280. The parameters 658 may be utilized to determine the expected value of an aftereffect of the location after different durations have elapsed since a user left the location. Optionally, the aftereffect function learned by the function learning module 280 (and represented by the parameters 658 or 659) is at least indicative of values v₁ and v₂ of expected affective response after durations Δt₁ and Δt₂ since leaving the location, respectively. Optionally, Δt₁≠Δt₂ and v₁≠v₂. Optionally, Δt₂ is at least 25% greater than Δt₁. In one example, Δt₁ is at least ten minutes and Δt₁ is at least twenty minutes. FIG. 49b also illustrates such a pairs of function pairs (Δt₁,v₁) and (Δt₂,v₂) for which Δ₁≠Δt₂ and v₁≠v₂.

[1320] The prior measurements 656 may be utilized in various ways by the function learning module 280, which may slightly change what is represented by the aftereffect function. In one embodiment, a prior measurement of a user is utilized to compute a baseline affective response value for the user. In this embodiment, values computed by the aftereffect function may be indicative of differences between the subsequent measurements 657 of the at least ten users and baseline affective response values for the at least ten users. In another embodiment, values computed by the aftereffect function may be indicative of an expected difference between the subsequent measurements 657 and the prior measurements 656.

[1321] Following is a description of different configurations of the function learning module 280 that may be used to learn an aftereffect function of a location. Additional details about the function learning module 280 may be found in this disclosure at least in section 17—Learning Function Parameters.

[1322] In one embodiment, the function learning module 280 utilizes machine learning-based trainer 286 to learn the parameters of the aftereffect function. Optionally, the machine learning-based trainer 286 utilizes the prior measurements 656 and the subsequent measurements 657 to train a model comprising parameters 658 for a predictor configured to predict a value of affective response of a user based on an input indicative of a duration that elapsed since the user left the location. In one example, each pair comprising a prior measurement of a user and a subsequent measurement of a user taken at a duration Δt after leaving the location, is converted to a sample (Δt,v), which may be used to train the predictor. Optionally, v is a value determined based on a difference between the subsequent measurement and the prior measurement and/or a difference between the subsequent measurement and baseline computed based on the prior measurement, as explained above.

[1323] When the trained predictor is provided inputs indicative of the durations Δt₁ and Δt₂, the predictor predicts the values v₁ and v₂, respectively. Optionally, the model comprises at least one of the following: a regression model, a model utilized by a neural network, a nearest neighbor model, a model for a support vector machine for regression, and a model utilized by a decision tree. Optionally, the parameters 658 comprise the parameters of the model and/or other data utilized by the predictor.

[1324] In an alternative embodiment, the function learning module 280 may utilize the binning module 290, which, in this embodiment, is configured to assign subsequent measurements 657 (along with their corresponding prior measurements) to one or more bins, from among a plurality of bins, based on durations corresponding to subsequent measurements 657. A duration corresponding to a subsequent measurement of a user is the duration that elapsed between when the user left the location and when the subsequent measurement is taken. Additionally, each bin, from among the plurality of bins, corresponds to a range of durations.

[1325] For example, if the location related to the aftereffect function is a vacation destination, then the plurality of bins may correspond to the duration that had elapsed since leaving the vacation destination. In this example, the first bin may include subsequent measurements taken within the first 24 hours after leaving, the second bin may include subsequent measurements taken 24-48 hours after leaving, the third bin may include subsequent measurements taken 48-72 hours after leaving, etc. Thus, each bin includes subsequent measurements (possibly along with other data such as corresponding prior measurements), which may be used to compute a value indicative of the aftereffect a user may be expected to have after a duration, which corresponds to the bin, has elapsed since the user left the vacation destination.

[1326] Additionally, in this embodiment, the function learning module 280 may utilize the aftereffect scoring module 302, which, in one embodiment, is configured to compute a plurality of aftereffect scores 659 corresponding to the plurality of bins. An aftereffect score corresponding to a bin is computed based on prior and subsequent measurements of at least five users, from among the at least ten users. The measurements of the at least five users used to compute the aftereffect score corresponding to the bin were taken at a time Δt after leaving the location, and the time Δt falls within the range of times that corresponds to the bin. Optionally, subsequent measurements used to compute the aftereffect score corresponding to the bin were assigned to the bin by the binning module 290. Optionally, with respect to the values Δt₁, Δt₂, v₁, and v₂ mentioned above, Δt₁ falls within a range of durations corresponding to a first bin, Δt₂ falls within a range of durations corresponding to a second bin, which is different from the first bin, and the values v₁ and v₂ are the aftereffect scores corresponding to the first and second bins, respectively.

