Welch Allyn, Inc.

Cylance Inc.

Welch Allyn Protocol, Inc.

Eric G Petersen

Herbert S Weiner

Kenneth G West

Steven D Baker

Ynjiun P Wang

Ervin Goldfain

Derek A Soeder

Matthew Wolff

David Neill Beveridge

Brian Michael Wallace

Jian Luan

Nhedti Colquitt

John Howard Irwin

Robert Paul Wilmington

William H Howell

Andrew Davis

Ryan Permeh

James J DelloStritto

Xuan Zhao

Michael Thomas Wojnowicz

John Brock

Frank LoMascolo

Cory Russell Gondek

Song Yong Chung

Chad Everett Craw

Mahdi Azarafrooz

Ronald James Blaszak

Ming Jin

James B Moon

Andy Wortman

Sean Michael Kelley

Albert Goldfain

Mahesh Narayan

Robert J Wood

Yaroslav Oliinyk

Hailey Buckingham

Brian Wallace

Brian T Moons

Steven J Marcaccini

Koo Lee

Michael Alan Hohimer

Andrew Wortman

Mark A Heritage

Jon K Fo

Douglas S Adams

Jon K F Fo

Nhedti L Colquitt

A system is provided for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

Machine learning model for analysis of instruction sequences

An artifact is received from which features are extracted and used to populate a vector. The features in the vector are then reduced using a feature reduction operation to result in a modified vector having a plurality of buckets. Features within the buckets of the modified vector above a pre-determined projected bucket clipping threshold are then identified. Using the identified features, and overflow vector is then generated. The modified vector is then input into a classification model to generate a score. This score is adjusted based on the overflow vector and can then be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Projected vector overflow penalty as mitigation for machine learning model string stuffing

An artifact is received from which features are extracted so as to populate a vector. The features in the vector can be reduced using a feature reduction operations to result in a modified vector having a plurality of buckets. A presence of predetermined types of features are identified within buckets of the modified vector influencing a score above a pre-determined threshold. A contribution of the identified features within the high influence buckets of the modified vector is then attenuated. The modified vector is input into a classification model to generate a score which can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Projected vector modification as mitigation for machine learning model string stuffing

An artifact is received and features are extracted therefrom to form a feature vector. Thereafter, a determination is made to alter a malware processing workflow based on a distance of one or more features in the feature vector relative to one or more indicator centroids. Each indicator centroid specifying a threshold distance to trigger an action. Based on such a determination, the malware processing workflow is altered.

Indicator centroids for malware handling

An artefact is received and parsed into a plurality of observations. A first subset of the observations are inputted into a machine learning model trained using historical data to classify the artefact. In addition, a second subset of the observations are inputted into a xenospace centroid configured to classify the artefact. Thereafter, the artefact is classified based on a combination of an output of the machine learning model and an output of xenospace centroid. Related apparatus, systems, techniques and articles are also described.

Artefact classification using xenospace centroids

A nested file having a primary file and at least one secondary file embedded therein is parsed using at least one parser of a cell. The cell assigns a maliciousness score to each of the parsed primary file and each of the parsed at least one secondary file. Thereafter, the cell generates an overall maliciousness score for the nested file that indicates a level of confidence that the nested file contains malicious content. The overall maliciousness score is provided to a data consumer indicating whether to proceed with consuming the data contained within the nested file.

Avoidance of malicious content in nested files

In one respect, there is provided a system for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

In one respect, there is provided a system for training a neural network adapted for classifying one or more instruction sequences. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: training, based at least on training data, a machine learning model to detect one or more predetermined interdependencies amongst a plurality of tokens in the training data; and providing the trained machine learning model to enable classification of one or more instruction sequences. Related methods and articles of manufacture, including computer program products, are also provided.

Training a machine learning model for analysis of instruction sequences

The invention relates generally to a medical device wireless adapter, and more particularly, to a module that adapts an existing legacy or newly designed medical device to a healthcare provider's wireless infrastructure.

Medical device wireless adapter

An example eye fundus imaging apparatus can include: a display device; an image capture device configured to capture at least one image of a fundus of an eye; and a control module programmed to: display a target, the target indicating a desired position for the image capture device to capture the at least one image of the fundus of the eye; monitor a relative position of the eye with respect to the target; and when the relative position of the eye corresponds to the target, automatically capture the at least one image of the fundus of the eye.

Opthalmoscope device

A method for displaying medical data includes receiving physiological data from a first medical monitoring device. The physiological data is obtained on a continuous basis. Physiological data is received from a second medical monitoring device. The physiological data from the second medical monitoring device is obtained on a non-continuous basis. The physiological data received from the first medical monitoring device and the physiological data received from the second medical monitoring device are displayed on a central display station. The central display station is located centrally within a care unit of a caregiving facility.

Central station integration of patient data

An ophthalmoscope device includes a support structure, an image capture device, and a display device. The support structure is configured to be worn by a subject. The image capture device is configured to capture images of the eye fundus of the subject. The display device is configured to overlay images in the field of view of the subject. The overlaid images are used to align the pupil/fovea orientation axis with the optical axis of the image capture device.

One method for measuring variability between direct heartbeat data and indirect heartbeat data includes: receiving first heartbeat data from an electrocardiogram over a period of time; receiving second heartbeat data from an indirect measure of heartbeat data over at least a portion of the period of time; calculating, by a computing device, a difference between the first heartbeat data and the second heartbeat data; and comparing the difference to determine a variation.

Analysis of direct and indirect heartbeat data variations

Growing community of inventors

Aloha, OR, United States of America

Eric G Petersen