3m Innovative Properties Company

3m Innovative Property Companies

Ravishankar Sivalingam

Guruprasad Somasundaram

Evan J Ribnick

Cristin E Moran

David M Mahli

Richard J Moore

Anthony J Sabelli

Aya Eid

Diane R Wolk

Brian J Stankiewicz

Jennifer Frances Schumacher

Shannon D Scott

Golshan Golnari

Pontus Axelsson

Michael Rotstein

Roland Persson

John Allen Wheatley

Andrew P Bonifas

Glenn E Casner

Robert D Lorentz

Theresa M Meyer

Yanina Shkel

Kevin S Xu

Robert W Shannon

Deepti Pachauri

Linus Åkerlund

Jennifer F Shumacher

Linus ÅKerlund

Methods for recognizing or identifying tooth types using digital 3D models of teeth. The methods include receiving a segmented digital 3D model of teeth and selecting a digital 3D model of a tooth from the segmented digital 3D model. An aggregation of the plurality of distinct features of the tooth is computed to generate a single feature describing the digital 3D model of the tooth. A type of the tooth is identified based upon the aggregation, which can include comparing the aggregation with features corresponding with known tooth types. The methods also include identifying a type of tooth, without segmenting it from an arch, based upon tooth widths and a location of the tooth within the arch.

Method for automatic tooth type recognition from 3D scans

Methods for aligning a digital 3D model of teeth represented by a 3D mesh to a desired orientation within a 3D coordinate system. The method includes receiving the 3D mesh in random alignment and changing an orientation of the 3D mesh to align the digital 3D model of teeth with a desired axis in the 3D coordinate system. The methods can also detect a gum line in the digital 3D model to remove the gingiva from the model.

Method of aligning intra-oral digital 3D models

Systems and methods for authenticating material samples are provided. Characteristic features are measured for a batch of material samples that comprise substantially the same composition and are produced by substantially the same process. The measured characteristic features have respective variability that is analyzed to extract statistical parameters. In some cases, reference ranges are determined based on the extracted statistical parameters for the batch of material samples. The corresponding statistical parameters of a test material sample are compared to the reference ranges to verify whether the test material sample is authentic.

Batch authentication of materials for automated anti counterfeiting

A method for detecting tooth wear using digital 3D models of teeth taken at different times. The digital 3D models of teeth are segmented to identify individual teeth within the digital 3D model. The segmentation includes performing a first segmentation method that over segments at least some of the teeth within the model and a second segmentation method that classifies points within the model as being either on an interior of a tooth or on a boundary between teeth. The results of the first and second segmentation methods are combined to generate segmented digital 3D models. The segmented digital 3D models of teeth are compared to detect tooth wear by determining differences between the segmented models, where the differences relate to the same tooth to detect wear on the tooth over time.

Detecting tooth wear using intra-oral 3D scans

Methods for estimating and predicting tooth wear based upon a single 3D digital model of teeth. The 3D digital model is segmented to identify individual teeth within the model. A digital model of a tooth is selected from the segmented model, and its original shape is predicted. The digital model is compared with the predicted original shape to estimate wear areas. A mapping function based upon values relating to tooth wear can also be applied to the selected digital model to predict wear of the tooth.

Estimating and predicting tooth wear using intra-oral 3D scans

Systems and methods for generating random bits by using physical variations present in material samples are provided. Initial random bit streams are derived from measured material properties for the material samples. In some cases, secondary random bit streams are generated by applying a randomness extraction algorithm to the derived initial random bit streams.

Systems and methods for generating random numbers using physical variations present in material samples

Techniques for creating and manipulating software notes representative of physical notes are described. A computing device includes a processor, an image collection module executable by the processor and configured to receive an input image of an environment having a plurality of overlapping physical notes, and an image processing engine executable by the processor and configured to process the input image with the computing device to identify the plurality of overlapping physical notes in the input image. The image processing engine determines a boundary of each note in the plurality of overlapping physical notes in the input image, and generates a plurality of digital notes corresponding to the determined boundary of each of the overlapping physical notes identified in the input image.

Note recognition for overlapping physical notes

This disclosure describes techniques for creating and manipulating software notes representative of physical notes. For example, techniques are described for recognizing physical notes present within a physical environment, capturing information therefrom and creating corresponding digital representations of the physical notes, referred to herein as digital notes or software-based notes. At least some aspects of the present disclosure feature system and methods for note recognition using color classification. The system receives a visual representation of a scene having one or more notes, where each note has a color. The system generates indicators indicative of color classes of pixels in the visual representation. The system further determines a general boundary of one of the notes based on the indicators.

Note recognition and management using color classification

At least some aspects of the present disclosure feature a computing device configured to receive an input image of an environment having a plurality of physical notes. The computing device automatically processes the input image to identify at least some of the plurality of the physical notes in the input image and displays the input image and indications indicative of the identified physical notes on a user interface. The computing device receives a user input indicating a position within the input image via a user interface and, responsive to the user input, recognizes proximate to the position a missed one of the physical notes that was not identified by the computing device when initially processing the input image.

Note capture and recognition with manual assist

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Woodbury, MN, United States of America

Ravishankar Sivalingam