公开(公告)号：US12285309B2

公开(公告)日：2025-04-29

申请号：US17413652

申请日：2019-12-24

Applicant: Solventum Intellectual Properties Company

Inventor： Alexandra R. Cunliffe ,  Benjamin D. Zimmer ,  Guruprasad Somasundaram ,  Deepti Pachauri ,  Jonathan D. Gandrud ,  Arash Sangari ,  Shawna L. Thomas ,  Nancy M. Amato

IPC: A61C7/00 ,  G16H20/00 ,  G16H50/50

Abstract: Methods for automatically removing collisions between digital mesh objects and moving digital mesh objects between spatial arrangements. The collisions are removed from a set of digital mesh objects using a perturbation method or a mesh deformation method. After removing the collisions, the digital mesh objects are output in a state without collisions between them. The digital mesh objects can be moved between initial and final states based upon motion constraints of the mesh objects and interpolated states of them between the initial and final states. Based upon the constraints and interpolated states, a number of states for movement of the set of digital mesh objects is determined either collectively for the set or individually for each mesh object. The states can be used as digital setups for dental or orthodontic treatment planning.

公开(公告)号：US20240320382A1

公开(公告)日：2024-09-26

申请号：US18618192

申请日：2024-03-27

Applicant: SOLVENTUM INTELLECTUAL PROPERTIES COMPANY

Inventor： Jonathan D. Gandrud ,  Cameron M. Fabbri ,  Joseph C. Dingeldein ,  James D. Hansen ,  Benjamin D. Zimmer

IPC: G06F30/10 ,  A61C13/00 ,  A61C13/34 ,  G06T17/20

CPC classification number: G06F30/10 ,  A61C13/0004 ,  A61C13/34 ,  G06T17/20

Abstract: Techniques are described for automating the design of dental restoration appliances using machine learning models. 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 machine learning model 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 machine learning model using the input to produce placement information for the dental appliance component with respect to the current dental anatomy of the dental restoration patient.