Address normalization using deep learning and address feature vectors
US10839156B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 3, 2019 |
| Grant date | Nov 17, 2020 |
| Priority date | — |
| Expiry date | May 23, 2039 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L67/52
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Generally described, one or more aspects of the present application correspond to a machine learning address normalization system. A system of deep learning networks can normalize the tokens of a free-form address into an address component hierarchy. Feature vectors representing various characters and words of the address tokens can be input into a bi-directional long short term memory network (LSTM) to generate a hidden state representation of each token, which can be individually passed through a softmax layer to generate probabilistic values of the token being each of the components in the address hierarchy. Thereafter, a conditional random field (CRF) model can select a particular address component for each token by using learned parameters to optimize a path through the collective outputs of the softmax layer for the tokens. Thus, the free-form address can be normalized to determine the values it contains for different components of a specified address hierarchy.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.