Chenlei Guo, Age 754381 148Th Ave NE UNIT N204, Bellevue, WA 98007

2012006, Mar 15, 2012

Sep 14, 2010

12/882139

Christopher Ball - Seattle WA, US
Chinna Polinati - Snoqualmie WA, US
Chenlei Guo - Redmond WA, US

Microsoft Corporation - Redmond WA

G06F 17/30

707713, 707812, 707756, 707E17032, 707E17017

Distributed and local processes analyze usage data and transform it into objects including timestamps and dimensions. Objects include a position vector to represent dimension analysis and additional attributes associated with measurements of different types. The objects are stored in a multidimensional database indexed on the vector and timestamp attributes.

2023008, Mar 23, 2023

Jun 29, 2022

17/853013

- Seattle WA, US
Zheng Chen - Seattle WA, US
Yuan Ling - Bellevue WA, US
Lambert Leo Mathias - Seattle WA, US
Chenlei Guo - Redmond WA, US

G10L 15/197
G10L 15/22
G10L 15/30
G10L 15/18

A system is provided for reducing friction during user interactions with a natural language processing system, such as voice assistant systems. The system determines a pre-trained model using dialog session data corresponding to multiple user profiles. The system determines a fine-tuned model using the pre-trained model and a fine-tuning dataset that corresponds to a particular task, such as query rewriting. The system uses the fine-tuned model to process a user input and determine an alternative representation of the input that can result in a desired response from the natural language processing system.

2021037, Dec 2, 2021

Jun 1, 2020

16/889420

- Seattle WA, US
Julia Kennedy Nemer - Seattle WA, US
Nikko Strom - Kirkland WA, US
Steven Mack Saunders - Bellevue WA, US
Laura Maggia Panfili - Seattle WA, US
Anna Caitlin Jentoft - Seattle WA, US
Sungjin Lee - Woodinville WA, US
David Thomas - Woodinville WA, US
Young-Bum Kim - Kirkland MA, US
Pablo Cesar Ganga - Bellevue WA, US
Chenlei Guo - Redmond WA, US
Shuting Tang - Seattle WA, US
Zhenyu Yao - Sammamish WA, US

G10L 15/18
G10L 15/22
G10L 15/26

Described herein is a system for responding to a frustrated user with a response determined based on spoken language understanding (SLU) processing of a user input. The system detects user frustration and responds to a repeated user input by confirming an action to be performed or presenting an alternative action, instead of performing the action responsive to the user input. The system also detects poor audio quality of the captured user input, and responds by requesting the user to repeat the user input. The system processes sentiment data and signal quality data to respond to user inputs.

2019018, Jun 13, 2019

Dec 11, 2017

15/837751

- Redmond WA, US
Chenlei GUO - Redmond WA, US
Divya JETLEY - Kirkland WA, US
Pavel METRIKOV - Moscow, RU

Microsoft Technology Licensing, LLC - Redmond WA

G06Q 10/10

Systems and methods are disclosed to provide optimized scheduling of calendar events based on flexibility scores of calendar events. A flexibility score may be representative of a probability or likelihood that a calendar event can or will be rescheduled in response to a conflicting calendar event. Flexibility scores of calendar events may be calculated based on one or more factors, which may be weighted, using one or more machine-learning models. Factors may include: event densities of invitees' calendars, organizational rankings of respective invitees, the remaining time before an event start time, an urgency of respective calendar events, etc. In this way, if open time slots are not available for all invitees to a proposed calendar request, an event organizer may identify time slots occupied by existing calendar events with the highest likelihood of being rescheduled in view of the proposed calendar event, thereby facilitating scheduling of the proposed calendar event.

2019005, Feb 21, 2019

Aug 21, 2017

15/682321

- Redmond WA, US
Abhishek Kumar CHATURVEDI - Vancouver, CA
Chenlei GUO - Redmond WA, US
Byungki BYUN - Issaquah WA, US
Karen Catelyn STABILE - Redmond WA, US

Microsoft Technology Licensing, LLC - Redmond WA

G06Q 10/10
G06Q 10/02

Described herein are systems and methods for scheduling a resource that is shared by multiple people. The shared resource is included in a plurality of shared resources, and a number of attributes are associated with the plurality of shared resources. The attributes are grouped and arranged in a hierarchy. When a shared resource is to be used or scheduled, the hierarchy is analyzed to determine one or more shared resources in the plurality of shared resources to suggest to a requestor scheduling the shared resource.

2016032, Nov 3, 2016

Jul 28, 2015

14/811397

- Redmond WA, US
Xinying Song - Bellevue WA, US
Jianfeng Gao - Woodinville WA, US
Chenlei Guo - Redmond WA, US
Byungki Byun - Issaquah WA, US
Brian D. Remick - Morgan Hill CA, US
Edward Thiele - Mountain View CA, US
Mohammed Aatif Ali - Union City CA, US
Marcus Gois - San Jose CA, US
Yang Zou - Mountain View CA, US
Mariana Stepp - Santa Clara CA, US
Divya Jetley - Bellevue WA, US
Stephen Friesen - Dublin CA, US

G06F 17/30
H04L 12/58

Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation. In an exemplary embodiment, a plurality of conversation boxes associated with communications between a user and target recipients, or between other users and recipients, are collected and stored as user history. During a training phase, the user history is used to train encoder and decoder blocks in a de-noising auto-encoder model. During a prediction phase, the trained encoder and decoder are used to predict one or more recipients for a current conversation box composed by the user, based on contextual and other signals extracted from the current conversation box. The predicted recipients are ranked using a scoring function, and the top-ranked individuals or entities may be recommended to the user.

2016027, Sep 22, 2016

Mar 6, 2016

15/062167

- Redmond WA, US
MIchal Lewowski - Bellevue WA, US
Jiantao Sun - Bellevue WA, US
Thomas Lin - Bellevue WA, US
Chenlei Guo - Seattle WA, US
Vipul Agarwal - Bellevue WA, US
Elbio Renato Torres Abib - Bellevue WA, US

Microsoft Technology Licensing, LLC - Redmond WA

G06F 17/30
G06N 99/00

Architecture that utilizes server-based signals (e.g., past engagement, application popularity, spell-correction, mined search patterns, machine learning models, etc.) to improve relevance of search results for local applications and settings. The architecture works for any operating system (OS) and any client device that has local settings or applications installed. The architecture also covers instances where server-signals are being used to improve queries on devices where settings are searched but no applications are installed or will not be installed.