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 .
Amendments
This action is in response to amendments filed June 8th, 2026, in which Claims 1 and 3 are amended. No claims have been cancelled nor added. The amendments have been entered, and Claims 1-20 are currently pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites to select the machine learning model for determining the final SG value but also previously had recited using at least one machine learning model to calculate a final sensor glucose (SG) value. The antecedent basis for the machine learning model for determining is unclear, because the language differs – is a machine learning model to calculate a final SG the antecedent basis for the machine learning for determining the final SG ? Further, the first recitation recites at least one machine learning model, which renders the machine learning model indefinite in the case where more than one model can be used (which is clearly intended in the claimed invention, see for example, Claim 4). For the purpose of examination, the claim will be interpreted as if it had read to select the at least one machine learning model for calculating the final SG value.
Dependent claims are rejected for inheriting the indefiniteness of a parent claim.
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.
Claim 2 is 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 2 recites wherein the at least one machine is one of genetic programming and a regression decision tree; however, Claim 1 has previously recited that discrete wavelet decomposition is used to select the machine learning model. Examination of the specification reveals that the only machine learning models ever selected using the discrete wavelet decomposition are linear models or a regression decision sub-tree (see Figs. 114, 115 where a regression decision subtree is selected based on wavelet decomposition values w7 and w10 and [0704]). No genetic programming models are ever selected on the basis of discrete wavelet decomposition in the disclosed invention.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 recites a method, thus a process, one of the four statutory categories of patentable subject matter. However, Claim 1 recites the mathematical process steps of preprocessing values, decomposing values, to select a machine learning model using the discrete wavelet decomposition and to calculate a final value using the discrete wavelet decomposition. Thus, the claim recites an abstract idea of calibrating data from a sensor using mathematical steps.
The claim does not include any additional elements which could integrate the abstract idea into a practical application, because the additional elements consist of:
a microcontroller and a recorder (interpreted as memory for storing information), both recited as generic computer equipment, and the implementation of an abstract idea on generic computer equipment cannot integrate the abstract idea into a practical application (see MPEP 2106.05(f)(2))
measuring an electrode current for the working electrode is insignificant extra-solution activity of data gathering, see MPEP 2106.05(g)
storing a plurality of values in said recorder and retrieving the plurality of values from said recorder are insignificant extra-solution activity of data gathering, see MPEP 2106.05(g)
using at least one machine learning model to calculate by the microcontroller again is merely using a computer or other machinery as a tool to perform the abstract idea, which by MPEP 2106.05(f)(2) cannot integrate the abstract idea into a practical application.
Thus, the claim is directed to the abstract idea of using mathematical steps to calibrate data from a sensor.
Further, the additional elements cannot provide significantly more than the abstract idea itself:
a microcontroller and a recorder (interpreted as memory for storing information), both recited as generic computer equipment, and the implementation of an abstract idea on generic computer equipment is not significantly more than the abstract idea itself (see MPEP 2106.05(f)(2))
measuring the electrode current for the working electrode is routine, conventional, and well-understood (see Telson, US PG Pub 2011/0040163, [0030], “Conventional in vivo CGM sensors may be configured [as] … ‘a sensor that produces an electrical current that is proportional to the blood or subcutaneous tissue level’ … the current may then be read to indicate glucose level”)
storing a plurality of values in said recorder and retrieving the plurality of values from said recorder are well-understood, routine, and conventional by MPEP 2106.05(d), “storing and retrieving data in memory”
using at least one machine learning model to calculate by the microcontroller again is merely using a computer or other machinery as a tool to perform the abstract idea, which by MPEP 2106.05(f)(2) cannot provide an inventive concept.
Thus, the additional elements of Claim 1, taken individually and in combination, do not represent significantly more than the abstract idea, and the claim is ineligible.
Claims 2-20, dependent upon Claim 1, recite additional details regarding the mental and mathematical processes, but no additional elements which integrate the abstract idea into a practical application nor are arguably an inventive concept nor significantly more than the abstract idea itself (Claims 2, 4, and 5 specify the machine learning model used, which by MPEP 2106.05(h) merely specifies the field of use; Claim 6 recites fused SG is calculated based on an EIS parameter, which merely specifies the data used in the mental process; Claims 7 and 8 specify what data is to be manipulated in the mental process; Claims 3, 9, 12-16, 18, and 19 each recite smoothing or blanking data, which are additional mathematical steps of data manipulation; Claims 10, 11, and 17 recite repeating the process over time; and Claim 20 recites calculating signal noise, which is an additional mathematical step of manipulating information) or additional steps which are insignificant extra-solution activity and routine, conventional, and well-understood (Claim 6 recites performing an electrochemical impedance spectroscopy procedure, see Telson, [0036], “Since the 1970’s, EIS has been used as a tool to analyze difficult and complicated systems”). Thus, dependent Claims 2-20 remain directed to the abstract idea of calibrating data from a sensor, without significantly more.
