System and method for providing access to data using customer profiles

• US 8,056,100 B2
• Filed: 05/20/2005
• Issued: 11/08/2011
• Est. Priority Date: 11/29/1994
• Status: Expired due to Fees

• Current Assignee: Fred HERZ Patents LLC
• Original Assignee: Pinpoint Incorporated (Altitude Capital Partners)
• Inventors: Zhang, Jian, Wachob, David, Ungar, Lyle, Herz, Frederick
• Primary Examiner(s): Hirl; Joseph P
• Assistant Examiner(s): RABOVIANSKI, JIVKA A
• Application Number:
  US11/133,766
• Publication Number:
  US 20060161952A1
• Time in Patent Office: 2,363 Days
• Field of Search: 725/46, 725/34, 725/36, 725/45
• US Class Current: 725/46
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06Q 20/306 using TV related infrastructures external card used for payment with client device H04N21/4185
• CPC Class Codes: G06Q 20/383 Anonymous user system
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H01H 1/00 Contacts liquid contacts H01H29/04
• CPC Class Codes: H04H 60/06 Arrangements for scheduling broadcast services or broadcast-related services
• CPC Class Codes: H04H 60/31 Arrangements for monitoring the use made of the broadcast services
• CPC Class Codes: H04H 60/46 for recognising users' preferences
• CPC Class Codes: H04L 2209/60 Digital content management, e.g. content distribution
• CPC Class Codes: H04L 9/0662 with particular pseudorandom sequence generator
• CPC Class Codes: H04L 9/0825 using asymmetric-key encryption or public key infrastructure [PKI], e.g. key signature or public key certificates
• CPC Class Codes: H04N 21/222 Secondary servers, e.g. proxy server, cable television Head-end provisioning of proxy services in data packet switching networks H04L67/56
• CPC Class Codes: H04N 21/2223 being a public access point, e.g. for downloading to or uploading from clients arrangements specially adapted to plural spots in a confined site in broadcast systems H04H20/63
• CPC Class Codes: H04N 21/23106 involving caching operations prefetching while addressing of a memory level in which the access to the desired data or data block requires associative addressing means within memory systems or architectures G06F12/0862; caching at an intermediate stage in a data network H04L67/568
• 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 21/262 Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists scheduling strategies for dispatcher in multiprogramming arrangements G06F9/4881; arrangements for scheduling broadcast services or broadcast-related services H04H60/06; flow control in packet networks H04L47/10; establishing a time schedule or organising the servicing of application requests H04L67/62
• CPC Class Codes: H04N 21/26241 involving the time of distribution, e.g. the best time of the day for inserting an advertisement or airing a children program
• CPC Class Codes: H04N 21/26603 for automatically generating descriptors from content, e.g. when it is not made available by its provider, using content analysis techniques
• CPC Class Codes: H04N 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/4332 : by placing content in organized collections, e.g. local EPG data repository interfaces, Database management systems or updating for information retrieval G06F16/23; details of retrieval of video data and associated meta data in video database G06F16/739
• 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 21/4532 : 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 21/4622 : Retrieving content or additional data from different sources, e.g. from a broadcast channel and the Internet web site content organization and management for information retrieval from the Internet G06F16/958; transmission by internet of broadcast information H04H60/82; stock exchange data over packet-switching network H04L12/1804; push services including data channel over packet-switching network H04L12/1859
• 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 21/4665 : involving classification methods, e.g. Decision trees
• CPC Class Codes: H04N 21/47 : End-user applications
• CPC Class Codes: H04N 21/4755 : for defining user preferences, e.g. favourite actors or genre retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306
• CPC Class Codes: H04N 21/4782 : Web browsing , e.g. WebTV
• CPC Class Codes: H04N 21/812 : involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 7/14 : Systems for two-way working H04N7/12, H04N7/173 take precedence
• CPC Class Codes: H04N 7/163 : by receiver means only
• CPC Class Codes: H04N 7/165 : Centralised control of user terminal subsequent to an upstream request signal H04N7/17345; Registering at central by two-way working H04N7/17309
• CPC Class Codes: H04N 7/1675 : Providing digital key or authorisation information for generation or regeneration of the scrambling sequence pseudo-random number generators in general G06F7/58
• CPC Class Codes: H04N 7/173 : with two-way working, e.g. subscriber sending a programme selection signal
• CPC Class Codes: H04N 7/17318 : Direct or substantially direct transmission and handling of requests
• CPC Class Codes: H04N 7/17354 : in an intermediate station common to a plurality of user terminals
• CPC Class Codes: Y10S 707/99936 : Pattern matching access
• CPC Class Codes: Y10S 707/99939 : Privileged access

