INTELLIGENT PEER-TO-PEER SYSTEM AND METHOD FOR COLLABORATIVE SUGGESTIONS AND PROPAGATION OF MEDIA

• US 20120016934A1
• Filed: 09/25/2011
• Published: 01/19/2012
• Est. Priority Date: 12/21/1999
• Status: Active Grant

• Current Assignee: TiVo Solutions Inc. (Adeia Inc.)
• Original Assignee: Wijnand Van Stam
• Inventors: Van Stam, Wijnand
• Granted Patent: US 9,055,197 B2
• Time in Patent Office: Days
• US Class Current: 709/203
• CPC Class Codes: G06F 16/1834 implemented based on peer-to-peer networks, e.g. gnutella p2p communication protocols H04L67/104
• 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 16/435 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: G11B 2220/20 Disc-shaped record carriers
• CPC Class Codes: G11B 27/105 of operating discs
• CPC Class Codes: G11B 27/34 Indicating arrangements indicating means incorporated in magazine or cassette G11B23/046 and G11B23/0875; indicating measured values in general G01D
• CPC Class Codes: H04H 60/35 Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04N 21/25 Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies maintenance or administration in data networks H04L41/00
• CPC Class Codes: H04N 21/252 Processing of multiple end-users' preferences to derive collaborative data
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; 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 21/4147 PVR [Personal Video Recorder] H04N5/76 takes precedence ; arrangements for broadcast with accumulation-type receivers H04H20/40
• CPC Class Codes: H04N 21/4316 for displaying supplemental content in a region of the screen, e.g. an advertisement in a separate window
• CPC Class Codes: H04N 21/442 Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk arrangements for monitoring broadcast services or broadcast-related services H04H60/29; arrangements for identifying or recognising characteristics with a direct linkage to broadcast information H04H60/35; monitoring of user activities for profile generation for accessing a video database G06F16/739; monitoring in wireless networks H04W24/00
• 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 21/443 OS processes, e.g. booting an STB, implementing a Java virtual machine in an STB or power management in an STB
• CPC Class Codes: H04N 21/45 Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
• 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 21/454 Content or additional data filtering, e.g. blocking advertisements
• 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 21/4663 : involving probabilistic networks, e.g. Bayesian networks
• CPC Class Codes: H04N 21/4668 : for recommending content, e.g. movies
• CPC Class Codes: H04N 21/47 : End-user applications
• CPC Class Codes: H04N 21/4756 : for rating content, e.g. scoring a recommended movie
• CPC Class Codes: H04N 21/4826 : using recommendation lists, e.g. of programs or channels sorted out according to their score
• CPC Class Codes: H04N 21/4828 : for searching program descriptors retrieval of video data G06F16/739
• CPC Class Codes: H04N 21/485 : End-user interface for client configuration
• CPC Class Codes: H04N 21/632 : using a connection between clients on a wide area network, e.g. setting up a peer-to-peer communication via Internet for retrieving video segments from the hard-disk of other client devices broadcast-related systems characterised by transmission among terminal devices H04H60/80; distributed application using peer-to-peer [P2P] networks H04L67/104
• CPC Class Codes: H04N 21/6543 : for forcing some client operations, e.g. recording remote booting in general G06F9/4416
• CPC Class Codes: H04N 21/812 : involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 5/76 : Television signal recording
• CPC Class Codes: H04N 5/782 : on tape
• CPC Class Codes: H04N 7/17318 : Direct or substantially direct transmission and handling of requests
• CPC Class Codes: H04N 7/17336 : Handling of requests in head-ends

Abstract

In a network-based system for recommending media content items based on user preferences, clients contact a server on a periodic basis, independent of the user. In addition to client-server interaction, clients also interact with one another in peer-to-peer fashion. Peers query one another and evaluate their similarity to each other in an interactive comparison of user preferences. When two clients are sufficiently similar, the interaction culminates in the originating client downloading content listings from the targeted peer to generate suggestions for their user. If the two clients are dissimilar, the query may be terminated, or the targeted peer may route the query to a second targeted peer. In addition to the lists of preferences, the originating client may download actual content items from the targeted peers.

