Methods and systems for selecting and presenting content based on context sensitive user preferences

• US 8,429,188 B2
• Filed: 09/01/2010
• Issued: 04/23/2013
• 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): Kim, Charles
• Assistant Examiner(s): MINA, FATIMA P
• Application Number:
  US12/873,622
• Publication Number:
  US 20100325111A1
• Time in Patent Office: 965 Days
• Field of Search: 707/709, 707/727, 707/730, 707/748, 707/752, 707/770
• US Class Current: 707/770
• 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 context-sensitive learned user preferences is provided. The method includes providing a set of content items having descriptive terms. The method includes receiving user input for identifying items and, in response thereto, presenting a subset of items. The method includes receiving user selections of said items and analyzing the descriptive terms of those items to learn the user'"'"'s content preferences. The method includes determining the context in which the user performed the selections and associating those contexts with the user content preferences learned from the corresponding user selections. The method includes, in response to subsequent user input, determining a context of said subsequent input and selecting and ordering a collection of items based on comparing those items'"'"' descriptive terms with the user'"'"'s learned content preferences associated with the determined context in which the user entered the subsequent input.

Claims

1. A computer implemented user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on analyzing user selections of content items to learn the content preferences of the user according to a context within which the user selected the content and using the learned content preferences to select and order subsequent user content search results when the user is within the same context, the method comprising:

providing access to a set of content items, each content item having at least one associated descriptive term to describe the content item, the content items of the set being organized into categories of related content items;

receiving incremental input entered by the user for incrementally identifying desired content items of the set, wherein the incremental input includes at least one character in a series of characters;

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

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

analyzing the date, day, and time of day of the user selection actions and analyzing the descriptive terms of the selected content items to learn content preferences of the user and to learn a plurality of periodicities of user selections of similar content items, wherein similarity is determined by comparing the descriptive terms of the selected content item with the previously selected content item, and wherein each periodicity indicates an amount of time between user selections of similar content items relative to a reference point;

associating each learned periodicity with the respective similar content items and the respective descriptive terms of the similar content items;

determining the context in which the user performed the selection actions, the context including a geographic location of the user at the time of the selection actions and at least one of;

date, day, and time of day;

associating the determined contexts of the user selection actions, including the geographic location of the user at the time of the selection actions, with the user content preferences learned from the corresponding user selections;

in response to receiving subsequent incremental input entered by the user, determining a context of said subsequent incremental input and selecting and ordering a collection of content items from the set based on a comparison of the descriptive terms of the content items of the collection with the learned content preferences of the user associated with the determined context in which the user entered the subsequent incremental input and further based on (i) promoting the ranking of those content items associated with descriptive terms further associated with periodicities similar to the date, day, and time of day of the subsequent incremental input, and (ii) promoting the ranking of those content items associated with a first periodicity similar to the date, day, and time of day of the subsequent incremental input relative to the ranking of those content items associated with a second periodicity similar to the date, day, and time of day of the subsequent incremental input; and

presenting said collection of content items to the user on a display screen.

2. 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 ordering the collection of content items is further based on the weighted learned content preferences so that content items associated with descriptive terms comparable to the learned content preferences having relatively higher weights are ranked relatively more highly.

3. The method of claim 2, wherein the weights of the learned content preferences are decayed as time passes from the act of learning the content preferences of the user.

4. The method of claim 2, wherein the weights of the learned content preferences are decayed according to a specified reoccurring interval of time.

5. The method of claim 2, 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.

6. The method of claim 2, wherein the weights of the learned content preferences associated with a given geographic location of the user are decayed based upon an amount of time that has elapsed since the last act of decaying said weights and based upon the user selecting a content item while within said given geographic location.

7. The method of claim 1, further comprising analyzing the categories of the selected content items to learn category preferences of the user, wherein selecting and ordering the collection of content items is further based on a comparison of the categories of the content items of the collection with the learned user category preferences.

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 1, 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, wherein the display screen is included in 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 subset of characters 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 subsets of characters 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 descriptive terms, determining the context in which the user performed the selection actions, associating the determined contexts, determining a context of the subsequent incremental input, 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 descriptive terms, determining the context in which the user performed the selection actions, associating the determined contexts, determining a context of the subsequent incremental input, and selecting and ordering the collection of content items is performed on a user client device.

22. The method of claim 1, wherein the learned content preferences are 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, wherein the context includes a category of the selected content items.

24. The method of claim 2, wherein the weights of the learned content preferences associated with a given geographic location of the user are not decayed if there are no user selection actions whose context includes said given geographic location.

25. The method of claim 1, wherein the amount of time indicated by the learned periodicity is longer than one week.

26. The method of claim 1, further comprising measuring the time that elapses between the user selections of similar content items relative to a reference point to learn the periodicity.

27. The method of claim 1, wherein the amount of time indicated by the first periodicity is longer than the amount of time indicated by the second periodicity.

28. The method of claim 1, wherein the context includes a frequency of visits to the geographic location of the user at the time of the selection actions.