Abstract

A system and method for scheduling the receipt of desired movies and other forms of data from a network which simultaneously distributes many sources of such data to many customers, as in a cable television system. Customer profiles are developed for the recipient describing how important certain characteristics of the broadcast video program, movie or other data are to each customer. From these profiles, an “agreement matrix” is calculated by comparing the recipient'"'"'s profiles to the actual profiles of the characteristics of the available video programs, movies, or other data. The agreement matrix thus characterizes the attractiveness of each video program, movie, or other data to each prospective customer. “Virtual” channels are generated from the agreement matrix to produce a series of video or data programming which will provide the greatest satisfaction to each customer. Feedback paths are also provided so that the customer'"'"'s profiles and/or the profiles of the video programs or other data may be modified to reflect actual usage. Kiosks are also developed which assist customers in the selection of videos, music, books, and the like in accordance with the customer'"'"'s objective profiles.

Claims

1. A method for recommending one or more textual information items to customers from a content collection of textual information items, the method comprising the steps of:

predetermining descriptive characteristics of the content in said collection;

creating content profiles of said textual information items, said content profiles indicating the presence or absence of said descriptive characteristics of said textual information items;

creating one or more customer profiles without a customer explicitly expressing preference for said predetermined characteristics;

said customer profiles representing the customers'"'"' preferences for said predetermined characteristics;

storing said customer profiles in a memory in association with respective customer identities;

retrieving a customer profile subsequently from said memory, by name or customer identity;

operating a computer adapted by stored programming to find a subset of said textual information items having content profiles that closely match said customer profile; and

electronically sending said subset at least partly via a data communications network to said customer for selection of a textual information item in the subset.

2. The method of claim 1, wherein said predetermined characteristics comprise the author.

3. A method as in claim 1 wherein the content profiles comprise values indicating the degree to which said predetermined characteristics apply to said content.

4. The method of claim 3 wherein the degree is expressed as the presence or absence of one or more said predetermined characteristics.

5. A method as in claim 1 wherein the customer profiles comprise values indicating the degree of preference for said predetermined characteristics in said content.

6. The method of claim 5 wherein the degree is expressed as the presence or absence of one or more said predetermined characteristics.

7. A method as in claim 1 wherein said customer profiles are time of day and/or day of week dependent.

8. A method as in claim 1 wherein said finding step is accomplished by correlating customer and content profiles.

9. A method as in claim 1 wherein said customer profiles are updated automatically based upon the content profiles of content selected by said customers.

10. A method as in claim 1 wherein said customer profiles further comprise weighting values indicating the importance of one or more predetermined characteristics.

11. A method as in claim 1 wherein said creating customer profiles step further comprises the step of:

determining the weighting values for one or more predetermined characteristics.

12. A method as in claim 1 wherein said creating customer profiles step is accomplished by assigning a typical customer profile.

13. A method as in claim 1 wherein said finding step further comprises removing from consideration the customer'"'"'s recently obtained items.

14. The method of claim 1, wherein said profile includes a content ID that uniquely identifies the content to which the profile pertains.

15. The method of claim 1 wherein at least one of the descriptive characteristics of the content represents an aspect of the content that is changeable after creation of the content.

16. The method of claim 1 wherein at least one said textual information item comprises a newspaper.

17. The method of any of claim 1 wherein said step of finding a subset is performed at a location remote from said customer, and wherein said subset is sent to said customer from said remote location.

18. The method of claim 1 wherein a content profile includes an identification of one or more customers who expressed a preference with respect to said textual information item.

19. The method of claim 1 wherein a customer identifier comprises a customer identity.

20. The method of claim 1 wherein a customer identifier comprises a pseudonymous customer identifier.

21. The method of claim 1 wherein said data communications network includes the global internet.

22. The method of claim 1 wherein a customer identity is indicated by a customer identifier.

23. The method of claim 1 wherein at least one descriptive characteristic of the content is non-inherent in the content.

24. A method for recommending data objects to customers from a content collection comprising the steps of:

predetermining descriptive characteristics of the content in said collection;

creating content profiles indicating the presence or absence of said predetermined characteristics in said data objects;

creating customer clusters;

creating customer-cluster profiles indicating the cluster'"'"'s preference for said predetermined characteristics;

storing said profiles in a memory;

retrieving said profiles subsequently from said memory;

operating a computer adapted by stored programming to find a subset of said data objects having content profiles that most closely match said customer-cluster profiles; and

electronically sending said subset at least partly via a data communications network to a customer for selection of a data object in the subset, based on the customer'"'"'s cluster membership.

