Microsoft Technology Licensing, LLC

Kateryna Lytvynets

Christopher M White

Darren Keith Edge

Weiwei Yang

Amber Dawn Hoak

Techniques for synthesizing and analyzing data are disclosed. A ML model anonymizes microdata to generate synthesized data. This anonymizing is performed by reproducing attributes identified within microdata and by applying constraints to prevent rare attribute combinations from being reproduced in the synthesized data. User input selects attributes to filter the synthesized data, thereby generating a subset of records. A UI displays a synthesized aggregate count representing how many records are in the subset. Pre-computed aggregate counts are accessed to indicate how many records in the microdata embody certain attributes. Based on the user input, there is an attempt to identify a particular count from the pre-computed aggregate counts. This count reflects how many records of the microdata would remain if the selected attributes were used to filter the microdata. That count is displayed along with the synthesized aggregate count. The two counts are juxtaposed next to one another.

Privacy-preserving data platform

A system and method for managing operation of electrical devices includes a control module that monitors status of multiple sources of electrical power to one or more electrical devices and electrical usage of the one or more electrical devices that receive electricity from the source of electrical power. The operation of the one or more electrical devices is managed using a machine learning model that forecasts status of the at least one source of electrical power and generates operational rules for the one or more electrical devices from historical values of control parameters of the one or more electrical devices, the status of the source of electrical power, and the electrical usage of the one or more electrical devices. The system may optimize renewable energy utilization, power grid stabilization, cost of electrical power usage, and the like.

Power grid aware machine learning device

A system and method for regulating charge/discharge of a battery to stabilize a regional power grid includes a regulation control module that monitors a frequency regulation signal from the regional power grid and market conditions for obtaining power from the regional power grid. A machine learning module predicts from the frequency regulation signal and market conditions a future beneficial period when the battery may be exposed to the regional power grid to charge/discharge power in accordance with the frequency regulation signal to stabilize the regional power grid through participation in the regulation of the regional power grid. Another machine learning module calculates a regulation control signal that tracks the frequency regulation signal during the future beneficial period and outputs the regulation control signal to at least one battery control module that manages charging/discharging of the battery to selectively withdraw/apply power from/to the regional power grid during the future beneficial period.

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Redmond, WA, United States of America

Kateryna Lytvynets