Farzad E Ehsani

US Patent:

8229728, Jul 24, 2012

Filed:

Jan 4, 2008

Appl. No.:

11/969518

Inventors:

Jun Huang - Fremont CA, US
Yookyung Kim - Los Altos CA, US
Demitrios Master - Cupertino CA, US
Farzad Ehsani - Sunnyvale CA, US

Assignee:

Fluential, LLC - Sunnyvale CA

International Classification:

G06F 17/20
G06F 17/21
G06F 17/28

US Classification:

704 4, 704 1, 704 10

Abstract:

The present invention adopts the fundamental architecture of a statistical machine translation system which utilizes statistical models learned from the training data and does not require expert knowledge for rule-based machine translation systems. Out of the training parallel data, a certain amount of sentence pairs are selected for manual alignment. These sentences are aligned at the phrase level instead of at the word level. Depending on the size of the training data, the optimal amount for manual alignment may vary. The alignment is done using an alignment tool with a graphical user interface which is convenient and intuitive to the users. Manually aligned data are then utilized to improve the automatic word alignment component. Model combination methods are also introduced to improve the accuracy and the coverage of statistical models for the task of statistical machine translation.

US Patent:

8374871, Feb 12, 2013

Filed:

Mar 11, 2002

Appl. No.:

10/096194

Inventors:

Farzad Ehsani - Los Altos Hills CA, US
Eva M. Knodt - La Honda CA, US

Assignee:

Fluential, LLC - Sunnyvale CA

International Classification:

G10L 15/00
G10L 21/00

US Classification:

704257, 704275

Abstract:

The invention enables creation of grammar networks that can regulate, control, and define the content and scope of human-machine interaction in natural language voice user interfaces (NLVUI). More specifically, the invention concerns a phrase-based modeling of generic structures of verbal interaction and use of these models for the purpose of automating part of the design of such grammar networks.

US Patent:

8442812, May 14, 2013

Filed:

Apr 5, 2004

Appl. No.:

10/818219

Inventors:

Farzad Ehsani - Sunnyvale CA, US
Eva M. Knodt - La Honda CA, US
Demitrios L. Master - Cupertino CA, US

Assignee:

Fluential, LLC - Sunnyvale CA

International Classification:

G06F 17/27
G10L 15/00

US Classification:

704 9, 704231

Abstract:

The invention enables creation of grammar networks that can regulate, control, and define the content and scope of human-machine interaction in natural language voice user interfaces (NLVUI). The invention enables phrase-based modeling of generic structures of verbal interaction to be used for the purpose of automating part of the design of such grammar networks. Most particularly, the invention enables such grammar networks to be used in providing a voice-controlled user interface to human readable text data that is also machine-readable (such as a Web page, a word processing document, a PDF document, or a spreadsheet).

US Patent:

8478578, Jul 2, 2013

Filed:

Jan 9, 2009

Appl. No.:

12/351793

Inventors:

Farzad Ehsani - Sunnyvale CA, US
Demitrios Master - Cupertino CA, US
Elaine Drom Zuber - Cupertino CA, US

Assignee:

Fluential, LLC - Sunnyvale CA

International Classification:

G06F 17/28

US Classification:

704 2, 704 5, 704 8, 704 9, 704246, 704251

Abstract:

Interpretation from a first language to a second language via one or more communication devices is performed through a communication network (e. g. phone network or the internet) using a server for performing recognition and interpretation tasks, comprising the steps of: receiving an input speech utterance in a first language on a first mobile communication device; conditioning said input speech utterance; first transmitting said conditioned input speech utterance to a server; recognizing said first transmitted speech utterance to generate one or more recognition results; interpreting said recognition results to generate one or more interpretation results in an interlingua; mapping the interlingua to a second language in a first selected format; second transmitting said interpretation results in the first selected format to a second mobile communication device; and presenting said interpretation results in a second selected format on said second communication device.

