Personalized experience scores based on measurements of affective response

• US 20160055236A1
• Filed: 08/21/2015
• Published: 02/25/2016
• Est. Priority Date: 08/21/2014
• Status: Active Grant

• Current Assignee: Affectomatics ltd.
• Original Assignee: Affectomatics ltd.
• Inventors: Frank, Ari M., Thieberger, Gil
• Granted Patent: US 10,198,505 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 40/295 Named entity recognition
• CPC Class Codes: G06F 40/35 Discourse or dialogue representation
• CPC Class Codes: G06F 40/56 Natural language generation
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 50/01 Social networking

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.

Claims

1. A system configured to utilize profiles of users to compute personalized experience scores based on measurements of affective response of the users, comprising:

a memory storing computer executable modules; and

a processor configured to execute the computer executable modules;

the computer executable modules comprising;

a collection module configured to receive measurements of affective response of at least ten users;

wherein each measurement of a user corresponds to an event in which the user has the experience;

a personalization module configured 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; and

a scoring module configured to compute an experience score for the certain user based on the measurements and the output;

wherein, for at least a certain first user and a certain second user, who have different profiles, the scoring module computes respective first and second experience scores that are different.

2. The system of claim 1, wherein a profile of a user comprises information that describes one or more of the following:

an indication of an experience the user had, 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.

3. The system of claim 1, wherein the first experience score is computed based on at least one measurement that is not utilized for computing the second experience score.

4. The system of claim 1, wherein a measurement utilized to compute both the first and second experience scores has a first weight when utilized to compute the first experience score and the measurement has a second weight, different from the first weight, when utilized to compute the second experience score.

5. The system of claim 1, further comprising a recommender module configured to recommend an experience to a user in a manner that belongs to a set comprising first and second manners;

wherein when recommending an experience in the first manner, the recommender module provides a stronger recommendation for the experience, compared to a recommendation for the experience that the recommender module provides when recommending in the second manner.

6. The system of claim 5, wherein the recommender module is further configured to:

recommend the experience in the first manner when an experience score for the experience reaches a predetermined threshold, and to recommend the experience in the second manner when the experience score does not reach the predetermined threshold.

7. The system of claim 6, wherein responsive to the first experience score reaching the predetermined threshold, the experience is recommended, by the recommender module, to the certain first user in the first manner;

and wherein responsive to the second experience score not reaching the predetermined threshold, the experience is recommended, by the recommender module, to the certain second user in the second manner.

8. The system of claim 1, wherein the personalization module comprises:

a profile comparator module configured to compute a value indicative of an extent of a similarity between a pair of profiles of users;

a weighting module configured to receive a profile of a certain user and profiles of the at least ten users and to generate weights for the measurements of the at least ten users;

wherein the weight for a measurement of a user, from among the at least ten users, is proportional to the extent of a similarity computed by the profile comparator module between a pair comprising 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;

and wherein the experience score is computed based on the measurements of the at least ten users and the weights.

9. The system of claim 1, wherein the personalization module comprises:

a clustering module configured to receive profiles of the at least ten users, and to cluster the users into clusters based on profile similarity, with each cluster comprising a single user or multiple users with similar profiles; and

a selector module 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;

wherein, 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;

the selector module is further configured to select at least eight users from among the users belonging to clusters in the subset;

and wherein the experience score is computed based on measurements of the at least eight users.

10. A system configured to utilize comparison of profiles of users to compute personalized experience scores based on measurements of affective response of the users, comprising:

a memory storing computer executable modules; and

a processor configured to execute the computer executable modules;

the computer executable modules comprising;

a collection module configured to receive measurements of affective response of at least ten users;

wherein each measurement of a user corresponds to an event in which the user has the experience;

a profile comparator module configured to compute a value indicative of an extent of a similarity between a pair of profiles of users;

a weighting module configured to receive a profile of a certain user and profiles of the at least ten users and to generate weights for the measurements of the at least ten users;

wherein 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 between a pair comprising 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; and

a scoring module configured to compute an experience score for the certain user based on the measurements and weights computed based on the profile of the certain user;

wherein, for at least a certain first user and a certain second user, who have different profiles, the scoring module computes different experience scores.

11. The system of claim 10, wherein the profile comparator is further configured to utilize a distance metric that is applied to a pair of vectors of feature values, each representing one of the profiles of the pair of profiles.

12. The system of claim 11, wherein the distance metric is based on one or more of the following metrics applied to the pair of vectors:

a dot-product between the pair of vectors, a Euclidean distance between the pair of vectors, and the Hamming distance between the pair of vectors.

13. The system of claim 10, wherein the profile comparator is further configured to utilize a predictor to compute a similarity between the pair of profiles;

wherein the predictor is trained on data comprising samples and their corresponding labels; and

wherein each sample comprises feature values derived from a certain pair of profiles of users, and the sample'"'"'s corresponding label is indicative of the similarity between the certain pair of profiles.

14. The system of claim 10, wherein a first set of weights for the measurements, generated based on the profile of the certain first user, is different from a second set of weights for the measurements, generated based on the profiles of the certain second user, such that the weight in the first set for a certain measurement from among the measurements is different than the weight in the second set for the certain measurement.

15. A system configured to utilize clustering of profiles of users to compute personalized experience scores based on measurements of affective response of the users, comprising:

a memory storing computer executable modules; and

a processor configured to execute the computer executable modules;

the computer executable modules comprising;

a collection module configured to receive measurements of affective response of at least ten users;

wherein each measurement of a user corresponds to an event in which the user has the experience;

a clustering module configured to receive profiles 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;

a selector module 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;

wherein, 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;

the selector module is further configured to select at least eight users from among the users belonging to clusters in the subset; and

a scoring module configured to compute an experience score for the certain user based on the measurements of the at least eight users;

wherein, for at least a certain first user and a certain second user, who have different profiles, the scoring module computes different personalized scores.

16. The system of claim 15, wherein clustering the users is done utilizing one or more of the following clustering approaches:

hierarchical clustering, partition-based clustering, and clustering utilizing an Expectation-Maximization algorithm.

17. The system of claim 15, wherein a profile of a user comprises information that describes one or more of the following:

an indication of an experience the user had, 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;

further comprising a profile comparator module configured to compute a value indicative of an extent of a similarity between a pair of profiles of users;

wherein the clustering module is further configured to utilize the profile comparator to perform the clustering.

18. The system of claim 17, wherein the selector module is further configured to utilize the profile comparator to select the one or more clusters.

19. The system of claim 17, wherein the profile comparator is further configured to utilize a distance metric applied to a pair of vectors of feature values, each representing one of the profiles of the pair of profiles.

20. The system of claim 19, wherein the distance metric is based on one or more of the following metrics applied to the pair of vectors:

a dot-product between the pair of vectors, a Euclidean distance between the pair of vectors, and the Hamming distance between the pair of vectors.