Chun C Wong

7730081, Jun 1, 2010

Oct 18, 2005

11/252899

Kristin D. Bromm - Mountain View CA, US
Denise D. Hui - San Mateo CA, US
Joshua T. Goodman - Redmond WA, US
Omar H. Shahine - San Francisco CA, US
Ethan N. Ray - Redmond WA, US
Matthew S. Carr - Seattle WA, US
Thomas A. Leung - Seattle WA, US
Wende E. Copfer - Bothell WA, US
Aly Valli - Seattle WA, US
Ewa Dominowska - Kirkland WA, US
Ying Li - Bellevue WA, US
Chun Yu Wong - Cupertino CA, US
Paul R. Weber - Seattle WA, US

Microsoft Corporation - Redmond WA

G06F 17/30

707769, 707783, 715752, 715747, 715758

While interacting with a messaging interface, keywords or other search criteria are automatically identified and used to perform a search. Search results and sponsored links (or advertisements) are displayed to the user within the messaging interface. To alleviate privacy concerns, this process will not be performed unless the user has explicitly opted-in to the search feature. In another embodiment, the user can highlight keywords in an outbound or inbound message to trigger a search without leaving the messaging interface. In another embodiment, the user can input a search keyword or phrase to trigger a search without leaving the messaging interface.

20060149746, Jul 6, 2006

Jan 4, 2005

11/028890

Aditya Bansod - San Francisco CA, US
Walter Hsueh - San Jose CA, US
Chun Wong - Sunnyvale CA, US

Microsoft Corporation - Redmond WA

G06F 17/30

707010000

A method and protocol for communication between a first computer and a second computer. The protocol includes a request from the first computer to the second computer including a function identifier for a function on the second computer and an argument for the function. The argument may be defined by a type for a function called by the function identifier. The protocol also includes a reply from the second computer to the first computer including the results of the function, with the reply defined as a script input for the first computer.

20210224995, Jul 22, 2021

Apr 2, 2021

17/221146

- Armonk NY, US
Chun Lok Wong - San Jose CA, US

G06T 7/00
G06K 9/00
G16H 50/20
G06N 20/00
G06N 3/04
G16H 50/70
G06K 9/62
G06N 3/08
G16H 30/40

Disease detection from medical images is provided. In various embodiments, a medical image of a patient is read. The medical image is provided to a trained anatomy segmentation network. A feature map is received from the trained anatomy segmentation network. The feature map indicates the location of at least one feature within the medical image. The feature map is provided to a trained classification network. The trained classification network was pre-trained on a plurality of feature map outputs of the segmentation network. A disease detection is received from the trained classification network. The disease detection indicating the presence or absence of a predetermined disease.

20210106286, Apr 15, 2021

Oct 11, 2019

16/599824

- Armonk NY, US
Hongzhi Wang - San Jose CA, US
Joy Tzung-yu Wu - San Jose CA, US
Chun Lok Wong - San Jose CA, US

A61B 5/00
G06T 7/10
G06K 9/68
G06N 3/08

A system for automated detection and type classification of central venous catheters. The system includes an electronic processor that is configured to, based on an image, generate a segmentation of a potential central venous catheter using a segmentation method and extract, from the segmentation, one or more image features associated with the potential central venous catheter. The electronic processor is also configured to, based on the one or more image features, determine, using a first classifier, whether the image includes a central venous catheters and determine, using a second classifier, a type of central venous catheter included in the image.

20210073588, Mar 11, 2021

Sep 11, 2019

16/567566

- Hartland WI, US
Chun Lok Wong - San Jose CA, US

G06K 9/62
G06T 7/11
G06N 3/08
G06N 3/04

Systems and methods generate a segmentation network for image segmentation using global optimization. A method for automatic generation of at least one segmentation network includes providing an initial set of hyperparameters to construct a segmentation network. The hyperparameters define operations for a set of block structures and connections between the block structures. The segmentation network is trained using a first set of images with ground truth. An objective function value for the trained segmentation network is generated using a second set of images having ground truth. The set of hyperparameters is updated by performing a derivative-free optimization algorithm on the objective function value to construct an updated segmentation network. The training of the segmentation network, the generating of the objective function, and the updating of the set of hyperparameters for the updated segmentation network are iterated to generate a network architecture for the segmentation network.

20210074000, Mar 11, 2021

Sep 11, 2019

16/567573

- Hartland WI, US
Chun Lok Wong - San Jose CA, US

G06T 7/174
G06K 9/62
G06T 7/00
G06T 7/136
G06T 7/162

Systems and methods generate a segmentation network for image segmentation using global optimization. A method for automatic generation of at least one segmentation network includes providing an initial set of hyperparameters to construct a segmentation network. The hyperparameters define operations for a set of block structures and connections between the block structures. The segmentation network is trained using a first set of images with ground truth. An objective function value for the trained segmentation network is generated using a second set of images having ground truth. Generating the objective function includes setting the objective function to a predetermined value responsive to identifying an untrainable condition of the trained initial segmentation network. The set of hyperparameters is updated by performing an optimization algorithm on the objective function value to construct an updated segmentation network. The training of the segmentation network, the generating of the objective function, and the updating of the set of hyperparameters for the updated segmentation network are iterated to generate a network architecture for the segmentation network.

20200321101, Oct 8, 2020

Apr 8, 2019

16/377944

- Armonk NY, US
Chun Lok Wong - San Jose CA, US
Joy Wu - San Jose CA, US
Mehdi Moradi - San Jose CA, US

G16H 30/40
G06N 20/00
G06K 9/62
G06T 7/00
G06F 17/24
G16H 15/00

Methods and systems are directed to training an artificial intelligence engine. One system includes an electronic processor configured obtain a set of reports corresponding to a set of medical images, determine a label for a finding of interest, and identify one or more ambiguous reports in the set of repots. Ambiguous reports do not include a positive label or a negative label for the finding of interest. The electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation for each of the one or more ambiguous reports and non-ambiguous reports in the set of reports. A result of the training is generation of a classification model for the label for the finding of interest.

20200074637, Mar 5, 2020

Aug 28, 2018

16/114947

- Armonk NY, US
Chun Lok Wong - San Jose CA, US

G06T 7/143
G06N 3/08
G06N 3/04
G06T 7/00

3D segmentation with exponential logarithmic loss for highly unbalanced object sizes is provided. In various embodiments, an artificial neural network is trained to label an anatomical feature in medical imagery by: i) providing at least one medical image to the artificial neural network; ii) determining from the artificial neural network a predicted segmentation for the at least one medical image; iii) comparing the predicted segmentation to ground truth segmentation, and computing therefrom a loss function, the loss function having an exponential-logarithmic term; and iv) updating the artificial neural network based on the loss function.