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US Patent 11868883 Intelligent control with hierarchical stacked neural networks

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
118688830
Patent Inventor Names
Michael Lamport Commons0
Date of Patent
January 9, 2024
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Patent Application Number
167160280
Date Filed
December 16, 2019
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Patent Citations
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US Patent 8355514 Audio encoding to convey auxiliary information, and media embodying same
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US Patent 8687879 Method and apparatus for generating special-purpose image analysis algorithms
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US Patent 8711919 Systems and methods for adaptive blind mode equalization
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US Patent 8712424 Inter-cell power control in the presence of fractional frequency reuse
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US Patent 8735009 Control unit for fuel-cell power generation apparatus, and control method, control program and computer-readable record medium with control program for the same
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US Patent 8762307 Control system for plant
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US Patent 8775341 Intelligent control with hierarchical stacked neural networks
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US Patent 8778441 Ready-to-eat oat-based food product
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Patent Citations Received
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US Patent 12125496 Methods for neural network-based voice enhancement and systems thereof
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Patent Primary Examiner
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Shane D Woolwine
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CPC Code
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G10L 15/16
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G06Q 10/107
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G06F 40/253
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G06F 16/3344
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G06N 20/20
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G06N 3/02
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G06N 3/08
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G06N 20/00
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Patent abstract

A system and method of detecting an aberrant message is provided. An ordered set of words within the message is detected. The set of words found within the message is linked to a corresponding set of expected words, the set of expected words having semantic attributes. A set of grammatical structures represented in the message is detected, based on the ordered set of words and the semantic attributes of the corresponding set of expected words. A cognitive noise vector comprising a quantitative measure of a deviation between grammatical structures represented in the message and an expected measure of grammatical structures for a message of the type is then determined. The cognitive noise vector may be processed by higher levels of the neural network and/or an external processor.

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