METHOD FOR EXTRACTING SALIENT DIALOG USAGE FROM LIVE DATA

• US 20190034040A1
• Filed: 09/27/2018
• Published: 01/31/2019
• Est. Priority Date: 12/06/2013
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: SHAH, Rushin N., NAIK, Devang K.
• Granted Patent: US 11,314,370 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 40/295 Named entity recognition
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Systems and processes are disclosed for virtual assistant request recognition using live usage data and data relating to future events. User requests that are received but not recognized can be used to generate candidate request templates. A count can be associated with each candidate request template and can be incremented each time a matching candidate request template is received. When a count reaches a threshold level, the corresponding candidate request template can be used to train a virtual assistant to recognize and respond to similar user requests in the future. In addition, data relating to future events can be mined to extract relevant information that can be used to populate both recognized user request templates and candidate user request templates. Populated user request templates (e.g., whole expected utterances) can then be used to recognize user requests and disambiguate user intent as future events become relevant.

Claims

1-25. -25. (canceled)

26. A system for request recognition for a virtual assistant, the system comprising:

a memory;

one or more processors; and

one or more programs stored in the memory, the one or more programs including instructions for;

receiving a textual representation of user speech;

generating a plurality of request templates based on the textual representation;

associating a count with a request template of the plurality of request templates based on a number of times the request template is received; and

in response to a determination that the count is more than a predetermined amount, displaying a notification including the request template.

27. The system of claim 26, wherein the one or more programs include further instructions for:

in response to a determination that the count is more than the predetermined amount, training a language model with the request template, the language model being associated with the virtual assistant.

28. The system of claim 26, wherein the one or more programs include further instructions for:

in response to a determination that the count is more than the predetermined amount, generating a plurality of populated templates, each of the plurality of populated templates comprising one of the plurality of generated request templates populated with an entity.

29. The system of claim 28, wherein the one or more programs include further instructions for:

training a language model with the plurality of populated templates.

30. The system of claim 26, wherein each of the generated plurality of request templates comprises at least one word or phrase that indicates a language domain and at least one entity related to the language domain.

31. The system of claim 26, wherein the one or more programs include further instructions for:

receiving contextual data relating to the textual representation of user speech;

wherein the plurality of request templates is generated based on the textual representation and the contextual data relating to the textual representation.

32. The system of claim 26, wherein displaying the notification including the request template comprises displaying contextual information associated with the textual representation.

33. The system of claim 26, wherein the request template includes at least one variable corresponding to at least one type of a plurality of entities.

34. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions for request recognition for a virtual assistant, which when executed by one or more processors of an electronic device, cause the device to:

receive a textual representation of user speech;

generate a plurality of request templates based on the textual representation;

associate a count with a request template of the plurality of request templates based on a number of times the request template is received; and

in response to a determination that the count is more than a predetermined amount, display a notification including the request template.

35. The non-transitory computer readable storage medium of claim 34, wherein the one or more programs comprise further instructions, which when executed by the one or more processors, cause the device to:

in response to a determination that the count is more than the predetermined amount, train a language model with the request template, the language model being associated with the virtual assistant.

36. The non-transitory computer readable storage medium of claim 34, wherein the one or more programs comprise further instructions, which when executed by the one or more processors, cause the device to:

in response to a determination that the count is more than the predetermined amount, generate a plurality of populated templates, each of the plurality of populated templates comprising one of the plurality of generated request templates populated with an entity.

37. The non-transitory computer readable storage medium of claim 36, wherein the one or more programs comprise further instructions, which when executed by the one or more processors, cause the device to:

train a language model with the plurality of populated templates.

38. The non-transitory computer readable storage medium of claim 34, wherein each of the generated plurality of request templates comprises at least one word or phrase that indicates a language domain and at least one entity related to the language domain.

39. The non-transitory computer readable storage medium of claim 34, wherein the one or more programs comprise further instructions, which when executed by the one or more processors, cause the device to:

receive contextual data relating to the textual representation of user speech;

wherein the plurality of request templates is generated based on the textual representation and the contextual data relating to the textual representation.

40. The non-transitory computer readable storage medium of claim 34, wherein displaying the notification including the request template further comprises displaying contextual information associated with the textual representation.

41. The non-transitory computer readable storage medium of claim 34, wherein the request template includes at least one variable corresponding to at least one type of a plurality of entities.

42. A method for request recognition for a virtual assistant, the method comprising:

receiving a textual representation of user speech;

generating a plurality of request templates based on the textual representation;

associating a count with a request template of the plurality of request templates based on a number of times the request template is received; and

in response to a determination that the count is more than a predetermined amount, displaying a notification including the request template.

43. The method of claim 42, further comprising:

in response to a determination that the count is more than the predetermined amount, training a language model with the request template, the language model being associated with the virtual assistant.

44. The method of claim 42, further comprising:

in response to a determination that the count is more than the predetermined amount, generating a plurality of populated templates, each of the plurality of populated templates comprising one of the plurality of generated request templates populated with an entity.

45. The method of claim 44, further comprising:

training a language model with the plurality of populated templates.

46. The method of claim 42, wherein each of the generated plurality of request templates comprises at least one word or phrase that indicates a language domain and at least one entity related to the language domain.

47. The method of claim 42, further comprising:

receiving contextual data relating to the textual representation of user speech;

wherein the plurality of request templates is generated based on the textual representation and the contextual data relating to the textual representation.

48. The method of claim 42, wherein displaying the notification including the request template further comprises displaying contextual information associated with the textual representation.

49. The method of claim 42, wherein the request template includes at least one variable corresponding to at least one type of a plurality of entities.