Abstract: Software agents that correct biases in measurements of affective response. In one embodiment, a sensor takes a measurement of affective response of a user, where the measurement corresponds to an event in which the user has an experience. A computer generates a description of the event that includes factors characterizing the event which correspond to at least one of the following: the user, the experience, and the instantiation of the event. The computer identifies, based on the description, a certain factor characterizes the event, and computes a corrected measurement, which is different from the measurement taken by the sensor. The corrected measurement is computed by modifying the sensor values utilizing a model trained on data comprising: measurements of affective response of the user corresponding to events involving the user having various experiences, and descriptions of the events.

Abstract: Software agents collect measurements of affective response and provide them in a selective manner that maintains user privacy. In one embodiment, a sensor takes measurements of affective response of the user. A computer receives a request for an affective value indicative of an emotional response to an experience. The computer selects, from among the measurements, a certain measurement of affective response of the user that corresponds to an event in which the user had the experience. The computer then utilizes a model to calculate, based on the certain measurement, the affective value indicative of the emotional response of the user to experience. Optionally, the model is generated based on previously taken measurements of affective response of the user, taken while the user had various experiences, and indications of emotional responses the user had while said previously taken measurements were taken. The computer sends the affective value to fulfil the request.

Abstract: Some aspects of this disclosure involve generation of crowd-based results based on measurements of affective response of users. In some embodiments described herein, sensors are used to take measurements of affective response of at least ten users who were at a certain location. The measurements may include various values indicative of physiological signals and/or behavioral cues of the at least ten users. Some examples of locations in this disclosure include vacation destinations, businesses, establishments that provide entertainment, certain geographical regions, and virtual environments. User interfaces are configured to receive data describing a location score computed based on the measurements of the at least ten users, which represents the affective response of the at least ten users to being at the certain location. The user interfaces may be used to report the location score (e.g., to a user who may be interested in visiting the certain location).

Abstract: Software agents collect measurements of affective response and provide them in a selective manner that maintains user privacy. In one embodiment, a sensor takes measurements of affective response of the user. A computer receives a request for an affective value indicative of an emotional response to an experience. The computer selects, from among the measurements, a certain measurement of affective response of the user that corresponds to an event in which the user had the experience. The computer then utilizes a model to calculate, based on the certain measurement, the affective value indicative of the emotional response of the user to experience. Optionally, the model is generated based on previously taken measurements of affective response of the user, taken while the user had various experiences, and indications of emotional responses the user had while said previously taken measurements were taken. The computer sends the affective value to fulfil the request.

Abstract: Software agents and smart contracts may assist users to control how their measurements of affective response are used and how they are compensated for providing their measurements. In one embodiment, a computer receives a measurement of affective response of a user to an experience, and also receives a request indicative of compensation to be provided to the user for a disclosure of a score for the experience. The score is to be calculated based on the measurement of the user and additional measurements of affective response of other users to the experience. The computer calculates a risk to privacy of the user due to a disclosure of the score. Responsive to the risk being below a first threshold and the compensation reaching a second threshold, the computer signs on behalf of the user, a smart contract that facilitates the disclosure of the score and a transfer of the compensation.

Abstract: Some aspects of this disclosure include systems, methods, and/or computed readable media that may be used to generate crowd-based results based on measurements of affective response of users. In some embodiments described herein, sensors are used to take measurements of affective response of at least ten users who have a certain experience. The measurements may include various values indicative of physiological signals and/or behavioral cues of the at least ten users. Some examples of experiences mentioned herein include going on vacations, eating in restaurants, and utilizing various products. User interfaces are configured to receive data describing a score computed based on the measurements of the at least ten users, which represents the affective response of the at least ten users to having the certain experience. The user interfaces may be used to report the score (e.g., to a user who may be interested in having the certain experience).

Abstract: Some aspects of this disclosure include systems, methods, and/or computer programs that may be used to collect measurements of affective response of users and to utilize the collected measurements to generate a crowd-based result, such as a score for an experience which the users had. Some embodiments described herein involve sending requests for measurements of affective response to software agents operating on behalf of the users. The requests may provide details regarding the type of measurements requested and may also provide assurances. The assurances may relate to various aspects such as the number of users whose measurements are to be used to generate the crowd-based result, the disclosure of the crowd-based result (e.g., regarding the recipients of the crowd-based result), and the compensation for providing the measurements. Software agents that accept the assurances and have relevant measurements may provide the measurements, which are then utilized to generate the crowd-based result.

Abstract: Some aspects of this disclosure include systems, methods, and/or computer programs that may be used to filter measurements of affective response collected using sensors that measure physiological signals and/or behavioral cues of users. A bias may be tendency, attitude, and/or inclination, which may influence the affective response a user has to an experience. Some embodiments described herein involve learning a model of biases of the user from measurements of affective response of the user and descriptions of events to which the measurements correspond. The model may be used to filter measurements of the user, which may be provided to other entities, if the model indicates that the measurements are likely to contain a certain level and/or type of bias (e.g., bias to certain factors). Such a filtration process can help make results generated based on the measurements more accurate and also may help protect the privacy of the user.

Abstract: Some aspects of this disclosure involve ranking types of food based on measurements of affective response of users who consumed the types of food. In one embodiment, a system that ranks types of food includes sensors that take measurements of affective response of users and a computer. The computer receives indications of times at which the users consumed the types of food and selects for each type of food, based on the indications, measurements of at least five of the users taken up to four hours after consuming the type of food. The computer ranks the types of food based on the selected measurements. Optionally, each of the types of food is a different item on a menu comprising food items.

