METHODS AND APPARATUS FOR PROVIDING INFORMATION OF INTEREST TO ONE OR MORE USERS

• US 20120150874A1
• Filed: 12/30/2011
• Published: 06/14/2012
• Est. Priority Date: 05/01/2008
• Status: Active Grant

• Current Assignee: Primal Fusion Inc.
• Original Assignee: Primal Fusion Inc.
• Inventors: Sweeney, Peter Joseph, Ilyas, Ihab Francis, Dupuis, Jean-Paul, Yampolska, Nadiya
• Granted Patent: US 9,378,203 B2
• Time in Patent Office: Days
• US Class Current: 707/749
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute

Abstract

Methods and system for providing information selected from a large set of digital content to at least one user. One such method comprises receiving user context information associated with the at least one user and identifying or generating, using at least one processor executing stored program instructions, a first concept in a semantic network, the first concept representing at least a portion of the user context information. The method further comprises obtaining at least one concept, including a second concept, semantically relevant to the first concept at least in part, by synthesizing the second concept based on the first concept and at least one other concept in the semantic network; and providing information to the at least one user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept, wherein the first concept in a semantic network is represented by a data structure storing any data associated with a node in the semantic network.

Claims

1. A computer-implemented method for providing information selected from a large set of digital content to at least one user, the method comprising:

receiving user context information associated with the at least one user;

identifying or generating, using at least one processor executing stored program instructions, a first concept in a semantic network, the first concept representing at least a portion of the user context information;

obtaining at least one concept, including a second concept, semantically relevant to the first concept at least in part, by synthesizing the second concept based on the first concept and at least one other concept in the semantic network; and

providing information to the at least one user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept,

wherein the first concept in the semantic network is represented by a data structure storing data associated with a node in the semantic network.

2. The computer-implemented method of claim 1, wherein synthesizing the second concept comprises:

identifying the at least one other concept in the semantic network based at least in part on the structure of the semantic network; and

synthesizing the second concept from the first concept and the at least one other concept.

3. The computer-implemented method of claim 1, wherein synthesizing the second concept comprises using an addition operation based on an attribute co-definition technique.

4. The computer-implemented method of claim 1, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-parent technique and/or an analogy-by-sibling technique.

5. The computer-implemented method of claim 1, wherein synthesizing the second concept comprises using an addition operation based on an attribute commonality technique.

6. The computer-implemented method of claim 1, wherein synthesizing the second concept comprises using a substitution operation, wherein the substitution operation comprises using a retrieval operation and/or an addition operation.

7. The computer-implemented method of claim 1, wherein obtaining the at least one concept comprises:

obtaining a plurality of concepts semantically relevant to the first concept;

computing a score for one or more concepts in the plurality of concepts, wherein the score for a specific concept is indicative of the semantic relevance of the specific concept to the first concept; and

selecting the at least one concept based on the scores computed for the one or more concepts.

8. The computer-implemented method of claim 7, wherein computing a score for a concept comprises using at least one measure of relevance from among generation certainty, concept productivity, Jaccard, statistical coherence, and/or cosine similarity.

9. The computer-implemented method of claim 1, wherein identifying or generating the first concept comprises:

determining whether the portion of the user context information matches an identifier of a concept in the semantic network; and

when it is determined that the at least a portion of the user context information does not match an identifier of a concept in the semantic network, generating the first concept in the semantic network.

10. The computer-implemented method of claim 1, wherein generating the first concept in the semantic network comprises identifying a concept in the semantic network covering more words in the portion of the user context information than any other concept in the semantic network.

11. The computer-implemented method of claim 1, wherein the user-specific context information comprises at least one of a search query provided by the user, demographic information associated with the user, information from the user'"'"'s browsing history, information typed by the user, and/or information highlighted by the user.

12. The computer-implemented method of claim 1, wherein the semantic network is represented by a data structure embodying a directed graph comprising a plurality of nodes and a plurality of edges, wherein each node is associated with a concept and an edge between two nodes represents a relationship between the two corresponding concepts.

13. The computer-implemented method of claim 1, wherein the information comprises one or more advertisements and/or one or more product recommendations from one or more other users.

14. The computer-implemented method of claim 1, wherein the information comprises content appearing on or accessible through a website.

15. The computer-implemented method of claim 1, wherein providing information to the at least one user comprises:

creating a search query that includes terms from the first concept and the at least one obtained concept; and

providing the user with information associated with the search results obtained based on the search query.

