Continuous learning models across edge hierarchies
US12380362B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 29, 2021 |
| Grant date | Aug 5, 2025 |
| Priority date | — |
| Expiry date | Apr 27, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/04
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods are provided for continuous learning of models across hierarchies under a multi-access edge computing. In particular, an on-premises edge server, using a model, generates inference data associated with captured stream data. A data drift determiner determines a data drift in the inference data by comparing the data against reference data generated using a golden model. The data drift indicates a loss of accuracy in the inference data. A gateway model maintains one or more models in a model cache for update the model. The gateway model instructs the one or more servers to train the new model. The gateway model transmits the trained model to update the model in the on-premises edge server. Training the new model includes determining an on-premises edge server with computing resources available to train the new model while generating other inference data for incoming stream data in the data analytic pipeline.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.