25. A method as in claim 24 wherein the customer-cluster profiles comprise values indicating the degree of preference for said predetermined characteristics in said content.

26. A method as in claim 24 wherein said customer-cluster profiles are updated passively and automatically based upon the content profiles of content obtained by said customers in the clusters.

27. A method as in claim 24 wherein said customer-cluster profiles further comprise weighting values indicating the importance of predetermined characteristics.

28. A method as in claim 24 wherein said creating customer-cluster profiles step further comprises the step of:

determining the weighting values for one or more predetermined characteristics.

29. A method as in claim 24 wherein customer-clusters are created by clustering customers on the basis of the content customers access.

30. A method as in claim 24 wherein said customer-clusters are created based on the number of common content accessed by customers.

31. A method as in claim 24 wherein said creating content profiles step is accomplished by clustering the profiles of the customers who have accessed the content.

32. A method as in claim 24 further comprising the step of:

forming a composite of content items.

33. The method of claim 24 wherein at least one of the descriptive characteristics of the content represents an aspect of the content that is changeable after creation of the content.

34. The method of claim 24 wherein said data communications network includes the global internet.

35. The method of claim 24 wherein said step of finding a subset is performed at a location remote from said customer, and wherein said subset is provided sent to said customer from said remote location.

36. A method for recommending one or more textual information items to customers from a content collection of textual information items and content profiles of said textual information items, said content profiles indicating the presence or absence or degree of presence or absence of one or more predetermined descriptive characteristics of said textual information items, the method comprising the steps of:

creating one or more customer profiles with or without a customer explicitly expressing preference for said predetermined characteristics, said customer profiles representing the customers'"'"' preferences for said predetermined characteristics;

storing said customer profiles in a memory in association with respective customer identifiers;

retrieving a customer profile subsequently from said memory, by name or other customer identifier;

operating a computer adapted by stored programming to find a subset of said textual information items having content profiles that most closely match said customer profile; and

electronically sending said subset at least partly via a data communications network to said customer for selection.

37. The method of claim 36 wherein the indication in a content profile of the presence, absence or degree of presence or absence of a descriptive characteristic is obtained from other customers'"'"' feedback regarding the textual information item.

38. The method of claim 36, wherein at least one of the descriptive characteristics of the content represents an aspect of the content that is changeable after creation of the content.

39. The method of claim 36 wherein said step of finding a subset is performed at a location remote from said customer, and wherein said subset is provided sent to said customer from said remote location.

40. The method of claim 36 wherein said data communications network includes the global internet.

41. The method of claim 36 wherein at least one said textual information item comprises a newspaper.

42. A method for recommending data objects to customers from a content collection and a collection of corresponding content profiles indicating the presence or absence, or degree of presence or absence, of one or more predetermined characteristics in said data objects, comprising the steps of:

creating customer clusters;

creating customer-cluster profiles indicating the cluster'"'"'s preference for said predetermined characteristics;

storing said profiles in a memory;

retrieving said profiles subsequently from said memory;

operating a computer adapted by stored programming to find a subset of said one or more data objects having content profiles that most closely match said customer-cluster profiles; and

electronically sending said subset at least partly via a data communications network to a customer for selection, based on the customer'"'"'s cluster membership.

43. A method as in claim 42 wherein the customer-cluster profiles comprise values indicating the degree of preference for said predetermined characteristics in said content.

44. A method as in claim 42 wherein said customer-cluster profiles are updated passively and automatically based upon the content profiles of content obtained by said customers in the clusters.

45. A method as in claim 42 wherein said customer-cluster profiles further comprise weighting values indicating the importance of predetermined characteristics.

46. A method as in claim 42 wherein said creating customer-cluster profiles step further comprises the step of:

determining the weighting values for one or more predetermined characteristics.

47. A method as in claim 42 wherein customer-clusters are created by clustering customers on the basis of the content customers access.

48. A method as in claim 42 wherein said customer-clusters are created based on the number of common content accessed by customers.

49. A method as in claim 42 further comprising the step of:

forming a composite of content items.

50. The method claim 42 wherein at least one of the descriptive characteristics of the content represents an aspect of the content that is changeable after creation of the content.

51. The method of claim 42 wherein said step of finding a subset is performed at a location remote from said customer, and wherein said subset is provided sent to said customer from said remote location.

52. The method of claim 42 wherein a content profile includes an identification of one or more customers who expressed a preference with respect to said textual information item.

53. The method of claim 42 wherein said data communications network includes the global internet.