Subscriber identification based on electronic program guide data

• US 6,714,917 B1
• Filed: 08/09/2000
• Issued: 03/30/2004
• Est. Priority Date: 12/03/1998
• Status: Expired due to Term

• Current Assignee: Prime Research Alliance E, LLC
• Original Assignee: Expanse Networks, Inc.
• Inventors: Eldering, Charles A., Flickinger, Gregory C.
• Primary Examiner(s): Stamber, Eric W.
• Assistant Examiner(s): Alvarez, Raquel
• Application Number:
  US09/635,253
• Time in Patent Office: 1,329 Days
• Field of Search: 705/1, 705/10, 705/14, 725/14, 725/15, 725/19, 725/34, 725/35, 725/46
• US Class Current: 705/7.33
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0277 Online advertisement
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• 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 21/4662 characterized by learning algorithms
• CPC Class Codes: H04N 21/6582 Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

A subscriber identification system is presented in which EPG related data including scrolling rates, paging rates, information screen viewing times, and manner and frequency of EPG activation are used to identify a subscriber from a group of subscribers. In one instance, the subscriber viewing data is recorded and a signal processing algorithm, such as a Fourier transform, is used to produce a processed version of the EPG related data. The processed version of the EPG related data can be correlated with stored common identifiers of subscriber profiles to determine which subscriber from the group is presently viewing the programming. A neural network or fuzzy logic can be used as the mechanism for identifying the subscriber from clusters of information, which are associated with individual subscribers.

Claims

1. In a telecommunications environment comprising a residential unit for providing a subscriber the ability to select and interact with content, the residential unit including at least a storage medium and a processor, a computer implemented method of associating a subscriber interacting with a device with particular characteristics, the method comprising utilizing the processor to:

monitor subscriber interactions with a device;

process at least a portion of the subscriber interactions to generate a session profile, wherein the session profile is generated without knowledge of the identity of the subscriber and identifies subscriber traits including non-subscriber interaction traits;

compare at least a portion of the session profile with at least a portion of subscriber profiles stored in the storage medium, the subscriber profiles being a compilation of session profiles generated over multiple interaction sessions that have been grouped together based on similarities; and

associate, based on similarities between the subscriber profile and the session profile, the subscriber with particular characteristics identified in one of the subscriber profiles.

2. The method of claim 1, wherein the subscriber traits and the particular characteristics include probabilistic demographics.

3. The method of claim 2, wherein said comparing includes comparing at least some subset of the probabilistic demographic characteristics of the subscriber within the session profile with similar attributes in the subscriber profiles.

4. The method of claim 1, wherein the subscriber traits and the particular characteristics include probabilistic program characteristics.

5. The method of claim 4, wherein said comparing includes comparing at least some subset of the probabilistic program characteristics associated with the subscriber within the session profile with similar attributes in the subscriber profiles.

6. The method of claim 1, wherein the session profiles and the subscriber profiles include a subscriber signature.

7. The method of claim 6, wherein the subscriber signature is a discrete spectrum representation of at least same subset of the subscriber interactivity.

8. The method of claim 6, wherein the subscriber signature is a Fourier transform of at least some subset of the subscriber interactivity.

9. The method of claim 6, wherein said comparing includes comparing the subscriber signature within the session profile with similar attributes in the subscriber profiles.

10. The method of claim 1, wherein said comparing is performed by a neural network.

11. The method of claim 1, wherein the session profiles are clustered based on similar subscriber signatures in order to generate the subscriber profiles.

12. The method of claim 1, wherein the session profiles are clustered based on similarities between at least some subset of probabilistic demographic characteristics and probabilistic program characteristics in order to generate the subscriber profiles.

13. The method of claim 1, further comprising retrieving program data associated with content selected within the subscriber interactivity.

14. The method of claim 13, wherein the program data defines at least some subset of program characteristics and program demographics.

