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:
May. 30, 2023

Filed:

Jun. 04, 2020
Applicant:

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

Inventors:

Alberto Polleri, London, 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;

Larissa Cristina Dos Santos Romualdo Suzuki, Wokingham, 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 20/00 (2019.01); G06F 8/41 (2018.01); G06F 9/54 (2006.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 8/41 (2013.01); G06F 9/541 (2013.01);
Abstract

The present disclosure relates to systems and methods for a machine learning platform that generates a library of components to generate machine learning models and machine learning applications. The machine learning infrastructure system allows a user (i.e., a data scientist) to generate machine learning applications without having detailed knowledge of the cloud-based network infrastructure or knowledge of how to generate code for building the model. The machine learning platform can analyze the identified data and the user provided desired prediction and performance characteristics to select one or more library components and associated API to generate a machine learning application. The machine learning can monitor and evaluate the outputs of the machine learning model to allow for feedbacks and adjustments to the model. The machine learning application can be trained, tested, and compiled for export as stand-alone executable code.


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