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公开(公告)号：US20240347210A1
公开(公告)日：2024-10-17
申请号：US18291821
申请日：2022-08-05
Applicant: 3M INNOVATIVE PROPERTIES COMPANY
Inventor： Cameron M. Fabbri , Wenbo Dong , James L. Graham , Cody J. Olson
IPC: G16H50/50 , G06T5/60 , G06T5/77 , G06T11/00 , G06V10/40 , G06V10/764 , G06V10/774 , G06V10/82
CPC classification number: G16H50/50 , G06T5/60 , G06T5/77 , G06T11/00 , G06V10/40 , G06V10/764 , G06V10/774 , G06V10/82 , G06T2207/30036 , G06T2210/41
Abstract: A method for displaying teeth after planned orthodontic treatment in order to show persons how their smiles will look after the treatment. The method includes receiving a digital 3D model of teeth or rendered images of teeth, and an image of a person such as a digital photo. The method uses a generator network to produce a generated image of the person showing teeth of the person, the person's smile, after the planned orthodontic treatment. The method uses a discriminator network processing input images, generated images, and real images to train the generator network through deep learning models to product a photo-realistic image of the person after the planned treatment.
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公开(公告)号：US20230153476A1
公开(公告)日：2023-05-18
申请号：US17917211
申请日：2021-05-14
Applicant: 3M INNOVATIVE PROPERTIES COMPANY
Inventor： Cameron M. Fabbri , Jonathan D. Gandrud , Joseph C. Dingeldein , James D. Hansen , Benjamin D. Zimmer , Jianbing Huang
CPC classification number: G06F30/10 , A61C13/0004 , A61C13/34 , G06T17/20
Abstract: Techniques are described for automating the design of dental restoration appliances using neural networks. An example computing device receives transform information associated with a current dental anatomy of a dental restoration patient, provides the transform information associated with the current dental anatomy of the dental restoration patient as input to a neural network trained with transform information indicating placement of a dental appliance component with respect to one or more teeth of corresponding dental anatomies, the dental appliance being used for dental restoration treatment for the one or more teeth, and executes the neural network using the input to produce placement information for the dental appliance component with respect to the current dental anatomy of the dental restoration patient.
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公开(公告)号：US11960795B2
公开(公告)日：2024-04-16
申请号：US17917211
申请日：2021-05-14
Applicant: 3M INNOVATIVE PROPERTIES COMPANY
Inventor： Cameron M. Fabbri , Jonathan D. Gandrud , Joseph C. Dingeldein , James D. Hansen , Benjamin D. Zimmer , Jianbing Huang
CPC classification number: G06F30/10 , A61C13/0004 , A61C13/34 , G06T17/20
Abstract: Techniques are described for automating the design of dental restoration appliances using neural networks. An example computing device receives transform information associated with a current dental anatomy of a dental restoration patient, provides the transform information associated with the current dental anatomy of the dental restoration patient as input to a neural network trained with transform information indicating placement of a dental appliance component with respect to one or more teeth of corresponding dental anatomies, the dental appliance being used for dental restoration treatment for the one or more teeth, and executes the neural network using the input to produce placement information for the dental appliance component with respect to the current dental anatomy of the dental restoration patient.
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公开(公告)号：US20240008955A1
公开(公告)日：2024-01-11
申请号：US18253699
申请日：2021-12-02
Applicant: 3M INNOVATIVE PROPERTIES COMPANY
Inventor： Jonathan D. Gandrud , Alexandra R. Cunliffe , James D. Hansen , Cameron M. Fabbri , Wenbo Dong , En-Tzu Yang , Jianbing Huang , Himanshu Nayar , Guruprasad Somasundaram , Jineng Ren , Joseph C. Dingeldein , Seyed Amir Hossein Hosseini , Steven C. Demlow , Benjamin D. Zimmer
CPC classification number: A61C7/002 , A61C13/0004 , A61C2007/004
Abstract: Machine learning, or geometric deep learning, applied to various dental processes and 5 solutions. In particular, generative adversarial networks apply machine learning to smile design—finished smile, appliance rendering, scan cleanup, restoration appliance design, crown and bridges design, and virtual debonding. Vertex and edge classification apply machine learning to gum versus teeth detection, teeth type segmentation, and brackets and other orthodontic hardware. Regression applies machine learning to coordinate systems, diagnostics, case complexity, and 0 prediction of treatment duration. Automatic encoders and clustering apply machine learning to grouping of doctors, or technicians, and preferences.
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