Yu U Zhang

7656818, Feb 2, 2010

Oct 28, 2005

11/261366

Mihyar Baroudi - San Jose CA, US
Thanh Luu - San Jose CA, US
Jason Pfeifer - Austin TX, US
Clyde L. Wildes - San Francisco CA, US
Yu Zhang - Sunnyvale CA, US

Cisco Technology, Inc. - San Jose CA

H04L 12/28

370254, 709220

Methods and systems for providing customizable network device management are disclosed. In one such method, a user-created policy is received and registered on a network device, such as a router. An event identified by the policy is detected at the network device. In response, the policy runs on the network device and an action specified by the policy is performed. The policy can be prepared by a user of a remote computing device, uploaded to the network device, and registered on the network device. In various implementations, the policy can be provided in the form of a script or applet executable by the network device.

8144631, Mar 27, 2012

Dec 13, 2006

11/639078

Yu Zhang - Santa Clara CA, US
Mark J. Conner - Boulder Creek CA, US
Sabita Jasty - San Jose CA, US

Cisco Technology, Inc. - San Jose CA

H04L 12/66

370261, 370264, 370352, 370503, 709227, 709248, 709249

In one embodiment, a method includes sending, from an originating gateway device (OGW) to a terminating gateway device (TGW), a setup message for setting up a call on a primary bearer channel (B-channel) of an Integrated Services Digital Network (ISDN). The OGW interconnects with a first Internet Protocol (IP) video endpoint, and the TGW interconnects with a second IP video endpoint. In response to an alerting message sent from the TGW, the OGW initiates procedures for synchronizing a plurality of secondary B-channels between the OGW and the TGW prior to the call entering into a connect state. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure.

20070258952, Nov 8, 2007

May 4, 2007

11/744236

Alex W. Tong - Dallas TX, US
Yu An Zhang - Plano TX, US
John J. Nemunaitis - Dallas TX, US
Yuqiao Shen - Orinda CA, US
Po-hsun Chen - Addison TX, US
Shirley K. Samuel - Irving TX, US

BAYLOR RESEARCH INSTITUTE - Dallas TX
MUREX PHARMACEUTICALS - Dallas TX
BAYLOR UNIVERSITY - Waco TX

A61K 48/00
C12N 15/861

424 932, 435456

The present invention includes compositions and methods for the knockdown of one or more genes to a target cell in need of gene therapy by using an siRNA transgene that is integrated into a replication-competent, oncolytic adenovirus.

20080259907, Oct 23, 2008

Apr 18, 2007

11/737017

Parameswaran Kumarasamy - San Jose CA, US
Yu Zhang - Cupertino CA, US
Sabita Jasty - San Jose CA, US
Mark Conner - Boulder Creek CA, US
Sandeep Singh Bhupinder Kohli - Fremont CA, US
Paul R. P. Chu - Saratoga CA, US

H04L 12/66

370352, 370410

Disclosed are a method, apparatus and system for interworking between H.320 or H.324 and SIP. The method comprises receiving a SIP message indicative of capabilities supported by a first endpoint device and deferring responding to the SIP message. The method further comprises receiving from a second endpoint device an H.320 or H.324 message indicative of the capabilities supported by the second endpoint device in response thereto responding to the SIP message thereby to establish a media communication channel between the first and second endpoint devices based on their respective capabilities.

60887125, Jul 11, 2000

Dec 19, 1997

8/994996

Kuang-Hua Ken Huang - Cupertino CA
Yong Zeng - Sunnyvale CA
Yu Zhang - Cupertino CA

Knights Technology, Inc. - Sunnyvale CA

G06F 1730

707526

An intelligent, state-based macro facility is provided (as opposed to the unintelligent keystroke macro facilities typical of the prior art). Instead of a record time and an end time, an intelligent, state-based macro, or "smart macro," has only a single "capture" time, determined by the user pressing a single "smart macro" key. The state of the application, including objects displayed and various properties or attributes of those objects, is then captured and stored under a name supplied by the user. That same state may then be applied to different data to produce the same kinds of results. The smart macro may be applied interactively by the user to different data, or different data sets. Even more advantageously, a batch job may be scheduled, in which the smart macro is scheduled to run on specified data at a specified time with the results being saved to disk under a user-specified name. Batch jobs may be scheduled as one-time jobs or recurring jobs.

20220413181, Dec 29, 2022

Jun 29, 2021

17/362501

- Menlo Park CA, US
Nicole Hu - Mountain View CA, US
Feyera Hirpa - Menlo Park CA, US
Wang Zhan - Menlo Park CA, US
Shabaz Patel - Menlo Park CA, US
Samuel Lamont - Menlo Park CA, US
Yi Liu - Menlo Park CA, US
Zhuo Liu - Menlo Park CA, US
Ting Li - Menlo Park CA, US
Yu Zhang - Menlo Park CA, US

G01V 99/00
G06F 30/28
G06F 30/12
G08B 21/18

Methods, systems, and computer programs are presented for determining flood levels within a region. One method includes an operation for detecting an alert generated by one of a riverine, a coastal, or an urban model. Further, the method includes operations for selecting one or more regions for estimating flood data based on the detected alert, and for calculating, by an inundation model, region flood data for each of the selected regions based on outputs from the riverine model, the coastal model, and the urban model. Additionally, the method includes an operation for combining the region flood data for the selected one or more regions to obtain combined flood data. The combined flood data is presented on a user interface, such as on a flood inundation map.

20220351713, Nov 3, 2022

Jul 19, 2022

17/813361

- Mountain View CA, US
Zhifeng Chen - Mountain View CA, US
Yonghui Wu - Fremont CA, US
Jonathan Shen - Mountain View CA, US
Ruoming Pang - Mountain View CA, US
Ron J. Weiss - New York NY, US
Ignacio Lopez Moreno - Brooklyn NY, US
Fei Ren - Mountain View CA, US
Yu Zhang - Mountain View CA, US
Quan Wang - Hoboken NJ, US
Patrick An Phu Nguyen - Palo Alto CA, US

Google LLC - Mountain View CA

G10L 13/04
G10L 17/04
G10L 19/00

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.

20220343894, Oct 27, 2022

Jun 15, 2021

17/348118

- Mountain View CA, US
Wei Han - Mountain View CA, US
Min Ma - Mountain View CA, US
Zhiyun Lu - Mountain View CA, US
Chung-Cheng Chiu - Sunnyvale CA, US
Ruoming Pang - New York NY, US
Arun Narayanan - Santa Clara CA, US
Ananya Misra - Mountain View CA, US
Yu Zhang - Mountain View CA, US
Liangliang Cao - Mountain View CA, US

Google LLC - Mountain View CA

G10L 15/06
G06N 3/04
G10L 15/18
G10L 15/08

A method for training a streaming automatic speech recognition student model includes receiving a plurality of unlabeled student training utterances. The method also includes, for each unlabeled student training utterance, generating a transcription corresponding to the respective unlabeled student training utterance using a plurality of non-streaming automated speech recognition (ASR) teacher models. The method further includes distilling a streaming ASR student model from the plurality of non-streaming ASR teacher models by training the streaming ASR student model using the plurality of unlabeled student training utterances paired with the corresponding transcriptions generated by the plurality of non-streaming ASR teacher models.