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 Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 and 12-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “continuously and automatically calibrating the sensor device, without manual user input, based on the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model” in lines 14-16, which is new matter. There is no indication in the original specification that the calibration is performed continuously, automatically, and without manual user input. Additionally, there is no indication of how both the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model provide a basis for the calibration. At most, paragraphs [0111]-[0115] of the published application discuss (A) generically using past estimation results in calibration and (B) a two-point calibration method which uses reference measurements and corresponding blood glucose results. There is no indication of how the calibration methods are performed continuously, automatically, and without user input and how the calibration methods are based on both the current glucose level and the corrected tissue glucose levels. Claims 2-10, 11-12, and 16-20 are rejected by virtue of their dependence from claim 1. Claims 13-15 recite similar limitations, so they are rejected for similar reasons.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 8-9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US 2016/0073964 A1 (Cobelli 2016) (previously cited) in view of US 2014/0273042 A1 (Saint) (previously cited), “Meal detection based on non-individualized moving horizon estimation and classification” (Kölle) (cited by Applicant), and US 2020/0015738 A1 (Doron)
With regards to claim 1, Cobelli 2016 teaches a process for determining a current glucose level in a transport fluid of an organism (Abstract, ¶¶ [0012]-[0013], and Fig. 3 disclose a “retrofitting” method for improving continuous glucose monitoring (CGM) data for determining blood glucose (BG). Because the specification and claim do not redefine “current glucose level”, the term is being given its broadest reasonable interpretation. A most-recent retrofitted CGM value amounts to a “current glucose value” because it is the newest and most recent value for the patient), the process comprising the steps of: determining, using a sensor device, measurements separated in time for actual tissue glucose levels in tissue surrounding the transport fluid (¶ [0031] and Fig. 3 discloses a sub-block A which receives CGM time series; ¶¶ [0008], [0013] discloses the CGM trace is originally measured by a sensor; Abstract discloses CGM devices provide glucose concentration measurements in subcutaneous tissue); determining corrected tissue glucose levels using the measurements, based on a sensor model, in which the measurements are correlated to glucose concentration while taking into account measurement noise (¶ [0031] and Fig. 3 discloses sub-block A outputs preprocessed CGM while detecting unreliable data and outliers (i.e., “taking into account measurement noise”); ¶¶ [0032]-[0033] discloses multiple algorithms (i.e., “sensor models”) for correlating the original CGM with reliable CGM values; ¶ [0033] further recites exploiting other relevant inputs that could help in describing fluctuations in glucose dynamics (e.g. meals quantity and scheduling, hypo treatments, drugs, physical activity and stress information, etc.) opportunely modeled by other relevant models (e.g. models for meal absorption, insulin action, etc.), when available); providing a state transition model by which actual glucose levels in the transport fluid are correlated to the corrected tissue glucose levels while taking into account process noise (Fig. 3 and ¶¶ [0035]-[0036], [0039]-[0041] depict sub-blocks B and C, the combination of which amount to the state transition model; ¶¶ [0039]-[0041] discloses sub-block C uses a model of blood-to-insterstitium glucose kinetics (i.e., state transition kinetics) to output a retrofitted glucose profile, wherein inputs include the preprocessed BG and a retrospectively calibrated CGM; ¶¶ [0035]-[0036] discloses the sub-block B receives preprocessed CGM and preprocessed BG and outputs the retrospectively calibrated CGM while taking into account systemic under/overestimation of CGM time series due to e.g., drift in time, errors in CGM calibration, and changes in sensor sensitivity); determining the current glucose level based on the state transition model and the corrected tissue glucose level (¶¶ [0039]-[0041] discloses a sub-block C which uses a model of blood-to-insterstitium glucose kinetics to output a retrofitted glucose profile, wherein inputs include the preprocessed BG and a retrospectively calibrated CGM).
Cobelli 2016 is silent regarding continuously determining a current glucose in a transport fluid, continuously determining measurements for actual tissue glucose levels.
In the same field of endeavor of monitoring glucose levels, Saint teaches continuously determining a current glucose in a transport fluid (¶ [0003] discloses substantially continuous estimation of blood glucose levels), continuously determining measurements for actual tissue glucose levels (¶ [0003] discloses a CGM which uses a transcutaneous sensor for continuously measuring glucose in the patient’s interstitial fluid). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above process of Cobelli 2016 to incorporate continuously determining a current glucose in a transport fluid and continuously determining measurements for actual tissue glucose levels as taught by Saint. The motivation would have been to provide a more complete blood glucose profile, thereby providing a better diagnostic picture of the patient.
The above combination is silent with regards to whether at least step d) is performed using at least one Moving Horizon Estimation Method.
