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:
Apr. 05, 2022

Filed:

Jul. 26, 2017
Applicant:

James R. Glidewell Dental Ceramics, Inc., Newport Beach, CA (US);

Inventors:

Sergei Azernikov, Irvine, CA (US);

Sergey Nikolskiy, Coto de Caza, CA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
A61C 13/00 (2006.01); G06T 7/00 (2017.01); G06K 9/62 (2022.01); G06K 9/00 (2022.01); G06N 3/08 (2006.01); G06N 5/04 (2006.01); A61C 7/00 (2006.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
A61C 13/0004 (2013.01); G06K 9/6274 (2013.01); G06N 3/08 (2013.01); G06N 5/04 (2013.01); G06T 7/0012 (2013.01); G06V 20/653 (2022.01); A61C 7/002 (2013.01); A61C 2007/004 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30036 (2013.01);
Abstract

A computer-implemented method of recognizing dental information associated with a dental model of dentition includes training a deep neural network to map a plurality of training dental models representing at least a portion of each one of a plurality of patients' dentitions to a probability vector including probability of the at least a portion of the dentition belonging to each one of a set of multiple categories. The category of the at least a portion of the dentition represented by the training dental model corresponds to the highest probability in the probability vector. The method includes receiving a dental model representing at least a portion of a patient's dentition and recognizing dental information associated with the dental model by applying the trained deep neural network to determine a category of the at least a portion of the patient's dentition represented by the received dental model.


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