Rui Fang Zhang

8081583, Dec 20, 2011

Jul 11, 2008

12/171913

Jae Hong Ruy - Daejeon, KR
Jong Suk Chae - Daejeon, KR
Cheol Sig Pyo - Daejeon, KR
Bong Soo Kim - Daejeon, KR
Eun Ju Lee - Daejeon, KR
Myung Jong Lee - Paramus NJ, US
Tae Rim Park - New York NY, US
Rui Zhang - New York NY, US

Electronics & Telecommunications Research Institute - Daejeon
Research Foundation of the City University of New York - New York NY

H04L 12/26

370256

Provided is a method of supporting node portability in a sensor network, wherein data transmission to a portable node can be guaranteed since even when the portable node is assigned with a different address by being associated with another network, the newly assigned address is transmitted to a parent node or a sink node that was previously associated with the portable node.

20220414684, Dec 29, 2022

Jun 23, 2021

17/356084

- McLean VA, US
Rui ZHANG - New York NY, US
Erik MUELLER - Chevy Chase MD, US
Victor Alvarez MIRANDA - McLean VA, US

Capital One Services, LLC - McLean VA

G06Q 30/02
G06F 17/18
G06K 9/62
G06N 3/08

Provided herein are systems and methods for using multi-modal regression to predict customer intent to contact a merchant. Multi-modal data including numerical data and unstructured data are extracted from customer interactions with the merchant. Features of the numerical data and the unstructured data are separately extracted and classified using techniques specific to the data types. The features for each type are then separately used to predict probabilities of customer intent. A neural network is used to combine the predictions into a single set of estimates of customer intent. This set of estimates of customer intents is used to estimate a probability that the customer will contact the merchant. The customer is then contacted based on the estimate.

20230008022, Jan 12, 2023

Nov 25, 2019

17/295497

Dane CLAUSEN - Kenilworth NJ, US
Ping CHEN - Kenilworth NJ, US
Xavier FRADERA - Boston MA, US
Liangqin GUO - Kenilworth NJ, US
Yongxin HAN - Boston MA, US
Shuwen HE - Fanwood NJ, US
Xianhai HUANG - Warren NJ, US
Joseph KOZLOWSKI - Princeton NJ, US
Guoqing LI - Kenilworth NJ, US
Theodore A. MARTINOT - Southborough MA, US
Alexander PASTERNAK - Boston MA, US
Andreas VERRAS - New York NY, US
Li XIAO - Kenilworth NJ, US
Wensheng YU - Kenilworth NJ, US
Rui ZHANG - Kenilworth NJ, US
- Rahway NJ, US

Merck Sharp & Dohme Corp. - Rahway NJ

C07D 231/56
C07D 491/052
C07D 231/54
A61K 31/496
C07D 401/12
C07D 401/14
C07D 413/12
A61K 31/5377
C07D 498/10
C07D 491/107
C07D 498/08
C07D 471/10
C07D 403/12
C07D 405/12
C07D 498/04
C07D 487/04
C07D 487/10
A61K 31/541
C07D 413/14

Disclosed herein are compounds of formula (I) which are inhibitors of an IDO enzyme: (I). Also disclosed herein are uses of the compounds in the potential treatment or prevention of an IDO-associated disease or disorder. Also disclosed herein are compositions comprising these compounds. Further disclosed herein are uses of the compositions in the potential treatment or prevention of an IDO-associated disease or disorder.

20210395240, Dec 23, 2021

Oct 28, 2019

17/287256

Dane CLAUSEN - Kenilworth NJ, US
- Rahway NJ, US
Liangqin Guo - Monroe Township NJ, US
Yongxin Han - Needham MA, US
Shuwen He - Fanwood NJ, US
Joseph Kozlowski - Princeton NJ, US
Guoqing Li - Belle Mead NJ, US
Theodore A. Martinot - Southborough MA, US
Alexander Pasternak - Jamaica Plain MA, US
David Sloman - Newton MA, US
Li Xiao - Cranbury NJ, US
Wensheng Yu - Edison NJ, US
Rui Zhang - Plainsboro NJ, US

Merck Sharp & Dohme Corp. - Rahway NJ

C07D 413/14
C07D 401/14

Disclosed herein are compounds of formula (I) which are inhibitors of an IDO enzyme: (I). Also disclosed herein are uses of the compounds in the potential treatment or prevention of an IDO-associated disease or disorder. Also disclosed herein are compositions comprising these compounds. Further disclosed herein are uses of the compositions in the potential treatment or prevention of an IDO-associated disease or disorder.

