Intelligent peer-to-peer system and method for collaborative suggestions and propagation of media

• US 20030014759A1
• Filed: 06/21/2002
• Published: 01/16/2003
• Est. Priority Date: 06/21/2002
• Status: Active Grant

• Current Assignee: TiVo Solutions Inc. (Adeia Inc.)
• Original Assignee: TiVo Inc. (Adeia Inc.)
• Inventors: Van Stam, Wijnand
• Granted Patent: US 8,132,219 B2
• Time in Patent Office: Days
• US Class Current: 725/97
• 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 peer-to-peer system for collaborative suggestions and media propagation comprising:

a network, said network comprising a plurality of clients and at least one server;

one or more lists for each client indicative of interests of a corresponding user;

means for estimating similarity between said clients, based on said lists; and

means for automatically querying one client by another;

so that similar clients may share information;

wherein said peer-to-peer system operates in parallel with a client-sever system, making opportunistic use of an existing network connection.

2. The system of claim 1, wherein each of said clients is periodically in contact with said server, and wherein at least a portion of said clients are simultaneously in contact with said server.

3. The system of claim 2, wherein said server organizes said clients simultaneously in contact with said server into one or more groups, and wherein said means for automatically querying one client by another comprises a network address for each client, wherein said server transmits the network address for each client within a group to all other clients within said group, so that clients within a group may query each other directly.

4. The system of claim 3, wherein said one or more lists include at least one of:

a list of items of media content stored on the respective client;

a list of items desired by said user; and

a list of items rated by said user.

5. The system of claim 4, wherein an originating client queries a first targeted peer within said group, and wherein said originating client transmits a list of items indicative of said user'"'"'s interests.

6. The system of claim 5, wherein said means for estimating similarity between said clients comprises an algorithm, said algorithm residing on each of said clients, and wherein said targeted peer compares said transmitted list with a list of its own and estimates similarity between the two lists according to said algorithm, said similarity expressed as a value.

7. The system of claim 6, wherein said algorithm includes the steps of:

determining the number of items common to both abbreviated lists; and

dividing the number of common items by the total number of items on the list from the originating client, wherein said value results.

8. The system of claim 7, wherein said value is in a range of approximately 0 to 1.

9. The system of claim 6, wherein the targeted peer responds by any of:

for a similarity estimate falling below a predetermined threshold value, said targeted peer returns said query without providing additional information, while adding a reference to said query so that said originating client doesn'"'"'t direct further queries to said first targeted peer;

for a similarity estimate falling below said predetermined threshold value, said first targeted peer redirects said query to a second targeted peer, wherein said first targeted peer adds a reference to said query so that subsequent peers receiving the query don'"'"'t redirect the query to the first targeted peer, and so that said originating client has a record of peers already queried; and

for a similarity estimate equaling or exceeding said predetermined threshold value, said first targeted peer responds to said originating client by transmitting the similarity estimate along with sizes of complete lists relative to complete list sizes on said originating client.

10. The system of claim 9, wherein said originating client evaluates similarity estimates, and relative list sizes received from responding targeted peers and responds by any of:

terminating said query;

directing said query to peers that have not yet been queried;

requesting one or more lists from one or more of said responding targeted peers.

11. The system of claim 10, wherein said originating client evaluates lists received from said responding targeted peers and utilizes unique items from said lists to generate suggestions and recommendations for said user.

12. The system of claim 11, wherein said originating client requests items of media content from said responding peers, based on contents of said lists.

13. The system of claim 11, wherein said lists are transmitted in discrete packets.

14. The system of claim 5, wherein said originating client iteratively queries said targeted peer, wherein an abbreviated list is transmitted with each query, said abbreviated lists comprising successive blocks of n consecutive entries from a complete list.

15. The system of claim 14, wherein said targeted peer iteratively evaluates each abbreviated list received for items common to both originating client and targeted peer and transmits a list of said common items to said originating client.

16. The system of claim 15, wherein said originating client evaluates said lists of common items received from said targeted peer and estimates similarity for the corresponding abbreviated list, and wherein an average of said similarity estimates constitutes an estimate of similarity between said originating client and said targeted peer.

17. The system of claim 16, wherein said lists exchanged between said originating client and said targeted peer also include user ratings for each item, and wherein a correlation is calculated for each abbreviated list based on said user ratings.

18. The system of claim 17, wherein correlations derived from said abbreviated lists converges, as the number of common items grows, to an actual correlation that would be obtained if all available data from said originating server and said targeted peer were known.

19. The system of claim 18, wherein said iterative queries continue until confidence in said calculated correlation equals or exceeds a minimum confidence level, based on total number of items shared from said originating server and total number of common items.

