IP Library › Granted Patent US 10,915,588
Granted Patent B2
US 10,915,588 · App. 16/053,726 · Granted Feb 9, 2021

Implicit dialog approach operating a conversational access interface to web content

Inventors: Raimo Bakis (Briarcliff Manor, NY); Song Feng (New York, NY); Jatin Ganhotra (White Plains, NY); Chulaka Gunasekara (Yorktown Heights, NY); David Nahamoo (Great Neck, NY); Lazaros Polymenakos (West Harrison, NY); Sunil D Shashidhara (White Plains, NY); Cheng Wu (Bellevue, WA); Li Zhu (Yorktown Heights, NY)
Assignee: International Business Machines Corporation
G06F16/9535G06F16/90332G06F40/30G06N5/02G10L15/22G10L2015/225
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Quick Facts
Patent No.
US 10,915,588
App. No.
16/053,726
Granted
Feb 9, 2021
Kind
B2
Abstract

A method, apparatus and computer program product for presenting a user interface for a conversational system is described. A user input is received in a dialog between a user and the conversational system, the user input in a natural language. A domain trained semantic matcher is used to determine a set of entities and a user intent from the user input. One or more queries is generated to selected ones of a plurality of knowledge sources, the knowledge sources created from domain specific knowledge. The results from the one or more queries are ranked based on domain specific knowledge. A system response is presented in the dialog based on at least a highest ranked result from the plurality of knowledge sources.

Claims (43)

1. A method for presenting a user interface for a conversational system comprising:

receiving a user input produced in a dialog between a user and the conversational system, the user input in a natural language;

using a domain trained semantic matcher to determine a set of entities and a user intent from the user input, the domain trained semantic matcher trained according to tasks of a target web site;

generating a plurality of queries from the set of entities and the user intent from the user input, each of the queries to selected ones of a plurality of knowledge sources, the knowledge sources created from domain specific knowledge, the domain specific knowledge related to tasks of the target web site;

ranking results from the plurality of queries based on the domain specific knowledge; and

presenting a system response in the dialog based on at least a highest ranked result from the plurality of knowledge sources.

2. The method as recited in claim 1 , wherein the plurality of queries are generated including a first query to a first knowledge source and a second query to a second knowledge source in the plurality of knowledge sources and a result from the first knowledge source to the first query has a higher confidence score than a result from the second knowledge source to the second query.

3. The method as recited in claim 2 , wherein the system response is generated based on the result from the first knowledge source.

4. The method as recited in claim 1 , wherein the plurality of queries are generated including a first query to a first knowledge source and a second query to a second knowledge source in the plurality of knowledge sources and a result from the first knowledge source to the first query is used to generate a first part of the system response and a result from the second knowledge source to the second query is used to generate a second part of the system response.

5. The method as recited in claim 1 , further comprising generating the system response with a natural language process from the highest ranked result from the plurality of knowledge sources.

6. The method as recited in claim 5 , further comprising:

generating one or more queries to an external knowledge source in addition to the plurality of knowledge sources; and

using a result from the one or more queries to the external knowledge source to generate the system response in the dialog.

7. The method as recited in claim 1 , further comprising updating selected ones of the plurality of knowledge sources based on conversational history in the dialog.

8. The method as recited in claim 1 , wherein the user intent is determined based on conversational history between the user and the conversational system.

9. Apparatus, comprising:

a processor;

computer memory holding computer program instructions executed by the processor for presenting a user interface for a conversational system, the computer program instructions comprising:

program code, operative to receive a user input produced in a dialog between a user and the conversational system, the user input in a natural language;

program code, operative to use a domain trained semantic matcher to determine a set of entities and a user intent from the user input, the domain trained semantic matcher trained according to tasks of a target web site;

program code, operative to generate a plurality of queries from the set of entities and the user intent from the user input, each of the queries to selected ones of a plurality of knowledge sources, the knowledge sources created from domain specific knowledge, the domain specific knowledge related to tasks of the target web site;

program code, operative to rank results from the plurality of queries based on the domain specific knowledge; and

program code, operative to present a system response in the dialog based on at least a highest ranked result from the plurality of knowledge sources.

10. The apparatus as recited in claim 9 , wherein the plurality of queries are generated including a first query to a first knowledge source and a second query to a second knowledge source in the plurality of knowledge sources and a result from the first knowledge source to the first query has a higher confidence score than a result from the second knowledge source to the second query.

11. The apparatus as recited in claim 10 , further comprising program code, operative to generate the system response based on the result from the first knowledge source.

12. The apparatus as recited in claim 9 , wherein the plurality of queries are generated including a first query to a first knowledge source and a second query to a second knowledge source in the plurality of knowledge sources and a result from the first knowledge source to the first query is used to generate a first part of the system response and a result from the second knowledge source to the second query is used to generate a second part of the system response.

13. The apparatus as recited in claim 9 , further comprising program code, operative to generate the system response with a natural language process from the highest ranked result from the plurality of knowledge sources.

14. The apparatus as recited in claim 13 , further comprising:

program code, operative to generate one or more queries to an external knowledge source in addition to the plurality of knowledge sources; and

program code, operative to use a result from the one or more queries to the external knowledge source to generate the system response in the dialog.

15. A computer program product in a non-transitory computer readable medium for use in a data processing system, the computer program product holding computer program instructions executed by the data processing system for presenting a user interface for a conversational system, the computer program instructions comprising:

program code, operative to receive a user input produced in a dialog between a user and the conversational system, the user input in a natural language;

program code, operative to use a domain trained semantic matcher to determine a set of entities and a user intent from the user input, the domain trained semantic matcher trained according to tasks of a target web site;

program code, operative to generate one or more a plurality of queries from the set of entities and the user intent from the user input, each of the queries to selected ones of a plurality of knowledge sources, the knowledge sources created from domain specific knowledge, the domain specific knowledge related to tasks of the target web site;

program code, operative to rank results from the plurality of queries based on the domain specific knowledge; and

program code, operative to present a system response in the dialog based on at least a highest ranked result from the plurality of knowledge sources.

16. The computer program product as recited in claim 15 , wherein the plurality of queries are generated including a first query to a first knowledge source and a second query to a second knowledge source in the plurality of knowledge sources and a result from the first knowledge source to the first query has a higher confidence score than a result from the second knowledge source to the second query.

17. The computer program product as recited in claim 16 , further comprising program code, operative to generate the system response based on the result from the first knowledge source.

18. The computer program product as recited in claim 15 , wherein the plurality of queries are generated including a first query to a first knowledge source and a second query to a second knowledge source in the plurality of knowledge sources and a result from the first knowledge source to the first query is used to generate a first part of the system response and a result from the second knowledge source to the second query is used to generate a second part of the system response.

19. The computer program product as recited in claim 15 , further comprising program code, operative to generate the system response with a natural language process from the highest ranked result from the plurality of knowledge sources.

20. The computer program product as recited in claim 19 , further comprising:

program code, operative to generate one or more queries to an external knowledge source in addition to the plurality of knowledge sources; and

program code, operative to use a result from the one or more queries to the external knowledge source to generate the system response in the dialog.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 17, 2018
From: BAKIS, RAIMO; FENG, SONG; GANHOTRA, JATIN; GUNASEKARA, CHULAKA; NAHAMOO, DAVID; POLYMENAKOS, LAZAROS; SHASHIDHARA, SUNIL D; WU, CHENG; ZHU, LI
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 047204/0573 →
Continuity (1)
Related Publication 20200042649A1 · Feb 6, 2020
Cited By (1)
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