16. A system for providing information selected from a large set of digital content to at least one user, the system comprising:

at least one processor configured to perform a method comprising

receiving user context information associated with the at least one user;

identifying or generating a first concept in a semantic network, the first concept representing at least a portion of the user context information;

obtaining at least one concept, including a second concept, semantically relevant to the first concept at least in part, by synthesizing the second concept based on the first concept and at least one other concept in the semantic network; and

providing information to the at least one user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept,

wherein the first concept in the semantic network is represented by a data structure storing data associated with a node in the semantic network.

17. The system of claim 16, wherein synthesizing the second concept comprises:

identifying the at least one other concept in the semantic network based at least in part on the structure of the semantic network; and

synthesizing the second concept from the first concept and the at least one other concept.

18. The system of claim 16, wherein synthesizing the second concept comprises using an addition operation based on an attribute co-definition technique.

19. The system of claim 16, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-parent technique and/or an analogy-by-sibling technique.

20. The system of claim 16, wherein synthesizing the second concept comprises using an addition operation based on an attribute commonality technique.

21. The system of claim 16, wherein synthesizing the second concept comprises using a substitution operation, wherein the substitution operation comprises using a retrieval operation and/or an addition operation.

22. The system of claim 16, wherein obtaining the at least one concept comprises:

obtaining a plurality of concepts semantically relevant to the first concept;

computing a score for one or more concepts in the plurality of concepts, wherein the score for a specific concept is indicative of the semantic relevance of the specific concept to the first concept; and

selecting the at least one concept based on the scores computed for the one or more concepts.

23. The system of claim 22, wherein computing a score for a concept comprises using at least one measure of relevance from among generation certainty, concept productivity, Jaccard, statistical coherence, and/or cosine similarity.

24. The system of claim 16, wherein identifying or generating the first concept comprises:

determining whether the portion of the user context information matches an identifier of a concept in the semantic network; and

when it is determined that the at least a portion of the user context information does not match an identifier of a concept in the semantic network, generating the first concept in the semantic network.

25. The system of claim 16, wherein generating the first concept in the semantic network comprises identifying a concept in the semantic network covering more words in the portion of the user context information than any other concept in the semantic network.

26. The system of claim 16, wherein the user-specific context information comprises at least one of a search query provided by the user, demographic information associated with the user, information from the user'"'"'s browsing history, information typed by the user, and/or information highlighted by the user.

27. The system of claim 16, wherein the semantic network is represented by a data structure embodying a directed graph comprising a plurality of nodes and a plurality of edges, wherein each node is associated with a concept and an edge between two nodes represents a relationship between the two corresponding concepts.

28. The system of claim 16, wherein the information comprises one or more advertisements and/or one or more product recommendations from one or more other users.

29. The system of claim 16, wherein the information comprises content appearing on or accessible through a website.

30. The system of claim 16, wherein providing information to the at least one user comprises:

creating a search query that includes terms from the first concept and the at least one obtained concept; and

providing the user with information associated with the search results obtained based on the search query.

31. At least one non-transitory computer readable storage medium storing processor-executable instructions that when executed by at least one processor, cause the at least one processor to perform a method for providing information selected from a large set of digital content to at least one user, the method comprising:

receiving user context information associated with the at least one user;

identifying or generating a first concept in a semantic network, the first concept representing at least a portion of the user context information;

obtaining at least one concept, including a second concept, semantically relevant to the first concept at least in part, by synthesizing the second concept based on the first concept and at least one other concept in the semantic network; and

providing information to the at least one user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept,

wherein the first concept in the semantic network is represented by a data structure storing data associated with a node in the semantic network.

32. The at least one non-transitory computer readable storage medium of claim 31, wherein synthesizing the second concept comprises:

identifying the at least one other concept in the semantic network based at least in part on the structure of the semantic network; and

synthesizing the second concept from the first concept and the at least one other concept.

33. The at least one non-transitory computer readable storage medium of claim 31, wherein synthesizing the second concept comprises using an addition operation based on an attribute co-definition technique.

34. The at least one non-transitory computer readable storage medium of claim 31, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-parent technique and/or an analogy-by-sibling technique.

35. The at least one non-transitory computer readable storage medium of claim 31, wherein synthesizing the second concept comprises using an addition operation based on an attribute commonality technique.

36. The at least one non-transitory computer readable storage medium of claim 31, wherein synthesizing the second concept comprises using a substitution operation, wherein the substitution operation comprises using a retrieval operation and/or an addition operation.

