Abstract: A computer-implemented method, computer program product and computing system for receiving a plurality of detection events concerning a plurality of security events occurring on multiple security-relevant subsystems within one or more computing platforms; storing the plurality of detection events to form an event repository; and processing the event repository using a machine learning model to identify attack patterns defined within the plurality of detection events stored within the event repository, thus defining one or more identified attack patterns.

Abstract: The disclosed embodiments are directed to a wearable automatic drug delivery device configured to provide basal-only dosing of insulin. In a primary embodiment, the wearable drug delivery device is configured to provide automatic operation and provides audible alerts and visual status indicators to the patient. In other embodiments, the patient may have some degree of control over the operation of the device by providing tapping gestures on housing of the device. In yet another embodiment, the patient may provide input and receive status from the device via an application executing on a portable computing device in wireless communication with the wearable drug delivery device.

Abstract: Exemplary embodiments may address the problem of missing blood glucose concentration readings from a glucose monitor that transmits blood glucose concentration readings over a wireless connection due to problems with the wireless connection. In the exemplary embodiments, an automated insulin delivery (AID) device uses an estimate in place of a missing blood glucose concentration reading in determining a predicted future blood glucose concentration reading for a user. Thus, the AID device is able to operate normally in generating insulin delivery settings despite not receiving a current blood glucose concentration reading for a current cycle. There is no need to suspend delivery of insulin to the user due to the missing blood glucose concentration reading.

Abstract: A computer-implemented method, computer program product and computing system for receiving a plurality of detection events concerning a plurality of security events occurring on two or more security-relevant subsystems within a computing platform; identifying two or more associated detection events included within the plurality of detection events; and grouping the two or more associated detection events to define a security incident.

Abstract: A computer-implemented method, computer program product and computing system for: obtaining hardware performance information concerning hardware deployed within a computing platform; obtaining platform performance information concerning the operation of the computing platform; obtaining application performance information concerning one or more applications deployed within the computing platform; and generating a holistic platform report concerning the computing platform based, at least in part, upon the hardware performance information, the platform performance information and the application performance information.

Abstract: A computer-implemented method, computer program product and computing system for: obtaining one or more artifacts concerning a detected security event; obtaining artifact information concerning the one or more artifacts; and generating a conclusion concerning the detected security event based, at least in part, upon the detected security event, the one or more artifacts, and the artifact information.

Abstract: A computer-implemented method, computer program product and computing system for receiving a plurality of detection events concerning a plurality of security events occurring on multiple security-relevant subsystems within one or more computing platforms; storing the plurality of detection events to form an event repository; and processing the event repository using a machine learning model to identify attack patterns defined within the plurality of detection events stored within the event repository, thus defining one or more identified attack patterns.

Abstract: A computer-implemented method, computer program product and computing system for defining a first query for a first security-relevant subsystem within a computing platform; processing the first query on the first security-relevant subsystem to generate a first data set concerning security events occurring on the first security-relevant subsystem; and receiving the first data set concerning the security events occurring on the first security-relevant subsystem.

Abstract: Disclosed are techniques and a device operable to determine a total amount of insulin delivered to the user over a predetermined time period. The total amount of insulin includes a total basal dosage delivered in basal dosages and a total bolus dosage delivered in bolus dosages over the predetermined time period. A proportion of the total amount of insulin delivered to the user provided via the total basal dosage amount over the predetermined time period is calculated. In response determining the proportion of the total amount of insulin attributed to the total basal dosage amount of insulin exceeds a threshold, an average basal dosage to be delivered within a subsequent time period that is approximately equal to the threshold may be determined. An instruction may be generated and output to deliver a modified basal dosage that substantially maintains the average basal dosage over the subsequent time period.

Abstract: A computer-implemented method, computer program product and computing system for: obtaining one or more artifacts concerning a detected security event; obtaining artifact information concerning the one or more artifacts; and generating a conclusion concerning the detected security event based, at least in part, upon the detected security event, the one or more artifacts, and the artifact information.

Abstract: Disclosed are a system, methods and computer-readable medium products that provide safety constraints for an insulin-delivery management program. Various examples provide safety constraints for a control algorithm-based drug delivery system that provides automatic delivery of a drug based on sensor input. Glucose measurement values may be received at regular time intervals from a sensor. A processor may predict future glucose values based on prior glucose measurement values. The safety constraints assist in safe operation of the drug delivery system during various operational scenarios. In some examples, predicted future glucose values may be used to implement safety constraints that mitigate under-delivery or over-delivery of the drug while not overly burdening the user of the drug delivery system and without sacrificing performance of the drug delivery system. Other safety constraints are also disclosed.

Abstract: Disclosed herein are systems and methods for the delivery of a co-formulation of insulin and a second drug, such as GLP-1, using an automated insulin delivery system. In a first embodiment, a dose of insulin is calculated by a medication delivery algorithm and a reduction factor is applied to account for the effect of second drug on the user's daily insulin requirement. In a second embodiment of the invention, a total amount of the second drug administered to the user during the past 24 hours is used to modify the correction factor and the insulin-to-carbohydrate ratio used by the medication delivery algorithm to cause a reduction in the insulin delivered to the user to account for the effect of the administration of the second drug portion of the co-formulation.

