DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 10, 17, and 19 are objected to because of the following informalities:
Claim 10 recites “a predetermined fixed length of usage follow the onboarding period.” The word “follow” should be “following.” The claim should read “a predetermined fixed length of usage following the onboarding period.” This is a grammatical error that, while not necessarily rendering the claim indefinite, should be corrected for clarity.
Claim 17 recites “a predetermined fixed length of usage follow the onboarding period.” The word “follow” is grammatically incorrect; the correct form is “following.” The claim should read “a predetermined fixed length of usage following the onboarding period.” This is a grammatical error that, while not necessarily rendering the claim indefinite, should be corrected for clarity.
Claim 19 recites “the target mean glucose value represent an ideal diabetes control scenario.” The word “represent” is grammatically incorrect; the correct form is “representing.” The claim should read “the target mean glucose value representing an ideal diabetes control scenario.” This is a grammatical error that, while not necessarily rendering the claim indefinite, should be corrected for clarity.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 3, 6, 8, 9, 10, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over O’Connor et al. (US 2021/0038813A1; hereinafter “O’Connor”) in view of Hayter et al. (US 2021/0050085A1; hereinafter “Hayter”).
In relation to independent claim 1, this claim recites a method comprising: receiving, at a controller for an automated drug delivery device, one or more indications of drug delivery from the automated drug delivery device; calculating, using a processor of the controller, an amount of progress towards a target system performance of the automated drug delivery device based on the one or more indications of drug delivery; and displaying, on a display of the controller, the amount of progress toward target system performance.
receiving, at a controller for an automated drug delivery device, one or more indications of drug delivery from the automated drug delivery device.
O’Connor discloses receiving, at a controller for an automated drug delivery device, indications of drug delivery from the automated drug delivery device. Specifically, O’Connor discloses: “[a] sensor coupled to a user can collect information regarding the user. A controller can use the collected information to determine an amount of medication to provide the user. The controller can instruct a drug delivery device to dispense the medication to the user.” (O’Connor ¶ [0012].) O’Connor further discloses that the controller can receive sensor data and dosage data from the drug delivery device: “[m]onitoring data (e.g., glucose level data and/or dosage data) can be provided to a monitoring device (e.g., the local device 116 or a remote device 112) for storage or review (e.g., presentation of current or past data related to delivery of the insulin to the user).” (O’Connor ¶ [0036].)
calculating, using a processor of the controller, an amount of progress towards a target system performance of the automated drug delivery device based on the one or more indications of drug delivery.
O’Connor discloses a controller with a processor that determines amounts of medication based on received data. Specifically, O’Connor discloses: “[t]he controller can determine the amount of insulin to provide based on received sensor data (e.g., glucose levels of the user). The controller can then instruct the medical device 102 of the automated medication delivery systems 100, 150, and 200 to automatically deliver the determined amount of insulin to a user.” (O’Connor ¶ [0032].) To the extent O’Connor does not expressly disclose calculating an amount of progress towards a target system performance based on indications of drug delivery, Hayter fills this gap by disclosing calculating and displaying progress during a learning/onboarding period. Specifically, Hayter discloses: “[u]ser feedback can provide an indication to the user that the system is making progress. The DGA can prompt the user for feedback (e.g., input or confirmation) as to any aspect of dose guidance, including a lack of information about an aspect of administered doses, analyte history, patient behavior or activities, dosing strategy generally, the type of a particular dose, confirmation that a DGA determined (e.g., learned by the system) dose type or strategy is correct, and others.” (Hayter ¶ [0160].)
displaying, on a display of the controller, the amount of progress toward target system performance.
O’Connor discloses a display on the controller. Specifically, O’Connor discloses: “[t]he user-output device may be a speaker for playing sound, a vibration generator (e.g., a motorized gear with an offset center of gravity) for creating vibrations, metal terminals for delivering an electric shock to the body of the person, a visual display and/or one or more lights for providing a visual alarm, or any other such output device.” (O’Connor ¶ [0045].) To the extent O’Connor does not expressly disclose displaying an amount of progress toward target system performance, Hayter fills this gap by disclosing outputting an indication on a user interface device. Specifically, Hayter discloses: “[d]uring (or after) the learning period, the DGA can output a prompt or other indication on UID 200 that requests user feedback.” (Hayter ¶ [0161].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine O’Connor with Hayter because both references relate to automated drug delivery systems that adapt to a user’s insulin needs over time. A person of ordinary skill in the art would have been motivated to incorporate Hayter’s teaching of providing progress indications during the learning period into O’Connor’s automated drug delivery system to keep users informed of the system’s adaptation status, thereby improving user engagement and reducing the likelihood of therapy discontinuation due to frustration with a perceived lack of progress.
