WO2024192362A1 - Method for mitigating hypoglycemia rebound in an automated insulin delivery system - Google Patents

Abstract

Embodiments relate to a control module for storing and accessing insulin delivery parameters to accurately set an insulin dosage amount. The control module can include a processor and a memory having instructions stored thereon that when executed by the processor will cause the processor to: receive glucose data including glucose concentration measurements spanning a time period; identify a minimum glucose concentration measurement (G_min) within the time period; identify a current glucose concentration measurement (G_c); set an upper bound constraint based on the G_min and the G_c; generate an adapted insulin dosage schedule by imposing a maximum insulin dosage on an insulin dosage schedule based on the upper bound constraint; and either: administer an insulin dosage in accordance with the adapted insulin dosage schedule; or control administration of insulin via an insulin dosage signal based on the adapted insulin dosage schedule.

Description

METHOD FOR MITIGATING HYPOGLYCEMIA REBOUND IN AN AUTOMATED

INSULIN DELIVERY SYSTEM

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent application is related to and claims the benefit of priority to U.S. provisional patent application no. 63/452,569, filed on March 16, 2023, the entire contents of which is incorporated by reference.

FIELD

[0002] Embodiments can relate to a systems and methods for storing and accessing insulin delivery parameters to accurately set an insulin dosage amount.

BACKGROUND INFORMATION

[0003] With automated insulin delivery (AID) systems becoming widely adopted in the management of type 1 diabetes, there has been an increase in occurrences of rebound hypoglycemia, or generated hypoglycemia induced by the controller’s response to rapid glucose rises following rescue carbohydrates. Furthermore, as AID systems aim to enable complete automation of prandial control, algorithms are designed to react even more strongly to glycemic rises. Rebound hypoglycemia and similar glycemic effects and occurrences present a problem for AID systems.

SUMMARY

[0004] An exemplary embodiment can relate to a control module for storing and accessing insulin delivery parameters to accurately set an insulin dosage amount. The control module can include a processor. The control module can include a memory having instructions stored thereon that when executed by the processor will cause the processor to perform one or more of the method steps or algorithmic functions disclosed herein. The instructions can cause the processor to receive glucose data including glucose concentration measurements spanning a time period. The instructions can cause the processor to identify a minimum glucose concentration measurement (G_min) within the time period. The instructions can cause the processor to identify a current glucose concentration measurement (G_c). The instructions can cause the processor to set an upper bound constraint based on the G_min and the G_c. The instructions can cause the processor to generate an adapted insulin dosage schedule by imposing a maximum insulin dosage on an insulin dosage schedule based on the upper bound constraint. The instructions can cause the processor to either: administer an insulin dosage in accordance with the adapted insulin dosage schedule; or control administration of insulin via an insulin dosage signal based on the adapted insulin dosage schedule.

[0005] An exemplary embodiment can relate to a computer readable medium having instructions stored therein for causing an insulin delivery processor to store and access insulin delivery⁷ parameters to accurately set an insulin dosage amount. The instructions when executed will cause an insulin delivery⁷ processor to perform one or more of the method steps or algorithmic functions disclosed herein. The instructions can cause the processor to receive glucose data including glucose concentration measurements spanning a time period. The instructions can cause the processor to identify a minimum glucose concentration measurement (G_min) within the time period. The instructions can cause the processor to identify a current glucose concentration measurement (G_c). The instructions can cause the processor to set an upper bound constraint based on the G_min and the G_c. The instructions can cause the processor to generate an adapted insulin dosage schedule by imposing a maximum insulin dosage on an insulin dosage schedule based on the upper bound constraint. The instructions can cause the processor to either: administer an insulin dosage in accordance with the adapted insulin dosage schedule; or control administration of insulin via an insulin dosage signal based on the adapted insulin dosage schedule.

[0006] An exemplary⁷ embodiment can relate to a method for storing and accessing insulin delivery⁷ parameters to accurately set an insulin dosage amount. The method can involve receiving glucose data including glucose concentration measurements spanning a time period. The method can involve identifying a minimum glucose concentration measurement (G_min) within the time period. The method can involve identifying a current glucose concentration measurement (G_c). The method can involve setting an upper bound constraint based on the G_min and the G_c. The method can involve generating an adapted insulin dosage schedule by imposing a maximum insulin dosage on an insulin dosage schedule based on the upper bound constraint. The method can involve either: administering an insulin dosage in accordance with the adapted insulin dosage schedule; or controlling administration of insulin via an insulin dosage signal based on the adapted insulin dosage schedule. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Other features and advantages of the present disclosure will become more apparent upon reading the following detailed description in conjunction with the accompanying drawings, wherein like elements are designated by like numerals, and wherein:

[0008] FIG. 1 shows an exemplary' system diagram for an embodiment a sy stem for accurately setting an insulin dosage amount;

[0009] FIG. 2 shows an exemplary' system diagram for an embodiment a system for accurately setting an insulin dosage amount;

[0010] FIG. 3 shows an exemplary’ method flow diagram for an embodiment a method for accurately setting an insulin dosage amount;

[0011] FIG. 4 shows an exemplary' diagram illustrating integration of an embodiment of a control module into an Automated Insulin Delivery System;

[0012] FIG. 5 shows an exemplary’ implementation of an embodiment of the method for basal modulation and bolus modulation;

[0013] FIG. 6 shows exemplary constraint curves for basal Modulation and bolus modulation;

[0014] FIG. 7 shows a comparison of glucose, basal, and bolus traces;

[0015] FIG. 8 is an exemplary high-level functional block diagram for an embodiment of the system;

[0016] FIG. 9 shows an exemplary' network configuration;

[0017] FIG. 10 is a block diagram that illustrates an exemplary system;

[0018] FIG. 11 illustrates an exemplary system; and

[0019] FIG. 12 is a block diagram of an exemplary machine.

DETAILED DESCRIPTION

[0020] Embodiments of the system 100 can relate to a control module 102 for storing and accessing insulin delivery parameters to accurately set an insulin dosage amount. The control module 102 can include a one or more processors 104. The control module 102 can include a memory 106. The control module 102 can include a memory 106 having instructions 108 stored thereon that when executed by the processor 104 can cause the processor 104 to perform one or more of the method steps or algorithmic functions disclosed herein. Embodiments disclosed herein improve operation of the processor 104. This can be achieved by implementing algorithmic functions that improve efficiency of processor operation by requiring less iterations (e.g., the algorithmic functions prevent overshooting a target), requiring less computational resources (e.g., the algorithmic functions allow the processor to operate on a subset of data as opposed to processing all of the datapoints in the signal), etc. For instance, the algorithmic functions set an upper bound constraint on data to be processed for generating an insulin dosage schedule. This upper bound constraint can prevent or reduce overshooting (e.g., improves accuracy) while at the same time can reduce the amount of computational resources that would otherwise be needed to process data above the upper bound. It can also prevent or reduce repetitive processing steps by obviating the need to compensate for overshooting a target. With the upper bound constraint being based on G_min and G_c values, the processor need only process a limited amount of data (e.g., 1 -hour’s worth of data), which further reduces computational resources and processing iterations. Thus, the inventive methods and systems disclosed herein provide for higher precision, require less computational resources, require less processing steps, etc.

[0021] Any of the processors 104 disclosed herein can be part of or in communication with a machine (e.g., a computer device, a logic device, a circuit, an operating module (hardware, software, and/or firmware), etc.). The processor 104 can be hardware (e.g., processor, integrated circuit, central processing unit, microprocessor, core processor, computer device, etc.), firmware, software, etc. configured to perform operations by execution of instructions embodied in computer program code, algorithms, program logic, control, logic, data processing program logic, artificial intelligence programming, machine learning programming, artificial neural network programming, automated reasoning programming, etc. The processor 104 can receive, process, and/or store data related to glucose measurements.

[0022] Any of the processors 104 disclosed herein can be a scalable processor, a parallelizable processor, a multi-thread processing processor, etc. The processor 104 can be a computer in which the processing power is selected as a function of anticipated network traffic (e.g. data flow). The processor 104 can include any integrated circuit or other electronic device (or collection of devices) capable of performing an operation on at least one instruction, which can include a Reduced Instruction Set Core (RISC) processor, a Complex Instruction Set Computer (CISC) microprocessor, a Microcontroller Unit (MCU), a CISCbased Central Processing Unit (CPU), a Digital Signal Processor (DSP), a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA), etc. The hardware of such devices may be integrated onto a single substrate (e.g., silicon "die"), or distributed among two or more substrates. Various functional aspects of the processor 104 may be implemented solely as software or firmware associated with the processor 104.

[0023] The processor 104 can include one or more processing or operating modules. A processing or operating module can be a software or firmware operating module configured to implement any of the functions disclosed herein. The processing or operating module can be embodied as software and stored in memory 106, the memory 106 being operatively associated with the processor 104. A processing module can be embodied as a web application, a desktop application, a console application, etc.