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.
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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1, 2, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, US Patent 6,424,847, in view of Shao, “Multivariate calibration methods in near infrared spectroscopic analysis” (as cited by the applicant in the information disclosure statement dated 10/16/2023), and further in view of Dobra et al., “SECRET: A Scalable Linear Regression Tree Algorithm.”
Regarding Claim 1, Mastrototaro teaches a method for retrospective calibration of a glucose sensor for measuring the level of glucose in a body of a user (title, “Glucose Monitor Calibration Methods”), said sensor including physical sensor electronics (column 3, lines 50-52, “The glucose sensor is electrically coupled to the glucose monitor to supply the glucose monitor data”), a microcontroller (column 3, lines 55-56, “the processor includes software to calculate calibration characteristics” & column 7, lines 30-31, “to control the infusion pump based on glucose sensor measurements”), a recorder (column 2, lines 23-24, “obtaining glucose monitor data at a predetermined memory storage rate” denotes the data is stored in a recorder), and a working electrode (column 5, lines 57-58, “electrodes of the glucose sensor terminating in the user’s subcutaneous tissue”), the method comprising: measuring, by said physical sensor electronics, [an] electrode current for the working electrode (column 8, lines 38-40, “the glucose monitor measures a continuous electrical current signal (ISIG) generated by the glucose sensor”); storing a plurality of said Isig values for said working electrode in said recorder; retrieving the plurality of Isig values from said recorder (column 8, lines 51-58, “at a glucose monitor memory storage rate … 3 interval values are averaged and stored in a glucose monitory memory as memory values. The memory values are retained in memory and may be downloaded to the data processor”), preprocessing said retrieved Isig values by said microcontroller (column 9, lines 7-12, referring to measured Isig values, “Clipping limits may be used to limit the signal magnitude variation from one value to the next thereby reducing the effects of extraneous data … in preferred embodiments, clipping limits are applied to the interval values. For instance, interval values that are above a maximum clipping limit or below a minimum clipping limit are replaced with the nearest clipping limit value”) … and using at least one machine learning model to calculate, by said microcontroller, a final sensor glucose (SG) value based on said Isig values (column 10, lines 56-57, “valid ISIG values are used to calculate blood glucose levels by the glucose monitor or post-processor” & Abstract, “the calculation of the calibration is obtained by regression … [or] alternatively … non-linear regression” where “regression” and “non-linear regression” are various machine learning models).
Mastrototaro is silent regarding decomposing said preprocessed Isig values using discrete wavelet decomposition and to calculate a final sensor glucose value based on … said discrete wavelet decomposition. However, Shao teaches a method for measurement calibration (Shao, title, “Multivariate calibration methods”) using a discrete wavelet decomposition (Shao, pg. 1664, 1st column, 3rd paragraph, “wavelet transform (WT) was introduced into the local regression” & 2nd column, “signal decomposition such as wavelet transform” indicates that they use a discrete wavelet decomposition to perform their regression model for calibration). It would have been obvious to incorporate such a transform into the regression model for calibration (and thus to calculate a final SG value) of Mastrototaro (Mastrototaro, Abstract, “the calculation of the calibration characteristics is obtained using linear [or alternatively, non-linear] regression”) because both inventions are about the calibration of sensor-measured data. The motivation to do so is that an “approach to avoid non-linearity is local regression” which can be performed with the discrete wavelet decomposition transform (Shao, pg. 1664, 1st column, 3rd paragraph).
