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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 12635867 B1

PDFFull Text
Date of Patent:
May. 26, 2026

Filed:

Oct. 16, 2023

Image quality assessment and multi mode dynamic camera for dental images

Applicant:

Align Technology, Inc., San Jose, CA (US);

Inventors:

Guotu Li, Apex, NC (US);

Chao Shi, Morrisville, NC (US);

Chad Clayton Brown, Cary, NC (US);

Christopher E. Cramer, Durham, NC (US);

Phillip Thomas Harris, Cary, NC (US);

Adam Sill, Raleigh, NC (US);

Will Neville, Bluffton, SC (US);

Ritvik Bansal, San Francisco, CA (US);

Assignee:

ALIGN TECHNOLOGY, INC., San Jose, CA (US);

Attorney:

FisherBroyles, LLP

Primary Examiner:

Howard D Brown, Jr

Int. Cl.
CPC ...
G16H 40/63 (2018.01); A61B 1/00 (2006.01); A61B 1/045 (2006.01); A61B 1/24 (2006.01); A61B 1/32 (2006.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06V 20/50 (2022.01); G06V 40/16 (2022.01); H04N 23/60 (2023.01); H04N 23/611 (2023.01); H04N 23/667 (2023.01);
U.S. Cl.
CPC ...
A61B 1/24 (2013.01); A61B 1/00006 (2013.01); A61B 1/000096 (2022.02); A61B 1/045 (2013.01); A61B 1/32 (2013.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06V 20/50 (2022.01); G06V 40/166 (2022.01); G06V 40/171 (2022.01); H04N 23/611 (2023.01); H04N 23/64 (2023.01); H04N 23/667 (2023.01); G06T 2207/20084 (2013.01); G06T 2207/20132 (2013.01); G06T 2207/30036 (2013.01); G06T 2207/30168 (2013.01); G06V 2201/034 (2022.01);
Abstract

Various apparatuses are disclosed (e.g., system, device, method, or the like) for guiding the capture and assessing the quality of an image, including dental images. The apparatuses may use trained neural networks to examine images and provide users feedback regarding image quality. The neural networks may be trained based on images within image groups that have been ranked based on perceived image quality.

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