Microsoft Technology Licensing, LLC

Choo Yei Chong

Neha Parikh Shah

Wende E Copfer

Venkata Sreekanth Kannepalli

Christopher Michael Dollar

Heidi Kenyon

Shabnam Erfani

Amy Lieh-An Huang

Edward Michael Doran

Megan Elizabeth Slater

Xiaoyu Chai

Jin Young Kim

Deepa Shenvi Priolkar

Aaron Nash Melhaff

Harald Becker

Dale R Johnson

Ioana Laura Marginas

Xinyi Zhang

Amy L Huang

Eleanor Ann Robinson

Xiao Cai

A system and method for facilitating convergence includes receiving a request to schedule a meeting at a meeting time, retrieve at least one of user data, contextual data, facility data and map data, the user data including a list of a plurality of meeting participants for the meeting, providing at least one of the list of the plurality of meeting participants, the meeting time, the user data, and the facility data to a trained machine-learning (ML) model for predicting a location at which two or more of the plurality of meeting participants will be located within a given time period prior to the meeting time, receiving the predicted location as an output from the trained ML model, and identifying a meeting location from among one or more meeting venues based on the predicted location of the two or more of the plurality of meeting participants, and providing the meeting location for display in a first selectable user interface element.

Method and system for facilitating convergence

A system and method for facilitating convergence includes receiving data relating to a first user's history of connections with other users, receiving contextual data about the first user, the contextual data including organizational graph data, providing the data and the contextual data as inputs to a trained machine-learning (ML) model for identifying a second user for the first user to connect with based on the data and the contextual data, receiving the identified second user as an output from the trained ML model, and providing the identified second user as a connection suggestion for display in a user interface element.

A method for friction reduction during professional network expansion is implemented via a computing system including a processor. The method includes executing, via a network, an enterprise application on a remote computing system operated by a user associated with an enterprise and surfacing a professional networking UI on a display of the remote computing system during execution of the enterprise application. The method includes generating parameters that are representative of a connection between the user and each suggested professional contact based on enterprise-level data corresponding to the user and each suggested professional contact and generating friction-reducing UI elements for each suggested professional contact based on the generated parameters. The method includes receiving, via the professional networking UI, user input including a command to open a contact connection page corresponding to one of the suggested professional contacts and surfacing the corresponding contact connection page including the generated friction-reducing UI elements.

Friction reduction during professional network expansion

A system and method for managing one or more physical spaces includes receiving data relating to location of a plurality of users, determining based on the location, an occupancy rate of the plurality of users for at least one of the one or more physical spaces, identifying based on the occupancy rate and features provided at the one or more physical spaces, one or more user preferred features, providing the occupancy rate and the user preferred features to a trained machine-learning (ML) model for determining optimal uses for the one or more physical spaces in a future time period, receiving as an output from the trained ML model suggested plans for use or management of the one or more physical spaces in the future time period, and providing the suggested plans for display in a user interface (UI) screen.

Method and system for intelligently managing facilities

Technologies are described for utilizing machine learning ('ML') to adjust operational characteristics of a computing system based upon detected HID activity. Labeled training data is collected with user consent that includes data describing HID activity and data that identifies user activity taking place on a computing device when the data HID activity took place. A ML model is trained using the labeled training data that can receive data describing current HID activity and identify user activity currently taking place on another computing device based upon the current HID activity. The ML model can then select features of the other computing device that are beneficial to the identified user activity. The ML model can then cause one or more operational characteristics of the other computing device to be adjusted based upon the identified user activity, thereby saving valuable computing resources. A UI can also be presented that describes the identified features.

Machine learning system for adjusting operational characteristics of a computing system based upon HID activity

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Redmond, WA, United States of America

Choo Yei Chong