Many people are diagnosed with Type 1 diabetes mellitus (TIDM) each year, according to the Food and Drug Administration. These patients may require insulin pumps and may desire to utilize an automatic insulin delivery (AID) system. However, the potential new users of AID systems have no pump parameters and no historical total daily insulin (TDI) data at all and nearly 50% of the new users will enter into honeymoon phase, where patients' insulin needs vary rapidly. TDI is a total amount of insulin delivered to a user in a day. It is based on how much insulin a user needs for the day. The honeymoon period may last up to six months, or as long as it takes the insulin-producing cells of the pancreas to die.
TDI of newly diagnosed patient may be determined in a number of way. Presently, initialization of TDI for a newly diagnosed diabetic patient is based on: 1) weight only; and 2) weight, glycated hemoglobin (HbA1C) (which is used for manual insulin delivery) and ketones (For example, TDI may be determined using a formula such as TDI=(−38)+1.4*weight (kg)+0.17*HbA1C (in mmol/mol)+4.3*ketones (mmol/L)). When weight alone is used, children with moderate hyperglycemia but without ketonuria or acidosis may be started with a single daily subcutaneous injection of 0.3-0.5 u/kg of intermediate-acting insulin alone. Children with hyperglycemia and ketonuria but without acidosis or dehydration may be started on a TDI established using a formulation of 0.5-0.7 u/kg of intermediate-acting insulin.
The TDI determined in this manner is only a generalization and may not satisfy needs of all new patients. It would be beneficial and advantageous to have a system, a device and/or a technique for optimizing a TDI for newly-diagnosed diabetic uses who may desire to use an automatic insulin delivery system.
Disclosed is a non-transitory computer readable medium embodied with programming code executable by a processor, and the processor when executing the programming code is operable to perform functions, and a process. A processor may be operable to receive a request via a graphical user interface to establish initial settings for an automatic insulin delivery device. An input of at least one physical characteristic of a user may be received. An adjusted total daily insulin factor usable to calculate a total daily insulin dosage may be determined. The adjusted total daily insulin factor may be a total daily insulin per unit of the user's weight reduced by a reduction factor. A comparison result generated by comparing the adjusted total daily insulin factor to a maximum algorithm delivery threshold. A total daily insulin dosage may be set based on the comparison result. Blood glucose measurement values may be obtained over a period of time. Based on the obtained blood glucose measurement values, a level of glycated hemoglobin of a user may be determined. In response to the determined level of glycated hemoglobin, the set total daily insulin dosage may be modified to provide a modified total daily insulin dosage. A control signal may be output that includes the modified total daily insulin dosage instructing a controller of a drug delivery device to actuate delivery of insulin according to the modified total daily insulin dosage.
Also disclosed is a device that includes a processor, a memory, a wireless communication device and an artificial pancreas application executable by the processor. The processor may be operable to execute programming code and applications including the artificial pancreas application. The memory may be coupled to the processor and operable to store programming code, an artificial pancreas application and data. The wireless communication device may be operable to wirelessly communicate with a paired device and communicatively coupled to the processor. The artificial pancreas application may be executable by the processor, and the processor, when executing the artificial pancreas application, is operable to perform functions. The functions may include attaining information associated with a user. An adjusted total daily insulin factor may be established. The processor may determine whether the adjusted total daily insulin factor exceeds a maximum algorithm delivery threshold. In response to a result of the determination, a total daily insulin dosage using the attained information may be set. The processor may obtain blood glucose measurement values over a period of time. Based on the obtained blood glucose measurement values, the processor may determine a level of glycated hemoglobin of a user. In response to the determined level of glycated hemoglobin, the set total daily insulin dosage may be modified to provide a modified total daily insulin dosage. A control signal may be output that includes the modified total daily insulin dosage instructing a controller to actuate delivery of insulin according to the modified total daily insulin dosage.
An example provides a process that may be used with any additional algorithms or computer applications that manage blood glucose levels and insulin therapy. Such algorithms may be referred to as an “artificial pancreas” algorithm-based system, or more generally, an artificial pancreas (AP) application or automatic insulin delivery (AID) algorithm, that provides automatic delivery of an insulin based on a blood glucose sensor input, such as that received from a CGM or the like. In an example, the artificial pancreas (AP) application when executed by a processor may enable a system to monitor a user's glucose values, determine an appropriate level of insulin for the user based on the monitored glucose values (e.g., blood glucose concentrations or blood glucose measurement values) and other information, such as user-provided information, such as carbohydrate intake, exercise times, meal times or the like, and take actions to maintain a user's blood glucose value within an appropriate range. The appropriate blood glucose value range may be considered a target blood glucose value of the particular user. For example, a target blood glucose value may be acceptable if it falls within the range of 80 mg/dL to 120 mg/dL, which is a range satisfying the clinical standard of care for treatment of diabetes. Alternatively, in addition, an AP application (or AID algorithm) as described herein may be able to establish a target blood glucose value more precisely and may set the target blood glucose value at, for example, 110 mg/dL, or the like. As described in more detail with reference to the examples of
The total daily insulin (TDI) described in the following description is intended as an initialization input into an automatic insulin delivery system that utilizes a closed-loop control operation and is not intended for use in open-loop control operation.
Typically, persons diagnosed with diabetes, such as Type 1, are suffering from a failure of their pancreas to produce enough insulin for their bodies to properly regulate blood glucose levels. The failure of the pancreas to produce enough insulin may be due the dying of cells of the pancreas and not being regenerated. As the cells of the pancreas die, and the amount of insulin produced by the remaining pancreas cells diminishes, a point is reached where it is recommended that the user begin receiving supplemental insulin. This time period as the pancreatic cells die off, and the user may become dependent on the supplemental insulin is referred to as the “honeymoon period.” A “honeymoon period” may last, in some instances, 6-18 months given the degree of failure of the user's pancreas and the time of the user's initial diabetes diagnosis. Of course, the honeymoon period may be shorter or longer for each individual user as there is no set time period for pancreatic failure. A measurement of a user's insulin dose adjusted glycated hemoglobin (HbA1c), which may be abbreviated as IDAA1c, may be used to determine whether the user is still within the “honeymoon period,” or not. In addition, the AP application or AID algorithm may determine that a user is requiring additional insulin over time as the pancreas fails.
In general, an automatic insulin delivery (AID) system can incorporate a variety of methods to 1) calculate the initial TDI based on linear relationship between TDI and weight. 2) An advantage of the described techniques and devices it that by adjusting TDI in consideration of the impact of the particular user's honeymoon phase allows the respective users to enter into closed loop systems without need for pre-existing insulin settings.
Due to the complicated and dynamic nature of the human body's response to insulin users may end up in a hypoglycemic or hyperglycemic state after being treated with insulin therapy. This outcome is undesirable for many reasons: hypoglycemia creates an immediate risk of a severe medical event (such as a seizure, a coma, or a death) while hyperglycemia creates long term negative health effects as well as the risk of ketoacidosis. Whether a person ends up in one of these states depends on a very complicated combination of many factors and sources of error.
Individuals affected with diabetes have a plethora of complicated decisions to make throughout the day to ensure a user is providing themselves with adequate insulin therapy. An automatic insulin delivery system that utilizes algorithms and/or an artificial pancreas (AP) application is operable to make many insulin delivery and insulin therapy-related decisions for a user so that the user can live their lives as close to the average non-diabetic individual as possible. In order to assist users (including new users) with making the many insulin delivery and insulin therapy-related decisions, the AP algorithm may generate alarms and notifications via a personal diabetes management (PDM) device.
In an example, an AID algorithm within any AID system may utilize the user's initial TDI to determine starting insulin delivery settings, and as time passes and additional data regarding the new user's condition is collected (for example from a continuous glucose monitor or the like), the AID algorithm may be operable to modify the starting (or initial) insulin delivery settings.
Examples of a process for initially setting insulin delivery and modifying the stating insulin delivery settings and devices for implementing a computer application are described with reference to the figures.
In the example process 100 of
TDI=0.53 u/kg*weight in kg,
where 0.53 is an insulin coefficient that may be based on historical clinical data for a population of newly-diagnosed diabetics, typical clinical guidance by endocrinologists as heuristic rules of thumb that provide reasonable initial glucose control, or the like.
The above value may be determined based on an assumption that a linear relationship exists between a user's weight and a user's TDI and that all user's on-boarding are in the honeymoon phase to minimize risk of hypoglycemia.
The artificial pancreas (AP) application or AID algorithm may be further operable to respond to the request or initial launch by causing a prompt to be generated and presented on a graphical user interface of a display of the personal diabetes management device (shown and described with reference to another figure). The artificial pancreas (AP) application or AID algorithm may be further operable to generate as part of the graphical user interface prompts requesting input of at least one physical characteristic of the user beginning the on-boarding process. In the process 100 example of
At 120, in response to the input of the at least one physical characteristic, the AP application or AID algorithm may execute logic to determine an adjusted total daily insulin factor (i.e., TDI/kg*(1−Reduction Proportion)). The process at 120 aims to optimize the TDI setting to minimize a user's risk of hypoglycemia and hyperglycemia. The adjustment is based on an expectation that the new user still has a portion of their pancreas functioning to produce insulin (i.e., the user is still in the honeymoon phase). Based on the assumption that every newly diagnosed user is in honeymoon phase, the process 100 at step reduces the TDI per kg by a certain proportion. In the example, the adjusted TDI factor may be equal to:
Adjusted TDI factor=0.53 u/kg*(1−X),
where X is a TDI reduction proportion.
In an example, the reduction proportion may be based on a result of an evaluation performed in response to a request sent to a server. The reduction proportion may be determined using various methods. In a specific example, one or more cost functions may be defined to determine the reduction proportion of TDI per kg. The respective cost functions may be based on a high blood glucose index (HBGI) and a low blood glucose index (LBGI) that are selected to balance both hypoglycemic and hyperglycemic risk based on the expected overdose or underdose when utilizing the adjusted TDI factor versus actual clinical data (which may be used in an open-loop calculation or when a user is no longer considered “newly-diagnosed” (e.g., 6-18 months). The respective cost functions may be:
Either HBGI or LBGI may remain at 0 depending on the value of the current glucose for which the risk is assessed. The non-zero value is utilized to calculate the cost. In other examples, values of HBGI or LBGI may be chosen or calculated based on selected relative risks of hyperglycemia and hypoglycemia.
Alternatively, the cost functions may be based on a weighted mean squared error (MSE) of overdose and underdose compared to the true TDI, which also balances both hypoglycemia risk and hyperglycemia risk when utilizing the adjusted TDI factor versus actual clinical data. The respective cost functions may be formulated as:
where n is the number of overdosed cases and m is the number of underdosed TDI cases, and w1 and w2 are the weights to balance the hypoglycemia risk and hyperglycemia risk, respectively.
Returning to the example of
The AP application or AID algorithm may have initially set an upper bound for a maximum algorithm delivery threshold. In the example process 100, the maximum algorithm delivery threshold may be set, for example, 0.5 u/kg/day (where u is units of insulin, kg is kilograms, day is a unit of time). In an example, the maximum algorithm delivery threshold may be approximately the maximum amount of insulin that either the AP application or AID algorithm may instruct a drug delivery device to deliver to the newly-diagnosed user. For example, the maximum algorithm delivery threshold may be referred to as the approximate maximum because in some instances, the maximum amount of insulin may be relaxed (e.g., increased) or constrained (e.g., decreased) by the AP application or AID algorithm based on detected conditions of the user and historical data.
At 130, the adjusted total daily insulin factor calculated at 120 may be compared to the maximum algorithm delivery threshold. For example, the inequality may be used to determine a result for the TDI setting:
Adjusted TDI factor>maximum algorithm daily threshold (e.g., 0.5 u/kg/day).
Should the result, at 130, of the inequality be NO, the adjusted TDI factor (with the reduction proportion) is not greater than the maximum algorithm daily threshold, the process 100 may proceed to 140. For example, the AP application or AID algorithm may determine that the comparison result indicates that the adjusted total daily insulin factor is less than the maximum algorithm delivery threshold, which causes the process 100 to proceed to 140. At 140, the AP application or the AID algorithm executed by the processor may set the TDI for delivery of insulin to the user using the adjusted TDI factor (with the reduction proportion). For example, the AP application or the AID algorithm may calculate the set total daily insulin dosage using the adjusted total daily insulin factor and the weight of the user.
