DETAILED ACTION
Notice to Applicant
This communication is in response to the amendment submitted July 6, 2026. Claim 8 and 17 are amended. Claims 1 – 7 were previously cancelled. Claims 8 – 26 are pending.
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
The objection to claim 8 is withdrawn based upon the amendment submitted July 6, 2026.
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 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.
Claim(s) 8 – 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shaya (U.S. Publication Number 2010/0262434 A1) in view of Dobbles et al., herein after Dobbles (U.S. Publication Number 2014/0039383 A1) further in view of Breton et al., herein after Breton (U.S. Publication Number 2011/0264378 A1).
Claim 8 (Currently Amended): Shaya teaches a system comprising:
a glucose monitor configured to provide user glucose readings (paragraph 66 discloses a continuous blood glucose monitor, and means for entering blood glucose readings and the time the readings were taken into a database; claim 3);
an insulin delivery mechanism (paragraph 104 discloses insulin may be delivered by injection (syringe) or infused from an insulin pump; paragraph 244 discloses a closed loop monitoring insulin pump for providing dynamic delivery of insulin);
a memory, the memory configured to store a record of insulin doses previously administered to a user as a function of time (paragraph 61 discloses a memory for storing a database comprising blood glucose readings; paragraph 222 discloses the patient’s historical insulin dosing information database is handled in a set of buffers each with instructions on how many values to retain in memory), a record of one or more external factors (paragraph 89 discloses proportionality coefficients relating the effect of carbohydrates, insulin, or exercise (external factors) as they relate to blood glucose, which are recorded in a patient’s daily log), one or more mathematical models of responses of the user’s glucose concentration to one or more external factors (paragraph 140 discloses a processor and computer program set to perform data quality control and mathematical operations on the data to calculate sensitivity factors based on fitting a linear model of the correlated transformed parameters; the abstract discloses statistically characterized sensitivity factors to advise the patient on the optimal bolus insulin dosages), and a confidence interval of a response of a user's glucose concentration to the one or more external factors (paragraph 122 discloses calculation of a confidence interval; paragraph 123 discloses recording blood glucose levels (BG), carbs, insulin (I), and a second BG level to generate graphs of the transformed variables, as well as the uncertainty for any confidence level; paragraph 134 discloses sensitivity factors can be obtained from a database stored within a blood glucose meter or an insulin pump, which automatically calculates variables, and a chart of included data points can be displayed and the slope, intercept, and confidence intervals can be automatically calculated).
Shaya fails to explicitly teach the following limitations met by Dobbles as cited:
a processor (Figure 9; paragraph 85 discloses a processor designed to perform arithmetic or logic operations using logic circuitry that responds to and processes the basic instructions that drive a computer) configured to:
project a time series of future glucose concentrations over a predetermined amount of time (paragraph 165 discloses the receiver can be programmed with standard medicaments and dosages, and can be used to increase the intelligence of the algorithms used in determining the glucose trends and patterns useful in predicting and analyzing present, past, and future glucose trends, and providing therapy recommendations; paragraph 206 discloses the processor module is programmed to project glucose trends based on data from the integrated system; paragraph 211 discloses a graphical representation of the measured analyte over a period of time (predetermined time period) at least partially by applying the one or more mathematical models of the responses of the user’s glucose concentrations to one or more external factors (paragraph 166 discloses a predictive algorithm that predicts a glucose value for the upcoming 15 – 20 minutes using a mathematical algorithm such as regression or smoothing which consider the amount, type, and time of medication delivery (external factors)), the record of one or more external factors, and the record of insulin doses previously administered to the user as a function of time (paragraph 171 discloses tracking glucose levels long-term using manual integration of delivery devices with a continuous glucose sensor); and