[0024] The processor 104 can include or be associated with a computer or machine readable medium. The computer or machine readable medium can include memory 106. Any of the memory 106 discussed herein can be computer readable memory configured to store data. The memory 106 can include a volatile or non-volatile, transitory⁷ or non-transitory memory, and be embodied as an in-memory, an active memory, a cloud memory, etc. Examples of memory 106 can include flash memory', Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read only Memory (PROM), Erasable Programmable Read only Memory⁷ (EPROM), Electronically Erasable Programmable Read only Memory⁷ (EEPROM), FLASH-EPROM, Compact Disc (CD)-ROM, Digital Optical Disc DVD), optical storage, optical medium, a carrier wave, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by the processor.

[0025] The memory 106 can be a non-transitory computer-readable medium. The term "computer-readable medium" (or "machine-readable medium") as used herein is an extensible term that refers to any medium or any memory, that participates in providing instructions 108 to the processor 104 for execution, or any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). Such a medium may store computer-executable instructions to be executed by a processing element and/or control logic, and data which is manipulated by a processing element and/or control logic, and may take many forms, including but not limited to, non-volatile medium, volatile medium, transmission media, etc. The computer or machine readable medium can be configured to store one or more instructions 108 thereon. The instructions 108 can be in the form of algorithms, program logic, etc. that cause the processor to execute any of the functions disclosed herein. [0026] Embodiments of the memory 106 can include a processor 104 module and other circuitry to allow for the transfer of data to and from the memory 106, which can include to and from other components of a communication system. This transfer can be via hardwire or wireless transmission. The communication system can include transceivers, which can be used in combination with switches, receivers, transmitters, routers, gateways, wave-guides, etc. to facilitate communications via a communication approach or protocol for controlled and coordinated signal transmission and processing to any other component or combination of components of the communication system. The transmission can be via a communication link. The communication link can be electronic-based, optical-based, opto-electronic-based, quantum-based, etc. Communications can be via Bluetooth, near field communications, cellular communications, telemetry communications, Internet communications, etc.

[0027] Transmission of data and signals can be via transmission media. Transmission media can include coaxial cables, copper wire, fiber optics, etc. Transmission media can also take the form of acoustic or light w aves, such as those generated during radio-w ave and infrared data communications, or other form of propagated signals (e.g., carrier waves, digital signals, etc.).

[0028] Any of the processors 104 can be in communication with other processors of other devices (e.g., a computer device, a computer system, a laptop computer, a desktop computer, etc.). For instance, the processor of the control module 102 can be in communication with a processor 104 of an insulin pump, etc. Any of the processors 104 can have transceivers or other communication devices / circuitry to facilitate transmission and reception of wireless signals. Any of the processors 104 can include an Application Programming Interface (API) as a softw are intermediary that allow s two or more applications to talk to each other. Use of an API can allow software of the processor 104 of the control module 102 to communicate with software of the processor 104 of the other device(s).

[0029] The instructions 108 can cause the processor 104 to receive glucose data (e.g., blood glucose, interstitial glucose, etc.). The glucose data can include glucose concentration measurements. The glucose concentration measurements can span a time period (e.g., 5 minutes, 30 minutes. 1 hour. 5 hours, 24 hours, etc.). It is contemplated for the period of time to be 1 hour or more, as embodiments of the system 100 can effectively and accurately generate an insulin dosage schedule by only using 1 hour’s worth of data. The time period is a time range, and the glucose data is data collected from a patient during that time range. The time range can be an immediately preceding time period (e.g., glucose data collected in the immediately preceding 1-hour), some other preceding time period (e.g., glucose data collected spanning a 1-hour time period that occurred yesterday), or a combination of both. The glucose data can be received by a glucose monitor, a data source (e.g., data store) containing the glucose data, etc. The receipt of glucose data can be a via a push operation (e.g., the glucose monitor can push the data to the processor 104), via a pull operation (e.g., the processor 104 can pull the data from the glucose monitor), or a combination of both. The receipt of glucose data can be in real-time, in a delayed manner, in a continuous manner, in a continuous-batch manner, set by a periodic schedule, set by some other schedule, via on- demand, prescribed by algorithmic function, etc. The received glucose data can be processes by the processor 104, stored in memory' 106 associated with the processor 104, processed by another processor 104, etc. The glucose data can be encoded, tagged, time stamped, clustered, etc.

[0030] The instructions 108 can cause the processor 104 to identify a minimum glucose concentration measurement (G_jnin) of the glucose data. This can be a G_min of the glucose data that is within the time period. For instance, the glucose data can include one or more glucose data points spanning the range of the time period. The G_min for that time period can be identified by the processor 104. The G_min can be the absolute minimum, a central tendency (e.g., average, standard deviation, etc.) minimum, the minimum that occurs the most. etc. Once identified, the G_min value can be recorded in a data table, for example. The instructions 108 can cause the processor 104 to identify a cunent glucose concentration measurement (G_c). The G_c can be the glucose measurement most recently received, the glucose measurement most recently received within the time period, the glucose measurement having a time stamp that is the most current, etc. Once identified, the G_c value can be recorded in a data table, for example.

[0031] The instructions 108 can cause the processor 104 to set an upper bound constraint based on the G_min and the G_c. The upper bound constraint can be a constraint limiting an amount of insulin used for an insulin infusion. This constraint can be used for generating or converted into a command or recommendation signal sent to or used by an insulin delivery system 110 to control or influence the amount of insulin to be administered by the insulin delivery system 110 (e.g., an Automated Insulin Delivery System). The upper bound can prevent the insulin delivery' system 110 from administering an amount of insulin that is greater than an amount limited by the upper bound. It is understood that the receipt of glucose data and generation of the upper bound constraint can be done for a single instance, on a continual basis, on a periodic basis, etc. Thus, the system 100 can generate one or more upper bound constraints, depending on how the data is processed. A current upper bound constraint can be generated and last for a predetermine amount of time, last until it is overridden manually or overridden by another glycemic model, last until a subsequent upper bound constraint is generated, etc. There can be one or more upper bound constraints for one or more time periods, one or more G_min values, one or more the G_c values, one or more other physiological or metabolic variables, etc. The upper bound constraint can be one or more curves. The one or more curves can be defined by a fixed G_min value and be a function of G_c values, defined by fixed G_c value and be a function of G_min values, etc. It is understood that other physiological or metabolic variables can be included (e.g., they too can be fixed or variable parameters for the curve(s)).

[0032] As a non-limiting example, the instructions 108 can cause the processor 104 to generate one or more upper bound constraints as an exponential decay function using the

G_min and/or the G_c as input variables. An exemplary embodiment can be configured to generate one or more upper bound constraint curves, wherein the one or more upper bound constraint curves are based on multiple G_c values for a constant G_min value. For instance, the instructions 108 can cause the processor 104 to generate: a G_min_40 upper bound constraint curve for constant G_min value equal to 40 mg/dL; a G_min_50 upper bound constraint curve for constant G_min value equal to 50 mg/dL; a G_min_60 upper bound constraint curve for constant G_min value equal to 60 mg/dL; a G_min_70 upper bound constraint curve for constant G_min value equal to 70 mg/dL; a G_min_80 upper bound constraint curve for constant G_min value equal to 80 mg/dL; a G_min_90 upper bound constraint curve for constant G_min value equal to 90 mg/dL; a G_min_100 upper bound constraint curve for constant G_min value equal to 100 mg/dL; a G_min_l 10 upper bound constraint curve for constant G_min value equal to 110 mg/dL; and/or a G_min_l 20 upper bound constraint curve for constant G_min value equal to 120 mg/dL.

[0033] With the one or more upper bound constraints being constructed as an exponential decay function, the instructions 108 can cause the processor 104 to: set the upper bound constraint to be inversely proportional with the G_min set the upper bound constraint to be inversely proportional with G_c; set the upper bound constraint to have no effect on an insulin dosage schedule when the G_min is greater than or equal to a glucose concentration target; and/or set the upper bound constraint to match a basal rate profile when the G_min is less than a than or equal to a glucose concentration value indicative of hypoglycemia.

[0034] The instructions 108 can cause the processor 104 to generate an adapted insulin dosage schedule by imposing a maximum insulin dosage on an insulin dosage schedule based on the upper bound constraint. For instance, the upper bound constraint can operate as an upper bound mathematical function that is applied to an insulin dosage schedule. The insulin dosage schedule can be an insulin dosage schedule set by the algorithmic function(s) of the insulin delivery- system 110. This demonstrates the ability to use the inventive system 100 with any the insulin delivery system 110, as the inventive system 100 can communicate the upper bound mathematical function to the insulin delivery' system 110 allowing the insulin delivery' system 110 or forcing it to adjust its insulin dosage schedule - e.g., generate an adapted insulin dosage schedule. In addition, or in the alternative, the processor 104 can pull the insulin dosage schedule from the insulin delivery system 110 and generate an adapted insulin dosage schedule which can then be transferred to the insulin delivery system 110, again allowing insulin delivery' system 110 or forcing it to adjust its insulin dosage schedule. [0035] As can be appreciated from the above, the instructions 108 can cause the processor 104 to either: administer an insulin dosage in accordance with the adapted insulin dosage schedule; or control administration of insulin via an insulin dosage signal based on the adapted insulin dosage schedule.