Conversely, at 130, if the result of the inequality is YES, the adjusted TDI factor (with the reduction proportion) is greater than the maximum algorithm daily threshold, the process 100 may proceed to 150. For example, at 130, the AP application or AID algorithm may determine that the comparison result indicates that the adjusted total daily insulin factor exceeds the maximum algorithm delivery threshold, which causes the process 100 to proceed to 150. At 150, the AP application or the AID algorithm executed by the processor may set the TDI for delivery of insulin to the user using the maximum algorithm daily threshold. In response, the AP application or AID algorithm at 150 may replace the adjusted total daily insulin factor with a predetermined total daily insulin factor, which may be, for example, 0.3-0.5 units/kilogram/day or the like. The AP application or the AID algorithm may calculate the set total daily insulin dosage using the predetermined total daily insulin factor and the weight of the user. For example, the AP application or the AID algorithm may set the TDI using the predetermined total daily insulin factor, which may be, example, 0.5 units/kilograms/day, in which case if the user weights 100 kg, the TDI is set to 50 units of insulin per day. 100 kg×0. units/kilograms/day. The actual TDI delivered may be dependent upon the inputted weight of the user. After setting the TDI either at 140 or 150, the process 100 proceeds to step 160. The device on which the AP application or AID algorithm is executing may be operable to establish communication links with other devices, such as a continuous glucose monitor (CGM) and a medical device, such as drug delivery device (shown in another example). For example, the processor of the device on which the AP application or AID algorithm is executing may be operable to generate a pairing request directed to a controller of a drug delivery device, a controller of a sensor or both. Based on a response to the pairing request from the processor, the processor of the device may establish a communication link with the controller or respective controllers. The control signal as well as other signals may be sent via the established communication link.
At step 160, the AP application or AID algorithm may output a control signal or control signals that control a drug delivery device (shown in another example) to begin administering insulin to the user based on the TDI setting from either 140 or 150. After beginning the outputting of the control signal(s) at step 160, the process 100 may proceed to 165. At 165, the AP application or the AID algorithm executed by the processor may begin collecting user blood glucose measurement values output by a continuous blood glucose monitor, also referred to as a continuous glucose monitor (CGM) (shown in another example). After a period of time of collecting the blood glucose measurement values of the user, the AP application or the AID algorithm executed by the processor may use the obtained blood glucose measurement values to calculate an attribute of the user's blood. For example, the AP application or AID algorithm may calculate, as a blood attribute, an Insulin-dose adjusted A1c (IDAA1c), or glycated hemoglobin value, for the user. In an example, IDAA1c is a model of insulin-dose adjusted glycated hemoglobin A1c, calculated as “A1c (%)+4× insulin dose (units per kilogram per 24 h (u/kg/24 hours)).” Alternatively, IDAA1c is defined as actual HbA1c+(4×insulin dose (u/kg/24 h)). If IDAA1c<9, the patient is in honeymoon phase and the patient partly depends on external insulin; otherwise IDAA1c>9, the patient is no longer able to produce insulin by themselves, they fully depend on external insulin.
The AP application or the AID algorithm may evaluate the calculated glycated hemoglobin (or IDAA1c) value at 170 with reference to a standard value indicative of a honeymoon threshold value of when the pancreas fails to produce sufficient insulin. In an example, the standard value for the blood attribute, such as IDAA1c, may be a honeymoon threshold value may be approximately 9. The honeymoon threshold value of approximately 9 may represent a percentage of the user's hemoglobin that is glycated hemoglobin. If a user's calculated amount of glycated hemoglobin is equal to or less than 9, the user may still be in the honeymoon period, also known as remission phase, while user's whose calculated glycated hemoglobin is greater than 9 may no longer be in the honeymoon period.
At 170, if the calculated glycated hemoglobin is determined by the AP application or the AID algorithm to be less than 9 (i.e., YES), which indicates the user is still in the honeymoon period, the process 100 may proceed back to 150. At 150, the AP application or the AID algorithm may set the TDI to the maximum daily insulin threshold and steps 160, 165 and 170 are repeated until the determination at 170 is NO, the user's glycated hemoglobin is no longer less than or equal to 9. In the response to the NO determining, the process 100 proceeds to 180. At 180, the AP application or the AID algorithm may increase the TDI setting because a honeymoon phase upper bound is unnecessary. The TDI setting may be the default setting, which may be above the maximum daily threshold value of 0.5 u/kg*weight, such as, for example, 0.53 u/kg. After increasing the TDI setting at 180, the AP application or the AID algorithm may generate and output control signals to the drug delivery device to actuate delivery of insulin according to the increased TDI setting (190).
In the example of
In the example of
In an alternate example, the initial TDI may be determined based on the user's weight and age. For example, when a starting TDI value is not available, the AID system can refer to the algorithm's predicted settings based on user's input weight and age to continue making insulin dosing decisions. This is predicated on the assumption that every newly-diagnosed user is in honeymoon phase, and the processes are intended to determine an optimal TDI reduction proportion to minimize the risk of both hyperglycemia and hypoglycemia for the newly-diagnosed user.
In response to the received inputs, the AP application or AID algorithm may determine an adjusted total daily insulin factor that may be usable to calculate a total daily insulin dosage (230). For example, based on the inputted weight and age, the AP application or AID algorithm may access a lookup table of different ages and a value for a corresponding TDI per kilogram (kg).
The lookup table, for example, may be something like the table below that has a list of ages (2 years old to 24+years old) and a corresponding total daily insulin factor for each respective age. The table below as an example is available online at Researchgate.net as a publication entitled “Daily insulin requirement of children and adolescents . . . mode of therapy. Of course, other tables of data may be used or generated based on data from multiple user's, diabetes treatment organizations and research, or the like. In the table below, the total daily insulin factor may range from 0.61 (for individuals 4 years of age) to 0.80 (for individuals 14 and 15 years of age). It should be noted that the corresponding total daily insulin factors follow a bell shaped curve with a lowest value at age 2 (i.e., 0.62) to the highest factor values being at ages 14 and 15 (i.e., 0.80) to a factor value of 0.66 for those 24 years old and over.
Upon determining the appropriate total daily insulin factor from the lookup table, the processor may generate an adjusted total daily insulin factor according to the following formula:
Adj. TDI Parameter=TDI/kg*weight in kg,
where (TDI/kg) is the total daily insulin factor, and XX is the reduction proportion.
The process may continue at 240, at which the AP application or AID algorithm may compare the determined adjusted total daily insulin factor to an upper bound for a maximum algorithm delivery threshold, which may be a value such as 0.5 u/kg (/day)(note here TDI or 0.5 is considered a per-day value, so the total daily insulin factor may be in units of u/kg or u/kg/day). The AP application or AID algorithm may generate a comparison result based on the comparison. In response to generating a comparison result by comparing the adjusted total daily insulin factor to a maximum algorithm delivery threshold, the AP application or AID algorithm may set a total daily insulin dosage based on the comparison result at 250. In a further example, in response to setting a total daily insulin dosage, the AP application or AID algorithm may output a control signal instructing a pump-mechanism controller to actuate a pump-mechanism to administer insulin according to the set total daily insulin dosage to begin delivering insulin to the user.
In another example, the personal diabetes management device, or the like, on which the AP application or AID algorithm is executing may be operable to establish communication links with other devices, such as a continuous glucose monitor (CGM) and a medical device, such as drug delivery device (shown in another example). For example, the processor of the device on which the AP application or AID algorithm is executing may be operable to generate a pairing request directed to a controller of a drug delivery device, a controller of a sensor, or both. Based on a response to the pairing request from the processor, the processor of the device may establish a communication link with the controller or respective controllers of a sensor and/or the drug delivery device. The control signal as well as other signals may be sent via the established communication link. Upon setting the total daily insulin dosage based on the comparison result, the AP application or AID algorithm may begin outputting a control signal or signals to a medical device or drug delivery device that instructs a controller of the respective device to actuate a pump to deliver insulin according to the present total daily insulin dosage setting.
In the example process 200, the AP application or AID algorithm may be operable to receive inputs, such as blood glucose measurement values from a continuous glucose monitor. For example, the processor of the personal diabetes management device may be communicatively coupled to the continuous glucose monitor. The AP application or AID algorithm may be operable to collect the user's blood glucose measurement values (260).
At 270, the AP application or AID algorithm may be operable to determine a level of an attribute of a user' blood based on the user's blood glucose measurement values. For example, the attribute of a user' blood may be an amount of glycated hemoglobin in the user's blood. Glycated hemoglobin also referred to insulin-dose adjusted glycated hemoglobin A1c (IDAA1c) as discussed with reference to the example of
In the example of
In the proposed example, the reduction proportion Y can be defined as 60% based on a minimum value obtained from one or more cost functions in a similar fashion as the selection of the 50% reduction proportion as discussed above with reference to
Either HBGI or LBGI, in this example, remains at 0 depending on the value of the current glucose for which the risk is assessed. The non-zero value of either HBGI or LGBI may be utilized to calculate the cost.
The described examples of
In either process 100 of
In further alternate examples, different conditions may be used to determine the honeymoon period that may not be doctor-guided or based on a determination of an IDAA1c value (as in the foregoing examples) These different conditions may, for example, be used to build a classifier that predicts a honeymoon phase with gender, diabetic ketoacidosis (DKA) and other relative parameters, like a latest A1c value, as well as weight, age or both.
In further examples, the initial TDI estimates calculated according to the foregoing examples may be ignored after sufficient insulin delivery history is available, such as 48-72 hours of history based on a system that utilizes an AP application or an AID algorithm, and the insulin delivery history is the only component that is utilized for subsequent TDI calculations. For example, early in the processes 100 of
However, newly-diagnosed users, when determining insulin dosages on their own, may not deliver the correct amount of insulin needed to control their blood glucose. Therefore, the described examples calculate a starting TDI estimate that may be used as a component of the TDI estimate in the subsequent TDI calculations in addition to the TDI calculated from the insulin delivery history.
In the examples of
It may be helpful to discuss an example of a drug delivery system that may implement the process example of
The drug delivery system 300 may be operable to implement the process examples illustrated in
The drug delivery system 300 may be an automatic drug delivery system that may include a medical device 302 (also referred to as “a drug delivery device” or “a wearable drug delivery device”), a blood glucose sensor 304 (also referred to as “a continuous glucose monitor” or “a blood glucose measurement device”), and a personal diabetes management (PDM) device 306. The system 300, in an example, may also include a smart device 307, which may be operable to communicate with the PDM 306 and other components of system 300 either via a wired or wireless communication link, such as 391, 392 or 393. In a specific example, the smart device 307 is coupled to the PDM 306 only via a wireless communication link 393, which may be a wireless communication link that utilizes the Bluetooth communication protocol or the like.
In an example, the medical device 302 may be attached to the body of a user, such as a patient or diabetic via, for example, an adhesive, and may deliver any therapeutic agent, including any drug or medicine, such as insulin, morphine or the like, to the user. The medical device 302 may, for example, be a wearable device worn by the user. For example, the medical device 302 may be directly coupled to a user (e.g., directly attached to a body part and/or skin of the user via an adhesive or the like). In an example, a surface of the medical device 302 may include an adhesive (not shown) to facilitate attachment to a user.
The medical device 302 may include a number of components to facilitate automatic delivery of a drug (also referred to as a therapeutic agent) to the user. The medical device 302 may be operable to store the drug (i.e., insulin) and to provide the drug to the user. The medical device 302 is often referred to as a pump, or an insulin pump, in reference to the operation of expelling insulin from the reservoir 325 for delivery to the user. While the examples refer to the reservoir 325 storing insulin, the reservoir 325 may be operable to store other drugs or therapeutic agents, such as morphine or the like, that are suitable for automatic delivery.
In various examples, the medical device 302 may be an automatic, wearable drug delivery device. For example, the medical device 302 may include a reservoir 325 for storing the drug (such as insulin), a needle or cannula (not shown) for delivering the drug into the body of the user (which may be done subcutaneously, intraperitoneally, or intravenously), and a pump mechanism (mech.) 324, or other drive mechanism, for transferring the drug from the reservoir 325, through a needle or cannula (not shown), and into the user. The pump mechanism 324 may be fluidly coupled to reservoir 325, and communicatively coupled to the medical device controller 321. The medical device 302 may also include a power source 328, such as a battery, a piezoelectric device, or the like, for supplying electrical power to the pump mechanism 324 and/or other components (such as the controller 321, memory 323, and the communication device 326) of the medical device 302. Although not shown, an electrical power supply for supplying electrical power may similarly be included in each of the sensor 304, the smart device 307 and the PDM device 306.
