Methods and systems for selecting and presenting content on a first system based on user preferences learned on a second system

• US 8,543,516 B2
• Filed: 02/04/2011
• Issued: 09/24/2013
• Est. Priority Date: 03/06/2006
• Status: Active Grant

• Current Assignee: Veveo, Inc. (Adeia Inc.)
• Original Assignee: Veveo, Inc. (Adeia Inc.)
• Inventors: Aravamudan, Murali, Rajasekharan, Ajit, Ramakrishnan, Kajamalai G.
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Kim, David H
• Application Number:
  US13/021,086
• Publication Number:
  US 20110131161A1
• Time in Patent Office: 963 Days
• Field of Search: 706/11, 707/732, 707/734, 707/767
• US Class Current: 706/11
• CPC Class Codes: G06F 16/152 using file content signatures, e.g. hash values
• CPC Class Codes: G06F 16/2453 Query optimisation
• CPC Class Codes: G06F 16/2457 with adaptation to user needs
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/2462 Approximate or statistical queries
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/284 Relational databases
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/435 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/437 Administration of user profiles, e.g. generation, initialisation, adaptation, distribution
• CPC Class Codes: G06F 16/438 Presentation of query results
• CPC Class Codes: G06F 16/686 using information manually generated, e.g. tags, keywords, comments, title or artist information, time, location or usage information, user ratings
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• 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 21/44224 : Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/4524 : involving the geographical location of the client retrieval from the Internet by querying based on geographical locations G06F16/9537; systems specially adapted for using geographical information in broadcast systems H04H60/70; protocols in which the network application is adapted for the location of the user terminal in communication control or processing H04L67/52; services making use of the location of users or terminals in wireless networks H04W4/02; locating users or terminals in wireless networks H04W64/00
• CPC Class Codes: H04N 21/4532 : involving end-user characteristics, e.g. viewer profile, preferences monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/466 : Learning process for intelligent management, e.g. learning user preferences for recommending movies services using the results of monitoring in broadcast systems H04H60/61
• CPC Class Codes: H04N 21/4828 : for searching program descriptors retrieval of video data G06F16/739
• CPC Class Codes: Y10S 707/99933 : Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99934 : Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99937 : Sorting
• CPC Class Codes: Y10S 707/99943 : Generating database or data structure, e.g. via user interface
• CPC Class Codes: Y10S 707/99945 : Object-oriented database structure processing

Abstract

A method of selecting and presenting content on a first system based on user preferences learned on a second system is provided. The method includes receiving a user'"'"'s input for identifying items of the second content system and, in response thereto, presenting a subset of items of the second content system and receiving the user'"'"'s selection actions thereof. The method includes analyzing the selected items to learn the user'"'"'s content preferences for the content of the second content system and determining a relationship between the content of the first and second content systems to determine preferences relevant to items of the first content system. The method includes, in response subsequent user input for items of the first content system, selecting and ordering a collection of items of the first content system based on the user'"'"'s learned content preferences determined to be relevant to the items of the first content system.

Claims

1. A user-interface method of selecting and presenting a collection of content items of a first content system in which the presentation is ordered at least in part based on content preferences of the user learned from user interactions with at least one second content system, the method comprising:

monitoring user interactions with the at least one second content system, the at least one second content system having a set of content items, each content item having at least one associated descriptive term to describe the content item;

analyzing the descriptive terms associated with content items with which the user interacted to learn the content preferences of the user for the content of the at least one second content system;

determining a subset of learned user content preferences that are relevant to the content items of the first content system; and

subsequent to determining the subset of relevant learned user content preferences, presenting at least one content item of the first content system selected and ordered based on the subset of learned content preferences of the user determined to be relevant to the content items of the first content system.

2. The method of claim 1, the presenting the at least one content item of the first content system subsequent to determining the subset of relevant learned user content preferences being in response to receiving incremental input entered by the user for incrementally identifying desired content items of the first content system.

3. The method of claim 1, the learned content preferences of the user being based on analyzing descriptive terms of content items with which the user interacted from a plurality of content systems.

4. The method of claim 2, wherein the incremental input entered by the user is the first interaction between the user and the first content system.

5. The method of claim 1, wherein the first content system has not characterized the content preferences of the user.

6. The method of claim 1, wherein the first content system and second content system are different systems.

7. The method of claim 1, wherein the first content system is on a server system remote from the user and the second content system is on a user client device.

8. The method of claim 1, wherein the second content system is on a server system remote from the user and the first content system is on a user client device.

9. The method of claim 1, wherein the selecting and ordering the at least one content item is further based on popularity values associated with the content items, each popularity value indicating a relative measure of a likelihood that the corresponding content item is desired by the user.

10. The method of claim 1, wherein the content items of at least one of the first and the second content systems is contained on at least one of a cable television system, a video-on-demand system, an IPTV system, and a personal video recorder.

11. The method of claim 1, the presenting the at least one content item including presenting the content item on at least part of a television screen.

12. The method of claim 1, the presenting the at least one content item including presenting the content item on at least one of a telephone, a PDA, and a remote control.

13. The method of claim 2, wherein the incremental input comprises at least one prefix of a word for describing the desired content items.

14. The method of claim 1, wherein the learned content preferences are stored on a user client device.

15. A user-interface system for selecting and presenting a collection of content items of a first content system in which the presentation is ordered at least in part based on content preferences of the user learned from user interactions with at least one second content system, the user-interface system comprising:

at least one non-transitory computer-readable media in at least one of a user device or a remote server system in communication with the user device, the computer-readable media comprising instructions that when executed cause a computer system to;

monitor user interactions with the at least one second system, the at least one second content system having a set of content items, each content item having at least one associated descriptive terms to described the content items;

analyze the descriptive terms associated with content items with which the user interacted to learn the content preferences of the user for the content of the at least one second content system;

determine a subset of learned user content preferences that are relevant to the content items of the first content system; and

present at least one content item of the first content system selected and ordered based on the subset of learned content preferences of the user determined to be relevant to the content items of the first content system.

16. The system of claim 15, wherein the presenting the at least one content item of the first content system occurs subsequent to determining the subset of relevant learned user content preferences and is responsive to receiving incremental input entered by the user for incrementally identifying desired content items of the first content system.

17. The system of claim 15, the learned content preferences of the user being based on analyzing descriptive terms associated with content items with which the user interacted from a plurality of content systems.

18. The system of claim 16, wherein the incremental input entered by the user is the first interaction between the user and the first content system.

19. The system of claim 15, wherein the first content system has not characterized the content preferences of the user.

20. The system of claim 15, wherein the first content system and second content system are different systems.

21. The system of claim 15, wherein the first content system is on a server system remote from the user and the second content system is on a user client device.

22. The system of claim 15, wherein the second content system is on a server system remote from the user and the first content system is on a user client device.

23. The system of claim 15, wherein the selecting and ordering the at least one content item is further based on popularity values associated with the content items, each popularity value indicating a relative measure of a likelihood that the corresponding content item is desired by the user.

24. The system of claim 15, wherein the content items of at least one of the first and second content systems is contained on at least one of a cable television system, a video-on-demand system, an IPTV system, and a personal video recorder.

25. The system of claim 15, wherein the presenting the at least one content item of the first content system includes presenting the at least one content item on at least part of a television screen.

26. The system of claim 15, wherein the presenting at least one content item of the first system presents the content item on at least one of a telephone, a PDA, and a remote control.

27. The system of claim 16, wherein the incremental input comprises at least one prefix of a word for describing the desired content items.

28. The system of claim 15, the computer-readable media further comprising instructions that when executed cause a computer system to receive the learned content preferences stored on a user client device.