IP Library › Granted Patent US 12,740,712
Granted Patent B2
US 12,740,712 · App. 18/656,338 · Granted Sep 22, 2026

Systems and methods for identifying and correcting food traps

Inventors: Avi Kopelman (Palo Alto, CA); Zelko Relic (Pleasanton, CA); Mitra Derakhshan (Herndon, VA)
Assignee: Align Technology, Inc.
A61B5/0088A61C7/002A61C9/0053A61C13/34
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,740,712
App. No.
18/656,338
Granted
Sep 22, 2026
Kind
B2
Abstract

A system for treating food traps may include an intraoral scanner and a processor coupled in electronic communication with the intraoral scanner, a processor, and memory comprising instructions that when executed by the processor cause the system to carry out a method. The method may include receiving a 3D digital model of a patient's detention and analyzing the 3D digital model of the patient's detention by detecting anatomic structures in the 3D digital model of the patient's detention that correspond to food traps. The 3D digital model of the patient's detention may be displayed on a display and digital feedback may be provided on the displayed 3D digital model of the patient's detention. The feedback may identify a location of the food traps.

Claims (77)

1 . A system for treating food traps, the system comprising:

a processor; and

memory comprising instructions that when executed by the processor cause the system to carry out a method of:

receiving a plurality of 3D digital models of a patient's detention taken over time;

analyzing the plurality of 3D digital models of the patient's detention by detecting changes in anatomic structures between each of the plurality of 3D digital models of the patient's detention;

determining that the detected changes indicate a formation of a food trap between the anatomic structures;

displaying the 3D digital model of the patient's detention on a display; and

providing digital feedback on the displayed 3D digital model of the patient's detention that identifies a location of the formation of the food trap.

2 . The system of claim 1 , wherein:

determining that the detected changes indicate a formation of a food trap includes extrapolating future tooth movement based on the plurality of 3D digital models; and

the memory further comprises instructions that when executed by the processor cause the system to carry out the method, the method further comprising:

generating a treatment plan for treating the formation of the food traps, the treatment plan including moving teeth of the patient's dentition from a first arrangement to a second arrangement that avoids the extrapolated future tooth movement.

3 . The system of claim 1 , wherein:

the memory further comprises instructions that when executed by the processor cause the system to carry out the method, the method further comprising:

generating a treatment plan for treating the formation of the food traps, the treatment plan to halt or reverse gingiva recession detected in the plurality of 3D digital models.

4 . A system for treating food traps, the system comprising:

an intraoral scanner;

a processor coupled in electronic communication with the intraoral scanner; and

memory comprising instructions that when executed by the processor cause the system to carry out a method comprising:

receiving a 3D digital model of a patient's detention;

analyzing the 3D digital model of the patient's detention by detecting food traps between anatomic structures in the 3D digital model of the patient's detention;

displaying the 3D digital model of the patient's detention on a display; and

providing digital feedback on the displayed 3D digital model of the patient's detention that identifies a location of the food traps.

5 . The system of claim 4 , wherein:

receiving the 3D digital model of the patient's dentition includes scanning the patient's detention with a 3D intraoral scanner, and wherein the 3D digital model is a 3D surface model.

6 . The system of claim 4 , wherein:

receiving the 3D digital model of the patient's dentition includes scanning the patient's detention with a multi-modal 3D intraoral scanner, and wherein the 3D digital model is a 3D surface and volumetric model.

7 . The system of claim 6 , wherein:

the 3D model includes near infrared subsurface data or CBCT data.

8 . The system of claim 4 , wherein:

analyzing the 3D digital model of the patient's detention by detecting anatomic structures includes detecting apertures between adjacent teeth that extend from a buccal side to a lingual side of the adjacent teeth.

9 . The system of claim 4 , wherein:

analyzing the 3D digital model of the patient's detention by detecting anatomic structures includes detecting recession of the patient's dentition to form an aperture between adjacent teeth that extend from a buccal side to a lingual side of the adjacent teeth.

