IP Library › Granted Patent US 12,702,528
Granted Patent B2
US 12,702,528 · App. 18/149,037 · Granted Aug 11, 2026

Relapse prevention retainer

Inventors: Ken Wu (San Jose, CA); Huizhong Li (San Jose, CA); Mitra Derakhshan (Herndon, VA); Zhou Yu (Santa Clara, CA); James Nishimuta (Durham, NC); Yuxiang Wang (Newark, CA); Xi Cai (San Jose, CA); Rohit Tanugula (San Jose, CA); Jeeyoung Choi (Sunnyvale, CA); Eric Yau (Santa Clara, CA); Jun Sato (San Jose, CA); John Y. Morton (San Jose, CA)
Assignee: Align Technology, Inc.
A61C7/08A61C7/002A61C13/34G06F30/20
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Quick Facts
Patent No.
US 12,702,528
App. No.
18/149,037
Filed
Dec 30, 2022
Granted
Aug 11, 2026
Kind
B2
Art Unit
3695
USPC
703/1
Abstract

Apparatuses and methods for customized retainers to prevent relapse. In particular, described herein are customized retainers, and methods and apparatuses for making customized retainers that specifically reenforce and prevent movement of one or more teeth having a higher likelihood of relapse following completion of an orthodontic treatment plan.

Claims (33)

1 . A method of creating a retainer for a patient, the method comprising:

estimating a stability estimate for one or more of the patient's teeth from a model of the patient's teeth and a current orthodontic treatment plan for the patient, wherein the stability estimate corresponds to a likelihood that the one or more of the patient's teeth will move out of a target final position from the current orthodontic treatment plan; and

generating a model of the retainer, wherein the retainer is reinforced in one or more regions configured to be in communication with the one or more of the patient's teeth in which the stability estimate exceeds a threshold.

2 . The method of claim 1 , wherein the model of the retainer is a digital model.

3 . The method of claim 1 , where estimating comprises using one or more prior treatment plans specific to the patient in addition to the current orthodontic treatment plan for the patient to estimate a final post-treatment relapse position for the one or more of the patient's teeth.

4 . The method of claim 1 , further comprising forming the retainer from the model of the retainer.

5 . The method of claim 1 , wherein estimating the stability estimate comprises using a machine-learning algorithm trained on a plurality of treatment plans and tooth models.

6 . The method of claim 1 , wherein estimating comprises estimating a plurality of stability estimates, wherein each stability estimate corresponds to a different tooth of the patient's teeth.

7 . The method of claim 1 , further comprising, for each stability estimate that exceeds the threshold, determining a force vector corresponding to a movement of the one or more of the patient's teeth beyond the target final position.

8 . The method of claim 7 , wherein generating the model of the retainer comprises including reinforcement to counter the force vector.

9 . The method of claim 1 , wherein estimating the stability estimate for one or more of the patient's teeth comprises estimating the stability estimate for one or more of: diastema relapse and general spacing relapse, anterior or posterior mesial/distal or buccal/lingual or rotational relapse, lingual crown tip in/buccal crown tip out, deep bite relapse, extrusion/intrusion relapse, arch expansion relapse and/or extraction, Class II treatment relapse, and Class III treatment relapse.

10 . The method of claim 1 , wherein generating the model of the retainer comprises including reinforcement of the one or more regions by making the retainer thicker, stiffer and/or having a longer trim line in the one or more regions configured to be in communication with the one or more of the patient's teeth.

11 . The method of claim 1 , wherein generating the model of the retainer comprises including reinforcement of the one or more regions by including a strut or the retainer thicker in the one or more regions configured to be in communication with the one or more of the patient's teeth.

12 . The method of claim 1 , wherein generating the model of the retainer comprises including reinforcement of the one or more regions so that the retainer applies a force on the one or more of the patient's teeth in which the stability estimate exceeds a threshold to overcorrect the one or more of the patient's teeth.

13 . A computer-implemented method of custom designing a retainer for a patient, the method comprising:

estimating, in a processor, from a model of the patient's teeth, a current orthodontic treatment plan for the patient, a stability estimate for one or more of the patient's teeth, wherein the stability estimate corresponds to a likelihood that the one or more of the patient's teeth will move beyond a target final position from the current orthodontic treatment plan in one or more relapse categories including: diastema relapse and general spacing relapse, anterior or posterior mesial/distal or buccal/lingual or rotational relapse, lingual crown tip in/buccal crown tip out, deep bite relapse, extrusion/intrusion relapse, arch expansion relapse and/or extraction; and

generating a model of the retainer, wherein the retainer is modified in one or more regions configured to be in communication with the one or more of the patient's teeth in which the stability estimate exceeds a threshold.