20200387119, Dec 10, 2020

Mar 10, 2020

16/814388

- Valhalla NY, US
Younghun Kim - White Plains NY, US
Tarun Kumar - Mohegan Lake NY, US
Abhishek Raman - Santa Clara CA, US
Rui Zhang - Ossining NY, US

Utopus Insights, Inc. - Valhalla NY

G05B 13/02
G01F 22/02
H04L 12/26
H04L 29/08
G01M 3/28
G05D 7/06

Technical solutions are described for predicting linepack delays. An example method includes receiving temporal sensor measurements of a first fluid-delivery pipeline network and generating a causality graph of the first fluid-delivery pipeline network. The method also includes determining a topological network of the stations based on the causality graph, where the topological network identifies a temporal delay between a pair of stations. The method also includes generating a temporal delay prediction model based on the topological network and predicting the linepack delays of a second fluid-delivery pipeline network based on the temporal delay prediction model, where a compressor station of the second fluid-delivery pipeline network compresses fluid based on the predicted linepack delays to maintain a predetermined pressure.

20200159976, May 21, 2020

Oct 23, 2019

16/661445

- ARMONK NY, US
Chandramouli Visweswariah - Croton-on-Hudson NY, US
Bo Zhang - Hoboken NJ, US
Rui Zhang - Chappaqua NY, US

G06F 30/20
G06F 17/18

A method for locating an anomaly in a fluid transmission pipeline system is provided. The method may include receiving data for one or more physical conditions measured at an input and output of a pipeline portion, performing multiple simulations on a model of the pipeline portion to determine sets of simulated conditions that respectively correspond to simulated leak locations, determining a probability for a leak at one or more of the simulated leak locations by comparing one or more sets of simulated conditions to the received output data, and determining a highest probability location for the leak based on the probability for the leak at the one or more of the simulated leak locations. At least one simulation of the multiple simulations may be performed as a stochastic process and may be based on the received input data.

20190064392, Feb 28, 2019

Aug 30, 2017

15/690312

- ARMONK NY, US
ILDAR KHABIBRAKHMANOV - Syosset NY, US
TARUN KUMAR - Mohegan Lake NY, US
MARK A. LAVIN - Katonah NY, US
KEVIN W. WARREN - Hopewell Junction NY, US
RUI ZHANG - Chappaqua NY, US
WEI ZHANG - Elmsford NY, US

G01W 1/10
H02J 3/38
G06N 99/00
G06Q 50/06
G05B 13/02
G06F 17/18

A method, computer system, and computer program product. Weather forecast data is generated with respect to an area encompassing a location of a solar farm by a computer system. Solar power output by the solar farm is forecasted by the computer system based on the generated weather forecast data. Forecasted solar power output data is generated by the computer system based on the forecasted solar power output by the solar farm. A power grid operation, including one or both of a power grid balancing operation and a power grid optimization operation, is performed based on the forecasted solar power output data.

20180105713, Apr 19, 2018

Mar 21, 2016

15/564779

- Ludwigshafen, DE
Thomas STURM - Schifferstadt, DE
Danny SEYFRIED - Ludwigshafen, DE
Rui ZHANG - West New York NJ, US

BASF SE - Ludwigshafen

C09D 11/52
C09D 7/40
C09D 11/108
C09D 11/107
C09D 11/037
B05D 1/00
B05D 1/40
B05D 1/28
B05D 1/32
H01B 1/02
H05K 3/12
H05K 1/03
H05K 1/09

Described is a composition suitable for the preparation of an electroconductive transparent layer, said composition comprising a mixture of water and one or more alcohols, electroconductive nanoobjects and one or more dissolved styrene/(meth)acrylic copolymers.