37. The at least one non-transitory computer readable storage medium of claim 31,

wherein obtaining

the at least one concept comprises:

obtaining a plurality of concepts semantically relevant to the first concept;

computing a score for one or more concepts in the plurality of concepts, wherein the score for a specific concept is indicative of the semantic relevance of the specific concept to the first concept; and

selecting the at least one concept based on the scores computed for the one or more concepts.

38. The at least one non-transitory computer readable storage medium of claim 37, wherein computing a score for a concept comprises using at least one measure of relevance from among generation certainty, concept productivity, Jaccard, statistical coherence, and/or cosine similarity.

39. The at least one non-transitory computer readable storage medium of claim 31, wherein identifying or generating the first concept comprises:

determining whether the portion of the user context information matches an identifier of a concept in the semantic network; and

when it is determined that the at least a portion of the user context information does not match an identifier of a concept in the semantic network, generating the first concept in the semantic network.

40. The at least one non-transitory computer readable storage medium of claim 31, wherein generating the first concept in the semantic network comprises identifying a concept in the semantic network covering more words in the portion of the user context information than any other concept in the semantic network.

41. The at least one non-transitory computer readable storage medium of claim 31, wherein the user-specific context information comprises at least one of a search query provided by the user, demographic information associated with the user, information from the user'"'"'s browsing history, information typed by the user, and/or information highlighted by the user.

42. The at least one non-transitory computer readable storage medium of claim 31, wherein the semantic network is represented by a data structure embodying a directed graph comprising a plurality of nodes and a plurality of edges, wherein each node is associated with a concept and an edge between two nodes represents a relationship between the two corresponding concepts.

43. The at least one non-transitory computer readable storage medium of claim 31, wherein the information comprises one or more advertisements and/or one or more product recommendations from one or more other users.

44. The at least one non-transitory computer readable storage medium of claim 31, wherein the information comprises content appearing on or accessible through a website.

45. The at least one non-transitory computer readable storage medium of claim 31, wherein providing information to the at least one user comprises:

creating a search query that includes terms from the first concept and the at least one obtained concept; and

providing the user with information associated with the search results obtained based on the search query.

46. A computer-implemented method for providing information selected from a large set of digital content, by using a user-specific knowledge representation, to at least one user, the method comprising:

receiving user context information associated with the at least one user;

identifying or generating, using at least one processor executing stored program instructions, a first concept in the user-specific knowledge representation representing at least information associated with the user, the first concept representing at least a portion of the user context information;

obtaining at least one concept in the user-specific knowledge representation that is semantically relevant to the first concept; and

providing information to the at least one user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept,

wherein a concept in the user-specific knowledge representation is represented by a data structure storing data associated with a node in the user-specific knowledge representation.

47. The computer-implemented method of claim 46, further comprising:

determining whether a saved user-specific knowledge representation associated with the user exists;

using the saved user-specific knowledge representation as the user-specific knowledge representation when it is determined that the saved user-specific knowledge representation exists, and

generating a new user-specific knowledge representation and using it as the user-specific knowledge representation, when it is determined that the saved user-specific knowledge representation does not exist.

48. The computer-implemented method of claim 46, wherein the at least one concept comprises a second concept, and obtaining the at least one concept comprises synthesizing the second concept based at least in part on the structure of the user-specific knowledge representation.

49. The computer-implemented method of claim 48, wherein synthesizing the second concept comprises using an addition operation based on an attribute co-definition technique.

50. The computer-implemented method of claim 48, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-parent technique and/or an analogy-by-sibling technique.

51. The computer-implemented method of claim 48, wherein synthesizing the second concept comprises using an addition operation based on an attribute commonality technique.

52. The computer-implemented method of claim 48, wherein synthesizing the second concept comprises using a substitution operation, wherein the substitution operation comprises using a retrieval operation and/or an addition operation.

53. The computer-implemented method of claim 46, wherein obtaining the at least one concept comprises:

obtaining a plurality of concepts semantically relevant to the first concept;

computing a score for one or more concepts in the plurality of concepts, wherein the score for a specific concept is indicative of the semantic relevance of the specific concept to the first concept; and

selecting the at least one concept based on the scores computed for the one or more concepts.

54. The computer-implemented method of claim 53, wherein computing a score for a concept comprises using at least one measure of relevance from among generation certainty, concept productivity, Jaccard, statistical coherence, and/or cosine similarity.

55. The computer-implemented method of claim 46, wherein identifying or generating the first concept comprises:

determining whether the portion of the user context information matches an identifier of a concept in the user-specific knowledge representation; and

when it is determined that the at least a portion of the user context information does not match an identifier of a concept in the user-specific knowledge representation, generating the first concept in the user-specific knowledge representation.