In relation to claim 2, this claim depends from claim 1 and further recites: wherein the amount of progress is divided into an onboarding period, a dynamic, automatic titration period, and a target system performance period and the on boarding period is a predetermined fixed length of usage.
Base rejection incorporated.
The rejection of claim 1 is incorporated herein.
wherein the amount of progress is divided into an onboarding period, a dynamic, automatic titration period, and a target system performance period and the on boarding period is a predetermined fixed length of usage.
O’Connor does not expressly disclose dividing the amount of progress into an onboarding period, a dynamic titration period, and a target performance period. Hayter fills this gap by disclosing a learning/onboarding period followed by a titration period. Specifically, Hayter discloses: “[t]his process can aid both HCPs and users by streamlining the DGA onboarding and titration, while also helping to ensure that the DGA [is properly initialized].” (Hayter ¶ [0117].) Hayter further discloses that the learning period is a predetermined fixed length: “[t]he learning period can last any time period sufficient to achieve the requisite information. In many embodiments, this period is at least two days, more preferable a week or longer (e.g., 14 days), and can vary depending on how well the DGA can learn the trends.” (Hayter ¶ [0124].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine O’Connor with Hayter because Hayter explicitly describes the onboarding and titration phases of dose guidance, and defining these phases as distinct periods with predetermined lengths provides users with clear expectations about the system’s adaptation timeline. A person of ordinary skill in the art would have been motivated to incorporate Hayter’s specific period structure into O’Connor’s progress display to clearly delineate the system’s learning phases for the user.
In relation to claim 3, this claim depends from claim 2 and further recites: wherein the dynamic, automatic titration period is a predetermined fixed length of usage following the onboarding period.
Base rejection incorporated.
The rejection of claim 2 is incorporated herein.
wherein the dynamic, automatic titration period is a predetermined fixed length of usage following the onboarding period.
Hayter discloses that the learning/titration period following onboarding is a predetermined fixed length. Specifically, Hayter discloses: “[t]he learning period can last any time period sufficient to achieve the requisite information. In many embodiments, this period is at least two days, more preferable a week or longer (e.g., 14 days), and can vary depending on how well the DGA can learn the trends.” (Hayter ¶ [0124].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Hayter’s teachings because defining a fixed length for the titration/learning period ensures that sufficient data is collected for accurate adaptation before the system transitions to target performance. A person of ordinary skill in the art would have been motivated to use a predetermined fixed length to provide users with a reliable timeline for when the system will reach its target performance.
In relation to claim 6, this claim depends from claim 2 and further recites: wherein the dynamic, automatic titration period is calculated, at least in part, based on a comparison of a user’s current mean glucose value as compared to a target mean glucose value for the user, the target mean glucose value representing an ideal diabetes control scenario.
Base rejection incorporated.
The rejection of claim 2 is incorporated herein.
wherein the dynamic, automatic titration period is calculated, at least in part, based on a comparison of a user’s current mean glucose value as compared to a target mean glucose value for the user, the target mean glucose value representing an ideal diabetes control scenario.
Hayter discloses comparing a user’s current glucose value to a target glucose value. Specifically, Hayter discloses: “[i]n some embodiments, the DGA may also determine a target glucose level, where the user is adjusting or correcting the mealtime dose when their level is above or predicted to be above the target glucose level.” (Hayter ¶ [0123].) Hayter further discloses the formula for comparing current glucose to target glucose: “the correction dose guidance can be determined based on the following formula, where (BG(t)) is the current glucose value and BGtarget is the target glucose.” (Hayter ¶ [0288].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Hayter’s target glucose comparison because comparing current glucose to a target is fundamental to determining whether titration is achieving the desired glucose control. A person of ordinary skill in the art would have been motivated to incorporate this comparison to accurately calculate the necessary titration period, as it provides a direct measure of whether the system is achieving its glucose management goals.
In relation to independent claim 8, this claim recites: a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a controller for an automated drug delivery device, cause the controller to: receive, at the controller, one or more indications of drug delivery from the automated drug delivery device; calculate, using a processor of the controller, an amount of progress towards a target system performance of the automated drug delivery device based on the one or more indications of drug delivery; and display, on a display of the controller, the amount of progress.