[1327] In one embodiment, an aftereffect score for a location is indicative of an extent of feeling at least one of the following emotions after leaving the location: pain, anxiety, annoyance, stress, aggression, aggravation, fear, sadness, drowsiness, apathy, anger, happiness, contentment, calmness, attentiveness, affection, and excitement. Optionally, the aftereffect score is indicative of a magnitude of a change in the level of the at least one of the emotions due to being in the location.

[1328] Embodiments described herein in may involve various types of locations for which an aftereffect function may be learned using the system illustrated in FIG. 49a. Following are a few examples of locations and functions of aftereffects that may be learned.

[1329] Vacation Destination—In one embodiment, the location for which the aftereffect function is computed is a vacation destination. For example, the vacation destination may be a certain country, a certain city, a certain resort, a certain hotel, and/or a certain park. The aftereffect function in this example may describe to what extent a user feels relaxed and/or happy (e.g., on a scale from 1 to 10) at a certain time after returning from the vacation destination; the certain time in this example may be 0 to 10 days from the return. In this embodiment, a prior measurement of the user may be taken before the user goes on the vacation (or while the user is on the vacation), and a subsequent measurement is taken at a time Δt after the user returns from the vacation. Optionally, in addition to the input value indicative of Δt, the aftereffect function may receive additional input values. For example, in one embodiment, the aftereffect function receives an additional input value d indicative of how long the vacation was (i.e., how many days a user spent at the vacation destination). Thus, in this example, the aftereffect function may be considered to behave like a function of the form ƒ(Δt,d)=v, and it may describe the affective response v a user is expected to feel at a time Δt after spending a duration of d at the vacation destination.

[1330] Virtual Location—In one embodiment, the location for which the aftereffect function is computed is virtual location, such as a virtual world. Optionally, the virtual location may correspond to a certain server that a user may log into. The aftereffect function in this example may describe to what extent a user feels relaxed and/or happy (e.g., on a scale from 1 to 10) at a certain time after leaving the virtual location (e.g., by logging out of the server). The certain time in this example may be 0 to 24 hours from the return from the vacation. In this embodiment, a prior measurement of the user may be taken before the user enters the virtual location (e.g., before putting on a head-mounted display) and/or while the user is in the virtual location; a subsequent measurement is taken at a time Δt after the user leaves the virtual location (e.g., by removing the head-mounted display). Optionally, in addition to the input value indicative of Δt, the aftereffect function may receive additional input values. For example, in one embodiment, the aftereffect function receives an additional input value d indicative of how much time the user spent in the virtual location. Thus, in this example, the aftereffect function may be considered to behave like a function of the form ƒ(Δt,d)=v, and it may describe the affective response v a user is expected to feel at a time Δt leaving the virtual location, after having spent a duration of d in the virtual location.

[1331] Environment—In one embodiment, the location for which the aftereffect function is computed is an environment characterized by a certain environmental parameter being in a certain range. Examples of environmental parameters include temperature, humidity, altitude, air quality, and allergen levels. The aftereffect function in this example may describe how well a user feels (e.g., on a scale from 1 to 10) after spending time in an environment characterized by an environmental parameter being in a certain range (e.g., the temperature in the environment is between 10° F. and 30° F., the altitude is above 5000 ft., the air quality is good, etc.) The certain time in this example may be 0 to 12 hours from the time the user left the environment. In this embodiment, a prior measurement of the user may be taken before the user enters the environment (or while the user is in the environment), and a subsequent measurement is taken at a time Δt after the user leaves the environment. Optionally, in addition to the input value indicative of Δt, the aftereffect function may receive additional input values. In one example, the aftereffect function receives an additional input value d that is indicative of a duration spent in the environment. Thus, in this example, the aftereffect function may be considered to behave like a function of the form ƒ(Δt,d)=v, and it may describe the affective response v a user is expected to feel at a time Δt after spending a duration d in the environment. In another example, an input value may represent the environmental parameter. For example, an input value q may represent the air quality index (AQI). Thus, the aftereffect function in this example may be considered to behave like a function of the form ƒ(Δt,d,q)=v, and it may describe the affective response v a user is expected to feel at a time Δt after spending a duration d in the environment that has air quality q.

