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公开(公告)号：WO2018005071A1
公开(公告)日：2018-01-04
申请号：PCT/US2017/037110
申请日：2017-06-13
Applicant: 3M INNOVATIVE PROPERTIES COMPANY
Inventor： ALVAREZ, Alberto , HANSEN, Eric S. , DEMLOW, Steven C. , RABY, Richard E.
Abstract: A method for determining virtual articulation from dental scans. The method includes receiving digital 3D models of a person's maxillary and mandibular arches, and digital 3D models of a plurality of different bite poses of the arches. The digital 3D models of the maxillary and mandibular arches are registered with the bite poses to generate transforms defining spatial relationships between the arches for the bite poses. Based upon the digital 3D models and transforms, the method computes a pure rotation axis representation for each bite pose of the mandibular arch with respect to the maxillary arch. The virtual articulation can be used in making restorations or for diagnostic purposes.
Abstract translation: 用于从牙科扫描确定虚拟关节的方法
。 该方法包括接收人的上颌和下颌拱的数字3D模型以及拱的多个不同咬合姿态的数字3D模型。 上颌骨和下颌骨拱的数字三维模型与咬合姿势配准以生成定义咬合姿势的牙弓之间的空间关系的变换。 基于数字3D模型和变换，该方法计算下颌弓相对于上颌弓的每个咬合姿态的纯旋转轴表示。 虚拟关节可用于修复或诊断目的。
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公开(公告)号：WO2022123402A1
公开(公告)日：2022-06-16
申请号：PCT/IB2021/061230
申请日：2021-12-02
Applicant: 3M INNOVATIVE PROPERTIES COMPANY [US]/[US]
Inventor： GANDRUD, Jonathan D. , CUNLIFFE, Alexandra R. , HANSEN, James D. , FABBRI, Cameron M. , DONG, Wenbo , YANG, En-Tzu , HUANG, Jianbing , NAYAR, Himanshu , SOMASUNDARAM, Guruprasad , REN, Jineng , DINGELDEIN, Joseph C. , HOSSEINI, Seyed Amir Hossein , DEMLOW, Steven C. , ZIMMER, Benjamin D.
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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公开(公告)号：WO2020026117A1
公开(公告)日：2020-02-06
申请号：PCT/IB2019/056450
申请日：2019-07-29
Applicant: 3M INNOVATIVE PROPERTIES COMPANY
Inventor： BEN-GAL NGUYEN, Nitsan , ZIMMER, Benjamin D. , SOMASUNDARAM, Guruprasad , CUNLIFFE, Alexandra R. , STANKIEWICZ, Brian J. , COLLINS, Elisa J. , MAIDEN MUELLER, David T. , DEMLOW, Steven C. , OLSON, Cody J. , AFRIDI, Muhammad J. , AMATO, Nancy , THOMAS, Shawna L. , GUTIERREZ, Alexander , SANGARI, Arash , GANDRUD, Jonathan D.
Abstract: An automated method for generating a final setup for orthodontic treatment. The method includes receiving a digital 3D model of teeth in an initial state. The method computes a scoring function based upon metrics related to the initial state to generate a corresponding score, perturbs the state of the teeth through translations and rotations of a tooth or set of teeth to generate a perturbed state of the teeth, and updates the state of the teeth based upon the score and the perturbed state of the teeth to generate one or more final setups representing a final state of the teeth after the orthodontic treatment.
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Patent Agency Ranking
公开(公告)号：EP3478218A1
公开(公告)日：2019-05-08
申请号：EP17820884.9
申请日：2017-06-13
Applicant: 3M Innovative Properties Company
Inventor： ALVAREZ, Alberto , HANSEN, Eric S. , DEMLOW, Steven C. , RABY, Richard E.
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公开(公告)号：EP4239538A3
公开(公告)日：2023-11-08
申请号：EP23181450.0
申请日：2019-07-29
Applicant: 3M Innovative Properties Company
Inventor： BEN-GAL NGUYEN, Nitsan , ZIMMER, Benjamin D. , SOMASUNDARAM, Guruprasad , CUNLIFFE, Alexandra R. , STANKIEWICZ, Brian , COLLINS, Elisa J. , MAIDEN MUELLER, David T. , DEMLOW, Steven C. , OLSON, Cody J. , AFRIDI, Muhammad J. , AMATO, Nancy , THOMAS, Shawna L. , GUTIERREZ, Alexander , SANGARI, Arash , GANDRUD, Jonathan D.
Abstract: The present invention relates to a computer-implemented method of generating a final setup for orthodontic treatment using supervised machine learning for direct mapping, comprising receiving, by one or more computer processors, a training set of patient case data which includes malocclusion states and ground truth images representing final setup states for the teeth of one or more patients, wherein the final setup states represent orthodontic post-treatment positions of a person's teeth and the malocclusion states are pre-treatment positions of the teeth and are represented by digital 3D models of teeth in an initial state, generating, by the one or more computer processors using a deep neural network, a generated output image, computing, by the one or more computer processors, a distance between a generated output image and the ground truth image representing the final state using a loss function, and minimizing, by the one or more computer processors, the distance to train, at least in part, the deep neural network.
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公开(公告)号：EP4260278A1
公开(公告)日：2023-10-18
申请号：EP21902819.8
申请日：2021-12-02
Applicant: 3M Innovative Properties Company
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公开(公告)号：EP4239538A2
公开(公告)日：2023-09-06
申请号：EP23181450.0
申请日：2019-07-29
Applicant: 3M Innovative Properties Company
Inventor： BEN-GAL NGUYEN, Nitsan , ZIMMER, Benjamin D. , SOMASUNDARAM, Guruprasad , CUNLIFFE, Alexandra R. , STANKIEWICZ, Brian , COLLINS, Elisa J. , MAIDEN MUELLER, David T. , DEMLOW, Steven C. , OLSON, Cody J. , AFRIDI, Muhammad J. , AMATO, Nancy , THOMAS, Shawna L. , GUTIERREZ, Alexander , SANGARI, Arash , GANDRUD, Jonathan D.
IPC: G06N20/00
Abstract: The present invention relates to a computer-implemented method of generating a final setup for orthodontic treatment using supervised machine learning for direct mapping, comprising receiving, by one or more computer processors, a training set of patient case data which includes malocclusion states and ground truth images representing final setup states for the teeth of one or more patients, wherein the final setup states represent orthodontic post-treatment positions of a person's teeth and the malocclusion states are pre-treatment positions of the teeth and are represented by digital 3D models of teeth in an initial state, generating, by the one or more computer processors using a deep neural network, a generated output image, computing, by the one or more computer processors, a distance between a generated output image and the ground truth image representing the final state using a loss function, and minimizing, by the one or more computer processors, the distance to train, at least in part, the deep neural network.
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公开(公告)号：EP3829481A1
公开(公告)日：2021-06-09
申请号：EP19844870.6
申请日：2019-07-29
Applicant: 3M Innovative Properties Company
Inventor： BEN-GAL NGUYEN, Nitsan , ZIMMER, Benjamin D. , SOMASUNDARAM, Guruprasad , CUNLIFFE, Alexandra R. , STANKIEWICZ, Brian J. , COLLINS, Elisa J. , MAIDEN MUELLER, David T. , DEMLOW, Steven C. , OLSON, Cody J. , AFRIDI, Muhammad J. , AMATO, Nancy , THOMAS, Shawna L. , GUTIERREZ, Alexander , SANGARI, Arash , GANDRUD, Jonathan D.
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