US Patent:

8583416, Nov 12, 2013

Filed:

Dec 27, 2007

Appl. No.:

11/965711

Inventors:

Jun Huang - Fremont CA, US
Yookyung Kim - Los Altos CA, US
Youssef Billawala - Campbell CA, US
Farzad Ehsani - Sunnyvale CA, US
Demitrios Master - Cupertino CA, US

Assignee:

Fluential, LLC - Sunnyvale CA

International Classification:

G06F 17/28
G10L 15/00
G10L 21/00

US Classification:

704 3, 704 4, 704251, 704277

Abstract:

The performance of traditional speech recognition systems (as applied to information extraction or translation) decreases significantly with, larger domain size, scarce training data as well as under noisy environmental conditions. This invention mitigates these problems through the introduction of a novel predictive feature extraction method which combines linguistic and statistical information for representation of information embedded in a noisy source language. The predictive features are combined with text classifiers to map the noisy text to one of the semantically or functionally similar groups. The features used by the classifier can be syntactic, semantic, and statistical.

US Patent:

20020032564, Mar 14, 2002

Filed:

Apr 19, 2001

Appl. No.:

09/840005

Inventors:

Farzad Ehsani - Los Altos Hills CA, US
Eva Knodt - La Honda CA, US
Demitrios Master - Cupertino CA, US

International Classification:

G10L015/12
G10L015/26
G10L015/00
G10L021/00

US Classification:

704/235000, 704/270100, 704/275000

Abstract:

The invention enables creation of grammar networks that can regulate, control, and define the content and scope of human-machine interaction in natural language voice user interfaces (NLVUI). The invention enables phrase-based modeling of generic structures of verbal interaction to be used for the purpose of automating part of the design of such grammar networks. Most particularly, the invention enables such grammar networks to be used in providing a voice-controlled user interface to human readable text data that is also machine-readable (such as a Web page, a word processing document, a PDF document, or a spreadsheet).

US Patent:

20080133245, Jun 5, 2008

Filed:

Dec 4, 2006

Appl. No.:

11/633859

Inventors:

Guillaume Proulx - Cupertino CA, US
Youssef Billawala - Campbell CA, US
Elaine Drom - Sunnyvale CA, US
Farzad Ehsani - Sunnyvale CA, US
Yookyung Kim - Los Altos CA, US
Demitrios Master - Cupertino CA, US

International Classification:

G10L 21/00
G06F 17/28
G10L 11/00

US Classification:

704277, 704 5, 704E11001, 704E21001

Abstract:

The present invention disclose modular speech-to-speech translation systems and methods that provide adaptable platforms to enable verbal communication between speakers of different languages within the context of specific domains. The components of the preferred embodiments of the present invention includes: (1) speech recognition; (2) machine translation; (3) N-best merging module; (4) verification; and (5) text-to-speech. Characteristics of the speech recognition module here are that the modules are structured to provide N-best selections and multi-stream processing, where multiple speech recognition engines may be active at any one time. The N-best lists from the one or more speech recognition engines may be handled either separately or collectively to improve both recognition and translation results. A merge module is responsible for integrating the N-best outputs of the translation engines along with confidence/translation scores to create a ranked list or recognition-translation pairs.

US Patent:

20130204813, Aug 8, 2013

Filed:

Jan 17, 2013

Appl. No.:

13/744056

Inventors:

Farzad Ehsani - Sunnyvale CA, US
Silke Maren Witt-Ehsani - Sunnyvale CA, US

Assignee:

FLUENTIAL, LLC - Sunnyvale CA

International Classification:

G06N 99/00

US Classification:

706 12

Abstract:

A virtual assistant learning system is presented. A monitoring device, a cell phone for example, observes user interactions with an environment by acquiring sensor data. The monitoring device uses the sensor data to identify the interactions, which in turn is provided to an inference engine. The inference engine leverages the interaction data and previously stored knowledge elements about the user to determine if the interaction exhibits one or more user preferences. The inference engine can use the preferences and interactions to construct queries targeting search engines to seek out possible future interactions that might be of interest to the user.