A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a controller for an automated drug delivery device, cause the controller to: receive, at the controller, one or more indications of drug delivery from the automated drug delivery device.
O’Connor discloses a controller with a memory storing instructions for receiving indications of drug delivery. Specifically, O’Connor discloses: “[t]he handheld electronic computing device 702 can include a controller or processor and a memory. The memory can store instructions that can be executed by the controller or processor. The instructions can implemented an ‘artificial pancreas’ algorithm.” (O’Connor ¶ [0050].) O’Connor further discloses that the controller receives dosage data from the drug delivery device: “[m]onitoring data (e.g., glucose level data and/or dosage data) can be provided to a monitoring device (e.g., the local device 116 or a remote device 112) for storage or review (e.g., presentation of current or past data related to delivery of the insulin to the user).” (O’Connor ¶ [0036].)
calculate, using a processor of the controller, an amount of progress towards a target system performance of the automated drug delivery device based on the one or more indications of drug delivery; and display, on a display of the controller, the amount of progress.
O’Connor does not expressly disclose calculating and displaying an amount of progress towards a target system performance. Hayter fills this gap by disclosing calculating and displaying progress. Specifically, Hayter discloses: “User feedback can provide an indication to the user that the system is making progress. The DGA can prompt the user for feedback (e.g., input or confirmation) as to any aspect of dose guidance.” (Hayter ¶ [0160].) Hayter further discloses: “[d]uring (or after) the learning period, the DGA can output a prompt or other indication on UID 200 that requests user feedback.” (Hayter ¶ [0161].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine O’Connor with Hayter because both references relate to automated drug delivery systems that adapt to a user’s insulin needs over time. A person of ordinary skill in the art would have been motivated to incorporate Hayter’s teaching of providing progress indications during the learning period into O’Connor’s automated drug delivery system to keep users informed of the system’s adaptation status, thereby improving user engagement and reducing the likelihood of therapy discontinuation.
In relation to claim 9, this claim depends from claim 8 and further recites: wherein the amount of progress is divided into an onboarding period, a dynamic, automatic titration period, and a target system performance period and the onboarding period is a predetermined fixed length of usage.
Base rejection incorporated.
The rejection of claim 8 is incorporated herein.
wherein the amount of progress is divided into an onboarding period, a dynamic, automatic titration period, and a target system performance period and the onboarding period is a predetermined fixed length of usage.
Hayter discloses onboarding and titration periods with predetermined lengths. Specifically, Hayter discloses: “[t]his process can aid both HCPs and users by streamlining the DGA onboarding and titration, while also helping to ensure that the DGA [is properly initialized].” (Hayter ¶ [0117].) Hayter further discloses: “[t]he learning period can last any time period sufficient to achieve the requisite information. In many embodiments, this period is at least two days, more preferable a week or longer (e.g., 14 days), and can vary depending on how well the DGA can learn the trends.” (Hayter ¶ [0124].)
Motivation to combine.
Based on the above teachings for an artisan skilled in the art, it would have been obvious to combine O’Connor with Hayter because Hayter explicitly describes the onboarding and titration phases of dose guidance. A person of ordinary skill in the art would have been motivated to incorporate Hayter’s specific period structure into O’Connor’s progress display to clearly delineate the system’s learning phases for the user.
In relation to claim 10, this claim depends from claim 9 and further recites: wherein the dynamic, automatic titration period is a predetermined fixed length of usage follow the onboarding period.
Base rejection incorporated.
The rejection of claim 9 is incorporated herein.
wherein the dynamic, automatic titration period is a predetermined fixed length of usage follow the onboarding period.
Hayter discloses a learning/titration period of predetermined fixed length following the onboarding period. Specifically, Hayter discloses: “[t]he learning period can last any time period sufficient to achieve the requisite information. In many embodiments, this period is at least two days, more preferable a week or longer (e.g., 14 days), and can vary depending on how well the DGA can learn the trends.” (Hayter ¶ [0124].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Hayter’s teachings because defining a fixed length for the titration/learning period ensures that sufficient data is collected for accurate adaptation before the system transitions to target performance. A person of ordinary skill in the art would have been motivated to use a predetermined fixed length to provide users with a reliable timeline for when the system will reach its target performance.
