CONTENT ENGINE

• US 20100030713A1
• Filed: 05/24/2007
• Published: 02/04/2010
• Est. Priority Date: 05/24/2006
• Status: Abandoned Application

• Current Assignee: ICOM Incorporated
• Original Assignee: ICOM Incorporated
• Inventors: Simpson, Alastair, Darnell, Ed, Eardley, Mark, Bryant, Peter
• Application Number:
  US12/302,280
• Publication Number:
  US 20100030713A1
• Time in Patent Office: Days
• US Class Current: 706/11
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

There is described a computer system for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users. The computer system comprises a content engine comprising user profile information, representative of interests and/or attributes of individual users, and a processor for determining a correlation between content and said user profile information to determine content most relevant to a particular user.

Claims

1. A computer system for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users, the computer system comprising:

a content engine comprising user profile information representative of interests and/or attributes of individual users; and

a processor for determining relevance of content to a particular user based on said user profile information.

2. A computer system as in claim 1, wherein the user profile information comprises information about the user which is declared by the user.

3. A computer system as in claim 2, comprising means for generating prompts for capturing declared information from the user, wherein said prompts comprise one or more of a form and a series of questions to the user.

4. A computer system as in claim 3, wherein said means for generating prompts generates a series of questions, such that one or more of the questions in the series is generated dependent on an answer to a previous question in the series.

5. A computer system as in claim 3, wherein said means for generating prompts generates a plurality of series of questions, such that one or more series of questions is generated responsive to at least one answer in a previous series of questions.

6. A computer system as in claim 1, comprising a module for receiving usage information and wherein the user profile information comprises information about a user which is derived from observations of the user'"'"'s usage behaviour.

7. A computer system as in claim 1, comprising a statistical engine and wherein the user profile information comprises predicted information which is deduced based on statistical processing of information declared by users.

8. A computer system as in claim 1, comprising a statistical engine and wherein the user profile information comprises predicted information which is deduced based on statistical processing of information derived based on the usage behaviour of users.

9. A computer system as in claim 1, comprising a statistical engine and wherein the user profile information comprises predicted information which is deduced based on statistical processing of (i) information declared by users and (ii) information derived based on the usage behaviour of users.

10. A computer system as in claim 1, wherein the user profile information comprises records of relationships between one or more of (i) users, (ii) content and (iii) categories, and wherein the content engine records score information indicating the significance of relationships recorded in the user profile information.

11. A computer system as in claim 10, comprising means for generating a prompt inviting a user to input score information indicating the relevance of content presented to the user by the content engine, wherein the score information comprises score information provided by users responsive to said prompts.

12. A computer system as in claim 10, comprising a module for receiving usage information and wherein said content engine records score information indicating the significance of relationships recorded in the user profile information, wherein the score information comprises score information derived by monitoring usage behaviour.

13. A computer system as in claim 10, comprising a statistical engine, wherein said content engine records score information indicating the significance of relationships recorded in the user profile information, and wherein the score information comprises score information deduced from statistical determinations.

14. A computer system as in claim 10, wherein the score information takes into account the relative age user profile information.

15. A computer system as in claim 14, wherein the score information takes into account time elapsed since user profile information was declared by a user.

16. A computer system as in claim 14, wherein the score information takes into account time elapsed since events providing usage-based user profile information occurred.

17. A computer system as in claim 10, wherein the score information takes into account the current popularity of a particular content or category.

18. A computer system as in claim 10, wherein the score information takes into account a measure of the user'"'"'s interest in a particular content or category, which measure is derived from usage observations.

19. A computer system as in claim 10, wherein the content engine is provided with means for generation a nominal score which is or replaced by one or more of score information based on user declarations or score information based on usage observations.

20. A computer system as in claim 1, wherein the content engine comprises a category table comprising a list of categories each having a unique category ID.

21. A computer system as in claim 1, wherein the content engine comprises a content table comprising a list of content items each having a unique content ID.

22. A computer system as in claim 21, wherein the content table comprises content items.

23. A computer system as in claim 20, wherein the content table comprises pointers to items of content.

24. A computer system as in claim 23, wherein said pointers to items of content comprise links to items of content accessible via the Internet.

25. A computer system as in claim 1, wherein the content engine comprises a user table comprising a plurality of users each having a unique user ID.