15. The method of claim 1, wherein said monitoring includes monitoring electronic program guide interactions.

16. The method of claim 15, wherein the electronic program guide interactions include at least some subset of scrolling speed, paging speed, information screen viewing time, manner of electronic program guide activation, and frequency of electronic program guide activation.

17. A system for associating a subscriber interacting with a device with particular characteristics, the system comprising:

a device;

a storage medium; and

a processor for;

monitoring subscriber interactivity with the device;

processing at least a portion of the subscriber interactivity to generate a session profile, wherein the session profile is generated with no knowledge of the identity of the subscriber and identifies subscriber traits including non-subscriber interactivity traits;

comparing at least a portion of the session profile with at least a portion of subscriber profiles, the subscriber profiles being a compilation of session profiles generated over multiple interaction sessions that have been grouped together based on similarities; and

associating, based on similarities between the subscriber profile and the session profile, the subscriber with particular characteristics identified in one of the subscriber profiles.

18. The system of claim 17, wherein said processor generates a session profile that includes probabilistic demographic characteristics and compares at least some subset of the probabilistic demographic characteristics of the subscriber included within the session profile with similar attributes in the subscriber profiles.

19. The system of claim 17, wherein said processor generates a session profile that includes a subscriber signature and compares the subscriber signature included within the session profile with similar attributes in the subscriber profiles.

20. The system of claim 17, wherein said processor monitors subscriber interactions with an electronic program guide.

21. The system of claim 20, wherein said processor monitors at least some subset of scrolling speed, paging speed, information screen viewing time, manner of electronic program guide activation, and frequency of electronic program guide activation.

22. A computer program embodied on a computer-readable medium for associating a subscriber interacting with a device with particular characteristics, said computer program comprising:

a source code segment for monitoring subscriber interactivity;

a source code segment for processing at least a portion of the subscriber interactivity to generate a session profile, wherein the session profile is generated with no knowledge of the identity of the subscriber and identifies subscriber traits and at least some of the subscriber traits are non-subscriber interactivity traits;

a source code segment for comparing at least a portion of the session profile with at least a portion of subscriber profiles, the subscriber profiles being a compilation of session profiles generated over multiple interaction sessions that have been grouped together based on similarities; and

a source code segment for associating, responsive to said source code segment for comparing, the subscriber with particular characteristics identified in one of the subscriber profiles.

23. The computer program of claim 22, wherein said source code segment for monitoring, monitors electronic program guide interactions.

24. The computer program of claim 23, wherein said source code segment for monitoring, monitors at least some subset of scrolling speed, paging speed, information screen viewing time, manner of electronic program guide activation, and frequency of electronic program guide activation.

25. In a television network environment consisting of a display device, a storage medium, and a processor, a computer implemented method for associating a subscriber with particular characteristics, the method comprising:

monitoring subscriber television viewing interactions;

utilizing the processor to process at least a portion of the subscriber television viewing interactions to generate a session profile, wherein the session profile identifies non-television viewing interaction characteristics;

comparing at least a portion of the session profile with at least a portion of subscriber profiles stored in the storage medium, the subscriber profiles being a compilation of session profiles generated over multiple television viewing sessions that have been grouped together based on similarities; and

associating, based on similarities between the subscriber profile and the session profile, the subscriber with particular characteristics identified in one of the subscriber profiles, wherein the identity of the subscriber need not be known in order to associate particular characteristics with the subscriber.

26. The method of claim 25, wherein the particular characteristics include demographic characteristics.

27. The method of claim 25, wherein the particular characteristics are probabilistic.

28. The method of claim 25, wherein the particular characteristics are generated by applying heuristic rules to the subscriber television interactions, wherein the heuristic rules associate the subscriber television interactions to particular characteristics.

29. The method of claim 25, wherein the television viewing interactions include at least some subset of scrolling speed, paging speed, information screen viewing time, manner of electronic program guide activation, and frequency of electronic program guide activation.