In relation to claim 13, this claim depends from claim 9 and further recites: wherein the dynamic, automatic titration period is calculated, at least in part, based on a comparison of a user’s current mean glucose value as compared to a target mean glucose value for the user, the target mean glucose value representing an ideal diabetes control scenario.
Base rejection incorporated.
The rejection of claim 9 is incorporated herein.
wherein the dynamic, automatic titration period is calculated, at least in part, based on a comparison of a user’s current mean glucose value as compared to a target mean glucose value for the user, the target mean glucose value representing an ideal diabetes control scenario.
Hayter discloses comparing a user’s current glucose value to a target glucose value. Specifically, Hayter discloses: “[i]n some embodiments, the DGA may also determine a target glucose level, where the user is adjusting or correcting the mealtime dose when their level is above or predicted to be above the target glucose level.” (Hayter ¶ [0123].) Hayter further discloses the formula for comparing current glucose to target glucose: “the correction dose guidance can be determined based on the following formula, where (BG(t)) is the current glucose value and BGtarget is the target glucose.” (Hayter ¶ [0288].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Hayter’s target glucose comparison because comparing current glucose to a target is fundamental to determining whether titration is achieving the desired glucose control. A person of ordinary skill in the art would have been motivated to incorporate this comparison to accurately calculate the necessary titration period.
Claims 4, 5, 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over O’Connor et al. (US 2021/0038813A1; hereinafter “O’Connor”) in view of Hayter et al. (US 2021/0050085A1; hereinafter “Hayter”), as discussed above, and in further view of Lee et al. (US 2022/0280721A1).
In relation to claim 4, this claim depends from claim 2 and further recites: wherein the dynamic, automatic titration period is calculated based on a rate of change in a parameter being adapted.
Base rejection incorporated.
The rejection of claim 2 is incorporated herein.
wherein the dynamic, automatic titration period is calculated based on a rate of change in a parameter being adapted.
To the extent the base combination does not expressly disclose calculating the titration period based on a rate of change in a parameter being adapted, Lee fills this gap by disclosing adapting parameters based on the degree of change over time. Specifically, Lee discloses: “The degree of adaptivity of the basal insulin amount per a time period, like per day or per hour, may be based on how much historical data is available. More extensive historical data being available may result in greater adaptivity of the TDI, greater adaptivity of the ratio of basal insulin amount to TDI and ultimately greater adaptivity in the basal amount.” (Lee ¶ [0025].) Lee further discloses updating TDI based on the rate of change between adaptation periods: “TDInew = (1 – X · Ndays) TDIold + X · Ndays · Itotal, where Ndays is a number of days in the period of days, TDIold is the estimated TDI for the user, S is a ratio, and TDInew is the average actual TDI for the user over the extended period of days. X is a parameter defining the weighting the new insulin delivery history will be applied versus the prior TDI setting.” (Lee ¶ [0007].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Lee because adjusting the adaptation period based on the rate of change of parameters optimizes the learning process by allowing the system to exit the titration period sooner when the user’s parameters stabilize quickly, and to extend the period when the parameters continue to change significantly. A person of ordinary skill in the art would have been motivated to incorporate Lee’s adaptive parameter adjustment to make the titration period more responsive to the user’s actual data.
In relation to claim 5, this claim depends from claim 4 and further recites: wherein the parameter being adapted is a user’s total daily insulin use.
Base rejection incorporated.
The rejection of claim 4 is incorporated herein.
wherein the parameter being adapted is a user’s total daily insulin use.
Lee expressly discloses that total daily insulin (TDI) is the parameter being adapted. Specifically, Lee discloses: “establishing an initial current basal amount of insulin to be delivered to the user per a time period of up to a day as a portion of an estimated total daily insulin (TDI) for the user; determining an average actual TDI for the user over a period of days, wherein the average actual TDI for each day in the period of days is a sum of basal insulin and bolus insulin delivered for the day; and updating the current basal amount of insulin to be delivered to the user for the time period from the initial current basal amount to a new basal amount based on the average actual TDI for the user over the period of days.” (Lee ¶ [0004].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Lee because total daily insulin is a critical parameter for automated insulin delivery, and using TDI as the adapted parameter is well-established in the art. A person of ordinary skill in the art would have been motivated to use TDI as the adapted parameter because it provides a comprehensive measure of the user’s overall insulin needs.
