Method and system for predicting audience viewing behavior

• US 9,514,436 B2
• Filed: 03/29/2010
• Issued: 12/06/2016
• Est. Priority Date: 09/05/2006
• Status: Active Grant

• Current Assignee: Nielsen Consumer LLC (Nielsen Holdings plc)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: Marci, Carl D., Levine, Brian, Kothuri, Ravi Kanth V, Siefert, Caleb J.
• Primary Examiner(s): Padot, Timothy
• Application Number:
  US12/749,376
• Publication Number:
  US 20100211439A1
• Time in Patent Office: 2,444 Days
• Field of Search: 705/7.29
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/225 of the fingers, e.g. by monitoring hand-grip force
• CPC Class Codes: A61B 5/6897 Computer input devices, e.g. mice or keyboards
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: H04H 60/33 Arrangements for monitoring the users' behaviour or opinions
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25883 being end-user demographical data, e.g. age, family status or address arrangements for identifying locations of users in broadcast systems H04H60/52
• CPC Class Codes: H04N 21/42201 biosensors, e.g. heat sensor for presence detection, EEG sensors or any limb activity sensors worn by the user input arrangements for interaction with the human body based on nervous system activity detection G06F3/015
• CPC Class Codes: H04N 21/44218 Detecting physical presence or behaviour of the user, e.g. using sensors to detect if the user is leaving the room or changes his face expression during a TV program methods or arrangements for recognising human body or animal bodies or body parts G06V40/10; methods or arrangements for acquiring or recognising human faces, facial parts, facial sketches, facial expressions G06V40/16; methods or arrangements for recognising movements or behaviour G06V40/20; arrangements for identifying users in broadcast systems H04H60/45
• CPC Class Codes: H04N 21/4532 involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/4662 characterized by learning algorithms
• CPC Class Codes: H04N 21/84 Generation or processing of descriptive data, e.g. content descriptors systems specially adapted for using meta-information in broadcast systems H04H60/73

Abstract

The present invention is directed to a method and system for predicting the behavior of an audience based on the biologically based responses of the audience to a presentation that provides a sensory stimulating experience and determining a measure of the level and pattern of engagement of that audience to the presentation. In particular, the invention is directed to a method and system for predicting whether an audience is likely to view a presentation in its entirety. In addition, the present invention may be used to determine the point at which an audience is likely to change their attention to an alternative sensory stimulating experience including fast forwarding through recorded content, changing the channel or leaving the room when viewing live content, or otherwise redirecting their engagement from the sensory stimulating experience.

Claims

1. A method for predicting viewership of a content stimulus comprising:

exposing an audience to the content stimulus using a media device communicatively coupled to a computer processing system, the computer processing system receives data relating to the content stimulus;

measuring a biologically based response during the exposure of the content stimulus using a biometric sensor device communicatively coupled to the computer processing system, the computer processing system receives data related to the biologically based response; and

executing instructions with the computer processing system to;

divide the content stimulus into a number of intervals;

determine for each interval an intensity value as a function of the biologically based response and a synchrony value as a function of a rate of change of the biologically based response for members of the audience during each interval, the synchrony value representing a number of members of the audience with a same biologically based response;

obtain an engagement score for each interval of the content stimulus, the engagement score being a function of the intensity value and the synchrony value;

use the engagement score to determine a metric including one or more of negative buildup, positive buildup, average engagement score, maximum engagement score, or combinations thereof; and

generate for display or output information indicating a prediction of viewership based on the metric.

2. The method of claim 1, wherein the computer processing system executes instructions to divide the content stimulus into the number of intervals by dividing the content stimulus into at least two intervals, the measuring of the biologically based response during the exposure of the content stimulus includes measuring the biologically based response during exposure of the content stimulus for each member of the audience, and the computer processing system obtains the engagement score by:

determining the intensity value as a function of the biologically based response for each interval;

determining the synchrony value as a function of a rate of change of the biologically based response for each interval; and

determining the engagement score for each interval as a function of the intensity value and the synchrony value.

3. The method of claim 1, wherein the measuring of the biologically based response includes measuring at least one of heart rate, skin conductance, respiration state, motion, or eye tracking.

4. The method of claim 1, wherein the computer processing system uses the engagement score to determine the metric of positive buildup by:

setting a threshold value;

dividing an engagement curve into ascending segments defined by intervals in which the engagement value remains relatively constant or increases;

computing the area above the threshold value for each such ascending segment;

summing the areas; and

dividing the sum by the duration of the content.

5. The method of claim 1, wherein the computer processing system uses the engagement score to determine the metric of negative buildup by:

setting a threshold value;

dividing an engagement curve into descending segments defined by intervals in which the engagement value remains relatively constant or decreases;

computing the area below the threshold value for each such descending segment;

summing the areas; and

dividing the sum by the duration of the content.

