System and method for providing supervised learning to associate profiles in video audiences

• US 8,930,989 B2
• Filed: 03/25/2008
• Issued: 01/06/2015
• Est. Priority Date: 08/20/2007
• Status: Expired due to Fees

• Current Assignee: Ads-Vantage
• Original Assignee: Adsvantage
• Inventors: Knoller, Raviv, Litvak-Hinenzon, Anna, Cohen, Reuven, Paker, Alex
• Primary Examiner(s): Tran, Hai V
• Application Number:
  US12/054,937
• Publication Number:
  US 20090055858A1
• Time in Patent Office: 2,478 Days
• Field of Search: 705/1, 705/7, 705/10, 705/14, 725/9, 725/10, 725/42, 725/46, 725/11, 725/13, 725/14, 725/16, 725/20, 725 34- 36, 725/61, 725/105, 725/135
• US Class Current: 725/34
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0264 based upon schedule
• CPC Class Codes: G06Q 30/04 Billing or invoicing
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: H04N 21/23424 involving splicing one content stream with another content stream, e.g. for inserting or substituting an advertisement
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/2665 Gathering content from different sources, e.g. Internet and satellite
• CPC Class Codes: H04N 21/44222 Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/6405 Multicasting data broadcast and multicast in packet switching networks H04L12/18
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

A system and method for providing supervised learning to associate profiles in video audiences is provided. The method includes: receiving data providing an association of consumer profiles and video decoders to households within a network; recording zapping events (patterns) created by consumers; and associating zapping patterns of consumers with households. The step of associating further includes: collecting external data and converting a format of the external data into an internal format; converting zapping logs into different data models that can be used to provide set top box signatures; providing the set top box signatures; using the set top box signatures with a list of set top boxes and profiles to provide an association rule; and applying the association rule to the set top box signatures to determine a list of profiles of the consumer profiles associated with a specific set top box of the set top boxes.

Claims

1. A method of performing supervised learning to associate one or more consumer profiles within a video audience with one or more video decoders within a network, comprising the steps of:

obtaining consumer profile data for a subset of consumer households, wherein the consumer profile data for each consumer household includes information associating one or more consumers in the consumer household to one or more video decoders in the consumer household based on which consumers in the consumer household use each of the one or more video decoders;

receiving zapping events recorded from at least one video decoder, within said network, also referred to as zapping patterns, wherein said zapping events are for a given time period;

receiving data from questionnaires providing an association of at least one consumer profile of said one or more consumer profiles to one or more video decoders for which said zapping events were recorded, wherein said received data serves as profile data;

using statistical analysis on said received profile data that provides an association of at least one consumer profile of said one or more consumer profiles to one or more video decoders for which said zapping events were recorded to infer a relation between one or more of the zapping patterns and at least one of the consumer profiles of said one or more consumer profiles that is associated with one of the video decoders; and

determining, based on the inferred relation, said received data and said received zapping events, a set of consumer profiles associated with one or more of the video decoders for which no consumer profile data was obtained; and

determining a probability that each consumer profile in said set of consumer profiles has been using said one or more of the video decoders for which no profile data was received from questionnaires during a time period represented in said zapping events recorded.

2. The method of claim 1, wherein obtaining the consumer profile data for the subset of consumers in the video audience comprises:

providing questionnaires to one or more of the consumers; and

receiving at least some of said questionnaires filled out by the one or more of the consumers.

3. The method of claim 1, wherein said zapping events include events where there is a switching from a current service to another service and/or other system for communicating with the one or more video decoders for which the zapping events were received.

4. The method of claim 1, wherein some or all of the video decoders are set top boxes.

5. The method of claim 4, wherein said external data is selected from the group consisting of a zapping log, a broadcast schedule, set top box information, and sample information.

6. The method of claim 4, wherein obtaining the consumer profile data for the subset of consumers in the video audience comprises:

providing questionnaires to one or more of the consumers; and

receiving at least some of the provided questionnaires filled out by said one or more of the consumers.

7. The method of claim 6, further comprising the step of, for any households that did not fill out the questionnaire, using identification functionality to associate set top boxes to remaining consumer profiles, that were not associated via use of said association rule.