14 . A non-transitory computer-readable medium including contents that are configured to cause one or more processors to perform a method comprising:

estimating, from a model of the patient's teeth and a current orthodontic treatment plan for a patient, a stability estimate for one or more of the patient's teeth, wherein the stability estimate corresponds to a likelihood that the one or more of the patient's teeth will move out of a target final position from the current orthodontic treatment plan; and

generating a model of a retainer, wherein the retainer is reinforced in one or more regions configured to be in communication with the one or more of the patient's teeth in which the stability estimate exceeds a threshold.

15 . The non-transitory computer-readable medium of claim 14 , where estimating comprises using one or more prior treatment plans specific to the patient in addition to the current orthodontic treatment plan for the patient to estimate a final post-treatment relapse position for the one or more of the patient's teeth.

16 . The non-transitory computer-readable medium of claim 14 , wherein the contents are further configured to cause the one or more processors to form the retainer from the model of the retainer.

17 . The non-transitory computer-readable medium of claim 14 , wherein estimating the stability estimate comprises using a machine-learning algorithm trained on a plurality of treatment plans and tooth models.

18 . The non-transitory computer-readable medium of claim 14 , wherein estimating comprises estimating a plurality of stability estimates, wherein each stability estimate corresponds to a different tooth of the patient's teeth.

19 . The non-transitory computer-readable medium of claim 14 , wherein the contents are further configured to cause the one or more processors to determine, for each stability estimate that exceeds the threshold, a force vector corresponding to a movement of the one or more of the patient's teeth beyond the target final position.

20 . The non-transitory computer-readable medium of claim 19 , wherein generating the model of the retainer comprises including reinforcement to counter the force vector.

21 . The non-transitory computer-readable medium of claim 14 , wherein estimating the stability estimate for one or more of the patient's teeth comprises estimating the stability estimate for one or more of: diastema relapse and general spacing relapse, anterior or posterior mesial/distal or buccal/lingual or rotational relapse, lingual crown tip in/buccal crown tip out, deep bite relapse, extrusion/intrusion relapse, arch expansion relapse and/or extraction, Class II treatment relapse, and Class III treatment relapse.

22 . The non-transitory computer-readable medium of claim 14 , wherein generating the model of the retainer comprises including reinforcement of the one or more regions by making the retainer thicker, stiffer and/or having a longer trim line in the one or more regions configured to be in communication with the one or more of the patient's teeth.

23 . The non-transitory computer-readable medium of claim 14 , wherein generating the model of the retainer comprises including reinforcement of the one or more regions by including a strut or the retainer thicker in the one or more regions configured to be in communication with the one or more of the patient's teeth.

24 . The non-transitory computer-readable medium of claim 14 , wherein generating the model of the retainer comprises including reinforcement of the one or more regions so that the retainer applies a force on the one or more of the patient's teeth in which the stability estimate exceeds a threshold to overcorrect the one or more of the patient's teeth.

25 . A non-transitory computer-readable medium including contents that are configured to cause one or more processors to perform a method comprising:

estimating, in a processor, from a model of the patient's teeth, a current orthodontic treatment plan for a patient, a stability estimate for one or more of the patient's teeth, wherein the stability estimate corresponds to a likelihood that the one or more of the patient's teeth will move beyond a target final position from the current orthodontic treatment plan in one or more relapse categories including: diastema relapse and general spacing relapse, anterior or posterior mesial/distal or buccal/lingual or rotational relapse, lingual crown tip in/buccal crown tip out, deep bite relapse, extrusion/intrusion relapse, arch expansion relapse and/or extraction; and

