Anomalous text detection and entity identification using exploration-exploitation and pre-trained language models
US12299543B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 11, 2020 |
| Grant date | May 13, 2025 |
| Priority date | — |
| Expiry date | Jan 16, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/045
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
There is a need for more effective and efficient anomalous text detection. This need can be addressed by, for example, solutions for anomalous text detection that include the steps of performing a group of exploration-exploitation keyword extraction iterations based at least in part on one or more training corpus data entries until a per-iteration keyword list for an ultimate exploration-exploitation keyword extraction iteration satisfies a keyword list threshold condition; and subsequent to performing the exploration-exploitation keyword extraction iterations: processing one or more input corpus data entries using the language-model-based binary classification model to generate one or more inferred anomaly probabilities, processing the one or more input corpus data entries using the keyword model to generate explanatory metadata for the one or more inferred anomaly probabilities, and performing one or more prediction-based actions based at least in part on the one or more inferred anomaly probabilities and the explanatory metadata.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.