The blood glucose sensor 304 may be a device communicatively coupled to the PDM processor 361 or controller 321 and may be operable to measure a blood glucose value at a predetermined time interval, such as every 5 minutes, or the like. The blood glucose sensor 304 may provide a number of blood glucose measurement values to the AP applications (e.g., 329, 349, 369, or 379) operating on the respective devices (e.g., 302, 304, 306, or 307). While the AP applications 329, 349, 369 and 379 were discussed in detail and shown in the system 300 example of
The medical device 302 may provide the insulin stored in reservoir 325 to the user based on information (e.g., blood glucose measurement values, predicted future blood glucose measurements, evaluations based on a user request for a bolus, an user interaction with PDM 306, medical device 302, sensor 304 or smart device 307), evaluations of missing blood glucose measurements and the other information provided by the sensor 304, smart device 307, and/or the management device (PDM) 306. For example, the medical device 302 may contain analog and/or digital circuitry that may be implemented as a controller 321 for controlling the delivery of the drug or therapeutic agent. The circuitry used to implement the controller 321 may include discrete, specialized logic and/or components, an application-specific integrated circuit, a microcontroller or processor that executes software instructions, firmware, programming instructions or programming code (enabling, for example, the artificial pancreas application (AP App) 329 as well as the process examples of
In an operational example, the AP application 369 executing in the personal diabetes management device 306 may be operable to control delivery of insulin to a user. For example, the AP application 369 may be operable to determine timing of an insulin dose and may output a command signal to the medical device 302 that actuates the pump mechanism 324 to deliver an insulin dose. In addition, the AP application (or AID algorithm) 369 when loaded with programmed code that provides instructions for the functionality of
The other devices in the system 300, such as personal diabetes management device 306, smart device 307 and sensor 304, may also be operable to perform various functions including controlling the medical device 302. For example, the personal diabetes management device 306 may include a communication device 364, a PDM processor 361, and a personal diabetes management device memory 363. The personal diabetes management device memory 363 may store an instance of the AP application 369 that includes programming code, that when executed by the PDM processor 361 provides the process examples described with reference to the examples of
The smart device 307 may be, for example, a smart phone, an Apple Watch®, another wearable smart device, including eyeglasses, provided by other manufacturers, a global positioning system-enabled wearable, a wearable fitness device, smart clothing, or the like. Similar to the personal diabetes management device 306, the smart device 307 may also be operable to perform various functions including controlling the medical device 302. For example, the smart device 307 may include a communication device 374, a processor 371, and a memory 373. The memory 373 may store an instance of the AP application 379 and/or an instance of an AID application (not shown) that includes programming code for providing the process examples described with reference to the examples of
The sensor 304 of system 300 may be a continuous glucose monitor (CGM) as described above, that may include a processor 341, a memory 343, a sensing or measuring device 344, and a communication device 346. The memory 343 may, for example, store an instance of an AP application 349 as well as other programming code and be operable to store data related to the AP application 349 and process examples described with reference to
Instructions for determining the delivery of the drug or therapeutic agent (e.g., as a bolus dosage) to the user (e.g., the size and/or timing of any doses of the drug or therapeutic agent) may originate locally by the medical device 302 or may originate remotely and be provided to the medical device 302. In an example of a local determination of drug or therapeutic agent delivery, programming instructions, such as an instance of the artificial pancreas application 329, stored in the memory 323 that is coupled to the medical device 302 may be used to make determinations by the medical device 302. In addition, the medical device 302 may be operable to communicate with the cloud-based services 311 via the communication device 326 and the communication link 388. In an example, the system 300 may include one or more components operable to implement the process examples of
Alternatively, the remote instructions may be provided to the medical device 302 over a wired or wireless communication link (such as 331) by the personal diabetes management device (PDM) 306, which has a PDM processor 361 that executes an instance of the artificial pancreas application 369, or the smart device 307 (via communication link 391), which has a processor 371 that executes an instance of the artificial pancreas application 369 as well as other programming code for controlling various devices, such as the medical device 302, smart device 307 and/or sensor 304. In an example, the send a message to a server, for example, in the cloud services 311 or the like, requesting downloading of the one or more cost functions to a personal diabetes management (PDM) 306 or smart device 307. The medical device 302 may execute any received instructions (originating internally or from the personal diabetes management device 306) for the delivery of the drug or therapeutic agent to the user. In this way, the delivery of the drug or therapeutic agent to a user may be automatic.
In various examples, the medical device 302 may communicate via a wireless communication link 331 with the personal diabetes management device 306. The personal diabetes management device 306 may be an electronic device such as, for example, a smart phone, a tablet, a dedicated diabetes therapy personal diabetes management device, or the like. The personal diabetes management device 306 may be a wearable wireless accessory device. The wireless communication links 308, 331, 322, 391, 392 and 393 may be any type of wireless communication link provided by any known wireless standard. As an example, the wireless communication links 308, 331, 322, 391, 392 and 393 may enable communications between the medical device 302, the personal diabetes management device 306 and sensor 304 based on, for example, Bluetooth®, Wi-Fi®, a near-field communication standard, a cellular standard, or any other wireless optical or radio-frequency protocol.
The sensor 304 may be a glucose sensor operable to measure blood glucose and output a blood glucose value or data that is representative of a blood glucose value. For example, the sensor 304 may be a glucose monitor or a continuous glucose monitor (CGM). The sensor 304 may include a processor 341, a memory 343, a sensing/measuring device 344, and communication device 346. The communication device 346 of sensor 304 may include one or more sensing elements, an electronic transmitter, receiver, and/or transceiver for communicating with the personal diabetes management device 306 over a wireless communication link 322 or with medical device 302 over the link 308. The sensing/measuring device 344 may include one or more sensing elements, such as a glucose measurement, heart rate monitor, or the like. The processor 341 may include discrete, specialized logic and/or components, an application-specific integrated circuit, a microcontroller or processor that executes software instructions, firmware, programming instructions stored in memory (such as memory 343), or any combination thereof. For example, the memory 343 may store an instance of an AP application 349 that is executable by the processor 341.
Although the sensor 304 is depicted as separate from the medical device 302, in various examples, the sensor 304 and medical device 302 may be incorporated into the same unit. That is, in various examples, the sensor 304 may be a part of the medical device 302 and contained within the same housing of the medical device 302 (e.g., the sensor 304 may be positioned within or embedded within the medical device 302). Glucose monitoring data (e.g., measured blood glucose values) determined by the sensor 304 may be provided to the medical device 302, smart device 307 and/or the personal diabetes management device 306 and may be used to perform the functions and deliver doses of insulin for automatic delivery of insulin by the medical device 302 as described with reference to the examples of
The sensor 304 may also be coupled to the user by, for example, adhesive or the like and may provide information or data on one or more medical conditions and/or physical attributes of the user. The information or data provided by the sensor 304 may be used to adjust drug delivery operations of the medical device 302.
In an example, the personal diabetes management device 306 may be a mobile computing device operable to manage a personal diabetes treatment plan via an AP application or an AID algorithm. The personal diabetes management device 306 may be used to program or adjust operation of the medical device 302 and/or the sensor 304. The personal diabetes management device 306 may be any portable electronic, computing device including, for example, a dedicated controller, such as PDM processor 361, a smartphone, or a tablet. In an example, the personal diabetes management device (PDM) 306 may include a PDM processor 361, a personal diabetes management device memory 363, and a communication device 364. The personal diabetes management device 306 may contain analog and/or digital circuitry that may be implemented as a PDM processor 361 (or controller) for executing processes to manage a user's blood glucose levels and for controlling the delivery of the drug or therapeutic agent to the user. The PDM processor 361 may also be operable to execute programming code stored in the personal diabetes management device memory 363. For example, the personal diabetes management device memory 363 may be operable to store an artificial pancreas (AP) application 369 that may be executed by the PDM processor 361. The PDM processor 361 may when executing the artificial pancreas application 369 may be operable to perform various functions, such as those described with respect to the examples in
The medical device 302 may communicate with the sensor 304 over a wireless communication link 308 and may communicate with the personal diabetes management device 306 over a wireless communication link 331. The sensor 304 and the personal diabetes management device 306 may communicate over a wireless communication link 322. The smart device 307, when present, may communicate with the medical device 302, the sensor 304 and the personal diabetes management device 306 over wireless communication links 391, 392 and 393, respectively. The wireless communication links 308, 331, 322, 391, 392 and 393 may be any type of wireless communication link operating using known wireless standards or proprietary standards. As an example, the wireless communication links 308, 331, 322, 391, 392 and 393 may provide communication links based on Bluetooth®, Wi-Fi, a near-field communication standard, a cellular standard, or any other wireless protocol via the respective communication devices 326, 346 and 364. In some examples, the medical device 302 and/or the personal diabetes management device 306 may include a user interface 327, 378 and 368, respectively, such as a keypad, a touchscreen display, levers, buttons, a microphone, a speaker, a light, a display, or the like, that is operable to allow a user to enter information and allow the personal diabetes management device to output information for presentation to the user. Note that the respective user interface devices 327, 378 and 368 may serve with the associated hardware, such as a touchscreen display, as both an input device and an output device. For example, the user interface devices may present graphical user interfaces that guide a user, for example, through the presentation of prompts, to input information or provide data to the user as well as other functions.
In various examples, the drug delivery system 300 may implement the artificial pancreas (AP) algorithm (and/or provide AP functionality) to govern or control automatic delivery of insulin to a user (e.g., to maintain euglycemia—a normal level of glucose in the blood). The AP application (or an AID algorithm) may be implemented by the medical device 302 and/or the sensor 304. The AP application may be operable to determine an initial total daily insulin dosage as described with reference to the examples of
As described herein, the drug delivery system 300 or any component thereof, such as the medical device may be considered to provide AP functionality or to implement an AP application. Accordingly, references to the AP application (e.g., functionality, operations, or capabilities thereof) are made for convenience and may refer to and/or include operations and/or functionalities of the drug delivery system 300 or any constituent component thereof (e.g., the medical device 302 and/or the personal diabetes management device 306). The drug delivery system 300—for example, as an insulin delivery system implementing an AP application—may be considered to be a drug delivery system or an AP application-based delivery system that uses sensor inputs (e.g., data collected by the sensor 304).
In an example, one or more of the devices, 302, 304, 306 or 307 may be operable to communicate via a wireless communication link 388 with cloud-based services 311. The cloud-based services 311 may utilize servers and data storage (not shown). The communication link 388 may be a cellular link, a Wi-Fi link, a Bluetooth link, or a combination thereof, that is established between the respective devices 302, 304, 306 or 307 of system 300. The data storage provided by the cloud-based services 311 may store insulin delivery history related to the user, cost function data related to general delivery of insulin to users, or the like. In addition, the cloud-based services 311 may process anonymized data from multiple users to provide generalized information related to the various parameters used by the AP application.
In an example, the device 302 includes a communication device 364, which as described above may be a receiver, a transmitter, or a transceiver that operates according to one or more radio-frequency protocols, such as Bluetooth, Wi-Fi, a near-field communication standard, a cellular standard, that may enable the respective device to communicate with the cloud-based services 311. For example, outputs from the sensor 304 or the medical device 302 may be transmitted to the cloud-based services 311 for storage or processing via the transceivers of communication device 364. Similarly, medical device 302, personal diabetes management device 306 and sensor 304 may be operable to communicate with the cloud-based services 311 via the communication link 388.
In an example, the respective receiver or transceiver of each respective device, 302, 306 or 307, may be operable to receive signals containing respective blood glucose measurement values of the number of blood glucose measurement values that may be transmitted by the sensor 304. The respective processor of each respective device 302, 306 or 307 may be operable to store each of the respective blood glucose measurement values in a respective memory, such as 323, 363 or 373. The respective blood glucose measurement values may be stored as data related to the artificial pancreas algorithm, such as 329, 349, 369 or 379. In a further example, the AP application operating on any of the personal diabetes management device 306, the Smart device 307, or sensor 304 may be operable to transmit, via a transceiver implemented by a respective communication device, 364, 374, 346, a control signal for receipt by a medical device. In the example, the control signal may indicate an amount of insulin to be expelled by the medical device 302.
