METHOD AND SYSTEM FOR DETECTING NON-POWERED VIDEO PLAYBACK DEVICES

• US 20130084056A1
• Filed: 11/27/2012
• Published: 04/04/2013
• Est. Priority Date: 04/06/2011
• Status: Active Grant

• Current Assignee: comScore, Inc.
• Original Assignee: comScore, Inc.
• Inventors: Harsh, Aaron, Yazdani, Amir, Vinson, Michael, Goerlich, Bruce
• Granted Patent: US 9,009,749 B2
• Time in Patent Office: Days
• US Class Current: 386/248
• CPC Class Codes: H04N 21/24 Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth, upstream requests monitoring of server performance or load G06F11/34; arrangements for observation, testing or troubleshooting for broadcast or for distribution combined with broadcast H04H20/12
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/658 Transmission by the client directed to the server
• CPC Class Codes: H04N 21/81 Monomedia components thereof
• CPC Class Codes: H04N 5/91 Television signal processing therefor

Abstract

A method and system for identifying tune data from set top boxes associated with televisions, video monitors, or other video playback devices that are likely powered off. In some embodiments, survival curves are constructed that predict a length of time before a video playback device is powered off after a tuning event. The survival curves are used to predict the likelihood that a video playback device is powered off. Viewership estimates made from tune data reported from set top boxes can be adjusted to take account of the video playback devices that are predicted to be powered off.

Claims

1. A computing system for analyzing video playback device viewing data, comprising:

a memory for storing programmed instructions;

a processor configured to execute the programmed instructions to perform operations including;

receiving tune data indicative of content tuned to by a number of set top boxes, the tune data associated with each set top box including an identification of the set top box and a plurality of tuning events, each tuning event including a time of the tuning event and a corresponding content identifier that was tuned at the tuning event;

analyzing the tune data to identify tune data from set top boxes associated with video playback devices that are likely powered off; and

adjusting video playback device viewing data by excluding at least some tune data from set top boxes associated with video playback devices that are likely powered off.

2. The computing system of claim 1, wherein analyzing the tune data to identify tune data from set top boxes associated with video playback devices that are likely powered off comprises:

compiling statistics predicting a length of time before video playback devices are likely powered off after a tuning event; and

using the statistics to identify tuning data from set top boxes associated with video playback devices that are likely powered off.

3. The computing system of claim 2, wherein the statistics regarding a length of time before video playback devices are likely powered off are compiled by detecting power off signals in the tune data.

4. The computing system of claim 2, wherein the statistics regarding a length of time before video playback devices are likely powered off are compiled by:

identifying a number of reliable set top boxes in the tune data; and

detecting power off signals in the tune data from the reliable set top boxes.

5. The computing system of claim 2, wherein the statistics that predict a length of time before a video playback device is powered off are compiled by analyzing the tune data for times between two successive tuning events and a power off signal received from a video playback device between those two successive tuning events.

6. The computing system of claim 5, wherein the statistics are compiled for different times of a day, days of a week, or months of a year.

7. The computing system of claim 5, wherein the statistics are compiled for types of broadcast programs.

8. The computing system of claim 5, wherein the statistics are compiled for types of networks.

9. A non-transitory, computer readable media with instructions stored thereon that are executable by a processor to perform operations of:

receiving tune data indicative of content tuned to by a number of set top boxes, the tune data associated with each set top box including an identification of the set top box and a plurality of tuning events, each tuning event including a time of the tuning event and a corresponding content identifier that was tuned at the tuning event;

analyzing the tune data to identify tune data from set top boxes associated with video playback devices that are likely powered off; and

adjusting viewing data to account for tune data from set top boxes associated with video playback devices that are likely powered off.

10. The non-transitory computer readable media of claim 9, wherein analyzing the tune data to identify tune data from set top boxes associated with video playback devices that are likely powered off comprises:

compiling statistics predicting a length of time before video playback devices are likely powered off after a tuning event; and

using the statistics to identify tuning data from set top boxes associated with video playback devices that are likely powered off.

11. The non-transitory computer readable media of claim 10, wherein compiling statistics predicting a length of time before video playback devices are likely powered off after a tuning event includes the operation of detecting power off signals in the tune data.

12. The non-transitory computer readable media of claim 10, wherein compiling statistics predicting a length of time before video playback devices are likely powered off after a tuning event includes the operations of:

identifying a number of reliable set top boxes in the tune data; and

detecting power off signals in the tune data from the reliable set top boxes.

13. A computer-implemented method of analyzing tune data, comprising:

receiving, at a computing system, tune data from a number of set top boxes that indicates tuning events for the set top boxes, the tune data associated with each set top box including an identification of the set top box and a plurality of tuning events, each tuning event including a time of the tuning event and a corresponding content identifier that was tuned at the tuning event;

analyzing, by the computing system, the tune data to identify set top boxes that are associated with video playback devices that are likely powered off; and

adjusting viewership estimates associated with the number of set top boxes to take into account the identified set top boxes that are associated with likely-powered-off video playback devices.

14. The computer-implemented method of claim 13, wherein the tune data is analyzed to identify set top boxes that are associated with video playback devices that are likely powered off by:

constructing, with the computing system, survival curves that indicate a likelihood that a video playback device is powered off within a time interval after a tuning event, wherein the survival curves are constructed from set top boxes that report a power off event in the tune data; and

using the survival curves to identify, with the computer, one or more set top boxes that do not report power off events in the tune data and that are associated with video playback devices that are likely powered off.

15. The computer-implemented method of claim 14, wherein the survival curves are constructed by the computing system for different hours of the day.

16. The computer-implemented method of claim 14, wherein the survival curves are constructed by the computing system for different days of the week or months of the year.

17. The computer-implemented method of claim 14, wherein the survival curves are constructed by the computing system for different types of broadcast programs.

18. The computer-implemented method of claim 14, wherein the survival curves are constructed by the computing system for different types of networks.

19. A method, implemented by a computing system having a processor and memory, for estimating when a video playback device was powered off based on tuning data received from a video monitoring device associated with the video playback device, the method comprising:

selecting, by the computing system, a first tuning event and a second tuning event from tuning data that is received from a video monitoring device that is associated with a video playback device,

wherein neither the first nor the second tuning events are power-off events of the video playback device, and

wherein the second tuning event follows the first tuning event;

selecting, by the computing system, a model associated with the duration of a time interval between the first tuning event and the second tuning event; and

applying the selected model in order to determine, by the computing system, a portion of the time interval in which to consider the video playback device to be powered off,

wherein the portion begins after the first tuning event and ends at the second tuning event; and

adjusting viewership estimates associated with the viewing playback device to take into account the portion of the time interval in which the video playback device was likely powered off.

20. The method of claim 19, wherein adjusting viewership estimates comprises excluding the video playback device from being counted in a number of video playback devices tuned to the program of interest.

21. The method of claim 20, wherein the video monitoring device is a set top box.

22. The method of claim 19, wherein the model approximates a 50% probability that the video playback device was powered off.

23. The method of claim 19, wherein the model uses a Monte Carlo technique to determine a probability that the video playback device has being powered off.

24. The method of claim 23, wherein the probability that the video playback device is powered off is selected from a random and uniform distribution of numbers between zero and one.

25. The method of claim 19, wherein the model is a survival curve.

26. The method of claim 25, wherein the model is determined using a mathematical formula.