Method and system for analysis of intended meaning of natural language

• US 20070094221A1
• Filed: 08/15/2006
• Published: 04/26/2007
• Est. Priority Date: 05/28/1998
• Status: Active Grant

• Current Assignee: DataCloud Technologies, LLC (IP Investments Group LLC)
• Original Assignee: QPS Tech LLC (Intellectual Ventures LLC)
• Inventors: Au, Lawrence
• Granted Patent: US 7,526,466 B2
• Time in Patent Office: Days
• US Class Current: 706/55
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A computer implemented data processor system automatically disambiguates a contextual meaning of natural language symbols to enable precise meanings to be stored for later retrieval from a natural language database, so that natural language database design is automatic, to enable flexible and efficient natural language interfaces to computers, household appliances and hand-held devices.

Claims

1-19. -19. (canceled)

20. A method of analysis of intended meaning of natural language, the method comprising:

receiving a natural language input, wherein the natural language input includes at least one word;

analyzing each of the at least one word of the natural language input using a semantic inheritance network; and

determining a contextual meaning for the natural language input using each of the at least one word of the natural language input analyzed using the semantic inheritance network.

21. The method of claim 20, wherein analyzing each of the at least one word of the natural language input using a semantic inheritance network includes:

analyzing each of the at least one word of the natural language input using a plurality of candidate nodes and at least one contextual distance; and

determining at least one preferred contextual distance.

22. The method of claim 21, wherein determining a contextual meaning for the natural language input using each of the at least one word of the natural language input analyzed using the semantic inheritance network includes:

applying the at least one contextual distance to the plurality of candidate nodes to produce a contextual meaning for the natural language input.

23. The method of claim 22, wherein the semantic inheritance network includes a stored natural language context including a plurality of context nodes, and wherein analyzing each of the at least one word of the natural language input using a plurality of candidate nodes and at least one contextual distance includes:

identifying a plurality of links, wherein each of the plurality of links traverses from one of the plurality of candidate nodes to one of the plurality of context nodes; and

computing each of the at least one contextual distance for each of the plurality of candidate nodes, each of the at least one contextual distance occurring between one of the plurality of candidate nodes and one of the plurality of context nodes.

24. The method of claim 23, wherein determining at least one preferred contextual distance includes:

comparing each of the at least one contextual distance to one another.

25. The method of claim 24, wherein determining at least one preferred contextual distance further includes:

selecting a subset of candidate nodes from the plurality of candidate nodes.

26. The method of claim 25, wherein determining at least one preferred contextual distance further includes:

determining a preferred contextual meaning corresponding to the natural language input;

wherein each of the subset of candidate nodes has an associated contextual distance approximating an optimal contextual distance, such that each of the input nodes has at least one candidate node selected from the subset of candidate nodes, the at least one associated candidate node being the preferred contextual meaning for the corresponding one from the set of input nodes.

27. The method of claim 26, wherein the semantic inheritance network includes a plurality of semantic nodes, and wherein each of the subset of candidate nodes inherits from a common semantic network node, the common semantic network node being selected from the plurality of semantic nodes.

28. The method of claim 26, wherein determining at least one preferred contextual distance further includes:

determining a greatest minimal distance among each of the plurality of candidate nodes and each of the plurality of context nodes.

29. The method of claim 26, wherein, if no links are identified as traversing among one of the plurality of candidate nodes and any one of the plurality of context nodes, estimating a distance between each of the plurality of candidate nodes and each of the plurality of context nodes.

30. The method of claim 26, wherein each of the plurality of links has a corresponding one of a plurality of semantic link types.

31. The method of claim 26, wherein no links are identified as traversing from one of the plurality of candidate nodes to one from the plurality of context nodes for at least one of the plurality of semantic link types.

32. The method of claim 26, wherein identifying a plurality of links includes:

excluding each of the plurality of links having the at least one of the plurality of semantic link types for which no links are identified.

33. The method of claim 20, wherein the method is performed by a processor.

34. The method of claim 33, wherein the processor is imbedded in a device having voice recognition capability.

35. The method of claim 33, wherein the device is a household appliance.

36. The method of claim 33, wherein the device is a hand-held device.

37. The method of claim 34, wherein the plurality of input candidate nodes are received via the voice recognition capability of the device.

38. The method of claim 33, wherein the processor is imbedded in a device having voice synthesis capability.

39. The method of claim 38, wherein outputting the best contextual meaning corresponding to the natural language input includes:

providing a voice synthesis output of the preferred contextual meaning corresponding to the natural language input.

40. The method of claim 36, wherein the hand-held device allows performance of network functions.