Various operational scenarios and examples of processes performed by the system 300 are described herein. For example, the system 300 may be operable to implement the process examples of
The techniques described herein for providing functionality to set an adjusted total daily insulin factor and determine whether the adjusted total daily insulin factor exceeds a maximum algorithm delivery threshold. In response to a result of the determination, set a total daily insulin dosage using the attained information and obtain blood glucose measurement values over a period of time. Based on the obtained blood glucose measurement values, a level of glycated hemoglobin of a user may be determined. The set total daily insulin dosage may be modified to provide a modified total daily insulin dosage in response to the determined level of glycated hemoglobin. A control signal including the modified total daily insulin dosage may be output instructing a controller to actuate delivery of insulin according to the modified total daily insulin dosage.
For example, the system 300 or any component thereof may be implemented in hardware, software, or any combination thereof. Software related implementations of the techniques described herein may include, but are not limited to, firmware, application specific software, or any other type of computer readable instructions that may be executed by one or more processors. Hardware related implementations of the techniques described herein may include, but are not limited to, integrated circuits (ICs), application specific ICs (ASICs), field programmable arrays (FPGAs), and/or programmable logic devices (PLDs). In some examples, the techniques described herein, and/or any system or constituent component described herein may be implemented with a processor executing computer readable instructions stored on one or more memory components.
Some examples of the disclosed device may be implemented, for example, using a storage medium, a computer-readable medium, or an article of manufacture which may store an instruction or a set of instructions that, if executed by a machine (i.e., processor or microcontroller), may cause the machine to perform a method and/or operation in accordance with examples of the disclosure. Such a machine may include, for example, any suitable processing platform, computing platform, computing device, processing device, computing system, processing system, computer, processor, or the like, and may be implemented using any suitable combination of hardware and/or software. The computer-readable medium or article may include, for example, any suitable type of memory unit, memory, memory article, memory medium, storage device, storage article, storage medium and/or storage unit, for example, memory (including non-transitory memory), removable or non-removable media, erasable or non-erasable media, writeable or re-writeable media, digital or analog media, hard disk, floppy disk, Compact Disk Read Only Memory (CD-ROM), Compact Disk Recordable (CD-R), Compact Disk Rewriteable (CD-RW), optical disk, magnetic media, magneto-optical media, removable memory cards or disks, various types of Digital Versatile Disk (DVD), a tape, a cassette, or the like. The instructions may include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, encrypted code, programming code, and the like, implemented using any suitable high-level, low-level, object-oriented, visual, compiled and/or interpreted programming language. The non-transitory computer readable medium embodied programming code may cause a processor when executing the programming code to perform functions, such as those described herein.
Certain examples of the present disclosure were described above. It is, however, expressly noted that the present disclosure is not limited to those examples, but rather the intention is that additions and modifications to what was expressly described herein are also included within the scope of the disclosed examples. Moreover, it is to be understood that the features of the various examples described herein were not mutually exclusive and may exist in various combinations and permutations, even if such combinations or permutations were not made express herein, without departing from the spirit and scope of the disclosed examples. In fact, variations, modifications, and other implementations of what was described herein will occur to those of ordinary skill in the art without departing from the spirit and the scope of the disclosed examples. As such, the disclosed examples are not to be defined only by the preceding illustrative description.
Program aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of executable code and/or associated data that is carried on or embodied in a type of non-transitory, machine readable medium. Storage type media include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. It is emphasized that the Abstract of the Disclosure is provided to allow a reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, various features are grouped together in a single example for streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed examples require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed example. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate example. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein,” respectively. Moreover, the terms “first,” “second,” “third,” and so forth, are used merely as labels and are not intended to impose numerical requirements on their objects.
The foregoing description of examples has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. Many modifications and variations are possible in light of this disclosure. It is intended that the scope of the present disclosure be limited not by this detailed description, but rather by the claims appended hereto. Future filed applications claiming priority to this application may claim the disclosed subject matter in a different manner and may generally include any set of one or more limitations as variously disclosed or otherwise demonstrated herein.
Number | Name | Date | Kind |
---|---|---|---|
303013 | Horton | Aug 1884 | A |
2797149 | Skeggs | Jun 1957 | A |
3631847 | Hobbs | Jan 1972 | A |
3634039 | Brondy | Jan 1972 | A |
3812843 | Wootten et al. | May 1974 | A |
3841328 | Jensen | Oct 1974 | A |
3963380 | Thomas, Jr. et al. | Jun 1976 | A |
4055175 | Clemens et al. | Oct 1977 | A |
4146029 | Ellinwood, Jr. | Mar 1979 | A |
4151845 | Clemens | May 1979 | A |
4245634 | Albisser et al. | Jan 1981 | A |
4368980 | Aldred et al. | Jan 1983 | A |
4373527 | Fischell | Feb 1983 | A |
4403984 | Ash et al. | Sep 1983 | A |
4464170 | Clemens et al. | Aug 1984 | A |
4469481 | Kobayashi | Sep 1984 | A |
4475901 | Kraegen et al. | Oct 1984 | A |
4526568 | Clemens et al. | Jul 1985 | A |
4526569 | Bernardi | Jul 1985 | A |
4529401 | Leslie et al. | Jul 1985 | A |
4559033 | Stephen et al. | Dec 1985 | A |
4559037 | Franetzki et al. | Dec 1985 | A |
4573968 | Parker | Mar 1986 | A |
4624661 | Arimond | Nov 1986 | A |
4633878 | Bombardieri | Jan 1987 | A |
4657529 | Prince et al. | Apr 1987 | A |
4685903 | Cable et al. | Aug 1987 | A |
4731726 | Allen, III | Mar 1988 | A |
4743243 | Vaillancourt | May 1988 | A |
4755173 | Konopka et al. | Jul 1988 | A |
4781688 | Thoma et al. | Nov 1988 | A |
4781693 | Martinez et al. | Nov 1988 | A |
4808161 | Kamen | Feb 1989 | A |
4854170 | Brimhall et al. | Aug 1989 | A |
4886499 | Cirelli et al. | Dec 1989 | A |
4900292 | Berry et al. | Feb 1990 | A |
4919596 | Slate et al. | Apr 1990 | A |
4925444 | Orkin et al. | May 1990 | A |
4940527 | Kazlauskas et al. | Jul 1990 | A |
4975581 | Robinson et al. | Dec 1990 | A |
4976720 | Machold et al. | Dec 1990 | A |
4981140 | Wyatt | Jan 1991 | A |
4994047 | Walker et al. | Feb 1991 | A |
5007286 | Malcolm et al. | Apr 1991 | A |
5097834 | Skrabal | Mar 1992 | A |
5102406 | Arnold | Apr 1992 | A |
5109850 | Blanco et al. | May 1992 | A |
5125415 | Bell | Jun 1992 | A |
5134079 | Cusack et al. | Jul 1992 | A |
5153827 | Coutre et al. | Oct 1992 | A |
5165406 | Wong | Nov 1992 | A |
5176662 | Bartholomew et al. | Jan 1993 | A |
5178609 | Ishikawa | Jan 1993 | A |
5207642 | Orkin et al. | May 1993 | A |
5232439 | Campbell et al. | Aug 1993 | A |
5237993 | Skrabal | Aug 1993 | A |
5244463 | Cordner, Jr. et al. | Sep 1993 | A |
5257980 | Van Antwerp et al. | Nov 1993 | A |
5273517 | Barone et al. | Dec 1993 | A |
5281808 | Kunkel | Jan 1994 | A |
5299571 | Mastrototaro | Apr 1994 | A |
5308982 | Ivaldi et al. | May 1994 | A |
5342298 | Michaels et al. | Aug 1994 | A |
5377674 | Kuestner | Jan 1995 | A |
5380665 | Cusack et al. | Jan 1995 | A |
5385539 | Maynard | Jan 1995 | A |
5389078 | Zalesky | Feb 1995 | A |
5411889 | Hoots et al. | May 1995 | A |
5421812 | Langley et al. | Jun 1995 | A |
5468727 | Phillips et al. | Nov 1995 | A |
5505709 | Funderburk et al. | Apr 1996 | A |
5505828 | Wong et al. | Apr 1996 | A |
5507288 | Bocker et al. | Apr 1996 | A |
5533389 | Kamen et al. | Jul 1996 | A |
5558640 | Pfeiler et al. | Sep 1996 | A |
5569186 | Lord et al. | Oct 1996 | A |
5584813 | Livingston et al. | Dec 1996 | A |
5609572 | Lang | Mar 1997 | A |
5665065 | Colman et al. | Sep 1997 | A |
5678539 | Schubert et al. | Oct 1997 | A |
5685844 | Marttila | Nov 1997 | A |
5685859 | Kornerup | Nov 1997 | A |
5693018 | Kriesel et al. | Dec 1997 | A |
5697899 | Hillman et al. | Dec 1997 | A |
5700695 | Yassinzadeh et al. | Dec 1997 | A |
5703364 | Rosenthal | Dec 1997 | A |
5714123 | Sohrab | Feb 1998 | A |
5716343 | Kriesel et al. | Feb 1998 | A |
5722397 | Eppstein | Mar 1998 | A |
5741228 | Lambrecht et al. | Apr 1998 | A |
5746217 | Erickson et al. | May 1998 | A |
5755682 | Knudson et al. | May 1998 | A |
5758643 | Wong et al. | Jun 1998 | A |
5800405 | McPhee | Sep 1998 | A |
5800420 | Gross et al. | Sep 1998 | A |
5801057 | Smart et al. | Sep 1998 | A |
5804048 | Wong et al. | Sep 1998 | A |
5817007 | Fodgaard et al. | Oct 1998 | A |
5820622 | Gross et al. | Oct 1998 | A |
5823951 | Messerschmidt | Oct 1998 | A |
