Methods and apparatus for providing information of interest to one or more users

• US 9,378,203 B2
• Filed: 12/30/2011
• Issued: 06/28/2016
• 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
• Primary Examiner(s): Hill, Stanley K
• Assistant Examiner(s): BUSS, BENJAMIN J
• Application Number:
  US13/340,792
• Publication Number:
  US 20120150874A1
• Time in Patent Office: 1,642 Days
• Field of Search: 706/55, 707/794
• US Class Current: 1/1
• 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 a user. Techniques include receiving user context information associated with the user and identifying or generating 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 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, wherein, after performance of the identifying or generating, the semantic network comprises a first node representing the first concept;

synthesizing a second concept, semantically relevant to the first concept, and augmenting the semantic network with a second node representing the second concept, the second node being different from the first node, the synthesizing comprising;

identifying in the semantic network a third concept that, together with the first concept or a parent or sibling concept of the first concept, co-defines a fourth concept in the semantic network, and

combining the first concept and the third concept to synthesize the second concept; and

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

wherein the first and second concepts in the semantic network are represented by at least one data structure storing data associated with the first and second nodes.

2. The computer-implemented method of claim 1, wherein synthesizing the second concept comprises identifying the third concept in the semantic network based at least in part on the structure of the semantic network.

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.

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

6. The computer-implemented method of claim 1, wherein the semantic network is user-specific.

7. The computer-implemented method of claim 6, further comprising:

determining whether a saved user-specific semantic network associated with the user exists;

using the saved user-specific semantic network as the user-specific semantic network when it is determined that the saved user-specific semantic network exists; and

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

8. The computer-implemented method of claim 1, further comprising:

obtaining a plurality of concepts semantically relevant to the first concept, including the second 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 second concept for providing information to the at least one user based on the scores computed for the one or more concepts.

9. The computer-implemented method of claim 8, 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.

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

determining whether the at least a 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.

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

12. The computer-implemented method of claim 1, wherein the user context information comprises at least one item selected from the group consisting of:

a search query provided by the at least one user, demographic information associated with the at least one user, information from the at least one user'"'"'s browsing history, information typed by the at least one user, and/or information highlighted by the at least one user.

13. 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.

14. 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.

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

16. 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 second concept; and

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

17. 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, wherein, after performance of the identifying or generating, the semantic network comprises a first node representing the first concept;

synthesizing a second concept, semantically relevant to the first concept, and augmenting the semantic network with a second node representing the second concept, the second node being different from the first node, the synthesizing comprising;

identifying in the semantic network a third concept that, together with the first concept or a parent or sibling concept of the first concept, co-defines a fourth concept in the semantic network, and

combining the first concept and the third concept to synthesize the second concept; and

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

wherein the first and second concepts in the semantic network are represented by at least one data structure storing data associated with the first and second nodes.

18. The at least one non-transitory computer readable storage medium of claim 17, wherein synthesizing the second concept comprises identifying the third concept in the semantic network based at least in part on the structure of the semantic network.

19. The at least one non-transitory computer readable storage medium of claim 17, wherein synthesizing the second concept comprises using an addition operation based on an attribute co-definition technique.

20. The at least one non-transitory computer readable storage medium of claim 17, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-parent technique.

21. The at least one non-transitory computer readable storage medium of claim 17, wherein synthesizing the second concept comprises using an addition operation based on an analogy-by-sibling technique.

22. The at least one non-transitory computer readable storage medium of claim 17, wherein the semantic network is user-specific.

23. The at least one non-transitory computer readable storage medium of claim 17, wherein the method further comprises:

obtaining a plurality of concepts semantically relevant to the first concept, including the second 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 second concept for providing information to the at least one user based on the scores computed for the one or more concepts.

24. The at least one non-transitory computer readable storage medium of claim 23, 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.

25. The at least one non-transitory computer readable storage medium of claim 17, wherein identifying or generating the first concept comprises:

determining whether the at least a 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.

26. The at least one non-transitory computer readable storage medium of claim 17, wherein identifying or generating the first concept in the semantic network comprises identifying a concept in the semantic network covering more words in the at least a portion of the user context information than any other concept in the semantic network.

27. The at least one non-transitory computer readable storage medium of claim 17, wherein the user context information comprises at least one item selected from the group consisting of:

a search query provided by the at least one user, demographic information associated with the at least one user, information from the at least one user'"'"'s browsing history, information typed by the at least one user, and/or information highlighted by the at least one user.

28. The at least one non-transitory computer readable storage medium of claim 17, 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.

29. The at least one non-transitory computer readable storage medium of claim 17, wherein the information comprises one or more advertisements and/or one or more product recommendations from one or more other users.

30. The at least one non-transitory computer readable storage medium of claim 17, wherein the information comprises content appearing on or accessible through a website.

31. The at least one non-transitory computer readable storage medium of claim 17, wherein providing information to the at least one user comprises:

creating a search query that includes terms from the first concept and the second concept; and

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