Microsoft Technology Licensing, LLC

Neha Parikh Shah

Wende E Copfer

Venkata Sreekanth Kannepalli

Choo Yei Chong

Christopher Michael Dollar

Heidi Kenyon

Aaron James Harrison

Shabnam Erfani

Amit Prem Manghani

Amy Lieh-An Huang

Edward Michael Doran

Megan Elizabeth Slater

Aleksey Ashikhmin

Tannaz Sattari Tabrizi

Amol Dattatray Dhaygude

Sunil Kumar

Sanjay H Ramaswamy

Sreeram Nivarthi

Jin Young Kim

Vindana Madhuwantha

Jagadeesh Virupaksha Huliyar

Manjit Singh Gill

Christopher Michael Trevino

Nishtha Mehrotra

Neha Choudhary

Carlos Andres Morales Torrado

Deepa Shenvi Priolkar

Sai Sumana Pagidipalli

Aaron Nash Melhaff

Robert Allen Donahue

Harald Becker

Diego Fernando Suarez Castillo

Sida Peng

Wilson Waikon Ung

Amy L Huang

Nikolay Mitev Trandev

V S Srujana Oruganti

Nimesh Ravindra Desai

Ritu Lakhotia

Apurva Bharatkumar Shah

Ganesh Jaiswal

Siddarth Rajendra Kumar

Vishnu Gajendran

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 configuring an application is described. The system identifies user collaboration data of users of an application. The user collaboration data indicates durations of collaborations between one or more users using the application. The system computes an influencer score of a first user based on the duration of collaborations between the first user and the other users. The system determines that the influencer score of the first user exceeds an influencer score threshold and generates a configuration setting of the application for the first user. The system applies the configuration setting to the application for the first user.

Adoption content propagation based on user collaboration data

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

A system and method for identifying a user is described. The system identifies collaboration metrics based on user interaction data of users of an application from an enterprise. The system accesses enterprise organizational data of the enterprise and identifies topic data from the user interaction data and the enterprise organizational data. The system trains a machine learning model based on the collaboration metrics, the enterprise organizational data, and the topic data.

User connector based on organization graph

A method may include presenting a graphical user interface (GUI) configured to define a network for analysis, the GUI including: a first portion configured to define a network boundary of an organizational network graph (ONG) in a dataset; a second portion configured to define a beginning time and ending time for the network analysis; and a third portion configured to define a set of one or more graph metrics for vertices in the ONG; retrieving a portion of the dataset based on the network boundary, beginning time, and ending time; transmitting instructions to a distributed computing platform to generate the set of graph metrics on the portion of the dataset; and storing the generated set of graph metrics as associated with the network analysis.

System for offline processing of organizational networks

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New York, NY, United States of America

Neha Parikh Shah