5840020 | Heinonen et al. | Nov 1998 | A |
5848991 | Gross et al. | Dec 1998 | A |
5851197 | Marano et al. | Dec 1998 | A |
5858005 | Kriesel | Jan 1999 | A |
5865806 | Howell | Feb 1999 | A |
5871470 | McWha | Feb 1999 | A |
5879310 | Sopp et al. | Mar 1999 | A |
5902253 | Pfeiffer et al. | May 1999 | A |
5931814 | Alex et al. | Aug 1999 | A |
5932175 | Knute et al. | Aug 1999 | A |
5935099 | Peterson et al. | Aug 1999 | A |
5947911 | Wong et al. | Sep 1999 | A |
5971941 | Simons et al. | Oct 1999 | A |
5993423 | Choi | Nov 1999 | A |
5997501 | Gross et al. | Dec 1999 | A |
6017318 | Gauthier et al. | Jan 2000 | A |
6024539 | Blomquist | Feb 2000 | A |
6032059 | Henning et al. | Feb 2000 | A |
6036924 | Simons et al. | Mar 2000 | A |
6040578 | Malin et al. | Mar 2000 | A |
6049727 | Crothall | Apr 2000 | A |
6050978 | Orr et al. | Apr 2000 | A |
6058934 | Sullivan | May 2000 | A |
6066103 | Duchon et al. | May 2000 | A |
6071292 | Makower et al. | Jun 2000 | A |
6072180 | Kramer et al. | Jun 2000 | A |
6077055 | Vilks | Jun 2000 | A |
6090092 | Fowles et al. | Jul 2000 | A |
6101406 | Hacker et al. | Aug 2000 | A |
6102872 | Doneen et al. | Aug 2000 | A |
6115673 | Malin et al. | Sep 2000 | A |
6123827 | Wong et al. | Sep 2000 | A |
6124134 | Stark | Sep 2000 | A |
6126637 | Kriesel et al. | Oct 2000 | A |
6128519 | Say | Oct 2000 | A |
6142939 | Eppstein et al. | Nov 2000 | A |
6143164 | Heller et al. | Nov 2000 | A |
6157041 | Thomas et al. | Dec 2000 | A |
6161028 | Braig et al. | Dec 2000 | A |
6162639 | Douglas | Dec 2000 | A |
6196046 | Braig et al. | Mar 2001 | B1 |
6200287 | Keller et al. | Mar 2001 | B1 |
6200338 | Solomon et al. | Mar 2001 | B1 |
6214629 | Freitag et al. | Apr 2001 | B1 |
6226082 | Roe | May 2001 | B1 |
6244776 | Wiley | Jun 2001 | B1 |
6261065 | Nayak et al. | Jul 2001 | B1 |
6262798 | Shepherd et al. | Jul 2001 | B1 |
6270455 | Brown | Aug 2001 | B1 |
6271045 | Douglas et al. | Aug 2001 | B1 |
6280381 | Malin et al. | Aug 2001 | B1 |
6285448 | Kuenstner | Sep 2001 | B1 |
6309370 | Haim et al. | Oct 2001 | B1 |
6312888 | Wong et al. | Nov 2001 | B1 |
6334851 | Hayes et al. | Jan 2002 | B1 |
6375627 | Mauze et al. | Apr 2002 | B1 |
6379301 | Worthington et al. | Apr 2002 | B1 |
6402689 | Scarantino et al. | Jun 2002 | B1 |
6470279 | Samsoondar | Oct 2002 | B1 |
6475196 | Vachon | Nov 2002 | B1 |
6477901 | Tadigadapa et al. | Nov 2002 | B1 |
6484044 | Lilienfeld-Toal | Nov 2002 | B1 |
6491656 | Morris | Dec 2002 | B1 |
6512937 | Blank et al. | Jan 2003 | B2 |
6525509 | Petersson et al. | Feb 2003 | B1 |
6528809 | Thomas et al. | Mar 2003 | B1 |
6540672 | Simonsen et al. | Apr 2003 | B1 |
6544212 | Galley et al. | Apr 2003 | B2 |
6546268 | Ishikawa et al. | Apr 2003 | B1 |
6546269 | Kurnik | Apr 2003 | B1 |
6553841 | Blouch | Apr 2003 | B1 |
6554798 | Mann et al. | Apr 2003 | B1 |
6556850 | Braig et al. | Apr 2003 | B1 |
6558351 | Steil et al. | May 2003 | B1 |
6560471 | Heller et al. | May 2003 | B1 |
6561978 | Conn et al. | May 2003 | B1 |
6562001 | Lebel et al. | May 2003 | B2 |
6562014 | Lin et al. | May 2003 | B2 |
6569125 | Jepson et al. | May 2003 | B2 |
6572542 | Houben et al. | Jun 2003 | B1 |
6572545 | Knobbe et al. | Jun 2003 | B2 |
6574490 | Abbink et al. | Jun 2003 | B2 |
6575905 | Knobbe et al. | Jun 2003 | B2 |
6580934 | Braig et al. | Jun 2003 | B1 |
6618603 | Varalli et al. | Sep 2003 | B2 |
6633772 | Ford et al. | Oct 2003 | B2 |
6645142 | Braig et al. | Nov 2003 | B2 |
6653091 | Dunn et al. | Nov 2003 | B1 |
6662030 | Khalil et al. | Dec 2003 | B2 |
6669663 | Thompson | Dec 2003 | B1 |
6678542 | Braig et al. | Jan 2004 | B2 |
6699221 | Vaillancourt | Mar 2004 | B2 |
6718189 | Rohrscheib et al. | Apr 2004 | B2 |
6721582 | Trepagnier et al. | Apr 2004 | B2 |
6728560 | Kollias et al. | Apr 2004 | B2 |
6740059 | Flaherty | May 2004 | B2 |
6740072 | Starkweather et al. | May 2004 | B2 |
6751490 | Esenaliev et al. | Jun 2004 | B2 |
6758835 | Close et al. | Jul 2004 | B2 |
6780156 | Haueter et al. | Aug 2004 | B2 |
6810290 | Lebel et al. | Oct 2004 | B2 |
6837858 | Cunningham et al. | Jan 2005 | B2 |
6837988 | Leong et al. | Jan 2005 | B2 |
6846288 | Nagar et al. | Jan 2005 | B2 |
6862534 | Sterling et al. | Mar 2005 | B2 |
6865408 | Abbink et al. | Mar 2005 | B1 |
6890291 | Robinson et al. | May 2005 | B2 |
6936029 | Mann et al. | Aug 2005 | B2 |
6949081 | Chance | Sep 2005 | B1 |
6958809 | Sterling et al. | Oct 2005 | B2 |
6989891 | Braig et al. | Jan 2006 | B2 |
6990366 | Say et al. | Jan 2006 | B2 |
7008404 | Nakajima | Mar 2006 | B2 |
7009180 | Sterling et al. | Mar 2006 | B2 |
7016713 | Gardner et al. | Mar 2006 | B2 |
7018360 | Flaherty et al. | Mar 2006 | B2 |
7025743 | Mann et al. | Apr 2006 | B2 |
7025744 | Utterberg et al. | Apr 2006 | B2 |
7027848 | Robinson et al. | Apr 2006 | B2 |
7043288 | Davis, III et al. | May 2006 | B2 |
7060059 | Keith et al. | Jun 2006 | B2 |
7061593 | Braig et al. | Jun 2006 | B2 |
7096124 | Sterling et al. | Aug 2006 | B2 |
7115205 | Robinson et al. | Oct 2006 | B2 |
7128727 | Flaherty et al. | Oct 2006 | B2 |
7139593 | Kavak et al. | Nov 2006 | B2 |
7139598 | Hull et al. | Nov 2006 | B2 |
7144384 | Gorman et al. | Dec 2006 | B2 |
7171252 | Scarantino et al. | Jan 2007 | B1 |
7190988 | Say et al. | Mar 2007 | B2 |
7204823 | Estes et al. | Apr 2007 | B2 |
7248912 | Gough et al. | Jul 2007 | B2 |
7267665 | Steil et al. | Sep 2007 | B2 |
7271912 | Sterling et al. | Sep 2007 | B2 |
7278983 | Ireland et al. | Oct 2007 | B2 |
7291107 | Hellwig et al. | Nov 2007 | B2 |
7291497 | Holmes et al. | Nov 2007 | B2 |
7303549 | Flaherty et al. | Dec 2007 | B2 |
7303622 | Loch et al. | Dec 2007 | B2 |
7303922 | Jeng et al. | Dec 2007 | B2 |
7354420 | Steil et al. | Apr 2008 | B2 |
7388202 | Sterling et al. | Jun 2008 | B2 |
7402153 | Steil et al. | Jul 2008 | B2 |
7404796 | Ginsberg | Jul 2008 | B2 |
7429255 | Thompson | Sep 2008 | B2 |
7460130 | Salganicoff | Dec 2008 | B2 |
7481787 | Gable et al. | Jan 2009 | B2 |
7491187 | Van Den Berghe et al. | Feb 2009 | B2 |
7500949 | Gottlieb et al. | Mar 2009 | B2 |
7509156 | Flanders | Mar 2009 | B2 |
7547281 | Hayes et al. | Jun 2009 | B2 |
7569030 | Lebel et al. | Aug 2009 | B2 |
7608042 | Goldberger et al. | Oct 2009 | B2 |
7651845 | Doyle, III et al. | Jan 2010 | B2 |
7680529 | Kroll | Mar 2010 | B2 |
7734323 | Blomquist et al. | Jun 2010 | B2 |
7766829 | Sloan et al. | Aug 2010 | B2 |
7785258 | Braig et al. | Aug 2010 | B2 |
7806854 | Damiano et al. | Oct 2010 | B2 |
7806886 | Kanderian, Jr. | Oct 2010 | B2 |
7918825 | OConnor et al. | Apr 2011 | B2 |
7946985 | Mastrototaro et al. | May 2011 | B2 |
7972296 | Braig et al. | Jul 2011 | B2 |
8221345 | Blomquist | Jul 2012 | B2 |
8251907 | Sterling et al. | Aug 2012 | B2 |
8449524 | Braig et al. | May 2013 | B2 |
8452359 | Rebec et al. | May 2013 | B2 |
8454576 | Mastrototaro et al. | Jun 2013 | B2 |
8467980 | Campbell et al. | Jun 2013 | B2 |
8478557 | Hayter et al. | Jul 2013 | B2 |
8547239 | Peatfield et al. | Oct 2013 | B2 |
8597274 | Sloan et al. | Dec 2013 | B2 |
8622988 | Hayter | Jan 2014 | B2 |
8810394 | Kalpin | Aug 2014 | B2 |
9061097 | Holt et al. | Jun 2015 | B2 |
9171343 | Fischell et al. | Oct 2015 | B1 |
9233204 | Booth et al. | Jan 2016 | B2 |
9486571 | Rosinko et al. | Nov 2016 | B2 |
9579456 | Budiman et al. | Feb 2017 | B2 |
9743224 | San Vicente et al. | Aug 2017 | B2 |
9907515 | Doyle, III et al. | Mar 2018 | B2 |
9980140 | Spencer et al. | May 2018 | B1 |
9984773 | Gondhalekar et al. | May 2018 | B2 |
10248839 | Levy et al. | Apr 2019 | B2 |
10335464 | Michelich | Jul 2019 | B1 |
10583250 | Mazlish et al. | Mar 2020 | B2 |
10737024 | Schmid | Aug 2020 | B2 |
10987468 | Mazlish et al. | Apr 2021 | B2 |
11197964 | Sjolund et al. | Dec 2021 | B2 |
11260169 | Estes | Mar 2022 | B2 |
20010021803 | Blank et al. | Sep 2001 | A1 |
20010034023 | Stanton, Jr. et al. | Oct 2001 | A1 |
20010034502 | Moberg et al. | Oct 2001 | A1 |
20010051377 | Hammer et al. | Dec 2001 | A1 |
20010053895 | Vaillancourt | Dec 2001 | A1 |
20020010401 | Bushmakin et al. | Jan 2002 | A1 |
20020010423 | Gross et al. | Jan 2002 | A1 |
20020016568 | Lebel et al. | Feb 2002 | A1 |
20020040208 | Flaherty et al. | Apr 2002 | A1 |
20020123740 | Flaherty et al. | Sep 2002 | A1 |
20020128543 | Leonhardt | Sep 2002 | A1 |
20020147423 | Burbank et al. | Oct 2002 | A1 |
20020155425 | Han et al. | Oct 2002 | A1 |
20020161288 | Shin et al. | Oct 2002 | A1 |
20030023148 | Lorenz et al. | Jan 2003 | A1 |
20030050621 | Lebel et al. | Mar 2003 | A1 |
20030060692 | Ruchti et al. | Mar 2003 | A1 |
20030086074 | Braig et al. | May 2003 | A1 |
20030086075 | Braig et al. | May 2003 | A1 |
20030090649 | Sterling et al. | May 2003 | A1 |
20030100040 | Bonnecaze et al. | May 2003 | A1 |
20030130616 | Steil et al. | Jul 2003 | A1 |
20030135388 | Martucci et al. | Jul 2003 | A1 |
20030144582 | Cohen et al. | Jul 2003 | A1 |
20030163097 | Fleury et al. | Aug 2003 | A1 |
20030195404 | Knobbe et al. | Oct 2003 | A1 |
20030208113 | Mault et al. | Nov 2003 | A1 |
20030208154 | Close et al. | Nov 2003 | A1 |
20030212379 | Bylund et al. | Nov 2003 | A1 |
20030216627 | Lorenz et al. | Nov 2003 | A1 |
20030220605 | Bowman, Jr. et al. | Nov 2003 | A1 |
20040010207 | Flaherty et al. | Jan 2004 | A1 |
20040034295 | Salganicoff | Feb 2004 | A1 |
20040045879 | Shults et al. | Mar 2004 | A1 |
20040051368 | Caputo et al. | Mar 2004 | A1 |
20040064259 | Haaland et al. | Apr 2004 | A1 |
20040097796 | Berman et al. | May 2004 | A1 |
20040116847 | Wall | Jun 2004 | A1 |
20040122353 | Shahmirian et al. | Jun 2004 | A1 |
20040133166 | Moberg et al. | Jul 2004 | A1 |
20040147034 | Gore et al. | Jul 2004 | A1 |
20040171983 | Sparks et al. | Sep 2004 | A1 |
20040203357 | Nassimi | Oct 2004 | A1 |
20040204868 | Maynard et al. | Oct 2004 | A1 |
20040215492 | Choi | Oct 2004 | A1 |
20040220517 | Starkweather et al. | Nov 2004 | A1 |
20040241736 | Hendee et al. | Dec 2004 | A1 |
20040249308 | Forssell | Dec 2004 | A1 |
20050003470 | Nelson et al. | Jan 2005 | A1 |
20050020980 | Inoue et al. | Jan 2005 | A1 |
20050022274 | Campbell et al. | Jan 2005 | A1 |
