Microsoft Technology Licensing, LLC

Jonathan Daniel Keech

Mark Alistair Wilson-Thomas

Kesavan Shanmugam

Vivian Julia Lim

Shengyu Fu

Simon Calvert

German David Obando Chacon

Srivatsn Narayanan

Ram Kumar Donthula

Ritesh Rambhai Parikh

Ryan Alexander Dawson

Sarika Calla

Neeraja Reddy Singireddy

Deniz Duncan

Mark A Wilson-thomas

Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Providing custom machine-learning models

Access control enhancements reduce security risks and management burdens when a user with access to a primary asset seeks access to a related supplementary asset. When a sufficient proof of access to the primary asset is provided, and the relationship of the primary and supplementary assets is recognized, access to the supplementary asset is granted without requiring a separate sign-in, a permission query to the supplementary asset's owner, or an authorization through an authenticated identity of the requestor, for example. Automatic access to the supplementary asset can be granted without the security risks inherent in a file share or a share link. In particular, a developer with access to one component of a project can be automatically and conveniently granted access to the rest of the project. Likewise, a custom machine learning model for autocompletion becomes accessible to all developers working on the repository source used to train the model.

Related asset access based on proven primary asset access

Improving the results and process of machine learning service in computer program development. A client's codebase is accessed. A set of features are extracted from the client's codebase. One or more features from the set of features are then selected. Thereafter, at least one of the selected features is sent to a machine learning service that uses the received feature(s) to build custom model(s) for the client's computer system.

Extracting program features for assisting software development

Systems and methods for sending in-product notifications to individual users of a software product or a specifically identified subset of users of the software product selected via their previously observed interactions with the software product. In addition, targeted notifications of bug fixes can be sent to specific users who have encountered an error condition or performance issue that a particular bug fix is designed to correct.

Targeted user notification of bug fixes

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Kirkland, WA, United States of America

Jonathan Daniel Keech