System and method for machine-learning-based position estimation for use in micro-assembly control with the aid of a digital computer
US11762348B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 21, 2021 |
| Grant date | Sep 19, 2023 |
| Priority date | — |
| Expiry date | Sep 20, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Control loop latency can be accounted for in predicting positions of micro-objects being moved by using a hybrid model that includes both at least one physics-based model and machine-learning models. The models are combined using gradient boosting, with a model created during at least one of the stages being fitted based on residuals calculated during a previous stage based on comparison to training data. The loss function for each stage is selected based on the model being created. The hybrid model is evaluated with data extrapolated and interpolated from the training data to prevent overfitting and ensure the hybrid model has sufficient predictive ability. By including both physics-based and machine-learning models, the hybrid model can account for both deterministic and stochastic components involved in the movement of the micro-objects, thus increasing the accuracy and throughput of the micro-assembly.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.