Habituation-compensated library of affective response
First Claim
1. A method for generating a habituation-compensated library, comprising:
- receiving samples comprising temporal windows of token instances to which a user was exposed, wherein the temporal windows of token instances comprise a window comprising instantiations of first and second tokens that have overlapping instantiation periods;
receiving data on previous instantiations of the first and second tokens, to which the user was exposed;
receiving target values corresponding to the temporal windows of token instances;
the target values represent affective responses of the user to the token instances from the temporal windows of token instances;
wherein the affective responses are values comprising representations of emotional responses;
training a machine learning-based user response model using data comprising;
the samples, the data on previous instantiations of the first and second tokens, and the corresponding target values; and
generating, based on the machine learning-based user response model, the habituation-compensated library that comprises for each token of the first and second tokens;
a first expected affective response of the user to an instance of the token after a first number of previous exposures to instantiations of the token, and a second expected affective response of the user to an instance of the token after a second number, that is greater than the first number, of previous exposures to instantiations of the token;
wherein for the first token, the first expected affective response is stronger than the second expected affective response, while for the second token, the first expected affective response is weaker than the second expected affective response.
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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.
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Citations
18 Claims
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1. A method for generating a habituation-compensated library, comprising:
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receiving samples comprising temporal windows of token instances to which a user was exposed, wherein the temporal windows of token instances comprise a window comprising instantiations of first and second tokens that have overlapping instantiation periods; receiving data on previous instantiations of the first and second tokens, to which the user was exposed; receiving target values corresponding to the temporal windows of token instances;
the target values represent affective responses of the user to the token instances from the temporal windows of token instances;
wherein the affective responses are values comprising representations of emotional responses;training a machine learning-based user response model using data comprising;
the samples, the data on previous instantiations of the first and second tokens, and the corresponding target values; andgenerating, based on the machine learning-based user response model, the habituation-compensated library that comprises for each token of the first and second tokens;
a first expected affective response of the user to an instance of the token after a first number of previous exposures to instantiations of the token, and a second expected affective response of the user to an instance of the token after a second number, that is greater than the first number, of previous exposures to instantiations of the token;
wherein for the first token, the first expected affective response is stronger than the second expected affective response, while for the second token, the first expected affective response is weaker than the second expected affective response. - View Dependent Claims (2, 3, 4, 5, 6, 7, 17)
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8. A method for generating a habituation-compensated library, comprising:
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receiving samples comprising temporal windows of token instances to which a user was exposed, wherein the temporal windows of token instances comprise a window comprising instantiations of first and second tokens that have overlapping instantiation periods; receiving data on previous instantiations of the first and second tokens, to which the user was exposed; receiving target values corresponding to the temporal windows of token instances;
the target values, which are derived from values of a measurement channel of the user, represent affective responses of the user to the token instances from the temporal windows of token instances;
wherein the affective responses are values comprising representations of values of the measurement channel of the user;training a machine learning-based user response model using data comprising;
the samples, the data on previous instantiations of the first and second tokens, and the corresponding target values; andgenerating, based on the machine learning-based user response model, the habituation-compensated library that comprises for each token of the first and second tokens;
a first expected affective response of the user to an instance of the token after a first number of previous exposures to instantiations of the token, and a second expected affective response of the user to an instance of the token after a second number, that is greater than the first number, of previous exposures to instantiations of the token;
wherein for the first token, the first expected affective response is stronger than the second expected affective response, while for the second token, the first expected affective response is weaker than the second expected affective response. - View Dependent Claims (9, 10, 11, 12, 13, 18)
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14. A device comprising a processor and a memory;
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the memory configured to store samples and target values; the samples comprising temporal windows of token instances to which a user was exposed, wherein the temporal windows of token instances comprise a window comprising instantiations of first and second tokens that have overlapping instantiation periods;
the samples further comprise data on previous instantiations of the first and second tokens, to which the user was exposed; andthe target values correspond to the temporal windows of token instances and represent affective responses of the user to the token instances from the temporal windows of token instances;
wherein the affective responses comprise at least one of;
values representing emotional responses, and values of a user measurement channel of the user;the processor configured to train a machine learning-based user response model using date comprising;
the samples, the data on previous instantiations of the first and second tokens, and the corresponding target values stored in the memory; andthe processor is further configured to generate, based on the machine learning-based user response model, a habituation-compensated library that comprises for each token of the first and second tokens;
a first expected affective response of the user to an instance of the token after a first number of previous exposures to instantiations of the token, and a second expected affective response of the user to an instance of the token after a second number, that is greater than the first number, of previous exposures to instantiations of the token;
wherein for the first token, the first expected affective response is stronger than the second expected affective response, while for the second token, the first expected affective response is weaker than the second expected affective response. - View Dependent Claims (15, 16)
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Specification