20050033148 | Haueter et al. | Feb 2005 | A1 |
20050049179 | Davidson | Mar 2005 | A1 |
20050065464 | Talbot et al. | Mar 2005 | A1 |
20050065465 | Lebel et al. | Mar 2005 | A1 |
20050075624 | Miesel | Apr 2005 | A1 |
20050105095 | Pesach et al. | May 2005 | A1 |
20050137573 | McLaughlin | Jun 2005 | A1 |
20050171503 | Van Den Berghe et al. | Aug 2005 | A1 |
20050182306 | Sloan | Aug 2005 | A1 |
20050192494 | Ginsberg | Sep 2005 | A1 |
20050192557 | Brauker et al. | Sep 2005 | A1 |
20050197621 | Poulsen et al. | Sep 2005 | A1 |
20050203360 | Brauker et al. | Sep 2005 | A1 |
20050203461 | Flaherty et al. | Sep 2005 | A1 |
20050238507 | Dilanni et al. | Oct 2005 | A1 |
20050261660 | Choi | Nov 2005 | A1 |
20050272640 | Doyle, III et al. | Dec 2005 | A1 |
20050277912 | John | Dec 2005 | A1 |
20060009727 | OMahony et al. | Jan 2006 | A1 |
20060079809 | Goldberger et al. | Apr 2006 | A1 |
20060100494 | Kroll | May 2006 | A1 |
20060134323 | OBrien | Jun 2006 | A1 |
20060167350 | Monfre et al. | Jul 2006 | A1 |
20060173406 | Hayes et al. | Aug 2006 | A1 |
20060189925 | Gable et al. | Aug 2006 | A1 |
20060189926 | Hall et al. | Aug 2006 | A1 |
20060197015 | Sterling et al. | Sep 2006 | A1 |
20060200070 | Callicoat et al. | Sep 2006 | A1 |
20060204535 | Johnson | Sep 2006 | A1 |
20060229531 | Goldberger et al. | Oct 2006 | A1 |
20060253085 | Geismar et al. | Nov 2006 | A1 |
20060264895 | Flanders | Nov 2006 | A1 |
20060270983 | Lord et al. | Nov 2006 | A1 |
20060276771 | Galley et al. | Dec 2006 | A1 |
20060282290 | Flaherty et al. | Dec 2006 | A1 |
20070016127 | Staib et al. | Jan 2007 | A1 |
20070060796 | Kim | Mar 2007 | A1 |
20070060869 | Tolle et al. | Mar 2007 | A1 |
20070060872 | Hall et al. | Mar 2007 | A1 |
20070083160 | Hall et al. | Apr 2007 | A1 |
20070106135 | Sloan et al. | May 2007 | A1 |
20070116601 | Patton | May 2007 | A1 |
20070118405 | Campbell et al. | May 2007 | A1 |
20070129690 | Rosenblatt et al. | Jun 2007 | A1 |
20070142720 | Ridder et al. | Jun 2007 | A1 |
20070173761 | Kanderian et al. | Jul 2007 | A1 |
20070173974 | Lin | Jul 2007 | A1 |
20070179352 | Randlov et al. | Aug 2007 | A1 |
20070191716 | Goldberger et al. | Aug 2007 | A1 |
20070197163 | Robertson | Aug 2007 | A1 |
20070225675 | Robinson et al. | Sep 2007 | A1 |
20070244381 | Robinson et al. | Oct 2007 | A1 |
20070249007 | Rosero | Oct 2007 | A1 |
20070264707 | Liederman et al. | Nov 2007 | A1 |
20070282269 | Carter et al. | Dec 2007 | A1 |
20070287985 | Estes et al. | Dec 2007 | A1 |
20070293843 | Ireland et al. | Dec 2007 | A1 |
20080033272 | Gough et al. | Feb 2008 | A1 |
20080051764 | Dent et al. | Feb 2008 | A1 |
20080058625 | McGarraugh et al. | Mar 2008 | A1 |
20080065050 | Sparks et al. | Mar 2008 | A1 |
20080071157 | McGarraugh et al. | Mar 2008 | A1 |
20080071158 | McGarraugh et al. | Mar 2008 | A1 |
20080078400 | Martens et al. | Apr 2008 | A1 |
20080097289 | Steil et al. | Apr 2008 | A1 |
20080132880 | Buchman | Jun 2008 | A1 |
20080161664 | Mastrototaro et al. | Jul 2008 | A1 |
20080172026 | Blomquist | Jul 2008 | A1 |
20080177165 | Blomquist et al. | Jul 2008 | A1 |
20080188796 | Steil et al. | Aug 2008 | A1 |
20080200838 | Goldberger et al. | Aug 2008 | A1 |
20080206067 | De Corral et al. | Aug 2008 | A1 |
20080208113 | Damiano et al. | Aug 2008 | A1 |
20080214919 | Harmon et al. | Sep 2008 | A1 |
20080228056 | Blomquist et al. | Sep 2008 | A1 |
20080249386 | Besterman et al. | Oct 2008 | A1 |
20080269585 | Ginsberg | Oct 2008 | A1 |
20080269714 | Mastrototaro et al. | Oct 2008 | A1 |
20080269723 | Mastrototaro et al. | Oct 2008 | A1 |
20080287906 | Burkholz et al. | Nov 2008 | A1 |
20090006061 | Thukral et al. | Jan 2009 | A1 |
20090018406 | Yodat et al. | Jan 2009 | A1 |
20090030398 | Yodat et al. | Jan 2009 | A1 |
20090036753 | King | Feb 2009 | A1 |
20090043240 | Robinson et al. | Feb 2009 | A1 |
20090054753 | Robinson et al. | Feb 2009 | A1 |
20090069743 | Krishnamoorthy et al. | Mar 2009 | A1 |
20090069745 | Estes et al. | Mar 2009 | A1 |
20090069787 | Estes et al. | Mar 2009 | A1 |
20090099521 | Gravesen et al. | Apr 2009 | A1 |
20090105573 | Malecha | Apr 2009 | A1 |
20090131861 | Braig et al. | May 2009 | A1 |
20090156922 | Goldberger et al. | Jun 2009 | A1 |
20090156924 | Shariati et al. | Jun 2009 | A1 |
20090163781 | Say et al. | Jun 2009 | A1 |
20090198350 | Thiele | Aug 2009 | A1 |
20090221890 | Saffer et al. | Sep 2009 | A1 |
20090228214 | Say et al. | Sep 2009 | A1 |
20090318791 | Kaastrup | Dec 2009 | A1 |
20090326343 | Gable et al. | Dec 2009 | A1 |
20100057042 | Hayter | Mar 2010 | A1 |
20100114026 | Karratt et al. | May 2010 | A1 |
20100121170 | Rule | May 2010 | A1 |
20100137784 | Cefai et al. | Jun 2010 | A1 |
20100152658 | Hanson et al. | Jun 2010 | A1 |
20100174228 | Buckingham et al. | Jul 2010 | A1 |
20100211003 | Sundar et al. | Aug 2010 | A1 |
20100228110 | Tsoukalis | Sep 2010 | A1 |
20100262117 | Magni et al. | Oct 2010 | A1 |
20100262434 | Shaya | Oct 2010 | A1 |
20100295686 | Sloan et al. | Nov 2010 | A1 |
20100298765 | Budiman et al. | Nov 2010 | A1 |
20110021584 | Berggren et al. | Jan 2011 | A1 |
20110028817 | Jin et al. | Feb 2011 | A1 |
20110054390 | Searle et al. | Mar 2011 | A1 |
20110054399 | Chong et al. | Mar 2011 | A1 |
20110124996 | Reinke et al. | May 2011 | A1 |
20110144586 | Michaud et al. | Jun 2011 | A1 |
20110160652 | Yodat et al. | Jun 2011 | A1 |
20110178472 | Cabiri | Jul 2011 | A1 |
20110190694 | Lanier, Jr. et al. | Aug 2011 | A1 |
20110202005 | Yodat et al. | Aug 2011 | A1 |
20110218495 | Remde | Sep 2011 | A1 |
20110230833 | Landman et al. | Sep 2011 | A1 |
20110251509 | Beyhan et al. | Oct 2011 | A1 |
20110313680 | Doyle et al. | Dec 2011 | A1 |
20110316562 | Cefai et al. | Dec 2011 | A1 |
20120003935 | Lydon et al. | Jan 2012 | A1 |
20120010594 | Holt et al. | Jan 2012 | A1 |
20120030393 | Ganesh et al. | Feb 2012 | A1 |
20120053556 | Lee | Mar 2012 | A1 |
20120078067 | Kovatchev et al. | Mar 2012 | A1 |
20120078161 | Masterson et al. | Mar 2012 | A1 |
20120078181 | Smith et al. | Mar 2012 | A1 |
20120101451 | Boit et al. | Apr 2012 | A1 |
20120123234 | Atlas et al. | May 2012 | A1 |
20120136336 | Mastrototaro et al. | May 2012 | A1 |
20120190955 | Rao et al. | Jul 2012 | A1 |
20120203085 | Rebec | Aug 2012 | A1 |
20120203178 | Tverskoy | Aug 2012 | A1 |
20120215087 | Cobelli et al. | Aug 2012 | A1 |
20120225134 | Komorowski | Sep 2012 | A1 |
20120226259 | Yodat et al. | Sep 2012 | A1 |
20120232520 | Sloan et al. | Sep 2012 | A1 |
20120238851 | Kamen et al. | Sep 2012 | A1 |
20120271655 | Knobel et al. | Oct 2012 | A1 |
20120277668 | Chawla et al. | Nov 2012 | A1 |
20120282111 | Nip et al. | Nov 2012 | A1 |
20120295550 | Wilson et al. | Nov 2012 | A1 |
20130030358 | Yodfat et al. | Jan 2013 | A1 |
20130158503 | Kanderian, Jr. et al. | Jun 2013 | A1 |
20130178791 | Javitt | Jul 2013 | A1 |
20130231642 | Doyle et al. | Sep 2013 | A1 |
20130253472 | Cabiri | Sep 2013 | A1 |
20130261406 | Rebec et al. | Oct 2013 | A1 |
20130296823 | Melker et al. | Nov 2013 | A1 |
20130317753 | Kamen et al. | Nov 2013 | A1 |
20130338576 | OConnor et al. | Dec 2013 | A1 |
20140005633 | Finan | Jan 2014 | A1 |
20140066886 | Roy et al. | Mar 2014 | A1 |
20140074033 | Sonderegger et al. | Mar 2014 | A1 |
20140121635 | Hayter | May 2014 | A1 |
20140128839 | Dilanni et al. | May 2014 | A1 |
20140135880 | Baumgartner et al. | May 2014 | A1 |
20140146202 | Boss et al. | May 2014 | A1 |
20140180203 | Budiman et al. | Jun 2014 | A1 |
20140180240 | Finan et al. | Jun 2014 | A1 |
20140200426 | Taub et al. | Jul 2014 | A1 |
20140200559 | Doyle et al. | Jul 2014 | A1 |
20140230021 | Birthwhistle et al. | Aug 2014 | A1 |
20140276554 | Finan et al. | Sep 2014 | A1 |
20140276556 | Saint et al. | Sep 2014 | A1 |
20140278123 | Prodhom et al. | Sep 2014 | A1 |
20140309615 | Mazlish | Oct 2014 | A1 |
20140316379 | Sonderegger et al. | Oct 2014 | A1 |
20140325065 | Birtwhistle et al. | Oct 2014 | A1 |
20150018633 | Kovachev et al. | Jan 2015 | A1 |
20150025329 | Amarasingham et al. | Jan 2015 | A1 |
20150025495 | Peyser | Jan 2015 | A1 |
20150120317 | Mayou et al. | Apr 2015 | A1 |
20150134265 | Kohlbrecher et al. | May 2015 | A1 |
20150165119 | Palerm et al. | Jun 2015 | A1 |
20150173674 | Hayes et al. | Jun 2015 | A1 |
20150213217 | Amarasingham et al. | Jul 2015 | A1 |
20150217052 | Keenan et al. | Aug 2015 | A1 |
20150217053 | Booth et al. | Aug 2015 | A1 |
20150265767 | Vazquez et al. | Sep 2015 | A1 |
20150306314 | Doyle et al. | Oct 2015 | A1 |
20150351671 | Vanslyke et al. | Dec 2015 | A1 |
20150366945 | Greene | Dec 2015 | A1 |
20160015891 | Papiorek | Jan 2016 | A1 |
20160038673 | Morales | Feb 2016 | A1 |
20160038689 | Lee et al. | Feb 2016 | A1 |
20160051749 | Istoc | Feb 2016 | A1 |
20160082187 | Schaible et al. | Mar 2016 | A1 |
20160089494 | Guerrini | Mar 2016 | A1 |
20160175520 | Palerm et al. | Jun 2016 | A1 |
20160228641 | Gescheit et al. | Aug 2016 | A1 |
20160243318 | Despa et al. | Aug 2016 | A1 |
20160256087 | Doyle et al. | Sep 2016 | A1 |
20160287512 | Cooper et al. | Oct 2016 | A1 |
20160302054 | Kimura et al. | Oct 2016 | A1 |
20160331310 | Kovatchev | Nov 2016 | A1 |
20160354543 | Cinar et al. | Dec 2016 | A1 |
20170049386 | Abraham et al. | Feb 2017 | A1 |
20170143899 | Gondhalekar et al. | May 2017 | A1 |
20170143900 | Rioux et al. | May 2017 | A1 |
20170156682 | Doyle et al. | Jun 2017 | A1 |
20170173261 | O'Connor et al. | Jun 2017 | A1 |
20170189625 | Cirillo et al. | Jul 2017 | A1 |
20170281877 | Marlin et al. | Oct 2017 | A1 |
20170296746 | Chen et al. | Oct 2017 | A1 |
20170311903 | Davis et al. | Nov 2017 | A1 |
20170348482 | Duke et al. | Dec 2017 | A1 |
20180036495 | Searle et al. | Feb 2018 | A1 |
20180040255 | Freeman et al. | Feb 2018 | A1 |
20180075200 | Davis et al. | Mar 2018 | A1 |
20180075201 | Davis et al. | Mar 2018 | A1 |
20180075202 | Davis et al. | Mar 2018 | A1 |
20180092576 | Ambrosio et al. | Apr 2018 | A1 |
20180126073 | Wu et al. | May 2018 | A1 |
20180169334 | Grosman et al. | Jun 2018 | A1 |
20180200434 | Mazlish et al. | Jul 2018 | A1 |