[0036] As noted above, the time period can 1 hour. Use of G_min and G_c values allow the system 100 to effectively and efficiently generate an upper bound constraint with only an hour’s worth of time. A continuous glucose monitor (CGM) generally obtains glucose data every 5 minutes, and thus with a CGM as the glucose monitor, the system 100 can generate an upper bound constraint to accurately determine an insulin dosage based on 12 data points. [0037] The instructions 108 can cause the processor 104 to set one or more upper bound constraints for long-acting insulin, one or more upper bound constraints for short-acting insulin, etc. This can allow the system 100 to generate adapted insulin dosage schedule(s) by imposing maximum basal insulin dosage(s), maximum bolus insulin dosage(s), etc.

[0038] With reference to FIG. 2, the system 100 can include the control module 102 in combination with an insulin delivery’ processor 104. The insulin delivery processor 104 can be a component of or in communication with an automated insulin delivery system 1 10. [0039] Embodiments can relate to a computer readable medium having instructions 108 stored therein for causing an insulin deliver}’ processor 104 to store and access insulin delivery parameters to accurately set an insulin dosage amount. The instructions 108 when executed can cause an insulin deliver}’ processor to perform one or more of the method steps or algorithmic functions disclosed herein. The instructions 108 can cause the processor to receive glucose data including glucose concentration measurements spanning a time period. The instructions 108 can cause the processor 104 to identify a minimum glucose concentration measurement (G_min) within the time period. The instructions 108 can cause the processor 104 to identify a current glucose concentration measurement (G_c). The instructions 108 can cause the processor 104 to set an upper bound constraint based on the G_min and the G_c. The instructions 108 can cause the processor 104 to generate an adapted insulin dosage schedule by imposing a maximum insulin dosage on an insulin dosage schedule based on the upper bound constraint. The instructions 108 can cause the processor 104 to either: administer an insulin dosage in accordance with the adapted insulin dosage schedule: or control administration of insulin via an insulin dosage signal based on the adapted insulin dosage schedule.

[0040] Embodiments can relate to a method for storing and accessing insulin delivery parameters to accurately set an insulin dosage amount. The method can involve receiving glucose data including glucose concentration measurements spanning a time period. The method can involve identifying a minimum glucose concentration measurement G_min) within the time period. The method can involve identifying a current glucose concentration measurement (G_c). The method can involve setting an upper bound constraint based on the G_min and the G_c. The method can involve generating an adapted insulin dosage schedule by imposing a maximum insulin dosage on an insulin dosage schedule based on the upper bound constraint. The method can involve either: administering an insulin dosage in accordance with the adapted insulin dosage schedule; or controlling administration of insulin via an insulin dosage signal based on the adapted insulin dosage schedule.

[0041] In some embodiments, the time period can be 1 hour.

[0042] In some embodiments, the method can involve setting an upper bound constraint for at least one or more of long-acting insulin or short-acting insulin.

[0043] In some embodiments, the method can involve generating the adapted insulin dosage schedule by imposing at least one or more of a maximum basal insulin dosage or a maximum bolus insulin dosage. [0044] In some embodiments, the method can involve generating the upper bound constraint as an exponential decay function using the G_min and the G_c as input variables. [0045] In some embodiments, the method can involve generating one or more upper bound constraint curves, each upper bound constraint curve being based on multiple G_c values for a constant G_min value.

[0046] In some embodiments, the method can involve generating: a G_min_40 upper bound constraint curve for constant G_min value equal to 40 mg/dL; a G_min_50 upper bound constraint curve for constant G_min value equal to 50 mg/dL; a G_min_60 upper bound constraint curve for constant G_min value equal to 60 mg/dL; a G_min_70 upper bound constraint curve for constant G_min value equal to 70 mg/dL; a G_min_80 upper bound constraint curve for constant G_min value equal to 80 mg/dL; a G_min_90 upper bound constraint curve for constant G_min value equal to 90 mg/dL; a G_m,_n_100 upper bound constraint curve for constant G_min value equal to 100 mg/dL; a G_min_l 10 upper bound constraint curve for constant G_min value equal to 110 mg/dL; and/or a G_min_l 20 upper bound constraint curve for constant G_min value equal to 120 mg/dL;

[0047] In some embodiments, the method can involve: setting the upper bound constraint to be inversely proportional with the G_min setting the upper bound constraint to be inversely proportional with G_c: setting the upper bound constraint to have no effect on an insulin dosage schedule when the G_min is greater than or equal to a glucose concentration target; and/or setting the upper bound constraint to match a basal rate profile when the G_min is less than a than or equal to a glucose concentration value indicative of hypoglycemia.

[0048] FIG. 8 is an exemplary high-level functional block diagram for an embodiment of the present invention, or an aspect of an embodiment of the present invention. As shown in FIG. 8, a processor 804 or controller communicates with the glucose monitor or data source 812, and optionally an insulin delivery device (e.g., other device 810). The glucose monitor or device communicates with the subject 800 to monitor glucose levels of the subject 800. The processor 804 or controller is configured to perform the required calculations.

Optionally, the insulin delivery device communicates with the subject 800 to deliver insulin to the subject 800. The processor 804 or controller is configured to perform the required calculations. The glucose monitor and the insulin delivery' device may be implemented as a separate device or as a single device. The processor 804 can be implemented locally in the glucose monitor, the insulin delivery device, or a standalone device (or in any combination of two or more of the glucose monitor, insulin device, or a stand along device). The processor 804 or a portion of the system can be located remotely such that the device is operated as a telemedicine device.

[0049] Referring to FIG. 9, in its most basic configuration, computing device 900 ty pically includes at least one processor 904 and memory 906. Depending on the exact configuration and type of computing device, memory' 906 can be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.) or some combination of the two.

[0050] Additionally, the computing device 900 may also have other features and/or functionality. For example, the computing device 900 could also include additional removable and/or non-removable storage including, but not limited to, magnetic or optical disks or tape, as well as writable electrical storage media. Such additional storage is the figure by removable storage 902a and non-removable storage 902b. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. The memory', the removable storage and the nonremovable storage are all examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology CDROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by the device. Any such computer storage media may be part of, or used in conjunction with, the device.

[0051] The computing device 900 may also contain one or more communications connections 908 that allow the device to communicate with other devices (e.g. other computing devices). The communications connections carry information in a communication media. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery' media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode, execute, or process information in the signal. By way of example, and not limitation, communication medium includes wired media such as a wired network or direct-wired connection, and wireless media such as radio, RF, infrared and other wireless media. As discussed above, the term computer readable media as used herein includes both storage media and communication media.

[0052] In addition to a stand-alone computing machine, embodiments of the invention can also be implemented on a netw ork system comprising a plurality of computing devices that are in communication with a networking means, such as a network with an infrastructure or an ad hoc network. The network connection can be wired connections or wireless connections. As a way of example, FIG. 9 illustrates a network system in which embodiments of the invention can be implemented. In this example, the network system comprises computer 910 (e.g. a netw ork server), network connection means 912 (e.g. wired and/or wireless connections), computer terminal 914, and PDA (e.g. a smart-phone) 916 (or other handheld or portable device, such as a cell phone, laptop computer, tablet computer, GPS receiver, mp3 player, handheld video player, pocket projector, etc. or handheld devices (or non portable devices) with combinations of such features). In an embodiment, it should be appreciated that the module 914 may be glucose monitor device. In an embodiment, it should be appreciated that the module listed as 914 may be a glucose monitor device, artificial pancreas, and/or an insulin device (or other interventional or diagnostic device).

Any of the components may be multiple in number. The embodiments of the invention can be implemented in anyone of the devices of the system. For example, execution of the instructions or other desired processing can be performed on the same computing device 900. Alternatively, an embodiment of the invention can be performed on different computing devices of the network system. For example, certain desired or required processing or execution can be performed on one of the computing devices of the network (e g., server 910 and/or glucose monitor device), whereas other processing and execution of the instruction can be performed at another computing device (e.g., terminal 914) of the network system, or vice versa. In fact, certain processing or execution can be performed at one computing device (e.g. server 910 and/or insulin device, artificial pancreas, or glucose monitor device (or other interventional or diagnostic device)); and the other processing or execution of the instructions can be performed at different computing devices that may or may not be networked. For example, the certain processing can be performed at terminal 914, while the other processing or instructions are passed to a computing device 900 w here the instructions are executed. This scenario may be of particular value especially w hen the PDA device, for example, accesses to the network through computer terminal 914 (or an access point in an ad hoc network). For another example, software to be protected can be executed, encoded or processed with one or more embodiments of the invention. The processed, encoded or executed software can then be distributed to customers. The distribution can be in a form of storage media (e.g., disk) or electronic copy.

