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:
Sep. 08, 2026

Filed:

Mar. 14, 2024
Applicant:

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

Inventors:

Mikhail Minchenkov, Zhukovsky, RU;

Ran Katz, Hod Hasharon, IL;

Pavel Agniashvili, Moscow, RU;

Chad Clayton Brown, Cary, NC (US);

Jonathan Coslovsky, Rehovot, IL;

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/11 (2017.01); A61C 9/00 (2006.01); A61C 13/34 (2006.01); G06F 18/2431 (2023.01); G06N 3/044 (2023.01); G06N 3/08 (2023.01); G06T 7/00 (2017.01); G06T 17/00 (2006.01); G06T 19/20 (2011.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06T 7/11 (2017.01); A61C 9/0053 (2013.01); A61C 13/34 (2013.01); G06F 18/2431 (2023.01); G06N 3/044 (2023.01); G06N 3/08 (2013.01); G06T 7/0012 (2013.01); G06T 17/00 (2013.01); G06T 19/20 (2013.01); G06V 10/454 (2022.01); G06V 10/82 (2022.01); G06T 2207/10016 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/10048 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30036 (2013.01); G06T 2207/30052 (2013.01); G06T 2210/41 (2013.01); G06T 2219/008 (2013.01); G06T 2219/2021 (2013.01); G06V 20/653 (2022.01); G06V 2201/03 (2022.01);
Abstract

A system includes an intraoral scanner and a computing device. The intraoral scanner generates intraoral scans of a dental site. The computing device processes the intraoral scans using a trained machine learning model to classify points of the intraoral scans into first points having a first dental class and second points having a second dental class; generates a 3D model of the dental site from the intraoral scans; determines first points of the 3D model having the first dental class based on the first points of the intraoral scans and second points of the virtual 3D model having the second dental class based on the second points of the intraoral scans; and removes data for a plurality of the first points of the virtual 3D model having the first dental class to generate a modified virtual 3D model.


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