In the same field of endeavor of blood glucose determination, Kölle teaches d) determining the current glucose state based on a state transition mode and a tissue glucose level (III. SIMULATION STUDY: A. Estimator model and parameters disclose determining a rate of appearance of glucose in plasma based on subcutaneous (SC) tissue measurements), wherein at least step d) is performed using at least one Moving Horizon Estimation method (II. METHODS: A. Moving Horizon Estimation discloses that the current states are estimated based on N past measurements; also see III. SIMULATION STUDY: Estimation set-up). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the determination of the glucose profile of the above combination of Cobelli 2016 in view of Saint to incorporate the use of a moving horizon estimation method as taught by Kölle such that at least step d) is performed using a Moving Horizon Estimation method. The motivation would have been to provide an a more accurate state estimation based on a whole horizon (Kölle: II. METHODS: C. Motivation for using moving horizon estimation and linear discriminant analysis for meal detection).
The above combination is silent with regards to continuously and automatically calibrating the sensor device, without manual user input, based on the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model.
In the same field of endeavor of calibrating continuous glucose monitoring sensors, Doron discloses continuously and automatically calibrating the sensor device, without manual user input, based on determined glucose levels (¶ [0047] discloses automatic calibration of the model comprises a step of estimation of parameters of the differential equation system by minimization of a quantity representative of the error, during a past observation period, between the blood sugar estimated based on the physiological model and the blood sugar measured by the sensor;¶ [0080] discloses regularly recalibrating the model, for example every 1 to 20 minutes, without requiring physically measuring the time-dependent parameters). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process of the above combination to incorporate continuously and automatically calibrating the sensor device, without manual user input, based on determined glucose levels as taught by Doron such that the calibration is based on the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model. The motivation would have been to improve the accuracy of future CGM measurements.
With regards to claim 2, the above combination teaches or suggests that the sensor model is a linear or non-linear function between one or more of the measurements and the glucose concentration (¶ [0032] of Cobelli 2016 discloses a statistically-based estimation procedure (e.g. a Bayesian smoothing procedure), which is necessarily either a linear or non-linear function).
With regards to claim 8, the above combination teaches or suggests the measurements are filtered by a filter function whereby errors of the sensor device are suppressed (¶ [0031] and Fig. 3 of Cobelli 2016 discloses sub-block A outputs preprocessed CGM while detecting unreliable data and outliers, wherein the sub-block A amounts to a filter function).
With regards to claim 9, the above combination teaches or suggests that, in order to determine the errors of the sensor device, a gradient of an increase in the actual tissue glucose levels is evaluated and/or the actual tissue glucose levels are evaluated (¶ [0032] of Cobelli 2016 discloses that a first-order time derivative of the time series is used to detect outliers).
With regards to claim 16, the above combination is silent with regards to whether step b) and step c) are carried out using the at least one Moving Horizon Estimation Method.
In the same field of endeavor of blood glucose determination, Kölle teaches a Moving Horizon Estimation method is applied to previously provided measurements (II. METHODS: A. Moving Horizon Estimation discloses that the current states are estimated based on N past measurements; also see III. SIMULATION STUDY: Estimation set-up). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the measurements of steps b)-c) to incorporate the use of a moving horizon estimation method as taught by Kölle. The motivation would have been to provide an a more accurate state estimation based on a whole horizon (Kölle: II. METHODS: C. Motivation for using moving horizon estimation and linear discriminant analysis for meal detection).
Claims 3 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Cobelli 2016 in view of Saint, Kölle, and Doron, as applied to claim 1 above, and further in view of “Simultaneous Model Predictive Control and Moving Horizon Estimation for Blood Glucose Regulation in Type 1 Diabetes” (Copp) (previously cited).
With regards to claim 3, the above combination teaches or suggests a value for at least one horizon of the Moving Horizon Estimation Method is less than or equal to 300 min (Fig. 2 and discloses comparison of estimated glucose rate of appearance for different MHE horizons ranging from 60 min to 300 min) and that shorter horizons lead to more accurate estimations of certain parameters (III. SIMULATION STUDY: D. Estimation on data simulated with estimator model).
The above combination is silent with regards to whether the value is less than or equal to 10.
In the same field of endeavor of blood glucose determination, Copp teaches a value of a horizon being less than or equal to 10 (3. ESTIMATION AND CONTROL APPROACH: disclose horizons of 9, 5, and 3). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the value of the horizon of the MHE of the above combination to be less than or equal to 10 as taught by Copp. The motivation would have been to provide a more accurate estimation of the blood glucose level.
