Dexcom, Inc.

Lifescan Corporation

Lifescan Scotland Limited

Covidien LP

Cilag International Gmbh

University of Virginia Patent Foundation

David Price

Leif N Bowman

Apurv Ullas Kamath

Eli Reihman

Michael Robert Mensinger

Rian W Draeger

Jorge Valdes

Phil Mayou

Peter C Simpson

Katherine Yerre Koehler

Hari Hampapuram

Keri Leone

Robert James Boock

Adam R Greene

Dan Kraemer

Daniel Justin Wiedeback

Kristin Koenekamp Cote

Shane Philip Delmore

Jeffrey Grant Stewart

Lauren C Miller

Katherine Eng Kirby

Eric Cohen

Aarthi Mahalingam

Brian Christopher Smith

Arturo Garcia

Shawn Larvenz

Thomas Hall

Naresh Chandra Bhavaraju

Thomas Arnold Peyser

Matthew Lawrence Johnson

Andrew Attila Pal

Laura J Dunn

David DeRenzy

Kostyantyn Snisarenko

Alexander Strachan

Peter A Krulevitch

Anthony B Ross

Jason M Halac

Boris P Kovatchev

David J Van Tol

Nick Foley

James Glencross

Robert A Wilk

Mark Douglas Kempkey

Ulrich Kraft

Pinaki Ray

Kirk Harmon

Greg Kida

Rebecca Gimenez

Sarah Paige Elli

Georgios Zamanakos

Kevin Sayer

Murrad Kazalbash

Mark Wu

Zara Sieh

Kazanna Calais Hames

Keri Weindel

Gillian Teft

Robert Cavaye

Ian Shadforth

Gretchen Anderson

Mary McEvoy

Donna L Savage

David Shepherd

Lorraine Comstock

Alistair Longmuir

Chad Patterson

Eric David Bergman

Greg Matian

Douglas Graham

Aparna Srinivasan

David Rodbard

Brenda Montgomery

Erik Otto

Spencer Frank

Chuck Stroyeck

Kostantyn Snisarenko

Devon M Headen

Alexander Michael Diener

Samuel Isaac Epstein

Mark Edward Selander

Ryan Richard Ruehl

Miya Osaki

Kimberly Mingo Ventura

Lisa Powell

Kyia Downing

Graham Douglas

Hee Jun Rho

Eric Bergman

Mary Mcevoy

Alan Coulson

Katerine Yerre Koehler

David Robbard

Ray Pinaki

Methods and apparatus, including computer program products, are provided for processing analyte data. In some example implementations, a method may include generating glucose sensor data indicative of a host's glucose concentration using a glucose sensor; calculating a glycemic variability index (GVI) value based on the glucose sensor data; and providing output to a user responsive to the calculated glycemic variability index value. The GVI may be a ratio of a length of a line representative of the sensor data and an ideal length of the line. Related systems, methods, and articles of manufacture are also disclosed.

Systems and methods for managing glycemic variability

An ultrasonic surgical apparatus including a first signal generator outputting a drive signal at a predetermined voltage and frequency, a first oscillating structure receiving the drive signal and oscillating at the frequency of the drive signal, and a bridge circuit, detecting the mechanical motion of the first oscillating structure and outputting a signal representative of the mechanical motion. The apparatus also includes a second oscillating structure integrally formed within a portion of the first oscillating structure, the second oscillating structure outputting an electrical signal, and a microcontroller receiving the signal output by the bridge circuit and output by the second oscillating structure, the microcontroller determining an instantaneous frequency at which the first oscillating structure is oscillating based on the received signal, comparing the electrical signal from the second oscillating structure with a known signal value and determining the temperature of the second oscillating structure based on a the comparison.

Temperature estimation and tissue detection of an ultrasonic dissector from frequency response monitoring

Methods and apparatus, including computer program products, are provided for remote monitoring. In some example implementations, there is provided a method. The method may include receiving, at a remote monitor, a notification message representative of an event detected, by a server, from analyte sensor data obtained from a receiver monitoring an analyte state of a host; presenting, at the remote monitor, the notification message to activate the remote monitor, wherein the remote monitor is configured by the server to receive the notification message to augment the receiver monitoring of the analyte state of the host; accessing, by the remote monitor, the server, in response to the presenting of the notification message; and receiving, in response to the accessing, information including at least the analyte sensor data. Related systems, methods, and articles of manufacture are also disclosed.

Remote monitoring of analyte measurements

Systems and methods are described that provide a dynamic reporting functionality that can identify important information and dynamically present a report about the important information that highlights important findings to the user. The described systems and methods are generally described in the field of diabetes management, but are applicable to other medical reports as well. In one implementation, the dynamic reports are based on available data and devices. For example, useless sections of the report, such as those with no populated data, may be removed, minimized in importance, assigned a lower priority, or the like.

System and method for data analytics and visualization

A system is provided for monitoring analyte in a host, including a continuous analyte sensor that produces a data stream indicative of a host's analyte concentration and a device that receives and records data from the data stream from the continuous analyte sensor. In one embodiment, the device includes a single point analyte monitor, from which it obtains an analyte value, and is configured to display only single point analyte measurement values, and not any analyte measurement values associated with data received from the continuous analyte sensor. Instead, data received from the continuous analyte sensor is used to provide alarms to the user when the analyte concentration and/or the rate of change of analyte concentration, as measured by the continuous analyte sensor, is above or below a predetermined range. Data received from the continuous analyte sensor may also be used to prompt the diabetic or caregiver to take certain actions, such as to perform another single point blood glucose measurement. In another embodiment, the device provides for toggling between two modes, with one mode that allows for display of glucose concentration values associated with the continuous glucose sensor and a second mode that prevents the display of glucose concentration values associated with the continuous glucose sensor.

Continuous analyte monitor data recording device operable in a blinded mode

Techniques for data analysis and user guidance are provided. One or more current measurements of one or more current analyte levels for the user are received from a sensor. A pattern is generated based on the one or more current measurements and the one or more past measurements. A first alignment with a first user target is then determined based on the pattern, where the first user target relates to one or more of a mental state or physical state of the user. A first result is output to the user, based on the determined first alignment.

Evaluation of data to provide decision support for a ketogenic lifestyle

Systems and methods for detecting and reporting patterns in analyte concentration data are provided. According to some implementations, an implantable device for continuous measurement of an analyte concentration is disclosed. The implantable device includes a sensor configured to generate a signal indicative of a concentration of an analyte in a host, a memory configured to store data corresponding at least one of the generated signal and user information, a processor configured to receive data from at least one of the memory and the sensor, wherein the processor is configured to generate pattern data based on the received information, and an output module configured to output the generated pattern data. The pattern data can be based on detecting frequency and severity of analyte data in clinically risky ranges.

Systems and methods for detecting glucose level data patterns

Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, the observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data of a user population to predict a diabetes classification for an individual user. The historical glucose measurements of the user population may be provided by glucose monitoring devices worn by users of the user population, while the historical outcome data includes one or more diagnostic measurements obtained from sources independent of the glucose monitoring devices. Once trained, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device during an observation period spanning multiple days. The predicted diabetes classification may then be output, such as by generating one or more notifications or user interfaces based on the classification.

Diabetes prediction using glucose measurements and machine learning

Growing community of inventors

Carlsbad, CA, United States of America

David Price