Shared learning across separate entities with private data features
US11989633B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 25, 2019 |
| Grant date | May 21, 2024 |
| Priority date | — |
| Expiry date | May 1, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments herein use transfer learning paradigms to facilitate classification across entities without requiring the entities access to the other party's sensitive data. In one or more embodiments, one entity may train a model using its own data (which may include at least some non-shared data) and shares either the scores (or an intermediate representation of the scores). One or more other parties may use the scores as a feature in its own model. The scores may be considered to act as an embedding of the features but do not reveal the features. In other embodiments, parties may be used to train part of a model or participate in generating one or more nodes of a decision tree without revealing all its features. The trained models or decision trees may then be used for classifying unlabeled events or items.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.