Microsoft Technology Licensing, LLC

Princeton University

Siyu Yang

Neelakantan Sundaresan

Shengyu Fu

Barry P Rand

Michael Fusella

Roshanak Zilouchian Moghaddam

Ying Zhao

Jorge Banuelos

Sai Tulasi Neppali

A distributed sequential pattern data mining framework mines user data to determine statistically-relevant sequential patterns which are used to correlate the sequential patterns to a particular outcome. The correlation is provided by a statistical model, a binary predictive model and/or a logistic regression model which uses the sequential patterns to learn the behavior of end users during their usage of a software application.

Distributed sequential pattern data mining framework

A code completion tool uses machine learning models to more precisely predict the likelihood of a method invocation completing a code fragment that follows one or more method invocations of a same class in a same document during program development. In one aspect, the machine learning model is a n-order Markov chain model that is trained on features that represent characteristics of the context of method invocations of a class in commonly-used programs from a sampled population.

Code completion with machine learning

Measurements on organic single crystals reveal remarkable optical and electrical characteristics compared to disordered films but practical device applications require uniform, pinhole-free films. Disclosed herein is a process to reliably convert as-deposited amorphous thin films to ones that are highly crystalline, with grains on the order of hundreds of microns. The disclosed method results in films that are pinhole-free and that possess grains that individually are single crystal domains.

Method and device for using an organic underlayer to enable crystallization of disordered organic thin films

Growing community of inventors

Redmond, WA, United States of America

Siyu Yang