COLLECTING DATA FROM DIFFERENT SOURCES

• US 20120059825A1
• Filed: 09/08/2010
• Published: 03/08/2012
• Est. Priority Date: 09/07/2010
• Status: Active Grant

• Current Assignee: OpenTV, Inc. (Kudelski SA)
• Original Assignee: OpenTV, Inc. (Kudelski SA)
• Inventors: Fishman, Alex, Chai, Crx K.
• Granted Patent: US 10,210,160 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/4387 by the use of playlists
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04H 60/33 Arrangements for monitoring the users' behaviour or opinions
• CPC Class Codes: H04H 60/65 for using the result on users' side
• CPC Class Codes: H04N 21/26258 for generating a list of items to be played back in a given order, e.g. playlist, or scheduling item distribution according to such list retrieval of multimedia data based on playlists G06F16/40
• CPC Class Codes: H04N 21/44204 Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched monitoring of user activities for profile generation for accessing a video database G06F16/739; protecting generic digital content where the protection is independent of the precise nature of the content G06F21/10; arrangements for monitoring the use made of the broadcast services in broadcast systems H04H60/31
• CPC Class Codes: H04N 21/4667 Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections

Abstract

A system for collecting data from different sources is described. In one example embodiment, the system obtains content-related data from a plurality of source computer systems, automatically identifies, based on the content-related data, content items having respective popularity values greater than a predetermined threshold value as popular content items, and automatically generates a list of popular content items based on the popular content items.

Claims

1. A computer-implemented method comprising:

obtaining content-related data from a plurality of source computer systems, the content-related data being indicative of viewers'"'"' interest in respective content items;

based on the content-related data, automatically identifying content items having respective popularity values greater than a predetermined threshold value as popular content items; and

automatically generating, at a server computer system, a list of popular content items based on the popular content items.

2. The method of claim 1, wherein a source system from the plurality of source systems comprises a web search engine provider'"'"'s system.

3. The method of claim 1, wherein a source system from the plurality of source systems comprises a social network provider'"'"'s system.

4. The method of claim 1, wherein a source system from the plurality of source systems comprises a video-on-demand provider'"'"'s system.

5. The method of claim 1, wherein a source system from the plurality of source systems comprises a viewer'"'"'s client device.

6. The method of claim 5, wherein the viewer'"'"'s client device is a set top box.

7. The method of claim 5, wherein the viewer'"'"'s client device is a desktop computer.

8. The method of claim 5, wherein the viewer'"'"'s client device is a mobile device.

associated with a popularity value greater than a threshold value.

9. The method of claim 1, comprising:

for a target viewer, customizing the list of popular content items to generate a customized playlist, the customizing based on a profile of the target viewer; and

communicating the customized playlist to a client device of the target viewer.

10. The method of claim 1, comprising providing data associated with the list of popular content items to a client device of a target viewer.

11. A computer-implemented system comprising:

a collector module to obtain content-related data from a plurality of source computer systems, the content-related data being indicative of viewers'"'"' interest in respective content items; and

an analytics module to;

automatically identify, based on the content-related data, content items having respective popularity values greater than a predetermined threshold value as popular content items, and

automatically generate, at a server computer system, a list of popular content items based on the popular content items.

12. The system of claim 11, wherein a source system from the plurality of source systems comprises a web search engine provider'"'"'s system.

13. The system of claim 11, wherein a source system from the plurality of source systems comprises a social network provider'"'"'s system.

14. The system of claim 11, wherein a source system from the plurality of source systems comprises a video-on-demand provider'"'"'s system.

15. The system of claim 11, wherein a source system from the plurality of source systems comprises a viewer'"'"'s client device.

16. The system of claim 15, wherein the viewer'"'"'s client device is a set top box.

17. The system of claim 15, wherein the viewer'"'"'s client device is a desktop computer.

18. The system of claim 15, wherein the viewer'"'"'s client device is a mobile device.

associated with a popularity value greater than a threshold value.

19. The system of claim 11, comprising:

for a target viewer, customizing the list of popular content items to generate a customized playlist, the customizing based on a profile of the target viewer; and

communicating the customized playlist to a client device of the target viewer.

20. The system of claim 11, comprising providing data associated with the list of popular content items to a client device of a target viewer.

21. A machine-readable non-transitory medium having instruction data to cause a machine to:

obtain content-related data from a plurality of source computer systems, the content-related data being indicative of viewers'"'"' interest in respective content items;

automatically identify, based on the content-related data, content items having respective popularity values greater than a predetermined threshold value as popular content items; and

automatically generate, at a server computer system, a list of popular content items based on the popular content items.