CHARACTERIZING DYNAMIC REGIONS OF DIGITAL MEDIA DATA

• US 20120236143A1
• Filed: 03/23/2012
• Published: 09/20/2012
• Est. Priority Date: 11/04/2005
• Status: Active Grant

• Current Assignee: EyeTracking, Inc.
• Original Assignee: EyeTracking, Inc.
• Inventors: Weatherhead, James J.
• Granted Patent: US 8,564,660 B2
• Time in Patent Office: Days
• US Class Current: 348/135
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: H04H 60/45 for identifying users
• CPC Class Codes: H04H 60/48 for recognising items expressed in broadcast information
• CPC Class Codes: H04H 60/59 of video recognising characters or patterns in general G06F18/00, G06V20/00
• CPC Class Codes: H04H 60/66 for using the result on distributors' side

Abstract

A media analysis tool is provided for defining dynamic regions of a digital media segment. The dynamic regions may contain at least part of a visible feature of the segment. Correlation of the defined regions with external data quantifying attention of a subject viewing the segment to locations on the screen provides measures of interest level and attention to visible features in the segment. The dynamic regions may be defined in only some of the frames of a segment. The dynamic region may be interpolated or extrapolated for frames in which it is not explicitly defined.

Claims

1. A computer-implemented method comprising:

defining a dynamic region in a plurality of frames of a digital media segment, the digital media segment comprising a portion of a digital media file and being defined by at least two time markers comprising a start time and an end time, the dynamic region containing at least part of a visible feature in the one or more frames and being moveable from a first of the plurality of frames and a second of the plurality of frames;

analyzing a relationship between an external data set that is collected from one or more subjects viewing the digital media segment and the dynamic region, the analyzing comprising determining a correlation between the defined dynamic region and the external data set to measure at least one of an interest level of the one or more subjects in the digital media segment and an attention level of the one or more subjects to visible features in the digital media segment; and

reporting a metric indicative of the at least one of the interest level and the attention level.

2. A computer-implemented method as in claim 1, wherein the defining the dynamic region comprises using an image recognition algorithm to automatically produce a mathematical definition of a screen region containing at least part of the visible feature.

3. A computer-implemented method as in claim 1, further comprising:

incorporating the digital media segment into a digital media presentation in a stochastically determined order.

4. A computer-implemented method as in claim 1, further comprising:

defining a second dynamic region in the digital media segment;

analyzing a relationship between the external data and the second dynamic region in the digital media segment; and

reporting a second metric indicative of a second subject interest level in the second dynamic region.

5. A computer-implemented method as in claim 1, further comprising:

defining a second dynamic region in the digital media segment; and

recording a second metric related to intersection of the dynamic region with the second dynamic region, the second metric comprising at least one of one or more specific points at which the first and second dynamic regions cross and/or overlap, a duration of at least one of the intersection and the crossing and/or overlapping, a frequency with which at least one of the intersection and the crossing and/or overlapping occurs, and a number of repetitions of at least one of the intersection and the crossing and/or overlapping.

6. A computer-implemented method as in claim 1, wherein the external data set comprises at least one of eye-tracking data for the one or more subjects, a cognitive activity measurement of the one or more subjects, a cognitive engagement measurement of the one or more subjects, an emotional response measurement of the one or more subjects, information acquired from an ECG device, information acquired from an EEG device, information acquired from an EKG device, information acquired from a FNIR device, respiration information, information acquired from an EMG device, information acquired from a pupilometry device, and information acquired from a galvanic skin response device.

8. A computer-implemented method as in claim 1, wherein the external data set comprises eye-tracking data for the one or more subjects, the computer-implemented method further comprising:

analyzing a relationship between a second external data set and the dynamic region in the plurality of frames of the digital media segment, the second external data set comprising data collected from one or more subjects viewing the digital media segment and the dynamic region, the second external data set comprising one or more of a cognitive activity measurement of the one or more subjects, a cognitive engagement measurement of the one or more subjects, an emotional response measurement of the one or more subjects, and a physiological response measurement of the one or more subjects the physiological response measurement comprising information acquired from an ECG device, information acquired from an EEG device, information acquired from an EKG device, information acquired from a FNIR device, respiration information, information acquired from an EMG device, information acquired from a pupilometry device, information acquired from a galvanic skin response device; and

reporting a second metric indicative of one or more of a level of subject cognitive activity of the one or more subjects, a level of subject cognitive engagement of the one or more subjects, a level of emotional response of the one or more subjects, and a level of one or more physiological reactions of the one or more subjects stimulated by the visible feature.

