Microsoft Technology Licensing, LLC

General Cable Technologies Corporation

Shaun Miller

Neelakantan Sundaresan

Roshanak Zilouchian Moghaddam

Shibani Basava

Xin Shi

Ram Kumar Donthula

Dinesh Chandnani

Brian W Boon

Zhu Chen

Vijaya K Upadya

Andrew Christopher Neil

Kalpathy Sitaraman Sivaraman

Matthew Sloan Theodore Evans

Yijin Wei

Geoffrey Staneff

Evgenia Steshenko

Carl Brochu

Scott M Brown

Alice C Albrinck

Matthew S Mclinn

Rob S Gould

Richard Chamberlain

A rule-based attribution mechanism analyzes documents having different types of data in different formats through the application of script-based rules that apply a tag to the document identifying the type of sensitive data that is contained in the document. Documents having similar tags are aggregated so that the sensitive data is scrubbed from the document leaving the telemetric data available for downstream processing. The scrubbing entails different actions, such as, eliminating the sensitive data, obfuscating the sensitive data, and converting the sensitive data into a non-sensitive value.

Rule-based document scrubbing of sensitive data

Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.

Detection of runtime errors using machine learning

Growing community of inventors

Sammamish, WA, United States of America

Shaun Miller