Distributed computing system with a crowdsourcing engine

Patent metadata

Field	Value
Publication number	US-11615137-B2
Application number	US-201815995124-A
Country	US
Kind code	B2
Filing date	May 31, 2018
Priority date	May 31, 2018
Publication date	Mar 28, 2023
Grant date	Mar 28, 2023

Abstract

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.

First claim

The invention claimed is:

1. A system for implementing a distributed computing system crowdsourcing engine, the system comprising:
one or more computer processors; and
computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to execute:
a crowdsourcing engine configured to:
receive a source asset from a distributed synthetic data as a service (SDaaS) crowdsource interface;
receive a crowdsource tag for the source asset via the distributed SDaaS crowdsource interface, wherein the distributed SDaaS is associated with a synthetic data as a service (SDaaS) integrated development environment (IDE) that supports both SDaaS distributed computing service operations and SDaaS machine-learning training service operations,
wherein the SDaaS distributed computing service operations are based on a service-oriented architecture that supports the SDaaS machine-learning training service operations while abstracting underlying SDaaS distributed computing service operations that are managed via an SDaaS distributed computing service,
wherein the SDaaS distributed computer service operations comprise storing and retrieving source assets at varying levels of detail for performing the SDaaS machine-learning service operations on the source assets;
based in part on the crowdsource tag, ingest the source asset, wherein ingesting the source asset comprises automatically computing values for asset-variation parameters of the source asset, wherein the asset-variation parameters are programmable for machine-learning; and
generate a crowdsourced synthetic data asset comprising the values for the asset-variation parameters.

2. The system of claim 1 , wherein the SDaaS IDE supports identifying additional asset-variation parameters for source assets.

3. The system of claim 1 , wherein the values are associated with generating training datasets based on intrinsic-parameter variation and extrinsic-parameter variation, wherein intrinsic-parameter variation and extrinsic-parameter variation provide programmable machine-learning data representations of assets and scenes.

4. The system of claim 1 , wherein ingesting source assets is based on a machine-learning synthetic data standard comprising a file format and a dataset-training architecture.

5. The system of claim 1 , the crowdsourcing engine further configured to compute a value quantifier for the crowdsourced synthetic data asset.

6. The system of claim 1 , the crowdsourcing engine further configured to generate a crowdsourced synthetic data asset profile comprising asset-variation parameters.

7. The system of claim 1 , wherein the crowdsourced synthetic data asset is stored as an archive format file, wherein the archive format file stores the values of the asset-variation parameters.

8. One or more computer storage media storing instructions thereon for implementing a distributed computing system crowdsourcing engine, which, when executed by one or more processors of a computing device cause the computing device to perform actions comprising:
receiving a source asset from a distributed synthetic data as a service (SDaaS) crowdsource interface;
receiving a crowdsource tag for the source asset via the distributed SDaaS crowdsource interface, wherein the distributed SDaaS is associated with a synthetic data as a service (SDaaS) integrated development environment (IDE) that supports both SDaaS distributed computing service operations and SDaaS machine-learning training service operations,
wherein the SDaaS distributed computing service operations are based on a service-oriented architecture that supports the SDaaS machine-learning training service operations while abstracting underlying SDaaS distributed computing service operations that are managed via an SDaaS distributed computing service,
wherein the SDaaS distributed computer service operations comprise storing and retrieving source assets at varying levels of detail for performing the SDaaS machine-learning service operations on the source assets;
based in part on the crowdsource tag, ingest the source asset, wherein ingesting the source asset comprises automatically computing values for asset-variation parameters of the source asset, wherein the asset-variation parameters are programmable for machine-learning; and
generating a crowdsourced synthetic data asset having the asset-variation parameters.

9. The media of claim 8 , wherein the SDaaS IDE supports identifying additional asset-variation parameters for source assets.

10. The media of claim 8 , wherein the values are associated with generating training datasets based on intrinsic-parameter variation and extrinsic-parameter variation, wherein the intrinsic-parameter variation and the extrinsic-parameter variation provide programmable machine-learning data representations of assets and scenes.

11. The media of claim 8 , wherein the actions further comprise computing a value quantifier for the crowdsourced synthetic data asset.

12. The media of claim 8 , wherein the actions further comprise generating a synthetic data scene based on the crowdsourced synthetic data asset.

13. The media of claim 8 , wherein the actions further comprise generating a crowdsourced synthetic data asset profile comprising the asset-variation parameters.

14. The media of claim 8 , wherein the crowdsourced synthetic data asset is stored as an archive format file, wherein the archive format file stores the values of the asset-variation parameters.

15. A computer-implemented method for implementing a distributed computing system crowdsourcing engine, the method comprising:
receiving a source asset from a distributed synthetic data as a service (SDaaS) crowdsource interface;
receiving a crowdsource tag for the source asset via the distributed SDaaS crowdsource interface, wherein the distributed SDaaS is associated with a synthetic data as a service (SDaaS) integrated development environment (IDE) that supports both SDaaS distributed computing service operations and SDaaS machine-learning training service operations,
wherein the SDaaS distributed computing service operations are based on a service-oriented architecture that supports the SDaaS machine-learning training service operations while abstracting underlying SDaaS distributed computing service operations that are managed via an SDaaS distributed computing service,
wherein the SDaaS distributed computer service operations comprise storing and retrieving source assets at varying levels of detail for performing the SDaaS machine-learning service operations on the source assets;
based in part on the crowdsource tag, ingest the source asset, wherein ingesting the source asset comprises automatically computing values for asset-variation parameters of the source asset, wherein the asset-variation parameters are programmable for machine-learning; and
generating a crowdsourced synthetic data asset having the asset-variation parameters.

16. The method of claim 15 , wherein the values are associated with generating training datasets based on intrinsic-parameter variation and extrinsic-parameter variation, wherein the intrinsic-parameter variation and the extrinsic-parameter variation provide programmable machine-learning data representations of assets and scenes.

17. The method of claim 15 , wherein ingesting source assets is based on a machine-learning synthetic data standard comprising a file format and a dataset-training architecture.

18. The method of claim 15 , the method further comprising computing a

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Zargahi Kamran
• Ebstyne Michael John
• Escos Pedro Urbina
• Michelotti Stephen

Classifications

• G06N3/0475

  Generative networks · CPC title

• G06N3/094

  Adversarial learning · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title