Situation-dependent libraries of affective response
First Claim
1. A system configured to generate a situation-dependent library, comprising:
- at least one processor and at least one memory, the at least one processor and the at least one memory cooperating to function as;
a machine learning trainer configured to receive samples comprising temporal windows of token instances to which a user was exposed and situation identifiers corresponding to situations the user was in while being exposed to the temporal windows;
wherein the token instances have overlapping instantiation periods and are spread over a period of time that spans at least a day, and the situation identifiers comprise situation identifiers for first and second situations;
the machine learning trainer is further configured to receive 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;
the machine learning trainer is further configured to train first and second situation-dependent machine learning-based user response models based on respective first and second datasets;
wherein the first and second datasets are obtained from partitioning the samples according to the situations the user was in when exposed to the temporal windows of token instances corresponding to the samples; and
wherein the first dataset comprises samples comprising situation identifiers for the first situation and corresponding target values, and the second dataset comprises samples comprising situation identifiers for the second situation and corresponding target values; and
a model analyzer configured to generate, based on the first and second situation-dependent machine learning-based user response models, the situation-dependent library which describes a first set of expected affective responses of the user to certain tokens while being in the first situation, and a second set of expected affective responses of the user to the certain tokens while being in the second situation;
wherein the first set is not identical to the second set, and wherein at least some of the token instances from the temporal windows of token instances are instantiations of the certain tokens.
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Abstract
Generating a situation-dependent library comprising a user'"'"'s expected response to tokens representing stimuli that influence the user'"'"'s affective state, including: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods and are spread over a long period of time that spans different situations; wherein at least one token is expected to elicit from the user a noticeably different affective response in the different situations; receiving target values corresponding to the temporal windows of token instances; the target values represent the user'"'"'s responses to the token instances from the temporal windows of token instances; training a machine learning-based user response model using the samples and the corresponding target values; and analyzing the machine learning-based user response model to generate the situation-dependent library comprising the user'"'"'s expected response to tokens, which accounts for the variations in the user'"'"'s affective response in the different situations.
33 Citations
20 Claims
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1. A system configured to generate a situation-dependent library, comprising:
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at least one processor and at least one memory, the at least one processor and the at least one memory cooperating to function as; a machine learning trainer configured to receive samples comprising temporal windows of token instances to which a user was exposed and situation identifiers corresponding to situations the user was in while being exposed to the temporal windows;
wherein the token instances have overlapping instantiation periods and are spread over a period of time that spans at least a day, and the situation identifiers comprise situation identifiers for first and second situations;the machine learning trainer is further configured to receive 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;the machine learning trainer is further configured to train first and second situation-dependent machine learning-based user response models based on respective first and second datasets;
wherein the first and second datasets are obtained from partitioning the samples according to the situations the user was in when exposed to the temporal windows of token instances corresponding to the samples; and
wherein the first dataset comprises samples comprising situation identifiers for the first situation and corresponding target values, and the second dataset comprises samples comprising situation identifiers for the second situation and corresponding target values; anda model analyzer configured to generate, based on the first and second situation-dependent machine learning-based user response models, the situation-dependent library which describes a first set of expected affective responses of the user to certain tokens while being in the first situation, and a second set of expected affective responses of the user to the certain tokens while being in the second situation;
wherein the first set is not identical to the second set, and wherein at least some of the token instances from the temporal windows of token instances are instantiations of the certain tokens. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system configured to generate a situation-dependent library, comprising:
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at least one processor and at least one memory, the at least one processor and the at least one memory cooperating to function as; a machine learning trainer configured to receive samples comprising temporal windows of token instances to which a user was exposed and situation identifiers corresponding to situations the user was in while being exposed to the temporal windows;
wherein the token instances have overlapping instantiation periods and are spread over a period of time that spans at least a day, and the situation identifiers comprise situation identifiers for first and second situations;the machine learning trainer is further configured to receive target values corresponding to a proper subset of the temporal windows of token instances; the target values represent affective responses of the user after being exposed to the token instances from the proper subset of the temporal windows of token instances; the machine learning trainer is further configured to perform semi-supervised training of first and second situation-dependent machine learning-based user response models based on respective first and second datasets;
wherein the first and second datasets are obtained from partitioning the samples according to the situations the user was in when exposed to the temporal windows of token instances corresponding to the samples; and
wherein the first dataset comprises samples comprising situation identifiers for the first situation and corresponding target values, and the second dataset comprises samples comprising situation identifiers for the second situation and corresponding target values; anda model analyzer configured to generate, based on the first and second situation-dependent machine learning-based user response models, the situation-dependent library which describes a first set of expected affective responses of the user to certain tokens while being in the first situation, and a second set of expected affective responses of the user to the certain tokens while being in the second situation;
wherein the first set is not identical to the second set, and wherein at least some of the token instances from the temporal windows of token instances are instantiations of the certain tokens. - View Dependent Claims (13, 14, 15, 16, 17)
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18. A system configured to generate a situation-dependent affective response library, comprising:
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at least one processor and at least one memory, the at least one processor and the at least one memory cooperating to function as; a machine learning trainer configured to receive samples comprising temporal windows of token instances to which a user was exposed and situation identifiers corresponding to situations the user was in while being exposed to the temporal windows;
wherein the token instances have overlapping instantiation periods and are spread over a period of time that spans at least a day, and the situation identifiers comprise situation identifiers for first and second situations;the machine learning trainer is further configured to receive affective response annotations corresponding to the temporal windows of token instances;
wherein the annotations are expressed as user measurement channel values;the machine learning trainer is further configured to train first and second situation-dependent machine learning-based user affective response models based on respective first and second datasets;
wherein the first and second datasets are obtained from partitioning the samples according to the situations the user was in when exposed to the temporal windows of token instances corresponding to the samples; and
wherein the first dataset comprises samples comprising situation identifiers for the first situation and corresponding target values, and the second dataset comprises samples comprising situation identifiers for the second situation and corresponding target values; anda model analyzer configured to generate, based on the first and second situation-dependent machine learning-based user affective response models, the situation-dependent affective-response library, which describes a first set of expected affective responses of the user to certain tokens while being in the first situation, and a second set of expected affective responses of the user to the certain tokens while being in the second situation;
wherein the first set is not identical to the second set, and wherein at least some of the token instances from the temporal windows of token instances are instantiations of the certain tokens. - View Dependent Claims (19, 20)
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Specification