Thomson Licensing

Roche Diabetes Care, Inc.

Roche Diagnostics Operations, Inc.

Shenzhen Tcl New Technology Ltd.

Thomson Licensing S.a.

Rhomson Licensing S.a.

F. Hoffmann-la Roche Ag

Mark Gilmore Mears

Eric Stephen Carlsgaard

Phillip Edgar Pash

Vincent R Rizzo

Kristin M Westerfield

Bettina Steiner

Aaron Hal Dinwiddie

Paul J Galley

Mark Nierzwick

Chad Andrew Lefevre

Joseph Wayne Forler

David Bradley Markisohn

Richard W Wilson

Thomas Edward Gospel

James Duane Tenbarge

Benoit Pol Menez

Gene Harlow Johnson

Stacia R Davis

Richard Allen Zemer

Erin K McKinney

Abhishek S Soni

David L Duke

Robert E Reinke

Steven Bousamra

Michael J Celentano

Carolynn Rae Johnson

Timothy Nicholas Aykroyd

Nils Babion

Carol Jane Batman

Steven Lee Cooper

Scott Joseph Duggan

Robert G Davies

Valerie Sacrez Liebhold

Mark Sheridan Westlake

Paul S Rutkowski

Bret David Hawkins

Craig L Carlson

Wilfried Schmidt

Khelan M Modi

Robert John Giger

Justin David Adams

Jose Salazar-Galindo

Mark Alan Logan

Gregg Michael Morgan

Minging (Mike) Xing

Chad Andrew Le Fevre

Carlton Jethro Simmons, Jr

Roberto Montanez

Wesley Guy Munson

Amy S Stanley

Magnus Gyllensward

Benjamin E Strickrod

Hemlata Nayee

Chung Him Kwong

Benjamin E Stickrod

Stephen Kwong

Systems and methods for automatically displaying patterns in biological data may include one or more processors, and machine readable instructions. The machine readable instructions can cause the one or more processors to divide biological data into segments of interest. The one or more processers can transform, automatically, each of the segments of interest into a set of features according to a mathematical algorithm. Further, the one or more processers can cluster, automatically, the segments of interest into groups of clustered segments according to a clustering algorithm. The segments of interest can be grouped in the groups of clustered segments based at least in part upon the set of features. A cluster center can be associated with one of the groups of clustered segments. Moreover, the one or more processers can present, automatically, the cluster center on a human machine interface.

Systems and methods for automatically displaying patterns in biological monitoring data

Management of a diabetic condition may be performed by detecting a mealtime and/or an increased level of activity associated with a user. The mealtime is detected based one or more mealtime indicators that may indicate a likelihood of an occurrence of a mealtime. Each mealtime indicator provides additional context around the likelihood of a mealtime. To assist in managing the diabetic condition, a user's elevated activity levels may be detected. The activity level of a user is detected based on one or more activity indicators. Each activity indicator provides additional context around the likelihood of an increased level of activity by the user. Management of the diabetic condition may be performed proximate a mealtime or a user exercising to increase the efficacy of management of the diabetic condition.

Autonomous management of a diabetic condition based on mealtime and activity detection

An alert is used to inform a user that their blood glucose level has dropped below a threshold (e.g., the user is hypoglycemic) or has increased above a threshold (e.g., the user is hyperglycemic). There is a hierarchy of alerts from lowest priority to highest priority. The alert is communicated by a user device (e.g., a mobile device), which is, for example, a smartphone, smart watch, home automation device, or the like. The alert is modified in order to increase the likelihood that the user receives or acknowledges the alert within a minimal amount of time. An intensity level (e.g., a volume) of an alert is modified based on, for example, whether the user has acknowledged a previous alert. A modality or a sensory channel of an alert is changed if an initial alert does fails to elicit a response from the user.

Medical device alert, optimization, personalization, and escalation

A diabetes management system includes a handheld medical device, a mobile computing device, and a diabetes management application. The handheld medical device is configured to measure glucose in a sample of fluid residing in a test strip and associate a measurement time with the glucose measurement. The diabetes management application is configured to request a current device time from the RTC, determine a first device delta time by determining a difference between the current device time and an internal clock time, and associate a first timestamp with a glucose measurement, wherein the first timestamp is equal to the measurement time plus the first device delta time.

Bolus calculator time keeping between mobile phone application and bG meters

A blood glucose meter with a low cost user interface has a color glucose scale that is indexed by indicators programmed according to the user's blood glucose target range through a separating computing device due to the limited meter user interface. The color glucose scale is static and placed adjacent to the indicator such as by being molded into the housing, printed on the housing, or attached to the display. The low cost user interface has a monochrome segmented display with certain segments serving as a plurality of indicators. An indicator scaling module uses the target blood glucose range of the person with diabetes to calculate the blood glucose ranges for each indictor to index the color glucose measurement scale. The indicator scaling module can also assign attributes to the indicator such as flashing. The graphic representation of the user's blood glucose measurement aids in user interpretation.

Blood glucose meter with low cost user interface having programmed graphic indicators

A diabetes management system includes a handheld medical device, a mobile computing device, and a diabetes management application. The handheld medical device is configured to determine, in response to a port receiving a test strip, whether an auto-send feature is enabled on the handheld medical device, determine whether the handheld medical device is paired with a mobile computing device, and selectively instruct a wireless transceiver to establish a wireless connection and communicate a glucose measurement and identifying information to the mobile computing device. The mobile computing device is configured to execute the diabetes management application. The diabetes management application is configured to process a plurality of glucose measurements and identifying information associated with each of a plurality of glucose measurements.

Pairing and synchronizing a mobile phone application with a handheld glucose meter

Display screen with graphical user interface for glucose monitoring system

A method is provided for contextualizing manual entries of blood glucose measures for a patient into a patient log of a portable computing device. The method includes: administering a structured collection procedure, where the structured collection procedure specifies two or more collection actions for obtaining blood glucose measures from a patient; generating a reminder notification for a given collection action of the structured collection procedure, where the given collection action is associated with a given event of patient; and displaying a data entry interface upon receipt of an acceptance input from a user in response to the reminder notification, where the data entry interface includes a first input for a blood glucose measure and a second input for an event associated with the blood glucose measure, such that value for the second input is defaulted to the given event from the reminder notification.

User interface features for a diabetes management application

Display screen with continuous glucose monitor icon

A handheld diabetes manager is presented with an improved graphical user interface for a bolus calculator. The bolus calculator is configured to receive blood glucose measurements from a blood glucose measurement module and operates, in response to an input, to compute an insulin recommendation for a patient based in part on the blood glucose measurements. The graphical user interface for the bolus calculator includes a health adjustment screen that enables a user to input a value for health events associated with the insulin recommendation, where the input value represents a cumulative effect of the health events on insulin of the patient and the health adjustment screen presents a different icon for each of the health events associated with the insulin recommendation.

Graphical user interface pertaining to a bolus calculator residing on a handheld diabetes management device

A method and apparatus used in communications devices for associating images with data is described. The method includes the steps of comparing data associated with a plurality of images with data associated with a record and linking an image from the plurality of images to the record if at least a portion of the data associated with the image matches at least a portion of the data associated with the record. The apparatus includes a memory for storing images and records and data associated with the images and records, a processor coupled to the memory for comparing the data associated with the records and data associated with a plurality of images, and a display coupled to the controller for displaying the image if at least a portion of the data associated with the image matches at least a portion of the data associated with the record.

Method and apparatus for associating images with data

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Zionsville, IN, United States of America

Mark Gilmore Mears