Chao Ma Li

US Patent:

20110225464, Sep 15, 2011

Filed:

Jun 30, 2010

Appl. No.:

12/827349

Inventors:

Shai Guday - Redmond WA, US
Thomas W. Kuehnel - Seattle WA, US
Gregory J. Scott - Seattle WA, US
Alec G. Kwok - Remond WA, US
Chao Li - Redmond WA, US
Yang Zhang - Bellevue WA, US
Naile Daoud - Woodinville WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06F 11/00

US Classification:

714 48, 714E11001

Abstract:

A framework is provided for diagnosing and resolving wireless connectivity-related issues. For example, some embodiments of the invention provide a “health monitor” which monitors and logs wireless connectivity-related events occurring on the device, the network, and the one or more resources to which the device is connected. The health monitor may analyze these events and/or other information to determine when a connectivity problem may have arisen, and if a problem is determined to be imminent or to have occurred, may initiate recovery procedures. In some embodiments, the monitoring of events, analysis to determine whether a connectivity problem has arisen, and the recovery from the problem occur transparently to the user.

US Patent:

20220237670, Jul 28, 2022

Filed:

Apr 15, 2022

Appl. No.:

17/721594

Inventors:

- Bentonville AR, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US

International Classification:

G06Q 30/02
G06K 9/62
G06Q 20/20
G06N 20/00
G06Q 10/06

Abstract:

This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.

US Patent:

20220215660, Jul 7, 2022

Filed:

Oct 15, 2021

Appl. No.:

17/502438

Inventors:

- Menlo Park CA, US
Chao Li - Woodinville WA, US
Kiran Kumar Somasundaram - Bellevue WA, US

International Classification:

G06V 20/20
G06V 20/64
G06T 19/00
H04L 67/01

Abstract:

In particular embodiments, a computing system may determine a user intent to perform a task in a physical environment surrounding the user. The system may send a query based on the user intent to a mapping server that stores a three-dimensional (3D) occupancy map containing spatial and semantic information of physical items in the physical environment. The mapping server may be configured to identify a subset of the physical items that are relevant to the user intent. The system may receive, from the mapping server, a response to the query comprising a portion of the 3D occupancy containing the subset of the physical items specific to the user intent. The system may capture a plurality of video frames of the physical environment. The system may process the plurality of video frames and the portion of the 3D occupancy map to provide one or more action labels associated with the task.

US Patent:

20210117448, Apr 22, 2021

Filed:

Oct 21, 2019

Appl. No.:

16/659017

Inventors:

- Redmond WA, US
Jiayuan HUANG - Medina WA, US
Weizhu CHEN - Kirkland WA, US
Changhong YUAN - Sammamish WA, US
Ankit SARAF - Bellevue WA, US
Xiaoying GUO - Sammamish WA, US
Eslam K. ABDELREHEEM - Sammamish WA, US
Yunjing MA - Bellevue WA, US
Yuantao WANG - Sammamish WA, US
Justin Carl WONG - Seattle WA, US
Nan ZHAO - Sammamish WA, US
Chao LI - Kirkland WA, US
Tsuyoshi WATANABE - Bothell WA, US
Jaclyn Ruth Elizabeth PHILLIPS - Seattle WA, US

Assignee:

Microsoft Technology Licensing, LLC - Redmond WA

International Classification:

G06F 16/28

Abstract:

In some examples, iterative sampling based dataset clustering may include sampling a dataset that includes a plurality of items to identify a specified number of sampled items. The sampled items may be clustered to generate a plurality of clusters. Un-sampled items may be assigned from the plurality of items to the clusters. Remaining un-sampled items that are not assigned to the clusters may be identified. A ratio associated with the remaining un-sampled items and the plurality of items may be compared to a specified threshold. Based on a determination that the ratio is greater than the specified threshold, an indication of completion of clustering of the plurality of items may be generated.

US Patent:

20200380570, Dec 3, 2020

Filed:

May 30, 2019

Appl. No.:

16/427238

Inventors:

- Bentonville AR, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US

International Classification:

G06Q 30/02
G06K 9/62
G06Q 10/06
G06N 20/00
G06Q 20/20

Abstract:

This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.

US Patent:

20200380571, Dec 3, 2020

Filed:

May 30, 2019

Appl. No.:

16/427241

Inventors:

- Bentonville AR, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US

International Classification:

G06Q 30/02
G06K 9/00
G06F 17/11
G06Q 10/06
G06N 20/00
G06Q 20/20

Abstract:

This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.

US Patent:

20190332180, Oct 31, 2019

Filed:

Feb 17, 2017

Appl. No.:

16/477044

Inventors:

- Redmond WA, US
Li Li - Redmond WA, US
Chao Li - Redmond WA, US
Yang Shu - Redmond WA, US
Qi Ren - Redmond WA, US
Erlei Xu - Redmond WA, US
Niangjun Zhu - Redmond WA, US
Hongjun Qiang - Redmond WA, US

International Classification:

G06F 3/01
G06F 3/03
H04L 29/08
H04W 12/06

Abstract:

Techniques for remote control of applications are described. A method according to an aspect of the disclosure comprises receiving, from an input processing unit, one ore more commands for performing one or more operations of an application instance; identifying, through checking pairing information, the application instance paired with the input processing unit; and sending to the identified application instance, the received one or more commands.