Patent application number | Description | Published |
20120290511 | Database of affective response and attention levels - 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. | 11-15-2012 |
20120290512 | Methods for creating a situation dependent library of affective response - 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. | 11-15-2012 |
20120290513 | Habituation-compensated library of affective response - 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 | 11-15-2012 |
20120290514 | Methods for predicting affective response from stimuli - 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. | 11-15-2012 |
20120290515 | Affective response predictor trained on partial data - 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 corresponding target values. | 11-15-2012 |
20120290516 | Habituation-compensated predictor of affective response - 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 | 11-15-2012 |
20120290517 | Predictor of affective response baseline values - 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 âTâ; 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 T/2 | 11-15-2012 |
20120290520 | Affective response predictor for a stream of stimuli - 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. | 11-15-2012 |
20120290521 | Discovering and classifying situations that influence affective response - 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. | 11-15-2012 |
20140108309 | Training a predictor of emotional response based on explicit voting on content and eye tracking to verify attention - Utilizing eye tracking to collect naturally expressed affective responses for training an emotional response predictor, comprising: receiving a vote of a user on a segment of content consumed by the user; receiving eye tracking data of the user taken while the user consumed the segment of content; determining, based on the eye tracking data, that a gaze-based attention level to the segment reaches a predetermined threshold; utilizing the vote to generate a label related to an emotional response to the segment; receiving an affective response measurement of the user taken substantially while the user consumed the segment of content; and training a measurement emotional response predictor with the label and the affective response measurement. | 04-17-2014 |
20140108842 | Utilizing eye tracking to reduce power consumption involved in measuring affective response - Systems and methods that enable a reduction of the power consumption involved in measuring a user's affective response to content. The reduction in power consumption is achieved by utilizing eye tracking to determine when a user is paying attention to content, and accordingly setting a mode of operation of a device that measures the user. Thus, by using different modes of operation, which are characterized by different energy consumption rates, the total power consumption of the device may be reduced, without loss of relevant measurements. | 04-17-2014 |
20140149177 | Responding to uncertainty of a user regarding an experience by presenting a prior experience - Responding to uncertainty of a user regarding an experience, comprising: receiving a first token instance representing the experience for the user, an indication of uncertainty of the user regarding the experience, token instances representing prior experiences, and affective responses to the prior experiences. Identifying, from among the prior experiences, a prior experience represented by a second token instance that is more similar to the first token instance than most of the token instances representing the other prior experiences, and affective response to the prior experience reaches a predetermined threshold. Whereby reaching the predetermined threshold implies that the user may remember the prior experience. Generating an explanation regarding relevancy of the experience to the user based on the prior experience. And presenting the explanation to the user as a response to the indication of uncertainty. | 05-29-2014 |
20140195221 | Utilizing semantic analysis to determine how to measure affective response - A semantic analyzer receives a segment of content, analyzes it utilizing semantic analysis, and outputs an indication regarding whether a value related to a predicted emotional response to the segment reaches a predetermined threshold. Based on the indication, a controller selects a measuring rate, from amongst at least first and second measuring rates, at which a device is to take measurements of affective response of a user to the segment. The first rate may be selected when the value does not reach the predetermined threshold, while the second mode may be selected when the value does reach it. The device takes significantly fewer measurements while operating at the first measuring rate, compared to number of measurements it takes while operating at the second measuring rate. | 07-10-2014 |
20150033056 | Reducing power consumption of sensor by overriding instructions to measure - Systems and methods for reducing power consumption of a device utilized to measure affective response to content by overriding selections of a mode-selector. The mode-selector receives tags corresponding to segments of content. The mode-selector selects, based on the tags, modes for operating the device to measure affective response to the segments. A threshold module receives measurements of the user's state, taken by a sensor, and indicates whether a predefined threshold is reached by the measurements. If reached, the device is operated according to a first mode to measure the affective response. Otherwise, the device is operated according to a second mode to measure the affective response. The power consumption of the device when operating in the second mode is significantly lower than the power consumption of the device when operating in the first mode. | 01-29-2015 |
20150039405 | Collecting naturally expressed affective responses for training an emotional response predictor utilizing voting on a social network - Collecting naturally expressed affective responses for training an emotional response. In one embodiment, a label generator is configured to receive a vote of a user on a segment of content consumed by the user on a social network. The label generator determines whether the user consumed the segment during a duration that is shorter than a predetermined threshold, and utilizes the vote to generate a label related to an emotional response to the segment. The predetermined threshold is selected such that, while consuming the segment in a period of time shorter than the predetermined threshold, the user is likely to have a single dominant emotional response to the segment. A training module receives the label and measurement of an affective response of the user taken during the duration, and trains an emotional response predictor with the measurement and the label. | 02-05-2015 |
20150040139 | Reducing computational load of processing measurements of affective response - Systems and methods for reducing the computational load of processing measurements of affective response of a user to content. A content emotional response analyzer (content ERA) receives a segment of content, analyzes it, and outputs an indication regarding whether a value related to a predicted emotional response to the segment reaches a predetermined threshold. Based on the indication, a controller selects a processing level, from among at least first and second processing levels, for a processor to process measurements of affective response. The first level may be selected when the value does not reach the predetermined threshold, while the second level may be selected when the value reaches it. The processor is configured to utilize significantly fewer computation cycles to process data operating at the first processing level, compared to the number of computation cycles it utilizes to process data operating at the second processing level. | 02-05-2015 |
20150040149 | Reducing transmissions of measurements of affective response by identifying actions that imply emotional response - A system, method, and computer product for reducing volume of transmissions of measurements of affective response are described herein. In one embodiment, an interaction analyzer receives a description of an interaction of a user with a media controller that controls presentation of content to the user. The interaction analyzer identifies from the description an action that causes a deviation from a progression of presentation of the content that would have occurred had the action not taken place. A transmitter sends a request to transmit measurements of affective response, taken by a sensor, during a window during which the user likely expressed an affective response related to the action. In some embodiments, the request is received by a transceiver coupled to the sensor with memory sufficient to store measurements of affective response of the user taken since the start of the window, which precedes the time the request is sent. | 02-05-2015 |
20150058081 | Selecting a prior experience similar to a future experience based on similarity of token instances and affective responses - Systems, methods, and/or computer program code for selecting a prior experience a user had, which resembles a future experience that may be intended for the user, are described herein. In some examples, an experience may involve content for consumption by the user, an activity for the user to participate in, or an item to be purchased for the user. The future experience is compared to prior experiences the user had, in order to find a prior experience that has a certain similarity to the future experience. The similarity involves both a similarity of representations of the prior experience and future experience (e.g., as determined from a comparison of token instances that represent them), and a similarity in responses to the prior experience and future experience (e.g., as determined from a comparison between a predicted affective response to the future experience and an affective response to the prior experience). | 02-26-2015 |
20150058327 | Responding to apprehension towards an experience with an explanation indicative of similarity to a prior experience - Described herein are systems, methods, and computer programs for responding to apprehension of a user towards an experience. In some examples, an experience involves content for the user or an activity for the user to participate in. In some embodiments, a first token instance is extracted from an expression of the user that indicates apprehension towards the experience. A prior experience, represented by a second token instance similar to the first token instance, is selected from among prior experiences the user had. Additionally, an affective response the user had to the prior experience reaches a threshold, which implies that the user may remember the prior experience. An explanation of the relevance of the experience to the user, which is indicative of similarity between the first token instance and the second token instance is generated and optionally presented to the user to ameliorate the apprehension. | 02-26-2015 |