determine a dose of insulin that minimizes a numerical function that assigns a quantified cost to deviations away from a predetermined glucose range (paragraph 97 discloses the term “regression” refers to finding a line in which a set of data has a minimal measurement (for example, deviation) from that line; paragraph 123 discloses the term “target range” and refers to a range of glucose concentrations within which a host is to try and maintain his blood sugar (ideal glucose level), where the target range is considered euglycemic (a normal level of blood sugar); paragraph 239 discloses insulin delivery is calculated to maintain the host substantially at and/or within a target range (predetermined glucose range), in which the host is to maintain his blood sugar, and where the controller module is configured to adaptively/intelligently program or re-program the target range after the evaluation of the internally derived data and the host’s metabolic response to insulin therapy, indicating the target range (predetermined glucose range) is modified based on the user’s data, regardless of hypo- or hyper-glycemia; paragraph 281 discloses the controller module considers system error (e.g. sensor error, insulin activity/delivery errors) when calculating an insulin therapy (e.g. an insulin delivery rate, an insulin dose) such that, for example, a sensor error is initially +/- 20%, the controller module is configured to adjust the target glucose range by a similar amount up or down) exhibited in the projected time series of future glucose concentrations (Figure 13; paragraph 12 discloses the patient's sensitivity factors can be a function of their condition where exercise, stress, illness, etc. can be sources of variation that change how the patient is utilizing insulin; paragraph 134 discloses sensitivity factors can be obtained from a data set using a spreadsheet program, checking that data conform to rules can be included in the spreadsheet calculations. These rules include proper time intervals between insulin and blood glucose readings, and that data not conforming to the rules or that include a patient declared flag for uncertainty can be automatically eliminated. A chart of the included data points can be displayed and the slope and intercept and their confidence intervals can be automatically calculated, indicating above or below a threshold displayed on a chart),
a display to display the determined dose of insulin (paragraph 211 discloses a user interface which displays a representation of a target glucose value or target glucose range; paragraph 212 discloses the glucose concentration value measured from the single glucose monitor can be individually displayed; paragraph 226 discloses the receiver may display the recommended therapy).
Shaya discloses determining a diabetic patient’s carbohydrate to insulin ration, carbohydrate to blood glucose ration, and insulin sensitivity factor using the patient’s record of blood glucose readings, carbohydrate consumption, and insulin doses; this accounts for the patient’s observed blood glucose changes by linear regression of appropriately transformed variables. Dobbles discloses an integrated insulin delivery system with continuous glucose sensor for monitoring a glucose concentration in a patient (host) and for delivering insulin to a patient.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Shaya to further include an integrated insulin delivery system with a continuous glucose sensor for delivering medication to a patient as disclosed by Dobbles.
One of ordinary skill in the art, before the effective filing date of the claimed invention would have been motivated to expand the method of Shaya in this way by providing an integrated diabetes management system to provide an improved convenience and accuracy which allows the diabetic patient the improved convenience, safety, and functionality of care in their disease (Dobbles: paragraph 126).
Shaya and Dobbles fail to explicitly teach the following limitations met by Breton as cited:
wherein the numerical function is used in determining the dose of insulin and is asymmetrical and assigns a higher quantified cost to deviations of the glucose concentration below the predetermined glucose range to deviations of the glucose concentration above the predetermined glucose range (paragraph 8 discloses analyzing the amplitude of BG excursions to correct the numerical problem created by the asymmetry of the BG scale introduced as a mathematical transformation that symmetrizes the BG scale; paragraph 184 discloses computing standard deviation (SD) as a measure of glucose variability of CGM data is not recommended when analyzing BG data because the BG measurement scale is highly asymmetric, the hypoglycemic range is numerically narrower than the hyperglycemic range, and the distribution of the glucose values of an individual is typically quite skewed. Therefore SD would be predominantly influenced by hyperglycemic excursions and would not be sensitive to hypoglycemia. It is also possible for confidence intervals based on SD to assume unrealistic negative values; paragraph 199 discloses the memory stores blood glucose values of the patient, the insulin dose values, the insulin types, and the parameters used by the microprocessor to calculate future blood glucose values, supplemental insulin doses, and carbohydrate supplements).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Shaya and Dobbles to further include a novel method, system, and computer program for the visual and quantitative tracking of blood glucose variability in diabetes from self-monitoring blood glucose (SMBG) data and/or continuous glucose monitoring (CGM) data. as disclosed by Breton.
