US 11,768,636 B2
Generating a transformed dataset for use by a machine learning model in an artificial intelligence infrastructure
Brian Gold, Los Altos, CA (US); Emily Watkins, Houston, TX (US); Ivan Jibaja, San Jose, CA (US); Igor Ostrovsky, Mountain View, CA (US); and Roy Kim, Los Altos, CA (US)
Assigned to PURE STORAGE, INC., Santa Clara, CA (US)
Filed by PURE STORAGE, INC., Mountain View, CA (US)
Filed on Dec. 27, 2022, as Appl. No. 18/146,807.
Application 18/146,807 is a continuation of application No. 16/888,402, filed on May 29, 2020, granted, now 11,556,280.
Application 16/888,402 is a continuation of application No. 16/040,996, filed on Jul. 20, 2018, granted, now 10,671,435, issued on Jun. 2, 2020.
Claims priority of provisional application 62/650,736, filed on Mar. 30, 2018.
Claims priority of provisional application 62/648,368, filed on Mar. 26, 2018.
Claims priority of provisional application 62/620,286, filed on Jan. 22, 2018.
Claims priority of provisional application 62/576,523, filed on Oct. 24, 2017.
Claims priority of provisional application 62/574,534, filed on Oct. 19, 2017.
Prior Publication US 2023/0126789 A1, Apr. 27, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. 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)
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)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
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.