Utilizing eye-tracking to estimate affective response to a token instance of interest

• US 9,569,734 B2
• Filed: 03/14/2015
• Issued: 02/14/2017
• Est. Priority Date: 10/20/2011
• Status: Active Grant

• Current Assignee: Affectomatics ltd.
• Original Assignee: Affectomatics ltd.
• Inventors: Thieberger, Gil, Frank, Ari M
• Primary Examiner(s): SITIRICHE, LUIS A
• Application Number:
  US14/658,152
• Publication Number:
  US 20150220841A1
• Time in Patent Office: 703 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

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.

Claims

1. A system configured to utilize eye-tracking to estimate affective response to a token instance of interest, comprising:

a memory configured to store;

a total affective response of a user to token instances to which the user was exposed and representations of first and second attention levels in respective first and second token instances from among the token instances;

wherein the first and second attention levels are computed based on eye-tracking data;

wherein the first and second token instances are visual token instances that have overlapping instantiation periods, and each of the first and second token instances corresponds to an object or a character, and is generated utilizing analysis of images obtained from at least one of the following sources;

content to which the user was exposed, and images of the surroundings of the user acquired with a camera;

wherein each token instance, from among the first and second token instances, comprises an attribute value indicative of at least one of the following values;

a duration of exposure to the token instance, and a size of an object or character to which the token instance corresponds;

and wherein the first attention level is greater than the second attention level;

a processor configured to select, based on the representations of the first and second attention levels, the first token instance as the token instance of interest; and

the processor is further configured to estimate the affective response to the token instance of interest based on the total affective response and the attribute value of the first token instance.

2. The system of claim 1, wherein the eye-tracking data comprises eye-tracking data of the user obtained by monitoring a gaze of the user while the user was exposed to the token instances.

3. The system of claim 2, wherein the first and second attentions level are proportional to the time the user spent looking at images corresponding to the first and second token instances, respectively.

4. The system of claim 1, wherein the eye-tracking data comprises eye-tracking data of a second user, obtained by monitoring a gaze of the second user while the second user was exposed to the token instances.

5. The system of claim 1, wherein the total affective response is expressed as an emotional response.

6. The system of claim 1, wherein the total affective response is expressed as a value of a user measurement channel of the user.

7. The system of claim 1, wherein the total affective response is proportional to a difference between affective responses of the user before and after the user was exposed to the token instances.

8. The system of claim 1, wherein the memory is further configured to store a baseline value for affective response of the user, and the processor is further configured to calculate the total affective response with respect to the baseline value;

whereby the baseline value represents a usual affective response of the user, as computed from multiple values acquired over a period of at least a few hours.

9. The system of claim 1, wherein more than 50% of the total affective response is attributed to the affective response to the token instance of interest.

10. The system of claim 1, wherein the total affective response is based, at least in part, on a value of a user measurement channel that is not utilized to generate the representation of the first attention level.

11. A method for utilizing eye-tracking to estimate affective response to a token instance of interest;

comprising;

receiving a total affective response of a user to token instances to which the user was exposed;

wherein the token instances comprise first and second token instances that are visual token instances that have overlapping instantiation periods, and each of the first and second token instances corresponds to an object or a character, and is generated utilizing analysis of images obtained from at least one of the following sources;

content to which the user was exposed, and images of the surroundings of the user acquired with a camera; and

wherein each token instance, from among the first and second token instances, comprises an attribute value indicative of at least one of the following values;

a duration of exposure to the token instance, and a size of an object or character to which the token instance corresponds;

receiving representations of first and second attention levels in the first and second token instances, respectively;

wherein the first and second attention levels are computed based on eye-tracking data; and

wherein the first attention level is greater than the second attention level;

selecting the first token instance as the token instance of interest based on the representations of the first and second attention levels; and

estimating the affective response to the token instance of interest based on the total affective response and the attribute value of the first token instance.

12. The method of claim 11, further comprising monitoring a gaze of the user while the user was exposed to the token instances in order to generate the eye-tracking data.

13. The method of claim 12, further comprising determining the first and second attentions level in proportion to the time the user spent looking at images corresponding to the first and second token instances;

respectively.

14. The method of claim 11, further comprising calculating the total affective response to the token instances in proportion to a difference between affective responses of the user before and after the user was exposed to the token instances.

15. The method of claim 11 further comprising receiving a baseline value for affective response of the user, and calculating the total affective response with respect to the baseline value;

whereby the baseline value represents a usual affective response of the user, as computed from multiple values acquired over a period of at least a few hours.

16. The method of claim 11, wherein more than 50% of the total affective response is attributed to the affective response to the token instance of interest.

17. The method of claim 11, further comprising calculating the total affective response based, at least in part, on a value of a user measurement channel that is not utilized to generate the representation of the first attention level.

18. A non-transitory computer-readable medium for use in a computer to utilize eye-tracking to estimate affective response to a token instance of interest;

the computer comprises a processor, and the non-transitory computer-readable medium comprising;

program code for receiving a total affective response of a user to token instances to which the user was exposed;

wherein the token instances comprise first and second token instances that are visual token instances that have overlapping instantiation periods, and each of the first and second token instances corresponds to an object or a character, and is generated utilizing analysis of images obtained from at least one of the following sources;

content to which the user was exposed, and images of the surroundings of the user acquired with a camera; and

wherein each token instance, from among the first and second token instances, comprises an attribute value indicative of at least one of the following values;

a duration of exposure to the token instance, and a size of an object or character to which the token instance corresponds;

program code for receiving representations of first and second attention levels in the first and second token instances, respectively;

wherein the first and second attention levels are computed based on eye-tracking data; and

wherein the first attention level is greater than the second attention level;

program code for selecting the first token instance as the token instance of interest based on the representations of the first and second attention levels; and

program code for estimating the affective response to the token instance of interest based on the total affective response and the attribute value of the first token instance.

19. The non-transitory computer-readable medium of claim 18, further comprising program code for monitoring a gaze of the user while the user was exposed to the token instances in order to generate the eye-tracking data.

20. The non-transitory computer-readable medium of claim 19, further comprising program code for determining the first and second attentions level in proportion to the time the user spent looking at images corresponding to the first and second token instances, respectively.