generating a model of a retainer, wherein the retainer is modified in one or more regions configured to be in communication with the one or more of the patient's teeth in which the stability estimate exceeds a threshold.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 17, 2023
From: WU, KEN; LI, HUIZHONG; DERAKHSHAN, MITRA; YU, ZHOU; NISHIMUTA, JAMES; WANG, YUXIANG; CAI, XI; TANUGULA, ROHIT; CHOI, JEEYOUNG; YAU, ERIC; SATO, JUN; MORTON, JOHN Y.
To: ALIGN TECHNOLOGY, INC.
Reel/Frame 062737/0388 →
Continuity (2)
Provisional Application 63295506 · Dec 30, 2021
Related Publication 20230210637A1 · Jul 6, 2023
References Cited (175)
US 5820368A · Wolk · 1998 [cited by applicant]
US 6183248B1 · Chishti et al. · 2001 [cited by applicant]
US 6309215B1 · Phan et al. · 2001 [cited by applicant]
US 6386864B1 · Kuo · 2002 [cited by applicant]
US 6454565B2 · Phan et al. · 2002 [cited by applicant]
US 6471511B1 · Chishti et al. · 2002 [cited by applicant]
US 6524101B1 · Phan et al. · 2003 [cited by applicant]
US 6572372B1 · Phan et al. · 2003 [cited by applicant]
US 6607382B1 · Kuo et al. · 2003 [cited by applicant]
US 6705863B2 · Phan et al. · 2004 [cited by applicant]
US 6783604B2 · Tricca · 2004 [cited by applicant]
US 6790035B2 · Tricca et al. · 2004 [cited by applicant]
US 6814574B2 · Abolfathi et al. · 2004 [cited by applicant]
US 6830450B2 · Knopp et al. · 2004 [cited by applicant]
US 6947038B1 · Anh et al. · 2005 [cited by applicant]
US 7074039B2 · Kopelman et al. · 2006 [cited by applicant]
US 7104792B2 · Taub et al. · 2006 [cited by applicant]
US 7121825B2 · Chishti et al. · 2006 [cited by applicant]
US 7160107B2 · Kopelman et al. · 2007 [cited by applicant]
US 7192273B2 · McSurdy, Jr. · 2007 [cited by applicant]
US 7347688B2 · Kopelman et al. · 2008 [cited by applicant]
US 7354270B2 · Abolfathi et al. · 2008 [cited by applicant]
US 7448514B2 · Wen · 2008 [cited by applicant]
US 7481121B1 · Cao · 2009 [cited by applicant]
US 7543511B2 · Kimura et al. · 2009 [cited by applicant]
US 7553157B2 · Abolfathi et al. · 2009 [cited by applicant]
US 7600999B2 · Knopp · 2009 [cited by applicant]
US 7658610B2 · Knopp · 2010 [cited by applicant]
US 7766658B2 · Tricca et al. · 2010 [cited by applicant]
US 7771195B2 · Knopp et al. · 2010 [cited by applicant]
US 7854609B2 · Chen et al. · 2010 [cited by applicant]
US 7871269B2 · Wu et al. · 2011 [cited by applicant]
US 7878801B2 · Abolfathi et al. · 2011 [cited by applicant]
US 7878805B2 · Moss et al. · 2011 [cited by applicant]
US 7883334B2 · Li et al. · 2011 [cited by applicant]
US 7914283B2 · Kuo · 2011 [cited by applicant]
US 7947508B2 · Tricca et al. · 2011 [cited by applicant]
US 8152518B2 · Kuo · 2012 [cited by applicant]
US 8172569B2 · Matty et al. · 2012 [cited by applicant]
US 8235715B2 · Kuo · 2012 [cited by applicant]
US 8292617B2 · Brandt et al. · 2012 [cited by applicant]
US 8337199B2 · Wen · 2012 [cited by applicant]
US 8401686B2 · Moss et al. · 2013 [cited by applicant]
US 8517726B2 · Kakavand et al. · 2013 [cited by applicant]
US 8562337B2 · Kuo et al. · 2013 [cited by applicant]
US 8641414B2 · Borovinskih et al. · 2014 [cited by applicant]
US 8684729B2 · Wen · 2014 [cited by applicant]
US 8708697B2 · Li et al. · 2014 [cited by applicant]
US 8758009B2 · Chen et al. · 2014 [cited by applicant]
US 8771149B2 · Rahman et al. · 2014 [cited by applicant]
US 8899976B2 · Chen et al. · 2014 [cited by applicant]
US 8899977B2 · Cao et al. · 2014 [cited by applicant]
US 8936463B2 · Mason et al. · 2015 [cited by applicant]
