3m Innovative Properties Company

Golshan Golnari

Jennifer Frances Schumacher

Guruprasad Somasundaram

Ravishankar Sivalingam

Evan J Ribnick

Aya Eid

Ronald David Jesme

Andrew P Bonifas

Brock A Hable

Nicholas G Amell

Saber Taghvaeeyan

Shannon D Scott

Anthony J Sabelli

Nicholas Andrew Asendorf

Theresa M Meyer

Muhammad Jamal Afridi

Saber Taghaeeyan

Roger W Barton

Himanshu Nayar

Robert W Shannon

Robert D Lorentz

James B Snyder

Deepti Pachauri

Mojtaba Kadkhodaie Elyaderani

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

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

Systems and methods for detecting/identifying novel material samples are provided. A test sample image is processed with a trained transformation function to obtain a transformed matrix. A measure of similarity of the test image based on the transformed matrix is compared to a threshold to determine whether the test sample is novel to a batch of material samples that are provided to train the transformation function.

Image based novelty detection of material samples

Systems and methods are provided for optimally determining sensor or infrastructure placement in a fluid network, for determining an anomaly of interest in the fluid network, and for determining sensor coverage in a fluid network, which are based on a model of the fluid network represented by a directed graph.

Systems and methods for modeling, analyzing, detecting, and monitoring fluid networks

The present subject matter enables early or real-time detection of anomalies in electric networks. In various applications, the system detects anomalies, such as electricity theft, electricity surge, etc. It solves the difficult-to-detect problems in an electrical network, where anomalies like electricity theft or electrical surge may not be found until it has raised numerous concerns or complaints, or has created a significant impact on infrastructure functionality, service quality, or cost. In addition, the present subject matter decreases the requirement for large number of sensors and provides more cost effective and scalable solutions. The present subject matter provides a method for determining where a detected anomaly is occurring within an electrical network. Variations of the present subject matter include anomaly identification systems for addressing anomalies in large networks. Various applications of the present subject matter provide guidance or effective placement of sensors in the electrical network.

System and method for anomaly detection in an electrical network

Systems and methods are provided for determining sensor or infrastructure placement in a fluid network, for determining an anomaly of interest in the fluid network, and for optimally determining sensor coverage in a fluid network, which are based on a model of the fluid network represented by a directed graph.

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

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

Growing community of inventors

Roseville, MN, United States of America

Golshan Golnari