Kevin S Lin

US Patent:

20220405550, Dec 22, 2022

Filed:

Aug 24, 2022

Appl. No.:

17/822080

Inventors:

- Redmond WA, US
Kevin LIN - Seattle WA, US
Zicheng LIU - Bellevue WA, US
Kun LUO - Redmond WA, US

Assignee:

Microsoft Technology Licensing, LLC - Redmond WA

International Classification:

G06N 3/04
G06N 3/08
G06T 7/10
G06F 17/15
G06V 40/10

Abstract:

A computing system is provided. The computing system includes a processor configured to execute a convolutional neural network that has been trained, the convolutional neural network including a backbone network that is a concatenated pyramid network, a plurality of first head neural networks, and a plurality of second head neural networks. At the backbone network, the processor is configured to receive an input image as input and output feature maps extracted from the input image. The processor is configured to: process the feature maps using each of the first head neural networks to output corresponding keypoint heatmaps; process the feature maps using each of the second head neural networks to output corresponding part affinity field heatmaps; link the keypoints into one or more instances of virtual skeletons using the part affinity fields; and output the instances of the virtual skeletons.

US Patent:

20200272812, Aug 27, 2020

Filed:

Feb 21, 2019

Appl. No.:

16/281876

Inventors:

- Redmond WA, US
Zicheng Liu - Bellevue WA, US
Kevin Lin - Redmond WA, US
Kun Luo - Redmond WA, US

International Classification:

G06K 9/00
G06K 9/62

Abstract:

A machine accesses a training data set comprising multiple real images and multiple synthetic images. The machine trains a joint prediction module to predict joint locations in visual data using the multiple real images. The machine trains a part affinity field prediction module to identify adjacent joints in visual data using the multiple real images. The machine trains the joint prediction module to predict joint locations in visual data using the multiple synthetic images. The machine trains the part affinity field prediction module to identify adjacent joints in visual data using the multiple synthetic images. The machine trains a body part prediction module to identify body parts in visual data using the multiple synthetic images. The machine provides a trained human body part segmentation module comprising the trained joint prediction module, the trained part affinity field prediction module, and the trained body part prediction module.

US Patent:

20200272888, Aug 27, 2020

Filed:

Apr 26, 2019

Appl. No.:

16/396513

Inventors:

- Redmond WA, US
Kevin LIN - Seattle WA, US
Zicheng LIU - Bellevue WA, US
Kun LUO - Redmond WA, US

Assignee:

Microsoft Technology Licensing, LLC - Redmond WA

International Classification:

G06N 3/04
G06N 3/08
G06T 7/10
G06K 9/00
G06F 17/15

Abstract:

A computing system is provided. The computing system includes a processor configured to execute a convolutional neural network that has been trained, the convolutional neural network including a backbone network that is a concatenated pyramid network, a plurality of first head neural networks, and a plurality of second head neural networks. At the backbone network, the processor is configured to receive an input image as input and output feature maps extracted from the input image. The processor is configured to: process the feature maps using each of the first head neural networks to output corresponding keypoint heatmaps; process the feature maps using each of the second head neural networks to output corresponding part affinity field heatmaps; link the keypoints into one or more instances of virtual skeletons using the part affinity fields; and output the instances of the virtual skeletons.

US Patent:

20200081629, Mar 12, 2020

Filed:

Sep 6, 2018

Appl. No.:

16/123457

Inventors:

- Armonk NY, US
Nedlaya Y. Francisco - Tucson AZ, US
Nicolas M. Clayton - Warrington, GB
Mark L. Lipets - Tucson AZ, US
Carol S. Mellgren - Tucson AZ, US
Gregory E. McBride - Vail AZ, US
David Fei - Tucson AZ, US
Kevin Lin - Tuscon AZ, US

International Classification:

G06F 3/06
G06F 12/0802

Abstract:

Provided are techniques for performing a recovery copy command to restore a safeguarded copy backup to a production volume. In response to receiving a recovery copy command, a production target data structure is created. A read operation is received for data for a storage location. In response to determining that the data for the storage location is in a cache of a host and a generation number is greater than a recovery generation number, the data is read from the cache. In response to determining at least one of that the data for the storage location is not in the cache and that the generation number is not greater than the recovery generation number, the data is read from one of the production volume and a backup volume based on a value of an indicator for the storage location in the production target data structure.