Abstract: Disclosed are techniques and a device operable to determine a total amount of insulin delivered to the user over a predetermined time period. The total amount of insulin includes a total basal dosage delivered in basal dosages and a total bolus dosage delivered in bolus dosages over the predetermined time period. A proportion of the total amount of insulin delivered to the user provided via the total basal dosage amount over the predetermined time period is calculated. In response determining the proportion of the total amount of insulin attributed to the total basal dosage amount of insulin exceeds a threshold, an average basal dosage to be delivered within a subsequent time period that is approximately equal to the threshold may be determined. An instruction may be generated and output to deliver a modified basal dosage that substantially maintains the average basal dosage over the subsequent time period.

Abstract: The disclosed embodiments are directed to a wearable automatic drug delivery device configured to provide basal-only dosing of insulin. In a primary embodiment, the wearable drug delivery device is configured to provide automatic operation and provides audible alerts and visual status indicators to the patient. In other embodiments, the patient may have some degree of control over the operation of the device by providing tapping gestures on housing of the device. In yet another embodiment, the patient may provide input and receive status from the device via an application executing on a portable computing device in wireless communication with the wearable drug delivery device.

Abstract: Exemplary embodiments provide an approach to predicting meal and/or exercise events for an insulin delivery system that otherwise does not otherwise identify such events. The insulin delivery system may use a model of glucose insulin interactions that projects estimated future glucose values based on the history of glucose values and insulin deliveries for the user. The predictions of meal events and/or exercise events may be based on residuals between actual glucose values and predicted glucose values. The exemplary embodiments may calculate a rate of change of the residuals over a period of time and compare the rate of change to thresholds to determine whether there likely has been a meal event or an exercise event. The insulin delivery system may then take measures to account for the meal or exercise by the user.

Abstract: Disclosed are a system, methods and computer-readable medium products that provide safety constraints for an insulin-delivery management program. Various examples provide safety constraints for a control algorithm-based drug delivery system that provides automatic delivery of a drug based on sensor input. Glucose measurement values may be received at regular time intervals from a sensor. A processor may predict future glucose values based on prior glucose measurement values. The safety constraints assist in safe operation of the drug delivery system during various operational scenarios. In some examples, predicted future glucose values may be used to implement safety constraints that mitigate under-delivery or over-delivery of the drug while not overly burdening the user of the drug delivery system and without sacrificing performance of the drug delivery system. Other safety constraints are also disclosed.

Abstract: The exemplary embodiments attempt to identify impending hypoglycemia and/or hyperglycemia and take measures to prevent the hypoglycemia or hyperglycemia. Exemplary embodiments may provide a drug delivery system for delivering insulin and glucagon as needed by a user of the drug delivery system. The drug delivery system may deploy a control system that controls the automated delivery of insulin and glucagon to a patient by the drug delivery system. The control system seeks among other goals to avoid the user experiencing hypoglycemia or hyperglycemia. The control system may employ a clinical decision support algorithm as is described below to control delivery of insulin and glucagon to reduce the risk of hypoglycemia or hyperglycemia and to provide alerts to the user when needed. The control system assesses whether the drug delivery system can respond enough to avoid hypoglycemia or hyperglycemia and generates alerts when manual action is needed to avoid hypoglycemia or hyperglycemia.

Abstract: Disclosed are techniques to establish initial settings for an automatic insulin delivery device. An adjusted total daily insulin (TDI) factor usable to calculate a TDI dosage may be determined. The adjusted TDI factor may be a TDI per unit of a physical characteristic of the user (e.g., weight) times a reduction factor. The adjusted TDI factor may be compared to a maximum algorithm delivery threshold. Based on the comparison result, the application or algorithm may set a TDI dosage and output a control signal. Blood glucose measurement values may be collected from a sensor over a period of time. A level of glycated hemoglobin of the blood may be determined based on the obtained blood glucose measurement values. In response to the level of glycated hemoglobin, the set TDI dosage may be modified. A subsequent control signal including the modified TDI dosage may be output to actuate delivery of insulin.

Abstract: Disclosed are examples for a system for drug delivery and components thereof. The system may include an on-body pump device and a secondary unit. The on-body pump device may include a reservoir and a fluid pathway. The reservoir may be configured to hold a liquid drug. The secondary unit may be removably coupled to the on-body pump device. The secondary unit may be configured to receive a prefilled cartridge containing a liquid drug, expel the liquid drug from the prefilled cartridge, and deliver the liquid drug to the reservoir of the on-body pump device via the fluid pathway. Examples of variations to the secondary unit are also disclosed.

Abstract: Disclosed are a device, system, methods and computer-readable medium products that provide an updated insulin-to-carbohydrate ratio and an updated total daily insulin. The described processes may be used for periodic updating of the insulin-to-carbohydrate ratio and the total daily insulin. The insulin-to-carbohydrate ratio and/or the total may be used in the calculation of new doses of insulin that a drug delivery device may be commanded to deliver to a user.