Dexcom, Inc.

Gary A Morris

Apurv Ullas Kamath

Katherine Yerre Koehler

Rian W Draeger

Nicholas Polytaridis

Philip Thomas Pupa

Scott M Belliveau

Benjamin Elrod West

Christopher Robert Hannemann

Michael Robert Mensinger

Hari Hampapuram

Anna Leigh Davis

Alexandra Elena Constantin

Atiim Joseph Wiley

Esteban Cabrera, Jr

Peter C Simpson

Laura J Dunn

Brian Christopher Smith

Eli Reihman

Tomas C Walker

Leif N Bowman

Arturo Garcia

Aditya Sagar Mandapaka

Andrea Jean Jackson

Andrew Attila Pal

Nathaniel David Heintzman

Timothy Joseph Goldsmith

Naresh Chandra Bhavaraju

Daniel Smith

Stephen J Vanslyke

Thomas Hall

Lauren Hruby Jepson

Subrai Girish Pai

Anna Claire Harley-Trochimczyk

Matthew T Vogel

Rita M Castillo

Gary Brian Gable

Samuel Jere Marsh

Ashley Anne Rindfleisch

Sofie Wells Schunk

Douglas William Burnette

Francis William Pascual

Patrick Wile McBride

Jorge R Barreras

Philip Mansiel Pellouchoud

Kevin Shoemaker

Mario Remon

Aarthi Mahalingam

Matthew Lawrence Johnson

Peter Galuardi

John Michael Gray

Jennifer Blackwell

Paul D Kramer

Jorge Valdes

Paul Noble-Campbell

Sumitaka Mikami

Lauren Danielle Armenta

Hemant Mahendra Nirmal

Eric Cohen

Rian Draeger

Jr Cabrera Esteban

Derek James Escobar

Alexandra Elena Constatin

Clint Morris

Jr Esteban Cabrera

Patrick Wile Mcbride

Systems, devices, and methods are disclosed for wireless communication of analyte data. In embodiments, a method of using a diabetes management partner interface to configure an analyte sensor system for wireless communication with a plurality of partner devices is provided. The method includes the analyte sensor system receiving authorization to provide one of the partner devices with access to a set of configuration parameters via the diabetes management partner interface. The set of configuration parameters is stored in a memory of the analyte sensor system. The method also includes, responsive to input received from the one partner device via the diabetes management partner interface, the analyte sensor system setting or causing a modification to the set of configuration parameters, according to a system requirement of the one partner device.

Diabetes management partner interface for wireless communication of analyte data

Methods, devices and systems are disclosed for inter-app communications between software applications on a mobile communications device. In one aspect, a computer-readable medium on a mobile computing device comprising an inter-application communication data structure to facilitate transitioning and distributing data between software applications in a shared app group for an operating system of the mobile computing device includes a scheme field of the data structure providing a scheme id associated with a target software app to transition to from a source software app, wherein the scheme id is listed on a scheme list stored with the source software app; and a payload field of the data structure providing data and/or an identification where to access data in a shared file system accessible to the software applications in the shared app group, wherein the payload field is encrypted.

Methods for providing an alert or an alarm to a user of a mobile communications device

Systems and methods are disclosed that provide smart alerts to users, e.g., alerts to users about diabetic states that are only provided when it makes sense to do so, e.g., when the system can predict or estimate that the user is not already cognitively aware of their current condition, e.g., particularly where the current condition is a diabetic state warranting attention. In this way, the alert or alarm is personalized and made particularly effective for that user. Such systems and methods still alert the user when action is necessary, e.g., a bolus or temporary basal rate change, or provide a response to a missed bolus or a need for correction, but do not alert when action is unnecessary, e.g., if the user is already estimated or predicted to be cognitively aware of the diabetic state warranting attention, or if corrective action was already taken.

System and method for providing alerts optimized for a user

The present disclosure relates to systems, devices and methods for receiving biosensor data acquired by a medical device, e.g., relating to glucose concentration values, and controlling the access and distribution of that data. In some embodiments, systems and methods are disclosed for monitoring glucose levels, displaying data relating to glucose values and metabolic health information, and controlling distribution of glucose data between applications executing on a computer, such as a smart phone. In some embodiments, systems and methods are disclosed for controlling access to medical data such as continuously monitored glucose levels, synchronizing health data relating to glucose levels between multiple applications executing on a computer, and/or encrypting data.

Systems and methods for distributing continuous glucose data

A sensing system for a floating pool system for detecting and alerting a user of disturbances and detecting various levels and releasing chemicals into a liquid medium. The system comprises a controller that can comprise a wireless communication module and at least one sensor, and a receiving device can be configured to receive a signal from the wireless communication module. The controller can comprise at least one tilt sensor that is generally in an open position and closes with a waking event. The controller sends a message to the receiving device when a waking event occurs, alerting the user to a disturbance and possibly dangerous situation.

Sensing system for pool floating device

Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host.

Systems and methods for health data visualization and user support tools for continuous glucose monitoring

Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

Machine learning in an artificial pancreas

Growing community of inventors

La Jolla, CA, United States of America

Gary A Morris