[0053] FIG. 10 is a block diagram that illustrates a system 1000 including a computer system 1002 and the associated Internet 1004 connection upon which an embodiment may be implemented. Such configuration is typically used for computers (hosts) connected to the Internet 1004 and executing a server or a client (or a combination) software. A source computer such as laptop, an ultimate destination computer and relay servers, for example, as well as any computer or processor described herein, may use the computer system configuration and the Internet connection shown in FIG. 10. The system 1004 may be used as a portable electronic device such as a notebook/laptop computer, a media player (e.g.. MP3 based or video player), a cellular phone, a Personal Digital Assistant (PDA), a glucose monitor device, an artificial pancreas, an insulin delivery device (or other interventional or diagnostic device), an image processing device (e.g., a digital camera or video recorder), and/or any other handheld computing devices, or a combination of any of these devices. Note that while FIG. 10 illustrates various components of a computer system, it is not intended to represent any particular architecture or manner of interconnecting the components; as such details are not germane to the present invention. It will also be appreciated that network computers, handheld computers, cell phones and other data processing systems which have fewer components or perhaps more components may also be used. The computer system of FIG. 10 may, for example, be an Apple Macintosh computer or Power Book, or an IBM compatible PC. Computer system 1000 includes a bus 1006, an interconnect, or other communication mechanism for communicating information, and a processor 110, commonly in the form of an integrated circuit, coupled with bus 1006 for processing information and for executing the computer executable instructions. Computer system 1000 also includes a main memory 1008, such as a Random Access Memory (RAM) or other dynamic storage device, coupled to bus 1006 for storing information and instructions to be executed by processor 1010.

[0054] Main memory 1008 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 1010. Computer system 1000 further includes a Read Only Memory (ROM) 1008 (or other nonvolatile memory) or other static storage device coupled to bus 1006 for storing static information and instructions for processor 1010. A storage device 1012, such as a magnetic disk or optical disk, a hard disk drive for reading from and writing to a hard disk, a magnetic disk drive for reading from and writing to a magnetic disk, and/or an optical disk drive (such as DVD) for reading from and writing to a removable optical disk, is coupled to bus 1006 for storing information and instructions. The hard disk drive, magnetic disk drive, and optical disk drive may be connected to the system bus by a hard disk drive interface, a magnetic disk drive interface, and an optical disk drive interface, respectively. The drives and their associated computer-readable media provide non-volatile storage of computer readable instructions, data structures, program modules and other data for the general purpose computing devices. Typically computer system 1000 includes an Operating System (OS) stored in a non-volatile storage for managing the computer resources and provides the applications and programs with an access to the computer resources and interfaces. An operating system commonly processes system data and user input, and responds by allocating and managing tasks and internal system resources, such as controlling and allocating memory, prioritizing system requests, controlling input and output devices, facilitating networking and managing files. Non-limiting examples of operating systems are Microsoft Windows, Mac OS X, and Linux.

[0055] The term "processor" is meant to include any integrated circuit or other electronic device (or collection of devices) capable of performing an operation on at least one instruction including, without limitation, Reduced Instruction Set Core (RISC) processors, CISC microprocessors, Microcontroller Units (MCUs), CISC-based Central Processing Units (CPUs), and Digital Signal Processors (DSPs). The hardware of such devices may be integrated onto a single substrate (e.g., silicon "die"), or distributed among two or more substrates. Furthermore, various functional aspects of the processor may be implemented solely as software or firmware associated with the processor.

[0056] Computer system 1000 may be coupled via bus 1006 to a display 1014. such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), a flat screen monitor, a touch screen monitor or similar means for displaying text and graphical data to a user. The display may be connected via a video adapter for supporting the display. The display allows a user to view, enter, and/or edit information that is relevant to the operation of the system. An input device 1016. including alphanumeric and other keys, is coupled to bus 1006 for communicating information and command selections to processor 1010. Another type of user input device is cursor control 1018, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 1010 and for controlling cursor movement on display 1014. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.

[0057] The computer system 1002 may be used for implementing the methods and techniques described herein. According to one embodiment, those methods and techniques are performed by computer system 1002 in response to processor 1010 executing one or more sequences of one or more instructions contained in main memory 1020. Such instructions may be read into main memory 1022 from another computer-readable medium, such as storage device 1012. Execution of the sequences of instructions contained in main memory 1022 causes processor 1010 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the arrangement. Thus, embodiments of the invention are not limited to any specific combination of hardware circuitry and software.

[0058] The term "computer-readable medium" (or "machine-readable medium") as used herein is an extensible term that refers to any medium or any memory, that participates in providing instructions to a processor, (such as processor 1010) for execution, or any mechanism for storing or transmitting information in a form readable by a machine (e.g.. a computer). Such a medium may store computer-executable instructions to be executed by a processing element and/or control logic, and data which is manipulated by a processing element and/or control logic, and may take many forms, including but not limited to, nonvolatile medium, volatile medium, and transmission medium. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 1006. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infrared data communications, or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Common forms of computer- readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch-cards, papertape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory’ chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.

[0059] Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to processor 1010 for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 1000 can receive the data on the telephone line and use an infra-red transmitter to convert the data to an infrared signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 1006. Bus 1006 carries the data to main memory 1022, from which processor 1010 retrieves and executes the instructions. The instructions received by main memory 1022 may optionally be stored on storage device 1012 either before or after execution by processor 1010.

[0060] Computer system 1000 also includes a communication interface 1024 coupled to bus 1006. Communication interface 1024 provides a two-way data communication coupling to a network link 1026 that is connected to a local network 1028. For example, communication interface 1024 may be an Integrated Services Digital Network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another non-limiting example, communication interface 1024 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. For example, Ethernet based connection based on IEEE802.3 standard may be used such as 10/100BaseT, lOOOBaseT (gigabit Ethernet), 10 gigabit Ethernet (10 GE or 10 GbE or 10 GigE per IEEE Std 802.3ae-2002 as standard), 40 Gigabit Ethernet (40 GbE), or 100 Gigabit Ethernet (100 GbE as per Ethernet standard IEEE P802.3ba), as described in Cisco Systems, Inc. Publication number 1-587005-001-3 (6/99), "Internetworking Technologies Handbook", Chapter 7: "Ethernet Technologies", pages 7-1 to 7-38, which is incorporated in its entirety for all purposes as if fully set forth herein. In such a case, the communication interface 1818 typically include a LAN transceiver or a modem, such as Standard Microsystems Corporation (SMSC) LAN91C111 10/100 Ethernet transceiver described in the Standard Microsystems Corporation (SMSC) data-sheet "LAN91C111 10/100 Non-PCI Ethernet Single Chip MAC+PHY" Data-Sheet, Rev. 15 (02-20-04). which is incorporated in its entirety for all purposes as if fully set forth herein.

[0061] Wireless links may also be implemented. In any such implementation, communication interface 1024 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0062] Network link 1026 typically provides data communication through one or more networks to other data devices. For example, network link 1026 may provide a connection through local network 1028 to a host computer or to data equipment operated by an Internet Service Provider (ISP) 1025. ISP 1025 in turn provides data communication sen-ices through the world wide packet data communication network Internet 1004. Local network 1028 and Internet 1004 both use electrical, electromagnetic or optical signals that cany' digital data streams. The signals through the various networks and the signals on the network link 1026 and through the communication interface 1024, which carry the digital data to and from computer system 1000, are exemplary forms of carrier waves transporting the information. [0063] A received code may be executed by processor 1010 as it is received, and/or stored in storage device 1012, or other non-volatile storage for later execution. In this manner, computer system 1000 may obtain application code in the form of a carrier wave. [0064] FIG. 11 illustrates a system in which one or more embodiments of the invention can be implemented using a network, or portions of a network or computers. Although the present invention glucose monitor, artificial pancreas or insulin device (or other interventional or diagnostic device) may be practiced without a network. FIG. 11 diagrammatically illustrates an exemplary system in which examples of the invention can be implemented. In an embodiment the glucose monitor, artificial pancreas or insulin device (or other interv entional or diagnostic device) may be implemented by the subject (or patient) locally at home or other desired location. However, in an alternative embodiment it may be implemented in a clinic setting or assistance setting. For instance, a clinic setup 1100 provides a place for doctors (e.g. 1102) or clinician/assistant to diagnose patients (e.g. 1104) with diseases related with glucose and related diseases and conditions. A glucose monitoring device 1106 can be used to monitor and/or test the glucose levels of the patient — as a standalone device. It should be appreciated that hile only glucose monitor device 1106 is shown in the figure, the system of the invention and any component thereof may be used in the manner depicted by FIG. 1 1 . The system or component may be affixed to the patient or in communication with the patient as desired or required. For example the system or combination of components thereof - including a glucose monitor device 1106 (or other related devices or systems such as a controller, and/or an artificial pancreas, an insulin pump (or other interventional or diagnostic device), or any other desired or required devices or components) - may be in contact, communication or affixed to the patient through tape or tubing (or other medical instruments or components) or may be in communication through wired or wireless connections. Such monitor and/or test can be short term (e.g. clinical visit) or long term (e.g. clinical stay or family). The glucose monitoring device outputs can be used by the doctor (clinician or assistant) for appropriate actions, such as insulin injection or food feeding for the patient, or other appropriate actions or modeling. Alternatively, the glucose monitoring device output can be delivered to computer terminal 1112 for instant or future analyses. The delivery can be through cable or wireless or any other suitable medium. The glucose monitoring device output from the patient can also be delivered to a portable device, such as PDA 1110. The glucose monitoring device outputs with improved accuracy can be delivered to a glucose monitoring center 1112 for processing and/or analyzing. Such delivery can be accomplished in many ways, such as network connection 1114, which can be wired or wireless.

