Unsupervised model building for clustering and anomaly detection
US10373056B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 25, 2018 |
| Grant date | Aug 6, 2019 |
| Priority date | — |
| Expiry date | Jan 25, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/82
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
During training mode, first input data is provided to a first neural network to generate first output data indicating that the first input data is classified in a first cluster. The first input data includes at least one of a continuous feature or a categorical feature. Second input data is generated and provided to at least one second neural network to generate second output data. The at least one second neural network corresponds to a variational autoencoder. An aggregate loss corresponding to the second output data is determined, including at least one of evaluating a first loss function for the continuous feature or evaluating a second loss function for the categorical feature. Based on the aggregate loss, at least one parameter of at least one neural network is adjusted. During use mode, the neural networks are used to determine cluster identifications and anomaly likelihoods for received data samples.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.