Claims

1. A method comprising:

sharing, between clients in a group of clients, preference data of users associated with the group of clients;

based upon the shared preference data, a first client in the group of clients identifying one or more suggested items for a second client in the group of clients;

wherein the method is performed by one or more computing devices.

2. The method of claim 1, wherein the first client and the second client are different media devices.

3. The method of claim 1, further comprising:

receiving, at the first client, second preference data of a second user of the second client;

comparing, at the first client, the second preference data of the second user to first preference data of a first user of the first client;

based on the comparing, determining, at the first client, whether the second client is similar to the first client;

based on determining that the second client is similar to the first client, providing the one or more suggested items to the second client.

4. The method of claim 3, further comprising the second client transmitting the second preference data to the first client based on grouping data specifying that the first client belongs to the group of clients.

5. The method of claim 3, further comprising:

receiving, at a third client, second preference data of a second user of the second client;

comparing, at the third client, the second preference data of the second user to third preference data of a third user of the third client;

based on the comparing, determining, at the third client, whether the third client is not similar to the second client;

based on determining that the third client is not similar to the second client, the third client sending the second preference data to the first client based on grouping data specifying that the first client belongs to the group of clients.

6. The method of claim 3, wherein the receiving, comparing, and determining are repeated iteratively until the a minimum confidence standard is satisfied.

7. The method of claim 1, further comprising the first client and the second client receiving grouping data from a central server identifying the clients in the group of clients.

8. The method of claim 1, wherein the preference data for a given client of the first client and the second client, and the one or more suggested items comprise one or more of:

a list of media items stored on the given client;

a list of media items desired by a given user of the given client;

or a list of media items rated by the given user of the given client.

9. The method of claim 1, further comprising:

receiving, at the second client, a similarity score measuring similarity between first preference data of a first user of the first client and second preference data of a second user of the second client;

based on the similarity score, the second client requesting that the first client identify the one or more suggested items.

10. The method of claim 1, further comprising:

prior to the first client identifying the one or more suggested items, the first client receiving first grouping data indicating that the first client and the second client are in a first group of clients, the first group of clients being the group of clients;

the first client sharing first preference data with the clients in the first group of clients based on the first grouping data;

after the first client identifies the one or more suggested items, the first client receiving second grouping data indicating that the first client is in a second group of clients that does not include the second client;

based on the second grouping data, sharing, between clients in the second group of clients, second preference data of users associated with the second group of clients;

based upon the second preference data shared between the clients in the second group of clients, the first client identifying one or more suggested items for a third client in the second group of clients.

11. A method comprising:

comparing first preference data of a first user of a first client with second preference data of a second user of a second client;

identifying one or more suggested media items for the second client based on the comparison;

causing the first client to send to the second client the one or more suggested media items;

wherein the method is performed by one or more computing devices.

12. The method of claim 11, wherein the first client and the second client are different media devices.

13. The method of claim 11, further comprising the second client requesting to download the one or more suggested media items from the first client.

14. The method of claim 11, further comprising:

determining whether the first client is similar to the second client;

upon determining that the first client is similar to the second client, identifying the one or more suggested media items.

15. The method of claim 11, wherein the comparing, identifying, and causing are performed by one or both of the first client and the second client.

16. The method of claim 11, further comprising:

the first client and the second client receiving grouping data from a central server identifying clients in a group of clients;

wherein the comparing, identifying, and causing are performed in response to determining, based on the grouping data, that the first client and the second client are in the group of clients.

17. A system comprising one or more computing devices configured to cause performance of:

sharing, between clients in a group of clients, preference data of users associated with the group of clients;

based upon the shared preference data, a first client in the group of clients identifying one or more suggested items for a second client in the group of clients.