With regards to claim 6, the above combination teaches or suggests a number of the measurements is greater than the value for the at least one horizon of the Moving Horizon Estimation Method (III: SIMULATION STUDY: B. Estimation set-up of Kölle teaches sampling time of 5 minutes being chosen; 3. ESTIMATION AND CONTROL APPROACH of Copp disclose horizons of 9, 5, and 3, wherein 5 minutes is greater than 3).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Cobelli 2016 in view of Saint, Kölle, and Doron, as applied to claim 1 above, and further in view of “Receding Horizon Control of Type I Diabetes Based on a Data-Driven Linear Time-Varying State-Space Model” (Zhou) (previously cited)
With regards to claim 4, the above combination is silent with regards to whether a variance of the measurement noise and/or a variance of the process noise is estimated or interpolated and/or weighted.
In the same field of modeling glucose dynamics, Zhou teaches a variance of the measurement noise and/or a variance of the process noise is estimated or interpolated and/or weighted. (II. Modeling Insulin-Glucose Dynamics: A. Time-delayed State Space Model discloses determining variances Qw and Rv of process noise and measurement error). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above combination to incorporate a variance of the measurement noise and/or the variance of the process noise is estimated as taught by Zhou. The motivation would have been to provide a more accurate modeling of the glucose dynamics.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Cobelli 2016 in view of Saint, Kölle, Doron, and Zhou, as applied to claim 4 above, and further in view of “Kalman Smoothing for Objective and Automatic Preprocessing of Glucose Data” (Staal) (previously cited).
With regards to claim 5, the above combination teaches or suggests determining a variance of all measurements (Zhou: II. Modeling Insulin-Glucose Dynamics: A. Time-delayed State Space Model discloses determining variances Qw and Rv of process noise and measurement error). The above combination is silent with regards to whether required values only partially used to calculate an estimation of the measurement noise and/or an estimation of the process noise are temporarily stored, and other values that have not been temporarily stored are interpolated using the required values that have been temporarily stored.
In the system related to the problem of processing glucose data, Staal teaches measurements are temporarily stored, and measurements that have not been temporarily stored can be interpolated using the measurements that have been stored (I. Introduction indicates Kalman smoothing applied to glucose data, resulting in phaseless smoothing and interpolation, wherein a time series of irregularly spaced blood glucose measurements is converted to a continuous time series of interpolated estimates with mean and variance). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above combination to incorporate interpolation of needed data measurements as taught by Staal. The motivation would have been to provide a more complete analysis of tissue glucose measurements, thereby improving the accuracy of the derived blood glucose levels.
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Cobelli 2016 in view of Saint, Kölle, Doron, and Zhou, as applied to claim 8 above, and further in view of US 2008/0188796 A1 (Steil) (previously cited)
With regards to claim 10, the above combination is silent with regards to whether measurements that are below a first threshold level and/or above a second threshold level are discarded by means of the filter function, wherein the second threshold level is greater than the first level.
In the same field of endeavor of monitoring glucose levels, Steil teaches a filter that suppresses errors of the sensor device, wherein measurements that are below a first threshold level and/or above a second threshold level are discarded by means of the filter function, wherein the second threshold level is greater than the first level. (¶¶ [0213]-[0216] disclose a pre-filter 400 with high noise threshold 406 negative noise threshold 408 preset at 100% above average and 50% below the average, respectively, wherein a “discard” flag is set to indicate that the values should be ignored and not used). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the filtering scheme of the above combination to incorporate a pre-filter as taught by Steil. The motivation would have been to provide better noise suppression.
With regards to claim 18, the above combination is silent with regards to whether measurements are weighted by the filter function.
In the same field of endeavor of monitoring glucose levels, Steil teaches a filter that suppresses errors of the sensor device, wherein measurements are weighted the filter function. (¶¶ [0213]-[0216] disclose a pre-filter 400 with high noise threshold 406 negative noise threshold 408 preset at 100% above average and 50% below the average, respectively, wherein a “discard” flag is set to indicate that the values should be ignored and not used (i.e., weighted to 0)). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the filtering scheme of the above combination to incorporate a pre-filter as taught by Steil. The motivation would have been to provide better noise suppression.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Cobelli 2016 in view of Saint, Kölle, and Doron, as applied to claim 1 above, and further in view of US 2005/0154271 A1 (Rasdal) (previously cited)
With regards to claim 12, the above combination is silent with regards to whether the state transition model includes a diffusion model for time-dependent modeling of a diffusion process of glucose from the transport fluid into the tissue, and/or sensor model parameters of the sensor model and/or state transition parameters of the state transition model are estimated and/or updated at least on a regular basis.