20180200438 | Mazlish et al. | Jul 2018 | A1 |
20180200441 | Desborough et al. | Jul 2018 | A1 |
20180204636 | Edwards et al. | Jul 2018 | A1 |
20180277253 | Gondhalekar et al. | Sep 2018 | A1 |
20180289891 | Finan et al. | Oct 2018 | A1 |
20180296757 | Finan | Oct 2018 | A1 |
20180342317 | Skirble et al. | Nov 2018 | A1 |
20180369479 | Hayter et al. | Dec 2018 | A1 |
20190076600 | Grosman et al. | Mar 2019 | A1 |
20190240403 | Palerm et al. | Aug 2019 | A1 |
20190290844 | Monirabbasi et al. | Sep 2019 | A1 |
20190336683 | O'Connor et al. | Nov 2019 | A1 |
20190336684 | O'Connor et al. | Nov 2019 | A1 |
20190348157 | Booth et al. | Nov 2019 | A1 |
20200046268 | Patek et al. | Feb 2020 | A1 |
20200101222 | Lintereur et al. | Apr 2020 | A1 |
20200101223 | Lintereur et al. | Apr 2020 | A1 |
20200101225 | O'Connor et al. | Apr 2020 | A1 |
20200219625 | Kahlbaugh | Jul 2020 | A1 |
20200342974 | Chen et al. | Oct 2020 | A1 |
20210050085 | Hayter et al. | Feb 2021 | A1 |
20210098105 | Lee et al. | Apr 2021 | A1 |
20220023536 | Graham | Jan 2022 | A1 |
Number | Date | Country |
---|---|---|
2015200834 | Mar 2015 | AU |
2015301146 | Mar 2017 | AU |
1297140 | May 2001 | CN |
19756872 | Jul 1999 | DE |
0341049 | Nov 1989 | EP |
0496305 | Jul 1992 | EP |
0549341 | Jun 1993 | EP |
1491144 | Dec 2004 | EP |
1571582 | Sep 2005 | EP |
0801578 | Jul 2006 | EP |
2666520 | Oct 2009 | EP |
2139382 | Jan 2010 | EP |
2397181 | Dec 2011 | EP |
2695573 | Feb 2014 | EP |
2830499 | Feb 2015 | EP |
2943149 | Nov 2015 | EP |
3177344 | Jun 2017 | EP |
3314548 | May 2018 | EP |
2897071 | May 2019 | EP |
3607985 | Feb 2020 | EP |
2443261 | Apr 2008 | GB |
51125993 | Nov 1976 | JP |
02131777 | May 1990 | JP |
2004283378 | Oct 2007 | JP |
2017525451 | Sep 2017 | JP |
2018153569 | Oct 2018 | JP |
2019525276 | Sep 2019 | JP |
200740148 | Oct 2007 | TW |
M452390 | May 2013 | TW |
9800193 | Jan 1998 | WO |
9956803 | Nov 1999 | WO |
0030705 | Jun 2000 | WO |
0032258 | Jun 2000 | WO |
0172354 | Oct 2001 | WO |
2002015954 | Feb 2002 | WO |
0243866 | Jun 2002 | WO |
02082990 | Oct 2002 | WO |
03016882 | Feb 2003 | WO |
03039362 | May 2003 | WO |
03045233 | Jun 2003 | WO |
2004043250 | May 2004 | WO |
04092715 | Oct 2004 | WO |
2005051170 | Jun 2005 | WO |
2005082436 | Sep 2005 | WO |
05110601 | Nov 2005 | WO |
2005113036 | Dec 2005 | WO |
2006053007 | May 2006 | WO |
2007064835 | Jun 2007 | WO |
2007078937 | Jul 2007 | WO |
2008024810 | Feb 2008 | WO |
2008029403 | Mar 2008 | WO |
2008133702 | Nov 2008 | WO |
2009045462 | Apr 2009 | WO |
2009049252 | Apr 2009 | WO |
2009066287 | May 2009 | WO |
2009066288 | May 2009 | WO |
2009098648 | Aug 2009 | WO |
2009134380 | Nov 2009 | WO |
2010053702 | May 2010 | WO |
2010132077 | Nov 2010 | WO |
2010138848 | Dec 2010 | WO |
2010147659 | Dec 2010 | WO |
2011095483 | Aug 2011 | WO |
2012045667 | Apr 2012 | WO |
2012108959 | Aug 2012 | WO |
2012134588 | Oct 2012 | WO |
2012177353 | Dec 2012 | WO |
2012178134 | Dec 2012 | WO |
2013078200 | May 2013 | WO |
2013134486 | Sep 2013 | WO |
20130149186 | Oct 2013 | WO |
2013177565 | Nov 2013 | WO |
2013182321 | Dec 2013 | WO |
2014109898 | Jul 2014 | WO |
2014110538 | Jul 2014 | WO |
2014194183 | Dec 2014 | WO |
2015056259 | Apr 2015 | WO |
2015061493 | Apr 2015 | WO |
2015073211 | May 2015 | WO |
2015081337 | Jun 2015 | WO |
2015187366 | Dec 2015 | WO |
2016004088 | Jan 2016 | WO |
2016022650 | Feb 2016 | WO |
2016041873 | Mar 2016 | WO |
2016089702 | Jun 2016 | WO |
2016141082 | Sep 2016 | WO |
2016161254 | Oct 2016 | WO |
2017004278 | Jan 2017 | WO |
2017091624 | Jun 2017 | WO |
2017105600 | Jun 2017 | WO |
2017184988 | Oct 2017 | WO |
2017205816 | Nov 2017 | WO |
2018009614 | Jan 2018 | WO |
2018067748 | Apr 2018 | WO |
2018120104 | Jul 2018 | WO |
2018136799 | Jul 2018 | WO |
2018204568 | Nov 2018 | WO |
2019077482 | Apr 2019 | WO |
2019094440 | May 2019 | WO |
2019213493 | Nov 2019 | WO |
2019246381 | Dec 2019 | WO |
2020081393 | Apr 2020 | WO |
2021011738 | Jan 2021 | WO |
Entry |
---|
US 5,954,699 A, 09/1999, Jost et al. (withdrawn) |
European Search Report for the European Patent Application No. 21168591.2, dated Oct. 13, 2021, 04 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/041954, dated Oct. 25, 2021, 13 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/047771, dated Dec. 22, 2021, 11 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/052855, dated Dec. 22, 2021, 11 pages. |
Unger, Jeff, et al., “Glucose Control in the Hospitalized Patient,” Emerg. Med 36(9):12-18 (2004). |
Glucommander FAQ downloaded from https://adaendo.com/GlucommanderFAQ.html on Mar. 16, 2009. |
Finfer, Simon & Heritier, Stephane. (2009). The NICE-SUGAR (Normoglycaemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation) Study: statistical analysis plan. Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine. 11. 46-57. |
Letters to the Editor regarding “Glucose Control in Critically Ill Patients,” N Engl J Med 361: 1, Jul. 2, 2009. |
“Medtronic is Leading a Highly Attractive Growth Market,” Jun. 2, 2009. |
Davidson, Paul C., et al. “Glucommander: An Adaptive, Computer-Directed System for IV Insulin Shown to be Safe, Simple, and Effective in 120,618 Hours of Operation,” Atlanta Diabetes Associates presentation Nov. 16, 2003. |
Davidson, Paul C., et al. “Pumpmaster and Glucommander,” presented at the MiniMed Symposium, Atlanta GA, Dec. 13, 2003. |
Kanji S., et al. “Reliability of point-of-care testing for glucose measurement in critically ill adults,” Critical Care Med, vol. 33, No. 12, pp. 2778-2785, 2005. |
Krinsley James S., “Severe hypoglycemia in critically ill patients: Risk factors and outcomes,” Critical Care Med, vol. 35, No. 10, pp. 1-6, 2007. |
European Patent Office, “Notification of Transmittal of the ISR and the Written Opinion of the International Searching Authority, or the Declaration,” in PCT Application No. PCT/GB2015/050248, dated Jun. 23, 2015, 12 pages. |
Farkas et al. ““Single-Versus Triple-Lumen Central Catheter-Related Sepsis: A Prospective Randomized Study in a Critically Ill Population”” The American Journal of Medicine Sep. 1992vol. 93 p. 277-282. |
Davidson, Paul C., et al., A computer-directed intravenous insulin system shown to be safe, simple,and effective in 120,618 h of operation, Diabetes Care, vol. 28, No. 10, Oct. 2005, pp. 2418-2423. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/016283, dated Jun. 2, 2021, 15 pages. |
Gorke, A. “Microbial contamination of haemodialysis catheter connections.” EDTNA/ERCA journal (English ed.) vol. 31,2 (2005): 79-84. doi:10.1111/j.1755-6686.2005.tb00399.x. |
Lovich et al. “Central venous catheter infusions: A laboratory model shows large differences in drug delivery dynamics related to catheter dead volume” Critical Care Med 2007 vol. 35, No. 12. |
Van Den Berghe, Greet, M.D., Ph.D., et al., Intensive Insulin Therapy in Critically Ill Patients, The New England Journal of Medicine, vol. 345, No. 19, Nov. 8, 2001, pp. 1359-1367. |
Templeton et al., “Multilumen Central Venous Catheters Increase Risk for Catheter-Related Bloodstream Infection: Prospective Surveillance Study” Infection 2008; 36: 322-327. |
Wilson, George S., et al., Progress toward the Development of an Implantable Sensor for Glucose, Clin. Chem., vol. 38, No. 9, 1992, pp. 1613-1617. |
Yeung et al. “Infection Rate for Single Lumen v Triple Lumen Subclavian Catheters” Infection Control and Hospital Epidemiology, vol. 9, No. 4 (Apr. 1988) pp. 154-158 The University of Chicago Press. |
International Search Report and Written Opinion, International Application No. PCT/US2010/033794 dated Jul. 16, 2010 (OPTIS.247VPC). |
International Search Report and Written Opinion in PCT/US2008/079641 (Optis.203VPC) dated Feb. 25, 2009. |
Berger, ““Measurement of Analytes in Human Serum and Whole Blood Samples by Near-Infrared Raman Spectroscopy,”” Ph D. Thesis, Massachusetts Institute of Technology, Chapter 4, pp. 50-73, 1998. |
Berger, “An Enhanced Algorithm for Linear Multivariate Calibration,” Analytical Chemistry, vol. 70, No. 3, pp. 623-627, Feb. 1, 1998. |
Billman et. al.,“Clinical Performance of an In line Ex-Vivo Point of Care Monitor: A Multicenter Study,” Clinical Chemistry 48: 11, pp. 2030-2043, 2002. |
Widness et al., “Clinical Performance on an In-Line Point-of-Care Monitor in Neonates”; Pediatrics, vol. 106, No. 3, pp. 497-504, Sep. 2000. |
Finkielman et al., “Agreement Between Bedside Blood and Plasma Glucose Measurement in the ICU Setting”; retrieved from http://www.chestjournal.org; CHEST/127/5/May 2005. |
Fogt, et al., “Development and Evaluation of a Glucose Analyzer for a Glucose-Controlled Insulin Infusion System (Biostator)”; Clinical Chemistry, vol. 24, No. 8, pp. 1366-1372, 1978. |
Vonach et al., “Application of Mid-Infrared Transmission Spectrometry to the Direct Determination of Glucose in Whole Blood,” Applied Spectroscopy, vol. 52, No. 6, 1998, pp. 820-822. |
Muniyappa et al., “Current Approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage,” AJP—Endocrinol Metab, vol. 294, E15-E26, first published Oct. 23, 2007. |
R Anthony Shaw, et al., “Infrared Spectroscopy in Clinical and Dianostic Analysis,” Encyclopedia of Analytical Chemistry, ed. Robert A. Meyers, John Wiley & Sons, Ltd., pp. 1-20, 2000. |
International Preliminary Report on Patentability for the International Patent Application No. PCT/US2019/053603, dated Apr. 8, 2021, 9 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2019/053603, dated Jan. 7, 2020, 16 pages. |
Dassau et al., “Detection of a meal using continuous glucose monitoring: Implications for an artificial [beta]-cell.” Diabetes Care, American Diabetes Association, Alexandria, VA, US, 31(2):295-300 (2008). |
Cameron et al., “Probabilistic Evolving Meal Detection and Estimation of Meal Total Glucose Appearance Author Affiliations”, J Diabetes Sci and Tech.vol., Diabetes Technology Society ;(5):1022-1030 (2009). |
Lee et al., “A closed-loop artificial pancreas based on model predictive control: Human-friendly identification and automatic meal disturbance rejection”, Biomedical Signal Processing and Control, Elsevier, Amsterdam, NL, 4(4):1746-8094 (2009). |