10 . The system of claim 4 , wherein:

analyzing the 3D digital model of the patient's detention by detecting anatomic structures includes detecting recession of interdental papilla to form an aperture between adjacent teeth that extend from a buccal side to a lingual side of the adjacent teeth.

11 . The system of claim 4 , wherein:

analyzing the 3D digital model of the patient's detention by detecting anatomic structures includes detecting an undercut at a base of a crown of at least one tooth.

12 . The system of claim 4 , wherein:

analyzing the 3D digital model of the patient's detention by detecting anatomic structures includes detecting an undercut in at least one tooth proximate gingiva of the patient's detention.

13 . The system of claim 4 , wherein:

analyzing the 3D digital model of the patient's detention by detecting anatomic structures includes detecting an undercut in at least one tooth proximate gingiva of the patient's detention.

14 . The system of claim 4 , wherein:

analyzing the 3D digital model of the patient's detention by detecting anatomic structures includes detecting a gap between a tooth and gingiva.

15 . The system of claim 4 , wherein:

analyzing the 3D digital model of the patient's detention by detecting anatomic structures includes detecting a teeth that overlap each other in a buccal-lingual direction.

16 . The system of claim 4 , wherein:

receiving the 3D digital model of the patient's dentition includes receiving a first 3D digital model of the patient's detention in normal occlusion and a second 3D digital model of the patient's detention in bite occlusion with bite forces applied; and

analyzing the 3D digital model of the patient's detention by detecting anatomic structures includes detecting an aperture in an interproximal region between adjacent teeth in the second model that is larger than the aperture in the interproximal region between the adjacent teeth in the first model.

17 . The system of claim 4 , wherein:

wherein the feedback includes highlighting a region of the 3D model that includes the food trap.

18 . The system of claim 4 , wherein:

wherein the feedback includes modifying a color of a region of the 3D model that includes the food trap.

19 . The system of claim 4 , wherein:

wherein the feedback includes modifying a generated perimeter around a region of the 3D model that includes the food trap.

20 . The system of claim 4 , wherein:

the method further comprises generating a treatment plan for treating the food traps by generating an orthodontic treatment plan to move teeth of the patient's dentition from a first arrangement with the food trap to a second arrangement without the food trap.

21 . A system for treating food traps, the system comprising:

a processor; and

memory comprising instructions that when executed by the processor cause the system to carry out a method of:

receiving a 3D digital model of a patient's detention;

analyzing the 3D digital model of the patient's detention by detecting food traps between anatomic structures in the 3D digital model of the patient's detention;

displaying the 3D digital model of the patient's detention on a display; and

providing digital feedback on the displayed 3D digital model of the patient's detention that identifies a location of the food traps.

22 . The system of claim 21 , wherein:

the memory further comprises instructions that when executed by the processor cause the system to carry out the method, the method further comprising:

generating a treatment plan for treating the food traps;

receiving dental feedback on the treatment plan, wherein the dental feedback includes a change in a position of one or more teeth of the patient's dentition in a final arrangement; and

analyzing the dental feedback by detecting anatomic structures in the 3D digital model of the patient's detention that correspond to food traps.

23 . The system of claim 22 , wherein:

analyzing the dental feedback includes detecting anatomic structures in a final arrangement that correspond to food traps.

24 . The system of claim 21 , wherein:

the memory further comprises instructions that when executed by the processor cause the system to carry out the method, the method further comprising:

generating a treatment plan for treating the food traps;

receiving dental feedback on the treatment plan, wherein the dental feedback includes a change in a shape, position, or orientation of one or more prosthetics; and

analyzing the dental feedback by detecting anatomic structures in the 3D digital model of the patient's detention that correspond to food traps.