In the same field of endeavor of continuous analyte sensing, Rasdal teaches sensor model parameters of the sensor model and/or state transition parameters of the state transition model are estimated and/or updated, at least on a regular basis (¶ [0003] continuous glucose sensors having regular updates of calibration, which necessarily updates sensor model parameters). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the calibration of the sensor model parameters of the above combination to incorporate that they are updated on at least a regular basis as taught by Rasdal. The motivation would have been to provide a more constant updating of the sensor parameters, thereby providing a more accurate diagnostic analysis of the patient.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Cobelli 2016 in view of Saint, Kölle, US 5,408,999 A (Singh) (previously cited), and Doron.
With regards to claim 13, Cobelli 2016 teaches a device configured to determine a current glucose level in a transport fluid of an organism (Abstract, ¶¶ [0012]-[0013], and Fig. 3 disclose a method for continuous glucose monitoring (CGM) for determining blood glucose (BG); Claim 1 teaches a glucose monitoring system; Because the specification and claim do not redefine “current glucose level”, the term is being given its broadest reasonable interpretation. A most-recent retrofitted CGM value amounts to a “current glucose value” because it is the newest and most recent value for the patient), comprising: a sensor device configured to continuously determine a series of measurements separated in time for actual tissue glucose levels in the tissue (¶ [0031] and Fig. 3 discloses a sub-block A which receives CGM time series; ¶¶ [0008], [0013] discloses the CGM trace is originally measured by a sensor; Abstract discloses CGM devices provide glucose concentration measurements in subcutaneous tissue; Claim 1 recites a continuous glucose monitoring device); a memory configured to store (i) a sensor model, in which the measurements are correlated to glucose concentration taking into account measurement noise, (¶ [0031] and Fig. 3 discloses sub-block A outputs preprocessed CGM while detecting unreliable data and outliers; ¶¶ [0032]-[0033] discloses multiple algorithms (i.e., “sensor models”) for correlating the original CGM with reliable CGM values; ¶ [0033] further recites exploiting other relevant inputs that could help in describing fluctuations in glucose dynamics (e.g. meals quantity and scheduling, hypo treatments, drugs, physical activity and stress information, etc.) opportunely modeled by other relevant models (e.g. models for meal absorption, insulin action, etc.), when available; Claim 1 discloses a data preprocessing module, which is necessarily implemented on a memory and associated processing elements), and (ii) a state transition model, in which actual glucose levels in the transport fluid are correlated to corrected tissue glucose levels while taking into account process noise (¶¶ [0039]-[0041] discloses a sub-block C which uses a model of blood-to-insterstitium glucose kinetics to output a retrofitted glucose profile, wherein inputs include the preprocessed BG and a retrospectively calibrated CGM; ¶¶ [0035]-[0036] discloses the sub-block B outputs the retrospectively calibrated CGM while taking into account systemic under/overestimation of CGM time series due to e.g., drift in time, errors in CGM calibration, and changes in sensor sensitivity; Claim 1 discloses a constrained inverse problem solver module, which is necessarily implemented on a memory and associated processing elements), and an evaluating device configured to (i) determine a corrected tissue glucose level using the measurements, based on the sensor model (¶ [0031] and Fig. 3 discloses sub-block A outputs preprocessed CGM while detecting unreliable data and outliers) and (ii) determine the current glucose level based on the state transition model and the corrected tissue glucose level (¶¶ [0039]-[0041] discloses a sub-block C which uses a model of blood-to-insterstitium glucose kinetics to output a retrofitted glucose profile, wherein inputs include the preprocessed BG and a retrospectively calibrated CGM).
Cobelli 2016 is silent regarding continuously determining a current glucose in a transport fluid, continuously determining measurements for actual tissue glucose levels.
In the same field of endeavor of monitoring glucose levels, Saint teaches continuously determining a current glucose in a transport fluid (¶ [0003] discloses substantially continuous estimation of blood glucose levels), continuously determining measurements for actual tissue glucose levels (¶ [0003] discloses a CGM which uses a transcutaneous sensor for continuously measuring glucose in the patient’s interstitial fluid). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above process of Cobelli 2016 to incorporate continuously determining a current glucose in a transport fluid and continuously determining measurements for actual tissue glucose levels as taught by Saint. The motivation would have been to provide a more complete blood glucose profile, thereby providing a better diagnostic picture of the patient.
The above combination is silent with regards to determining the current glucose level using at least one Moving Horizon Estimation Method.
