Microsoft Technology Licensing, LLC

Stephen Michelotti

Michael Ebstyne

Kamran Rajabi Zargahi

Pedro Urbina Escos

Emanuel Shalev

Michael John Ebstyne

Various embodiments, methods and systems for implementing a distributed computing system crowdsourcing engine are provided. Initially, a source asset is received from a distributed synthetic data as a service (SDaaS) crowdsource interface. A crowdsource tag is received for the source asset via the distributed SDaaS crowdsource interface. Based in part on the crowdsource tag, the source asset is ingested. Ingesting the source asset comprises automatically computing values for asset-variation parameters of the source asset. The asset-variation parameters are programmable for machine-learning. A crowdsourced synthetic data asset comprising the values for asset-variation parameters is generated.

Distributed computing system with a crowdsourcing engine

Various embodiments, methods and systems for implementing a distributed computing system scene assembly engine are provided. Initially, a selection of a first synthetic data asset and a selection of a second synthetic data asset are received from a distributed synthetic data as a service (SDaaS) integrated development environment (IDE). A synthetic data asset is associated with asset-variation parameters and scene-variation parameters, the asset-variation parameters and scene-variation parameters are programmable for machine-learning. Values for generating a synthetic data scene are received. The values correspond to asset-variation parameters or scene-variation parameters. Based on the values, the synthetic data scene is generated using the first synthetic data asset and the second synthetic data asset.

Distributed computing system with a synthetic data as a service scene assembly engine

Various embodiments, methods and systems for implementing a distributed computing system feedback loop engine are provided. Initially, a training dataset report is accessed. The training dataset report identifies a synthetic data asset having values for asset-variation parameters. The synthetic data asset is associated with a frameset. Based on the training dataset report, the synthetic data asset with a synthetic data asset variation is updated. The frameset is updated using the updated synthetic data asset.

Distributed computing system with a synthetic data as a service feedback loop engine

Various embodiments, methods and systems for implementing a distributed computing frameset assembly engine are provided. Initially, a synthetic data scene is accessed. A first set of values for scene-variation parameters is determined. The first set of values is automatically determined for generating a synthetic data scene frameset. The synthetic data scene frameset is generated based on the first set of values. The synthetic data scene frameset comprises at least a first frame in the frameset comprising the synthetic data scene updated based on a value for a scene-variation parameter. The synthetic data scene frameset is stored.

Distributed computing system with a synthetic data as a service frameset assembly engine

Various embodiments, methods and systems for implementing a distributed computing system asset assembly engine are provided. Initially a first source asset is received from a first distributed Synthetic Data as a Service (SDaaS) upload interface. A second source asset is received from a second a distributed SDaaS upload interface. The first source asset and the second source asset are ingested. Ingesting a source asset comprises automatically computing values for asset-variation parameters of the source asset, where the asset-variation parameters are programmable for machine-learning. A first synthetic data asset comprising a first set of values for the asset-variation parameters is generated. A second synthetic data asset comprising a second set of values for the asset-variation parameters is generated. The first synthetic data asset and the second synthetic data asset in a synthetic data asset are stored.

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Ellicott City, MD, United States of America

Stephen Michelotti