One of ordinary skill in the art, before the effective filing date of the claimed invention would have been motivated to expand the method of Shaya and Dobbles in this way by acquiring a plurality of blood glucose data, tracking blood glucose variability based on said blood glucose data. The tracking may provide a signal(s) of optimal glucose control and signals indicating risk for hyperglycemia and hypoglycemia. (Breton: paragraph 21).
Claim 9 (Previously Presented): Shaya, Dobbles, and Breton teach the system of claim 8. Shaya teaches wherein a respective mathematical model (paragraph 140 discloses a processor and computer program set to perform data quality control and mathematical operations on the data to calculate sensitivity factors based on fitting a linear model of the correlated transformed parameters) is a linear regression algorithm (paragraph 51 discloses a functional relationship is a linear relationship and the functional fit is a linear fit; paragraph 121 discloses linear regression equations can be programmed to operate using the data set with a given set of data) where a dependent variable is change in glucose over possibly overlapping time intervals and independent variables are doses of insulin, dietary carbohydrates, and other factors that may affect glucose summed over windows of time prior to the time window during which the glucose change is being regressed (paragraph 50 discloses generating at least one transformed data set comprising a pair of transformed variables, where the first transformed variable being the difference between the first glucose reading and the second reading).
Claim 10 (Previously Presented): Shaya, Dobbles, and Breton teach the system of claim 9. Shaya teaches wherein the other factors that may affect glucose include a duration of exercise engaged in by a user (paragraph 12 discloses exercise can be a source of variation that changes how a patient utilizes insulin; paragraph 119 discloses if exercise is a factor used in calculating sensitivity values, the amount of exercise needs to input, and can be provided by devices that measure caloric expenditure based on heat dissipation – the database can be segmented by exercise metric and time after the exercise event).
Claim 11 (Previously Presented): Shaya, Dobbles, and Breton teach the system of claim 8. Shaya teaches wherein the mathematical model (paragraph 140 discloses a processor and computer program set to perform data quality control and mathematical operations on the data to calculate sensitivity factors based on fitting a linear model of the correlated transformed parameters) is a linear regression algorithm (paragraph 51 discloses a functional relationship is a linear relationship and the functional fit is a linear fit; paragraph 121 discloses linear regression equations can be programmed to operate using the data set with a given set of data) where a dependent variable is change in glucose over possibly overlapping time intervals and independent variables are doses of insulin, dietary carbohydrates , and other factors that may affect glucose (paragraph 50 discloses generating at least one transformed data set comprising a pair of transformed variables, where the first transformed variable being the difference between the first glucose reading and the second reading) multiplied by a polynomial dependent on the time delay between when the dose was administered and when the glucose response is being measured (paragraph 40 discloses a way to fit a polynomial curve to a series of corrected carbohydrate rations as a function of time of day).
Claim 12 (Previously Presented): Shaya, Dobbles, and Breton teach the system of claim 11. Shaya teaches a system wherein the other factors that may affect glucose include duration of exercise engaged in by the user (paragraph 12 discloses exercise can be a source of variation that changes how a patient utilizes insulin; paragraph 119 discloses if exercise is a factor used in calculating sensitivity values, the amount of exercise needs to input, and can be provided by devices that measure caloric expenditure based on heat dissipation – the database can be segmented by exercise metric and time after the exercise event).
Claim 13 (Previously Presented): Shaya, Dobbles, and Breton teach the system of claim 8. Shaya teaches a system wherein the insulin delivery mechanism comprising a delivery mechanism for delivering short-acting insulin (paragraph 35 discloses the concept of carb counting is designed to teach patients who are using multiple daily injections, or insulin pumps, how to match short acting insulin to carb intakes using carb to insulin ratios; paragraph 104 discloses insulin may be delivered by injection (syringe) or infused from an insulin pump; paragraph 244 discloses a closed loop monitoring insulin pump for providing dynamic delivery of insulin).
Claim 14 (Previously Presented): Shaya, Dobbles, and Breton teach the system of claim 13. Shaya teaches a system further comprising a second insulin delivery mechanism comprising a delivery mechanism for delivering long-acting insulin (paragraph 104 discloses insulin may be delivered by injection (syringe) or infused from an insulin pump; paragraph 244 discloses a closed loop monitoring insulin pump for providing dynamic delivery of insulin).