20. The system of claim 19, wherein said originating client:

terminates said query based on said calculated correlation;

or

requests one or more complete lists from said targeted client, based on said calculated correlation.

21. The system of claim 1, wherein said system is a component of a network-based personal video recording system.

22. A peer-to-peer method of generating collaborative suggestions and propagating items of media content comprising the steps of:

providing a network, said network comprising a plurality of clients and at least one server;

providing one or more lists for each client indicative of interests of a corresponding user;

estimating similarity between said clients, based on said lists; and

automatically querying one client by another, so that similar clients may share information, wherein said peer-to-peer method executes in parallel with a client-server method, opportunistically using an existing network connection.

23. The method of claim 22, wherein each of said clients is periodically in contact with said server, and wherein at least a portion of said clients are simultaneously in contact with said server.

24. The method of claim 22, further comprising the steps of:

organizing said clients simultaneously in contact with said server into one or more groups; and

transmitting a network address for each client within a group to all other clients within said group by said server, so that clients within a group may query each other directly.

25. The method of claim 24, wherein said one or more lists include at least one of:

a list of items of media content stored on the respective client;

a list of items desired by said user; and

a list of items rated by said user.

26. The method of claim 24, wherein said step of automatically querying one client by another comprises:

querying a first targeted peer within said group by an originating client, wherein said originating client transmits a list of items indicative of said user'"'"'s interests.

27. The method of claim 26, wherein said step of estimating similarity between said clients comprises the steps of:

comparing said transmitted list with a corresponding list of its own by said targeted peer; and

estimating similarity between the two lists according to an algorithm, said algorithm residing on each of said clients, said similarity expressed as a value.

28. The method of claim 27, wherein said algorithm includes the steps of:

determining the number of items common to both lists; and

dividing the number of common items by the total number of items on the abbreviated list from the originating client, wherein said value results.

29. The method of claim 28, wherein said value is in a range of approximately 0 to 1.

30. The method of claim 28, further comprising any of the steps of:

for a similarity estimate falling below a predetermined threshold value, returning said query by said first targeted peer without providing additional information, while adding a reference to said query so that said originating client doesn'"'"'t direct further queries to said first targeted peer;

for a similarity estimate falling below said predetermined threshold value, redirecting said query by said first targeted peer to a second targeted peer, wherein said first targeted peer adds a reference to said query so that subsequent peers receiving the query don'"'"'t redirect the query to the first targeted peer, and so that said originating client has a record of peers already queried; and

for a similarity estimate equaling or exceeding said threshold minimum value, responding to said originating client by said first targeted peer by transmitting the similarity estimate along with sizes of complete lists relative to complete list sizes on said originating client.

31. The method of claim 30, further comprising the step of:

evaluating similarity estimates and relative list sizes, by said originating client, received from responding targeted peers

32. The method of claim 31, further comprising one of the steps of:

terminating said query by said originating client;

directing said query to peers that have not yet been queried; and

requesting one or more lists from one or more of said responding targeted peers.

33. The method of claim 32 further comprising the step of:

utilizing unique items from said lists to generate suggestions and recommendations for said user by said originating client.

34. The method of claim 32, further comprising the step of:

by said originating client, requesting items of media content from said responding peers, based on contents of said lists.

35. The method of claim 32, wherein said lists are transmitted in discrete packets.

36. The method of claim 24, wherein said step of automatically querying one client by another comprises:

an originating client iteratively querying a targeted peer, wherein an abbreviated list is transmitted with each query, said abbreviated lists comprising consecutive blocks of n consecutive entries from a complete list, starting at a beginning of said complete list.

37. The method of claim 36, further comprising the steps of

said targeted peer iteratively evaluating each abbreviated list received for items common to both originating client and targeted peer;

and

and transmitting a list of said common items to said originating client.

38. The method of claim 37, wherein said step of estimating similarity between clients comprises the steps of:

said originating client evaluating said lists of common items received from said targeted peer;

and estimating similarity for the corresponding abbreviated list, wherein an average of said similarity estimates constitutes an estimate of similarity between said originating client and said targeted peer.

39. The method of claim 38, further comprising the step of:

calculating a correlation for each abbreviated list based on user ratings for each item, wherein said lists exchanged between said originating client and said targeted peer also include said user ratings.

40. The method of claim 39, wherein correlations derived from said abbreviated lists converge, as the number of common items grows, to an actual correlation that would be obtained if all available data from said originating server and said targeted peer were known.

41. The system of claim 39, wherein said iterative queries continue until confidence in said calculated correlation equals or exceeds a minimum confidence level, based on total number of items shared from said originating server and total number of common items.

42. The method of claim 41, further comprising one of the steps of:

said originating client terminating said query based on said calculated correlation; and

said originating client requesting one or more complete lists from said targeted client, based on said calculated correlation.