Cheng En Wu

US Patent:

7574358, Aug 11, 2009

Filed:

Feb 28, 2005

Appl. No.:

11/067819

Inventors:

Sabine Deligne - New York NY, US
Yuqing Gao - Mount Kisco NY, US
Vaibhava Goel - Elmsford NY, US
Cheng Wu - Mount Kisco NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G10L 15/00

US Classification:

704243, 704246, 704251, 704255, 7042565, 704257

Abstract:

A natural language business system and method is developed to understand the underlying meaning of a person's speech, such as during a transaction with the business system. The system includes a speech recognition engine, and action classification engine, and a control module. The control module causes the system to execute an inventive method wherein the speech recognition and action classification models may be recursively optimized on an unisolated performance metric that is pertinent to the overall performance of the natural language business system, as opposed to the isolated model-specific criteria previously employed.

US Patent:

8571869, Oct 29, 2013

Filed:

May 15, 2008

Appl. No.:

12/121329

Inventors:

Sabine Deligne - New York NY, US
Yuqing Gao - Mount Kisco NY, US
Vaibhava Goel - Elmsford NY, US
Cheng Wu - Mount Kisco NY, US

Assignee:

Nuance Communications, Inc. - Burlington MA

International Classification:

G10L 15/00

US Classification:

704257, 704246, 704247, 704251, 704252

Abstract:

A natural language business system and method is developed to understand the underlying meaning of a person's speech, such as during a transaction with the business system. The system includes a speech recognition engine, and action classification engine, and a control module. The control module causes the system to execute an inventive method wherein the speech recognition and action classification models may be recursively optimized on an unisolated performance metric that is pertinent to the overall performance of the natural language business system, as opposed to the isolated model-specific criteria previously employed.

US Patent:

20070168457, Jul 19, 2007

Filed:

Jan 18, 2006

Appl. No.:

11/334717

Inventors:

Juan Huerta - Bronx NY, US
David Lubensky - Brookfield CT, US
Cheng Wu - Mount Kisco NY, US

Assignee:

International Business Machines Corporation - Armonk NY

International Classification:

G06F 15/16

US Classification:

709217000

Abstract:

Techniques are provided for addressing a problem pertaining to a computer of a user. In an exemplary method, the obtaining of a problem statement associated with the problem pertaining to the computer of the user is facilitated. This can be carried out via speech over a telephony connection. The establishment of a remote access connection between the computer of the user and a remote help desk application can also be facilitated. Furthermore, substantially parallel interaction with the user can be facilitated, via both the telephony connection and the remote access connection to the remote help desk application.

US Patent:

20200042642, Feb 6, 2020

Filed:

Aug 2, 2018

Appl. No.:

16/053718

Inventors:

- Armonk NY, US
Song Feng - New York NY, US
Chulaka Gunasekara - Yorktown Heights NY, US
David Nahamoo - Great Neck NY, US
Lazaros Polymenakos - West Harrison NY, US
Sunil D. Shashidhara - White Plains NY, US
Cheng Wu - Bellevue WA, US
Li Zhu - Yorktown Heights NY, US

International Classification:

G06F 17/30
G06N 5/02
G06F 17/27
G06N 3/04
G06N 99/00

Abstract:

A method, apparatus and computer program product for creating a dialog system for web content is described. Knowledge is extracted from a target web application for the dialog system. The knowledge includes an organizational structure of the target web application and domain knowledge pertinent to the target web application. A deep learning process associates the domain knowledge with the organization structure of the target application. A plurality of knowledge sources of different respective types are created from the domain knowledge and the organizational structure. Each of the knowledge sources is used for providing answers to user queries to the dialog system. As part of the invention, a semantic matcher is provided to select among the answers provided by the plurality of knowledge sources for a best answer to a user query.

US Patent:

20200042649, Feb 6, 2020

Filed:

Aug 2, 2018

Appl. No.:

16/053726

Inventors:

- Armonk NY, US
Song Feng - New York NY, US
Chulaka Gunasekara - Yorktown Heights NY, US
David Nahamoo - Great Neck NY, US
Lazaros Polymenakos - West Harrison NY, US
Sunil D. Shashidhara - White Plains NY, US
Cheng Wu - Bellevue WA, US
Li Zhu - Yorktown Heights NY, US

International Classification:

G06F 17/30
G06F 17/27
G06N 5/02
G10L 15/22

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.

US Patent:

20180357216, Dec 13, 2018

Filed:

Dec 14, 2017

Appl. No.:

15/841703

Inventors:

- ARMONK NY, US
David NAHAMOO - Great Neck NY, US
Lazaros C. POLYMENAKOS - West Harrison NY, US
Cheng WU - Bellevue WA, US
John ZAKOS - Queensland, AU

International Classification:

G06F 17/27
G06F 17/21
G06F 17/30

Abstract:

A system and method performs automated domain concept discovery and clustering using word embeddings by receiving a set of documents for natural language processing for a domain, representing a plurality of entries in the set of documents as continuous vectors in a high dimensional continuous space, applying a clustering algorithm based on a mutual information optimization criterion to form a set of clusters, associating each entry of the plurality of entries with each cluster in the set of clusters through formalizing an evidence based model of each cluster given each entry, calculating a mutual information metric between each entry and each cluster using the evidence based model, and identifying a nominal center of each cluster by maximizing the mutual information.