Align Technology, Inc.

Anna Akopova

Chunhua Li

Jun Sato

Rohit Tanugula

Andrey Cherkas

Yuxiang Wang

Reza Shirazi Aghjari

Andrew Jang

Luyao Cai

Viktoria Medvinskaya

Arno Kukk

Kangning Su

Arev Hambardzumyan

Arseniy Korsun

Kirill Savin

Vladislav Shcherbinin

Systems, methods, and computer-readable media for managing customer contact based on context. Specifically, a provider in a provider network associated with provisioning a specific product to a specific user can be identified. Context information related to the provider provisioning one or more products in relation to one or more workflows for provisioning the one or more products through the provider network can be accessed. A model can be applied based on the context information to identify circumstances associated with a likelihood that the provider will contact an enterprise of the provider network with respect to provisioning of the specific product to the specific user. As follows, performance of one or more remedial actions through the enterprise to address the provider contacting the enterprise can be facilitated based on the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network.

Customer contact management in association with provisioning products through a provider network of an enterprise

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. 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.

Aligner damage prediction and mitigation

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Moscow, Russia

Anna Akopova