18. The system of claim 17, wherein the first client and the second client are different media devices.

19. The system of claim 17, wherein the one or more computing devices are further configured to cause performance of:

receiving, at the first client, second preference data of a second user of the second client;

comparing, at the first client, the second preference data of the second user to first preference data of a first user of the first client;

based on the comparing, determining, at the first client, whether the second client is similar to the first client;

based on determining that the second client is similar to the first client, providing the one or more suggested items to the second client.

20. The system of claim 19, wherein the one or more computing devices are further configured to cause performance of the second client transmitting the second preference data to the first client based on grouping data specifying that the first client belongs to the group of clients.

21. The system of claim 19, wherein the one or more computing devices are further configured to cause performance of:

receiving, at a third client, second preference data of a second user of the second client;

comparing, at the third client, the second preference data of the second user to third preference data of a third user of the third client;

based on the comparing, determining, at the third client, whether the third client is not similar to the second client;

based on determining that the third client is not similar to the second client, the third client sending the second preference data to the first client based on grouping data specifying that the first client belongs to the group of clients.

22. The system of claim 19, wherein the receiving, comparing, and determining are repeated iteratively until the a minimum confidence standard is satisfied.

23. The system of claim 17, wherein the one or more computing devices are further configured to cause performance of the first client and the second client receiving grouping data from a central server identifying the clients in the group of clients.

24. The system of claim 17, wherein the preference data for a given client of the first client and the second client, and the one or more suggested items comprise one or more of:

a list of media items stored on the given client;

a list of media items desired by a given user of the given client;

or a list of media items rated by the given user of the given client.

25. The system of claim 17, wherein the one or more computing devices are further configured to cause performance of:

receiving, at the second client, a similarity score measuring similarity between first preference data of a first user of the first client and second preference data of a second user of the second client;

based on the similarity score, the second client requesting that the first client identify the one or more suggested items.

26. The system of claim 17, wherein the one or more computing devices are further configured to cause performance of:

prior to the first client identifying the one or more suggested items, the first client receiving first grouping data indicating that the first client and the second client are in a first group of clients, the first group of clients being the group of clients;

the first client sharing first preference data with the clients in the first group of clients based on the first grouping data;

after the first client identifies the one or more suggested items, the first client receiving second grouping data indicating that the first client is in a second group of clients that does not include the second client;

based on the second grouping data, sharing, between clients in the second group of clients, second preference data of users associated with the second group of clients;

based upon the second preference data shared between the clients in the second group of clients, the first client identifying one or more suggested items for a third client in the second group of clients.

27. An apparatus configured to cause performance of:

comparing first preference data of a first user of a first client with second preference data of a second user of a second client;

identifying one or more suggested media items for the second client based on the comparison;

causing the first client to send to the second client the one or more suggested media items.

28. The apparatus of claim 27, wherein the first client and the second client are different media devices.

29. The apparatus of claim 27, wherein the apparatus is further configured to cause performance of the second client requesting to download the one or more suggested media items from the first client.

30. The apparatus of claim 27, wherein the apparatus is further configured to cause performance of:

determining whether the first client is similar to the second client;

upon determining that the first client is similar to the second client, identifying the one or more suggested media items.

31. The apparatus of claim 27, wherein the comparing, identifying, and causing are performed by one or both of the first client and the second client.

32. The apparatus of claim 27, wherein the apparatus is further configured to cause performance of:

the first client and the second client receiving grouping data from a central server identifying clients in a group of clients;

wherein the comparing, identifying, and causing are performed in response to determining, based on the grouping data, that the first client and the second client are in the group of clients.

33. One or more non-transitory computer-readable media storing instructions that, when executed by one or more computing devices, cause performance of:

sharing, between clients in a group of clients, preference data of users associated with the group of clients;

based upon the shared preference data, a first client in the group of clients identifying one or more suggested items for a second client in the group of clients.

34. One or more non-transitory computer-readable media storing instructions that, when executed by one or more computing devices, cause performance of:

comparing first preference data of a first user of a first client with second preference data of a second user of a second client;

identifying one or more suggested media items for the second client based on the comparison;

causing the first client to send to the second client the one or more suggested media items.