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US Patent 10275285 Data transformation caching in an artificial intelligence infrastructure

Patent 10275285 was granted and assigned to Pure Storage on April, 2019 by the United States Patent and Trademark Office.

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Contents

Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Applicant
Pure Storage
Pure Storage
Current Assignee
Pure Storage
Pure Storage
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10275285
Date of Patent
April 30, 2019
Patent Application Number
16046337
Date Filed
July 26, 2018
Patent Citations
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US Patent 10025813 Distributed data transformation system
Patent Citations Received
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US Patent 12067466 Artificial intelligence and machine learning hyperscale infrastructure
3
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US Patent 11494692 Hyperscale artificial intelligence and machine learning infrastructure
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US Patent 11861423 Accelerating artificial intelligence (‘AI’) workflows
6
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US Patent 12061899 Infrastructure as code (IaC) pre-deployment analysis via a machine-learning model
7
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US Patent 11496521 Feedback loop for security audit logs
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US Patent 11556280 Data transformation for a machine learning model
‌
US Patent 10771240 Dynamic blockchain system and method for providing efficient and secure distributed data access, data storage and data transport
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US Patent 11768636 Generating a transformed dataset for use by a machine learning model in an artificial intelligence infrastructure
...
Patent Primary Examiner
‌
Le H Luu
Patent abstract

Data transformation caching in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to a dataset; generating, in dependence upon the one or more transformations, a transformed dataset; storing, within one or more of the storage systems, the transformed dataset; receiving a plurality of requests to transmit the transformed dataset to one or more of the GPU servers; and responsive to each request, transmitting, from the one or more storage systems to the one or more GPU servers without re-performing the one or more transformations on the dataset, the transformed dataset.

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