Predictive model for anomaly detection and feedback-based scheduling
US9699049B2 · kind B2 · utility
40Cited by
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20Claims
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Key dates
| Filing date | Dec 30, 2014 |
| Grant date | Jul 4, 2017 |
| Priority date | — |
| Expiry date | May 28, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
In an example embodiment, clusters of nodes in a network are monitored. Then the monitored data may be stored in an open time-series database. Data from the open time-series database is collected and labeled it as training data. Then a model is built through machine learning using the training data. Additional data is retrieved from the open time-series database. The additional data is left as unlabeled. Anomalies in the unlabeled data are computed using the model, producing prediction outcomes and metrics. Finally, the prediction outcomes and the network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.