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:
Sep. 26, 2023

Filed:

Dec. 27, 2022
Applicant:

Pure Storage, Inc., Mountain View, CA (US);

Inventors:

Brian Gold, Los Altos, CA (US);

Emily Watkins, Houston, TX (US);

Ivan Jibaja, San Jose, CA (US);

Igor Ostrovsky, Mountain View, CA (US);

Roy Kim, Los Altos, CA (US);

Assignee:

PURE STORAGE, INC., Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 67/12 (2022.01); G06F 3/06 (2006.01); G06N 20/00 (2019.01); G06F 16/245 (2019.01); G06F 16/178 (2019.01); G06Q 30/0242 (2023.01); G06F 9/48 (2006.01); G06F 9/50 (2006.01); G06N 3/063 (2023.01); G06N 3/08 (2023.01); G06T 1/20 (2006.01); G06T 1/60 (2006.01); G06F 16/958 (2019.01); G06F 16/248 (2019.01);
U.S. Cl.
CPC ...
G06F 3/0679 (2013.01); G06F 3/0604 (2013.01); G06F 3/067 (2013.01); G06F 3/0608 (2013.01); G06F 3/0646 (2013.01); G06F 3/0649 (2013.01); G06F 9/4881 (2013.01); G06F 9/5027 (2013.01); G06F 16/1794 (2019.01); G06F 16/245 (2019.01); G06N 3/063 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 30/0243 (2013.01); G06T 1/20 (2013.01); G06T 1/60 (2013.01); G06F 16/248 (2019.01); G06F 16/972 (2019.01); G06T 2200/28 (2013.01);
Abstract

Generating a transformed dataset for use by a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit ('GPU') servers, including: storing, within one or more storage systems, a transformed dataset generated by applying one or more transformations to a dataset that are identified based on one or more expected input formats of data received as input data by one or more machine learning models to be executed on one or more servers; and transmitting, from the one or more storage systems to the one or more servers without reapplying the one or more transformations on the dataset, the transformed dataset including data in the one or more expected formats of data to be received as input data by the one or more machine learning models.


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