Chris C Wang

US Patent:

20050259793, Nov 24, 2005

Filed:

May 19, 2004

Appl. No.:

10/848812

Inventors:

In Yeo - Richmond Hill, CA
Sandra McIntosh - Athens GA, US
Chris Wang - Chamblee GA, US
Akbar Beiki-Ardakani - Thornhill, CA

International Classification:

A61N005/10
G03B042/04

US Classification:

378182000

Abstract:

A phantom and film cassette therefor, a composition of high atomic number elements and tissue-equivalent material for a phantom, and an adjustable holder for a phantom. The film cassette comprises sections of tissue-equivalent material, wherein the sections retain a sheet of film when closed together and the phantom composition comprises tissue-equivalent material and high atomic number elements. In operation, dose distributions are determined via computation at various depths within a simulated water-equivalent phantom. After calculating dose distributions, an actual beam is delivered to a phantom containing the cassette, or utilizing a phantom containing high atomic number elements, wherein the phantom mimics human tissue, wherein the phantom houses radiographic film. Images are then generated on the film, the images are converted into an actual dose distribution, and the actual dose distribution is compared with the calculated dose distributions. Finally, a patient is treated based on the beam delivery thus verified.

US Patent:

20210256366, Aug 19, 2021

Filed:

Feb 14, 2020

Appl. No.:

16/791380

Inventors:

- Mountain View CA, US
Chris J. WANG - San Francisco CA, US

International Classification:

G06N 3/08
G06N 3/04
G06F 16/9535

Abstract:

Certain aspects of the present disclosure provide techniques for generating a recommendation of third-party applications to a user by a recommendation engine. The recommendation engine includes two deep-learning models that use various data sources (e.g., user data and application data) to generate the recommendation. One deep-learning model generates a relevance score for each available third-party application. The relevance score is used to determine a relevant application(s). The other deep-learning model generates a connection score for each relevant application. The recommendation engine uses the relevance score and the connections to generate an engagement score for each relevant application to determine whether the user would use the third-party application if recommended to the user. Those relevant applications with an engagement score that meet pre-determined criteria are determined and displayed to the user in the application as a recommendation.

US Patent:

20210089840, Mar 25, 2021

Filed:

Sep 18, 2020

Appl. No.:

17/026068

Inventors:

- Stanford CA, US
Andrew Y. Ng - Stanford CA, US
Pranav Rajpurkar - Stanford CA, US
Mark Sabini - Stanford CA, US
Chris Wang - Stanford CA, US
Nguyet Minh Phu - Stanford CA, US
Amirhossein Kiani - Stanford CA, US
Jeremy Irvin - Stanford CA, US
Matthew Lungren - Stanford CA, US

Assignee:

The Board of Trustees of the Leland Stanford Junior University - Stanford CA

International Classification:

G06K 9/62
G06T 7/00

Abstract:

Systems and methods for radiology image classification from noisy images in accordance with embodiments of the invention are illustrated. One embodiment includes noisy image classification device, including a processor, camera circuitry, and a memory containing a noisy image classification application, where the noisy image classification application directs the processor to obtain image data describing a first image taken of a second image using the camera circuitry, where the second image was produced by a medical imaging device, and where the first image is a noisy version of the second image, classify the image data using a neural network trained to be robust to noise, generate an investigation recommendation based on the classification, and provide the investigation recommendation via a display.

US Patent:

20200052988, Feb 13, 2020

Filed:

Oct 18, 2019

Appl. No.:

16/657184

Inventors:

- Schenectady NY, US
Soujanya ALURI - Bangalore, IN
Chris J. WANG - San Ramon CA, US
Priyanka VAKEEL - Bangalore, IN

International Classification:

H04L 12/26
H04L 29/08
H04W 4/70

Abstract:

The example embodiments are directed to a system and method for monitoring the health of analytical applications. In certain embodiments, these analytic applications may be a part of a broader IoT solution that may optionally be hosted on a cloud platform. In one example, the method includes receiving an output of an IoT analytic application deployed on a computing platform, selecting at least one performance metric based on a type of the application, calculating a performance of the application based on the selected performance metric, the received output of the application, and an expected output of the application, and in response to detecting the calculated performance of the application is below a predetermined threshold, outputting an alert to a user device. Accordingly, the health of an IoT analytic application can be monitored and an alert can be provided when the application operates below a predetermined threshold.

US Patent:

20180183693, Jun 28, 2018

Filed:

Dec 23, 2016

Appl. No.:

15/389615

Inventors:

- Schenectady NY, US
Soujanya ALURI - Bangalore, IN
Chris J. WANG - San Ramon CA, US
Priyanka VAKEEL - Bangalore, IN

International Classification:

H04L 12/26
H04L 29/08
H04L 12/24

Abstract:

The example embodiments are directed to a system and method for monitoring the health of analytical applications. In certain embodiments, these analytic applications may be a part of a broader IoT solution that may optionally be hosted on a cloud platform. In one example, the method includes receiving an output of an IoT analytic application deployed on a computing platform, selecting at least one performance metric based on a type of the application, calculating a performance of the application based on the selected performance metric, the received output of the application, and an expected output of the application, and in response to detecting the calculated performance of the application is below a predetermined threshold, outputting an alert to a user device. Accordingly, the health of an IoT analytic application can be monitored and an alert can be provided when the application operates below a predetermined threshold.