Distributed learning anomaly detector
US12067489B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 2, 2021 |
| Grant date | Aug 20, 2024 |
| Priority date | — |
| Expiry date | Mar 22, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L41/0806
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In a network discovery and management system, a machine learning (ML) DLAD processor trains, validates, updates, and stores machine learning models. A ML training data preparation program performs operations to process and format input data to generate ML training data that can be used to train ML models. ML training program uses the ML training data to train ML models, thereby generating trained ML models. The ML training program can re-train or update the training of ML models as the system collects additional data and produces additional estimates, predictions, and forecasts. ML model validation program performs validation testing on trained ML models to generate one or more metrics that can indicate accuracy of predictions generated by the trained models. The resulting ML model(s) can be used to manage the network including but not limited to retrieve, instantiate and execute dynamic applications based on predictions made based on the models.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.