Patents by Inventor EMILY WATKINS
EMILY WATKINS has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240028266Abstract: 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.Type: ApplicationFiled: September 12, 2023Publication date: January 25, 2024Inventors: BRIAN GOLD, EMILY WATKINS, IVAN JIBAJA, IGOR OSTROVSKY, ROY KIM
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Publication number: 20230325272Abstract: An illustrative method may include identifying, based on data associated with an operation of a hardware component, an anomaly in the data; determining that the anomaly is representative of an issue associated with the hardware component; and performing, based on the determining that the anomaly is representative of the issue associated with the hardware component, a remedial action that affects a performance of the operation of the hardware component.Type: ApplicationFiled: June 14, 2023Publication date: October 12, 2023Inventors: Christopher Golden, Emily Watkins
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Patent number: 11768636Abstract: 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.Type: GrantFiled: December 27, 2022Date of Patent: September 26, 2023Assignee: PURE STORAGE, INC.Inventors: Brian Gold, Emily Watkins, Ivan Jibaja, Igor Ostrovsky, Roy Kim
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Patent number: 11734097Abstract: An illustrative method includes identifying, based on an output of a machine learning model that receives data associated with an operation of a hardware component as an input, an anomaly in the data, determining that the anomaly is representative of an issue associated with the hardware component, and performing, based on the determining that the anomaly is representative of the issue associated with the hardware component, a remedial action that affects a performance of the operation of the hardware component.Type: GrantFiled: January 27, 2021Date of Patent: August 22, 2023Assignee: Pure Storage, Inc.Inventors: Christopher Golden, Emily Watkins
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Publication number: 20230126789Abstract: 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.Type: ApplicationFiled: December 27, 2022Publication date: April 27, 2023Inventors: BRIAN GOLD, EMILY WATKINS, IVAN JIBAJA, IGOR OSTROVSKY, ROY KIM
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Publication number: 20230073931Abstract: A hyperscale artificial intelligence and machine learning infrastructure includes a plurality of racks, where: at least one or more of the racks include one or more GPU servers; at least one or more of the racks include one or more storage systems; each of the racks include one or more switches coupled to at least one switch in another rack; and the one or more GPU servers are configured to execute one or more artificial intelligence or machine learning applications, wherein data stored within the one or more storage systems is used as input to the one or more artificial intelligence or machine learning applications.Type: ApplicationFiled: November 2, 2022Publication date: March 9, 2023Inventors: EMILY WATKINS, RAMNATH SAI SAGAR THUMBAVANAM PADMANABHAN, JAMES FISHER, HARRY LYDIKSEN
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Patent number: 11556280Abstract: 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.Type: GrantFiled: May 29, 2020Date of Patent: January 17, 2023Assignee: PURE STORAGE, INC.Inventors: Brian Gold, Emily Watkins, Ivan Jibaja, Igor Ostrovsky, Roy Kim
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Patent number: 11494692Abstract: A hyperscale artificial intelligence and machine learning infrastructure includes a plurality of racks, where: at least one or more of the racks include one or more GPU servers; at least one or more of the racks include one or more storage systems; each of the racks include one or more switches coupled to at least one switch in another rack; and the one or more GPU servers are configured to execute one or more artificial intelligence or machine learning applications, wherein data stored within the one or more storage systems is used as input to the one or more artificial intelligence or machine learning applications.Type: GrantFiled: March 26, 2019Date of Patent: November 8, 2022Assignee: PURE STORAGE, INC.Inventors: Emily Watkins, Ramnath Sai Sagar Thumbavanam Padmanabhan, James Fisher, Harry Lydiksen
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Publication number: 20220253443Abstract: Improving machine learning models in an artificial intelligence infrastructure includes: storing, within one or more storage systems of an artificial intelligence infrastructure, information describing a dataset and one or more transformations applied to the dataset resulting in a transformed dataset; and storing, within the one or more storage systems, information describing only portions of previous versions of a machine learning model that differ from a current version of the machine learning model, wherein the previous versions used the transformed dataset as input during one or more prior executions by the artificial intelligence infrastructure.Type: ApplicationFiled: April 26, 2022Publication date: August 11, 2022Inventors: BRIAN GOLD, EMILY WATKINS, IVAN JIBAJA, IGOR OSTROVSKY, ROY KIM
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Patent number: 11403290Abstract: Ensuring reproducibility in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, by a unified management plane, one or more transformations applied to a dataset by the artificial intelligence infrastructure, wherein applying the one or more transformations to the dataset causes the artificial intelligence infrastructure to generate a transformed dataset; storing, within the one or more storage systems, information describing the dataset, the one or more transformations applied to the dataset, and the transformed dataset; identifying, by the unified management plane, one or more machine learning models executed by the artificial intelligence infrastructure using the transformed dataset as input; and storing, within the one or more storage systems, information describing one or more machine learning models executed using the transformed dataset as input.Type: GrantFiled: July 18, 2019Date of Patent: August 2, 2022Assignee: PURE STORAGE, INC.Inventors: Brian Gold, Emily Watkins, Ivan Jibaja, Igor Ostrovsky, Roy Kim
