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

• US 8,825,576 B2
• Filed: 08/05/2013
• Issued: 09/02/2014
• 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): Chang, Li-Wu
• Assistant Examiner(s): Kim, David H
• Application Number:
  US13/959,289
• Publication Number:
  US 20130325843A1
• Time in Patent Office: 393 Days
• Field of Search: None
• US Class Current: 706/45
• 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 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 providing to a first content system preferences learned from user interactions with at least one second content system, the method comprising:

monitoring user interactions with the at least one second content system, the at least one second content system having a set of content items, each content item having at least one associated descriptive term to describe the content item;

analyzing the descriptive terms associated with content items with which a user interacted to learn the user content preferences for the content of the at least one second content system and associating at least one of a user identifier and a device identifier with the learned preferences; and

in response to a request from the first content system for content preferences associated with at least one of a specified user identifier and a specified device identifier, providing the learned preferences associated with a matching identifier.

2. The method of claim 1, wherein the at least one of the user identifier and the device identifier is an encrypted identifier.

3. The method of claim 2, wherein the encrypted identifier is encrypted using a hash function.

4. The method of claim 1, wherein the at least one second content system is on a user client device.

5. The method of claim 4, wherein the monitoring user interactions monitors the user interactions with a plurality of second content systems, each second content system of the plurality being on a single corresponding user client device.

6. The method of claim 1, wherein the first content system is on a server system remote from the user.

7. The method of claim 1, wherein the first content system and the at least one second content system are different systems.

8. The method of claim 1, wherein the at least one second content system relates to at least one of a phone, phone book, address book, calendar, downloadable media content, television, travel, web, and book dataspaces.

9. The method of claim 1, wherein the first content system has not characterized the content preferences of the user.

10. The method of claim 1, wherein the learned content preferences are stored on a user client device.