9. A computer-implemented method as in claim 1, further comprising generating a predicted definition of the dynamic region for an additional frame in the digital media segment in which the dynamic region is not defined.

10. A computer-implemented method as in claim 9, wherein the generating comprises interpolating the predicted definition based on two or more of the plurality of frames.

25. A computer-implemented method as in claim 1, wherein the digital media segment comprises a digital video segment.

26. A computer-implemented method as in claim 3, further comprising:

displaying the digital media presentation to the one or more subjects; and

collecting the external data set during the displaying.

7. (canceled)

11. (canceled)

12. (canceled)

13. (canceled)

14. (canceled)

15. (canceled)

16. (canceled)

17. A computer-implemented method comprising:

identifying a visible feature that is displayed in a digital media segment, the digital media segment comprising a portion of a digital media file and being defined by at least two time markers comprising a start time and an end time;

defining a first screen region that contains at least part of the visible feature in a first frame of the digital media segment;

defining a second screen region that contains at least part of the visible feature in a second frame of the digital media segment;

predicting a definition of a third screen region that contains at least part of the visible feature in a third frame based on the definitions of the first screen region and the second screen region;

determining a correlation between the first screen region, the second screen region and the third screen region and an external data set to measure at least one of an interest level of the one or more subjects in the digital media segment and an attention level of the one or more subjects to visible features in the digital media segment, the external data set comprising eye-tracking data collected for one or more subjects while the one or more subjects view the digital media segment; and

reporting a metric indicative of the at least one of the interest level and the attention level.

18. A computer-implemented method as in claim 17, wherein the defining of the first screen region comprises specifying a first location of a vertex point of a polygon in the first frame, and the defining of the second screen region comprises specifying a second location the vertex point in the second frame.

19. A computer-implemented method as in claim 18, wherein the predicting comprises interpolating a third location of the vertex point in the third frame.

20. A computer-implemented method as in claim 17, wherein the third frame appears between the first and the second frames.

21. A computer-implemented method as in claim 17, wherein the defining the first screen region comprises using a image recognition algorithm to automatically produce a mathematical definition of the first screen region that contains at least part of the visible feature.

22. A computer-implemented method as in claim 17, further comprising:

displaying the digital media segment to a subject;

recording a metric pertaining to a location of subject attention in the third frame;

and comparing the location of subject attention with the predicted definition of the third region to estimate subject interest in the visible feature.

23. A computer-implemented method as in claim 17, further comprising:

presenting the digital media segment to the one or more subjects; and

recording the external data related to reactions of the one or more subjects during viewing by the one or more subject of the digital media segment.

27. A computer-implemented method as in claim 17, wherein the digital media segment comprises a digital video segment.

28. A computer-implemented method as in claim 17, further comprising:

analyzing a relationship between a second external data set and the first, the second, and the third screen regions in the digital media segment, the second external data set comprising data collected from one or more subjects viewing the digital media segment, the second external data set comprising one or more of a cognitive activity measurement of the one or more subjects, a cognitive engagement measurement of the one or more subjects, an emotional response measurement of the one or more subjects, and a physiological response measurement of the one or more subjects, the physiological response measurement comprising information acquired from an ECG device, information acquired from an EEG device, information acquired from an EKG device, information acquired from a FNIR device, respiration information, information acquired from an EMG device, information acquired from a pupilometry device, information acquired from a galvanic skin response device; and

reporting a second metric indicative of one or more of a level of cognitive activity of the one or more subjects, a level of subject cognitive engagement of the one or more subjects, a level of emotional response of the one or more subjects, and a level of one or more physiological reactions of the one or more subjects stimulated by the visible feature.

24. (canceled)