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Patent number: 11010233Abstract: An exemplary monitoring system receives log data associated with an operation of a hardware component, applies the log data as an input to an unsupervised machine learning model, and identifies, based on an output of the unsupervised machine learning model, an anomaly in the log data.Type: GrantFiled: January 16, 2019Date of Patent: May 18, 2021Assignee: Pure Storage, IncInventors: Christopher Golden, Emily Watkins
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Patent number: 10915813Abstract: An apparatus for artificial intelligence acceleration is provided. The apparatus includes a storage and compute system having a distributed, redundant key value store for metadata. The storage and compute system having distributed compute resources configurable to access, through a plurality of authorities, data in the solid-state memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store.Type: GrantFiled: June 21, 2019Date of Patent: February 9, 2021Assignee: Pure Storage, Inc.Inventors: Fabio Margaglia, Emily Watkins, Hari Kannan, Cary A. Sandvig
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Publication number: 20200293378Abstract: 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.Type: ApplicationFiled: May 29, 2020Publication date: September 17, 2020Inventors: BRIAN GOLD, EMILY WATKINS, IVAN JIBAJA, IGOR OSTROVSKY, ROY KIM
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Patent number: 10671435Abstract: 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.Type: GrantFiled: July 20, 2018Date of Patent: June 2, 2020Assignee: PURE STORAGE, INC.Inventors: Brian Gold, Emily Watkins, Ivan Jibaja, Igor Ostrovsky, Roy Kim
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Patent number: 10671434Abstract: Data transformation offloading in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: storing, within the storage system, a dataset; identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to the dataset; and generating, by the storage system in dependence upon the one or more transformations, a transformed dataset.Type: GrantFiled: July 20, 2018Date of Patent: June 2, 2020Assignee: PURE STORAGE, INC.Inventors: Brian Gold, Emily Watkins, Ivan Jibaja, Igor Ostrovsky, Roy Kim
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Patent number: 10649988Abstract: An artificial intelligence and machine learning infrastructure system, including: one or more storage systems comprising, respectively, one or more storage devices; and one or more graphical processing units, wherein the graphical processing units are configured to communicate with the one or more storage systems over a communication fabric; where the one or more storage systems, the one or more graphical processing units, and the communication fabric are implemented within a single chassis.Type: GrantFiled: July 27, 2018Date of Patent: May 12, 2020Assignee: Pure Storage, Inc.Inventors: Brian Gold, Emily Watkins, Ivan Jibaja, Igor Ostrovsky, Roy Kim
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Publication number: 20200125941Abstract: An artificial intelligence and machine learning infrastructure system, including: one or more storage systems comprising, respectively, one or more storage devices; and one or more graphical processing units, wherein the graphical processing units are configured to communicate with the one or more storage systems over a communication fabric; where the one or more storage systems, the one or more graphical processing units, and the communication fabric are implemented within a single chassis.Type: ApplicationFiled: July 27, 2018Publication date: April 23, 2020Inventors: BRIAN GOLD, EMILY WATKINS, IVAN JIBAJA, IGOR OSTROVSKY, ROY KIM
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Patent number: 10467527Abstract: An apparatus for artificial intelligence acceleration is provided. The apparatus includes a storage and compute system having a distributed, redundant key value store for metadata. The storage and compute system having distributed compute resources configurable to access, through a plurality of authorities, data in the solid-state memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store.Type: GrantFiled: January 31, 2018Date of Patent: November 5, 2019Assignee: Pure Storage, Inc.Inventors: Fabio Margaglia, Emily Watkins, Hari Kannan, Cary A. Sandvig
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Publication number: 20190318243Abstract: An apparatus for artificial intelligence acceleration is provided. The apparatus includes a storage and compute system having a distributed, redundant key value store for metadata. The storage and compute system having distributed compute resources configurable to access, through a plurality of authorities, data in the solid-state memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store.Type: ApplicationFiled: June 21, 2019Publication date: October 17, 2019Inventors: Fabio Margaglia, Emily Watkins, Hari Kannan, Cary A. Sandvig
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Patent number: 10360214Abstract: Ensuring reproducibility in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, by a unified management plane, one or more transformations applied to a dataset by the artificial intelligence infrastructure, wherein applying the one or more transformations to the dataset causes the artificial intelligence infrastructure to generate a transformed dataset; storing, within the one or more storage systems, information describing the dataset, the one or more transformations applied to the dataset, and the transformed dataset; identifying, by the unified management plane, one or more machine learning models executed by the artificial intelligence infrastructure using the transformed dataset as input; and storing, within the one or more storage systems, information describing one or more machine learning models executed using the transformed dataset as input.Type: GrantFiled: July 26, 2018Date of Patent: July 23, 2019Assignee: Pure Storage, Inc.Inventors: Brian Gold, Emily Watkins, Ivan Jibaja, Igor Ostrovsky, Roy Kim