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US Patent 10523342 Autonomous reinforcement learning method of receiver scan schedule control

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
105233421
Patent Inventor Names
Jonathan M. Sussman-Fort1
Scott A Kuzdeba1
Date of Patent
December 31, 2019
1
Patent Application Number
163510371
Date Filed
March 12, 2019
1
Patent Citations
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US Patent 10222474 Lidar systems including a gallium and nitrogen containing laser light source
1
Patent Citations Received
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US Patent 12137350 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
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US Patent 12127007 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
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US Patent 12133082 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
4
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US Patent 12133083 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
5
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US Patent 12133084 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
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US Patent 12137349 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
7
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US Patent 11316596 Method for detecting at least one compromised computer device in an information system
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US Patent 11924648 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
10
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Patent Primary Examiner
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James M Perez
1
Patent abstract

A method of detecting electromagnetic signal sources of interest includes applying reinforcement learning to automatically and continuously update a receiver scan schedule wherein an agent is reinforced according to comparisons between expected and actual degrees of success after each schedule update, actual degrees of success being estimated by applying to signal data a plurality of value scales applicable to a plurality of reward classes. An exponential scale can be applied across the plurality of reward classes. A companion system can provide data analysis to the agent. The agent can include an actor module that determines schedule updates and a critic module that determines the degrees of scanning success and awards the reinforcements. Embodiments implement a plurality of agents according to asynchronous multiple-worker actor/critic reinforcement learning. The method can be initially applied to training data comprising synthetic and/or previously measured signal data for which the signal sources are fully characterized.

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