US Patent:

20200081792, Mar 12, 2020

Filed:

Sep 6, 2018

Appl. No.:

16/123618

Inventors:

- Armonk NY, US
Kevin Lin - Tucson AZ, US
David Fei - Tucson AZ, US
Nedlaya Y. Francisco - Tucson AZ, US

International Classification:

G06F 11/14
G06F 12/0868
G06F 3/06

Abstract:

Provided are a computer program product, system, and method for copying point-in-time data in a storage to a point-in-time copy data location in advance of destaging data to the storage. A point-in-time copy is created to maintain tracks in a source storage unit as of a point-in-time. A source copy data structure indicates tracks in the source storage unit to copy from the storage to a point-in-time data location. An update to write to a source track is received and a determination is made as to whether the source copy data structure indicates to copy the source track from the storage to the point-in-time data location. The update is written to a cache. A copy operation is initiated to copy the source track from the storage to the point-in-time data location asynchronous before the source track is destaged from the cache to the storage unit.

US Patent:

20190294511, Sep 26, 2019

Filed:

Jun 13, 2019

Appl. No.:

16/440926

Inventors:

- Armonk NY, US
Nicolas M. Clayton - Warrington, GB
Nedlaya Y. Francisco - Tucson AZ, US
Kevin Lin - Tucson AZ, US
Gregory E. McBride - Vail AZ, US
Carol S. Mellgren - Tucson AZ, US
Raul E. Saba - Tucson AZ, US
Matthew Sanchez - Tucson AZ, US

International Classification:

G06F 11/14

Abstract:

Consistency groups are asynchronously copied to a remote computational device, from a local computational device, wherein point in time copy operations are performed at the local computational device while the consistency groups are being asynchronously copied to the remote computational device. Indicators are stored at the remote computational device to identify those point in time copy operations that are to be restored as part of a recovery operation performed at the remote computational device in response to a failure of the local computational device.

US Patent:

20190034293, Jan 31, 2019

Filed:

Jul 27, 2017

Appl. No.:

15/662144

Inventors:

- Armonk NY, US
Nicolas M. Clayton - Warrington, GB
Nedlaya Y. Francisco - Tucson AZ, US
Kevin Lin - Tucson AZ, US
Gregory E. McBride - Vail AZ, US
Carol S. Mellgren - Tucson AZ, US
Raul E. Saba - Tucson AZ, US
Matthew Sanchez - Tucson AZ, US

International Classification:

G06F 11/14

Abstract:

Consistency groups are asynchronously copied to a remote computational device, from a local computational device, wherein point in time copy operations are performed at the local computational device while the consistency groups are being asynchronously copied to the remote computational device. Indicators are stored at the remote computational device to identify those point in time copy operations that are to be restored as part of a recovery operation performed at the remote computational device in response to a failure of the local computational device.

US Patent:

20180341694, Nov 29, 2018

Filed:

May 24, 2017

Appl. No.:

15/603736

Inventors:

- Armonk NY, US
Nicolas M. Clayton - Cheshire, GB
Kevin J. Lin - Tucson AZ, US
Gregory E. McBride - Vail AZ, US
Carol S. Mellgren - Tucson AZ, US
Matthew Sanchez - Tucson AZ, US

International Classification:

G06F 17/30
H04L 29/08

Abstract:

A method, system and computer-usable medium are disclosed for improved point-in-time copying of data within an asynchronous data mirroring environment comprising: receiving a request to initiate an asynchronous data mirroring operation associated with a first point-in-time copying process; processing a first set of establish data to generate a point-in-time establish reservation, the first set of establish data associated with the first point-in-time copying process establish; using the point-in-time establish reservation to generate a second set of establish data if the second point-in-time copying process can be performed; using the second set of establish data to initiate a second point-in-time copying process; and, tracking establish operations in progress between the first point-in-time copying process and the second point-in-time copying process.