Adapting simulation data to real-world conditions encountered by physical processes
US11679506B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 10, 2022 |
| Grant date | Jun 20, 2023 |
| Priority date | — |
| Expiry date | Mar 10, 2042 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02P90/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
One embodiment of the present invention sets forth a technique for generating simulated training data for a physical process. The technique includes receiving, as input to at least one machine learning model, a first simulated image of a first object, wherein the at least one machine learning model includes mappings between simulated images generated from models of physical objects and real-world images of the physical objects. The technique also includes performing, by the at least one machine learning model, one or more operations on the first simulated image to generate a first augmented image of the first object. The technique further includes transmitting the first augmented image to a training pipeline for an additional machine learning model that controls a behavior of the physical process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.