3m Innovative Properties Company

Attenti Electronic Monitoring Ltd

Solventum Intellectual Properties Company

Guruprasad Somasundaram

Richard J Moore

Diane R Wolk

Cristin E Moran

David M Mahli

Ravishankar Sivalingam

Pontus Axelsson

Evan J Ribnick

Robert W Shannon

Deepti Pachauri

Roland Persson

Jonathan Dale Gandrud

Michael Rotstein

Gautam Singh

Arash Sangari

Alexandra R Cunliffe

James W Howard

Linus Åkerlund

Brian J Stankiewicz

Jennifer Frances Schumacher

Travis L Potts

Robert D Lorentz

Aya Eid

Caroline M Ylitalo

Glenn E Casner

Scott D Pearson

Kui Chen-Ho

Michael A McCoy

David Solomon Segal

Shannon D Scott

Golshan Golnari

Tasuku Nakayama

Anthony J Sabelli

Andreas M Geldmacher

Anne C Gold

Benjamin D Zimmer

John Allen Wheatley

Richard E Raby

Saber Taghvaeeyan

Nicholas Andrew Asendorf

Justin M Johnson

Brian L Linzie

Himanshu Nayar

Kristopher W Clover

James B Snyder

Willem V Bastiaens

Nitsan Ben-Gal Nguyen

Stephen M June

Olen Ronning

Dennard J Powell

Gabriel Alexander Biller

Silvia Geciova-Borovova Guttmann

Masuma W Henry

Mojtaba Kadkhodaie Elyaderani

James Bevan Snyder

Neeraj Sharma

Britton G Billingsley

Steven H Kong

Andrew H Tilstra

Nathan J Anderson

Michael C Leighton

Timothy J Gardner

Peter See

Theresa M Meyer

Muhammad Jamal Afridi

Jonathan T Kahl

Fumio Karasawa

Charles Allen Shaklee

Katja Hansen

Markus G W Lierse

Héctor M Aybar López

Staffan H Kjellberg

Claire R Donoghue

Alberto Alvarez

Olof Roland Persson

Catherine L Aune

Maja Giese

Nancy M Amato

Shawna L Thomas

Kevin S Xu

Steve Lastavich

Kelly L Stein

Kyle W English

Michael E O'Brien

Judy J Ma

Lindsay Labahn

Emre Berge Ergenekon

Vera Shuman

Shingo Moriyasu

Mojtaba K Elyaderani

Tamara M Meehan-Russell

Matthew R Cruikshank

Linus Âkerlund

Brian R Hackerson

Tyler William Olson

James William Howard

Héctor M Aybar LÓPEZ

A method for generating and reusing digital setups for an orthodontic treatment path. The method receives a digital 3D model of teeth, optionally performs interproximal reduction on the model, and generates an initial treatment path with stages including an initial, final, and intermediate setups. The method divides the initial treatment path into initial steps of feasible motion of the teeth resulting in a final treatment path with setups corresponding with the initial steps. For a treatment redesign, the method computes new steps of feasible motion for only a portion of the initial treatment path and based upon the initial steps, and generates the final path with new setups corresponding with the new steps. The setups can be used to make orthodontic appliances, such as clear tray aligners.

Automated process for intermediate orthodontic digital setup reuse due to treatment plan modifications

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.

Methods to automatically remove collisions between digital mesh objects and smoothly move mesh objects between spatial arrangements

The present disclosure includes in one instance an optical article comprising a data rich plurality of retroreflective elements that are configured in a spatially defined arrangement, where the plurality of retroreflective elements comprise retroreflective elements having at least two different retroreflective properties, and where data rich means information that is readily machine interpretable. The present disclosure also includes a system comprising the previously mentioned optical article, an optical system, and an inference engine for interpreting and classifying the plurality of retroreflective elements wherein the optical system feeds data to the inference engine.

Optical articles and systems interacting with the same

Optical articles including a spatially defined arrangement of a plurality of data rich retroreflective elements, wherein the plurality of retroreflective elements comprise retroreflective elements having at least two different retroreflective properties and at least two different optical contrasts with respect to a background substrate when observed within an ultraviolet spectrum, a visible spectrum, a near-infrared spectrum, or a combination thereof.

The present disclosure includes in one instance an optical article comprising a data rich plurality of retroreflective meats that are configured in a spatially defined arrangement, where the plurality of retroreflective elements comprise retroreflective elements having at least two different retroreflective properties, and where data rich means information that is readily machine interpretable. The present disclosure also includes a system comprising the previously mentioned optical article, an optical system, and an inference engine for interpreting and classifying the plurality of retroreflective elements wherein the optical system feeds data to the inference engine.

Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Compliance metrics for offender monitoring devices

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

A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, at least one motion sensor, at least one processor, and at least one memory device. The at least one memory device includes instructions that, when executed by the at least one processor, cause the at least one processor to determine, based on data generated by the at least one motion sensor, a net distance between the last known location of the BWTD and a current location of the BWTD. The instructions further cause the at least one processor to determine, based on the net distance, whether the BWTD is within a bounded area that includes the last known location; and responsive to determining that BWTD is not within the bounded area, output an indication that the BWTD is not within the bounded area.

Geographic boundary compliance detection using body-worn offender monitoring electronic devices

An article includes a substrate having a physical surface, a multi-dimensional machine-readable code embodied on the physical surface, wherein the multi-dimensional machine-readable optical code includes a static data (SD) optical element set and a dynamic lookup data (DLD) optical element set. Each of the SD and the DLD sets are embodied on the physical surface. The DLD optical element set encodes a look-up value that references dynamically changeable data, wherein the SD optical element set encodes static data that does not reference other data. The DLD optical element set is not decodable at the distance greater than the threshold distance.

Multi-dimensional optical code with static data and dynamic lookup data optical element sets

In an example, a system comprises at least one body-worn tracking device (BWTD) configured to transmit location data that indicates a location of the BWTD and a computing system configured to communicate with the at least one BWTD. The computing system is configured to receive the location data from the at least one BWTD, determine, based on a set of weighted filters associated with at least one monitoring attribute, at least one gradient value corresponding to the location data, where the at least one gradient value represents a level of the at least one monitoring attribute, and perform at least one operation based on the at least one gradient value.

Characterizing monitoring attributes for offender monitoring

A device comprising an imaging unit for capturing an image of a target object and of an accessory. The accessory comprises a reference target object having a known retroreflective coefficient. The device also comprises a calculating unit for calculating a value of a retroreflective coefficient of the target object based on a comparison of the image of the target to the image of the accessory.

Measuring device, system, method, and program

In some examples, an article includes a substrate having a surface; a parent optical element set comprising one or more parent optical elements disposed on the surface of the substrate, a child optical element set comprising one or more child optical elements disposed on the surface of the substrate. Each of the parent optical elements has a first retroreflective property and each of the child optical elements has a second retroreflective property different from the first retroreflective property.

Retroreflective multiscale codes

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

Growing community of inventors

Minneapolis, MN, United States of America

Guruprasad Somasundaram