METHODS AND SYSTEMS FOR SELECTING AND PRESENTING CONTENT BASED ON LEARNED PERIODICITY OF USER CONTENT SELECTION

• US 20070271205A1
• Filed: 03/06/2007
• Published: 11/22/2007
• 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
• Granted Patent: US 7,774,294 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• 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 receiving search input from the user for identifying desired content items and receiving content selection actions from the user. The method further includes analyzing the date, day, and time of content selection actions by the user and analyzing descriptive terms associated with the selected content items to learn a periodicity of user selections of similar content items. In response to subsequent searches by the user, the method calls for selecting and ordering a collection of content items for presentation to the user based on comparing the user'"'"'s search input to descriptive terms associated with content items and based on the learned periodicities 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 periodicities of user selections of content items, the method comprising:

providing a set of content items, each content item having at least one associated descriptive term to describe the content item;

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 date, day, and time of the user selection actions and analyzing the descriptive terms associated with the selected content items to learn a periodicity of user selections of similar content items, wherein similarity is determined by comparing the descriptive terms associated with 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;

associating the learned periodicity with descriptive terms associated with the similar content items; and

in response to receiving subsequent incremental input entered by the user, selecting and ordering a collection of content items wherein content items associated with descriptive terms similar to the subsequent incremental input and associated with descriptive terms further associated with periodicities similar to the date, day, and time of the subsequent incremental input are presented as more relevant content.

2. The method of claim 1, wherein the learned periodicity is about one day.

3. The method of claim 1, wherein the learned periodicity is about one week.

4. The method of claim 1, wherein the learned periodicity is about one month.

5. The method of claim 1, wherein the reference point is a day of the week.

6. The method of claim 1, wherein the reference point is a day of the month.

7. The method of claim 1, wherein the reference point is a day of the year.

8. The method of claim 1, wherein the selecting and ordering 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.

9. 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.

10. 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 descriptive terms includes at least one of title, cast, director, content description, and keywords associated with the content.

11. 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.

12. 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.

13. The method of claim 12, wherein the input constrained device has a plurality of overloaded keys, each of the overloaded keys representing two or more characters.

14. 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.

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

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

17. The method of claim 16, wherein the display constrained device is at least one of a telephone, a PDA, and a remote control.

18. 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.

19. The method of claim 18, 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.

20. 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 date, day and time, analyzing the descriptive terms, associating the learned periodicity, and selecting and ordering the collection of content items is performed on a server system remote from the user.

21. 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 date, day and time, analyzing the descriptive terms, associating the learned periodicity, and selecting and ordering the collection of content items is performed on a user client device.

22. The method of claim 1, wherein at least one learned periodicity is stored on a user client device and selecting and ordering the collection of content items includes selecting and ordering content items stored on the client device.

23. The method of claim 1, further comprising:

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, day, date, and time, and the group into which the selected content items are organized; and

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

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

24. The method of claim 1, further comprising:

associating more than one descriptive term with the learned periodicity; and

weighting the descriptive terms associated with the learned periodicity according to at least one of recency of the user selections of content items and number of user selections of content items;

wherein the selecting and ordering the collection of content items is further based on the weights of the descriptive terms.

25. The method of claim 1, further comprising learning more than one periodicity of user selections of a set of similar content items, wherein each periodicity is associated with a descriptive term that differs from the descriptive terms of the other periodicities, wherein selecting and ordering the collection of content items is based on a comparison of the more than one periodicity of user selections to the date, day, and time of the subsequent incremental input.

26. The method of claim 1, further comprising analyzing the descriptive terms associated with the selected content items to learn content preferences of the user, wherein the selecting and ordering the collection of content items is further based on comparing the descriptive terms associated with the content items of the collection with the learned content preferences of the user.

27. The method of claim 1, further comprising:

organizing the set of content items by genre information that characterizes the content items, wherein the genre information is specified by the content system; and

analyzing the genre information characterizing the selected content items to learn the preferred genres of the user;

wherein the selecting and ordering 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.

28. The method of claim 1, wherein the time of the user selection actions and the time of the subsequent incremental input are represented as time slots of a specified duration.

29. The method of claim 28, wherein the specified duration is about one second.

30. The method of claim 28, wherein the specified duration is about one minute.

31. The method of claim 28, wherein the specified duration is about 30 minutes.

32. The method of claim 28, wherein the specified duration is about one hour.

33. The method of claim 28, wherein the specified duration is about one day.

34. The method of claim 28, wherein the specified duration is about one week.

35. The method of claim 28, wherein the specified duration is about one month.

36. The method of claim 1, further comprising weighting the learned periodicities of user selections 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 ordering the collection of content items is further based on the weighted learned periodicities so that content items associated with descriptive terms further associated with learned periodicities having relatively higher weights are ranked relatively more highly.

37. The method of claim 36, wherein the weights of the learned periodicities are decayed as time passes from the act of learning the periodicities.

38. The method of claim 36, wherein the weights of the learned periodicities are decayed according to a specified reoccurring interval of time.

39. The method of claim 36, wherein the weights of the learned periodicities are decayed based upon the number of user selections occurring after the act of learning the periodicities.