3m Innovative Properties Company

James B Snyder

James W Howard

Justin M Johnson

Travis L Potts

James L C Werness, Jr

Guruprasad Somasundaram

Kui Chen-Ho

Suman K Patel

Deepti Pachauri

Caroline M Ylitalo

Steven T Awiszus

Kiran S Kanukurthy

Neeraj Sharma

Glenn E Casner

Jennifer Frances Schumacher

Thomas J Dahlin

Nicholas Andrew Asendorf

Tien Yi T H Whiting

Muhammad Jamal Afridi

Paul L Acito

Charles Allen Shaklee

Katja Hansen

Kenneth L Smith

Michael E Hamerly

Jonathan Dale Gandrud

Robert D Lorentz

Arash Sangari

Golshan Golnari

Tadesse G Nigatu

Onur Sinan Yordem

Jason M Patterson

Elisa J Collins

Andrew W Long

Wesley M Barbee

Matthew Shannon

Johnathan R Graves

Panagiotis D Stanitsas

Carla H Barnes

Tamara M Meehan-Russell

Matthew M Shettel

Jerome Shanko, Jr

Payas Tikotekar

Ravi Raagav Srinivas

Systems and methods for authenticating material samples are provided. Digital images of the samples are processed to extract computer-vision features, which are used to train a classification algorithm along with location and optional time information. The extracted features/information of a test sample are evaluated by the trained classification algorithm to identify the test sample. The results of the evaluation are used to track and locate counterfeits or authentic products.

Image based counterfeit detection

A system, wearable device and management device provided for predicting a flashover event. According to one aspect of the disclosure, a wearable device for predicting a flashover event is provided. The wearable device includes at least one thermal sensor configured to generate thermal data associated with an environment, and processing circuitry configured to: determine a risk of ignition of at least one combustible gas in the environment based on the thermal data, and trigger at least one action based on the determined risk of ignition.

System and method for risk classification and warning of flashover events

In general, techniques are described for a personal protective equipment (PPE) management system (PPEMS) that uses images of optical patterns embodied on articles of personal protective equipment (PPEs) to identify safety conditions that correspond to usage of the PPEs. In one example, an article of personal protective equipment (PPE) includes a first optical pattern embodied on a surface of the article of PPE; a second optical pattern embodied on the surface of the article of PPE, wherein a spatial relation between the first optical pattern and the second optical pattern is indicative of an operational status of the article of PPE.

Personal protective equipment management system using optical patterns for equipment and safety monitoring

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

In some examples, a method includes selecting, by a coding computing device, a particular identifier from a set of identifiers generated by an identifier allocation authority, wherein the set of identifiers are included within a global set of identifiers maintained by the identifier allocation authority; coding, by the coding computing device, the particular identifier from the set of identifiers into an optical element set that represents an optical code comprising a plurality of finder optical elements arranged in a pattern to localize the optical code within an image, a plurality of context optical elements representing context information, and a plurality of content optical elements representing content information; and generating a visual output of the optical code that is decodable to the particular identifier which, in combination with at least one other parameter associated with the optical element set, comprises a globally unique identifier in a set of globally unique identifiers.

Identifier allocation for optical element sets in machine-read articles

Systems and methods for authenticating material samples are provided. Digital images of the samples are processed to extract computer-vision features, which are used to train a classification algorithm. The computer-vision features of a test sample are evaluated by the trained classification algorithm to identify the test sample.

The disclosure is directed to an article, such as a pathway article or a sheeting. The article includes a physical surface having a code embodied thereon. The code is associated with pathway information in transaction data stored by a blockchain managed by a consensus network of node. The pathway information indicates one or more characteristics of a vehicle pathway, wherein the pathway information provides at least one of: information descriptive of at least a portion of the vehicle pathway, or vehicle operation instructions associated with the portion of the vehicle pathway.

Pathway article authentication

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 some examples, a computing device includes one or more computer processors, a communication device, and a memory comprising instructions that cause the one or more computer processors to: receive, using the communication device and from a set of vehicles, different sets of infrastructure data for a particular infrastructure article that is proximate to each respective vehicle of the set of vehicles, wherein each respective vehicle in the set of vehicles comprises at least one infrastructure sensor that generates infrastructure data descriptive of infrastructure articles that are proximate to the respective vehicle; determine, based at least in part on the different sets of infrastructure data for the particular infrastructure article from each respective vehicle of the set of vehicles, a quality metric for the infrastructure article; and perform at least one operation based at least in part on the quality metric for the infrastructure article.

Vehicle-sourced infrastructure quality metrics

In some examples, an optically active article includes a retroreflective substrate; and at least one security element disposed at the retroreflective substrate; an article message disposed at the retroreflective substrate, and wherein the at least one security element comprises validation information that is detectable outside a visible light spectrum, and wherein a combination of the article message and the validation information of the at least one security element indicates whether the optically active article is counterfeit.

Counterfeit detection of an optically active article using security elements

In some examples, a computing device receives an image of an optically active article that includes a set of one or more symbols of a symbol set, wherein at least one symbol of the set of one or more symbols comprises a set of encoding regions that are embedded with the symbol. In response to receiving the image, the computing device may determine that a particular image region of the image represents the at least one symbol. For encoding regions within the at least one symbol, the computing device may determine, based at least in part on the determination that the particular image region of the image represents the at least one symbol, whether the one or more encoding regions are active or inactive. The computing device may perform, based at least in part on whether the one or more encoding regions are active or inactive, one or more operations.

Encoding data in symbols disposed on an optically active article

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

The inventors of the present application developed novel retroreflective materials, and articles. One example of the present application is a retroreflective article, comprising a substrate and a machine-readable information disposed on the substrate, wherein the machine-readable information corresponds to a predetermined human-readable information.

Increasing dissimilarity of characters disposed on an optically active article

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

Growing community of inventors

Minneapolis, MN, United States of America

James B Snyder