In the same field of endeavor of blood glucose determination, Kölle teaches determining a current glucose state based on a state transition mode and a tissue glucose level (III. SIMULATION STUDY: A. Estimator model and parameters disclose determining a rate of appearance of glucose in plasma based on subcutaneous (SC) tissue measurements), using a Moving Horizon Estimation method (II. METHODS: A. Moving Horizon Estimation discloses that the current states are estimated based on N past measurements; also see III. SIMULATION STUDY: Estimation set-up). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the determination of the glucose profile of Cobelli 2016 to incorporate the use of a moving horizon estimation method as taught by Kölle such that Moving Horizon Estimation method is carried out to provide the current glucose level in step d) and applied to previously provided glucose measurements and at least one previous tissue glucose measurement. The motivation would have been to provide an a more accurate state estimation based on a whole horizon (Kölle: II. METHODS: C. Motivation for using moving horizon estimation and linear discriminant analysis for meal detection).
The above combination is silent with regards to whether the sensor device is for measuring fluorescence in a tissue surrounding the transport fluid by a fiber optic probe configured to continuously determine measurements separated in time for actual tissue glucose levels in the tissue.
In the same field of endeavor, Singh teaches a sensor device for measuring fluorescence in a tissue surrounding the transport fluid, by a fiber optic probe configured to continuously determine measurements separated in time for actual tissue glucose levels in the tissue (Col. 1, lines 41-57). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have substituted the sensor of Cobelli 2016 with the sensor of Singh. Because both elements are capable of determining tissue glucose (Col. 1, lines 41-57 of Singh and ¶¶ [0008], [0013] of Cobelli 2016), it would have been the simple substitution of one known equivalent element for another to obtain predictable results.
The above combination is silent with regards to continuously and automatically calibrating the sensor device, without manual user input, based on the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model.
In the same field of endeavor of calibrating continuous glucose monitoring sensors, Doron discloses continuously and automatically calibrating the sensor device, without manual user input, based on determined glucose levels (¶ [0047] discloses automatic calibration of the model comprises a step of estimation of parameters of the differential equation system by minimization of a quantity representative of the error, during a past observation period, between the blood sugar estimated based on the physiological model and the blood sugar measured by the sensor;¶ [0080] discloses regularly recalibrating the model, for example every 1 to 20 minutes, without requiring physically measuring the time-dependent parameters). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process of the above combination to incorporate continuously and automatically calibrating the sensor device, without manual user input, based on determined glucose levels as taught by Doron such that the calibration is based on the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model. The motivation would have been to improve the accuracy of future CGM measurements.
Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cobelli 2016 in view of Saint, Kölle, US 10,182,750 B1 (Philippine) (previously cited), and Doron.
With regards to claim 14, Cobelli 2016 teaches an evaluation device configured to determine a current glucose level in a transport fluid of an organism (Abstract, ¶¶ [0012]-[0013], and Fig. 3 disclose a method for continuous glucose monitoring (CGM) for determining blood glucose (BG); Claim 1 teaches a glucose monitoring system. Because the specification and claim do not redefine “current glucose level”, the term is being given its broadest reasonable interpretation. A most-recent retrofitted CGM value amounts to a “current glucose value” because it is the newest and most recent value for the patient), the evaluation device comprising: a sensor device configured to provide measurements separated in time for actual tissue glucose level in tissue surrounding the transport fluid (¶ [0031] and Fig. 3 discloses a sub-block A which receives CGM time series; ¶¶ [0008], [0013] discloses the CGM trace is originally measured by a sensor; Abstract discloses CGM devices provide glucose concentration measurements in subcutaneous tissue; Claim 1 recites a continuous glucose monitoring device); (i) a sensor model, in which the measurements are correlated to reference tissue glucose levels taking into account measurement noise, (In view of the rejection under 35 U.S.C. §112(b) above, “reference tissue glucose levels” is being interpreted to be “the corrected tissue glucose levels”. ¶ [0031] and Fig. 3 discloses sub-block A outputs preprocessed CGM while detecting unreliable data and outliers; ¶¶ [0032]-[0033] discloses multiple algorithms (i.e., “sensor models”) for correlating the original CGM with reliable CGM values; ¶ [0033] further recites exploiting other relevant inputs that could help in describing fluctuations in glucose dynamics (e.g. meals quantity and scheduling, hypo treatments, drugs, physical activity and stress information, etc.) opportunely modeled by other relevant models (e.g. models for meal absorption, insulin action, etc.), when available; Claim 1 discloses a data preprocessing module, which is necessarily implemented on a memory and associated processing elements), and (ii) a state transition model, in which actual glucose levels in the transport fluid are correlated to corrected tissue glucose levels while taking into account process noise (¶¶ [0039]-[0041] discloses a sub-block C which uses a model of blood-to-insterstitium glucose kinetics to output a retrofitted glucose profile, wherein inputs include the preprocessed BG and a retrospectively calibrated CGM; ¶¶ [0035]-[0036] discloses the sub-block B outputs the retrospectively calibrated CGM while taking into account systemic under/overestimation of CGM time series due to e.g., drift in time, errors in CGM calibration, and changes in sensor sensitivity; Claim 1 discloses a constrained inverse problem solver module, which is necessarily implemented on a memory and associated processing elements), and a calculating device configured to (i) determine a corrected tissue glucose level using the measurements, based on the sensor model (¶ [0031] and Fig. 3 discloses sub-block A outputs preprocessed CGM while detecting unreliable data and outliers) and (ii) determine the current glucose level based on the state transition model and the corrected tissue glucose level (¶¶ [0039]-[0041] discloses a sub-block C which uses a model of blood-to-insterstitium glucose kinetics to output a retrofitted glucose profile, wherein inputs include the preprocessed BG and a retrospectively calibrated CGM).
