Dexcom, Inc.

Arima Genomics, Inc.

Nathaniel David Heintzman

Apurv Ullas Kamath

Eli Reihman

Andrew Attila Pal

Lauren Hruby Jepson

Peter C Simpson

Katherine Yerre Koehler

Hari Hampapuram

Rian W Draeger

Naresh Chandra Bhavaraju

Arturo Garcia

Anna Leigh Davis

Nicholas Polytaridis

Philip Thomas Pupa

Scott M Belliveau

Alexandra Elena Constantin

Tomas C Walker

Gary A Morris

Subrai Girish Pai

Matthew T Vogel

Daniel Smith

Leif N Bowman

Stephen J Vanslyke

Aditya Sagar Mandapaka

Laura J Dunn

Thomas Hall

Benjamin Elrod West

Christopher Robert Hannemann

Atiim Joseph Wiley

Anna Claire Harley-Trochimczyk

Andrea Jean Jackson

Sofie Wells Schunk

Ashley Anne Rindfleisch

Samuel Jere Marsh

Gary Brian Gable

Rita M Castillo

Michael Robert Mensinger

Eric Cohen

Phil Mayou

John Michael Gray

Jennifer Blackwell

Basab Dattaray

Matthew Lawrence Johnson

Patrick Wile McBride

Daniel Justin Wiedeback

Sumitaka Mikami

Esteban Cabrera, Jr

Jorge Valdes

Jacob S Leach

Mark Derdzinski

Siddarth Selvaraj

Christian Edgar Laing

Esteban Cabrera

Jr Esteban Cabrera

Daniel Justin Weideback

In implementations of systems for determining a similarity of sequences of glucose values, a computing device implements a similarity system to receive input data describing a sequence of user glucose values measured by a continuous glucose monitoring (CGM) system. The similarity system computes similarity scores for a plurality of sequences of glucose values by comparing each glucose values included in the sequence of user glucose values with ever glucose value included in each sequence of the plurality of sequences. A particular sequence of glucose values that is associated with a highest similarity score is identified. The similarity system determines an externality associated with the particular sequence. The similarity system generates an indication of the externality for display in a user interface.

Systems for determining similarity of sequences of glucose values

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

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

The present disclosure relates to methods to deconvolute a mixture sample of genetic material from different origins or sources. The disclosed methods can be used in various applications, including, the non-invasive determination of a fetal genome, a fetal -ome (e.g. exome). or other targeted fetal locus from cell-free nucleic acids in maternal plasma or other body fluids; the determination of cancer-associated mutations from cell-free nucleic acids in a body fluid sample that contains a mixture of nucleic acids from normal cells and tumor cells; and quantification of donor cell contamination using a body fluid from a transplantation recipient to monitor and/or predict the outcome of a transplantation procedure.

Accurate molecular deconvolution of mixture samples

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

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.

Growing community of inventors

San Diego, CA, United States of America

Nathaniel David Heintzman