Jian Ren Huang

US Patent:

7391803, Jun 24, 2008

Filed:

Sep 2, 2003

Appl. No.:

10/653509

Inventors:

Tao He - San Diego CA, US
Jignesh Shah - San Diego CA, US
Jian Huang - Poway CA, US

Assignee:

Nokia Corporation - Espoo

International Classification:

H04B 1/707

US Classification:

375142, 375144, 375148, 375150

Abstract:

A system and method are provided for canceling interference present in a local channel signal received at a receiver. The system includes a controller and a finger demodulator assembly. The controller is capable of selecting at least one interfering pilot channel signal that has a signal strength above a threshold, where the selected interfering pilot channel signals comprises an interfering set of pilot channel signals. The finger demodulator assembly can then be capable of producing a corrected local channel signal based upon the interfering set of pilot channel signals during the idle state and/or access state.

US Patent:

20220375560, Nov 24, 2022

Filed:

Aug 8, 2022

Appl. No.:

17/882824

Inventors:

- PARSIPPANY NJ, US
Rajneesh Patil - Durham NC, US
Nithyanandan Thangavelu - Bangalore, IN
Jian Huang - Parsippany NJ, US
Vijay Pratap A - Bangalore, IN

International Classification:

G16H 10/60
G16H 50/20
G16H 10/20
G16H 20/10

Abstract:

Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.

US Patent:

20200410614, Dec 31, 2020

Filed:

Jun 25, 2019

Appl. No.:

16/451097

Inventors:

- Parsippany NJ, US
Rajneesh Patil - Durham NC, US
Nithyanandan Thangavelu - Bangalore, IN
Jian Huang - Parsippany NJ, US
Vijay Pratap A - Bangalore, IN

International Classification:

G06Q 50/18
G16H 10/20
G16H 10/60
G06N 20/00

Abstract:

Aspects of the subject matter described in this specification are embodied in systems and methods that utilize machine-learning techniques to evaluate clinical trial data using one or more learning models trained to identify anomalies representing adverse events associated with a clinical trial investigation. In some implementations, investigation data collected at a clinical trial site is obtained. A set of models corresponding to the clinical trial site is selected. Each model included in the set of models is trained to identify, based on historical investigation data collected at the clinical trial site, a distinct set of one or more indicators that indicate a compliance risk associated with the investigation data. A score for the clinical trial site is determined based on the investigation data relative to the historical investigation data. The score represents a likelihood that the investigation data is associated with at least one indicator representing the compliance risk.

Address:

4308 Puffin Pl, Raleigh, NC 27616

Phone:

9192170089

VIN:

5FNYF3H20BB015370

Make:

HONDA

Model:

PILOT

Year:

2011