IP Library Granted Patent US 8,249,879
Granted Patent B2
US 8,249,879 · App. 13/290,501 · Granted Aug 21, 2012

System and method of providing a spoken dialog interface to a website

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Quick Facts
Patent No.
US 8,249,879
App. No.
13/290,501
Granted
Aug 21, 2012
Kind
B2
Abstract

Disclosed is a system and method for training a spoken dialog service component from website data. Spoken dialog service components typically include an automatic speech recognition module, a language understanding module, a dialog management module, a language generation module and a text-to-speech module. The method includes converting data from a structured database associated with a website to a structured text data set and a structured task knowledge base, extracting linguistic items from the structured database, and training a spoken dialog service component using at least one of the structured text data, the structured task knowledge base, or the linguistic items. The system includes modules configured to implement the method.

Claims (31)

1. A method comprising:

translating, via a processor, data associated with a website into a structured text data set and a structured task knowledge base;

extracting from the website a linguistic item; and

combining the linguistic item, the structured task knowledge base, and the structured text data set with a generalized language model, to yield a website specific language model.

2. The method of claim 1 , wherein the linguistic item comprises one of a named-entity, a nominal phrase, a verbal phrase, and an adjectival phrase.

3. The method of claim 1 , wherein the structured text data set comprises a hierarchical tree.

4. The method of claim 3 , wherein the hierarchical tree comprises a plurality of non-leaf tree nodes having a node alias that is human comprehendible.

5. The method of claim 4 , wherein each non-leaf tree node further comprises a verbose description document and a concise summary.

6. The method of claim 5 , wherein the verbose description document and concise summary are used for information matching and help prompt construction during dialog execution.

7. The method of claim 1 , further comprising encoding each webpage in the website as a sequence of feature vectors.

8. A system comprising:

a processor; and

a non-transitory storage device storing instructions for controlling the processor to perform steps comprising:

translating data associated with a website into a structured text data set and a structured task knowledge base;

extracting from the website a linguistic item; and

combining the linguistic item, the structured task knowledge base, and the structured text data set with a generalized language model, to yield a website specific language model.

9. The system of claim 8 , wherein the linguistic item comprises one of a named-entity, a nominal phrase, a verbal phrase, and an adjectival phrase.

10. The system of claim 8 , wherein the structured text data set comprises a hierarchical tree.

11. The system of claim 10 , wherein the hierarchical tree comprises a plurality of non-leaf tree nodes having a node alias that is human comprehendible.

12. The system of claim 11 , wherein each non-leaf tree node further comprises a verbose description document and a concise summary.

13. The system of claim 12 , wherein the verbose description document and concise summary are used for information matching and help prompt construction during dialog execution.

14. The system of claim 8 , wherein the non-transitory storage medium stores further instructions comprising encoding each webpage in the website as a sequence of feature vectors.

15. A non-transitory computer-readable storage medium storing instructions which, when executed by a computing device, cause the computing device to perform steps comprising:

translating data associated with a website into a structured text data set and a structured task knowledge base;

extracting from the website a linguistic item; and

combining the linguistic item, the structured task knowledge base, and the structured text data set with a generalized language model, to yield a website specific language model.

16. The non-transitory computer-readable storage medium of claim 15 , wherein the linguistic item comprises one of a named-entity, a nominal phrase, a verbal phrase, and an adjectival phrase.

17. The non-transitory computer-readable storage medium of claim 15 , wherein the structured text data set comprises a hierarchical tree.

18. The non-transitory computer-readable storage medium of claim 17 , wherein the hierarchical tree comprises a plurality of non-leaf tree nodes having a node alias that is human comprehendible.

19. The non-transitory computer-readable storage medium of claim 18 , wherein each non-leaf tree node further comprises a verbose description document and a concise summary.

20. The non-transitory computer-readable storage medium of claim 19 , wherein the verbose description document and concise summary are used for information matching and help prompt construction during dialog execution.

Assignments (5)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 13, 2023
From: NUANCE COMMUNICATIONS, INC.
To: MICROSOFT TECHNOLOGY LICENSING, LLC
Reel/Frame 065552/0934 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 26, 2017
From: AT&T INTELLECTUAL PROPERTY II, L.P.
To: NUANCE COMMUNICATIONS, INC.
Reel/Frame 041512/0608 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 28, 2016
From: AT&T CORP.
To: AT&T PROPERTIES, LLC
Reel/Frame 038275/0041 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 28, 2016
From: AT&T PROPERTIES, LLC
To: AT&T INTELLECTUAL PROPERTY II, L.P.
Reel/Frame 038275/0130 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 7, 2011
From: BANGALORE, SRINIVAS; FENG, JUNLAN
To: AT&T CORP.
Reel/Frame 027185/0245 →