Dexcom, Inc.

Phil Mayou

Michael Robert Mensinger

Eli Reihman

Katherine Yerre Koehler

Eric Cohen

Rian W Draeger

Angela Marie Traven

Hari Hampapuram

Apurv Ullas Kamath

Peter C Simpson

Aarthi Mahalingam

Naresh Chandra Bhavaraju

Leif N Bowman

Basab Dattaray

Arturo Garcia

David Price

Laura J Dunn

Thomas Hall

Andrew Attila Pal

Nicholas Polytaridis

Jorge Valdes

Alexandra Lynn Carlton

Stephen M Madigan

Kevin Sayer

Murrad Kazalbash

Robert James Boock

Thomas Arnold Peyser

John Michael Gray

Jennifer Blackwell

Anna Leigh Davis

Philip Thomas Pupa

Scott M Belliveau

Alexandra Elena Constantin

Lauren Hruby Jepson

Nathaniel David Heintzman

Tomas C Walker

Kostyantyn Snisarenko

Matthew Lawrence Johnson

Patrick Wile McBride

Daniel Justin Wiedeback

Sumitaka Mikami

Sheryl Sadsarin Gaano

Brian Christopher Smith

Sebastian Bohm

Douglas William Burnette

Esteban Cabrera, Jr

Disha B Sheth

Subrai Girish Pai

Matthew T Vogel

Michael Levozier Moore

Katherine Yerre Grubstein

Derek James Escobar

Jim Stephen Amidei

Keri Weindel

Shawn Larvenz

Keri Leone

Randy Tompot

Kostantyn Snisarenko

Stephen Madigan

Daniel Justin Weideback

Katherine Yerre Kohler

Rian Draeger

Jr Cabrera Esteban

Eli Reihmann

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

Systems and methods are provided to determine a time to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a model or pattern. The time to deliver guidance may be calculated to be useful to a user in the management of a glucose concentration level.

System and method for providing personalized guidance to diabetes patients

Systems and methods that continuously adapt aspects of a continuous monitoring device based on collected information to provide an individually tailored configuration are described. The adaptations may include adapting the user interface, the alerting, the motivational messages, the training, and the like. Such adaptation can allow a patient to more readily identify and understand the information provided by/via the device.

Adaptive interface for continuous monitoring devices

Display screen or portion thereof with a graphical user interface with icons

Systems and methods are provided to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a patient glucose concentration level. The glucose concentration level may be provided to a stored model to determine a state. The guidance may be determined based at least in part on the determined state.

System and method for decision support

The present disclosure relates to techniques for receiving glucose data from a continuous glucose sensor and controlling the use and redistribution of that data so it is used in an intended manner. In one aspect, a system includes a plurality of continuous glucose monitor (CGM) devices; a plurality of display devices to receive data from the CGM devices classified into a plurality of classifications based on data type; a cloud server architecture to receive the data from the display devices on an intermittent basis, in which the data routed to a particular server of the plurality of servers is determined by the data type, and in which the intermittent basis varies depending upon data type; a plurality of remote monitor display devices; and an analysis and report engine.

Distributed system architecture for continuous glucose 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 provided to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a patient glucose concentration data sensed by a glucose concentration sensor. A host state change associated with the host glucose concentration data may be determined. A guidance message based at least in part on the host state change may also be determined. The guidance message may be delivered through a user interface.

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

Systems and methods for remote and host monitoring communication are disclosed. In some implementations, monitoring systems can comprise a host monitoring device associated with a Host communicatively coupled to one or more remote monitoring devices associated with Remote Monitors. The host monitoring device can send communications based at least in part on analyte measurements of a Host sensor and/or other contextual data giving such measurements context. Different remote monitoring devices can receive different communications based at least in part on the role of the respective Remote Monitors relative to the Host. These roles can be reflected in classifications of Remote Monitors.

Systems and methods for remote and host monitoring communications

Growing community of inventors

San Diego, CA, United States of America

Phil Mayou