Habituation-compensated library of affective response

• US 20120290513A1
• Filed: 06/25/2011
• Published: 11/15/2012
• Est. Priority Date: 05/11/2011
• Status: Active Grant

• Current Assignee: Affectomatics ltd.
• Original Assignee: Affectivon Limited
• Inventors: Frank, Ari M., Thieberger, Gil, Ben-Haim, Anat Thieberger
• Granted Patent: US 8,918,344 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 3/013 Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

Generating a habituation-compensated library comprising a user'"'"'s expected response to tokens representing stimuli that influence the user'"'"'s affective state, the method comprising: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods; the samples further comprise data on previous instantiations of at least one of the token instances from the temporal windows; receiving target values corresponding to the temporal windows of token instances; the target values represent the user'"'"'s response to the token instances from the temporal windows of token instances; training a machine learning-based user response model using the samples, the data on previous instantiations, and the corresponding target values; and analyzing the machine learning-based user response model to generate the habituation-compensated library, which accounts for the influence of the user'"'"'s previous exposure to tokens

Claims

1. A method for generating a habituation-compensated library comprising a user'"'"'s expected response to tokens representing stimuli that influence the user'"'"'s affective state, the method comprising:

receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods;

the samples further comprise data on previous instantiations of at least one of the token instances from the temporal windows of token instances;

receiving target values corresponding to the temporal windows of token instances;

the target values represent the user'"'"'s response to the token instances from the temporal windows of token instances;

training a machine learning-based user response model using the samples, the data on previous instantiations, and the corresponding target values; and

analyzing the machine learning-based user response model to generate the habituation-compensated library, which accounts for the influence of the user'"'"'s previous exposure to tokens.

2. The method of claim 1, wherein the target values are affective response annotations, and the habituation-compensated library is a habituation-compensated library comprising the user'"'"'s expected affective response to tokens.

3. The method of claim 1, wherein the data on previous instantiations comprises the time elapsed since the last time the user was exposed to a similar token instance.

4. The method of claim 3, further comprising a classifier configured to classify token instances into a predefined set of classes;

and similar token instances refer to token instances belonging to the same class.

5. The method of claim 1, wherein the habituation-compensated library is generated by analyzing at least two different machine learning-based user response models that were trained on data collected over periods during which the user was in different situations;

and the habituation-compensated library comprises the user'"'"'s expected responses to essentially the same tokens in the different situations.

6. The method of claim 1, wherein the step of training the machine learning-based user response model comprises training a neural network;

and the habituation-compensated library comprises partial derivatives of the neural networks output values, with respect to the input neurons that correspond to token instances.

7. The method of claim 1, wherein the step of training the machine learning-based user response model comprises training a regression model;

and

the habituation-compensated library comprises a regression parameter vector.

8. The method of claim 1, wherein the step of training the machine learning-based user response model comprises training a maximum entropy model classifier;

and the habituation-compensated library comprises feature function weights.

9. A method for generating a habituation-compensated library comprising expected changes to a user'"'"'s measurement channel values in response to exposure to token instances representing stimuli, the method comprising:

receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods;

the samples further comprise data on previous instantiations of at least one of the token instances from the temporal windows of token instances;

receiving target values corresponding to the temporal windows of token instances;

the target values, which are derived from the user'"'"'s measurement channel, represent the user'"'"'s response to the token instances from the temporal windows of token instances;

training a machine learning-based user response model using the samples, the data on previous instantiations, and the corresponding target values; and

analyzing the machine learning-based user response model to generate the habituation-compensated library, which accounts for the influence of the user'"'"'s previous exposure to tokens.

10. The method of claim 9, wherein the data on previous instantiations comprises the time elapsed since the last time the user was exposed to a similar token instance.

11. The method of claim 10, further comprising a classifier configured to classify token instances into a predefined set of classes;

and similar token instances refer to token instances belonging to the same class.

12. The method of claim 9, wherein the habituation-compensated library is generated by analyzing at least two different machine learning-based user response models that were trained on data collected over periods during which the user was in different situations;

and the habituation-compensated library comprises the user'"'"'s expected responses to essentially the same tokens in the different situations.

13. The method of claim 9, wherein the step of training the machine learning-based user response model comprises training a neural network;

and the habituation-compensated library comprises partial derivatives of the neural networks output values, with respect to the input neurons that correspond to token instances.

14. The method of claim 9, wherein the step of training the machine learning-based user response model comprises training a regression model;

and

the habituation-compensated library comprises a regression parameter vector.

15. The method of claim 9, wherein the step of training the machine learning-based user response model comprises training a maximum entropy model classifier;

and the habituation-compensated library comprises feature function weights.

16. A device comprising a processor and a memory;

the memory configured to store samples and target values;

the samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods;

the samples further comprise data on previous instantiations of at least one of the token instances from the temporal windows of token instances; and

the target values corresponding to the temporal windows of token instances;

the target values represent the user'"'"'s response to the token instances from the temporal windows of token instances;

the processor configured to train a machine learning-based user response model using the samples, the data on previous instantiations, and the corresponding target values stored in the memory; and

the processor further configured to analyze the machine learning-based user response model to generate a habituation-compensated library comprising a user'"'"'s expected response to tokens representing stimuli that influence the user'"'"'s affective state, while taking in account the influence of the user'"'"'s previous exposure to tokens.

17. The device of claim 16, wherein the target values are affective response annotations, and the habituation-compensated library is a habituation-compensated library comprising the user'"'"'s expected affective response to tokens.