Cobelli 2016 is silent regarding continuously determining a current glucose in a transport fluid, continuously determining measurements for actual tissue glucose levels.
In the same field of endeavor of monitoring glucose levels, Saint teaches continuously determining a current glucose in a transport fluid (¶ [0003] discloses substantially continuous estimation of blood glucose levels), continuously determining measurements for actual tissue glucose levels (¶ [0003] discloses a CGM which uses a transcutaneous sensor for continuously measuring glucose in the patient’s interstitial fluid). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above process of Cobelli 2016 to incorporate continuously determining a current glucose in a transport fluid and continuously determining measurements for actual tissue glucose levels as taught by Saint. The motivation would have been to provide a more complete blood glucose profile, thereby providing a better diagnostic picture of the patient.
The above combination is silent with regards to determining the current glucose level using at least one Moving Horizon Estimation Method.
In the same field of endeavor of blood glucose determination, Kölle teaches determining a current glucose state based on a state transition mode and a tissue glucose level (III. SIMULATION STUDY: A. Estimator model and parameters disclose determining a rate of appearance of glucose in plasma based on subcutaneous (SC) tissue measurements), using a Moving Horizon Estimation method (II. METHODS: A. Moving Horizon Estimation discloses that the current states are estimated based on N past measurements; also see III. SIMULATION STUDY: Estimation set-up). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the determination of the glucose profile of Cobelli 2016 to incorporate the use of a moving horizon estimation method as taught by Kölle such that Moving Horizon Estimation method is carried out to provide the current glucose level in step d) and applied to previously provided glucose measurements and at least one previous tissue glucose measurement. The motivation would have been to provide an a more accurate state estimation based on a whole horizon (Kölle: II. METHODS: C. Motivation for using moving horizon estimation and linear discriminant analysis for meal detection).
The above combination is silent with regards to an interface to connect a sensor device and at least one memory to store the models.
In the same field of endeavor of glucose monitoring, Philippine discloses an interface to connect to a sensor device (Col. 1, lines 23-37 disclose an interface device of a CGM) and at least one memory capable of storing methods and models (Col. 11, line 42 to Col. 12, line 11). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above combination to incorporate an interface and a memory as taught by Philippine. The motivation would have been to provide the necessary computer components for implementing the blood glucose determination.
The above combination is silent with regards to continuously and automatically calibrating the sensor device, without manual user input, based on the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model.
In the same field of endeavor of calibrating continuous glucose monitoring sensors, Doron discloses continuously and automatically calibrating the sensor device, without manual user input, based on determined glucose levels (¶ [0047] discloses automatic calibration of the model comprises a step of estimation of parameters of the differential equation system by minimization of a quantity representative of the error, during a past observation period, between the blood sugar estimated based on the physiological model and the blood sugar measured by the sensor;¶ [0080] discloses regularly recalibrating the model, for example every 1 to 20 minutes, without requiring physically measuring the time-dependent parameters). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process of the above combination to incorporate continuously and automatically calibrating the sensor device, without manual user input, based on determined glucose levels as taught by Doron such that the calibration is based on the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model. The motivation would have been to improve the accuracy of future CGM measurements.