In relation to claim 11, this claim depends from claim 9 and further recites: wherein the dynamic, automatic titration period is calculated based on a rate of change in a parameter being adapted.
Base rejection incorporated.
The rejection of claim 9 is incorporated herein.
wherein the dynamic, automatic titration period is calculated based on a rate of change in a parameter being adapted.
To the extent the base combination does not expressly disclose calculating the titration period based on a rate of change in a parameter being adapted, Lee fills this gap by disclosing adapting parameters based on the degree of change over time. Specifically, Lee discloses: “[t]he degree of adaptivity of the basal insulin amount per a time period, like per day or per hour, may be based on how much historical data is available. More extensive historical data being available may result in greater adaptivity of the TDI, greater adaptivity of the ratio of basal insulin amount to TDI and ultimately greater adaptivity in the basal amount.” (Lee ¶ [0025].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Lee because adjusting the adaptation period based on the rate of change of parameters optimizes the learning process. A person of ordinary skill in the art would have been motivated to incorporate Lee’s adaptive parameter adjustment to make the titration period more responsive to the user’s actual data.
In relation to claim 12, this claim depends from claim 11 and further recites: wherein the parameter being adapted is a user’s total daily insulin use.
Base rejection incorporated.
The rejection of claim 11 is incorporated herein.
wherein the parameter being adapted is a user’s total daily insulin use.
Lee expressly discloses that total daily insulin (TDI) is the parameter being adapted. Specifically, Lee discloses: “establishing an initial current basal amount of insulin to be delivered to the user per a time period of up to a day as a portion of an estimated total daily insulin (TDI) for the user; determining an average actual TDI for the user over a period of days, wherein the average actual TDI for each day in the period of days is a sum of basal insulin and bolus insulin delivered for the day; and updating the current basal amount of insulin to be delivered to the user for the time period from the initial current basal amount to a new basal amount based on the average actual TDI for the user over the period of days.” (Lee ¶ [0004].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Lee because total daily insulin is a critical parameter for automated insulin delivery. A person of ordinary skill in the art would have been motivated to use TDI as the adapted parameter because it provides a comprehensive measure of the user’s overall insulin needs.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over O’Connor et al. (US 2021/0038813A1; hereinafter “O’Connor”) in view of Hayter et al. (US 2021/0050085A1; hereinafter “Hayter”), as discussed above, and in further view of American Diabetes Association (ADA), “CGM & Time in Range”; hereinafter “ADA”).
In relation to claim 7, this claim depends from claim 2 and further recites: wherein the dynamic, automatic titration period is calculated, at least in part, based on a target time-in-range for the user’s glucose level over a predetermined period of time.
Base rejection incorporated.
The rejection of claim 2 is incorporated herein.
wherein the dynamic, automatic titration period is calculated, at least in part, based on a target time-in-range for the user’s glucose level over a predetermined period of time.
Hayter discloses displaying time-in-range data. Specifically, Hayter discloses: “[t]he TIR display 252 can also report the amount of time the patient’s glucose levels were below a low threshold (e.g., below 54 mg/dL), which is lower than a low boundary of the target range or above a high threshold (e.g., above 250 mg/dL), which is higher than a high boundary of the target range.” (Hayter ¶ [0119].) To the extent the base combination does not expressly disclose using a target time-in-range to calculate the titration period, ADA fills this gap by disclosing and demonstrating that the claimed time-in-range targets are well-known in the art. Specifically, ADA discloses: “[t]ime in range is the amount of time you spend in the target blood glucose (blood sugar) range—between 70 and 180 mg/dL for most people. … Most people [with type 1 and type 2 diabetes] should aim for a time in range of at least 70 percent of readings—meaning 70 percent of readings, you should aim for roughly 17 out of 24 hours each day to be in range (not high or low).” (ADA website, CGM & Time in Range.)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with ADA because time-in-range is a standard clinical metric for evaluating diabetes management success, as recognized by the American Diabetes Association. A person of ordinary skill in the art would have been motivated to use target time-in-range to determine the titration period, as it provides a comprehensive measure of glucose control success over a predetermined period of time, and is a metric that is readily available from the CGM data already being collected by the automated drug delivery system.
In relation to claim 14, this claim depends from claim 9 and further recites: wherein the dynamic, automatic titration period is calculated, at least in part, based on a target time-in-range for the user’s glucose level over a predetermined period of time.
Base rejection incorporated.
