Training a machine learning system for transaction data processing
US12118559B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 24, 2021 |
| Grant date | Oct 15, 2024 |
| Priority date | — |
| Expiry date | May 24, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q40/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method of training a supervised machine learning system to detect anomalies within transaction data is described. The method includes obtaining a training set of data samples; assigning a label indicating an absence of an anomaly to unlabelled data samples in the training set; partitioning the data of the data samples in the training set into two feature sets, a first feature set representing observable features and a second feature set representing context features; generating synthetic data samples by combining features from the two feature sets that respectively relate to two different uniquely identifiable entities; assigning a label indicating a presence of an anomaly to the synthetic data samples; augmenting the training set with the synthetic data samples; and training a supervised machine learning system with the augmented training set and the assigned labels.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.