Sdc U.S. Smilepay Spv

Smiledirectclub LLC

Jordan Katzman

Christopher Yancey

Alex Fenkell

Josh Chapman

David Katzman

Jessica Cicurel

Tim Wucher

Ryan Amelon

Josh Long

John Dargis

Clete Culp

Steve Cicurel

Andrew Wright

Aleksey Gurtovoy

Ramsey Jones

Jared Lafer

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

A method includes receiving representations of a first set of dental impressions including a first dental impression of an upper dental arch of a user and a second dental impression of a lower dental arch of the user having been created using contents from an initial dental impression kit sent to the user, identifying a defective dental impression, sending a retake dental impression kit to the user where the retake dental impression kit includes a set of dental trays consisting of dental trays corresponding to the dental arch associated with the defective dental impression, receiving representations of a second set of dental impressions of the dental arch associated with the defective dental impression, and manufacturing, based on the second set of dental impressions, a plurality of dental aligners specific to the user and being configured to reposition one or more teeth of the user.

Dental impression retake kit and methods therefor

A method includes sending a dental impression kit to a user including dental trays of different sizes including a first set of dental trays having a first size and a second set of dental trays having a second size, a base putty, and a catalyst putty, where the base putty and the catalyst putty are mixable to form a putty mixture for creating dental impressions. The method includes receiving dental impressions created using the first set of dental trays, the first set of dental trays having been selected by the user based on a fit of a first dental tray of the first set on a dental arch of the user relative to a fit of a second dental tray of the second set, and manufacturing a plurality of dental aligners specific to the user and being configured to reposition one or more teeth of the user.

Dental impression kit and methods therefor

A distributed system includes one or more servers configured to provide computing devices access to portals hosted by the one or more servers, where the portals include a patient intake portal accessible by a first computing device located at an office of an intake dentist or orthodontist, and an approving portal accessible by a second computing device remote from the first computing device. The distributed system includes a dental aligner fabrication system including a fabrication computing device and dental aligner fabrication equipment. The patient intake portal is configured to provide a plurality of status updates to the intake dentist or orthodontist regarding a status of the patient, where the plurality of status updates include an indication that the dental aligners have been sent to the patient.

Distributed system for fabricating dental aligners

Systems and methods for intraoral scanning include determining, by an appointment management system, a condition that dental impressions have not been received from a user within a threshold time period where the user was provided with an in-home dental impression kit, generating and communicating a message to the user based on the condition where the message prompts the user to schedule an appointment at an intraoral scanning site, receiving a request to schedule an appointment at the intraoral scanning site where the intraoral scanning site has an intraoral scanner configured to conduct an intraoral scan of a mouth of the user, and conducting the intraoral scan using the intraoral scanner at the intraoral scanning site where the intraoral scan generates three-dimensional data of the mouth of the user.

Arrangements for intraoral scanning

A method includes receiving, by a model generation engine, an image of a dental arch of a user, executing, by the model generation engine, a machine learning model that is trained to receive the image and output a representation of the image, where in at least one iteration during training, the machine learning model determines a difference between one or more features extracted from an iteration of the representation of the image and a corresponding one or more features extracted from the image, and the method further includes outputting, by the model generation engine, an output representation of the image.

Systems and methods for constructing a dental arch image using a machine learning model

Systems and methods include determining, by an appointment management system, a condition that an appointment to conduct an intraoral scan at an intraoral scanning site has been missed by a user, automatically generating a message to the user indicating that an at-home dental impression kit can be sent to the user based on determining that the appointment has been missed, and transmitting the automatically generated message to a communications device associated with the user.

A system includes one or more processors coupled to a non-transitory memory, where the one or more processors are configured to generate, using a training set comprising one or more two-dimensional (2D) training images of a dental arch and a three-dimensional (3D) dental arch model, a machine-learning model configured to generate 3D models of dental arches from 2D images of the dental arches, receive one or more 2D images of a dental arch of a user obtained by a user device of the user, and execute the machine-learning model using the one or more 2D images as input to generate a 3D model of the dental arch of the user.

Systems and methods for constructing a three-dimensional model from two-dimensional images

Systems and methods for generating a three-dimensional (3D) model of a user's dental arch based on two-dimensional (2D) images of dental impressions include a model training system that provides a machine learning model using training image(s) of a dental impression of a respective dental arch and a 3D training model of the respective dental arch. A model generation system receives first image(s) of a first dental impression of a user's dental arch and second image(s), which may be of the first dental impression or a second dental impression of the dental arch. The model generation system generates a first and second 3D model of the dental arch by applying the first image(s) and second image(s) to the machine learning model.

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.

A system includes an orientation determination system comprising a camera where the camera is configured to capture an image of an orientation feature of a physical dental model of a dental arch of a customer with material thermoformed thereon. The orientation determination system is configured to identify an offset of the physical dental model with respect to a fixture plate during positioning or before or after the physical dental model is positioned on the fixture plate by determining an actual orientation of the physical dental model based on the orientation feature. The system also includes a laser trimming system configured to cut the material along a trim line based on the identified offset while the fixture plate is moved about at least two axes to produce a dental aligner specific to the customer and being configured to reposition one or more teeth of the customer.

Systems for laser trimming dental aligners

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.

Systems and methods for generating a three-dimensional (3D) model of a user's dental arch based on two-dimensional (2D) images include a model training system that receives data packets of a training set. Each data packet may include data corresponding to training images of a respective dental arch and a 3D training model of the respective dental arch. The model training system identifies, for a data packet, correlation points between the one or more training images and the 3D training model of the respective dental arch. The model training system generates a machine learning model using the correlation points for the data packets of the training set. A model generation system receives one or more images of a dental arch. The model generation system generates a 3D model of the dental arch by applying the images of the dental arch to the machine learning model.

Systems and methods of remote orthodontic treatment include receiving a plurality of images from a customer where the plurality of images represent teeth of the customer, receiving a first approval from a staff computing device where the first approval is indicative of a staff user approving the plurality of images for assessment by a dental professional, receiving a second approval from a provider computing device where the second approval is indicative of the provider approving a fitness of the customer for treatment, generating, in response to receiving the second approval, a treatment plan including a series of steps to reposition at least one tooth of the teeth of the customer, and manufacturing one or more aligners based at least in part on the treatment plan where the aligners are specific to the customer and are configured to reposition the at least one tooth of the teeth of the customer.

Growing community of inventors

Nashville, TN, United States of America

Jordan Katzman