The rejection of claim 9 is incorporated herein.
wherein the dynamic, automatic titration period is calculated, at least in part, based on a target time-in-range for the user’s glucose level over a predetermined period of time.
Hayter discloses displaying time-in-range data. Specifically, Hayter discloses: “[t]he TIR display 252 can also report the amount of time the patient’s glucose levels were below a low threshold (e.g., below 54 mg/dL), which is lower than a low boundary of the target range or above a high threshold (e.g., above 250 mg/dL), which is higher than a high boundary of the target range.” (Hayter ¶ [0119].) To the extent the base combination does not expressly disclose using a target time-in-range to calculate the titration period, ADA fills this gap by disclosing time-in-range targets. Specifically, ADA discloses: “[t]ime in range is the amount of time you spend in the target blood glucose (blood sugar) range—between 70 and 180 mg/dL for most people. … Most people [with type 1 and type 2 diabetes] should aim for a time in range of at least 70 percent of readings—meaning 70 percent of readings, you should aim for roughly 17 out of 24 hours each day to be in range (not high or low).” (ADA website, CGM & Time in Range.)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with ADA because time-in-range is a standard clinical metric for evaluating diabetes management success. A person of ordinary skill in the art would have been motivated to use target time-in-range to determine the titration period, as it provides a comprehensive measure of glucose control success over a predetermined period of time.
Claims 15, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rosinko (US 9,486,571) in view of Hayter et al. (US 2021/0050085A1; hereinafter “Hayter”).
In relation to independent claim 15, this claim recites: a system comprising: an automated drug delivery device comprising: a reservoir storing a liquid drug, a delivery system for delivering the liquid drug to a user, a processor for calculating dosage amounts for the liquid drug, and a transmitter/receiver configured to transmit the dosage amounts; and a controller comprising: a transmitter/receiver, a display, a processor, and a memory storing instructions that, when executed by the processor, configure the controller to: receive, at the transmitter/receiver of the controller, the dosage amounts from the automated drug delivery device; calculate an amount of progress towards a target system performance of the automated drug delivery device based on the dosage amounts; and display, on the display of the controller, the amount of progress.
A system comprising: an automated drug delivery device comprising: a reservoir storing a liquid drug, a delivery system for delivering the liquid drug to a user, a processor for calculating dosage amounts for the liquid drug, and a transmitter/receiver configured to transmit the dosage amounts.
Rosinko discloses an automated drug delivery device with a reservoir, delivery system, processor, and transmitter/receiver. Specifically, Rosinko discloses: “the medical device is configured as a pump 12 such as an infusion pump that can include a pumping or delivery mechanism and reservoir for delivering medicament to a patient and an output/display 44.” (Rosinko col. 3, ll. 17–21.) Rosinko further discloses: “[t]he infusion pump 12 may also include a memory device 30, a transmitter/receiver 32, an alarm 34, a speaker 36, a clock/timer 38, an input device 40, a user interface suitable for accepting input and commands from a user such as a caregiver or patient, a drive mechanism 48, an estimator device 52 and a microphone (not pictured).” (Rosinko col. 3, ll. 57–62.) Rosinko further discloses: “In one embodiment, medical device can be a portable insulin pump configured to deliver insulin to a patient.” (Rosinko col. 3, ll. 37-38.)
and a controller comprising: a transmitter/receiver, a display, a processor, and a memory storing instructions that, when executed by the processor, configure the controller to: receive, at the transmitter/receiver of the controller, the dosage amounts from the automated drug delivery device.
Rosinko discloses a controller (safety processor) with a transmitter/receiver, display, processor, and memory. Specifically, Rosinko discloses: “[s]afety processor 102 can include a processor 142 that controls the overall functions of the safety processor 102. The safety processor 102 can also include a memory device 130, a transmitter/receiver 132, an alarm system 134, a speaker 136, a clock/timer 138 an input device 140, and a user interface 160 having a display 144 and that, in some embodiments, can include a touch sensitive screen 146 with input capability.” (Rosinko col. 4, ll. 51–58.) Rosinko further discloses: “[o]perating and/or programming commands sent from the remote control device 104 and intended for execution by the medical device are first received by the safety processor 102 where they are reviewed before potentially being sent on to the medical device 12.” (Rosinko col. 4, ll. 25–30.)
