# US 10,877,927 B2 · Distributed computing system with a synthetic data as a service asset assembly engine

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 10,877,927 B2
- Document ID: US10877927B2
- Status: Granted
- Kind: B2
- Application number: 15/995,114
- Filed: 2018-05-31
- Published: 2020-11-26
- Granted: 2020-12-29
- Earliest claimed: 2018-05-31
- Assignees: Microsofttechnology Licensing, LLC
- Applicants: Microsoft Technology Licensing, LLC
- Inventors: Kamran Zargahi; Michael John Ebstyne; Pedro Urbina Escos; Stephen Michelotti; Emanuel Shalev
- Web page: https://patentgrep.com/patent/US10877927B2

## CPC

- G06F 16/113: {Details of archiving (lifecycle management in storage systems G06F3/0649; point-in-time backing up or restoration of persistent data G06F11/1446)}
- G06Q 50/00: Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism (healthcare informatics G16H)
- G06F 8/33: Intelligent editors
- G06F 9/5077: {Logical partitioning of resources; Management or configuration of virtualized resources (specific details on emulation or internal functioning of virtual machines G06F9/455)}
- G06F 16/164: {File meta data generation}
- G06F 16/2458: Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06N 3/045: Combinations of networks
- G06N 3/047: Probabilistic or stochastic networks
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 20/00: Machine learning
- G06N 3/063: using electronic means
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

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.

## Claims

### Claim 1

A system for implementing a distributed computing system asset assembly engine, the system comprising:
one or more hardware computer processors; and
computer memory storing computer-useable instructions, that when used by the one or more computer processors, cause the one or more hardware computer processors to preform operations comprising:
an asset assembly engine configured to:
receive a first source asset from a first distributed Synthetic Data as a Service (SDaaS) upload interface of a synthetic data as a service (SDaaS) integrated development environment (IDE) associated with both SDaaS distributed computing service operations and SDaaS machine-learning training service operations that are part of a service-oriented architecture of an SDaaS service, wherein the service-oriented architecture abstracts underlying the SDaaS distributed computing service operations that are managed via the SDaaS service from the SDaaS machine-learning training service operations to provide machine-learning training services using the distributed computing system asset assembly engine;
receive a second source asset from a second distributed SDaaS upload interface;
ingest the first source asset and the second source asset, wherein ingesting a source asset comprises automatically computing values for asset-variation parameters of the source asset, wherein the asset-variation parameters are programmable for machine-learning;
generate a first synthetic data asset comprising a first set of values for the asset-variation parameters;
generate a second synthetic data asset comprising a second set of values for the asset-variation parameters; and
store the first synthetic data asset and the second synthetic data asset in a synthetic data asset store.

### Claim 2 (depends on 1)

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

### Claim 3 (depends on 1)

The system of claim 1, 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.

### Claim 4 (depends on 1)

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.

### Claim 5 (depends on 1)

The method of claim 1, wherein at least one SDaaS distributed computing service operation, managed via the SDaaS service, supports distributed computing availability of synthetic data assets, wherein the at least one SDaaS distributed computing service operation is different from the SDaaS machine-learning training service operations.

### Claim 6 (depends on 1)

The system of claim 1, wherein ingesting the source asset comprises further comprises automatically computing values for scene-variation parameters of the source asset, wherein the scene-variation parameters are programmable for machine-learning.

### Claim 7 (depends on 1)

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

### Claim 8

One or more computer-readable media storing instructions thereon for implementing a distributed computing system asset assembly engine, which, when executed by one or more processors of a computing device cause the computing device to perform actions comprising:
receiving a first source asset from a first distributed Synthetic Data as a Service (SDaaS) upload interface of a synthetic data as a service (SDaaS) integrated development environment (IDE) associated with both SDaaS distributed computing service operations and SDaaS machine-learning training service operations that are part of a service-oriented architecture of an SDaaS service, wherein the service-oriented architecture abstracts underlying the SDaaS distributed computing service operations that are managed via the SDaaS service from the SDaaS machine-learning training service operations to provide machine-learning training services using the distributed computing system asset assembly engine;
receiving a second source asset from a second distributed SDaaS upload interface;
ingesting the first source asset and the second source asset, wherein ingesting a source asset comprises automatically computing values for asset-variation parameters of the source asset, wherein the asset-variation parameters are programmable for machine-learning;
generating a first synthetic data asset comprising a first set of values for the asset-variation parameters;
generating a second synthetic data asset comprising a second set of values for the asset-variation parameters; and
storing the first synthetic data asset and the second synthetic data asset in a synthetic data asset store.

### Claim 9 (depends on 8)

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

### Claim 10 (depends on 8)

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.

### Claim 11 (depends on 8)

The media of claim 8, wherein ingesting source assets is based on a machine-learning synthetic data standard comprising a file format and a dataset-training architecture.

### Claim 12 (depends on 8)

The media of claim 8, wherein ingesting the source asset further comprises automatically computing values for scene-variation parameters of the source asset, wherein the scene-variation parameters are programmable for machine-learning.

### Claim 13 (depends on 8)

The media of claim 8, the actions further comprising generating a synthetic data scene based on the first synthetic data asset and the second synthetic data asset.

### Claim 14 (depends on 8)

The media of claim 8, wherein a synthetic data store data structure for the synthetic data assets integrates with a machine-learning training service for automatic retrieval of synthetic data assets and synthetic data scenes.

### Claim 15

A computer-implemented method for implementing a distributed computing system asset assembly engine, the method comprising:
receiving a source asset from a distributed Synthetic Data as a Service (SDaaS) upload interface of a synthetic data as a service (SDaaS) integrated development environment (IDE) associated with both SDaaS distributed computing service operations and SDaaS machine-learning training service operations that are part of a service-oriented architecture of an SDaaS service, wherein the service-oriented architecture abstracts underlying the SDaaS distributed computing service operations that are managed via the SDaaS service from the SDaaS machine-learning training service operations to provide machine-learning training services using the distributed computing system asset assembly engine;
ingesting 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 synthetic data asset comprising the values for the asset-variation parameters.

### Claim 16 (depends on 15)

The method of claim 15, wherein the values are associated with generating training datasets 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.

### Claim 17 (depends on 15)

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.

### Claim 18 (depends on 15)

The method of claim 15, wherein ingesting the source asset further comprises automatically computing values for scene-variation parameters of the source asset, wherein the scene-variation parameters are programmable for machine-learning.

### Claim 19 (depends on 15)

The method of claim 15, the method further comprising generating a synthetic data scene based on the first synthetic data asset and the second synthetic data asset.

### Claim 20 (depends on 15)

The method of claim 15, the method further comprising:
storing the synthetic data asset in an archive format file, wherein the archive format file stores the values of the asset-variation parameters; and
communicating the synthetic data asset to an integrated machine-learning training algorithm.