The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 17, 2023

Filed:

Jun. 04, 2020
Applicant:

Oracle International Corporation, Redwood Shores, CA (US);

Inventors:

Alberto Polleri, London, GB;

Larissa Cristina Dos Santos Romualdo Suzuki, Wokingham, GB;

Sergio Aldea Lopez, London, GB;

Marc Michiel Bron, London, GB;

Dan David Golding, London, GB;

Alexander Ioannides, London, GB;

Maria del Rosario Mestre, London, GB;

Hugo Alexandre Pereira Monteiro, London, GB;

Oleg Gennadievich Shevelev, London, GB;

Xiaoxue Zhao, London, GB;

Matthew Charles Rowe, Milton Keynes, GB;

Assignee:

Oracle International Corporation, Redwood Shores, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/20 (2019.01); G06N 5/00 (2006.01); G06F 16/36 (2019.01); G06N 20/00 (2019.01); G06F 16/901 (2019.01); G06F 11/34 (2006.01); G06F 16/907 (2019.01); G06F 16/9035 (2019.01); G06F 8/75 (2018.01); G06F 8/77 (2018.01); G06N 5/02 (2006.01); G06F 16/28 (2019.01); G06F 16/21 (2019.01); G06F 16/2457 (2019.01); H04L 9/08 (2006.01); H04L 9/32 (2006.01); G06K 9/62 (2022.01); G06F 16/23 (2019.01); G06F 11/30 (2006.01);
U.S. Cl.
CPC ...
G06N 20/20 (2019.01); G06F 8/75 (2013.01); G06F 8/77 (2013.01); G06F 11/3003 (2013.01); G06F 11/3409 (2013.01); G06F 11/3433 (2013.01); G06F 11/3452 (2013.01); G06F 11/3466 (2013.01); G06F 16/211 (2019.01); G06F 16/2365 (2019.01); G06F 16/24573 (2019.01); G06F 16/24578 (2019.01); G06F 16/285 (2019.01); G06F 16/367 (2019.01); G06F 16/907 (2019.01); G06F 16/9024 (2019.01); G06F 16/9035 (2019.01); G06K 9/6231 (2013.01); G06K 9/6232 (2013.01); G06K 9/6259 (2013.01); G06K 9/6298 (2013.01); G06N 5/003 (2013.01); G06N 5/025 (2013.01); G06N 20/00 (2019.01); H04L 9/088 (2013.01); H04L 9/3236 (2013.01);
Abstract

The present disclosure relates to systems and methods for using existing data ontologies for generating machine learning solutions for a high-precision search of relevant services to compose pipelines with minimal human intervention. Data ontologies can be used to create a combination of non-logic based and logic-based sematic services that can significantly outperform both kinds of selection in terms of precision. Quality of Service (QoS) and product Key Performance Indicator (KPI) constraints can be used as part of architecture selection in developing, training, validating, and improving machine learning models. For data sets without existing ontologies, one or more ontologies be generated and stored for future use.


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