Sdc U.S. Smilepay Spv

Ryan Amelon

Jordan Katzman

Tim Wucher

Ramsey Jones

Jared Lafer

Aleksey Gurtovoy

Martin Chobanyan

Systems and methods for generating a 3D model of a dentition are disclosed herein. A method includes generating a first 3D model of a dentition. The dentition includes a model tooth in a first position. The method includes receiving a digital representation comprising a patient tooth. The patient tooth corresponds to the model tooth of the first 3D model. The method includes determining a virtual camera parameter associated with the digital representation. The method includes adjusting the virtual camera parameter to determine a 3D position of the corresponding patient tooth. The method includes moving the model tooth of the first 3D model from the first position to a second position. The second position corresponds to the 3D position of the corresponding patient tooth. The method includes generating a second 3D model comprising the model tooth of the first 3D model in the second position.

Positioning individual three-dimensional model teeth based on two-dimensional images

Systems and methods disclosed herein use a first machine learning architecture and a second machine learning architecture where the first machine learning architecture executes on a first processor and receives a first image representing a mouth of a user, determines user feedback for outputting to the user based on a first machine learning model, and outputs the user feedback for capturing a second image representing the mouth of the user. The second machine learning architecture executes on a second processor and receives the first image and the second image, and generates a 3D model of at least a portion of a dental arch of the user based on the first image and the second image where the 3D model is generated based on a second machine learning model of the second machine learning architecture.

Machine learning architecture for imaging protocol detector

Systems and methods for generating a 3D model of a dentition are disclosed herein. A method includes generating a first 3D model of a dentition including a plurality of model teeth. The method includes receiving at least one digital representation comprising patient teeth. The method includes determining a virtual camera parameter. The virtual camera parameter corresponds with a camera used to capture the digital representation. The method includes comparing, based on the virtual camera parameter, a first position of a model tooth with a position of a corresponding patient tooth of the digital representation. The method includes moving the model tooth from the first position to a second position. The second position is based on the position of the corresponding patient tooth. The method includes generating a second 3D model comprising the model tooth of the first 3D model in the second position.

Disclosed is a machine learning architecture for a two-dimensional image protocol detector configured to receive a first image representing at least a portion of a mouth of a user, and output user feedback for capturing a second image representing a portion of the mouth of the user, where the machine learning architecture outputs the user feedback in response to an image quality score of the first image not satisfying an image quality threshold.

Growing community of inventors

Nashville, TN, United States of America

Ryan Amelon