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:
Apr. 11, 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;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/20 (2019.01); G06N 5/00 (2023.01); 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 5/025 (2023.01); 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 an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production. One or more parameters may also be discovered. The new parameters may be incorporated into the existing model in an off-line mode. The machine learning application with the new parameters can be tested off-line and the results can be compared with previous results with existing parameters. If the new parameters outperform the existing parameters as compared with ground-truth data, the machine learning application can be auto-promoted to production.


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