[0065] In addition to the glucose monitoring device outputs, errors, parameters for accuracy improvements, and any accuracy related information can be delivered, such as to computer and / or glucose monitoring center 1112 for performing error analyses. This can provide a centralized accuracy monitoring, modeling and/or accuracy enhancement for glucose centers (or other interventional or diagnostic centers), due to the importance of the glucose sensors (or other interventional or diagnostic sensors or devices).

[0066] Examples of the invention can also be implemented in a standalone computing device associated with the target glucose monitoring device, artificial pancreas, and/or insulin device (or other interventional or diagnostic device.

[0067] FIG. 12 is a block diagram illustrating an example of a machine upon which one or more aspects of embodiments of the present invention can be implemented. Referring to FIG. 12, an aspect of an embodiment of the present invention includes, but not limited thereto, a system, method, and computer readable medium, which illustrates a block diagram of an example machine 1200 upon w hich one or more embodiments (e.g., discussed methodologies) can be implemented (e.g., run).

[0068] FIG. 12 illustrates a block diagram of an example machine 1200 upon which one or more embodiments (e g., discussed methodologies) can be implemented (e.g., run).

[0069] Examples of machine 1200 can include logic, one or more components, circuits (e.g., modules), or mechanisms. Circuits are tangible entities configured to perform certain operations. In an example, circuits can be arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner. In an example, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardw are processors (processors) can be configured by software (e.g., instructions, an application portion, or an application) as a circuit that operates to perform certain operations as described herein. In an example, the software can reside (1) on a non-transitory machine readable medium or (2) in a transmission signal. In an example, the software, when executed by the underlying hardw are of the circuit, causes the circuit to perform the certain operations. [0070] In an example, a circuit can be implemented mechanically or electronically. For example, a circuit can comprise dedicated circuitry or logic that is specifically configured to perform one or more techniques such as discussed above, such as including a special-purpose processor, a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC). In an example, a circuit can comprise programmable logic (e.g., circuitry’, as encompassed within a general-purpose processor or other programmable processor) that can be temporarily configured (e.g., by software) to perform the certain operations. It will be appreciated that the decision to implement a circuit mechanically (e.g., in dedicated and permanently configured circuitry ), or in temporarily configured circuitry (e.g., configured by software) can be driven by cost and time considerations.

[0071] Accordingly, the term ‘'circuit’’ is understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily (e.g., transitorily) configured (e.g., programmed) to operate in a specified manner or to perform specified operations. In an example, given a plurality of temporarily configured circuits, each of the circuits need not be configured or instantiated at any one instance in time. For example, yvhere the circuits comprise a general-purpose processor configured via software, the general-purpose processor can be configured as respective different circuits at different times. Software can accordingly configure a processor, for example, to constitute a particular circuit at one instance of time and to constitute a different circuit at a different instance of time.

[0072] In an example, circuits can provide information to, and receive information from, other circuits. In this example, the circuits can be regarded as being communicatively coupled to one or more other circuits. Where multiple of such circuits exist contemporaneously, communications can be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the circuits. In embodiments in which multiple circuits are configured or instantiated at different times, communications between such circuits can be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple circuits have access. For example, one circuit can perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further circuit can then, at a later time, access the memory device to retrieve and process the stored output. In an example, circuits can be configured to initiate or receive communications with input or output devices and can operate on a resource (e.g., a collection of information).

[0073] The various operations of method examples described herein can be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute processor-implemented circuits that operate to perform one or more operations or functions. In an example, the circuits referred to herein can comprise processor-implemented circuits.

[0074] Similarly, the methods described herein can be at least partially processor- implemented. For example, at least some of the operations of a method can be performed by one or processors or processor-implemented circuits. The performance of certain of the operations can be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In an example, the processor or processors can be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other examples the processors can be distributed across a number of locations.

[0075] The one or more processors can also operate to support performance of the relevant operations in a "cloud computing" environment or as a "software as a service” (SaaS). For example, at least some of the operations can be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs).)

[0076] Example embodiments (e.g., apparatus, systems, or methods) can be implemented in digital electronic circuitry, in computer hardware, in firmware, in software, or in any combination thereof. Example embodiments can be implemented using a computer program product (e.g.. a computer program, tangibly embodied in an information carrier or in a machine readable medium, for execution by, or to control the operation of, data processing apparatus such as a programmable processor, a computer, or multiple computers).

[0077] A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a software module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

[0078] In an example, operations can be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Examples of method operations can also be performed by, and example apparatus can be implemented as, special purpose logic circuitry (e.g., a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)). [0079] The computing system can include clients and servers. A client and server are generally remote from each other and generally interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In embodiments deploying a programmable computing system, it will be appreciated that both hardware and software architectures require consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware can be a design choice. Below are set out hardware (e.g., machine 1200) and software architectures that can be deployed in example embodiments.

[0080] In an example, the machine 1200 can operate as a standalone device or the machine 1200 can be connected (e.g., networked) to other machines.

[0081] In a networked deployment, the machine 1200 can operate in the capacity of either a server or a client machine in server-client network environments. In an example, machine 1200 can act as a peer machine in peer-to-peer (or other distributed) network environments. The machine 1200 can be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) specifying actions to be taken (e.g., performed) by the machine 1200. Further, while only a single machine 1200 is illustrated, the term ‘'machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0082] Example machine (e.g., computer system) 1200 can include a processor 1250 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 1252a and a static memory 1252b, some or all of which can communicate with each other via a bus 1220. The machine 1200 can further include a display unit 1202, an alphanumeric input device 1204 (e.g., a keyboard), and a user interface (UI) navigation device 1206 (e.g., a mouse). In an example, the display unit 1202, input device 1204 and UI navigation device 1206 can be a touch screen display. The machine 1200 can additionally include a storage device (e.g., drive unit) 1208, a signal generation device 1210 (e.g., a speaker), a network interface device 1212, and one or more sensors 1214, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor. [0083] The storage device 1208 can include a machine readable medium 1216 on which is stored one or more sets of data structures or instructions 1254 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 1254 can also reside, completely or at least partially, within the main memory 1252a, within static memory 1252b, or within the processor 804 during execution thereof by the machine 1200. In an example, one or any combination of the processor 804, the main memory 1252a, the static memory 1252b, or the storage device 1208 can constitute machine readable media.

[0084] While the machine readable medium 1216 is illustrated as a single medium, the term "machine readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that configured to store the one or more instructions 1254. The term “machine readable medium’’ can also be taken to include any tangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine readable medium” can accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine readable media can include non-volatile memory, including, by way of example, semiconductor memory devices (e.g.. Electrically Programmable Read-Only Memory (EPROM). Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0085] The instructions 1254 can further be transmitted or received over a communications network 1218 using a transmission medium via the network interface device utilizing any one of a number of transfer protocols (e.g., frame relay, IP, TCP, UDP, HTTP, etc.). Example communication networks can include a local area network (LAN), a wide area network (WAN), a packet data network (e.g.. the Internet), mobile telephone networks (e.g.. cellular networks). Plain Old Telephone (POTS) networks, and wireless data networks (e.g., IEEE 802.11 standards family known as Wi-Fi®, IEEE 802.16 standards family known as WiMax®), peer-to-peer (P2P) networks, among others. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

[0086] Although example embodiments of the present disclosure are explained in some instances in detail herein, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the present disclosure be limited in its scope to the details of construction and arrangement of components set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or carried out in various ways.

