Utilizing analysis of content to reduce power consumption of a sensor that measures affective response to the content

• US 9,086,884 B1
• Filed: 04/29/2015
• Issued: 07/21/2015
• Est. Priority Date: 10/14/2012
• Status: Active Grant

• 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
• Application Number:
  US14/698,911
• Publication Number:
  US 20150227193A1
• Time in Patent Office: 83 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

Disclosed herein are systems, methods, and computer program products that utilize analysis of content in order to reduce power consumption of a sensor that measures affective response of a user. A content emotional response analyzer receives and analyzes a segment of content, and outputs an indication that is indicative of whether a predefined threshold related to an emotional response to the segment is reached. A controller selects a mode of operation for the sensor based on the indication. The mode of operation is selected from a set comprising first and second modes of operation. The sensor is configured to consume significantly less power while operating at the first mode of operation compared to the power it is configured to consume while operating at the second mode of operation.

Claims

1. A system configured to reduce power consumption of a sensor according to analysis of 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 mode of operation for a sensor for measuring affective response of a 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 an 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 mode of operation for the sensor for measuring affective response of the user to the second segment;

wherein the sensor is configured to consume at least 50% less power while operating at the first mode of operation compared to the power it is configured to consume while operating at the second mode of operation.

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

9. 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.

10. A method for reducing power consumption of a sensor that measures affective response 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;

selecting, based on the first indication, a first mode of operation for a sensor to measure affective response to the first segment;

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

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

selecting, based on the second indication, a second mode of operation for the sensor to measure affective response to the second segment;

wherein, per unit of measurement time, power consumed by the sensor for measuring the affective response of a user to the first segment, is at least 50% lower than power consumed for measuring the affective response of the user to the second segment.

11. The method of claim 10, wherein the first and second segments comprise text, or data that can be converted to text, and further comprising analyzing the first and second segments utilizing semantic analysis of the text to produce the first and second indications.

12. The method of claim 10, further comprising refraining from taking measurements of affective response of the user when operating the sensor in the first mode of operation.

13. The method of claim 10, further comprising forwarding, to a module that trains an emotional response model, a description of an expected emotional response to the second segment and data obtained from the measuring of the affective response of the user to the second segment.

14. The method of claim 10, further comprising forwarding data obtained from the measuring of the affective response of the user to the second segment to a measurement emotional response predictor (measurement ERP) that predicts emotional response.

15. The method of claim 10, wherein the sensor is battery powered, and further comprising measuring the affective response of the user by utilizing the sensor for at least one of:

taking a physiological measurement of the user and capturing an image of the user.

16. The method of claim 10, further comprising receiving the second segment from an interactive computer game that further provides information about an expected emotional response to the second segment.

17. A non-transitory computer-readable medium for use in a computer to reduce power consumption of a sensor that measures affective response 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;

program code for selecting, based on the first indication, a first mode of operation for a sensor to measure affective response to the first segment;

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

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

program code for selecting, based on the second indication, a second mode of operation for the sensor to measure affective response to the second segment;

wherein, per unit of measurement time, power consumed by the sensor for measuring the affective response of a user to the first segment, is at least 50% lower than power consumed for measuring the affective response of the user to the second segment.

18. The non-transitory computer-readable medium of claim 17, wherein the first and second segments comprise text, or data that can be converted to text, and further comprising program code for analyzing the first and second segments utilizing semantic analysis of the text to produce the first and second indications.

19. The non-transitory computer-readable medium of claim 17, further comprising program code for forwarding, to a module that trains an emotional response model, a description of an expected emotional response to the second segment and data obtained from the measuring of the affective response of the user to the second segment.

20. The non-transitory computer-readable medium of claim 17, wherein the sensor is battery powered, and further comprising program code for measuring the affective response of the user by utilizing the sensor for at least one of:

taking a physiological measurement of the user and capturing an image of the user.