公开(公告)号：US12282486B2

公开(公告)日：2025-04-22

申请号：US17733011

申请日：2022-04-29

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Iraklis Psaroudakis ,  Giulia Carocari ,  Andrea Ziani ,  Miroslav Cepek

IPC: G06F16/2457 ,  G06F16/2458 ,  G06F16/29 ,  G06N3/08 ,  G06N3/10

Abstract: Techniques are described herein for address matching from a single address string to an address matching score. In an embodiment, an address string is received and parsed into parsed address data. Once an address string is parsed into parsed address data, the parsed address data is standardized by converting the parsed address data into a standard format and replacing abbreviations, colloquial names with formal names. Once an address string has been standardized into a standardized street locale, candidate addresses that are identical to or similar to the standardized street locale are identified and are assigned a score. Each score comprises a probability that the respective candidate address and the standardized street locale represent a same place or location.

公开(公告)号：US20250077876A1

公开(公告)日：2025-03-06

申请号：US18239416

申请日：2023-08-29

Applicant: Oracle International Corporation

Inventor： Moein Owhadi Kareshk ,  Giulia Carocari ,  Yasha Pushak ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

IPC: G06N3/09

Abstract: Techniques for selecting machine-learned (ML) models using diversity criteria are provided. In one technique, for each ML model of multiple ML models, output data is generated based on input data to the ML model. Multiple pairs of ML models are identified, where each ML model in the multiple pairs is from the multiple ML models. For each pair of ML models in the multiple pairs of ML models: (1) first output data that was previously generated by a first ML model in the pair is identified; (2) second output data that was previously generated by a second ML model in the pair is identified; (3) a diversity value that is based on the first and second output data is generated; and (4) the diversity value is added to a set of diversity values. A subset of the multiple ML models is selected based on the set of diversity values.