The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 19, 2018

Filed:

May. 03, 2017
Applicant:

Spawar Systems Center Pacific, San Diego, CA (US);

Inventors:

Benjamin J. Migliori, San Diego, CA (US);

Daniel J. Gebhardt, San Diego, CA (US);

Daniel C. Grady, La Jolla, CA (US);

Riley Zeller-Townson, Hampstead, NC (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
H04L 1/20 (2006.01); H04L 27/00 (2006.01); H04B 1/00 (2006.01); G06N 99/00 (2010.01); H04L 27/14 (2006.01); G01R 29/06 (2006.01); H04B 17/309 (2015.01);
U.S. Cl.
CPC ...
H04L 27/0012 (2013.01); G01R 29/06 (2013.01); G06N 99/005 (2013.01); H04B 1/0003 (2013.01); H04B 17/309 (2015.01); H04L 27/0008 (2013.01); H04L 27/14 (2013.01);
Abstract

Class types of input signals having unknown class types are automatically classified using a neural network. The neural network learns features associated with a plurality of different observed signals having respective different known class types. The neural network then recognizes features of the input signals having unknown class types that at least partially match at least some of the features associated with the plurality of different observed signals having respective different known class types. The neural network determines probabilities that each of the input signals has each of the known class types based on strengths of the matches between the recognized features of the input signals and the features associated with plurality of different observed signals. The neural network classifies each of the input signals as having one of the respective different known class types based on a highest determined probability.


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