Granted Patent B1

US 12,456,015 · App. 18/193,693 · Granted Oct 28, 2025

Inventors: Anoop Kumar (Fountain Valley, CA); Yi Fung (Quincy, MA); Premkumar Natarajan (Rolling Hills Estates, CA); Aram Galstyan (Los Angeles, CA); Heng Ji (Champaign, IL)

Assignee: Amazon Technologies, Inc.

G06F40/30G06F16/3329G06F40/40

Abstract

Claims (108)

1. A computer-implemented method comprising:

receiving first input data representing a first question corresponding to a first dialog in a natural language;

receiving second input data representing a first response to the first question;

receiving third input data corresponding to a request to generate a second question following the first response;

using a first machine learning (ML) component configured to generate data associated with a predicted question of a dialog, processing the first input data, the second input data, and the third input data to determine:

a first category corresponding to the second question, and

a first explanation of relevance of the first category;

processing, using a second ML component, the first input data, the second input data, the third input data, the first category, and the first explanation to generate the second question; and

outputting the second question.

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

receiving fourth input data representing a third question corresponding to a second dialog in the natural language;

receiving fifth input data representing a second response to the third question;

processing, by a first preliminary ML component, the fourth input data and the fifth input data to determine:

a second category corresponding to a fourth question following the second response, and

a second explanation of relevance of the second category;

determining a first comparison of the second category and a training category corresponding to the third question;

determining a second comparison of the second explanation and a training natural explanation corresponding to the third question; and

based on the first comparison and the second comparison, determining a third ML component.

3. The computer-implemented method of claim 2 , further comprising:

after determining the third ML component, processing, using the third ML component, the fourth input data, the fifth input data, the second category, and the second explanation to generate the third question;

determining a third comparison of the third question and a training question; and

based on the third comparison, determining a fourth ML component, wherein the first ML component and the second ML component correspond to the fourth ML component.

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

prior to receiving the first input data, receiving a transcript of a description of a first event associated with the first dialog;

using a third ML component, processing the transcript to generate a third question associated with the first event; and

outputting the third question.

5. A computer-implemented method comprising:

receiving first input data corresponding to a first natural language question;

receiving second input data corresponding to first natural language response to the first natural language question;

using a first machine learning (ML) component configured to generate data associated with a predicted question of a dialog, processing the first input data and the second input data to determine:

a first category corresponding to a second natural language question following the first natural language response, and

a first explanation of relevance of the first category;

using the first input data, the second input data, the first category, and the first explanation, generating the second natural language question; and

outputting the second natural language question.

6. The computer-implemented method of claim 5 , further comprising:

prior to receiving the first input data, receiving third input data corresponding to a natural language description of a first event;

using a second ML component, processing the third input data to generate a third natural language question associated with the first event; and

outputting the third natural language question.

7. The computer-implemented method of claim 5 , wherein:

the first input data and the second input data are associated with a first dialog involving a first user and a second user,

the first natural language question corresponds to a question asked by the first user to the second user,

the first natural language response corresponds to a response of the second user to the question asked by the first user, and

the second natural language question is configured to be addressed to the second user.

8. The computer-implemented method of claim 5 , wherein:

generating the second natural language question comprises processing the first input data, the second input data, the first category, and the first explanation using a second ML component.

9. The computer-implemented method of claim 5 , further comprising:

determining third input data corresponding to a third natural language question previously presented, wherein:

determining the first category is further based on processing the third input data,

determining the first explanation is further based on processing the third input data, and

determining the second natural language question is further based on processing the third input data.

10. The computer-implemented method of claim 5 , further comprising:

after outputting the second natural language question, receiving third input data corresponding to a second natural language response to the second natural language question;

receiving fourth input data corresponding to the second natural language question;

using the first ML component, processing the third input data and the fourth input data to determine:

a second category corresponding to a third natural language question following the second natural language response, and

a second explanation of relevance of the second category;

processing the third input data, the fourth input data, the second category, and the second explanation to generate the third natural language question;

determining that the third natural language question is semantically similar to the second natural language question; and

ceasing processing with respect to the third natural language question.

11. The computer-implemented method of claim 5 , further comprising:

after outputting the second natural language question, receiving third input data corresponding to a request to output an explanation of relevance of the second natural language question; and

based on the third input data corresponding to the request to output the explanation of relevance of the second natural language question, outputting the first explanation.

12. The computer-implemented method of claim 5 , wherein:

the first input data and the second input data are associated with a first dialog, the first dialog corresponding to a first goal to be achieved during the first dialog, and

the method further comprises:

using the first ML component, processing the first input data, the second input data, the first category, and the first explanation to generate the second natural language question, wherein the second natural language question is configured to result in third input data associated with the first goal.

13. A computing system comprising:

at least one processor; and

at least one memory comprising instructions that, when executed by the at least one processor, cause the computing system to:

receive first input data corresponding to a first natural language question;

receive second input data corresponding to a first natural language response to the first natural language question;

using a first machine learning (ML) component configured to generate data associated with a predicted question of a dialog, process the first input data and the second input data to determine:

a first category corresponding to a second natural language question following the first natural language response, and

a first explanation of relevance of the first category;

using the first input data, the second input data, the first category, and the first explanation, generating the second natural language question; and

outputting the second natural language question.

14. The computing system of claim 13 , wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the computing system to:

prior to receiving the first input data, receive third input data corresponding to a natural language description of a first event;

using a second ML component, process the third input data to generate a third natural language question associated with the first event; and

output the third natural language question.

15. The computing system of claim 13 , wherein:

the first input data and the second input data are associated with a first dialog involving a first user and a second user,

the first natural language question corresponds to a question asked by the first user to the second user,

the first natural language response corresponds to a response of the second user to the question asked by the first user, and

the second natural language question is configured to be addressed to the second user.

16. The computing system of claim 13 , wherein:

generating the second natural language question comprises processing the first input data, the second input data, the first category, and the first explanation using a second ML component.

17. The computing system of claim 13 , wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the computing system to:

determine third input data corresponding to a third natural language question previously presented, wherein:

determining the first category is further based on processing the third input data,

determining the first explanation is further based on processing the third input data, and

determining the second natural language question is further based on processing the third input data.

18. The computing system of claim 13 , wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the computing system to:

after outputting the second natural language question, receive third input data corresponding to a second natural language response to the second natural language question;

receive fourth input data corresponding to the second natural language question;

using the first ML component, process the third input data and the fourth input data to determine:

a second category corresponding to a third natural language question following the second natural language response, and

a second explanation of relevance of the second category;

process the third input data, the fourth input data, the second category, and the second explanation to generate the third natural language question;

determine that the third natural language question is semantically similar to the second natural language question; and

cease processing with respect to the third natural language question.

19. The computing system of claim 13 , wherein the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the computing system to:

after outputting the second natural language question, receive third input data corresponding to a request to output an explanation of relevance of the second natural language question; and

based on the third input data corresponding to the request to output the explanation of relevance of the second natural language question, outputting the first explanation.

20. The computing system of claim 13 , wherein:

the first input data and the second input data are associated with a first dialog, the first dialog corresponding to a first goal to be achieved during the first dialog, and

the at least one memory further comprises instructions that, when executed by the at least one processor, further cause the computing system to:

using the first ML component, process the first input data, the second input data, the first category, and the first explanation to generate the second natural language question, wherein the second natural language question is configured to result in third input data associated with the first goal.

Assignments (2)

References Cited (70)