With regards to claim 15, Cobelli 2016 teaches a process for determining a current glucose level in a transport fluid of an organism (Abstract, ¶¶ [0012]-[0013], and Fig. 3 disclose a “retrofitting” method for improving continuous glucose monitoring (CGM) data for determining blood glucose (BG). Because the specification and claim do not redefine “current glucose level”, the term is being given its broadest reasonable interpretation. A most-recent retrofitted CGM value amounts to a “current glucose value” because it is the newest and most recent value for the patient), the process comprising the steps of: determining, using a sensor device, measurements separated in time for actual tissue glucose levels in tissue surrounding the transport fluid (¶ [0031] and Fig. 3 discloses a sub-block A which receives CGM time series; ¶¶ [0008], [0013] discloses the CGM trace is originally measured by a sensor; Abstract discloses CGM devices provide glucose concentration measurements in subcutaneous tissue); determining corrected tissue glucose levels using the measurements, based on a sensor model, in which the measurements are correlated to glucose concentration while taking into account measurement noise (¶ [0031] and Fig. 3 discloses sub-block A outputs preprocessed CGM while detecting unreliable data and outliers (i.e., “taking into account measurement noise”); ¶¶ [0032]-[0033] discloses multiple algorithms (i.e., “sensor models”) in which the input CGM are correlated to the preprocessed CGM data; ¶ [0033] further recites exploiting other relevant inputs that could help in describing fluctuations in glucose dynamics (e.g. meals quantity and scheduling, hypo treatments, drugs, physical activity and stress information, etc.) opportunely modeled by other relevant models (e.g. models for meal absorption, insulin action, etc.), when available); providing a state transition model by which actual glucose levels in the transport fluid are correlated to the corrected tissue glucose levels while taking into account process noise (Fig. 3 and ¶¶ [0035]-[0036], [0039]-[0041] depict sub-blocks B and C, the combination of which amount to the state transition model; ¶¶ [0039]-[0041] discloses sub-block C uses a model of blood-to-insterstitium glucose kinetics (i.e., state transition kinetics) to output a retrofitted glucose profile, wherein inputs include the preprocessed BG and a retrospectively calibrated CGM; ¶¶ [0035]-[0036] discloses the sub-block B receives preprocessed CGM and preprocessed BG and outputs the retrospectively calibrated CGM while taking into account systemic under/overestimation of CGM time series due to e.g., drift in time, errors in CGM calibration, and changes in sensor sensitivity); determining the current glucose level based on the state transition model and the corrected tissue glucose level (¶¶ [0039]-[0041] discloses a sub-block C which uses a model of blood-to-insterstitium glucose kinetics to output a retrofitted glucose profile, wherein inputs include the preprocessed BG and a retrospectively calibrated CGM).
Cobelli 2016 is silent with regards to a non-transitory computer-readable medium having a program stored thereon for causing a computer to perform the above process.
In the same field of endeavor of glucose monitoring, Philippine discloses a non-transitory computer-readable medium having a program stored thereon for causing a computer to perform a process (Col. 11, line 42 to Col. 12, line 11). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above combination to incorporate that it is implemented on the processor and computer-readable storage media as taught by Philippine. The motivation would have been to provide the necessary computer components for implementing the blood glucose determination.
The above combination is silent regarding continuously determining a current glucose in a transport fluid, continuously determining measurements for actual tissue glucose levels.
In the same field of endeavor of monitoring glucose levels, Saint teaches continuously determining a current glucose in a transport fluid (¶ [0003] discloses substantially continuous estimation of blood glucose levels), continuously determining measurements for actual tissue glucose levels (¶ [0003] discloses a CGM which uses a transcutaneous sensor for continuously measuring glucose in the patient’s interstitial fluid). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the above process of Cobelli 2016 to incorporate continuously determining a current glucose in a transport fluid and continuously determining measurements for actual tissue glucose levels as taught by Saint. The motivation would have been to provide a more complete blood glucose profile, thereby providing a better diagnostic picture of the patient.
The above combination is silent with regards to whether at least step d) is performed using at least one Moving Horizon Estimation Method.
In the same field of endeavor of blood glucose determination, Kölle teaches d) determining the current glucose state based on a state transition mode and a tissue glucose level (III. SIMULATION STUDY: A. Estimator model and parameters disclose determining a rate of appearance of glucose in plasma based on subcutaneous (SC) tissue measurements), wherein at least step d) is performed using at least one Moving Horizon Estimation method (II. METHODS: A. Moving Horizon Estimation discloses that the current states are estimated based on N past measurements; also see III. SIMULATION STUDY: Estimation set-up). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the determination of the glucose profile of the above combination of Cobelli 2016 in view of Saint to incorporate the use of a moving horizon estimation method as taught by Kölle such that at least step d) is performed using a Moving Horizon Estimation method. The motivation would have been to provide an a more accurate state estimation based on a whole horizon (Kölle: II. METHODS: C. Motivation for using moving horizon estimation and linear discriminant analysis for meal detection).
The above combination is silent with regards to continuously and automatically calibrating the sensor device, without manual user input, based on the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model.
