Methods and systems for selecting and presenting content based on learned periodicity of user content selection

• US 7,949,627 B2
• Filed: 07/26/2010
• Issued: 05/24/2011
• 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): FERNANDEZ RIVAS, OMAR F
• Application Number:
  US12/843,577
• Publication Number:
  US 20100293160A1
• Time in Patent Office: 302 Days
• Field of Search: None
• US Class Current: 706/62
• 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 computer-implemented user-interface method of selecting and presenting at least one content item in which the selection is based at least in part on learning periodicities of user selections of content items, 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;

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

in response to the input entered by the user, presenting content items to the user;

receiving actions from the user resulting in the selection of content items from the subset;

analyzing the descriptive terms associated with the selected content items 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;

analyzing the date, day, and time of at least two of the individual selection actions of the sets of actions to learn periodicities of 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 actions of the set;

associating the learned periodicities of the sets of actions resulting in the selection of similar content items with the corresponding descriptive terms associated with the similar content items that were selected; and

subsequent to learning at least one periodicity of the actions of the user, selecting and presenting at least one content item, wherein the selection of the at least one content item is based on at least one of the learned periodicities similar to the date, day, and time of said subsequent selecting and presenting of the at least one content item.

2. 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, 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 content items include 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 content items include 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.

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 the selection actions are entered by the user on at least one of a telephone, a PDA, a mobile computing device, a computer, and a remote control.

7. The method of claim 1, further comprising presenting the at least one content item on at least part of a television screen.

8. The method of claim 6, 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.

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

10. The method of claim 1, wherein at least one of receiving actions from the user, analyzing the descriptive terms, learning periodicities of user actions, associating the learned periodicities with descriptive terms, and selecting the at least one content item is performed on a server system remote from the user.

11. The method of claim 1, wherein at least one of receiving selection actions, analyzing the descriptive terms, learning periodicities of user actions, associating the learned periodicities with descriptive terms, and selecting the at least one content item is performed on a user client device.

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

13. The method of claim 1, further comprising:

organizing the 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 and the group into which the selected content items are organized; and

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

wherein only learned periodicities associated with the context in which the subsequent selecting and presenting of the at least one content item takes place are used in said selecting and presenting of the at least one content item.

14. The method of claim 1, wherein the learned periodicity is at least one of about one day, one week, and one month.

15. 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 presenting the at least one content item is further based on the weights of the descriptive terms.

16. 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 the selecting and presenting the at least one content item is based on a comparison of the more than one periodicity of user selections to the date, day, and time of the subsequent selecting and presenting of the at least one content item.

17. 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 presenting at least one content item is further based on comparing the descriptive terms associated with the content items of the set with the learned content preferences of the user.

18. The method of claim 1, wherein the time of the user selection actions and the time of the subsequent selecting and presentation of the at least one content item are represented as time slots of a specified duration.

19. The method of claim 18, wherein the specified duration is at least one of about one second, one minute, 30 minutes, one hour, one day, one week, and one month.