Dexcom, Inc.

Andrea Jean Jackson

Apurv Ullas Kamath

Peter C Simpson

Eli Reihman

Katherine Yerre Koehler

Rian W Draeger

Leif N Bowman

Arturo Garcia

Aditya Sagar Mandapaka

Gary A Morris

Philip Thomas Pupa

Scott M Belliveau

Atiim Joseph Wiley

Subrai Girish Pai

Daniel Smith

Hari Hampapuram

Naresh Chandra Bhavaraju

Stephen J Vanslyke

Anna Leigh Davis

Andrew Attila Pal

Laura J Dunn

Nicholas Polytaridis

Thomas Hall

Lauren Hruby Jepson

Benjamin Elrod West

Alexandra Elena Constantin

Christopher Robert Hannemann

Anna Claire Harley-Trochimczyk

Nathaniel David Heintzman

Tomas C Walker

Matthew T Vogel

Rita M Castillo

Gary Brian Gable

Samuel Jere Marsh

Ashley Anne Rindfleisch

Sofie Wells Schunk

Michael Robert Mensinger

Brian Christopher Smith

John Michael Gray

Jennifer Blackwell

Paul D Kramer

Esteban Cabrera, Jr

Paul Noble-Campbell

Timothy Joseph Goldsmith

Sumitaka Mikami

Lauren Danielle Armenta

Hemant Mahendra Nirmal

Mark Derdzinski

Joost Herman Van Der Linden

Maritza S Powell

Jessica S Larrabee

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

Certain aspects of the present disclosure relate to methods and systems for optimized delivery of communications including content to users of a software application. The method also includes obtaining, by a customer engagement platform (CEP), a set of cohort selection criteria for identifying a user cohort to deliver the content; identifying, by a data analytics platform (DAP), the user cohort to communicate with in accordance with the set of cohort selection criteria; identifying, by the DAP, one or more communication configurations for communicating with one or more sub-groups within the user cohort; and to each user of the user cohort, transmitting one or more communications based on the content and a corresponding communication configuration for a sub-group that may include the corresponding user; and measuring engagement outcomes associated with usage of the corresponding one or more communication configurations in communication with each of the sub-groups.

Machine learning techniques for optimized communication with users of a software application

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

Growing community of inventors

Solana Beach, CA, United States of America

Andrea Jean Jackson