Yu U Wang

US Patent:

20220197689, Jun 23, 2022

Filed:

May 23, 2019

Appl. No.:

17/601635

Inventors:

- Redmond WA, US
Bin DU - Redmond WA, US
Sajay ANTONY - Woodinville WA, US
Steven M. LASKER - Seattle WA, US
Dongjiang YOU - Redmond WA, US
Yu WANG - Bellevue WA, US
Samarth SHAH - Bellevue WA, US
Brian Timothy GOFF - Seattle WA, US
Shiwei ZHANG - Shanghai, CN

International Classification:

G06F 9/455
G06F 8/61

Abstract:

Computing system enhancements make container instantiation faster, reduce layer content storage demands, and make more container image formats available. A container instantiation location sends a container image pull request to a container registry, receives an image manifest, sends a layer mount request to the registry instead of a layer content download request, receives a layer mount, optionally repeats for additional layers, creates a union file system spanning the layers, and launches a container process based on the union file system without first downloading all the layer content. Inefficiencies and technical limitations of some other approaches are avoided, such as loopback mounts for snapshot expansion, creation or transmission of extra snapshots or extra container image clones, cluttering layer content with virtual machine settings, container system vendor lock-in, lack of container instantiation at a local system due to insufficient local storage, and lack of syscall optimization due to storage driver plugin usage.

US Patent:

20210326751, Oct 21, 2021

Filed:

May 22, 2020

Appl. No.:

16/882296

Inventors:

- Redmond WA, US
Hao Cheng - Bellevue WA, US
Yu Wang - Bellevue WA, US
Jianfeng Gao - Woodinville WA, US
Weizhu Chen - Kirkland WA, US
Pengcheng He - Sammamish WA, US
Hoifung Poon - Bellevue WA, US

Assignee:

Microsoft Technology Licensing, LLC - Redmond WA

International Classification:

G06N 20/00
G06N 3/08
G06F 40/284
G06K 9/62

Abstract:

This document relates to training of machine learning models. One example method involves providing a machine learning model having one or more mapping layers. The one or more mapping layers can include at least a first mapping layer configured to map components of pretraining examples into first representations in a space. The example method also includes performing a pretraining stage on the one or more mapping layers using the pretraining examples. The pretraining stage can include adding noise to the first representations of the components of the pretraining examples to obtain noise-adjusted first representations. The pretraining stage can also include performing a self-supervised learning process to pretrain the one or more mapping layers using at least the first representations of the training data items and the noise-adjusted first representations of the training data items.

US Patent:

20210106833, Apr 15, 2021

Filed:

Oct 9, 2019

Appl. No.:

16/597067

Inventors:

- Berlin, DE
Brian M. Taff - Portland OR, US
Kurt Swenson - Dayton OR, US
Joseph Simon Raven - Tigard OR, US
Yu Wang - Lake Oswego OR, US
Paul Stadnik - Lake Oswego OR, US
Ulrich Hugel - Hillsboro OR, US

International Classification:

A61N 1/37
A61B 5/00
A61N 1/372
A61N 1/375

Abstract:

Technology for near infrared (NIR) based communication and/or sensing of implantable medical devices and systems as well as method of operating the same.

US Patent:

20200273000, Aug 27, 2020

Filed:

Feb 25, 2019

Appl. No.:

16/285170

Inventors:

- Redmond WA, US
Xinying SONG - Redmond WA, US
Ah Young KIM - Redmond WA, US
Haiyuan CAO - Bellevue WA, US
Yu WANG - Redmond WA, US
Hui SU - Bellevue WA, US
Shahina FERDOUS - Redmond WA, US
Jianfeng GAO - Redmond WA, US
Karan SRIVASTAVA - Seattle WA, US
Jaideep SARKAR - Redmond WA, US

International Classification:

G06Q 10/10
G06Q 10/06
G06Q 10/04

Abstract:

Systems and methods are disclosed for intelligent scheduling of calls to sales leads, leveraging machine learning (ML) to optimize expected results. One exemplary method includes determining, using a connectivity prediction model, call connectivity rate predictions; determining timeslot resources; allocating, based at least on the call connectivity rate predictions and timeslot resources, leads to timeslots in a first time period; determining, within a timeslot and using a lead scoring model, lead prioritization among leads within the timeslot; configuring, based at least on the lead prioritization, the telephone unit with lead information for placing a phone call; and applying a contextual bandit (ML) process to update the connectivity prediction model, the lead scoring model, or both. During subsequent time periods, the updated connectivity prediction and lead scoring models are used, thereby improving expected results over time.

