Brian E Price

Address:

7324 Skillman St APT 1208, Dallas, TX 75231

VIN:

WBXPC93447WF04136

Make:

BMW

Model:

X3

Year:

2007

US Patent:

20080264076, Oct 30, 2008

Filed:

Apr 25, 2007

Appl. No.:

11/789642

Inventors:

Brian C. Price - Plano TX, US
Randal J. Winkler - Overland Park KS, US

International Classification:

F25J 5/00

US Classification:

62 96, 62190

Abstract:

A system and process to re-liquefy boil-off liquid natural gas in varied amounts by a process using a screw compressor having an effective compression range from about 10 to about 100 percent of its rated capacity.

US Patent:

20090205367, Aug 20, 2009

Filed:

Feb 15, 2008

Appl. No.:

12/069962

Inventors:

Brian C. Price - Parker TX, US

International Classification:

F25J 3/00
F25B 9/00

US Classification:

62612, 62623

Abstract:

A method and system for the separation of a synthesis gas and methane mixture which contains carbon monoxide, hydrogen and methane with the process producing synthesis gas and liquid natural gas (LNG).

US Patent:

20220207745, Jun 30, 2022

Filed:

Mar 18, 2022

Appl. No.:

17/655493

Inventors:

- San Jose CA, US
Long Mai - Portland OR, US
Jun Hao Liew - Singapore, SG
Brian Price - San Jose CA, US

International Classification:

G06T 7/12
G06T 7/194
G06T 3/40
G06V 10/25

Abstract:

The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing scale-diverse segmentation neural networks to analyze digital images at different scales and identify different target objects portrayed in the digital images. For example, in one or more embodiments, the disclosed systems analyze a digital image and corresponding user indicators (e.g., foreground indicators, background indicators, edge indicators, boundary region indicators, and/or voice indicators) at different scales utilizing a scale-diverse segmentation neural network. In particular, the disclosed systems can utilize the scale-diverse segmentation neural network to generate a plurality of semantically meaningful object segmentation outputs. Furthermore, the disclosed systems can provide the plurality of object segmentation outputs for display and selection to improve the efficiency and accuracy of identifying target objects and modifying the digital image.

US Patent:

20200202533, Jun 25, 2020

Filed:

Dec 24, 2018

Appl. No.:

16/231746

Inventors:

- San Jose CA, US
Long Mai - Portland OR, US
Jun Hao Liew - Singapore, SG
Brian Price - San Jose CA, US

International Classification:

G06T 7/12
G06T 7/194
G06K 9/32
G06T 3/40

Abstract:

The present disclosure relates to systems, non-transitory computer-readable media, and methods for training and utilizing scale-diverse segmentation neural networks to analyze digital images at different scales and identify different target objects portrayed in the digital images. For example, in one or more embodiments, the disclosed systems analyze a digital image and corresponding user indicators (e.g., foreground indicators, background indicators, edge indicators, boundary region indicators, and/or voice indicators) at different scales utilizing a scale-diverse segmentation neural network. In particular, the disclosed systems can utilize the scale-diverse segmentation neural network to generate a plurality of semantically meaningful object segmentation outputs. Furthermore, the disclosed systems can provide the plurality of object segmentation outputs for display and selection to improve the efficiency and accuracy of identifying target objects and modifying the digital image.