Claim 15 (Previously Presented): Shaya, Dobbles, and Breton teach the system of claim 14. Shaya discloses wherein the system is configured to suggest an ideal dose of the long-acting insulin and an ideal time to dose the long-acting insulin (Abstract discloses advising the diabetic patient on optimal bolus insulin dosages; paragraph 49 discloses updating an insulin pump with new sensitivity factors for calculation of a recommended dose).
Claim 16 (Previously Presented): Shaya, Dobbles, and Breton teach the system of claim 13. Shaya teaches a system wherein the system suggests an ideal correction dose of the short-acting insulin for different levels of hyperglycemia (paragraph 35 discloses the concept of carb counting is designed to teach patients who are using multiple daily injections, or insulin pumps, how to match short acting insulin to carb intakes using carb to insulin ratios; paragraph 104 discloses insulin may be delivered by injection (syringe) or infused from an insulin pump; paragraph 244 discloses a closed loop monitoring insulin pump for providing dynamic delivery of insulin).
Claim(s) 17 – 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shaya (U.S. Publication Number 2010/0262434 A1) in view of Dobbles et al., herein after Dobbles (U.S. Publication Number 2014/0039383 A1) further in view of Breton et al., herein after Breton (U.S. Publication Number 2011/0264378 A1) further in view of Abu-Rmileh et al., herein after Abu-Rmileh (Abu-Rmileh, A., & Garcia-Gabin, W. (2010). A gain-scheduling model predictive controller for blood glucose control in Type-1 diabetes. IEEE Transactions on Biomedical Engineering, 57(10), 2478-2484).
Claim 17 (Currently Amended): Shaya teaches a system comprising:
a glucose monitor configured to provide a user glucose readings (paragraph 66 discloses a continuous blood glucose monitor, and means for entering blood glucose readings and the time the readings were taken into a database; claim 3);
an insulin delivery mechanism (paragraph 104 discloses insulin may be delivered by injection (syringe) or infused from an insulin pump; paragraph 244 discloses a closed loop monitoring insulin pump for providing dynamic delivery of insulin);
a memory configured to receive and store a record of: insulin doses previously administered to the user as a function of time (paragraph 122 discloses calculation of a confidence interval; paragraph 123 discloses recording blood glucose levels (BG), carbs, insulin (I), and a second BG level to generate graphs of the transformed variables , as well as the uncertainty for any confidence level; paragraph 134 discloses sensitivity factors can be obtained from a database stored within a blood glucose meter or an insulin pump, which automatically calculates variables, and a chart of included data points can be displayed and the slope, intercept, and confidence intervals can be automatically calculated) and a user’s glucose concentrations as a function of time (paragraph 61 discloses a memory for storing a database comprising blood glucose readings; paragraph 222 discloses the patient’s historical insulin dosing information database is handled in a set of buffers each with instructions on how many values to retain in memory); and
a processor (paragraph 68 discloses a processor for executing a programmed set of instructions) configured to:
(i) derive rates of change in a user's glucose concentration as a function of time at least partially based on the stored record of the user’s glucose concentrations as a function of time (Figure 3; Abstract discloses sensitivity factors that best account for the patient’s observed blood glucose changes by linear regression; paragraph 59 discloses calculating and communicating the range of blood glucose outcomes that can be expected based on the historic variance of blood glucose outcomes (stored record of user’s glucose outcomes); paragraph 106 discloses recording carb intake at each meal, insulin delivered, and blood glucose readings before meals, and 3-4 hours after meals indicating a function of time; paragraph 214 discloses the insulin pump stores a history of the actual insulin dose delivered, along with a time stamp; paragraph 222 discloses the patient’s historical insulin dosing information database; paragraph 223 discloses the history of values stored in the patient database);
Shaya fails to explicitly teach the following limitations met by Dobbles as cited:
(ii) derive a time-dependent response curve of the user’s glucose concentration as a function of time that is a fit to a glucose concentrations of the stored record of the user’s glucose concentrations and insulin doses of the stored record of insulin doses previously administered to the user as a function of time (paragraph 165 discloses the receiver can be programmed with standard medicaments and dosages, and can be used to increase the intelligence of the algorithms used in determining the glucose trends and patterns useful in predicting and analyzing present, past, and future glucose trends, and providing therapy recommendations; paragraph 171 discloses tracking glucose levels long-term using manual integration of delivery devices with a continuous glucose sensor), the fit being at least partially based on a mathematical model (paragraph 107 discloses the change in blood glucose as a linear relationship; paragraph 166 discloses a predictive algorithm that predicts a glucose value for the upcoming 15 – 20 minutes using a mathematical algorithm such as regression or smoothing which consider the amount, type, and time of medication delivery; paragraph 214 discloses the insulin pump stores a history of the actual insulin dose delivered, along with a time stamp; paragraph 222 discloses the patient’s historical insulin dosing information database; paragraph 223 discloses the history of values stored in the patient database).
