Jie Chen

US Patent:

8423454, Apr 16, 2013

Filed:

Jan 6, 2012

Appl. No.:

13/344709

Inventors:

Jie Chen - Chappaqua NY, US
Timothy John Breault - Huntersville NC, US
Fernando Cela Diaz - New York NY, US
William Anthony Nobili - Charlotte NC, US
Sandi Setiawan - Charlottle NC, US
Harsh Singhal - Charlotte NC, US
Agus Sudjianto - Charlotte NC, US
Andrea Renee Turner - Rock Hill SC, US
Bradford Timothy Winkelman - Wilmington DE, US

Assignee:

Bank of America Corporation - Charlotte NC

International Classification:

G06Q 40/00

US Classification:

705 38

Abstract:

Embodiments of the present invention relate to methods and apparatuses for determining leading indicators and/or for modeling one or more time series. For example, in some embodiments, a method is provided that includes: (a) receiving first data indicating the value of a total income amount for a plurality of consumers over a period of time; (b) receiving second data indicating the value of a total debt amount for a plurality of consumers over a period of time; (c) selecting a consumer leverage time series that compares the total income amount to the total debt amount over a period of time; (d) modeling the consumer leverage time series based at least partially on the first and second data; (e) determining, using a processor, the value of the cycle component for a particular time; and (f) outputting an indication of the value of the cycle component for the particular time.

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:

20190251484, Aug 15, 2019

Filed:

Feb 11, 2019

Appl. No.:

16/272759

Inventors:

- Indianapolis IN, US
Amin Amini - Irvine CA, US
Jie Chen - Carmel IN, US

International Classification:

G06Q 10/04
G06F 1/3203
G06F 9/30
H02J 3/14

Abstract:

Systems and methods are provided for forecasting and managing daily electrical demands. In some embodiments, a computing platform may receive historical demand data corresponding to historical electrical demand over a first period of time. Next, the computing platform may determine a reference rate of change (ROC) and a statistical mode corresponding to a number of positive ROCs. After, the computing platform may receive current demand data corresponding to current electrical demand over a second period of time. Subsequently, the computing platform may determine an alarm condition corresponding to a daily peak electrical demand. Following, the computing platform may generate one or more commands directing a second computing platform to display the alarm condition. Afterwards, the computing platform may transmit the one or more commands directing the second computing platform to display the alarm condition.

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.

US Patent:

20150148059, May 28, 2015

Filed:

Nov 26, 2013

Appl. No.:

14/090166

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

Assignee:

AT&T Intellectual Property I, L.P. - Atlanta GA

International Classification:

H04W 64/00

US Classification:

4554561

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.