Reducing computational load of processing measurements of affective response

• US 9,058,200 B2
• Filed: 10/17/2014
• Issued: 06/16/2015
• Est. Priority Date: 10/14/2012
• Status: Expired due to Fees

• Current Assignee: Affectomatics ltd.
• Original Assignee: Ari M. Frank, Gil Thieberger
• Inventors: Frank, Ari M, Thieberger, Gil
• Primary Examiner(s): Tsai, Henry
• Assistant Examiner(s): ROCHE, JOHN B
• Application Number:
  US14/516,833
• Publication Number:
  US 20150040139A1
• Time in Patent Office: 242 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 1/28 Supervision thereof, e.g. detecting power-supply failure by out of limits supervision
• CPC Class Codes: G06F 1/3206 Monitoring of events, devices or parameters that trigger a change in power modality
• CPC Class Codes: G06F 1/3212 Monitoring battery levels, e.g. power saving mode being initiated when battery voltage goes below a certain level
• CPC Class Codes: G06F 1/3234 Power saving characterised by the action undertaken
• CPC Class Codes: G06F 1/3296 by lowering the supply or operating voltage
• CPC Class Codes: G06F 2203/011 Emotion or mood input determined on the basis of sensed human body parameters such as pulse, heart rate or beat, temperature of skin, facial expressions, iris, voice pitch, brain activity patterns
• CPC Class Codes: G06F 3/013 Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06F 3/015 Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06F 9/46 Multiprogramming arrangements
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

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.

Claims

1. A system configured to reduce computational load involved in processing measurements of affective response of a user to content, comprising:

a content emotional response analyzer (content ERA) configured to;

receive a first segment of content, analyze the first segment, and output a first indication indicating that a first value related to an emotional response to the first segment does not reach a first predetermined threshold; and

a hardware-based controller configured to select, based on the first indication, a first processing level for a processor to process measurements of affective response of the user to the first segment;

wherein the content ERA is further configured to;

receive a second segment of content, analyze the second segment, and output a second indication indicating that a second value related to a prediction of emotional response to the second segment reaches a second predetermined threshold; and

the hardware-based controller is further configured to select, based on the second indication, a second processing level for the processor to process measurements of affective response of the user to the second segment;

wherein, per volume unit of measurement data, number of computation cycles utilized by the processor to process, at the first processing level, measurements of the affective response of the user to the first segment, is at least 50% lower than the number of computation cycles utilized by the processor to process, at the second processing level, measurements of the affective response of the user to the second segment.

2. The system of claim 1, wherein the first and second indications indicate confidence in predictions of emotional response to the first and second segments, respectively;

whereby based on the first indication, it is determined that confidence in the prediction of the emotional response to the first segment does not reach the first predetermined threshold, and based on the second indication, it is determined that confidence in the prediction of the emotional response to the second segment does reach the second predetermined threshold.

3. The system of claim 1, wherein the first and second indications indicate expected magnitudes of change in affective response due to exposure to the first and second segments, respectively;

whereby based on the first indication, it is determined that expected magnitude of change in affective response due to exposure to the first segment does not reach the first predetermined threshold, and based on the second indication, it is determined that expected magnitude of change to affective response due to exposure to the second segment does reach the second predetermined threshold.

4. The system of claim 1, wherein the first and second indications indicate how beneficial it is to measure the affective response to the first and second segments, respectively;

whereby based on the first indication, it is determined that benefit from measuring the affective response to the first segment does not reach the first predetermine threshold, and based on the second indication it is determined that benefit from measuring affective response to the second segment does reach the second predetermined threshold.

5. The system of claim 1, wherein the first and second segments comprise visual content, and being exposed to the first and second segments is achieved by viewing the first and second segments.

6. The system of claim 1, wherein the first and second segments comprise data representing text, and the content ERA utilizes semantic analysis of the text to produce the first and second indications.

7. The system of claim 1, wherein measurements of the affective response of the user to the first and second segments are acquired by a sensor;

wherein the sensor is battery powered and is one or more of the following;

a physiological sensor, and an image capturing device.

8. The system of claim 1, wherein while operating at the first processing level the processor does not process measurements of affective response.

9. The system of claim 1, further comprising a measurement emotional response predictor (measurement ERP) configured to predict emotional response of the user from data comprising measurements of a sensor.

