Attenti Electronic Monitoring Ltd

3m Innovative Properties Company

Solventum Intellectual Properties Company

Arash Sangari

Jonathan Dale Gandrud

Guruprasad Somasundaram

David Solomon Segal

Saber Taghvaeeyan

Robert W Shannon

James W Howard

Deepti Pachauri

Gautam Singh

Nicholas Andrew Asendorf

Alexandra R Cunliffe

Jennifer Frances Schumacher

Himanshu Nayar

Nitsan Ben-Gal Nguyen

Mojtaba Kadkhodaie Elyaderani

James Bevan Snyder

John Allen Wheatley

Caroline M Ylitalo

Neeraj Sharma

Kui Chen-Ho

Glenn E Casner

Michael A McCoy

Travis L Potts

Andrew H Tilstra

Anne C Gold

Andreas M Geldmacher

Charles Allen Shaklee

Katja Hansen

Markus G W Lierse

Maja Giese

Evan J Ribnick

James B Snyder

Muhammad Jamal Afridi

Benjamin D Zimmer

Elisa J Collins

Silvia Geciova-Borovova Guttmann

Nancy M Amato

Shawna L Thomas

James William Howard

Tyler William Olson

Mojtaba K Elyaderani

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

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.

Systems and methods for image recognition are provided. A style-transfer neural network is trained for each real image to obtain a trained style-transfer neural network. The texture or style features of the real images are transferred, via the trained style-transfer neural network, to a target image to generate styled images which are used for training an image-recognition machine learning model (e.g., a neural network). In some cases, the real images are clustered and representative style images are selected from the clusters.

Neural style transfer for image varietization and recognition

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 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

A system for tracking a location of a body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) component, one or more sensors, one or more processors, and a memory device. The memory device includes instructions that, when executed by the one or more processors, cause the one or more processors to determine whether a current location of the BWTD can be determined using the GNSS component. The instructions also cause the one or more processors to determine, based in part on the activity of the wearer of the BWTD and based on a determination that the current location of the BWTD cannot be determined using the GNSS component, whether to perform an action. The instructions also cause the one or more processors to perform the action in response to a determination to perform the action.

Activity-based rules for compliance detection using body-worn offender monitoring electronic devices

A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, a cellular communication unit, 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 whether the BWTD is located on board an aircraft. Execution of the instructions further causes the at least one processor to, responsive to determining that the BWTD is located on board the aircraft: disable the GNSS device and the cellular communication unit; and temporarily refrain from generating an alert that indicates a current location of the BWTD cannot be determined.

Travel compliance detection using body-worn offender monitoring electronic devices

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 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

Growing community of inventors

Saint Paul, MN, United States of America

Arash Sangari