Yuan Chen

US Patent:

7287014, Oct 23, 2007

Filed:

Nov 15, 2002

Appl. No.:

10/294773

Inventors:

Yuan Yan Chen - Woodbridge VA, US
Joseph Chen - Woodbridge VA, US

International Classification:

G06E 1/00
G06E 3/00
G06F 15/18
G06G 7/00

US Classification:

706 16

Abstract:

A plausible neural network (PLANN) is an artificial neural network with weight connection given by mutual information, which has the capability of inference and learning, and yet retains many characteristics of a biological neural network. The learning algorithm is based on statistical estimation, which is faster than the gradient decent approach currently used. The network after training becomes a fuzzy/belief network; the inference and weight are exchangeable, and as a result, knowledge extraction becomes simple. PLANN performs associative memory, supervised, semi-supervised, unsupervised learning and function/relation approximation in a single network architecture. This network architecture can easily be implemented by analog VLSI circuit design.

US Patent:

20020010691, Jan 24, 2002

Filed:

Mar 15, 2001

Appl. No.:

09/808101

Inventors:

Yuan Chen - Woodbridge VA, US

International Classification:

G06F015/18

US Classification:

706/020000

Abstract:

An apparatus and method for performing parallel distributed processing are disclosed. A plurality of nodes are connected with weight connections. The weight connections are updated based on a likelihood function of the associated nodes. Also, inputs to nodes are aggregated using t-norm or t-conorm functions, with outputs representing the possibility and belief measures. The aggregation methods presented offer an improvement over many other classification methods. Because of the form of the output, additional data evidence, including additional attributes, may be taken into account to improve classification without retraining the original data.

US Patent:

20220365509, Nov 17, 2022

Filed:

May 23, 2022

Appl. No.:

17/750549

Inventors:

- Gaithersburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

Assignee:

Xometry, Inc. - Gaithersburg MD

International Classification:

G05B 19/4097
G06N 20/00
G05B 19/401
G06F 30/00
G06N 20/20
G06N 5/04

Abstract:

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

US Patent:

20200348646, Nov 5, 2020

Filed:

Jul 14, 2020

Appl. No.:

16/928499

Inventors:

- Gaithersburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

International Classification:

G05B 19/4097
G06N 20/00
G05B 19/401
G06F 30/00
G06N 5/04

Abstract:

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

US Patent:

20200183355, Jun 11, 2020

Filed:

Feb 10, 2020

Appl. No.:

16/786454

Inventors:

- Gaithersburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

International Classification:

G05B 19/4097
G06N 5/04
G05B 19/401
G06N 20/00
G06F 30/00

Abstract:

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

US Patent:

20190271966, Sep 5, 2019

Filed:

Apr 26, 2019

Appl. No.:

16/395940

Inventors:

- Gaithersburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

International Classification:

G05B 19/4097
G06N 20/00
G05B 19/401
G06N 5/04
G06F 17/50

Abstract:

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

US Patent:

20180341246, Nov 29, 2018

Filed:

Jul 26, 2018

Appl. No.:

16/046519

Inventors:

- Geithesburg MD, US
Yuan Chen - Rockville MD, US
Luke S. Hendrix - Bethesda MD, US
William J. Sankey - College Park MD, US
Joshua Ryan Smith - Washington DC, US
Daniel Wheeler - Darnestown MD, US

Assignee:

Xometry, Inc. - Geithesburg MD

International Classification:

G05B 19/4097
G06F 17/50
G05B 19/401
G06N 99/00
G06N 5/04

Abstract:

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.

US Patent:

20180120813, May 3, 2018

Filed:

Nov 1, 2016

Appl. No.:

15/340338

Inventors:

- Gaithersburg MD, US
Yuan CHEN - Rockville MD, US
Luke S. HENDRIX - Bethesda MD, US
William J. SANKEY - College Park MD, US
Joshua Ryan SMITH - Washington DC, US
Daniel WHEELER - Darnestown MD, US

Assignee:

Xometry, Inc. - Gaithersburg MD

International Classification:

G05B 19/4097
G06N 5/04
G06N 99/00

Abstract:

The subject technology is related to methods and apparatus for discretization and manufacturability analysis of computer assisted design models. In one embodiment, the subject technology implements a computer-based method for the reception of an electronic file with a digital model representative of a physical object. The computer-based method determines geometric and physical attributes from a discretized version of the digital model, a cloud point version of the digital model, and symbolic functions generated through evolutionary algorithms. A set of predictive machine learning models is utilized to infer predictions related to the manufacture process of the physical object.