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

• US 7,885,904 B2
• Filed: 09/15/2010
• Issued: 02/08/2011
• 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): Holmes; Michael B.
• Assistant Examiner(s): Kim; David H
• Application Number:
  US12/882,451
• Publication Number:
  US 20110004572A1
• Time in Patent Office: 146 Days
• Field of Search: 706/11, 707/732, 707/734, 707/767
• US Class Current: 706/11
• 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 21/4532 : 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 subset of content items of a first dataspace in which at least one content item of the subset is selected at least in part based on content preferences of the user learned from the user selecting content of a second dataspace, the method comprising:

providing access to a first collection of content items of a first dataspace, each content item of the first collection having at least one associated descriptive term to describe the content item;

providing access to a second collection of content items of a second dataspace, each content item of the second collection having at least one associated descriptive term to describe the content item;

receiving selection actions of content items of the second collection from the user;

a computer system determining a user preference signature by analyzing the descriptive terms of the selected content items of the second collection to learn the content preferences of the user for the content of the second dataspace;

determining a relationship between the content items of the first dataspace and the content items of the second dataspace, the relationship defining which learned user content preferences of the user preference signature are relevant to the content items of the first dataspace; and

subsequent to learning the content preferences of the user, selecting and presenting to the user at least one content item of the first dataspace based on the learned content preferences of the user determined to be relevant to the content items of the first dataspace.

2. The method of claim 1, further comprising:

analyzing the descriptive terms associated with the content items of the second collection selected by the user to identify sets of actions resulting in the selection of similar content items, wherein similarity is determined by comparing the descriptive terms associated with any one of the selected content items with any of the previously selected content items; and

analyzing the date, day, and time of at least two of the individual selection actions of the sets of actions to learn periodicities of the user actions resulting in the selections of similar content items, wherein the periodicity corresponding to a particular set of actions for selecting similar content items indicates the amount of time between the user selection actions of the set;

wherein the selecting of the at least one content item of the first dataspace for presentation to the user is further based on at least one of the learned periodicities of the sets of actions resulting in the selection of similar content items.

3. The method of claim 1, wherein the relationship between the content items of the first dataspace and the content items of the second dataspace is determined prior to receiving selection actions from the user.

4. The method of claim 1, further comprising:

recording information associated with at least one of the selection actions from the user; and

including the information in at least one of the first and second collections of content items.

5. The method of claim 1, wherein the first dataspace and second dataspace are different dataspaces.

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

7. The method of claim 6, 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 6, 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 presenting the at least one content item is further based on popularity values associated with the content items of the first dataspace, 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 second dataspaces includes at least one of television program items, movie items, audio/video media items, music items, contact information items, personal schedule items, web content items, and purchasable product items.

11. The method of claim 1, wherein the content items of at least one of the first and second dataspaces includes at least one of television program items, movie items, and audio/video media items and the descriptive terms includes at least one of title, cast, director, content description, and keywords associated with the content.

12. The method of claim 1, wherein the selection actions are entered by the user on at least one of a telephone, a PDA, and a mobile computing device.

13. The method of claim 12, wherein at least one of the telephone, the PDA, and the mobile computing device has a plurality of overloaded keys, each of the overloaded keys representing two or more characters.

14. The method of claim 1, wherein the presenting the at least one content item of the first dataspace includes presenting said at least one content item on at least one of a telephone, a PDA, and a mobile computing device.

15. The method of claim 1, wherein at least one of receiving selection actions, analyzing the descriptive terms, determining a relationship between the content items of the first and second dataspaces, and selecting the at least one content item of the first dataspace is performed on a server system remote from the user.

16. The method of claim 1, wherein at least one of receiving selection actions, analyzing the descriptive terms, determining a relationship between the content items of the first and second dataspaces, and selecting the at least one content item of the first dataspace is performed on a user client device.

17. The method of claim 1, wherein at least one of the learned content preferences is stored on a user client device and selecting and presenting the at least one content item of the first dataspace includes selecting and presenting content items stored on the client device.

18. The method of claim 1, further comprising weighting the learned content preferences of the user according to at least one of a measure of recency of selection of the content item having the analyzed descriptive terms, number of selections of the content item having the analyzed descriptive terms, and time of use of the content item having the analyzed descriptive terms, wherein the act of selecting and presenting the at least one content item of the first dataspace is further based on the weighted learned content preferences so that content items of the first dataspace associated with descriptive terms comparable to the learned content preferences having relatively higher weights are ranked relatively more highly.

19. The method of claim 18, wherein the weights of the learned content preferences are decayed as time passes from the act of learning the content preferences of the user.

20. The method of claim 18, wherein the weights of the learned content preferences are decayed according to a specified reoccurring interval of time.

21. The method of claim 18, wherein the weights of the learned content preferences are decayed based upon the number of user selections occurring after the act of learning the content preferences of the user.

22. The method of claim 1, further comprising:

organizing the content items of the first and second dataspaces into groupings based on the informational content of the content items;

determining a context in which the user performed the selection actions, the context including at least one of geographic location of the user, day, date, time, and the group into which the selected content items are organized; and

associating the contexts of the user selection actions with the content preferences of user selections learned from the corresponding user selections;

wherein only content preferences associated with the context in which the subsequent selecting and presenting of the at least one content item of the first dataspace to the user takes place are used in said selecting and presenting of the at least one content item.