(iii) integrate the derived response of the user’s glucose concentration as a function of time to obtain a total glucose response of the user’s glucose concentration to insulin doses (paragraph 33 discloses internally derived data comprising one of a glucose concentration, a glucose concentration range, a change in glucose concentration a rate of change of glucose concentration, an acceleration of the glucose concentration rate of change, a host insulin sensitivity, a change in host sensitivity, a host metabolic response to insulin therapy, the amount of insulin delivered, a time of insulin delivery, an insulin on board, and a time; paragraph 204 discloses evaluation of glucose response to medication information), and
(iv) project a time series of future glucose concentrations over a predetermined amount of time based at least in part on the obtained total response (paragraph 165 discloses the receiver can be programmed with standard medicaments and dosages, and can be used to increase the intelligence of the algorithms used in determining the glucose trends and patterns useful in predicting and analyzing present, past, and future glucose trends, and providing therapy recommendations; paragraph 206 discloses the processor module is programmed to project glucose trends based on data from the integrated system; paragraph 211 discloses a graphical representation of the measured analyte over a period of time (predetermined time period)); and
a display to display the optimal dose of insulin (paragraph 211 discloses a user interface which displays a representation of a target glucose value or target glucose range; paragraph 212 discloses the glucose concentration value measured from the single glucose monitor can be individually displayed; paragraph 226 discloses the receiver may display the recommended therapy).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Shaya to further include an integrated insulin delivery system with a continuous glucose sensor for delivering medication to a patient as disclosed by Dobbles.
One of ordinary skill in the art, before the effective filing date of the claimed invention would have been motivated to expand the method of Shaya in this way by providing an integrated diabetes management system to provide an improved convenience and accuracy which allows the diabetic patient the improved convenience, safety, and functionality of care in their disease (Dobbles: paragraph 126).
Shaya and Dobbles fail to explicitly teach the following limitations met by Breton as cited:
wherein the numerical function is asymmetrical and assigns a high quantified cost to deviations of the glucose concentration below the predetermined glucose range than to deviations of the glucose concentrations above the predetermined glucose range (paragraph 8 discloses analyzing the amplitude of BG excursions to correct the numerical problem created by the asymmetry of the BG scale introduced as a mathematical transformation that symmetrizes the BG scale; paragraph 184 discloses computing standard deviation (SD) as a measure of glucose variability of CGM data is not recommended when analyzing BG data because the BG measurement scale is highly asymmetric, the hypoglycemic range is numerically narrower than the hyperglycemic range, and the distribution of the glucose values of an individual is typically quite skewed (18). Therefore SD would be predominantly influenced by hyperglycemic excursions and would not be sensitive to hypoglycemia. It is also possible for confidence intervals based on SD to assume unrealistic negative values).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Shaya and Dobbles to further include a novel method, system, and computer program for the visual and quantitative tracking of blood glucose variability in diabetes from self-monitoring blood glucose (SMBG) data and/or continuous glucose monitoring (CGM) data. as disclosed by Breton.
One of ordinary skill in the art, before the effective filing date of the claimed invention would have been motivated to expand the method of Shaya and Dobbles in this way by acquiring a plurality of blood glucose data, tracking blood glucose variability based on said blood glucose data. The tracking may provide a signal(s) of optimal glucose control and signals indicating risk for hyperglycemia and hypoglycemia. (Breton: paragraph 21).