US 8936464B2 · Kopelman · 2015 [cited by applicant]
US 9022781B2 · Kuo et al. · 2015 [cited by applicant]
US 9119691B2 · Namiranian et al. · 2015 [cited by applicant]
US 9161823B2 · Morton et al. · 2015 [cited by applicant]
US 9241774B2 · Li et al. · 2016 [cited by applicant]
US 9326831B2 · Cheang · 2016 [cited by applicant]
US 9433476B2 · Khardekar et al. · 2016 [cited by applicant]
US 9610141B2 · Kopelman et al. · 2017 [cited by applicant]
US 9655691B2 · Li et al. · 2017 [cited by applicant]
US 9675427B2 · Kopelman · 2017 [cited by applicant]
US 9700385B2 · Webber · 2017 [cited by applicant]
US 9744001B2 · Choi et al. · 2017 [cited by applicant]
US 9844424B2 · Wu et al. · 2017 [cited by applicant]
US 10045835B2 · Boronkay et al. · 2018 [cited by applicant]
US 10111730B2 · Webber et al. · 2018 [cited by applicant]
US 10150244B2 · Sato et al. · 2018 [cited by applicant]
US 10201409B2 · Mason et al. · 2019 [cited by applicant]
US 10213277B2 · Webber et al. · 2019 [cited by applicant]
US 10299894B2 · Tanugula et al. · 2019 [cited by applicant]
US 10363116B2 · Boronkay · 2019 [cited by applicant]
US 10383705B2 · Shanjani et al. · 2019 [cited by applicant]
US D865180S · Bauer et al. · 2019 [cited by applicant]
US 10449016B2 · Kimura et al. · 2019 [cited by applicant]
US 10463452B2 · Matov et al. · 2019 [cited by applicant]
US 10470847B2 · Shanjani et al. · 2019 [cited by applicant]
US 10492888B2 · Chen et al. · 2019 [cited by applicant]
US 10517701B2 · Boronkay · 2019 [cited by applicant]
US 10537406B2 · Wu et al. · 2020 [cited by applicant]
US 10537463B2 · Kopelman · 2020 [cited by applicant]
US 10548700B2 · Fernie · 2020 [cited by applicant]
US 10555792B2 · Kopelman et al. · 2020 [cited by applicant]
US 10588776B2 · Cam et al. · 2020 [cited by applicant]
US 10613515B2 · Cramer et al. · 2020 [cited by applicant]
US 10639134B2 · Shanjani et al. · 2020 [cited by applicant]
US 10743964B2 · Wu et al. · 2020 [cited by applicant]
US 10758323B2 · Kopelman · 2020 [cited by applicant]
US 10781274B2 · Liska et al. · 2020 [cited by applicant]
US 10813720B2 · Grove et al. · 2020 [cited by applicant]
US 10874483B2 · Boronkay · 2020 [cited by applicant]
US 10881487B2 · Cam et al. · 2021 [cited by applicant]
US 10912629B2 · Tanugula et al. · 2021 [cited by applicant]
US 10959810B2 · Li et al. · 2021 [cited by applicant]
US 10993783B2 · Wu et al. · 2021 [cited by applicant]
US 11026768B2 · Moss et al. · 2021 [cited by applicant]
US 11026831B2 · Kuo · 2021 [cited by applicant]
US 11045282B2 · Kopelman et al. · 2021 [cited by applicant]
US 11045283B2 · Riley et al. · 2021 [cited by applicant]
US 11103330B2 · Webber et al. · 2021 [cited by applicant]
US 11123156B2 · Cam et al. · 2021 [cited by applicant]
US 11154382B2 · Kopelman et al. · 2021 [cited by applicant]
US 11166788B2 · Webber · 2021 [cited by applicant]
US 11174338B2 · Liska et al. · 2021 [cited by applicant]
US 11219506B2 · Shanjani et al. · 2022 [cited by applicant]
US 11259896B2 · Matov et al. · 2022 [cited by applicant]
US 11273011B2 · Shanjani et al. · 2022 [cited by applicant]
US 11278375B2 · Wang et al. · 2022 [cited by applicant]
US 11318667B2 · Mojdeh et al. · 2022 [cited by applicant]
US 11331166B2 · Morton et al. · 2022 [cited by applicant]
US 11344385B2 · Morton et al. · 2022 [cited by applicant]
US 11376101B2 · Sato et al. · 2022 [cited by applicant]
US 11419702B2 · Sato et al. · 2022 [cited by applicant]
US 11419710B2 · Mason et al. · 2022 [cited by applicant]
