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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.

Patent No.:

US 11630998 B1

PDFFull Text
Date of Patent:
Apr. 18, 2023

Filed:

Mar. 26, 2019

Systems and methods for automatically training neural networks

Applicant:

Cohda Wireless Pty Ltd., Wayville, AU;

Inventors:

Malik Khan, Wayville, AU;

Mohamed Elbanhawi, Wayville, AU;

Assignee:

Cohda Wireless Pty Ltd., Wayville, AU;

Attorney:

Schwegman Lundbrg & Woessner, P.A.

Primary Examiner:

Marthe Y Marc-Coleman

Int. Cl.
CPC ...
G06N 3/08 (2006.01); H04W 4/46 (2018.01); G06K 9/62 (2022.01); H04W 4/38 (2018.01); G01S 13/00 (2006.01); H04W 4/00 (2018.01); B60W 50/00 (2006.01); H04W 4/40 (2018.01); G06N 3/00 (2006.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01); H04W 4/18 (2009.01); H04L 67/12 (2022.01); H04W 4/02 (2018.01); G01S 13/86 (2006.01); G01S 17/931 (2020.01); G05D 1/02 (2020.01); G06N 3/008 (2023.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); B60W 50/00 (2013.01); G01S 13/00 (2013.01); G06K 9/6256 (2013.01); G06K 9/6267 (2013.01); G06N 3/008 (2013.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01); H04W 4/00 (2013.01); H04W 4/38 (2018.02); H04W 4/40 (2018.02); H04W 4/46 (2018.02); B60W 2554/00 (2020.02); G01S 13/865 (2013.01); G01S 17/931 (2020.01); G05D 1/0246 (2013.01); H04L 67/12 (2013.01); H04W 4/02 (2013.01); H04W 4/185 (2013.01);
Abstract

A method for automatically training a neural network includes at a trainer having a first communication device and a perception recorder, continuously recording the surroundings in the vicinity of the first object; receiving, at the trainer, a message from a communication device associated with an object in the vicinity of the trainer, the message including information about the position and the type of the object; identifying a recording corresponding to the time at which the message is received from the object; correlating the received positional information about the second object with a corresponding location in the recording to identify the object in the recording; classifying the identified object based on the type of information received in the message from the object; and using the classified recording to train the neural network.

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