Microsoft Technology Licensing, LLC

Aleksey Ashikhmin

Sreeram Nivarthi

Aaron James Harrison

Sanjay H Ramaswamy

Neha Parikh Shah

Amit Prem Manghani

Vindana Madhuwantha

Jagadeesh Virupaksha Huliyar

Sunil Kumar

Sergey M Yekhanin

Di Li

Dheepak Ramaswamy

Muthukrishnan Paramasivam

Ananthatejas Raghavan

Janardhan Dattatreya Kulkarni

Sivakanth Gopi

Amol Dattatray Dhaygude

Umashree Narayanaswamy

Tannaz Sattari Tabrizi

Apurva Bharatkumar Shah

Christopher Michael Trevino

V S Srujana Oruganti

Manjit Singh Gill

Andrew C Owen

Ritu Lakhotia

Ganesh Jaiswal

Vishnu Gajendran

Xinyun Sun

Sai Sumana Pagidipalli

Nishtha Mehrotra

Carlos Andres Morales Torrado

Siddarth Rajendra Kumar

Eric Radtke

Sida Peng

Omar Mustafa

Nimesh Ravindra Desai

Sriradha Selvaraj

Diego Fernando Suarez Castillo

Brian Quistorff

Neha Choudhary

Bingran Luo

Wilson Waikon Ung

Anjaneya Malpani

Robert Allen Donahue

Nikolay Mitev Trandev

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 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 system and method for applying noise to data is described. The system accesses a metric value of a metric of each user from a group of users of an application. The metric indicates a measure of an operation of the application by a corresponding user. The system generates noise values and defines a distribution of the noise values to the group of users. The system modifies the metric value of the metric of each user with a corresponding noise value from the noise values based on the distribution.

Noise generation for differential privacy

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

Growing community of inventors

Redmond, WA, United States of America

Aleksey Ashikhmin