Techniques for generating a training data set for a machine-learning model
US12299581B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 26, 2021 |
| Grant date | May 13, 2025 |
| Priority date | — |
| Expiry date | Feb 28, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods are provided herein for generating a synthetic training data set that can be used to train a machine-learning model to identify when two addresses match (e.g., when a user-defined address and an authoritative address match). The addresses may each be tokenized. Each candidate address can be scored based on a number of common tokens it shares with the user-defined address. The highest-scored candidate address may be selected as a matching address for the user-defined address. In some embodiments, a number of the remaining candidate address can be selected as negative examples (e.g., candidate addresses that do not match the user-defined address) based on, for example, historical delivery information associated with the corresponding addresses. In this manner, an expansive training data set may be generated using addresses associated with user profiles of an online service provider and a set of authoritative addresses obtained from an authoritative source.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.