Dexcom, Inc.

Christopher Robert Hannemann

Apurv Ullas Kamath

Nicholas Polytaridis

Benjamin Elrod West

Gary A Morris

Katherine Yerre Koehler

Hari Hampapuram

Rian W Draeger

Anna Leigh Davis

Laura J Dunn

Philip Thomas Pupa

Scott M Belliveau

Atiim Joseph Wiley

Michael Robert Mensinger

Alexandra Elena Constantin

Stephen J Vanslyke

Lauren Hruby Jepson

Matthew T Vogel

Peter C Simpson

Daniel Smith

Eli Reihman

Leif N Bowman

Naresh Chandra Bhavaraju

Arturo Garcia

Andrew Attila Pal

Aditya Sagar Mandapaka

Thomas Hall

Subrai Girish Pai

Anna Claire Harley-Trochimczyk

Tomas C Walker

Nathaniel David Heintzman

Andrea Jean Jackson

Rita M Castillo

Gary Brian Gable

Samuel Jere Marsh

Ashley Anne Rindfleisch

Sofie Wells Schunk

Brian Christopher Smith

Douglas William Burnette

Esteban Cabrera, Jr

Francis William Pascual

Timothy Joseph Goldsmith

Patrick Wile McBride

Philip Mansiel Pellouchoud

Kevin Shoemaker

Jorge R Barreras

Mario Remon

Kamuran Turksoy

Andrew Scott Parker

Sarah Kate Pickus

Ghazaleh R Esmaili

Alexandra Elena Constatin

Neha Vyas

Leah Ziegler

Todd N Haseyama

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

Various examples are directed to systems and methods for generating a bolus dose for a host. A bolus application may display a first bolus configuration parameter question at a user interface and receive, through the user interface, a first answer to the first bolus configuration parameter question. The first answer may describe a previous bolus determination technique of the host. The bolus application may select a second bolus configuration parameter question using the first answer and provide the second bolus configuration parameter question at the user interface. The bolus application may determine a set of at least one bolus configuration parameter using the first answer and a second answer to the second bolus configuration parameter question.

Managing bolus doses

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

Recommendations based on continuous glucose monitoring (CGM) are described. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a platform that provides a CGM system may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process. In implementations, a CGM platform includes a data analytics platform that obtains glucose measurements provided by a CGM system and also obtains additional data associated with a user. The data analytics platform processes these measurements and the additional data to predict a health indicator by using models. This prediction serves as a basis for generating a recommendation, such as a message recommending the user take action or adopt a behavior to mitigate a predicted negative health condition.

Recommendations based on continuous glucose monitoring

Growing community of inventors

San Diego, CA, United States of America

Christopher Robert Hannemann