Shaya, Dobbles, and Breton fail to explicitly teach the following limitations met by Abu-Rmileh as cited:
(v) determine a dose of insulin that minimizes a numerical function that assigns quantified costs to deviations away from a predetermined glucose range exhibited in the projected time series of future glucose concentrations obtained from the projection of step (iv) in an asymmetrical manner (page 2478, Abstract discloses the controller is provided with a feedforward loop to improve meal compensation, a gain-scheduling scheme to account for different blood glucoses levels, and an asymmetric cost function to reduce hypoglycemic risk; page 2480, column 1, paragraph 1 discloses asymmetry is to penalize the hypoglycemic events more aggressively than hyperglycemic events since hypoglycemia is more life threatening; page 2480, column 2, paragraph 2 discloses The UVa simulator is equipped with many numerical and graphical metrics that can be used to evaluate the performance of the control algorithms; page 2481, column 2, paragraph 1 discloses that when the feedforward loop is implemented (Fig. 1), the controller is announced about the upcoming meal, and the value of the meal is included in the prediction of future values of blood glucose, and the meal effect on blood glucose will be considered in calculating the future insulin dose; page 2483, Figure 5 shows a comparison between two blood glucose profiles, indicating “exhibited”).
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to expand the method of Shaya, Dobbles, and Breton to further include a gain-scheduling model predictive controller for blood glucose control in type-1 diabetes, where the gain-scheduling technique assigns a specific dosing profile for each glycemic range as disclosed by Abu-Rmileh.
One of ordinary skill in the art, before the effective filing date of the claimed invention would have been motivated to expand the method of Shaya, Dobbles, and Breton in this way by solving a blood glucose control problem including a prediction property of MPC that allows for anticipatory and careful insulin delivery (Abu-Rmileh: page 2479, column 1, paragraphs 3 and 4).
System and storage claims 18 – 26 repeat the subject matter of claims 8 – 17. As the underlying processes of claims 18 – 26 have been shown to be fully disclosed by the teachings of Shaya, Dobbles, Breton and Abu-Rmileh in the above rejections of claims 8 – 17; as such, these limitations (claims 18 – 26) are rejected for the same reasons given above for claims 8 – 17 and incorporated herein.
Response to Arguments
Applicant's arguments filed July 6, 2026 have been fully considered but they are not persuasive. The Applicant’s arguments have been addressed in the order in which they have been received.
The Applicant argues Shaya in view of Dobbles, when combined, do not teach or suggest “determine[ing] a dose of insulin that minimizes a numberical function that assigns a quantified cost to deviations away from a predetermined glucose range exhibited in the projected time series of future glucose concentrations, wherein the numerical function is used in determining the dose of insulin and is asymmetrical and assigns a higher quantified cost to deviations of the glucose concentration below the predetermined range than to deviations of the glucose concentration above the predetermined glucose range”. The Examiner respectfully disagrees. The Examiner submits Dobbles discloses the use of transformed variables to generate a predicted linear relationship based on the sensitivity factors needed to determine insulin and food dosages to correct blood glucose high and low imbalances (paragraph 135). A combination of time of day, recent data, and preceding blood glucose (BG) near to current BG can be used by an algorithm to predict the range of outcomes of the patient's endeavors to manage their blood glucose levels (paragraph 292), indicating future blood glucose levels. In addition, Dobbs discloses insulin delivery is calculated to maintain the host substantially at and/or within a target range (predetermined glucose range), in which the host is to maintain his blood sugar, and where the controller module is configured to adaptively/intelligently program or re-program the target range after the evaluation of the internally derived data and the host’s metabolic response to insulin therapy, indicating the target range (predetermined glucose range) is modified based on the user’s data, indicating the target range is modified based on hypo- or hyper-glycemic readings (paragraph 239). Breton further discloses the memory stores blood glucose values of the patient, the insulin dose values, the insulin types, and the parameters used by the microprocessor to calculate future blood glucose values, supplemental insulin doses, and carbohydrate supplements (paragraph 199), indicating the calculation of supplemental insulin doses. Thus, Applicant’s argument is not persuasive and the rejection is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K.K.R/Examiner, Art Unit 3682
/ROBERT A SOREY/Primary Examiner, Art Unit 3682
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