26. A computer system as in claim 1, wherein the content engine comprises at least one relational table.

27. A computer system as in claim 26, wherein said relational table comprises score information indicating the significance of relationships recorded therein.

28. A computer system as in claim 1, wherein the content engine comprises a behaviour table recording relationships between individual users and individual items of content.

29. A computer system as in claim 1, wherein the content engine comprises a classification table recording relationships between individual items of content and individual categories.

30. A computer system as in claim 1, wherein the content engine comprises a profile table recording relationships between individual users and individual categories.

31. A computer system as in claim 28, wherein at least one table recording relationships comprises predicted relationships based on statistical analysis.

32. A computer system as in claim 31, wherein a plurality of tables recording relationships comprise predicted relationships based on statistical analysis.

33. A computer system as in claim 6, wherein said content engine comprises means for comparing declared information of the user'"'"'s profile with information derived from the users based on usage.

34. A computer system as in claim 7, wherein said content engine comprises means for comparing predicted information of the user'"'"'s profile with one or more of information declared by the user or derived from the user'"'"'s usage.

35. A computer system as in claim 1, wherein said content engine comprises a table recording peer groups of users based on one or more of:

registration of a user with a particular group;

usage behaviour of a user; and

prediction based on statistical analysis.

36. A computer system as in claim 1, wherein a table records a peer group based on common content interests.

37. A computer system as in claim 1, wherein a table records content accessed by similar users as likely to be linked by a common theme.

38. A computer system as in claim 1, comprising a statistical engine arranged to determine the extent of relationship between first and second table populations having regard to a reference population.

39. A computer system as in claim 38, wherein said statistical engine is coupled to receive populations and determine the extent of relationships between one or more of:

first and second content populations;

a first category population and a second category population;

a first user population and a second user population;

a first content population and a category population;

a first category population and a user population; and

a first user population and a content population.

40. A computer system as in claim 39, wherein said statistical engine is capable of determining the extent of relationships between more than two populations.

41. A computer system as in claim 1, wherein the content engine comprises one or more relationship tables, and wherein the content engine comprises index representations of information in at least one relationship table.

42. A computer system as in claim 1, wherein said user profile information comprises all information about the user required to make the relevance determination.

43. A computer system as in claim 2, wherein information declared by the user falls into one or more of the following categories:

age;

gender;

demographic information;

psychographic information;

learning patterns;

personality type;

individual personality preferences;

learning preferences;

intelligence type;

skills;

interests;

region of domicile;

environment preferences;

entertainment preferences; and

general preferences.

44. A computer system as in claim 2, comprising software to support one or more of the following types of declared information gathering:

user testing;

filtering based on a user input;

a pre-search;

usage patterns;

purchasing patterns;

sociological patterns;

sociological attributes;

entertainment preferences; and

indications of groups and/or communities relevant to the user.

45. A computer system as in claim 1, wherein an interface presented to the user provides a means for the user to switch off relevance determination based on the user profile.

46. A computer system as in claim 1, wherein an interface presented to the user provides a means for the user to select from among user profile criteria applied in relevance determination.

47. A computer system as in claim 1, comprising software supporting notification of developments to user e-mail accounts.

48. A computer system as in claim 1, comprising communications interfaces facilitating communication between users.

49. A computer system as in claim 1, comprising means for presenting a plurality of channels of content to a user, wherein aspects of the selection and/or configuration of said channels are selectable by the content engine based on a relevance determination.

50. A computer system as in claim 1, wherein the content engine is arranged to select content within a channel presented to a user based on a relevance determination.

51. A computer system as in claim 1, comprising third-party content relating to products or services offered to users.

52. A computer system as in claim 51 , comprising an interface through which users can tailor aspects of the system as viewed by them.

53. A web portal supporting development of individual users over time, the web portal comprising:

a content engine for determining the relevance of content, said content engine comprising user profile information representative of individual interests and/or attributes, wherein said user profile information comprises one or more of;

(a) information declared by users about themselves;

(b) information about users derived from observations of their usage behaviour;

(c) information about users determined based on statistical analyses of (a) or (b) or both (a) and (b); and

means for comparing said user profile information with the content to determine relevance of a particular piece of content to a particular user.

