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公开(公告)号:US20230326242A1
公开(公告)日:2023-10-12
申请号:US18202778
申请日:2023-05-26
Applicant: SDC U.S. SmilePay SPV
Inventor: Tim Wucher , Ryan Amelon , Jordan Katzman , Aleksey Gurtovoy
CPC classification number: G06V40/171 , G06N20/00 , G06T7/0002 , G06V10/82 , G06V20/41 , G06V20/46 , G06T2207/20081 , G06T2207/30168 , G06T2207/30201
Abstract: A system includes one or more processors coupled to non-transitory memory, and the one or more processors are configured to receive a first image representing at least a portion of a mouth of a user, execute a first machine-learning architecture trained to generate a set of features from the first image, determine, based on the set of features, that the first image satisfies at least one criteria for executing a second machine-learning architecture based on the first image, and generate, based on the first image satisfying the at least one criteria, a prompt indicating feedback for capturing a second image representing at least a second portion of the mouth of the user.
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公开(公告)号:US20230386045A1
公开(公告)日:2023-11-30
申请号:US18202646
申请日:2023-05-26
Applicant: SDC U.S. SmilePay SPV
Inventor: Ryan Amelon , Saul Kohn , Aleksey Gurtovoy
CPC classification number: G06T7/11 , G06T7/136 , A61C7/002 , G06T7/55 , G06T2207/20081
Abstract: Systems and methods disclosed herein include a processor and a non-transitory computer readable medium containing instructions that when executed by the processor causes the processor to receive an image representing a first portion of a mouth of a user, segment the image to generate segmented regions of teeth present in the image, generate an imaging record by mapping the segmented regions of teeth present in the image to a template model where the imaging record indicates regions of the teeth that remain to be captured, and provide feedback identifying the regions of the teeth that remain to be captured based on the imaging record.
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公开(公告)号:US11423697B1
公开(公告)日:2022-08-23
申请号:US17401053
申请日:2021-08-12
Applicant: SDC U.S. SMILEPAY SPV
Inventor: Tim Wucher , Ryan Amelon , Jordan Katzman , Aleksey Gurtovoy
Abstract: 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.
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公开(公告)号:US11663852B2
公开(公告)日:2023-05-30
申请号:US17858734
申请日:2022-07-06
Applicant: SDC U.S. SmilePay SPV
Inventor: Tim Wucher , Ryan Amelon , Jordan Katzman , Aleksey Gurtovoy
CPC classification number: G06V40/171 , G06N20/00 , G06T7/0002 , G06V10/82 , G06V20/41 , G06V20/46 , G06T2207/20081 , G06T2207/30168 , G06T2207/30201
Abstract: 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.
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公开(公告)号:US20230048895A1
公开(公告)日:2023-02-16
申请号:US17858734
申请日:2022-07-06
Applicant: SDC U.S. SmilePay SPV
Inventor: Tim Wucher , Ryan Amelon , Jordan Katzman , Aleksey Gurtovoy
Abstract: 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.
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