Microsoft Technology Licensing, LLC

Ehimwenma Nosakhare

Vivek Gupta

Mawulolo Koku Ameko

Wee Hyong Tok

Jyh-Han Lin

Vu Minh Le

Fang Liu

Avrilia Floratou

Jenna Hong

Irene Rogan Shaffer

Mark A Kromer

Jordan Joseph Henkel

Joyce Yu Cahoon

Abhishek Uday Kumar Shah

Sean Gormley T Kelley

Jason Wang

Ashish Neupane

Kabir Walia

Abhishek Narain

Yanjie He

Gust Verbruggen

Noelle Yanhui Li

David Joseph Donahue

Daniel Muñoz Huerta

Andrea Treviño Gavito

Richard Anarfi

Shaily Jignesh Fozdar

Yan Qiu

A method for recommending a computer hardware configuration, including: receiving, by a processor, a machine-readable specification of a computing task; extracting, by the processor, a plurality of features from the machine-readable specification of the computing task; supplying, by the processor, the plurality of features to a reinforcement learning model to generate a proposed computer hardware configuration to execute the computing task; and providing, by the processor, the proposed computer hardware configuration to a user.

Automatic selection of computer hardware configuration for data processing pipelines

A computer-implemented method for proactively detecting and remediating anomalous devices includes accessing, via a network, device attributes corresponding to enterprise devices within an enterprise network, providing the device attributes to a supervised machine learning model, and predicting, via the supervised machine learning model, whether each enterprise device is healthy or anomalous, where the enterprise device is predicted to be healthy unless the supervised machine learning model determines that the probability of the enterprise device being anomalous exceeds a specified confidence threshold. The method includes, for each enterprise device that is predicted to be anomalous, perturbing a portion of the corresponding device attributes via an automated counterfactual generator to generate synthetic data representative of counterfactual healthy devices. The method includes generating recommended remedial action(s) that will cause each enterprise device to approximate each counterfactual healthy device and causing surfacing, via a user interface, of the recommended remedial action(s).

Proactively detecting and remediating anomalous devices using supervised machine learning model and automated counterfactual generator

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

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Newton, MA, United States of America

Ehimwenma Nosakhare