公开(公告)号：US12299581B1

公开(公告)日：2025-05-13

申请号：US17214448

申请日：2021-03-26

Applicant: Amazon Technologies, Inc.

Inventor： Patrick Ian Wilson ,  Dmitry Vladimir Zhiyanov ,  Lichao Wang ,  Jong Wan Kim ,  Srinivas K Yellala

IPC: G06N20/00 ,  G06F7/24 ,  G06N3/088

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.