US Patent:

20200142737, May 7, 2020

Filed:

Jan 8, 2020

Appl. No.:

16/737474

Inventors:

- Redmond WA, US
Yu Wang - Redmond WA, US
Karan Srivastava - Seattle WA, US
Jinfeng Gao - Woodinville WA, US
Prabhdeep Singh - Newcastle WA, US
Haiyuan Cao - Redmond WA, US
Xinying Song - Bellevue WA, US
Hui Su - Bellevue WA, US
Jaideep Sarkar - Redmond WA, US

International Classification:

G06F 9/48
G06F 9/50
G06K 9/62
G06N 20/00

Abstract:

Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

US Patent:

20190303197, Oct 3, 2019

Filed:

Apr 2, 2018

Appl. No.:

15/943206

Inventors:

- Redmond WA, US
Yu Wang - Redmond WA, US
Karan Srivastava - Seattle WA, US
Jianfeng Gao - Woodinville WA, US
Prabhdeep Singh - Newcastle WA, US
Haiyuan Cao - Redmond WA, US
Xinying Song - Bellevue WA, US
Hui Su - Bellevue WA, US
Jaideep Sarkar - Redmond WA, US

International Classification:

G06F 9/48
G06F 9/50
G06K 9/62
G06F 15/18

Abstract:

Generally discussed herein are devices, systems, and methods for scheduling tasks to be completed by resources. A method can include identifying features of the task, the features including a time-dependent feature and a time-independent feature, the time-dependent feature indicating a time the task is more likely to be successfully completed by the resource, converting the features to feature values based on a predefined mapping of features to feature values in a first memory device, determining, by a gradient boost tree model and based on a first current time and the feature values, a likelihood the resource will successfully complete the task, and scheduling the task to be performed by the resource based on the determined likelihood.

US Patent:

20190034604, Jan 31, 2019

Filed:

Jul 25, 2017

Appl. No.:

15/659555

Inventors:

- Suwon-si, KR
Yu Wang - Bellevue WA, US

International Classification:

G06F 21/32
H04L 29/06
G10L 15/30
H04L 12/28

Abstract:

A device and a method for authenticating a user. The method includes selecting the phrase key from a plurality of phrase keys. The method also includes receiving, from a target service a file that includes parsed data based on speech recognition processing of a phrase spoken by a user. Additionally, the method includes sending a notification the target service, upon a determination that the parsed data matches a phrase key. The method further includes receiving a set of user credentials from the target service and sending the set of user credentials to the virtual assistant device.

US Patent:

20180189032, Jul 5, 2018

Filed:

Dec 30, 2016

Appl. No.:

15/394968

Inventors:

ERIKO NURVITADHI - Hillsboro OR, US
YU WANG - Hillsboro OR, US
DEBORAH T. MARR - Portland OR, US

International Classification:

G06F 9/44

Abstract:

An apparatus and method are described for designing an accelerator for processing sparse data. For example, one embodiment comprises a machine-readable medium having program code stored thereon which, when executed by a processor, causes the processor to perform the operations of: analyzing input graph program code and parameters associated with a target accelerator in view of an accelerator architecture template; responsively mapping the parameters onto the architecture template to implement customizations to the accelerator architecture template; and generating a hardware description representation of the target accelerator based on the determined mapping of the parameters to apply to the accelerator architecture template.

Author:

Yu San Wang

ISBN #:

0275900460

Author:

Yu S. Wang

ISBN #:

0275934713

Author:

Yu Michael Wang

ISBN #:

0791835138