Multiple machine learning model anomaly detection framework
US12050628B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 6, 2023 |
| Grant date | Jul 30, 2024 |
| Priority date | — |
| Expiry date | Jul 6, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/2365
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Anomalies may be detected using a multiple machine learning model anomaly detection framework. A clustering model is trained using an unsupervised machine learning algorithm on a historical anomaly dataset. A plurality of clusters of records are determined by applying the historical anomaly dataset to the clustering model. Then it is determined whether each cluster of the plurality of clusters is an anomaly-type cluster or a normal-type cluster. The plurality of labels for the plurality of records are updated based on the particular record's cluster classification. Non-pure clusters are determined from among the plurality of clusters based on a purity threshold. A supervised machine learning model is trained for each of the non-pure clusters using the records in the given cluster and the labels for each of those records. Then, predictions of an anomaly are made using the clustering model and the supervised machine learning models.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.