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US Patent 10410111 Automated evaluation of neural networks using trained classifier

Patent 10410111 was granted and assigned to SparkCognition on September, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
SparkCognition
SparkCognition
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Current Assignee
SparkCognition
SparkCognition
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
104101110
Patent Inventor Names
Syed Mohammad Amir Husain0
Date of Patent
September 10, 2019
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Patent Application Number
157938660
Date Filed
October 25, 2017
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Patent Citations Received
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US Patent 11966570 Automated processing and dynamic filtering of content for display
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US Patent 11636285 Memory including examples of calculating hamming distances for neural network and data center applications
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US Patent 11687786 Pre-processing for data-driven model creation
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US Patent 11907500 Automated processing and dynamic filtering of content for display
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US Patent 11914844 Automated processing and dynamic filtering of content for display
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US Patent 10678680 Method for automatically generating search heuristics and performing method of concolic testing using automatically generated search heuristics
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US Patent 10853230 Method for automatically generating search heuristics and performing method of concolic testing using automatically generated search heuristics
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US Patent 11586380 Memory systems including examples of calculating hamming distances for neural network and data center applications
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Patent Primary Examiner
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Scott A. Waldron
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Patent abstract

A computer system includes a memory storing a data structure representing a neural network. The data structure includes a plurality of fields including values representing topology of the neural network. The computer system also includes one or more processors configured to perform neural network classification by operations including generating a vector representing at least a portion of the neural network based on the data structure. The operations also include providing the vector as input to a trained classifier to generate a classification result associated with at least the portion of the neural network, where the classification result is indicative of expected performance or reliability of the neural network. The operations also include generating an output indicative of the classification result.

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