Abstract: Some aspects of this disclosure include systems, methods, and/or computer programs that may be used to determine a cause of an inaccuracy in predicted affective response to an event that involves a user who has an experience. Some embodiments described herein involve identifying when a difference between a measurement of affective response corresponding to an event is different from the predicted affective response corresponding to the event. When such a discrepancy is identified, a presentation is made to the user of at least one of the following: one or more factors characterizing the event, and effects of the one or more factors on the user, as determined based on a model of the user. Based on a comment made by the user in response to the presentation, at least one of the following is identified: a discrepancy in a description of the event, and an inaccuracy in the model.

Abstract: Described herein are systems, methods and computer-readable media for recommending a repeated experience. One embodiment includes collecting a subset of the measurements that includes measurements of at least five of the users who had an experience; each measurement of a user is associated with a value indicative of an extent to which the user had previously experienced the experience. Calculating parameters of a function based on the measurements in the subset and their associated values, where the function describes, for different extents to which the experience had been previously experienced, an expected affective response to experiencing the experience again. And responsive to determining that an expected affective response to experiencing the experience again after having experiencing it for at least a certain extent reaches a threshold, recommending the experience.

Abstract: Described herein are embodiments of systems, method, and computer programs for recommending an experience to a user. In one embodiment, a sensor takes measurements of affective response of the user while the user is exposed to token instances that are instantiations of visual tokens. An eye tracker measures the gaze of the user while the user is exposed to the token instances. A computer calculates, based on the measurements of affective response and measurements of the gaze, values of expected affective response of the user to exposure to instantiations of the visual tokens. These values are utilized to select an experience for the user, such that based on the values, an expected affective response of the user to exposure to token instances corresponding to the selected experience is more positive than an expected affective response of the user to exposure to token instances corresponding to other experiences.

Abstract: This disclosure describes systems, methods, and computer program products that may be used to provide crowd-based scores for experiences, which are computed based on measurements of affective response of multiple users who had the experiences. The measurements of affective response may be collected using a wide array of sensors that measure physiological signals and/or behavioral cues. Additionally, the scores may be personalized for a certain user based on similarities between a profile of the certain user and profiles of the multiple users.

Abstract: Some aspects of this disclosure include systems, methods, and/or computer programs that may be used to notify a user about a cause of emotional imbalance. Some embodiments described herein involve determining when a user is in a state of emotional imbalance based on a measurement of affective response of the user, which is taken with a sensor. Factors characterizing an event to which the measurement corresponds are examined in order to select a certain factor that, based on a model of the user, has an effect on the user that is congruous with the measurement of affective response. The user is notified about the certain factor in order to make the user more aware of the certain factor and its effect on the user.

Abstract: Some aspects of this disclosure involve ranking types of food based on measurements of affective response of users who consumed the types of food. In one embodiment, a system that ranks types of food includes sensors that take measurements of affective response of users and a computer. The computer receives indications of times at which the users consumed the types of food and selects for each type of food, based on the indications, measurements of at least five of the users taken up to four hours after consuming the type of food. The computer ranks the types of food based on the selected measurements. Optionally, each of the types of food is a different item on a menu comprising food items.

Abstract: Some aspects of this disclosure involve computation of a preference score for a certain type of food. In some embodiments described herein, measurements of affective response of at least ten users are collected. The measurements may include various values indicative of physiological signals and/or behavioral cues of the at least ten users. Each measurement of a user is taken with a sensor coupled to the user up to four hours after the user consumed the certain type of food. A preference score is computed based on the measurements. The preference score is indicative of how much the at least ten users enjoyed consuming the certain type of food and/or how well they felt after consuming the certain type of food.

Abstract: Described herein are embodiments of systems, method, and computer programs for estimating affective response to a token instance of interest utilizing a predicted affective response to a background token instance. The affective response is computed based on the difference between a measured affective response of the user and the predicted affective response. Where the measured affective response is a measurement of the affective response of the user to a simultaneous exposure to both the background token instance and the token instance of interest.

Abstract: Described herein are embodiments of systems, methods, and computer programs for estimating affective response to a token instance of interest. A measurement of affective response of the user to a temporal window of token instances is taken. A token instance belonging to the window is selected as a token instance of interest based on an attention level of the user, and is removed from the temporal window of token instances. A predicted affective response to the temporal window of token instances from which the token instance of interest is removed is computed. The affective response to the token instance of interest is computed based on the difference between the measured affective response of the user and the predicted affective response.

Abstract: A user may be exposed to multiple token instances representing stimuli that may influence the affective state of the user. Described herein are embodiments of systems, method, and computer programs for estimating affective response to a token instance of interest, selected from among the token instances. The token instance of interest is selected based on attention levels derived from eye-tracking data. In one example embodiment, the token instance of interest is a token instance in which the attention of the user is higher than the attention level of the user in at least one other token instance to which the user is exposed at the same time.

Abstract: A user may be exposed to multiple token instances representing stimuli that may influence the affective state of the user. Described herein are embodiments of systems, method, and computer programs for estimating affective response to a token instance of interest, selected from among the token instances. The token instance of interest is selected based on attention levels received from an external source such as, information received from other users, information received from analysis of communications of the users, and/or information received from an external site such as an online retailer or a social network. In one example embodiment, the token instance of interest is a token instance for which attention level of the user is higher than at least one other token instance.