METHODS AND SYSTEMS FOR SELECTING AND PRESENTING CONTENT BASED ON DYNAMICALLY IDENTIFYING MICROGENRES ASSOCIATED WITH THE CONTENT

• US 20070266021A1
• Filed: 03/06/2007
• Published: 11/15/2007
• Est. Priority Date: 03/06/2006
• Status: Active Grant

• Current Assignee: Veveo, Inc. (Adeia Inc.)
• Original Assignee: Veveo, Inc. (Adeia Inc.)
• Inventors: RAJASEKHARAN, Ajit, ARAVAMUDAN, Murali, RAMAKRISHNAN, Kajamalai
• Granted Patent: US 7,774,341 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 based on learned user preferences is provided. The method includes providing a content system including a set of content items organized by genre characterizing the content items, and wherein the set of content items contains microgenre metadata further characterizing the content items. The method also includes receiving search input from the user for identifying desired content items and, in response, presenting a subset of content items to the user. The method further includes receiving content item selection actions from the user and analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user. The method includes, in response to receiving subsequent user search input, selecting and presenting content items in an order that portrays as relatively more relevant those content items containing microgenre metadata that more closely match the learned microgenre preferences of the user.

Claims

1. A user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on learning the preferred microgenres of content of the user as contained in content items selected by the user, the method comprising:

providing a content system including a set of content items organized by genre information that characterizes the content items, wherein the genre information is specified by the content system, and wherein the set of content items contains microgenre metadata further characterizing the content items;

receiving incremental input entered by the user for incrementally identifying desired content items;

in response to the incremental input entered by the user, presenting a subset of content items to the user;

receiving actions from the user selecting content items from the subset;

analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user; and

in response to receiving subsequent incremental input entered by the user, selecting and presenting a collection of content items in an order that portrays as relatively more relevant those content items containing microgenre metadata that more closely match the learned microgenre preferences of the user.

2. The method of claim 1, wherein the order of the collection of content items 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.

3. The method of claim 1, wherein the set of content items 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.

4. The method of claim 1, wherein the set of content items includes at least one of television program items, movie items, and audio/video media items and the microgenre metadata includes at least one of title, cast, director, content description, and keywords associated with the content.

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

6. The method of claim 1, wherein at least one of the incremental input and the subsequent incremental input are entered by the user on an input constrained device.

7. The method of claim 6, wherein the input constrained device has a plurality of overloaded keys, each of the overloaded keys representing two or more characters.

8. The method of claim 1, wherein at least one of the incremental input and the subsequent incremental input are entered by the user on at least one of a telephone, a PDA, a computer, and a remote control.

9. The method of claim 1, further comprising presenting the ordered collection of content items on at least part of a television screen.

10. The method of claim 1, further comprising presenting the ordered collection of content items on a display constrained device.

11. The method of claim 10, wherein the display constrained device is at least one of a telephone, a PDA, and a remote control.

12. The method of claim 1, wherein at least one of the incremental input and the subsequent incremental input comprises at least one prefix of a word for describing the desired content items.

13. The method of claim 12, wherein at least one of the incremental input and the subsequent incremental input comprises at least two prefixes of a phrase for describing the desired content items.

14. The method of claim 1, wherein at least one of receiving incremental input, presenting the subset of content items, receiving actions from the user, analyzing the microgenre metadata, and selecting and presenting the collection of content items is performed on a server system remote from the user.

15. The method of claim 1, wherein at least one of receiving incremental input, presenting the subset of content items, receiving actions from the user, analyzing the microgenre metadata, and selecting and presenting the collection of content items is performed on a user client device.

16. The method of claim 1, wherein at least one learned microgenre preference is stored on a user client device and selecting and presenting the collection of content items includes selecting and presenting content items stored on the client device.

17. The method of claim 1, further comprising:

further organizing the content items of the set of content items 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, date, day, time, and the group into which the selected content items are organized; and

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

wherein only microgenre preferences associated with the context in which the user entered the subsequent incremental input are used in the selecting and presenting of the collection of content items.

18. The method of claim 1, further comprising analyzing the genre information characterizing the selected content items to learn the preferred genres of the user, and wherein the selecting and presenting the collection of content items is further based on promoting the relevance of those content items characterized by genre information that more closely match the learned genre preferences of the user.

19. The method of claim 1, further comprising:

analyzing the date, day, and time of the user selection actions and analyzing at least one of the genre information and microgenre metadata of the selected content items to learn a periodicity of user selections of similar content items, wherein similarity is determined by comparing the at least one of the genre information and microgenre metadata of the selected content item with the previously selected content item, and wherein the periodicity indicates the amount of time between user selections of similar content items relative to a reference point; and

associating the learned periodicity with the at least one of the genre information and microgenre metadata of the similar content items;

wherein the selecting and presenting the collection of content items is further based on promoting the relevance of those content items characterized by genre information or containing microgenre metadata associated with periodicities similar to the date, day, and time of the subsequent incremental input.

20. The method of claim 1, further comprising weighting the learned microgenre preferences of the user according to at least one of a measure of recency of selection of the content item having the analyzed microgenre metadata, number of selections of the content item having the analyzed microgenre metadata, and time of use of the content item having the analyzed microgenre metadata, wherein the act of selecting and ordering the collection of content items is further based on the weighted learned microgenre preferences so that content items containing microgenre metadata comparable to the learned microgenre preferences having relatively higher weights are ranked relatively more highly.

21. The method of claim 20, wherein the weights of the learned microgenre preferences are decayed as time passes from the act of learning the microgenre preferences of the user.

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

23. The method of claim 20, wherein the weights of the learned microgenre preferences are decayed based upon the number of user selections occurring after the act of learning the microgenre preferences of the user.