Jie Chen

US Patent:

7062574, Jun 13, 2006

Filed:

May 11, 2000

Appl. No.:

09/568687

Inventors:

Jie Chen - Bridgewater NJ, US
Jit Kumar - Basking Ridge NJ, US

Assignee:

Agere Systems Inc. - Allentown PA

International Classification:

G06F 15/16

US Classification:

709250, 709230, 709236, 709245

Abstract:

A system for, and method of, selectively detaching point-to-point protocol header information from a packet received from a network and a network adapter incorporating the system or the method. In one embodiment, the system includes: (1) a sub-protocol detector that receives the packet and identifies therefrom a sub-protocol employed with respect to the packet and (2) a header detacher, coupled to the sub-protocol detector, that detaches at least some of the point-to-point protocol header information from the packet based on an identity of the sub-protocol thereby to reduce an overall size of the packet.

US Patent:

20220315580, Oct 6, 2022

Filed:

Dec 9, 2020

Appl. No.:

17/116016

Inventors:

- Princeton NJ, US
Amy C. Hart - Ewing NJ, US
John E. Macor - Washington Crossing PA, US
Michael E. Mertzman - New Hope PA, US
William J. Pitts - Newtown PA, US
Steven H. Spergel - Warrington PA, US
Scott Hunter Watterson - Pennington NJ, US
Murugaiah Andappan Murugaiah Subbaiah - Tamil Radu, IN
Jie Chen - Cambridge MA, US
Carolyn Diane Dzierba - Middletown CT, US
Guanglin Luo - Newtown PA, US
Jianliang Shi - Furlong PA, US

International Classification:

C07D 471/04

Abstract:

Compounds having formula (I)-(IX), and enantiomers, and diastereomers, stereoisomers, pharmaceutically acceptable salts and prodrugs thereof, are useful as kinase modulators, including RIPK1 modulation. All the variables are as defined herein.

US Patent:

20230060623, Mar 2, 2023

Filed:

Aug 24, 2021

Appl. No.:

17/410617

Inventors:

- Atlanta GA, US
Wenjie Zhao - Princeton NJ, US
Jie Chen - Watchung NJ, US

International Classification:

H04L 12/851
H04L 12/803
H04L 12/26

Abstract:

Intelligent, adaptive scheduling weight adjustment is enabled, e.g., to improve network performance. For instance, A non-transitory machine-readable medium can comprise executable instructions that, when executed by a processor, facilitate performance of operations, comprising based on key performance indicators corresponding to data traffic flows via a network, determining quality of service data representative of respective qualities of service for the data traffic flows, using a scheduling weight data traffic model generated using machine learning and trained using past quality of service data representative of past qualities of service of past data traffic flows via the network, from prior to the data traffic flows, and past scheduling weight settings applied to the past data traffic flows, determining a scheduling weight setting to be applied to a data traffic flow of the data traffic flows, and applying the scheduling weight setting to the data traffic flow.

US Patent:

20210377903, Dec 2, 2021

Filed:

Jun 2, 2020

Appl. No.:

16/890493

Inventors:

- Atlanta GA, US
Jie Chen - Watchung NJ, US
Wenjie Zhao - Princeton NJ, US

International Classification:

H04W 64/00
H04W 16/14
H04W 4/029
H04W 24/02
H04L 12/24
H04W 16/18

Abstract:

The technologies described herein are generally directed to facilitating operation of a system for implementing fifth generation (5G) or other next generation networks. In accordance with one or more embodiments, a method described herein can include identifying, by a device comprising a processor, predicted resource usage of a first antenna covering a geographic zone. Further, the method can include selecting, by the device, a group of geographic siting locations within the geographic zone for potentially siting ones of a group of second antennas. In addition, selecting, by the device, a spatial arrangement in relation to the first antenna, of a subset of the group of geographic siting locations can occur, with a selected spatial arrangement including an arrangement to maintain the predicted resource usage in the condition.

