Feng Gao, Age 651700 166Th Ct SE, Bothell, WA 98012

6392385, May 21, 2002

Dec 1, 1999

09/452267

Jeremy Barker - Redmond WA, 98052
Feng Gao - Laurel MD, 20733

H01M 1044

320130, 320131

The present invention provides a method for cycling a battery, containing lithium metal oxide, particularly lithium manganese oxide (LMO) active material, in a cell in a manner which reduces the extent to which loss of capacity occurs. The present method of operation extends the cycle life of such battery.

6444370, Sep 3, 2002

Nov 20, 1998

09/196882

Jeremy Barker - Redmond WA
Feng Gao - Henderson NV
Arnold Stux - Baltimore MD

Valence Technology, Inc. - Henderson NV

H01M 616

429332

The present invention provides a novel electrolyte solvent which is usable with a variety of carbonaceous and metal oxide electrode active materials, providing improved performance over a broad temperature range, and which is stabilized to maintain cell capacity over a number of cycles.

2002003, Apr 4, 2002

Sep 29, 1999

09/408065

JEREMY BARKER - REDMOND WA, US
FENG GAO - ANN ARBOR MI, US
EDWARD P. THURSTON - HOBOKEN NJ, US

H01M010/40
H01M004/52
H01M004/50

429/326000, 429/331000, 429/332000, 429/333000, 429/342000, 429/223000, 429/224000, 429/231100, 429/231300

The present invention provides an electrochemical cell having an electrolyte comprising a solvent and a solute, the solute comprising a lithium salt, and the solvent comprising an organic solvent selected from the group of lactones.

2003007, Apr 24, 2003

Apr 29, 2002

10/134317

Jeremy Barker - Redmond WA, US
Feng Gao - Henderson NV, US
Arnold Stux - Baltimore MD, US

H01M010/40
H01M004/50
H01M002/16

429/331000, 429/332000, 429/231100, 429/224000, 429/254000, 429/247000

The present invention provides a novel electrolyte solvent which is usable with a variety of carbonaceous and metal oxide electrode active materials, providing improved performance over a broad temperature range, and which is stabilized to maintain cell capacity over a number of cycles.

2022041, Dec 29, 2022

Aug 24, 2022

17/894363

- Beijing, CN
Youbing Yin - Kenmore WA, US
Junjie Bai - Seattle WA, US
Qi Song - Seattle WA, US
Kunlin Cao - Kenmore WA, US
Yi Lu - Seattle WA, US
Feng Gao - Seattle WA, US

Keya Medical Technology Co., Ltd. - Beijing

G16H 50/20
G06N 3/04
G06N 3/08

A method and system can be used for disease quantification modeling of an anatomical tree structure. The method may include obtaining a centerline of an anatomical tree structure and generating a graph neural network including a plurality of nodes based on a graph. Each node corresponds to a centerline point and edges are defined by the centerline, with an input of each node being a disease related feature or an image patch for the corresponding centerline point and an output of each node being a disease quantification parameter. The method also includes obtaining labeled data of one or more nodes, the number of which is less than a total number of the nodes in the graph neural network. Further, the method includes training the graph neural network by transferring information between the one or more nodes and other nodes based on the labeled data of the one or more nodes.

2022019, Jun 23, 2022

Mar 11, 2022

17/692337

- Shenzhen, CN
Youbing Yin - Kenmore WA, US
Qi Song - Seattle WA, US
Junjie Bai - Seattle WA, US
Yi Lu - Seattle WA, US
Yi Wu - Seattle WA, US
Feng Gao - Seattle WA, US
Kunlin Cao - Kenmore WA, US

SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION - Shenzhen

G06K 9/62
G06N 3/08
G06T 19/00
G06V 10/42

Methods and Systems for generating a centerline for an object in an image and computer readable medium are provided. The method includes receiving an image containing the object. The method also includes generating the centerline of the object, by a processor, using a reinforcement learning network configured to predict movement of a virtual agent that traces the centerline in the image. The reinforcement learning network is further configured to perform at least one auxiliary task that detects a bifurcation in a trajectory of the object. The reinforcement learning network is trained by maximizing a cumulative reward and minimizing an auxiliary loss of the at least one auxiliary task. Additionally, the method includes displaying the centerline of the object.

2023009, Mar 30, 2023

Apr 19, 2022

17/723863

- Shenzhen, CN
Junjie Bai - Seattle WA, US
Youbing Yin - Kenmore WA, US
Feng Gao - Seattle WA, US
Yue Pan - Seattle WA, US
Qi Song - Seattle WA, US

SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION - Shenzhen

G06T 7/33
G06T 17/00
G06T 7/00
G06T 7/73
G06T 11/00
G06T 19/20
A61B 6/00
A61B 8/08

Embodiments of the disclosure provide methods and systems for multi-modality joint analysis of a plurality of vascular images. The exemplary system may include a communication interface configured to receive the plurality of vascular images acquired using a plurality of imaging modalities. The system may further include at least one processor, configured to extract a plurality of vessel models for a vessel of interest from the plurality of vascular images. The plurality of vessel models are associated with the plurality of imaging modalities, respectively. The at least one processor is also configured to fuse the plurality of vessel models associated with the plurality of imaging modalities to generate a fused model for the vessel of interest. The at least one processor is further configured to provide a diagnostic analysis result based on the fused model of the vessel of interest.

2020041, Dec 31, 2020

Sep 10, 2020

17/017657

- Beijing, CN
Feng Gao - Seattle WA, US
Hanbo Chen - Seattle WA, US
Shanhui Sun - Princeton NJ, US
Junjie Bai - Seattle WA, US
Zheng Te - Beijing, CN
Youbing Yin - Kenmore WA, US

BEIJING KEYA MEDICAL TECHNOLOGY CO., LTD. - Beijing

G06T 7/00
G06N 3/04
G06K 9/80
G06N 3/08
G06F 16/50
G06F 40/10
G06F 40/174

Embodiments of the disclosure provide systems and methods for generating a report based on a medical image of a patient. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to automatically determine keywords from a natural language description of the medical image generated by applying a learning network to the medical image. The at least one processor is further configured to generate the report describing the medical image of the patient based on the keywords. The at least one processor is also configured to provide the report for display.