25 . The system of claim 24 , wherein:

analyzing the dental feedback includes detecting anatomic or prosthetic structures that correspond to food traps.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 17, 2024
From: KOPELMAN, AVI; RELIC, ZELKO; DERAKHSHAN, MITRA
To: ALIGN TECHNOLOGY, INC.
Reel/Frame 067746/0865 →
Continuity (2)
Provisional Application 63500815 · May 8, 2023
Related Publication 20240374144A1 · Nov 14, 2024
References Cited (101)
US 5975893A · Chishti et al. · 1999 [cited by applicant]
US 6099314A · Kopelman et al. · 2000 [cited by applicant]
US 6309215B1 · Phan et al. · 2001 [cited by applicant]
US 6334772B1 · Taub et al. · 2002 [cited by applicant]
US 6334853B1 · Kopelman et al. · 2002 [cited by applicant]
US 6450807B1 · Chishti et al. · 2002 [cited by applicant]
US 6463344B1 · Pavlovskaia et al. · 2002 [cited by applicant]
US 6542249B1 · Kofman et al. · 2003 [cited by applicant]
US 6633789B1 · Nikolskiy et al. · 2003 [cited by applicant]
US 6664986B1 · Kopelman et al. · 2003 [cited by applicant]
US 6697164B1 · Babayoff et al. · 2004 [cited by applicant]
US 6830450B2 · Knopp et al. · 2004 [cited by applicant]
US 6845175B2 · Kopelman et al. · 2005 [cited by applicant]
US 6979196B2 · Nikolskiy et al. · 2005 [cited by applicant]
US 7030383B2 · Babayoff et al. · 2006 [cited by applicant]
US 7202466B2 · Babayoff et al. · 2007 [cited by applicant]
US 7255558B2 · Babayoff et al. · 2007 [cited by applicant]
US 7286954B2 · Kopelman et al. · 2007 [cited by applicant]
US 7319529B2 · Babayoff · 2008 [cited by applicant]
US 7373286B2 · Nikolskiy et al. · 2008 [cited by applicant]
US 7507088B2 · Taub et al. · 2009 [cited by applicant]
US 7545372B2 · Kopelman et al. · 2009 [cited by applicant]
US 7698068B2 · Babayoff · 2010 [cited by applicant]
US 7916911B2 · Kaza et al. · 2011 [cited by applicant]
US 8108189B2 · Chelnokov et al. · 2012 [cited by applicant]
US 8244028B2 · Kuo et al. · 2012 [cited by applicant]
US 8587582B2 · Matov et al. · 2013 [cited by applicant]
US 8948482B2 · Levin · 2015 [cited by applicant]
US D742518S · Barak et al. · 2015 [cited by applicant]
US 9192305B2 · Levin · 2015 [cited by applicant]
US 9261356B2 · Lampert et al. · 2016 [cited by applicant]
US 9261358B2 · Atiya et al. · 2016 [cited by applicant]
US 9299192B2 · Kopelman · 2016 [cited by applicant]
US D760901S · Barak et al. · 2016 [cited by applicant]
US 9393087B2 · Moalem · 2016 [cited by applicant]
US 9408679B2 · Kopelman · 2016 [cited by applicant]
US 9431887B2 · Boltanski · 2016 [cited by applicant]
US 9439568B2 · Atiya et al. · 2016 [cited by applicant]
US 9451873B1 · Kopelman et al. · 2016 [cited by applicant]
US D768861S · Barak et al. · 2016 [cited by applicant]
US D771817S · Barak et al. · 2016 [cited by applicant]
US 9491863B2 · Boltanski · 2016 [cited by applicant]
US D774193S · Makmel et al. · 2016 [cited by applicant]
US 9510757B2 · Kopelman et al. · 2016 [cited by applicant]
US 9660418B2 · Atiya et al. · 2017 [cited by applicant]
US 9668829B2 · Kopelman · 2017 [cited by applicant]
