Microsoft Technology Licensing, LLC

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Irene Rogan Shaffer

Marc Todd Friedman

Alekh Jindal

Hiren Shantilal Patel

Shi Qiao

Markus Weimer

Gilbert Antonius

Abhishek Roy

Lucas Rosenblatt

Peter Orenberg

Soundararajan Srinivasan

Vijay Kumar Ramani

Remmelt Herbert Lieve Ammerlaan

H M Sajjad Hossain

Vu Minh Le

Fang Liu

Vivek Gupta

Avrilia Floratou

Jordan Joseph Henkel

Joyce Yu Cahoon

Ehimwenma Nosakhare

Wee Hyong Tok

Jason Wang

Yan Qiu

Abhishek Narain

Gust Verbruggen

Noelle Yanhui Li

David Joseph Donahue

Daniel Muñoz Huerta

Richard Anarfi

Shaily Jignesh Fozdar

A computer-implemented method for performing natural language-based data integration includes causing execution of a data integration application on a remote device via a network and causing surfacing of a GUI corresponding to the data integration application on a display of the remote device. The method includes receiving, via the GUI, a natural language input representing a data integration task, generating, via an LLM, a set of ordered activities corresponding to the data integration task represented by the natural language input, and selecting, via the LLM, one or more APIs for performing each activity within the set of ordered activities. The method also includes generating a data pipeline based on the set of ordered activities and the API(s) for performing each activity, as well as back-translating the data pipeline to a desired data format for execution by the data integration application.

Natural language-based data integration

Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.

System and method for machine learning for system deployments without performance regressions

A method and apparatus for accurately interpreting American Sign Language (ASL) including extracting facial features from a detected face and identifying an ASL user using these features. The linguistic markers are extracted and compared with linguistic markers stored in an ASL emotions database. An accurate emotion associated with the linguistic markers is received and displayed on a user interface.

Method and apparatus to accurately interpret facial expressions in American Sign Language

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Cambridge, MA, United States of America

Irene Rogan Shaffer