[0087] It should be appreciated that any element, part, section, subsection, or component described with reference to any specific embodiment above may be incorporated with, integrated into, or otherwise adapted for use with any other embodiment described herein unless specifically noted otherwise or if it should render the embodiment device nonfunctional. Likewise, any step described with reference to a particular method or process may be integrated, incorporated, or otherwise combined with other methods or processes described herein unless specifically stated otherwise or if it should render the embodiment method nonfunctional. Furthermore, multiple embodiment devices or embodiment methods may be combined, incorporated, or otherwise integrated into one another to construct or develop further embodiments of the invention described herein.

[0088] It should be appreciated that any of the components or modules referred to with regards to any of the present invention embodiments discussed herein, may be integrally or separately formed with one another. Further, redundant functions or structures of the components or modules may be implemented. Moreover, the various components may be communicated locally and/or remotely with any user/clinician/patient or machine/system/computer/processor. Moreover, the various components may be in communication via wireless and/or hardwire or other desirable and available communication means, systems and hardware. Moreover, various components and modules may be substituted with other modules or components that provide similar functions.

[0089] It should be appreciated that the device and related components discussed herein may take on all shapes along the entire continual geometric spectrum of manipulation of x, y and z planes to provide and meet the anatomical, environmental, and structural demands and operational requirements. Moreover, locations and alignments of the various components may vary as desired or required. [0090] It should be appreciated that various sizes, dimensions, contours, rigidity, shapes, flexibility and materials of any of the components or portions of components in the various embodiments discussed throughout may be varied and utilized as desired or required.

[0091] It should be appreciated that while some dimensions are provided on the aforementioned figures, the device may constitute various sizes, dimensions, contours, rigidity, shapes, flexibility and materials as it pertains to the components or portions of components of the device, and therefore may be varied and utilized as desired or required. [0092] It must also be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” or “approximately” one particular value and/or to “about” or “approximately” another particular value. When such a range is expressed, other exemplary embodiments include from the one particular value and/or to the other particular value.

[0093] By “comprising” or “containing” or “including” is meant that at least the named compound, element, particle, or method step is present in the composition or article or method, but does not exclude the presence of other compounds, matenals, particles, or method steps, even if the other such compounds, material, particles, or method steps have the same function as what is named.

[0094] In describing example embodiments, terminology will be resorted to for the sake of clarity. It is intended that each term contemplates its broadest meaning as understood by those skilled in the art and includes all technical equivalents that operate in a similar manner to accomplish a similar purpose. It is also to be understood that the mention of one or more steps of a method does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Steps of a method may be performed in a different order than those described herein without departing from the scope of the present disclosure. Similarly, it is also to be understood that the mention of one or more components in a device or system does not preclude the presence of additional components or intervening components between those components expressly identified.

[0095] Some references, which may include various patents, patent applications, and publications, are cited in a reference list and discussed in the disclosure provided herein. The citation and/or discussion of such references is provided merely to clarify the description of the present disclosure and is not an admission that any such reference is “prior art” to any aspects of the present disclosure described herein. In terms of notation, “[n]” corresponds to the n^th reference in the list. All references cited and discussed in this specification are incorporated herein by reference in their entireties and to the same extent as if each reference was individually incorporated by reference.

[0096] It should be appreciated that as discussed herein, a subject may be a human or any animal. It should be appreciated that an animal may be a variety of any applicable type, including, but not limited thereto, mammal, veterinarian animal, livestock animal or pet type animal, etc. As an example, the animal may be a laboratory animal specifically selected to have certain characteristics similar to human (e.g. rat. dog, pig. monkey), etc. It should be appreciated that the subject may be any applicable human patient, for example.

[0097] The term “about,” as used herein, means approximately, in the region of, roughly, or around. When the term “about” is used in conjunction with a numerical range, it modifies that range by extending the boundaries above and below the numerical values set forth. In general, the term “about” is used herein to modify a numerical value above and below the stated value by a variance of 10%. In one aspect, the term “about” means plus or minus 10% of the numerical value of the number with which it is being used. Therefore, about 50% means in the range of 45%-55%. Numerical ranges recited herein by endpoints include all numbers and fractions subsumed within that range (e.g., 1 to 5 includes 1, 1.5, 2. 2.75. 3.

3.90, 4, 4.24, and 5). Similarly, numerical ranges recited herein by endpoints include subranges subsumed within that range (e.g., 1 to 5 includes 1-1.5, 1.5-2, 2-2.75, 2.75-3, 3- 3.90, 3.90-4, 4-4.24, 4.24-5, 2-5, 3-5, 1-4, and 2-4). It is also to be understood that all numbers and fractions thereof are presumed to be modified by the term “about.” [0098] EXAMPLES

[0099] The following examples are exemplary' implementation and testing for embodiments of the disclosed system and methods. Embodiments of the system and methods discussed in the examples may be referred to as “HypoSafe”.

[0100] The examples demonstrate how a rebound hypoglycemia prevention layer (HypoSafe) can be integrated into any AID system. HypoSafe constrains the maximum permissible insulin delivery⁷ dose based on the minimum glucose reading from the previous hour and the current glucose level. To demonstrate its efficacy, we integrated HypoSafe into the latest UVA AID system and simulated two scenarios using the 100-adult cohort of the UVA/Padova T1D simulator: a nominal case including three unannounced meals, and another case where hypoglycemia was purposely induced by an overestimated manual bolus. In both simulation scenarios, rebound hypoglycemia events were reduced with HypoSafe (nominal: from 39 to 0. hypo-induced: from 55 to 7) by attenuating the commanded basal (nominal:

0.27U vs. 0.04U, hypo-induced: 0.27U vs. 0.03U) and bolus (nominal: 1.02U vs. 0.05U, hypo- induced: 0.43U vs. 0.02U) within the 30-minute interval after treating a hypoglycemia event. No clinically significant changes resulted for time in the range 70-180 mg/dL or in time above 180 mg/dL. HypoSafe was shown to be effective in reducing rebound hypoglycemia events without affecting achieved time in range when combined with an advanced AID system.

[0101] Automated Insulin Delivery⁷ (AID) systems represent a game-changing approach to treating type 1 diabetes (T1D). These advanced closed-loop systems aim to maintain the blood glucose (BG) level in a healthy range by integrating continuous glucose monitoring (CGM) sensors with insulin pumps through control algorithms that automatically regulate insulin infusion. AID systems can operate in hybrid closed-loop (HCL) mode, requiring manual announcements, or full closed-loop (FCL) mode, which may not require user interaction. Currently, commercially available AID systems are labeled for HCL use. with ongoing research into FCL mode. The Inreda system has become the only FCL AID system approved by the European Commission. While AID systems offer significant benefits in improving glycemic control and reducing the burden of constant diabetes management, they are not exempt from limitations. One crucial consideration is the risk of hypoglycemia, a condition that develops as an interplay of insulin excess and the compromised glucose counter regulation in T1D, and that is linked to consequences such as dizziness, blurred vision, cognitive impairments, and in extreme cases, seizures and diabetic coma.

[0102] Different strategies have been devised to prevent hypoglycemia episodes in diabetes management. Monitoring systems now incorporate low threshold alerts and/or hypoglycemia prediction alarms to enable users to take proactive measures. Personalized hypoglycemia predictive alerts have also been introduced using glucose-insulin models or long short-term memory' models to predict glucose trends. Additionally, different algorithms have been developed and integrated into AID systems to proactively reduce or suspend insulin infusion when hypoglycemia is predicted or present.

[0103] Despite these efforts, hypoglycemia events can still occur. When they do, people with T1D usually take fast-acting (or rescue) carbohydrates to rapidly restore normoglycemia. However, this sudden change in BG levels can trigger an excessive control response, potentially inducing a second hypoglycemia episode. Literature reported this oscillatory rebound hypoglycemia during a clinical validation of a HCL controller, where the controller overreacted to rapid BG increases after rescue carbohydrates. To address this issue, a dynamic insulin-on-board constraint was proposed to minimize the likelihood of controller-induced hypoglycemia following a rapid rise of glucose levels. Additionally, recent data from a clinical trial (ClinicalTrials.gov ID NCT04877730) evaluating HCL and FCL modes of the UVA AID system revealed instances of rebound hypoglycemia. Specifically, there were 5 rebound events during HCL mode and 3 in FCL mode across the 35 subjects, and 9% of hypoglycemic episodes were attributed to rebound lows induced by controller overcorrection. This underscores the need for strategies to counteract the controller’s overreaction to rescue carbohydrates.

[0104] In this work, a controller agnostic HypoSafe module is developed to prevent rebound hypoglycemia. Based only on preceding glucose conditions. HypoSafe sets constraints on subsequent control actions without relying on manual user inputs. To evaluate the efficacy of this strategy, we integrated HypoSafe into the University of Virginia (UVA) AID system within the Food and Drug Administration (FDA)-accepted UVA/Padova T1D Simulator. Rebound hypoglycemia events and glucose outcome metrics are reported for two different simulation scenarios: one including unannounced meals and another including an over-sized manual bolus.

