Data model generation using generative adversarial networks
US10460235B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 4, 2018 |
| Grant date | Oct 29, 2019 |
| Priority date | — |
| Expiry date | Oct 4, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.