[calculate an amount of progress towards a target system performance of the automated drug delivery device based on the dosage amounts; and display, on the display of the controller, the amount of progress]
Rosinko does not expressly disclose calculating and displaying an amount of progress towards a target system performance based on dosage amounts. Hayter fills this gap by disclosing calculating and displaying progress. Specifically, Hayter discloses: “[u]ser feedback can provide an indication to the user that the system is making progress. The DGA can prompt the user for feedback (e.g., input or confirmation) as to any aspect of dose guidance, including a lack of information about an aspect of administered doses, analyte history, patient behavior or activities, dosing strategy generally, the type of a particular dose, confirmation that a DGA determined (e.g., learned by the system) dose type or strategy is correct, and others.” (Hayter ¶ [0160].) Hayter further discloses: “[d]uring (or after) the learning period, the DGA can output a prompt or other indication on UID 200 that requests user feedback.” (Hayter ¶ [0161].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine Rosinko with Hayter because both references relate to automated drug delivery systems with controllers that communicate with medical devices. A person of ordinary skill in the art would have been motivated to incorporate Hayter’s teaching of providing progress indications during the learning period into Rosinko’s system to keep users informed of the system’s adaptation status via the controller’s display, thereby improving user engagement and reducing the likelihood of therapy discontinuation due to frustration with a perceived lack of progress.
In relation to claim 16, this depends from claim 15 and further recites: wherein the amount of progress is divided into an on boarding period, a dynamic, automatic titration period, and a target system performance period and the onboarding period is a predetermined fixed length of usage.
Base rejection incorporated.
The rejection of claim 15 is incorporated herein.
wherein the amount of progress is divided into an on boarding period, a dynamic, automatic titration period, and a target system performance period and the onboarding period is a predetermined fixed length of usage.
Hayter discloses onboarding and titration periods with predetermined lengths. Specifically, Hayter discloses: “[t]his process can aid both HCPs and users by streamlining the DGA onboarding and titration, while also helping to ensure that the DGA [is properly initialized].” (Hayter ¶ [0117].) Hayter further discloses: “[t]he learning period can last any time period sufficient to achieve the requisite information. In many embodiments, this period is at least two days, more preferable a week or longer (e.g., 14 days), and can vary depending on how well the DGA can learn the trends.” (Hayter ¶ [0124].)
Motivation to combine.
Based on the above teachings for an artisan skilled in the art, it would have been obvious to combine Rosinko with Hayter because Hayter explicitly describes the onboarding and titration phases of dose guidance. A person of ordinary skill in the art would have been motivated to incorporate Hayter’s specific period structure into Rosinko’s progress display to clearly delineate the system’s learning phases for the user.
In relation to claim 19, this claim depends from claim 16 and further recites: wherein the dynamic, automatic titration period is calculated, at least in part, based on a comparison of a user’s current mean glucose value as compared to a target mean glucose value for the user, the target mean glucose value represent an ideal diabetes control scenario.
Base rejection incorporated.
The rejection of claim 16 is incorporated herein.
wherein the dynamic, automatic titration period is calculated, at least in part, based on a comparison of a user’s current mean glucose value as compared to a target mean glucose value for the user, the target mean glucose value represent an ideal diabetes control scenario.
Hayter discloses comparing a user’s current glucose value to a target glucose value. Specifically, Hayter discloses: “[i]n some embodiments, the DGA may also determine a target glucose level, where the user is adjusting or correcting the mealtime dose when their level is above or predicted to be above the target glucose level.” (Hayter ¶ [0123].) Hayter further discloses the formula for comparing current glucose to target glucose: “the correction dose guidance can be determined based on the following formula, where (BG(t)) is the current glucose value and BGtarget is the target glucose.” (Hayter ¶ [0288].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Hayter’s target glucose comparison because comparing current glucose to a target is fundamental to determining whether titration is achieving the desired glucose control. A person of ordinary skill in the art would have been motivated to incorporate this comparison to accurately calculate the necessary titration period.
Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rosinko (US 9,486,571) in view of Hayter et al. (US 2021/0050085A1; hereinafter “Hayter”), as discussed above, and in further view of Lee et al. (US 2022/0280721A1; hereinafter “Lee”).
In relation to claim 17, this claim depends from claim 16 and further recites: wherein the dynamic, automatic titration period is: a predetermined fixed length of usage follow the onboarding period, or calculated based on a rate of change in a parameter being adapted.
Base rejection incorporated.
The rejection of claim 16 is incorporated herein.
wherein the dynamic, automatic titration period is: a predetermined fixed length of usage follow the onboarding period, or calculated based on a rate of change in a parameter being adapted.
