Tokitae LLC

Charles River Laboratories, Inc.

Staples, Inc.

Amplero, Inc.

Courosh Mehanian

Benjamin K Wilson

Liming Hu

Xinliang Zheng

Matthew P Horning

Robert A LoBosco

Yanning Zhu

Lucas C Siler

Richard E Long

Shawn McGuire

Clay Matthew Thompson

Timothy Wee

Karthik Kumara

Charles Delahunt

Sourabh Kulhare

Cary Richard Champlin

Oliver B Downs

Tchavdar Dangaltchev

Luca Cazzanti

Sebastian Wachsmann-Hogiu

Jing Pan

Julie Penzotti

Doug Scott

In an embodiment, an intelligent system includes an electronic circuit configured to execute a neural network, to detect at least one feature in an image of a body portion while executing the neural network, and to determine a respective position and a respective class of each of the detected at least one feature while executing the neural network. For example, such a system can execute a neural network to detect at least one feature in an image of a lung, to determine a respective position within the image of each detected feature, and to classify each of the detected features as one of the following: A-line, B-line, pleural line, consolidation, and pleural effusion.

Automated ultrasound video interpretation of a body part with one or more convolutional neural networks

Embodiments disclosed herein are directed to systems and methods for determining if a fluid is present in a body region. The systems and methods include using ultrasound systems having operational parameters that provide ultrasound echo maps having high resolution B-line artefacts.

Ultrasound systems and methods of identifying fluids in body regions using the same

One or more implementations allow for detecting B-lines in ultrasound video and images for diagnostic purposes through analysis of Q-mode images for B-line detection.

Devices, systems, and methods for diagnosis of pulmonary conditions through detection of b-lines in lung sonography

Technology for predicting online user shopping behavior, such as whether a user will purchase a product, is described. An example method includes receiving current session data describing a current session for a current user, extracting a current clickstream from the current session data classifying the current clickstream as a purchase clickstream or a non-purchase clickstream by processing the current clickstream using one or more sets of Hidden Markov Model parameters produced by one or more Hidden Markov Models, and computing, using the one or more computing devices, a purchase probability that the current user will purchase a product during the current session based on the classifying.

Clickstream purchase prediction using Hidden Markov Models

Techniques and technologies for automated microscopy scanning systems are disclosed wherein a microscopy system performs 'hunt mode' operations at coarsely-spaced locations throughout a scanning window until an acceptable quality scan result is achieved. The system then performs detailed scans at all fields of view within a grid cell that includes the location having the acceptable scan result. The system performs another evaluation of the scan results for the entire grid cell, and if the scan results for the grid cell are collectively acceptable, then the system proceeds to perform 'scan mode' operations. The scan mode operations include scanning and evaluating all of the fields of view within one or more grid cells adjacent to the acceptable grid cell from the hunt mode operations. The system may successively perform hunt mode operations and scan mode operations, compiling information regarding one or more aspects of the scanning process, until one or more termination criteria are satisfied.

Automated microscopy scanning systems and methods

Technology for selecting promotion(s) to display in a page of an application for display to a user is described. An example method includes determining a promotion for a product; calculating for the promotion a posterior distribution of a user-action probability reflecting estimates for a user response to a display of the promotion for the product on a computing device of the user; determining the posterior distribution as collapsing beyond a certain threshold; responsive thereto, calculating an uncollapsed posterior distribution of the user-action probability reflecting modified estimates for the user response to the display of the promotion for the product on a computing device of the user; storing the uncollapsed posterior distribution of the user-action probability in a response database; and determining whether to select the promotion from the promotion database for display on a computing device of the user based on the modified estimates.

Promotion selection for online customers using Bayesian bandits

Embodiments disclosed herein are directed to systems and methods for determining a presence and an amount of an analyte in a biological sample. The systems and methods for determining the presence of an analyte utilize a plurality of images of a sample slide including multiple fields-of-view having multiple focal planes therein. The systems and methods utilize algorithms configured to balance the color and grayscale intensity of the plurality of images and based thereon determine if the plurality of images contain the analyte therein.

Image analysis systems and related methods

Embodiments disclosed herein are directed to systems and methods for determining a presence and an amount of an analyte in a biological sample. The systems and methods for determining the presence of an analyte utilize a plurality of images of a sample slide including multiple fields-of-view having multiple focal planes therein. The systems and methods utilize algorithms configured to color and grayscale intensity balance the plurality of images and based thereon determine if the plurality of images contain the analyte therein.

Techniques are disclosed that automatically identify and order the most differentiated clusters from a given collection of clusters within a dataset. A measure of dissimilarity is computed for each cluster from a defined reference cluster, and the clusters are ordered according to the chosen dissimilarity. At least N clusters are selected as the most differentiated clusters relative to the defined reference. Within each cluster, the top-M most distinguishing cluster attributes can be automatically identified by an analogous process that computes the dissimilarity of each cluster attribute to its corresponding attribute in the reference cluster, and orders the attributes by dissimilarity. This then allows for automatic surfacing of what it is about a cluster that differentiates its members relative to the population as a whole, and to provide insight on what action or treatment might be made to address that specific segment of the underlying population.

Unsupervised prioritization and visualization of clusters

Computer display with a graphical user interface

Growing community of inventors

Redmond, WA, United States of America

Courosh Mehanian