Techniques for analyzing vehicle design deviations using deep learning with neural networks
US11468292B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 22, 2019 |
| Grant date | Oct 11, 2022 |
| Priority date | — |
| Expiry date | Jun 5, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A design application is configured to generate a latent space representation of a fleet of pre-existing vehicles. The design application encodes vehicle designs associated with the fleet of pre-existing vehicles into the latent space representation to generate a first latent space location. The first latent space location represents the characteristic style associated with the fleet of pre-existing vehicles. The design application encodes a sample design provided by a user into the latent space representation to produce a second latent space location. The design application then determines a distance between the first latent space location and the second latent space location. Based on the distance, the design application generates a style metric that indicates the aesthetic similarity between the sample design and the vehicle designs associated with the fleet of pre-existing vehicles. The design application can also generate new vehicle designs based on the latent space representation and the sample design.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.