US 9675430B2 · Verker et al. · 2017 [cited by applicant]
US 9693839B2 · Atiya et al. · 2017 [cited by applicant]
US 9717402B2 · Lampert et al. · 2017 [cited by applicant]
US 9724177B2 · Levin · 2017 [cited by applicant]
US 9844426B2 · Atiya et al. · 2017 [cited by applicant]
US 10076389B2 · Wu et al. · 2018 [cited by applicant]
US 10098714B2 · Kuo · 2018 [cited by applicant]
US 10108269B2 · Sabina et al. · 2018 [cited by applicant]
US 10111581B2 · Makmel · 2018 [cited by applicant]
US 10111714B2 · Kopelman et al. · 2018 [cited by applicant]
US 10123706B2 · Elbaz et al. · 2018 [cited by applicant]
US 10136972B2 · Sabina et al. · 2018 [cited by applicant]
US 10195006B2 · Freiberg · 2019 [cited by examiner]
US 10380212B2 · Elbaz et al. · 2019 [cited by applicant]
US 10390913B2 · Sabina et al. · 2019 [cited by applicant]
US 10453269B2 · Furst · 2019 [cited by applicant]
US 10456043B2 · Atiya et al. · 2019 [cited by applicant]
US 10499793B2 · Ozerov et al. · 2019 [cited by applicant]
US 10504386B2 · Levin et al. · 2019 [cited by applicant]
US 10507087B2 · Elbaz et al. · 2019 [cited by applicant]
US 10517482B2 · Sato et al. · 2019 [cited by applicant]
US 10695150B2 · Kopelman et al. · 2020 [cited by applicant]
US 10708574B2 · Furst et al. · 2020 [cited by applicant]
US 10772506B2 · Atiya et al. · 2020 [cited by applicant]
US 10813727B2 · Sabina et al. · 2020 [cited by applicant]
US 10888399B2 · Kopelman et al. · 2021 [cited by applicant]
US 10952816B2 · Kopelman · 2021 [cited by applicant]
US 10980613B2 · Shanjani et al. · 2021 [cited by applicant]
US 10993783B2 · Wu · 2021 [cited by examiner]
US 11013581B2 · Sabina et al. · 2021 [cited by applicant]
US D925739S · Shalev et al. · 2021 [cited by applicant]
US 11096765B2 · Atiya et al. · 2021 [cited by applicant]
US 11238586B2 · Minchenkov et al. · 2022 [cited by applicant]
US 11367192B2 · Kopelman et al. · 2022 [cited by applicant]
US 11455727B2 · Minchenkov et al. · 2022 [cited by applicant]
US 11478132B2 · Kopelman et al. · 2022 [cited by applicant]
US 11563929B2 · Saphier et al. · 2023 [cited by applicant]
US 11564777B2 · Kopelman · 2023 [cited by examiner]
US 11633268B2 · Moalem et al. · 2023 [cited by applicant]
US 11707238B2 · Moshe et al. · 2023 [cited by applicant]
US RE49605E · Kopelman · 2023 [cited by applicant]
US 11744681B2 · Kopelman et al. · 2023 [cited by applicant]
US 11759277B2 · Shalev et al. · 2023 [cited by applicant]
US 11801126B2 · Hansen · 2023 [cited by examiner]
US 11896461B2 · Saphier et al. · 2024 [cited by applicant]
US 11937996B2 · Peleg · 2024 [cited by applicant]
US 11995839B2 · Weiss et al. · 2024 [cited by applicant]
US 12370015B2 · Winchell · 2025 [cited by examiner]
US 20180153649A1 · Wu · 2018 [cited by examiner]
US 20190083208A1 · Hansen · 2019 [cited by examiner]
US 20210121049A1 · Rudnitsky et al. · 2021 [cited by applicant]
US 20210137653A1 · Saphier et al. · 2021 [cited by applicant]
US 20210196152A1 · Saphier et al. · 2021 [cited by applicant]
US 20220039917A1 · Winchell · 2022 [cited by examiner]
US 20230030704A1 · Weinstein · 2023 [cited by examiner]