3m Innovative Properties Company

Attenti Electronic Monitoring Ltd

Target Brands, Inc.

Solventum Intellectual Properties Company

Deepti Pachauri

Guruprasad Somasundaram

James W Howard

Jonathan Dale Gandrud

Robert W Shannon

Arash Sangari

Saber Taghvaeeyan

David Solomon Segal

Gautam Singh

Nicholas Andrew Asendorf

Justin M Johnson

Jennifer Frances Schumacher

Himanshu Nayar

James B Snyder

Nitsan Ben-Gal Nguyen

James Bevan Snyder

Mojtaba Kadkhodaie Elyaderani

Travis L Potts

Alexandra R Cunliffe

Matthew Nokleby

Caroline M Ylitalo

Christopher P Henderson

Roger W Barton

Ravishankar Sivalingam

Evan J Ribnick

Golshan Golnari

Claire R Donoghue

Benjamin D Zimmer

Kevin S Xu

Michael L Munson

Shawna L Thomas

Nancy M Amato

Kenneth Zins

Aloka Khanna

Benjamin W Watson

Tyler William Olson

Tamara M Meehan-Russell

James William Howard

A method and system of determining air quality are disclosed. In examples, a method comprises identifying one or more aerosol particle types based on an absorbance spectra of aerosol particles captured on a filter and determining a mass concentration of each of the one or more aerosol particle types based on the absorbance spectra and the aerosol particle type. The method further comprises detecting a median particle size of each of the one or more aerosol particle types based on a rate of change of the absorbance spectra and the aerosol particle type. The method further comprises determining an air quality metric based on the identified one or more aerosol particle types, the determined mass concentration of each of the one or more aerosol particle types, and the determined median particle size of each of the one or more aerosol particle types.

Air quality assessment based upon optical absorbance

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

Augmented reality-based training systems that can dynamically construct and render AR content based on physical attributes of a user to train the user to correctly fit one or more articles of a personal protective equipment (PPE) onto the user's body. In some examples, an AR system includes a display and a computing device having a memory and one or more processors. The memory can include instructions that, when executed by the processors, simulate a fitting of PPE to a worker. The system can capture an image of a worker, select a digital model of the PPE, determine alignment of the PPE to the worker, and output for display augmented reality content that includes a composite image of the worker overlaid with the digital model of the PPE in accordance with the determined alignment.

Personal protective equipment training system with user-specific augmented reality content construction and rendering

Methods and systems for automated detection of carton defects are disclosed. One method includes capturing one or more images of a carton via a camera system at a routing location within a warehouse of a retail supply chain, and applying a machine learning model to determine a likelihood of damage of the carton. The method can include, based on the likelihood of damage being above a particular threshold, identifying the carton as damaged. A carton assessment record can be stored in a carton damage tracking database, including the one or more images of the carton alongside the likelihood of damage and the routing location.

Automated detection of carton damage

Systems and methods for detecting the quality of signals captured by a sensor device monitoring a biological function. Sensor data associated with the sensor device is received, the sensor data representing time-series measurement samples of one or more parameters associated with the biological function, the sensor data including usable and unusable samples of the time-series measurements. Data representing two or more features of samples of the time-series measurements is extracted and filtered to reduce outliers in the extracted data based on an expected outlier ratio. A machine learning algorithm is then trained to identify events based on the filtered extracted data.

Sensing system and method for monitoring time-dependent processes

Methods and systems for automated detection of carton damage are disclosed. One method includes capturing one or more images of a carton via a camera system at a routing location within a warehouse of a retail supply chain, and applying a machine learning model to determine a likelihood of damage of the carton. The method can include, based on the likelihood of damage being above a particular threshold, identifying the carton as damaged. A carton assessment record can be stored in a carton damage tracking database, including the one or more images of the carton alongside the likelihood of damage and the routing location.

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

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

In some examples, an article includes a substrate and a plurality of optical element sets embodied on the substrate, wherein each optical element set includes a plurality of optical elements, wherein each respective optical element represents an encoded value in a set of encoded values, wherein the set of encoded values are differentiable based on visual differentiability of the respective optical elements, wherein each respective optical element set represents at least a portion of a message or error correction data to decode the message if one or more of the plurality of optical element sets are visually occluded, and wherein the optical element sets for the message and error correction data are spatially configured at the physical surface in a matrix such that the message is decodable from the substrate without optical elements positioned within at least one complete edge of the matrix that is visually occluded.

Occlusion-resilient optical codes for machine-read articles

An article includes a substrate with a physical surface, and a hierarchy of parent and child optical element sets embodied on the physical surface, such that a first encoded value represented by the parent optical element set is based at least in part on the visual appearance of a particular optical element in the child optical element set, and a second, different encoded value represented by the particular parent optical element is based at least in part on the visual appearance, and the second encoded value not being decodable from a distance greater than a threshold distance, the first encoded value being decodable from the distance greater than the threshold distance.

Hierarchichal optical element sets for machine-read articles

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

Saint Paul, MN, United States of America

Deepti Pachauri