International Search Report and Written Opinion for the InternationalPatent Application No. PCT/US2021/018297, dated May 18, 2021, 18 pages. |
An Emilia Fushimi: “Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement”, Journal of Diabetes Science and Technology Diabetes Technology Society, Mar. 22, 2019, pp. 1025-1043. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/017441, dated May 25, 2021, 12 pages. |
International Search Report and Written Opinion for the InternationalPatent Application No. PCT/US2021/017664, dated May 26, 2021, 16 pages. |
Mirko Messori et al: “Individualized model predictive control for the artificial pancreas: In silico evaluation of closed-loop glucose control”, IEEE Control Systems, vol. 38, No. 1, Feb. 1, 2018, pp. 86-104. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/017662, dated May 26, 2021, 14 pages. |
Anonymous: “Reservoir Best Practice and Top Tips” Feb. 7, 2016, URL: https://www.medtronic-diabetes.co.uk/blog/reservoir-best-practice-and-top-tips, p. 1. |
Gildon Bradford: “InPen Smart Insulin Pen System: Product Review and User Experience” Diabetes Spectrum, vol. 31, No. 4, Nov. 15, 2018, pp. 354-358. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/016050, dated May 27, 2021, 16 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2020/065226, dated May 31, 2021, 18 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/017659, dated May 31, 2021, 13 pages. |
Montaser Eslam et al., “Seasonal Local Models for Glucose Prediction in Type 1 Diabetes”, IEE Journal of Biomedical and Health Informatics, IEEE, Piscataway, NJ, USA, vol. 24, No. 7, Jul. 2020, pp. 2064-2072. |
Samadi Sediqeh et al., “Meal Detection and Carbohydrate Estimation Using Continuous Glucose Sensor Data” IEEE Journal of Biomedical and Health Informatics, IEEE, Piscataway, NJ, USA, vol. 21, No. 3, May 1, 2017, pp. 619-627. |
Samadi Sediqeh et al., “Automatic Detection and Estimation of Unannouced Meals for Multivariable Artificial Pancreas System”, Diabetis Technology & Therapeutics, vol. 20m No. 3, Mar. 1, 2018, pp. 235-246. |
Khodaei et al., “Physiological Closed-Loop Contol (PCLC) Systems: Review of a Modern Frontier in Automation”, IEEE Access, IEEE, USA, vol. 8, Jan. 20, 2020, pp. 23965-24005. |
E. Atlas et al., “MD-Logic Artificial Pancreas System: A pilot study in adults with type 1 diabetes”, Diabetes Care, vol. 33, No. 5, Feb. 11, 2010, pp. 1071-1076. |
Anonymous: “Fuzzy control system”, Wikipedia, Jan. 10, 2020. URL: https://en.wikipedia.org/w/index.php?title=Fuzzy_control_system&oldid=935091190. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/022694, dated Jun. 25, 2021, 13 pages. |
International Search Report and Written Opinion for International Patent Application No. PCT/US2021/051027, dated Jan. 7, 2022, 16 pages. |
International Search Report and Written Opinion for International Patent Application No. PCT/US2021/052372, dated Jan. 26, 2022, 15 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/046607, dated Jan. 31, 2022, 20 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2021/055745, dated Feb. 14, 2022, 13 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2020/052125, dated Aug. 12, 2020, 15 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2020/050332, dated Sep. 12, 2020, 12 pages. |
International Search Report and Written Opinion for Application No. PCT/EP2019/060365, dated Aug. 13, 2019, 12 pages. |
Anonymous: “Artificial pancreas—Wikipedia”, Mar. 13, 2018 (Mar. 13, 2018), XP055603712, Retrieved from the Internet: URL: https://en.wikipedia.org/wiki/Artificial_pancreas [retrieved on Jul. 9, 2019] section “Medical Equipment” and the figure labeled “The medical equipment approach to an artifical pancreas”. |
Kaveh et al., “Blood Glucose Regulation via Double Loop Higher Order Sliding Mode Control and Multiple Sampling Rate.” Paper presented at the proceedings of the 17th IFAC World Congress, Seoul, Korea (Jul. 2008). |
Dassau et al., “Real-Time Hypoglycemia Prediction Suite Using Contineous Glucose Monitoring,” Diabetes Care, vol. 33, No. 6, 1249-1254 (2010). |
International Search Report and Written Opinion for International Patent Application No. PCT/US17/53262, dated Dec. 13, 2017, 8 pages. |
Van Heusden et al., “Control-Relevant Models for Glucose Control using A Priori Patient Characteristics”, IEEE Transactions on Biomedical Engineering, vol. 59, No. 7, (Jul. 1, 2012) pp. 1839-1849. |
Doyle III et al., “Run-to-Run Control Strategy for Diabetes Management.” Paper presented at 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey (Oct. 2001). |
Bequette, B.W., and Desemone, J., “Intelligent Dosing Systems”: Need for Design and Analysis Based on Control Theory, Diabetes Technology and Therapeutics 9(6): 868-873 (2004). |
Parker et al., “A Model-Based Agorithm for Blood Gucose Control in Type 1 Diabetic Patients.” IEEE Transactions on Biomedical Engineering, 46 (2) 148-147 (1999). |
International Search Report and Written Opinion for International Patent Application No. PCT/US2017/015601, dated May 16, 2017, 12 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2018/018901, dated Aug. 6, 2018, 12 pages. |
International Search Report and Written Opinion for International Patent Application No. PCT/US2018/052467, dated Jan. 4, 2019, 13 pages. |
“How to Create a QR Code that Deep Links to Your Mobile App”, Pure Oxygen Labs, web<https://pureoxygenlabs.com/how-to-create-a-qr-codes-that-deep-link-to-your-mobile-app/>. Year:2017. |
“Read NFC Tags with an iPHone App on iOS 11”, GoToTags, Sep. 11, 2017, web <https://gototags.com/blog/read-nfc-tags-with-an-iphone-app-on-ios-11/>. (Year:2017). |
International Search Report and Written Opinion for International Patent Application No. PCT/US2016/063350, dated Mar. 27, 2017, 9 pages. |
Extended Search Report dated Aug. 13, 2018, issued in European Patent Application No. 16753053.4, 9 pages. |
International Search Report and Written Opinion for International Patent Application No. PCT/US16/18452, dated Apr. 29, 2015, 9 pages. |
International Preliminary Report on Patentability dated Aug. 31, 2017, issued in PCT Patent Application No. PCT/US2016/018452, 7 pages. |
International Search Report and Written Opinion for International Patent Application No. PCT/US2019/055862, dated Mar. 11, 2020. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2022/013470, dated May 6, 2022, 14 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2022/013473, dated May 6, 2022, 13 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2022/019079, dated Jun. 2, 2022, 14 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2022/018453, dated Jun. 2, 2022, 13 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US22/018700, dated Jun. 7, 2022, 13 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US22/019080, dated Jun. 7, 2022, 14 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US22/019664, dated Jun. 7, 2022, 14 pages. |
International Search Report and Written Opinion for the International Patent Application No. PCT/US21/060618, dated Mar. 21, 2022, 15 pages. |
Herrero Pau et al: “Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator—in silicoevaluation under intra-day variability”, Computer Methods and Programs in Biomedicine, Elsevier, Amsterdam, NL, vol. 146, Jun. 1, 2017 (Jun. 1, 2017), pp. 125-131, XP085115607, ISSN: 0169-2607, DOI:10.1016/J.CMPB.2017.05.010. |
Marie Aude Qemerais: “Preliminary Evaluation of a New Semi-Closed-Loop Insulin Therapy System over the prandial period in Adult Patients with type I diabetes: the WP6. 0 Diabeloop Study”, Journal of Diabetes Science and Technology Diabetes Technology Society Reprints and permissions, Jan. 1, 2014, pp. 1177-1184, Retrieved from the Internet: URL:http://journals.sagepub.com/doi/pdf/10.1177/1932296814545668 [retrieved on Jun. 6, 2022] chapter “Functioning of the Algorithm” chapter “Statistical Analysis” p. 1183, left-hand column, line 16-line 23. |
Anonymous: “Kernel density estimation”, Wikipedia, Nov. 13, 2020 (Nov. 13, 2020), pp. 1-12, XP055895569, Retrieved from the Internet: URL:https://en.wikipedia.org/w/index.php?title=Kernel_density_estimation&oldid=988508333 [retrieved on Jun. 6, 2022]. |
Anonymous: “openaps / oref0 /lib/determine-basal-js”, openaps repository, Nov. 9, 2019 (Nov. 9, 2019), pp. 1-17, XP055900283, Retrieved from the Internet: URL:https://github.com/openaps/oref0/blob/master/lib/determine-basal/determine-basal [retrieved on Jun. 6, 2022] line 116-line 118, line 439-line 446. |
Anonymous: “AndroidAPS screens”, AndroidAPS documentation, Oct. 4, 2020 (Oct. 4, 2020), pp. 1-12, XP055894824, Retrieved from the Internet: URL:https://github.com/openaps/AndroidAPSdocs/blob/25d8acf8b28262b411b34f416f173ac0814d7e14/docs/en/Getting-Started/Screenshots.md [retrieved on Jun. 6, 2022]. |
Kozak Milos et al: “Issue #2473 of AndroidAPS”, MilosKozak / AndroidAPS Public repository, Mar. 4, 2020 (Mar. 4, 2020), pp. 1-4, XP055900328, Retrieved from the Internet: URL:https://github.com/MilosKozak/AndroidAPS/Issues/2473 [retrieved on Jun. 6, 2022]. |
Medication Bar Code System Implementation Planning Section I: A Bar Code Primer for Leaders, Aug. 2013. |
Medication Bar Code System Implementation Planning Section II: Building the Case for Automated Identification of Medications, Aug. 2013. |
Villareal et al. (2009) in: Distr. Comp. Art. Intell. Bioninf. Soft Comp. Amb. Ass. Living; Int. Work Conf. Art. Neural Networks (IWANN) 2009, Leet. Notes Comp. Sci vol. 5518; S. Omatu et al. (Eds.), pp. 870-877. |
Fox, Ian G.; Machine Learning for Physiological Time Series: Representing and Controlling Blood Glucose for Diabetes Management; University of Michigan. ProQuest Dissertations Publishing, 2020. 28240142. (Year: 2020). |
International Search Report and Written Opinion for the International Patent Application No. PCT/US2022/012896, dated Apr. 22, 2022, 15 pages. |
Glucon Critical Care Blood Glucose Monitor; Glucon; retrieved on Dec. 29, 2010 from http://www.glucon.com. |
Number | Date | Country | |
---|---|---|---|
20210308377 A1 | Oct 2021 | US |