Teaching a machine classifier to recognize a new class
US11995403B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 11, 2021 |
| Grant date | May 28, 2024 |
| Priority date | — |
| Expiry date | Oct 8, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the technology described herein describe a machine classifier capable of continually learning new classes through a continual few-shot learning approach. A natural language processing (NLP) machine classifier may initially be trained to identify a plurality of other classes through a conventional training process. In order to learn a new class, natural-language training data for a new class is generated. The training data for the new class may be few-shot training data. The training also uses synthetic training data that represents each of the plurality of other classes. The synthetic training data may be generated through a model inversion of the original classifier. The synthetic training data and the natural-language training data are used to retrain the NLP classifier to identify text in the plurality of other classes and the new class using.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.