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

Abstract: Methods for detecting and validating an estimated situation using a situation-dependent predictor of a user response to token instances, including: receiving a temporal window of token instances and a putative situation for a user; predicting an expected response of the user to being exposed to the temporal window of token instances; receiving a value of a measurement channel of the user taken during exposure of the user to the temporal window of token instances; identifying that difference between the received value of the user measurement channel and the predicted expected response of the user is above a predefined threshold; and indicating that the putative situation is wrong.

Abstract: Predicting a user's response to a stream of token instances, including: receiving a stream of token instances; partitioning the stream of token instances into consecutive temporal windows of token instances; predicting response of the user to temporal windows of token instances; predicting response of the user to a certain temporal window of token instances; and forwarding the prediction of the user to the stream of token instances.

Abstract: Creating a machine learning-based affective response predictor to predict a user's emotional state after being exposed to tokens representing stimuli that influence the user's affective state, comprising: receiving samples comprising temporal windows of token instances to which the user was exposed; the token instances are spread over a long period of time, and a subset of the token instances originate from same source and have overlapping instantiation periods; receiving target values, which represent affective response annotations of the user and correspond to the temporal windows of token instances; and creating the machine learning-based affective response predictor for the user, which compensates for non-linear effects resulting from the user being exposed to the subset of token instances originating from the same source and having overlapping instantiation periods, by running a machine learning training procedure on input data comprising the samples and the corresponding target values.

Abstract: Creating a machine learning-based affective response predictor of a user when there are significantly more samples than target values available for training, comprising: receiving samples comprising temporal windows of token instances to which the user was exposed; the token instances are spread over a long period of time; receiving intermittent target values corresponding to a subset of the temporal windows of token instances; the target values represent affective response annotations of the user; creating the machine learning-based affective response predictor of the user, by running a semi-supervised machine learning training procedure on the samples and the intermittent corresponding target values; wherein the machine learning-based affective response predictor is more accurate than a predictor created when training only on the samples that have corresponding target values, since it is capable of learning additional information from the samples comprising temporal windows of token instances without corres

Abstract: Creating a machine learning-based habituation-compensated predictor of a user's response to token instances representing stimuli that influence the user's affective state, 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 responses to the token instances from the temporal windows of token instances; training the machine learning-based habituation-compensated predictor to predict the user's response to token instances, while accounting for the influence of the user's previous exposure to tokens; wherein the training uses the samples, the data on previous instantiations, and the corresponding target values

Abstract: Calculating a situation-dependent baseline value for a user response to token instances representing stimuli that influence the user's affective state, utilizing large time windows and rapid adjustments to changing situations, including: accessing a database storing annotations representing the user's response to token instances originating from multiple distinct token sources; calculating a first situation-dependent baseline value by weighting annotations retrieved from the database and associated with a first situation identifier, which are spread over a long period of time ‘T’; calculating a second situation-dependent baseline value by weighting annotations retrieved from the database and associated with a second situation identifier; wherein the difference between the first and second situation-dependent baseline values is significant, and the method rapidly adjusts to the situation change by exhibiting an extremely shorter transient time between the first and the second situation-dependent baselines than

Abstract: A data structure stored in memory including: token instances representing stimuli that influence a user's affective state; the token instances are spread over a long period of time that spans different situations, and a plurality of the token instances have overlapping instantiation periods; data representing levels of user attention in some of the token instances used by an application program to improve the accuracy of a machine learning based affective response model for the user; annotations representing emotional states of the user; the annotations are spread over a long period of time that spans different situations; and linkage information between the token instances, the data representing levels of user attention, and the annotations.

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