Medtronic Minimed, Inc.

David Dunleavy

Louis J Lintereur

Patrick E Weydt

Anirban Roy

Benyamin Grosman

Pratik J Agrawal

Lavie Golenberg

Neha J Parikh

Di Wu

Ali Dianaty

Alexander S Campbell

Taly G Engel

An example system for therapy delivery includes one or more processors configured to in response to a prediction indicating that the meal event is to occur, output instructions to an insulin delivery device to deliver a partial therapy dosage, to a device to notify the patient to use the insulin delivery device to take the partial therapy dosage, or to the insulin delivery device to prepare the partial therapy dosage prior to the meal event occurring, and in response to a determination indicating that the meal event is occurring (e.g., based on movement characteristics of a patient arm), output instructions to the insulin delivery device to deliver a remaining therapy dosage, to the device to notify the patient to use the insulin delivery device to take the remaining therapy dosage, or to the insulin delivery device to prepare the remaining therapy dosage.

Prediction based delivering or guiding of therapy for diabetes

One or more processors may be configured to detect whether a patient with diabetes is operating a vehicle based on one or more detected gestures. Based on the detection of operating the vehicle, the one or more processors may cause a patient device to output alerts according to a driving alert protocol. In another example, based on the detection of operating the vehicle, one or more processors may cause an insulin pump to operate according to a driving therapy protocol.

Adjusting diabetes alerts and therapy based on gesture-based detection of vehicle operation

A processor-implemented method for closed-loop control in steady-state conditions includes determining, based on data including historical bolus information but excluding historical basal information, an amount of unmetabolized therapeutic substance in a patient; determining, based on a difference between a most recent measurement of a physiological condition of the patient and a target value for the physiological condition, a first amount or rate of a basal dosage for delivery to the patient; adjusting, based on the amount of unmetabolized therapeutic substance, the first amount or rate of the basal dosage to determine a second amount or rate of the basal dosage; and causing delivery of the second amount or rate of the basal dosage based on communicating the second amount or rate in a delivery command.

Closed-loop control in steady-state conditions

Techniques related to automatically generating a super bolus may include determining an amount of an augmented meal bolus to be delivered to a patient for regulating the patient's glycemic response to a meal. The amount of the augmented meal bolus may exceed a sufficient amount for counteracting a glucose level increase caused by the meal. In some embodiments, the techniques may further include determining a duration of a postprandial reduction period during which basal dosage deliveries to the patient are to be reduced. In some other embodiments, the techniques may further include delivering the augmented meal bolus to the patient prior to determining whether or not to cause reduction of basal dosage deliveries. More specifically, a glucose level of the patient may be obtained after delivery of the augmented meal bolus, and the obtained glucose level may be used to determine whether to reduce basal dosage deliveries.

Automated super bolus generation

A system includes one or more processors and one or more processor-readable storage media storing instructions which, when executed by the one or more processors, cause performance of receiving, via a user interface, confirmation input that indicates user acceptance of a blood glucose (BG) measurement generated by a BG meter device, in response to receiving the confirmation input, using the BG measurement for performing both glucose sensor calibration and bolus calculation, and causing delivery of a bolus based on outputting a delivery command in accordance with the bolus calculation.

Efficient confirmation of blood glucose measurements

Dynamic super bolus generation

Devices, systems, and techniques for controlling delivery of therapy for diabetes are described. In one example, a system includes a wearable device configured to generate user activity data associated with an arm of a user; and one or more processors configured to: identify at least one gesture indicative of utilization of an injection device for preparation of an insulin injection based on the user activity data; based on the at least one identified gesture, generate information indicative of at least one of an amount or type of insulin dosage in the insulin injection by the injection device; compare the generated information to a criteria of a proper insulin injection; and output information indicative of whether the criteria is satisfied based on the comparison.

Gesture-based control of diabetes therapy

An insulin infusion device and a related operating method are disclosed. The method obtains a blood glucose (BG) measurement of a blood sample from a user, displays the BG measurement on a display element of the infusion device, and receives a confirmation input that indicates user acceptance of the displayed BG measurement. In response to the confirmation input, a glucose sensor calibration routine is automatically initiated, and an insulin bolus calculation is automatically initiated. The calibration routine and the bolus calculation use the confirmed BG measurement. The method continues by controlling delivery of insulin, in accordance with the calculated insulin bolus, from a fluid reservoir of the insulin infusion device. The sensor calibration routine and the insulin bolus calculation are both performed without any additional user involvement or interaction with the user interface other than the user acceptance of the displayed BG measurement.

Growing community of inventors

West Hills, CA, United States of America

David Dunleavy