Yuan Fang Chen

US Patent:

20120118776, May 17, 2012

Filed:

Nov 11, 2010

Appl. No.:

12/944553

Inventors:

Donald Gringer - New York NY, US
Yuan Fang Chen - Forest Hills NY, US

International Classification:

B65D 79/00

US Classification:

206361, 206525

Abstract:

There is provided in a preferred embodiment of the present invention an improved packaging device comprising a plastic casing having a depressed compartment, a plurality of wings extending from the plastic casing and a connector to secure the wings of the plastic casing around the product. Preferably, the improved packaging device also includes a folding hanger that extends from the proximate end of the plastic casing, and is used to display the product.

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:

20210293918, Sep 23, 2021

Filed:

Oct 29, 2020

Appl. No.:

17/084257

Inventors:

- Suwon-si, KR
Yuan CHEN - New York NY, US
Dae Won LEE - Princeton NJ, US
Colin PREPSCIUS - New York NY, US
Ibrahim Volkan ISLER - Saint Paul MN, US
Lawrence David JACKEL - Holmdel NJ, US
Hyunjune Sebastian SEUNG - Princeton NJ, US
Daniel Dongyuel LEE - Tenafly NJ, US

Assignee:

SAMSUNG ELECTRONICS CO., LTD. - Suwon-si

International Classification:

G01S 5/22
B25J 19/02

Abstract:

A method of collision localization on a robotic device includes obtaining audio signals from a plurality of acoustic sensors spaced apart along the robotic device; identifying, based on a collision being detected, a strongest audio signal; identifying a primary onset time for an acoustic sensor producing the strongest audio signal, the primary onset time being a time at which waves propagating from the collision reach the acoustic sensor producing the strongest audio signal; generating a virtual onset time set, by shifting a calibration manifold, based on the identified primary onset time, the calibration manifold representing relative onset times from evenly spaced marker locations on the robotic device to the plurality of acoustic sensors; determining scores for the marker locations based a standard deviation of elements in the virtual onset time set; and estimating a location of the collision based on a highest score of the determined scores.

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.