In the same field of endeavor of calibrating continuous glucose monitoring sensors, Doron discloses continuously and automatically calibrating the sensor device, without manual user input, based on determined glucose levels (¶ [0047] discloses automatic calibration of the model comprises a step of estimation of parameters of the differential equation system by minimization of a quantity representative of the error, during a past observation period, between the blood sugar estimated based on the physiological model and the blood sugar measured by the sensor;¶ [0080] discloses regularly recalibrating the model, for example every 1 to 20 minutes, without requiring physically measuring the time-dependent parameters). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process of the above combination to incorporate continuously and automatically calibrating the sensor device, without manual user input, based on determined glucose levels as taught by Doron such that the calibration is based on the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model. The motivation would have been to improve the accuracy of future CGM measurements.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Cobelli 2016 in view of Saint, Kölle, Doron, and Zhou, as applied to claim 4 above, and further in view of US 2020/0237271 A1 (Vanslyke) (Previously cited).
With regards to claim 17, the above combination is silent with regards to whether the variance of the measurement noise and/or the variance of the process noise is estimated using an exponential smoothing.
In a system relevant to the problem of smoothing noise, Vanslyke teaches calculation of a measurement error variance (¶ [0146]) and parameters and/or the physiologic noise factors may be smoothed, for example, using an exponential forgetting factor (¶ [0180]). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the calculation of variance of the measurement noise of the above combination to incorporate that it is based on parameters that have been smoothed using an exponential forgetting factor as taught by Vanslyke. The motivation would have been to provide a more accurate determination of noise severity.
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cobelli 2016 in view of Saint, Kölle, Doron, Zhou, and Steil, as applied to claim 10 above, and further in view of US 2010/0049015 A1 (Martini) (previously cited)
With regards to claim 19, the above combination is silent with regards to whether the first threshold level and the second threshold level correspond to physiological limits.
In a system relevant to the problem of analyzing glucose, Martini teaches that physiological range of diabetic individuals is 40 mg/dl to 400 mg/dl (¶ [0065]). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the thresholds of Steil such that they correspond to the physiological range of diabetic individuals of 40 mg/dl to 400 mg/dl as taught by Martini. Because both ranges can be used to distinguish between physiologically-relevant glucose measurements and outliers, it would have been the simple substitution of one known equivalent element for another to obtain predictable results.
With regards to claim 20, the above combination teaches or suggests that the first threshold level is between 10 mg/dL-50 mg/dL and the second threshold level is between 100 mg/dL-600 mg/dL (¶ [0065] of Martini discloses physiological range of diabetic individuals is 40 mg/dl to 400 mg/dl).
No Prior Art Rejections of Claim 7
With regards to claim 7, the prior art does not teach or suggest that “a variance of the measurement noise and/or a variance of the process noise is regularly adjusted based on a value of a sum of the at least one horizon of the Moving Horizon Evaluation Method and the number of the measurements”. The state of the art provides no teaching or suggestion that would reasonably lead one of skill to arrive at the above limitation absent improper hindsight or an otherwise inarticulate combination of inadequate teachings.
“Zone Model Predictive Control and Moving Horizon Estimation for the Regulation of Blood Glucose in Critical Care Patients” (Knab) (previously cited) teaches a moving horizon evaluation method in relation to measurement noise and process noise (see at least 2.5 State Estimation). However, Knab does not teach or suggest the above limitations.
Response to Arguments
Claim Rejections under 35 U.S.C. §112(b)
In view of the claim amendments filed 02/27/2026, the claim rejections under 35 U.S.C. §112(b) were withdrawn.
Claim Rejections under 35 U.S.C. §103
There are new grounds of claim rejections under 35 U.S.C. §103 necessitated by the claim amendments filed 02/27/2026
To the extent that the Applicant's arguments are applicable to the current rejections, the Examiner makes the following comments.
Applicant's arguments filed 02/27/2026 have been fully considered but they are not persuasive.
Applicant asserts that Saint teaches away from the claimed invention because the subject matter of Saint also requires manually determined values from a patient. However, this argument is not persuasive because Saint is not relied upon for teaching the automatic calibration. Instead, Doron discloses continuously and automatically calibrating the sensor device, without manual user input, based on determined glucose levels (¶ [0047] discloses automatic calibration of the model comprises a step of estimation of parameters of the differential equation system by minimization of a quantity representative of the error, during a past observation period, between the blood sugar estimated based on the physiological model and the blood sugar measured by the sensor;¶ [0080] discloses regularly recalibrating the model, for example every 1 to 20 minutes, without requiring physically measuring the time-dependent parameters). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the process of the above combination to incorporate continuously and automatically calibrating the sensor device, without manual user input, based on determined glucose levels as taught by Doron such that the calibration is based on the current glucose level determined using the state transition model and the corrected tissue glucose levels determined using the sensor model. The motivation would have been to improve the accuracy of future CGM measurements.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAMUEL C KIM whose telephone number is (571)272-8637. The examiner can normally be reached M-F 8:00 AM - 5:00 PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jacqueline Cheng can be reached at (571) 272-5596. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.C.K./Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791
Prosecution Insights