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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.

Patent No.:

US 11615271 B1

PDFFull Text
Date of Patent:
Mar. 28, 2023

Filed:

Jun. 03, 2020

Machine learning pipeline optimization

Applicant:

Cerebri Ai Inc., Austin, TX (US);

Inventors:

Eyal Ben Zion, Toronto, CA;

Alain Charles Briancon, Germantown, MD (US);

Pranav Mahesh Makhijani, Bethesda, MD (US);

Thejas Narayana Prasad, Spring, TX (US);

Sara Amini, Fairfax, VA (US);

Jian Deng, Kitchener, CA;

Ngoc Thu Nguyen, Potomac, MD (US);

Jean Joseph Belanger, Austin, TX (US);

Assignee:

Cerebri AI Inc., Austin, TX (US);

Attorney:

Pillsbury Winthrop Shaw Pittman, LLP

Primary Examiner:

Chameli Das

Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06F 8/20 (2018.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06F 30/20 (2020.01); G06F 8/10 (2018.01); G06F 8/30 (2018.01); G06Q 40/08 (2012.01); G06F 8/36 (2018.01); G06F 16/25 (2019.01); G06F 9/445 (2018.01); G06N 5/04 (2023.01); G06Q 10/0631 (2023.01); G06Q 10/0637 (2023.01); G06Q 10/0639 (2023.01); G06Q 10/067 (2023.01); G06Q 30/012 (2023.01); G06Q 30/016 (2023.01); G06Q 30/0204 (2023.01); G06Q 40/02 (2023.01); G06Q 30/0202 (2023.01);
U.S. Cl.
CPC ...
G06K 9/6257 (2013.01); G06F 8/10 (2013.01); G06F 8/24 (2013.01); G06F 8/315 (2013.01); G06F 8/36 (2013.01); G06F 9/44521 (2013.01); G06F 16/254 (2019.01); G06F 30/20 (2020.01); G06K 9/6256 (2013.01); G06K 9/6264 (2013.01); G06K 9/6282 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 10/067 (2013.01); G06Q 10/06316 (2013.01); G06Q 10/06375 (2013.01); G06Q 10/06393 (2013.01); G06Q 30/012 (2013.01); G06Q 30/016 (2013.01); G06Q 30/0202 (2013.01); G06Q 30/0204 (2013.01); G06Q 40/025 (2013.01); G06Q 40/08 (2013.01);
Abstract

Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.

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