56. The computer-implemented method of claim 46, wherein generating the first concept in the user-specific knowledge representation comprises identifying a concept in the user-specific knowledge representation covering more words in the portion of the user context information than any other concept in the user-specific knowledge representation.

57. The computer-implemented method of claim 46, wherein the user-specific context information comprises at least one of a search query provided by the user, demographic information associated with the user, information from the user'"'"'s browsing history, information typed by the user, and/or information highlighted by the user.

58. The computer-implemented method of claim 46, wherein the user-specific knowledge representation is represented by a data structure embodying a directed graph comprising a plurality of nodes and a plurality of edges, wherein each node is associated with a concept and an edge between two nodes represents a relationship between the two corresponding concepts.

59. The computer-implemented method of claim 46, wherein the information comprises one or more advertisements and/or one or more product recommendations from one or more other users.

60. The computer-implemented method of claim 46, wherein the information comprises content appearing on or accessible through a website.

61. The computer-implemented method of claim 46, wherein providing information to the at least one user comprises:

creating a search query that includes terms from the first concept and the at least one obtained concept; and

providing the user with information associated with the search results obtained based on the search query.

62. A system for providing information selected from a large set of digital content, by using a user-specific knowledge representation, to at least one user, the system comprising:

at least one processor configured to execute a method comprising;

receiving user context information associated with the at least one user;

identifying or generating, using at least one processor executing stored program instructions, a first concept in the user-specific knowledge representation representing at least information associated with the user, the first concept representing at least a portion of the user context information;

obtaining at least one concept in the user-specific knowledge representation that is semantically relevant to the first concept; and

providing information to the at least one user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept,

wherein a concept in the user-specific knowledge representation is represented by a data structure storing data associated with a node in the user-specific knowledge representation.

63. The system of claim 62, further comprising:

determining whether a saved user-specific knowledge representation associated with the user exists;

using the saved user-specific knowledge representation as the user-specific knowledge representation when it is determined that the saved user-specific knowledge representation exists, and

generating a new user-specific knowledge representation and using it as the user-specific knowledge representation, when it is determined that the saved user-specific knowledge representation does not exist.

64. The system of claim 62, wherein the at least one concept comprises a second concept, and obtaining the at least one concept comprises synthesizing the second concept based at least in part on the structure of the user-specific knowledge representation.

65. The system of claim 64, wherein synthesizing the second concept comprises using an addition operation based on an attribute co-definition technique.

66. The system of claim 64, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-parent technique and/or an analogy-by-sibling technique.

67. The system of claim 64, wherein synthesizing the second concept comprises using an addition operation based on an attribute commonality technique.

68. The system of claim 64, wherein synthesizing the second concept comprises using a substitution operation, wherein the substitution operation comprises using a retrieval operation and/or an addition operation.

69. The system of claim 62, wherein obtaining the at least one concept comprises:

obtaining a plurality of concepts semantically relevant to the first concept;

computing a score for one or more concepts in the plurality of concepts, wherein the score for a specific concept is indicative of the semantic relevance of the specific concept to the first concept; and

selecting the at least one concept based on the scores computed for the one or more concepts.

70. The system of claim 69, wherein computing a score for a concept comprises using at least one measure of relevance from among generation certainty, concept productivity, Jaccard, statistical coherence, and/or cosine similarity.

71. The system of claim 62, wherein identifying or generating the first concept comprises:

determining whether the portion of the user context information matches an identifier of a concept in the user-specific knowledge representation; and

when it is determined that the at least a portion of the user context information does not match an identifier of a concept in the user-specific knowledge representation, generating the first concept in the user-specific knowledge representation.

72. The system of claim 62, wherein generating the first concept in the user-specific knowledge representation comprises identifying a concept in the user-specific knowledge representation covering more words in the portion of the user context information than any other concept in the user-specific knowledge representation.

73. The system of claim 62, wherein the user-specific context information comprises at least one of a search query provided by the user, demographic information associated with the user, information from the user'"'"'s browsing history, information typed by the user, and/or information highlighted by the user.

74. The system of claim 62, wherein the user-specific knowledge representation is represented by a data structure embodying a directed graph comprising a plurality of nodes and a plurality of edges, wherein each node is associated with a concept and an edge between two nodes represents a relationship between the two corresponding concepts.

75. The system of claim 62, wherein the information comprises one or more advertisements and/or one or more product recommendations from one or more other users.