[0105] METHODS

[0106] HypoSafe module

[0107] The primary goal of the HypoSafe module is to prevent rebound hypoglycemia by informing the system with conditions preceding the current glucose value and then determining when to allow full control actions. To that end, a dynamic upper bound over the control action is imposed. It is to consider both basal and bolus actions, especially in the case of FCL controllers that can react to BG rises by triggering a bolus.

[0108] Based on the analysis of prior glucose trace, the following design principles are desired: (i) the lower the glucose levels have been in the recent past, the stricter the constraint should be, (ii) the lower the current glucose value, the stricter the constraint, (iii) if minimum glucose in the recent past is at or above the glucose target, then the constraint is maximum, allowing for full control actions; and (iv) if hypoglycemia is detected in the recent past, there should not be any insulin delivery above the subject’s predefined basal rate profile (BR_pr ) until reaching the glucose target. Hypoglycemia was defined as BG levels under 70 mg/dL based on the American Diabetes Association guidelines.

[0109] Following these principles, the safety layer is then designed to apply an exponential decay constraint over the insulin delivery dose as a response to the minimum glucose value recorded in the last hour (G_min) and the current glucose concentration (G_c). The lower the values of G_min and G_c. the stricter the constraint is meant to be. Considering a glucose target of 120 mg/dL, the insulin constraint is given by: constraint = 1.1

with maxvalue corresponding to the maximum desired value for the insulin signal that is being constrained, and factors a = 0.14299, /? = 0.0714, and c = 5.7143 determined via numerical optimization to fit the curve that satisfy the design principles mentioned above.

[0110] HypoSafe can be applied to both basal and bolus commands with independent boundary constraints. For instance, in FIG. 6 (HypoSafe constraint for different values of G_min and G_c. Left axis and right axes show the constraint applied to basal and bolus delivery, respectively), the set of curves depending on G_min and G_c can be observed for both basal (left axis) and bolus (right axis), where the ranges for each are a design selection depending on the signal to be modulated.

[0111] Integration with a fully Closed-Loop Algorithm

[0112] The UVA AID system integrates (i) a safety system for imminent hypoglycemia mitigation (legacy SSM), (ii) a hyperglycemia mitigation system (HMS), (iii) a bolus priming system (BPS) to compensate for abrupt positive disturbances, and (iv) a model predictive control (MPC) algorithm for background insulin regulation.

[0113] In summary', the MPC relies on a mathematical description of the insulin-glucose interaction to calculate an optimal control policy of insulin deviations from the subject’s predefined BR_prf that satisfies a set of constraints within a time horizon. As for the BPS, it estimates the probability of large positive disturbances (like meals) based on past glucose measurements. If the probability is above a certain threshold, the BPS triggers a bolus that depends on different ratios of the subject’s total daily insulin (TDI). FIG. 4 shows multiple panels. The upper panel shows integration of HypoSafe into the UVA AID system. The bottom-left panel shows basal modulation by constraining the output of the MPC. The bottom right-panel shows bolus modulation by constraining the output of the BPS. Abbreviations: MPC, model predictive controller; BPS, bolus priming system; HMS, hyperglycemia mitigation system; SSM. safety system; BR_prf, basal rate profile. TDI, total daily insulin.

[0114] To prevent rebound hypoglycemia from basal and bolus commands, HypoSafe is coupled to both the MPC and BPS as depicted in Figure 2. For basal, the constraint was set to allow a maximum of 10 times the subject-specific BR_prf (U/min), i.e., maxvalue — 10 in (1), obtaining:

HypoSafe_basai = BR_prf * constraint. (2) with u_basat = mm(u_MPC, HypoSaf e_basal ). [0115] In case of the BPS, the constraint was defined to represent fractions of the subject’s TDI (U) allowing up to a 10% of its value (maxvalue = 10):

[0116] The final priming bolus is given by u_botus = mm(u_BPS, HypoSafe_boius).

[0117] Simulation scenario and outcomes

[0118] Experiments were run on the 100-adult cohort of the FDA-accepted UVA/Padova T1D simulator to assess the efficacy of the HypoSafe module. Two different scenarios were considered: (i) a nominal scenario consisting of 30h with three unannounced meals provided to each subject at 7h, 13h, and 19h with carbohydrate content of 70, 50, and 80 g, respectively, (ii) A 16h hypoglycemia-induced scenario where an over-estimated manual bolus is provided for a single meal at 6h with 70g of carbohydrates. For both scenarios, intra-day variability in insulin and daw n phenomenon are included, and hypoglycemia treatments of 15g of fastabsorbing carbohydrates are provided every 20 min until glucose is above 60mg/dL.

[0119] The primary outcome is the number of rebound hypoglycemia episodes caused by insulin deliver}' in the next 30 minutes of the first hypoglycemia event. A rebound hypoglycemia event was defined if BG <70 mg/dL within a 2-hour window from a previous hypoglycemia event. In addition, basal and bolus commands within 30 min after a hypoglycemia event are reported. Overall glucose outcome metrics are also analyzed: mean BG, coefficient of variation (CV). and percentage of time spent between 70-180 mg/dL (time in range, TIR), above 180 mg/dL (time above range, TAR), and below 70 mg/dL (time below range, TBR), along to the percentage of time in below' 54 mg/dL, in tight range 70-140 mg/dL, and above 250 mg/dL.

[0120] FIG. 7 shows a comparison of glucose, basal and bolus traces in a representative subject during the hypo-induced scenario with and without HypoSafe. The triangle represents meal intake, solid and with pattern triangles represent rescue carbohydrates after first hypoglycemia event and after rebound hypoglycemia, respectively. FIG. 7 shows the evolution of glycemia, basal, and bolus of a representative subject under the closed-loop system with and without the HypoSafe module in the hypoglycemia-induced scenario. It is observed that around 11 :30 a hypoglycemia event occurs given the over-estimation of the meal bolus at breakfast. After the event, rescue carbohydrates are provided, rapidly raising the BG level. Without HypoSafe, basal infusion is increased and a priming bolus is triggered, causing a second hypoglycemia event. It is also observed that the rebound hypoglycemia is repeated generating an oscillatory response. In contrast, when integrating the HypoSafe module into the AID system, both basal and bolus commands are safely constrained, avoiding in this way the rebound toward hypoglycemia.

[0121] Population results for both scenarios are reported in Table 1. In the nominal scenario, the UVA AID strategy led to 39 rebound hypoglycemia events. In contrast, when adding the HypoSafe module, all these events were avoided. In addition, results show no clinically significant differences in TIR (FCL = 80.4 ± 8.6 vs. FCL+ HypoSafe= 79.8 ± 8.6). TBR (FCL = 2.0 ± 1.6 vs. FCL+ HypoSafe= 1.7 ± 1.4) or TAR (FCL =17.6 ± 8.2 vs. FCL+ HypoSafe= 18.5 ± 8.3). In the hypoglycemia-induced scenario, rebound hypoglycemia events were reduced from 55 to 7 with HypoSafe. Insulin delivery from basal and bolus doses was significantly reduced within the 30 min following a hypoglycemia event. Glucose outcome metrics show no clinically significant differences with and without HypoSafe.

Table 1 : Population outcome metrics in nominal and hypoglycemia-induced scenario.

[0122] There are currently several strategies reported in literature and implemented in commercial devices that aim to alert or suspend insulin infusion for hypoglycemia prevention. Typically, these strategies are based on glucose thresholds or short-term glucose predictions. However, once the subject treats a hypoglycemic event and BG levels rise rapidly there can be an overreaction of insulin leading to rebound hypoglycemia or rollercoaster patterns.

[0123] In particular, a recent analysis on rebound hypoglycemia for subjects under multiple daily injections and continuous subcutaneous insulin infusion found 29.4% of hypoglycemic episodes were rebound events. There was also an indication of behavioral overcompensation of carbohydrate intake when hypoglycemic, and aggressiveness to correct resulting hyperglycemia.

[0124] This overreaction to rapid glucose increases has also been observed when using AID systems. Features of the controller depending on glucose value or rate of change may trigger basal or bolus corrections after rescue carbohydrates, potentially leading to rebound hypoglycemia. This phenomenon is more common in FCL-enabled control strategies, which are designed to react more aggressively to positive glucose changes such as meals.

[0125] In this study, a HypoSafe module was developed to prevent rebound hypoglycemia caused by the controller overreacting to the effect of rescue carbohydrates. This safety layer is agnostic to the control strategy⁷, i.e., it acts as an outer layer that does not depend on the controller’s internal logic, it is simple to integrate, and only uses the last hour of CGM data as input, avoiding the need of requiring hypoglycemia treatment announcement and thus being suitable for FCL strategies.

[0126] The integration of this new control layer prevented rebound hypoglycemia events and lessened oscillatory⁷ glucose level behaviors in simulated experiments. The oscillatory response that can happen after a controller over-correction following rescue carbohydrates is explicitly shown in FIG. 7. This behavior is avoided by integrating HypoSafe as it restricts basal and bolus commands after the initial hypoglycemia treatment.

