Microsoft Technology Licensing, LLC

Amy Lieh-An Huang

Neha Parikh Shah

Wende E Copfer

Choo Yei Chong

Venkata Sreekanth Kannepalli

Heidi Kenyon

Christopher Michael Dollar

Shabnam Erfani

Edward Michael Doran

Megan Elizabeth Slater

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 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

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Seattle, WA, United States of America

Amy Lieh-An Huang