US 11471253B2 · Venkatasanthanam et al. · 2022 [cited by applicant]
US 11497586B2 · Kopelman · 2022 [cited by applicant]
US 11504214B2 · Wu et al. · 2022 [cited by applicant]
US 11523881B2 · Wang et al. · 2022 [cited by applicant]
US 11534268B2 · Li et al. · 2022 [cited by applicant]
US 11534974B2 · O'Leary · 2022 [cited by examiner]
US 11554000B2 · Webber · 2023 [cited by applicant]
US 11564777B2 · Kopelman et al. · 2023 [cited by applicant]
US 11571278B2 · Kopelman et al. · 2023 [cited by applicant]
US 11571279B2 · Wang et al. · 2023 [cited by applicant]
US 11576750B2 · Kopelman et al. · 2023 [cited by applicant]
US 11576752B2 · Morton et al. · 2023 [cited by applicant]
US 11589955B2 · Medvinskaya · 2023 [cited by examiner]
US 12263632B2 · Martínez González · 2025 [cited by examiner]
US 20020192617A1 · Phan et al. · 2002 [cited by applicant]
US 20040166462A1 · Phan et al. · 2004 [cited by applicant]
US 20040166463A1 · Wen et al. · 2004 [cited by applicant]
US 20050014105A1 · Abolfathi et al. · 2005 [cited by applicant]
US 20050186524A1 · Abolfathi et al. · 2005 [cited by applicant]
US 20050244768A1 · Taub et al. · 2005 [cited by applicant]
US 20060019218A1 · Kuo · 2006 [cited by applicant]
US 20060078841A1 · DeSimone et al. · 2006 [cited by applicant]
US 20060115782A1 · Li et al. · 2006 [cited by applicant]
US 20060115785A1 · Li et al. · 2006 [cited by applicant]
US 20060199142A1 · Liu et al. · 2006 [cited by applicant]
US 20060234179A1 · Wen et al. · 2006 [cited by applicant]
US 20080118882A1 · Su · 2008 [cited by applicant]
US 20080160473A1 · Li et al. · 2008 [cited by applicant]
US 20080286716A1 · Sherwood · 2008 [cited by applicant]
US 20080286717A1 · Sherwood · 2008 [cited by applicant]
US 20090280450A1 · Kuo · 2009 [cited by applicant]
US 20100055635A1 · Kakavand · 2010 [cited by applicant]
US 20100129763A1 · Kuo · 2010 [cited by applicant]
US 20110269092A1 · Kuo et al. · 2011 [cited by applicant]
US 20140067334A1 · Kuo · 2014 [cited by applicant]
US 20150366638A1 · Kopelman et al. · 2015 [cited by applicant]
US 20160193014A1 · Morton et al. · 2016 [cited by applicant]
US 20170007359A1 · Kopelman · 2017 [cited by examiner]
US 20170007361A1 · Boronkay et al. · 2017 [cited by applicant]
US 20170135793A1 · Webber et al. · 2017 [cited by applicant]
US 20170165032A1 · Webber et al. · 2017 [cited by applicant]
US 20180360567A1 · Xue et al. · 2018 [cited by applicant]
US 20190000592A1 · Cam et al. · 2019 [cited by applicant]
US 20190000593A1 · Cam et al. · 2019 [cited by applicant]
US 20190008613A1 · Cao · 2019 [cited by examiner]
US 20190046297A1 · Kopelman et al. · 2019 [cited by applicant]
US 20190099129A1 · Kopelman et al. · 2019 [cited by applicant]
US 20190125497A1 · Derakhshan et al. · 2019 [cited by applicant]
US 20190262101A1 · Shanjani et al. · 2019 [cited by applicant]
US 20190298494A1 · Webber et al. · 2019 [cited by applicant]
US 20200000553A1 · Makarenkova et al. · 2020 [cited by applicant]
US 20200100866A1 · Medvinskaya et al. · 2020 [cited by applicant]
US 20200155276A1 · Cam et al. · 2020 [cited by applicant]
US 20200188062A1 · Kopelman et al. · 2020 [cited by applicant]
US 20200214598A1 · Li et al. · 2020 [cited by applicant]
US 20200214801A1 · Wang et al. · 2020 [cited by applicant]
US 20200390523A1 · Sato et al. · 2020 [cited by applicant]
US 20210147672A1 · Cole et al. · 2021 [cited by applicant]
US 20210196429A1 · Shojaei et al. · 2021 [cited by applicant]
US 20220160467A2 · Shojaei · 2022 [cited by examiner]