Microsoft Technology Licensing, LLC

Vindana Madhuwantha

Sanjay H Ramaswamy

Sreeram Nivarthi

Aaron James Harrison

Aleksey Ashikhmin

Umashree Narayanaswamy

Tannaz Sattari Tabrizi

Sunil Kumar

Di Li

Dheepak Ramaswamy

Neha Parikh Shah

Amit Prem Manghani

Marilyn Nicole Harris

Jagadeesh Virupaksha Huliyar

Apurva Bharatkumar Shah

Andrew C Owen

V S Srujana Oruganti

Eric Radtke

Nimesh Ravindra Desai

Omar Mustafa

Navid Ahmed Tahiri

Ritu Lakhotia

Ganesh Jaiswal

Siddarth Rajendra Kumar

Vishnu Gajendran

Sai Sumana Pagidipalli

Nishtha Mehrotra

Andrei Catalin Zlati

Diego Fernando Suarez Castillo

Carlos Andres Morales Torrado

Pramod Dhandapani

Brian Quistorff

Sida Peng

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 validating user profile data from a directory application is described. The system accesses user collaboration data of a plurality of users of an application. The system also accesses a directory application that manages user profile data for each user of the plurality of users. A set of heuristics is applied on the user profile data. The system validates results of the applied set of heuristics on the user profile data with the user collaboration data. The quality of the user profile data is assessed based on the validation.

Assessing quality of an active directory

A method may include retrieving metric data on a plurality of groups of users, the metric data including: a value of a performance metric for each of the plurality of groups; and an indication that a first group of the plurality of groups is anomalous with respect to a value of the performance metric of a control group of the plurality of groups; and presenting a user interface, the user interface including: a first portion including a visualization of a comparison of the value of the performance metric for the first group and values of the performance metric of other groups in the plurality of groups; and a second portion including a visualization of trend data of the performance metric for the first group over a period of time.

System for identification of outlier groups

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Bothell, WA, United States of America

Vindana Madhuwantha