6. The method of claim 1, wherein the computer processing system predicts the viewership based on the metric based on correlations between the metric and actual viewership data previously collected.

7. The method of claim 1, wherein the computer processing system predicts the viewership based on the metric by determining whether a viewer will watch the entirety of the content stimulus when the content stimulus is previously recorded.

8. The method of claim 1, wherein the computer processing system predicts the viewership based on the metric by determining whether a viewer will watch the entirety of the content stimulus when the content stimulus is presented live.

9. The method of claim 1, wherein the content stimulus includes one or more of show segments, presentations, commercials, or combinations thereof.

10. The method of claim 1, wherein the metric is a first metric and the computer processing system predicts the viewership by evaluating the first metric and a second metric, the second metric including one or more of negative buildup, positive buildup, average engagement score, maximum engagement score, or combinations thereof.

11. A computerized system for predicting viewership of a content stimulus comprising:

a plurality of sensors to measure a biologically based response to the content stimulus over a time period, the time period being divided into two or more time intervals;

an intensity processor to determine, for each time interval, an intensity value as a function of the biologically based response;

a synchrony processor to determine, for each time interval, a synchrony value as a function of the biologically based response for members of an audience during each interval, the synchrony value representing a number of audience members with a same biologically based response;

an engagement processor to determine, for each time interval, an engagement value as a function of the intensity value and the synchrony value, and a metric including one or more of negative buildup, positive buildup, average engagement score, or maximum engagement score; and

a prediction generator to generate, for display or output, information indicating a prediction of viewership based on the metric.

12. The system of claim 11, wherein the content stimulus is first content stimulus and the system further includes:

a comparator to compare the engagement value to an engagement value stored in a database corresponding to a second content stimulus; and

an indicator to indicate the first content stimulus is similar to the second content stimulus based on the comparison.

13. The system of claim 11, wherein the biologically based response includes one or more of heart rate, skin conductance, respiration state, motion, or eye tracking.

14. The system of claim 11, wherein the intensity processor is to determine the intensity value as a function of a standardized score and the standardized score is determined as a function of at least one of a peak value, a trough value, a median value, an average value, or a rate of change value of the biologically based response.

15. A method of validating metrics associated with viewership of a content stimulus comprising:

exposing an audience to the content stimulus over a period of time using a media device, the period of time divided into at least two intervals;

measuring, with a biometric device, a biologically based response to the content stimulus for members of the audience; and

executing instructions with a computer processing system communicatively coupled to the media device and the biometric sensor device to;

determine an intensity value as a function of the biologically based response for each interval;

determine a synchrony value as a function of a rate of change of the biologically based response for members of the audience during each interval, the synchrony value representing a number of members of the audience with a same biologically based response;

determine engagement scores for the intervals as a function of the intensity value and the synchrony value;

use the engagement scores to determine engagement metrics including one or more of negative buildup, positive buildup, average engagement score, or maximum engagement score;

normalize the data;

determine correlations between the engagement metrics and viewership retention data; and

generate for display or output information indicating the determined correlations between the engagement metrics and the viewership retention data.

16. The method of claim 15, wherein the measuring of the biologically based response includes measuring one or more of heart rate, skin conductance, respiration rate, respiration state, motion, or eye tracking.

17. The method of claim 15, wherein the engagement scores are used to determine the engagement metrics by determining the negative buildup by:

setting a threshold value;

dividing a curve into descending segments defined by intervals in which the engagement scores remain relatively constant or decrease;

computing an area below the threshold value for each such descending segment;

summing the areas; and

dividing the sum by a duration of the content stimulus.

18. The method of claim 15, wherein the engagement scores are used to determine the engagement metrics by determining the positive buildup by:

setting a threshold value;

dividing a curve into ascending segments defined by intervals in which the engagement scores remain relatively constant or increase;

computing an area above the threshold value for each such ascending segment;

summing the areas; and

dividing the sum by a duration of the content stimulus.

19. The method of claim 15, further including collecting visual attention data with a visual attention device.

20. The method of claim 19, further including collecting the visual attention data during each exposure to the content stimulus.

21. A computerized system for predicting viewership of a content stimulus comprising:

a plurality of sensors to measure a biologically based response to the content stimulus of an audience over a time period, the time period being divided into two or more time intervals;

an intensity processor to determine, for each of the time intervals, an intensity value as a function of the biologically based response;

a synchrony processor to determine, for each of the time intervals, a synchrony value as a function of the biologically based response, the synchrony value representing a number of audience members with a same biologically based response;

an engagement processor to;

aggregate, for each of the time intervals, the measured biologically based response of members of the audience, and

determine, for each of the time intervals, an engagement index as a function of the intensity value and the synchrony value, and a metric including one or more of negative buildup, positive buildup, average engagement score, or maximum engagement score; and

a prediction generator to generate, for display or output, information indicating a prediction of viewership based on the metric.