Name matching engine boosted by machine learning
US12079282B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 10, 2020 |
| Grant date | Sep 3, 2024 |
| Priority date | — |
| Expiry date | Apr 6, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are described herein for a Name Matching Engine that integrates two Machine Learning (ML) module options. The first ML module is a feature-engineered classifier that boosts text-based name matching techniques with a binary classifier ML model. The feature-engineered classifier comprises a first stage of text-based candidate finding, and a second stage in which a binary classifier model predicts whether each string, of the candidate match list, is a match or not. The binary classifier model is based on features from two or more of: a name feature level, a word feature level, a character feature level, and an initial feature level. The second ML module of the Name Matching Engine comprises an end-to-end Recurrent Neural Network (RNN) model that directly accepts name strings as a sequence of n-grams and generates learned text embeddings. The text embeddings of matching name strings are close to each other in the feature space.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.