Methods for training saturation-compensating predictors of affective response to stimuli
First Claim
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1. A method comprising:
- receiving samples comprising temporal windows of token instances to which a user was exposed;
the token instances spread over at least a day, and a subset of the token instances originating from a same source and having overlapping instantiation periods;
receiving target values representing affective response annotations of the user in response to the temporal windows of token instances; and
training an emotional response predictor on input data comprising;
the samples, the corresponding target values, and values indicative of the number of token instances in the temporal windows of token instances;
wherein the emotional response predictor utilizes the values indicative of the number of the token instances to compensate for non-linear effects resulting from saturation of the user due to the user being exposed to the token instances in the temporal windows of token instances including the token instances originating from the same source and having overlapping instantiation periods.
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Abstract
Described herein are methods for training a machine learning-based predictor of affective response to stimuli. The methods involve receiving samples comprising temporal windows of token instances to which a user was exposed, and target values representing affective response annotations of the user in response to the temporal windows of token instances. This data is used for the training of the predictor along with values indicative of the number of the token instances in the temporal windows of token instances, which are used to compensate for non-linear effects resulting from saturation of the user.
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Citations
20 Claims
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1. A method comprising:
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receiving samples comprising temporal windows of token instances to which a user was exposed;
the token instances spread over at least a day, and a subset of the token instances originating from a same source and having overlapping instantiation periods;receiving target values representing affective response annotations of the user in response to the temporal windows of token instances; and training an emotional response predictor on input data comprising;
the samples, the corresponding target values, and values indicative of the number of token instances in the temporal windows of token instances;
wherein the emotional response predictor utilizes the values indicative of the number of the token instances to compensate for non-linear effects resulting from saturation of the user due to the user being exposed to the token instances in the temporal windows of token instances including the token instances originating from the same source and having overlapping instantiation periods. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A method comprising:
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receiving samples comprising temporal windows of token instances, to which a user was exposed, comprising representations of elements extracted from digital media content;
the token instances spread over at least a day, and a subset of the token instances originating from a same digital media source and having overlapping instantiation periods;receiving target values corresponding to the temporal windows of token instances;
the target values representing affective response annotations of the user in response to the temporal windows of token instances;training a machine learning-based predictor to predict values representing affective response of the user to being exposed to token instances;
wherein the training is performed on data comprising the samples, the corresponding target values, and values indicative of the number of token instances in the temporal windows of token instances; and
wherein the machine learning-based predictor utilizes the values indicative of the number of the token instances to compensate for non-linear effects resulting from saturation of the user due to the user being exposed to the token instances in the temporal windows of token instances including the token instances originating from the same digital media source and having overlapping instantiation periods. - View Dependent Claims (17, 18, 19)
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20. A method comprising:
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receiving samples comprising temporal windows of token instances to which a user was exposed;
the token instances spread over at least a day, and a subset of the token instances originating from same source and having overlapping instantiation periods;receiving target values representing a response of the user to the temporal windows of token instances expressed as values of a measurement channel of the user; and training a machine learning-based predictor on input data comprising;
the samples, the corresponding target values, and values indicative of the number of token instances in the temporal windows of token instances;
wherein the predictor utilizes the values indicative of the number of the token instances to compensate for non-linear effects resulting from saturation of the user due to the user being exposed to the token instances in the temporal windows of token instances including the token instances originating from the same source and having overlapping instantiation periods.
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Specification