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:
Nov. 07, 2023

Filed:

Sep. 12, 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 (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 9/08 (2006.01); G06N 20/20 (2019.01); 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 9/32 (2006.01); G06F 16/23 (2019.01); G06F 11/30 (2006.01); G06F 18/10 (2023.01); G06F 18/213 (2023.01); G06F 18/2115 (2023.01); G06F 18/214 (2023.01); G06N 5/01 (2023.01);
U.S. Cl.
CPC ...
H04L 9/0894 (2013.01); 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); G06F 18/10 (2023.01); G06F 18/213 (2023.01); G06F 18/2115 (2023.01); G06F 18/2155 (2023.01); G06N 5/01 (2023.01); G06N 5/025 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); H04L 9/088 (2013.01); H04L 9/3236 (2013.01);
Abstract

The present disclosure relates to systems and methods for a machine-learning platform for the safe serialization of a machine-learning application. Individual library components (e.g., a pipeline, a microservice routine, a software module, and an infrastructure model) can be encrypted using one or more keys. The keys can be stored in a location different from the storage location of the machine-learning application. Prior to incorporation of the library component into a machine-learning model, one or more keys can be retrieved from the remote storage location to authenticate that the one or more encrypted library components are authentic. The process can reject any of the one or more component, when the encrypted library component fails authentication. If a component is rejected, the system can roll back to a previous, authenticated version of the library component. The authenticated library components can be compiled into machine-learning software.


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