3m Innovative Properties Company

Benjamin D Zimmer

Guruprasad Somasundaram

Alexandra R Cunliffe

Jonathan Dale Gandrud

Peter See

Kelly L Stein

Caroline M Ylitalo

James D Hansen

Richard E Raby

Steve Lastavich

Brian J Stankiewicz

Richard J Moore

Joseph C Dingeldein

Evan J Ribnick

William K Smyth

Deepti Pachauri

Arash Sangari

Jianbing Huang

Claire R Donoghue

Nitsan Ben-Gal Nguyen

Alberto Alvarez

Shawna L Thomas

Mojtaba K Elyaderani

Cameron M Fabbri

Nancy M Amato

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.

Neural network-based generation and placement of tooth restoration dental appliances

A system and method of selecting personal protection equipment (PPE) for a worker. The system identifies sets of PPEs and simulates a fitting of the identified PPE sets to the worker. Fitting includes, for each of the identified PPE sets, selecting a three-dimensional computer model for each article of the PPE set, placing the computer model of each of the PPE articles of the PPE set being fitted on a computer model representing the worker, identifying collisions between the computer models as placed, and taking an action based on the identified collisions.

Automated fitting of multiple articles of personal protection equipment concurrently worn by a user

Methods for tracking gum line changes by comparing digital 3D models of teeth and gingiva taken at different times. The digital 3D models are segmented to digitally identify the teeth from the gingiva, and the segmented digital 3D models are compared to detect gum line changes by determining differences between them relating to the gum line. Gum line changes can also be estimated by comparing one of the digital 3D models with a 3D model having a predicted original location of the gum line. Gum line change maps can be displayed to show the gum line changes determined through the tracking or estimating of changes. The digital 3D models can also be displayed with periodontal measurements placed upon them.

Tools for tracking the gum line and displaying periodontal measurements using intra-oral 3D scans

A method for aligning digital 3D models of dental arch pairs, including a mandible and maxilla, to bring them into bite alignment. The method estimates representative planes for the mandible and maxilla 3D models and transforms the models such that the representative planes are each aligned with their respective coordinate systems and shown in a horizontal view. The method also transforms the models such that the mandible and maxilla are each aligned with a same coordinate system and shown in a front view. The digital 3D models are then brought into bite alignment such that the mandible is in occlusion with the maxilla or within a distance near to occlusion. The digital models of the bite-aligned arch pairs can also be transformed or rotated to be shown centered in a front view.

Automatic alignment and orientation of digital 3D dental arch pairs

At least some embodiments of the present disclosure feature systems and methods for assessing the impact of visual features within a region of a scene. With the input of a visual representation of a scene and at least one selected region within the scene, the system applies a visual attention model to the visual representation to determine visual conspicuity of the at least one selected region. The system computes feature-related data associated with a plurality of features of the at least one selected region. Based on the visual conspicuity and the feature-related data, the system assesses an impact that at least one of the features within the at least one selected region have on the visual conspicuity.

Systems and methods for computing and presenting results of visual attention modeling

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Maplewood, MN, United States of America

Benjamin D Zimmer