[0127] Additionally, it has been observed that including HypoSafe has no clinically significant effect on glucose outcome metrics. This is advantageous since the module does not compensate for the reduction of hypoglycemia events by lowering TIR or increasing TAR. Hence, the performance achieved with the base controller design is maintained.

[0128] This work is limited to in-silico results comparing the performance of an AID system with and without HypoSafe. Conditions for hypoglycemia treatments were standardized for all virtual subjects (15g every 20 min until BG> 60mg/dL) which can artificially reduce TBR for strategies with and without HypoSafe, but in free-living conditions, subject preferences and hypoglycemia awareness may differ from the testing scenario. This layer was integrated into the UVA AID system in a recent clinical trial (clinicaltrials.gov NCT05528770), where rebound hypoglycemia was not observed.

[0129] AID systems can increase the risk for rebound hypoglycemia after reacting to rescue carbohydrates. In this work, a HypoSafe module that can be easily integrated into an AID system has been developed to constrain insulin doses. HypoSafe is based on the minimum glucose measurement in the last hour and the current glucose concentration, avoiding the need of manual announcements. The proposed HypoSafe module was shown to be effective in reducing rebound hypoglycemia events without clinically affecting achieved TIR when combined with an advanced FCL system.

[0130] It will be understood that modifications to embodiments disclosed herein can be made to meet a particular set of design criteria. For instance, any of the components discussed herein can be any suitable number or type of each to meet a particular objective. Therefore, while certain exemplary embodiments of the system and methods of making and using the same disclosed herein have been discussed and illustrated, it is to be distinctly understood that the disclosure is not limited thereto but can be otherwise vanously embodied and practiced within the scope of the following claims.

[0131] It will be appreciated that some components, features, and/or configurations can be described in connection with only one particular embodiment, but these same components, features, and/or configurations can be applied or used with many other embodiments and should be considered applicable to the other embodiments, unless stated otherwise or unless such a component, feature, and/or configuration is technically impossible to use with the other embodiment. Thus, the components, features, and/or configurations of the various embodiments can be combined together in any manner and such combinations are expressly contemplated and disclosed by this statement.

[0132] It will be appreciated by those skilled in the art that the present disclosure can be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The presently disclosed embodiments are therefore considered in all respects to be illustrative and not restricted. The scope of the disclosure is indicated by the appended claims rather than the foregoing description and all changes that come within the meaning and range and equivalence thereof are intended to be embraced therein. Additionally, the disclosure of a range of values is a disclosure of every numerical value within that range, including the end points. [0133] The following references are incorporated herein by reference in their entireties.

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Claims

WHAT IS CLAIMED IS:

1. A control module for storing and accessing insulin delivery parameters to accurately set an insulin dosage amount, the control module comprising: a processor; a memory having instructions stored thereon that when executed by the processor will cause the processor to: receive glucose data including glucose concentration measurements spanning a time period; identify a minimum glucose concentration measurement (G_min) within the time period; identify a current glucose concentration measurement (G_c); set an upper bound constraint based on the G_min and the G_c. generate an adapted insulin dosage schedule by imposing a maximum insulin dosage on an insulin dosage schedule based on the upper bound constraint; and either: administer an insulin dosage in accordance with the adapted insulin dosage schedule; or control administration of insulin via an insulin dosage signal based on the adapted insulin dosage schedule.

2. The control module of claim 1, wherein: the time period is 1 hour.

3. The control module of claim 1, wherein instructions will cause the processor to: set an upper bound constraint for at least one or more of long-acting insulin or shortacting insulin.

4. The control module of claim 3, wherein instructions will cause the processor to: generate the adapted insulin dosage schedule by imposing at least one or more of a maximum basal insulin dosage or a maximum bolus insulin dosage.

5. The control module of claim 1, wherein instructions will cause the processor to: generate the upper bound constraint as an exponential decay function using the G_min and the G_c as input variables.

6. The control module of claim 1, wherein instructions will cause the processor to: generate one or more upper bound constraint curves, each upper bound constraint curve being based on multiple G_c values for a constant G_min value.

7. The control module of claim 6, wherein instructions will cause the processor to generate at least one or more of: a G_min_40 upper bound constraint curve for constant G_min value equal to 40 mg/dL; a G_min_50 upper bound constraint curve for constant G_min value equal to 50 mg/dL; a G_min_60 upper bound constraint curve for constant G_min value equal to 60 mg/dL; a G_min_70 upper bound constraint curve for constant G_min value equal to 70 mg/dL; a G_min_80 upper bound constraint curve for constant G_min value equal to 80 mg/dL; a G_min 90 upper bound constraint curve for constant G_min value equal to 90 mg/dL; a G_mln_ 00 upper bound constraint curve for constant G_min value equal to 100 mg/dL; a G_min_l 10 upper bound constraint curve for constant G_min value equal to 110 mg/dL; or a G_min_l 20 upper bound constraint curve for constant G_min value equal to 120 mg/dL.

8. The control module of claim 1, wherein instructions will cause the processor to at least one or more of: set the upper bound constraint to be inversely proportional with the G_mbl set the upper bound constraint to be inversely proportional with G_c. set the upper bound constraint to have no effect on an insulin dosage schedule when the G_min is greater than or equal to a glucose concentration target; or set the upper bound constraint to match a basal rate profde when the G_min is less than a than or equal to a glucose concentration value indicative of hypoglycemia.

9. The control module of claim 1, in combination with an insulin delivery processor.

10. The control module of claim 1, wherein the insulin delivery processor is a component of or in communication with an automated insulin delivery system.

11. A computer readable medium having instructions stored therein for causing an insulin deliver}' processor to store and access insulin delivery parameters to accurately set an insulin dosage amount, the instructions when executed will cause an insulin delivery processor to: receive glucose data including glucose concentration measurements spanning a time period; identify a minimum glucose concentration measurement (G_min) within the time period; identify a current glucose concentration measurement (G_c); set an upper bound constraint based on the G_min and the Gfy generate an adapted insulin dosage schedule by imposing a maximum insulin dosage on an insulin dosage schedule based on the upper bound constraint; and either: administer an insulin dosage in accordance with the adapted insulin dosage schedule; or control administration of insulin via an insulin dosage signal based on the adapted insulin dosage schedule.

12. A method for storing and accessing insulin delivery parameters to accurately set an insulin dosage amount, the method comprising: receiving glucose data including glucose concentration measurements spanning a time period; identifying a minimum glucose concentration measurement (G_min) within the time period; identifying a current glucose concentration measurement (G_c); setting an upper bound constraint based on the G_min and the G_c; generating an adapted insulin dosage schedule by imposing a maximum insulin dosage on an insulin dosage schedule based on the upper bound constraint; and either: administering an insulin dosage in accordance with the adapted insulin dosage schedule; or controlling administration of insulin via an insulin dosage signal based on the adapted insulin dosage schedule.

13. The method of claim 12, wherein: the time period is 1 hour.

14. The method of claim 12, comprising: setting an upper bound constraint for at least one or more of long-acting insulin or short-acting insulin.

15. The method of claim 14, comprising: generating the adapted insulin dosage schedule by imposing at least one or more of a maximum basal insulin dosage or a maximum bolus insulin dosage.

16. The method of claim 12, comprising: generating the upper bound constraint as an exponential decay function using the G_min and the G_c as input variables.

17. The method of claim 12, comprising: generating one or more upper bound constraint curves, each upper bound constraint curve being based on multiple G_c values for a constant G_min value.

18. The method of claim 17, comprising to generating at least one or more of: a G_min_40 upper bound constraint curve for constant G_min value equal to 40 mg/dL; a G_min_50 upper bound constraint curve for constant G_min value equal to 50 mg/dL; a G_min_60 upper bound constraint curve for constant G_min value equal to 60 mg/dL; a G_min_70 upper bound constraint curve for constant G_min value equal to 70 mg/dL; a G_min_80 upper bound constraint curve for constant G_min value equal to 80 mg/dL; a G_mitl_90 upper bound constraint curve for constant G_min value equal to 90 mg/dL; a G_min_l 00 upper bound constraint curve for constant G_min value equal to 100 mg/dL; a G,_nin_l 10 upper bound constraint curve for constant G_min value equal to 110 mg/dL; or a G_min_l 20 upper bound constraint curve for constant G_min value equal to 120 mg/dL.

19. The method of claim 1, comprising at least one or more of: setting the upper bound constraint to be inversely proportional with the G_min; setting the upper bound constraint to be inversely proportional with G_c; setting the upper bound constraint to have no effect on an insulin dosage schedule when the G_min is greater than or equal to a glucose concentration target; or setting the upper bound constraint to match a basal rate profile when the G_min is less than a than or equal to a glucose concentration value indicative of hypoglycemia.