54. A web portal as in claim 53, comprising data selected from or more of the following categories:

academic courses;

career guidance;

job opportunities; and

location based amenities.

55. A web portal as in claim 54, comprising information on job opportunities, and further providing an interface for one or more of:

job searching;

online job applications; and

tracking job applications.

56. A method of operating a computer system to determine relevance of content, the method comprising:

accessing recorded information about one or more of;

users;

categories; and

content, which information is declared by users and/or derived from observations of user'"'"'s behaviour, wherein said information comprises a plurality of populations;

establishing an intersection between selected ones of said plurality of populations;

comparing said intersection with a reference population to determine an extent of relationship between said selected pluralities of populations; and

using said extent of relationship to determine a predicted relationship.

57. A method as in claim 56, wherein said recorded information comprises index lists and said the step of establishing an intersection is achieved by scanning a plurality of such index lists.

58. A method as in claim 57, wherein said index lists comprise at least one inverted index list.

59. A method as in claim 57, wherein said scanning of a plurality of index lists occurs substantially contemporaneously.

60. A method as in claim 57, wherein the influence of an index list on the determination is dependent on the length of the list.

61. A method as in claim 57, wherein scanning of a index list is performed by means of an optimised merge-sort algorithm.

62. A method as in claim 56, wherein said predicted relationship comprises a relationship between one or more of:

a user and a category;

a user and an item of content;

an item of content and a category;

first and second items of content;

first and second users; and

first and second categories.

63. A method as in claim 57, wherein a plurality of index lists to be scanned are loaded into a memory to facilitate the step of establishing said intersection.

64. A method as in claim 57, wherein said index lists are extracted from information including information held relational tables of a content engine.

65. A method as in any of claim 64, further taking into account scores indicated in relational tables of a content engine.

66. A method as in claim 65, wherein scoring takes into account the age of information declared from the user and/or the age of information derived from the usage, such that more recent information is afforded a score giving it more significance.

67. A method as in claim 65, taking into account timing of declared and or derived information such that more pertinent information carries more significance.

68. A server application comprising a content table comprising a list of content items each having a unique content ID, wherein the list of content items comprises content items sourced from within a network administered by the operator of the server application as well as content items sourced from the Internet.

69. A server application as in claim 68, wherein said content table comprises links to content sourced from the Internet.

70. A method of building a content table in a server, the content table comprising content items sourced from within an environment administered by the operator of the server as well as content items sourced from the Internet;

the method comprising providing the server with software causing an entry to be made in said content table responsive to users registered with said server accessing items of content, irrespective of whether the item of content accessed is from within the environment administered by the operator of the server or from Internet.

71. A content engine for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users, comprising user profile information comprising information based on information about users declared by the relevant users and information about users derived from observations of usage behaviour.

72. A content engine for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users, comprising user profile information based on one or more of information about a users declared by the relevant users and information about users derived from observations of usage behaviour, wherein said user profile information further comprises information predicted about users based on statistical analysis of said declared and/or said derived information.

73. A content engine for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users, comprising user profile information representative of interests and/or attributes of individual users, wherein said user profile information comprises information predicted by said content engine, and said content engine further comprises means for receiving from the user an indication of accuracy of said predicted information.

74. A content engine for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users, comprising a table recording relationships between individual users and individual items of content.

75. A content engine for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users, comprising a table recording relationships between individual users and individual categories.

76. A content engine for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users, comprising a table recording relationships between individual items of content and individual categories.

77. A content engine for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users, comprising a first table recording relationships between individual items of content and individual categories and a second table recording relationships between individual users and individual categories.

78. A content engine for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users, comprising a first table recording relationships between individual items of content and individual users and a second table recording relationships between individual users and individual categories.

79. A content engine for determining the relevance of stored content to particular users and for facilitating the provision of relevant content to individual users, comprising a first table recording relationships between individual items of content and individual users and a second table recording relationships between individual items of content and individual categories.

80. A content engine comprising means for predicting aspects of relationships between one or more of users, content and categories, and further comprising means for determining a difference between predicted relationship information and corresponding information declared from the user or derived based on usage behaviour.

81. A content engine comprising relationship tables recording relationship information concerning two or more of users, content and categories, and further comprising index representations of at least some of said relationship information, wherein said index representations are accessible to a statistical engine for determining the extent of relationships for use in determinations of relevance.