10. The system of claim 1, wherein the first and second segments are received from a computer game that further provides information about expected emotional response to the first and second segments.

11. A method for reducing computational load of processing measurements of affective response of a user to content, comprising:

receiving a first indication derived from analysis of a first segment of content;

determining that the first indication indicates that a first value related to a prediction of emotional response to the first segment does not reach a first predetermined threshold, and configuring a processor to operate at a first processing level to process measurements of affective response of the user to the first segment;

receiving a second indication derived from analysis of a second segment of content; and

determining that the second indication indicates that a second value related to a prediction of emotional response to the second segment reaches a second predetermined threshold, and configuring the processor to operate at a second processing level to process measurements of affective response of the user to the second segment;

wherein, per volume unit of measurement data, number of computation cycles utilized by the processor to process, at the first processing level, measurements of the affective response of the user to the first segment, is at least 50% lower than the number of computation cycles utilized by the processor to process, at the second processing level, measurements of the affective response of the user to the second segment.

12. The method of claim 11, wherein the first and second predetermined thresholds represent first and second confidence levels in predictions of emotional response, respectively;

and wherein the first and second values represent confidence in predictions of emotional response to the first and second segments, respectively;

whereby the processor is configured to operate at the first processing level to process measurements of affective response of the user to the first segment when the first indication indicates that confidence in a prediction of emotional response to the first segment does not reach the first confidence level, and the processor is configured to operate at the second processing level to process measurements of affective response of the user to the second segment when the second indication indicates that confidence in a prediction of emotional response to the second segment does reach the second confidence level.

13. The method of claim 11, wherein the first and second predetermined thresholds represent first and second magnitudes of change in affective response, respectively;

and wherein the first and second values represent predicted changes in affective response due to exposure to the first and second segments, respectively;

whereby the processor is configured to operate at the first processing level to process measurements of affective response of the user to the first segment when the first indication indicates that a predicted change in affective response due to exposure to the first segment does not reach the first magnitude, and the processor is configured to operate at the second processing level to process measurements of affective response of the user to the second segment when the second indication indicates that a predicted change in affective response due to exposure to the second segment does reach the second magnitude.

14. The method of claim 11, wherein the first and second predetermined thresholds represent first and second benefit levels, respectively;

and wherein the first and second values represent predicted benefits to measuring affective response to the first and second segments, respectively;

whereby the processor is configured to operate at the first processing level to process measurements of affective response of the user to the first segment when the first indication indicates that a benefit of measuring affective response to the first segment does not reach the first benefit level, and the processor is configured to operate at the second processing level to process measurements of affective response of the user to the second segment when the second indication indicates that a benefit of measuring affective response to the second segment does reach the second benefit level.

15. The method of claim 11, wherein the first and second segments comprise data representing text, and the analysis of the first and second segments utilizes semantic analysis of the text to determine whether the predetermined threshold is reached.

16. The method of claim 11, wherein while operating at the first processing level no processing of measurements of affective response takes place.

17. The method of claim 11, further comprising forwarding, to a module that trains an emotional response model, a description of expected emotional response to the second segment and data obtained from processing the measurements of the affective response of the user to the second segment.

18. The method of claim 11, further comprising forwarding data obtained from processing the measurements of the affective response of the user to the second segment to a module that predicts emotional response.

19. The method of claim 11, wherein the second segment is received from an interactive computer game that further provides information about expected emotional response to the second segment.

20. A non-transitory computer-readable medium for use in a computer to reduce computational load of processing measurements of affective response of a user to content;

the computer comprising a processor, and the non-transitory computer-readable medium comprising;

program code for receiving a first indication derived from analysis of a first segment of content;

program code for determining that the first indication indicates that a first value related to a prediction of emotional response to the first segment does not reach a first predetermined threshold, and configuring a processor to operate at a first processing level to process measurements of affective response of the user to the first segment;

program code for receiving a second indication derived from analysis of a second segment of content; and

program code for determining that the second indication indicates that a second value related to a prediction of emotional response to the second segment reaches a second predetermined threshold, and configuring the processor to operate at a second processing level to process measurements of affective response of the user to the second segment;

wherein, per volume unit of measurement data, number of computation cycles utilized by the processor to process, at the first processing level, measurements of the affective response of the user to the first segment, is at least 50% lower than the number of computation cycles utilized by the processor to process, at the second processing level, measurements of the affective response of the user to the second segment.