8. The method of claim 1, further comprising the step of determining a targeted rating of a program per consumer profile.

9. The method of claim 1, further comprising the step of determining a total viewership.

10. The method of claim 1, further comprising the step of determining a viewership flow.

11. The method of claim 10, wherein the step of determining a viewership flow further comprises the steps of, for each channel made available by the video decoders, calculating a number, or percentage, of viewers of every consumer profile that join the channel and leave the channel during a short period of time.

12. The method of claim 11, wherein the step of determining a viewership flow is performed for every minimally defined region.

13. The method of claim 1, further comprising the step of determining a targeted rating distribution.

14. The method of claim 13, wherein the step of determining a targeted rating distribution further comprises the steps of, for every channel made available by the video decoders, for every consumer profile, calculating a rating of the channel during a short period of time.

15. The method of claim 14, wherein the step of determining a targeted rating distribution is performed for every minimally defined region.

16. The method of claim 1, wherein received data includes data associating consumer profiles and video decoders to households within said network, and wherein said method further comprises the step of associating at least one viewer profile to at least one household.

17. The method of claim 1 wherein a household is associated with one or more video decoders within said network.

18. The method of claim 1, further comprising the step of determining content to a viewer profile association.

19. The method of claim 18, wherein determining content to a viewer profile association includes associating at least one consumer profile to a pre-defined content, wherein the profile is associated to at least one of the video decoders within said network.

20. The method of claim 1, where the zapping patterns are aggregated over all video decoders in a specific facility, and are used to determine all consumer profiles associated with this facility,

21. The method of claim 20, wherein the received data, the received consumer profiles, and the determined set of consumer profiles are used to determine a probability that each individual consumer profile using a video decoder was viewing a specific content during the given time period of the zapping events.

22. The method of claim 1, wherein the received data, the received consumer profiles, and the determined set of consumer profiles are used to determine a probability that each individual consumer profile using a video decoder was viewing a specific content during the given time period of the zapping events.

23. A system for providing supervised learning to associate one or more consumer profiles within a video audience with one or more video decoders within a home network, wherein consumer profile data is obtained for a subset of consumers in the video audience and the consumer profile data for each consumer is associated with one or more of the video decoders in the home network, the system comprising:

logic to receive zapping events recorded from at least one video decoder, within said home network, also referred to as zapping patterns, wherein said zapping events are for a given time period;

logic to receive data providing an association of at least one of the consumer profile of said one or more consumer profiles to one or more of the video decoders for which said zapping events were recorded, wherein said received data serves as profile information; and

logic to use statistical analysis on the received data to infer a relation between one or more of the zapping patterns and at least one consumer profile of said one or more consumer profiles that is associated with one of the video decoders within the home network; and

determining from said received data and said received zapping patterns, a set of consumer profiles associated with one or more of said video decoders for which no consumer profile data was obtained from said received data; and

determining a probability that each consumer profile in said set of consumer profiles has been using said one or more video decoders during a time period represented in said zapping pattern,

further comprising a management application that comprises;

logic to convert a format of external data into an internal format;

logic to convert zapping logs into different data models that can be used to provide set top box signatures;

logic to provide said set top box signatures;

logic to use said set top box signatures with a list of set top boxes and of said one or more consumer profiles to provide an association rule; and

logic to apply said association rule to said set top box signatures to determine a list of profiles of said one or more consumer profiles associated with a specific set top box of said set top boxes.

24. The system of claim 23, further comprising:

a head end selected from the group consisting of an internet protocol television head end, a cable head end, a satellite head end, and a terrestrial head end.

25. The system of claim 23, further comprising:

a system for communicating selected from the group consisting of an access network multiplexer, a radio frequency interface, and a radio frequency modulation interface.

26. The system of claim 23, wherein said zapping patterns include events where there is a switching from a current service to another service and/or other system for communicating with a set top box.

27. The system of claim 23, wherein said external data is selected from the group consisting of a zapping log, a broadcast schedule, set top box information, and sample information.

28. The system of claim 23, wherein said management application further comprises logic configured to determine a targeted rating of a content per consumer profile.

29. The method of claim 28, wherein the content is a program.