[1332] In some embodiments, aftereffect functions of different locations are compared. Optionally, such a comparison may help determine which location is better in terms of its aftereffect on users (and/or on a certain user if the aftereffect functions are personalized for the certain user). Comparison of aftereffects may be done utilizing the function comparator module 284, which, in one embodiment, is configured to receive descriptions of at least first and second aftereffect functions that describe values of expected affective response at different durations after leaving respective first and second locations. The function comparator module 284 is also configured, in this embodiment, to compare the first and second functions and to provide an indication of at least one of the following: (i) the location, from among the first and second locations, for which the average aftereffect, from the time of leaving the respective location until a certain duration Δt, is greatest; (ii) the location, from among the first and second locations, for which the average aftereffect, from a time starting at a certain duration Δt after leaving the respective location and onwards, is greatest; and (iii) the location, from among the first and second locations, for which at a time corresponding to elapsing of a certain duration Δt since leaving the respective location, the corresponding aftereffect is greatest. Optionally, comparing aftereffect functions may involve computing integrals of the functions, as described in more detail in section 17—Learning Function Parameters.

[1333] In some embodiments, the personalization module 130 may be utilized to learn personalized aftereffect functions for different users by utilizing profiles of the different users. Given a profile of a certain user, the personalization module 130 may generate an output indicative of similarities between the profile of the certain user and the profiles from among the profiles 504 of the at least ten users. Utilizing this output, the function learning module 280 may select and/or weight measurements from among the prior measurements 656 and the subsequent measurements 657, in order to learn an aftereffect function personalized for the certain user. Optionally, the aftereffect function personalized for the certain user describes values of expected affective response that the certain user may have, at different durations after leaving the location.

[1334] Depending on the embodiment, a profile of a user, such as a profile from among the profiles 504 may include various values, each of may be characterized as being one or more of the following: a demographic characteristic of the user, a genetic characteristic of the user, a static attribute describing the body of the user, a medical condition of the user, an indication of a content item consumed by the user, an indication of a location visited by the user, and a feature value derived from semantic analysis of a communication of the user.

[1335] It is to be noted that personalized aftereffect functions are not necessarily the same for all users; for some input values, aftereffect functions that are personalized for different users may assign different target values. That is, for at least a certain first user and a certain second user, who have different profiles, the function learning module 280 learns different aftereffect functions, denoted ƒ₁ and ƒ₂, respectively. In one example, ƒ₁ is indicative of values v₁ and v₂ of expected affective responses after durations Δt₁ and τ₂ since leaving the location, respectively, and ƒ₂ is indicative of values v₃ and v₄ of expected affective responses after the durations Δt₁ and Δt₂ since leaving the location, respectively. Additionally, Δt₁≠Δt₂, v₁≠v₂, v₃≠v₄, and v₁≠v₃.

[1336] FIG. 50 illustrates such a scenario where personalized aftereffect functions are generated for different users. In this illustration, certain first user 662a and certain second user 662b have different profiles 663a and 663b, respectively. Given these profiles, the personalization module 130 generates different outputs that are utilized by the function learning module 280 to learn functions 664a and 664b for the certain first user 662a and the certain second user 662b, respectively. The different functions indicate different expected aftereffect trends for the different users; namely, that the aftereffect of the certain second user 662b initially falls much quicker than the aftereffect of the certain first user 662a.

[1337] Additional information regarding personalization, such as what information the profiles 504 of users may contain, how to determine similarity between profiles, and/or how the output may be utilized, may be found at least in section 11—Personalization.

[1338] FIG. 51 illustrates steps involved in one embodiment of a method for learning a function describing an aftereffect of a location. The steps illustrated in FIG. 51 may be used, in some embodiments, by systems modeled according to FIG. 49a. In some embodiments, instructions for implementing the method may be stored on a computer-readable medium, which may optionally be a non-transitory computer-readable medium. In response to execution by a system including a processor and memory, the instructions cause the system to perform operations of the method.

[1339] In one embodiment, a method for learning a function describing an aftereffect of a location includes at least the following steps:

[1340] In Step 660a, receiving, by a system comprising a processor and memory, measurements of affective response of users taken utilizing sensors coupled to the users. Optionally, the measurements include prior and subsequent measurements of at least ten users who were at the location. A prior measurement of a user is taken before the user leaves the location (or even before the user arrives at the location). A subsequent measurement of the user is taken after the user leaves the location (e.g., after elapsing of a duration of at least ten minutes after the user leaves the location). Optionally, the prior and subsequent measurements are received by the collection module 120. Optionally, the prior measurements that are received in this step are the prior measurements 656 and the subsequent measurements received in this step are the subsequent measurements 657.

[1341] And in Step 660b, learning, based on prior measurements 656 and the subsequent measurements 657, parameters of an aftereffect function, which describes values of expected affective response after different durations since leaving the location. Optionally, the aftereffect function is at least indicative of values v₁ and v₂ of expected affective response after durations Δt₁ and Δt₂ since leaving the location, respectively; where Δt₁≠Δt₂ and v₁≠v₂. Optionally, the aftereffect function is learned utilizing the function learning module 280.