76. The system of claim 62, wherein the information comprises content appearing on or accessible through a website.

77. The system of claim 62, wherein providing information to the at least one user comprises:

creating a search query that includes terms from the first concept and the at least one obtained concept; and

providing the user with information associated with the search results obtained based on the search query.

78. At least one non-transitory computer-readable storage medium storing processor-executable instructions that when executed by at least one processor, cause the at least one processor to perform a method for providing information selected from a large set of digital content, by using a user-specific knowledge representation, to at least one user, the method comprising:

receiving user context information associated with the at least one user;

identifying or generating, using at least one processor executing stored program instructions, a first concept in the user-specific knowledge representation representing at least information associated with the user, the first concept representing at least a portion of the user context information;

obtaining at least one concept in the user-specific knowledge representation that is semantically relevant to the first concept; and

providing information to the at least one user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept,

wherein a concept in the user-specific knowledge representation is represented by a data structure storing data associated with a node in the user-specific knowledge representation.

79. The at least one non-transitory computer-readable storage medium of claim 78, further comprising:

determining whether a saved user-specific knowledge representation associated with the user exists;

using the saved user-specific knowledge representation as the user-specific knowledge representation when it is determined that the saved user-specific knowledge representation exists, and

generating a new user-specific knowledge representation and using it as the user-specific knowledge representation, when it is determined that the saved user-specific knowledge representation does not exist.

80. The at least one non-transitory computer-readable storage medium of claim 78, wherein the at least one concept comprises a second concept, and obtaining the at least one concept comprises synthesizing the second concept based at least in part on the structure of the user-specific knowledge representation.

81. The at least one non-transitory computer-readable storage medium of claim 80, wherein synthesizing the second concept comprises using an addition operation based on an attribute co-definition technique.

82. The at least one non-transitory computer-readable storage medium of claim 80, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-parent technique and/or an analogy-by-sibling technique.

83. The at least one non-transitory computer-readable storage medium of claim 80, wherein synthesizing the second concept comprises using an addition operation based on an attribute commonality technique.

84. The at least one non-transitory computer-readable storage medium of claim 80, wherein synthesizing the second concept comprises using a substitution operation, wherein the substitution operation comprises using a retrieval operation and/or an addition operation.

85. The at least one non-transitory computer-readable storage medium of claim 78, wherein obtaining the at least one concept comprises:

obtaining a plurality of concepts semantically relevant to the first concept;

computing a score for one or more concepts in the plurality of concepts, wherein the score for a specific concept is indicative of the semantic relevance of the specific concept to the first concept; and

selecting the at least one concept based on the scores computed for the one or more concepts.

86. The at least one non-transitory computer-readable storage medium of claim 78, wherein computing a score for a concept comprises using at least one measure of relevance from among generation certainty, concept productivity, Jaccard, statistical coherence, and/or cosine similarity.

87. The at least one non-transitory computer-readable storage medium of claim 78, wherein identifying or generating the first concept comprises:

determining whether the portion of the user context information matches an identifier of a concept in the user-specific knowledge representation; and

when it is determined that the at least a portion of the user context information does not match an identifier of a concept in the user-specific knowledge representation, generating the first concept in the user-specific knowledge representation.

88. The at least one non-transitory computer-readable storage medium of claim 78, wherein generating the first concept in the user-specific knowledge representation comprises identifying a concept in the user-specific knowledge representation covering more words in the portion of the user context information than any other concept in the user-specific knowledge representation.

89. The at least one non-transitory computer-readable storage medium of claim 78, wherein the user-specific context information comprises at least one of a search query provided by the user, demographic information associated with the user, information from the user'"'"'s browsing history, information typed by the user, and/or information highlighted by the user.

90. The at least one non-transitory computer-readable storage medium of claim 78, wherein the user-specific knowledge representation is represented by a data structure embodying a directed graph comprising a plurality of nodes and a plurality of edges, wherein each node is associated with a concept and an edge between two nodes represents a relationship between the two corresponding concepts.

91. The at least one non-transitory computer-readable storage medium of claim 78, wherein the information comprises one or more advertisements and/or one or more product recommendations from one or more other users.

92. The at least one non-transitory computer-readable storage medium of claim 78, wherein the information comprises content appearing on or accessible through a website.

93. The at least one non-transitory computer-readable storage medium of claim 78, wherein providing information to the at least one user comprises:

creating a search query that includes terms from the first concept and the at least one obtained concept; and

providing the user with information associated with the search results obtained based on the search query.