Dual model incremental learning
US11727269B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 8, 2020 |
| Grant date | Aug 15, 2023 |
| Priority date | — |
| Expiry date | Jun 21, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In an approach to efficient model adjustment utilizing a dual model system, one or more computer processors create a subset of a dataset utilizing a trained primary model; create a secondary model based on the created subset of the dataset; calculate a confidence of a case utilizing the trained primary model, wherein the confidence is a robustness indicator of a model indicating a capacity of the model to meet or exceed performance when applied to the dataset; responsive to the calculated confidence not exceeding a confidence threshold, generate a score of the case utilizing the created secondary model; responsive to an incorrect classification, update the created subset of the dataset with the case; retrain the secondary model utilizing the updated subset of the dataset.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.