[1342] In one embodiment, Step 660a optionally involves utilizing a sensor coupled to a user who was at the location to obtain a prior measurement of affective response of the user and/or a subsequent measurement of affective response of the user. Optionally, Step 660a may involve taking multiple subsequent measurements of the user at different times after the user left the location.

[1343] In some embodiments, the method may optionally include Step 660c that involves displaying the aftereffect function learned in Step 660b on a display such as the display 252. Optionally, displaying the aftereffect function involves rendering a representation of the aftereffect function and/or its parameters. For example, the function may be rendered as a graph, plot, and/or any other image that represents values given by the function and/or parameters of the function.

[1344] As discussed above, parameters of the aftereffect function may be learned from measurements of affective response utilizing various approaches. Therefore, Step 660b may involve performing different operations in different embodiments.

[1345] In one embodiment, learning the parameters of the aftereffect function in Step 660b comprises utilizing a machine learning-based trainer that is configured to utilize the prior measurements 656 and the subsequent measurements 657 to train a model for a predictor configured to predict a value of affective response of a user based on an input indicative of a duration that elapsed since the user left the location. Optionally, the values in the model are such that responsive to being provided inputs indicative of the durations Δt₁ and Δt₂, the predictor predicts the values v₁ and v₂, respectively.

[1346] In another embodiment, learning the parameters of the aftereffect function in Step 660b involves performing the following operations: (i) assigning subsequent measurements to a plurality of bins based on durations corresponding to subsequent measurements (a duration corresponding to a subsequent measurement of a user is the duration that elapsed between when the user left the location and when the subsequent measurement is taken); and (ii) computing a plurality of aftereffect scores corresponding to the plurality of bins. Optionally, an aftereffect score corresponding to a bin is computed based on prior and subsequent measurements of at least five users, from among the at least ten users, selected such that durations corresponding to the subsequent measurements of the at least five users fall within the range corresponding to the bin; thus, each bin corresponds to a range of durations corresponding to subsequent measurements. Optionally, the aftereffect score is computed by the aftereffect scoring module 302. Optionally, Δt₁ falls within a range of durations corresponding to a first bin, Δt₂ falls within a range of durations corresponding to a second bin, which is different from the first bin, and the values v₁ and v₂ are the aftereffect scores corresponding to the first and second bins, respectively.

[1347] In some embodiments, aftereffect functions learned by a method illustrated in FIG. 51 may be compared (e.g., utilizing the function comparator 284). Optionally, performing such a comparison involves the following steps: (i) receiving descriptions of first and second aftereffect functions that describe values of expected affective response at different durations after leaving respective first and second locations; (ii) comparing the first and second aftereffect functions; and (iii) providing an indication derived from the comparison. Optionally, the indication indicates least one of the following: (i) the location from among the first and second location for which the average aftereffect, from the time of leaving the respective location until a certain duration Δt, is greatest; (ii) the location from among the first and second locations for which the average aftereffect, from a time starting at a certain duration Δt after leaving the respective location and onwards, is greatest; and (iii) the location from among the first and second locations for which at a time corresponding to elapsing of a certain duration Δt since leaving the respective location, the corresponding aftereffect is greatest.

[1348] An aftereffect function learned by a method illustrated in FIG. 51 may be personalized for a certain user. In such a case, the method may include the following steps: (i) receiving a profile of a certain user and profiles of at least some of the users (who contributed measurements used for learning the personalized functions); (ii) generating an output indicative of similarities between the profile of the certain user and the profiles; and (iii) utilizing the output to learn an aftereffect function personalized for the certain user that describes values of expected affective response at different durations after leaving the location. Optionally, the output is generated utilizing the personalization module 130. Depending on the type of personalization approach used and/or the type of function learning approach used, the output may be utilized in various ways to learn an aftereffect function for the location, as discussed in further detail above. Optionally, for at least a certain first user and a certain second user, who have different profiles, different aftereffect functions are learned, denoted ƒ₁ and ƒ₂, respectively. In one example, ƒ₁ is indicative of values v₁ and v₂ of expected affective responses after durations Δt₁ and Δt₂ since leaving the location, respectively, and ƒ₂ is indicative of values v₃ and v₄ of expected affective responses after the durations Δt₁ and Δt₂ since leaving the location, respectively. Additionally, in this example, Δt₁≠Δt₂, v₁≠v₂, v₃≠v₄, and v₁≠v₃.