Align Technology, Inc.

Luyao Cai

Chunhua Li

Jun Sato

Rohit Tanugula

Yuxiang Wang

Kangning Su

Andrew Jang

Reza Shirazi Aghjari

John Y Morton

Peter Webber

Bruce Cam

Andrey Cherkas

Eric Yau

Tzishing Jesse Lim

Jennifer Chavez

Anna Akopova

Viktoria Medvinskaya

Zhou (Jonelle) Yu

Arno Kukk

Pavel Pokotilov

Zhou Yu

Ping Liu

Methods for designing orthodontic appliances are provided. In some embodiments, a method includes receiving a treatment plan for a patient's dentition and generating an aligner model representing an aligner configured to implement a treatment stage of the treatment plan. The aligner model can include a thickness map having a thickness parameter for each location of the aligner model. The method can include adjusting the thickness map by using the aligner model to evaluate an outcome when the aligner is applied to the patient's dentition, and iteratively reducing the value of each thickness parameter of the thickness map based on the evaluation until a resulting thickness is determined for each thickness parameter. The method can also include providing instructions to manufacture the aligner based on the aligner model with the resulting thickness for each thickness parameter of the thickness map.

Methods for aligner design using thickness maps

The disclosed computer-implemented method for selecting locations of mandibular relocation features (MRFs) on oral appliances may include determining candidate locations for placing an MRF on a first oral appliance for a first stage of mandibular relocation (MR) treatment and determining candidate locations for placing an MRF on a second oral appliance for a second stage. The method may include selecting from the candidate locations for the first stage and the second stage based on decreasing a distance between the selected pair of candidate locations. The method may further include processing multiple MRFs in parallel. The method may also include increasing a number of initial candidate locations. Locations for intermediate stages may be selected based on interpolating between the first and second stages or based on decreasing a distance between the selected pair if the interpolation fails. The intermediate stages may be processed using a binary search or a linear search.

Systems and methods for precision wing placement

A support is coupled to an appliance to decrease warpage. The support may comprise a plurality of extensions coupled to the appliance to decrease warpage. The extensions can be coupled to the appliance at one or more of many locations, such as on an occlusal surface, a wall, an edge or an interior of the appliance. In some embodiments, the extensions are coupled to walls of the appliance such as a buccal wall and a lingual wall, and the extensions can be coupled to the walls of the appliance near edges of the walls, such as a gingivally facing edges of the walls that are oriented toward the gingiva when the appliance is worn.

Various structured supports for 3D printed aligners/mouth pieces

Methods and systems for producing orthodontic appliances are provided herein utilizing iterative modeling techniques to increase the efficiency and efficacy of said appliances. Further disclosed herein are the orthodontic appliances fabricated from such methods.

Automatic aligner design with robust parametric optimization method

Embodiments relate to an aligner breakage solution that tests damage to an aligner using machine learning. A method includes processing data from a digital design for an orthodontic aligner by a trained machine learning model and outputting, by the trained machine learning model, a probability that the orthodontic aligner associated with the digital design will be damaged during manufacturing of the orthodontic aligner. The method further includes making a comparison of the probability that the orthodontic aligner associated with the digital design will be damaged during manufacturing of the orthodontic aligner to a probability threshold and determining whether the orthodontic aligner is a high risk orthodontic aligner based on a result of the comparison. Responsive to determining that the orthodontic aligner is a high risk orthodontic aligner, the method includes performing at least one of a) a corrective action or b) selecting a manufacturing flow for high risk orthodontic aligners.

Aligner damage prediction using machine learning

Embodiments relate to an aligner breakage solution that tests probability of aligner breakage at weak points. A method includes gathering a digital model representing an aligner for a dental arch of a patient, receiving material property information for a material to be used to manufacture the aligner, and analyzing one or more regions of the aligner. Analyzing a region of the aligner comprises simulating application of a load around the region, determining at least one of a stress, a strain or a strain energy density at the region, evaluating a strength of the aligner at the region, and determining whether the region satisfies a damage criterion based on the strength of the aligner at the region.

Aligner damage prediction at weak spots with simulation

A support is coupled to an appliance to decrease warpage. The support may comprise a plurality of extensions coupled to the appliance to decrease warpage. The extensions can be coupled to the appliance at one or more of many locations, such as on an occlusal surface, a wall, an edge or an interior of the appliance. In some embodiments, the extensions are coupled to walls of the appliance such as a buccal wall and a lingual wall, and the extensions can be coupled to the walls of the appliance near edges of the walls, such as gingivally facing edges of the walls that are oriented toward the gingiva when the appliance is worn.

Embodiments relate to an aligner breakage solution that tests progressive damage to an aligner. A method includes gathering a digital model representing an aligner for a dental arch of a patient, and simulating progressive damage to the aligner. Simulating progressive damage for a region of the aligner comprises simulating, using at least the digital model, a sequence of loads on the aligner, determining an amount of damage to the region of the aligner for each load, and after each simulation of a load on the aligner, updating the digital model based on the amount of damage to the region of the aligner. The method further includes determining whether a damage criterion is satisfied for at least one region of the aligner and determining whether to implement one or more corrective actions for the aligner.

Prediction of aligner progressive damage using simulation

Embodiments relate to an aligner breakage solution. A method includes obtaining a digital design of a polymeric aligner for a dental arch of a patient. The polymeric aligner is shaped to apply forces to teeth of the dental arch. The method also includes performing an analysis on the digital design of the polymeric aligner using at least one of a) a trained machine learning model, b) a numerical simulation, c) a geometry evaluator or d) a rules engine. The method may also include determining, based on the analysis, whether the digital design of the polymeric aligner includes probable points of damage, wherein for a probable point of damage there is a threshold probability that breakage, deformation, or warpage will occur. The method may also include, responsive to determining that the digital design of the polymeric aligner comprises probable points of damage, performing corrective actions based on the probable points of damage.

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San Jose, CA, United States of America

Luyao Cai