Habituation-compensated library of affective response
First Claim
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.
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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
17 Claims
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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:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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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:
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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. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. 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 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; andthe 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. - View Dependent Claims (17)
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Specification