Hayter discloses a learning/titration period of predetermined fixed length following the onboarding period. Specifically, Hayter discloses: “[t]he learning period can last any time period sufficient to achieve the requisite information. In many embodiments, this period is at least two days, more preferable a week or longer (e.g., 14 days), and can vary depending on how well the DGA can learn the trends.” (Hayter ¶ [0124].) To the extent the base combination does not expressly disclose calculating the titration period based on a rate of change in a parameter being adapted, Lee fills this gap by disclosing adapting parameters based on the degree of change over time. Specifically, Lee discloses: “[t]he degree of adaptivity of the basal insulin amount per a time period, like per day or per hour, may be based on how much historical data is available. More extensive historical data being available may result in greater adaptivity of the TDI, greater adaptivity of the ratio of basal insulin amount to TDI and ultimately greater adaptivity in the basal amount.” (Lee ¶ [0025].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Lee because adjusting the adaptation period based on the rate of change of parameters optimizes the learning process. A person of ordinary skill in the art would have been motivated to incorporate Lee’s adaptive parameter adjustment to make the titration period more responsive to the user’s actual data, allowing the system to exit the titration period sooner when the user’s parameters stabilize quickly.
In relation to claim 18, this claim depends from claim 17 and further recites: wherein the parameter being adapted is a user’s total daily insulin use.
Base rejection incorporated.
The rejection of claim 17 is incorporated herein.
wherein the parameter being adapted is a user’s total daily insulin use.
Lee expressly discloses that total daily insulin (TDI) is the parameter being adapted. Specifically, Lee discloses: “establishing an initial current basal amount of insulin to be delivered to the user per a time period of up to a day as a portion of an estimated total daily insulin (TDI) for the user; determining an average actual TDI for the user over a period of days, wherein the average actual TDI for each day in the period of days is a sum of basal insulin and bolus insulin delivered for the day; and updating the current basal amount of insulin to be delivered to the user for the time period from the initial current basal amount to a new basal amount based on the average actual TDI for the user over the period of days.” (Lee ¶ [0004].)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with Lee because total daily insulin is a critical parameter for automated insulin delivery. A person of ordinary skill in the art would have been motivated to use TDI as the adapted parameter because it provides a comprehensive measure of the user’s overall insulin needs.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Rosinko (US 9,486,571) in view of Hayter et al. (US 2021/0050085A1; hereinafter “Hayter”), as discussed above, and in further view of American Diabetes Association (ADA), “CGM & Time in Range”; hereinafter “ADA”).
In relation to claim 20, this claim depends from claim 16 and further recites: wherein the dynamic, automatic titration period is calculated, at least in part, based on a target time-in-range for the user’s glucose level over a predetermined period of time.
Base rejection incorporated.
The rejection of claim 16 is incorporated herein.
wherein the dynamic, automatic titration period is calculated, at least in part, based on a target time-in-range for the user’s glucose level over a predetermined period of time.
Hayter discloses displaying time-in-range data. Specifically, Hayter discloses: “[t]he TIR display 252 can also report the amount of time the patient’s glucose levels were below a low threshold (e.g., below 54 mg/dL), which is lower than a low boundary of the target range or above a high threshold (e.g., above 250 mg/dL), which is higher than a high boundary of the target range.” (Hayter ¶ [0119].) To the extent the base combination does not expressly disclose using a target time-in-range to calculate the titration period, ADA fills this gap by disclosing time-in-range targets. Specifically, ADA discloses: “[t]ime in range is the amount of time you spend in the target blood glucose (blood sugar) range—between 70 and 180 mg/dL for most people. … Most people with type 1 and type 2 diabetes should aim for a time in range of at least 70 percent of readings—meaning 70 percent of readings, you should aim for roughly 17 out of 24 hours each day to be in range (not high or low).” (ADA, CGM & Time in Range.)
Motivation to combine.
Based on the above teachings, for an artisan skilled in the art, it would have been obvious to combine the base combination with ADA because time-in-range is a standard clinical metric for evaluating diabetes management success. A person of ordinary skill in the art would have been motivated to use target time-in-range to determine the titration period, as it provides a comprehensive measure of glucose control success over a predetermined period of time.
Conclusion
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Respectfully submitted,
/MANUEL A MENDEZ/ Primary Examiner, Art Unit 3783
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