US Patent:

20210081673, Mar 18, 2021

Filed:

Sep 9, 2020

Appl. No.:

17/016260

Inventors:

- Princeton NJ, US
Asim KADAV - Jersey City NJ, US
Jie CHEN - Bellevue WA, US

Assignee:

NEC LABORATORIES AMERICA, INC - Princeton NJ

International Classification:

G06K 9/00

Abstract:

Aspects of the present disclosure describe systems, methods, and structures that provide action recognition with high-order interaction with spatio-temporal object tracking. Image and object features are organized into into tracks, which advantageously facilitates many possible learnable embeddings and intra/inter-track interaction(s). Operationally, our systems, method, and structures according to the present disclosure employ an efficient high-order interaction model to learn embeddings and intra/inter object track interaction across the space and time for AR. Each frame is detected by an object detector to locate visual objects. Those objects are linked through time to form object tracks. The object tracks are then organized and combined with the embeddings as the input to our model. The model is trained to generate representative embeddings and discriminative video features through high-order interaction which is formulated as an efficient matrix operation without iterative processing delay.

US Patent:

20190389859, Dec 26, 2019

Filed:

Feb 12, 2018

Appl. No.:

16/484604

Inventors:

- Princeton NJ, US
Amy C. Hart - Ewing NJ, US
John E. Macor - Washington Crossing PA, US
Michael E. Mertzman - New Hope PA, US
William J. Pitts - Newtown PA, US
Steven H. Spergel - Warrington PA, US
Scott Hunter Watterson - Pennington NJ, US
Murugaiah Andappan Murugaiah Subbaiah - Tamil Radu, IN
Jie Chen - Cambridge MA, US
Carolyn Diane Dzierba - Middletown CT, US
Guanglin Luo - Newtown PA, US
Jianliang Shi - Furlong PA, US

International Classification:

C07D 471/04

Abstract:

Compounds having formula (I) (IX), and enantiomers, and diastereomers, stereoisomers, pharmaceutically acceptable salts and prodrugs thereof, are useful as kinase modulators, including RIPK1 modulation. All the variables are as defined herein: (I), (II), (III), (IV), (V), (VI), (VII), (VIII), (IX).

US Patent:

20180227876, Aug 9, 2018

Filed:

Apr 5, 2018

Appl. No.:

15/946163

Inventors:

- Atlanta GA, US
Jie Chen - Watchung NJ, US
Sam Houston Parker - Cranbury NJ, US
Huahui Wang - Bridgewater NJ, US
Wenjie Zhao - Princeton NJ, US

International Classification:

H04W 64/00
G01S 5/10

Abstract:

Techniques for locating a mobile device using a time distance of arrival (TDOA) method with disturbance scrutiny are provided. In an aspect, for respective combinations of three base station devices of a number of base station devices greater than or equal to three, intersections in hyperbolic curves, generated using a closed form function with input values based on differences of distances from the device to pairs of base station devices of the respective combinations of three base station devices, are determined. The intersection points are then tested for robustness against measurement errors associated with the input values and a subset of the intersection points that are associated with a degree of resistance to the measurement errors are selected to estimate a location of the device.

US Patent:

20170079005, Mar 16, 2017

Filed:

Nov 23, 2016

Appl. No.:

15/359843

Inventors:

- Atlanta GA, US
Jie Chen - Watchung NJ, US
Sam Houston Parker - Cranbury NJ, US
Huahui Wang - Bridgewater NJ, US
Wenjie Zhao - Princeton NJ, US

International Classification:

H04W 64/00
G01S 5/10

Abstract:

Techniques for locating a mobile device using a time distance of arrival (TDOA) method with disturbance scrutiny are provided. In an aspect, for respective combinations of three base station devices of a number of base station devices greater than or equal to three, intersections in hyperbolic curves, generated using a closed form function with input values based on differences of distances from the device to pairs of base station devices of the respective combinations of three base station devices, are determined. The intersection points are then tested for robustness against measurement errors associated with the input values and a subset of the intersection points that are associated with a degree of resistance to the measurement errors are selected to estimate a location of the device.