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US Patent 10338968 Distributed neuromorphic processing performance accountability

Patent 10338968 was granted and assigned to Sas (company) on July, 2019 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Sas (company)
Sas (company)
Current Assignee
Sas (company)
Sas (company)
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10338968
Date of Patent
July 2, 2019
Patent Application Number
16039863
Date Filed
July 19, 2018
Patent Citations
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US Patent 10095554 Automated generation of private federated areas
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US Patent 10095553 Hierarchical federated area trees
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US Patent 10002029 Automated transfer of neural network definitions among federated areas
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US Patent 10013656 Methods and apparatus for analytical processing of provenance data for HPC workflow optimization
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US Patent 10033816 Workflow service using state transfer
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US Patent 10078710 Distributed data set storage and analysis reproducibility
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US Patent 10095552 Automated transfer of objects among federated areas
Patent Citations Received
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US Patent 11714968 Identifying data of interest using machine learning
0
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US Patent 10929191 Loading models on nodes having multiple model service frameworks
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US Patent 11003501 Loading models on nodes having multiple model service frameworks
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US Patent 11288456 Identifying data of interest using machine learning
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US Patent 10725830 Loading models on nodes having multiple model service frameworks
Patent Primary Examiner
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Hiren P Patel
Patent abstract

An apparatus includes a processor to: receive a request to repeat an earlier performance of a first job flow described in a job flow definition; analyze the job flow definition to determine whether the first job flow uses a neural network; in response to a determination that the first job flow uses a neural network, analyze an object associated with the first job flow to determine whether the neural network was trained using training data from a second job flow that does not use a neural network; and in response to a determination that such training data was so used, repeat the earlier performance of the first job flow, perform the second job flow with the same input data values as used in the repeated performance of the first job flow, and analyze corresponding output data values of both performances to determine a degree of accuracy of the neural network.

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