Machine learning-based part determinations for computer-aided design (CAD) assemblies
US11790128B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 3, 2020 |
| Grant date | Oct 17, 2023 |
| Priority date | — |
| Expiry date | Jan 15, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2210/12
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A computing system may include an assembly access engine configured to access a computer-aided design (CAD) assembly that digitally represents a product component that includes multiple parts. The computing system may also include a part determination engine configured to determine a recommended part for the CAD assembly, including by providing the CAD assembly as an input to a machine-learning (ML) model trained with assembly structure data of CAD assemblies of a common product type as the CAD assembly, generating a candidate part set through the ML model, filtering the candidate part set based on physical and cost characteristics of the different candidate parts of the candidate part set, and identifying the recommended part from the filtered candidate part set. The part recommendation engine may also be configured to insert the recommended part into the CAD assembly and provide the CAD assembly in support of physical manufacture.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.