While the Mastrototaro/Shao combination teaches regression using discrete wavelet composition to determine a final sensor value, the combination does not teach using the discrete wavelet composition to select the machine learning model for determining the final SG value based on the plurality of Isig values. Mastrototaro teaches learning the calibration with linear or non-linear regression (Mastrototaro, Abstract) but also teaches that that “other alternative embodiments may utilize singular and multiple, non-linear regression techniques” (Mastrototaro, column 16, lines 28-29). Dobra teaches a non-linear regression technique that relies upon the independent variables (in the case of Mastrototaro/Shao, Isig and discrete wavelet decomposition values) in regression to select a machine learning model for determining the final regression output value (Dobra, Abstract, “Regression trees with linear models in the leaves” and pg. 1, 1st column, 1st paragraph, “predictor attributes” to decide the node splits. See the instant application, Fig. 114, to see that the linear regression tree of Dobra is exactly the claimed model, with “predictor attributes” such as Isig and discrete wavelet decomposition values at the nodes). It would been obvious to one of ordinary skill in the art to use the particular non-linear regression model of Dobra in the Mastrototaro/Shao combination to predict a final SG value. The motivation to do so is that Mastrototaro already says that any of various non-linear regression methods may be used, and Dobra states that “Regression trees with linear models in the leaves satisfy both these requirements … [e.g. are] accurate and easy to interpret” (Dobra, Abstract).
Regarding Claim 2, the Mastrototaro/Shao/Dobra combination of Claim 1 teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). The Mastrototaro/Shao/Dobra combination further teaches wherein the at least one learning model is … regression decision tree (Dobra, title, “Linear Regression Tree” & pg. 2, 2nd column, 2nd paragraph, “Regression trees … are the natural generalization of decision trees for regression problems”).
Regarding Claim 18, the Mastrototaro/Shao/Dobra combination of Claim 1 teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Mastrototaro further teaches smoothing of the preprocessed Isig values (Mastrototaro, column 10, lines 6-8, “Fig. 8 shows interval value R’, which is calculated by averaging sampled values N through Q”, see Fig. 8, sampled values are clipped and averaged, which results in smoothing).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, in view of Shao and Dobra, and further in view of Campetelli et al., “Improvements on Noninvasive Blood Glucose Biosensors Using Wavelets for Quick Fault Detection.”
Regarding Claim 3, the Mastrototaro/Shao/Dobra combination of Claim 1 teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Mastrototaro further teaches determining whether to perform blanking (Mastrototaro, column 9, lines 13-15, “interval values that are outside of the clipping limits are ignored” with column 10, lines 21-24, “clipping may be applied to the … calculated glucose values” shows that blanking/ignoring predicted SG values is envisioned by Mastrototaro, in particular values outside of the clipping limits). The combination does not teach that the determination to perform blanking is made based on the discrete wavelet decomposition, but Campetelli teaches detecting a fault based on the discrete wavelet decomposition (Campetelli, Abstract, “a fault detection system based on discrete wavelets transform (DWT) is applied here” & pg. 5, 2nd column, 2nd paragraph, “it can be easily seen how the wavelet detail at level 1 can detect the moment when the sensor gives the wrong measurement” & pg. 4, Fig. 4, “Typical failure”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to detect faults/failures/areas where the signal cannot be trusted based on the DWT (as does Campetelli) and then blank the signal at these areas (as does Mastrototaro). The motivation to do so is so as to not provide known incorrect/faulty values as output.
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, in view of Shao and Dobra, and further in view of Dinney, US PG Pub 2013/0058925.
Regarding Claim 4, the Mastrototaro/Shao/Dobra combination of Claim 1 teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Mastrototaro teaches learning the calibration with linear or non-linear regression (Mastrototaro, Abstract) but also teaches that that “other alternative embodiments may utilize singular and multiple, non-linear regression techniques” (Mastrototaro, column 16, lines 28-29). Shao teaches an ensemble method of creating a non-linear regression (Shao, pg. 1662, Abstract, “calibration methods … including non-linear approaches and ensemble techniques”) thus teaching wherein a first … sensor value is calculated and wherein a second … sensor value is calculated (Shao, pg. 1664, 2nd column, 1st paragraph, “Ensemble modeling is a statistical technique that combines the results of multiple individual models to produce a single prediction”). It would have been obvious to incorporate features, such as ensemble prediction, from Shao into the regression model for glucose prediction calibration of Mastrototaro (Mastrototaro, Abstract, “the calculation of the calibration characteristics is obtained using linear [or alternatively, non-linear] regression”) because both inventions are about the calibration of sensor-measured data. The motivation to do so is that “in ensemble modeling … multiple models will effectively identify and encode more aspects of the relationship between independent and dependent variables than a single model” (Shao, pg. 1663, 1st column, 3rd paragraph).
Dobra uses one the specific machine learning models of Claim 5 (regression decision tree), but none of Mastrototaro, Shao, nor Dobra teaches using genetic programming. However, Dinney teaches both of these methods (Dinney, [0011], “using … classification and regression trees (CART), or genetic programming”) to perform prediction. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use some of the non-linear predictive machine learning models of Dinney in the invention of Mastrototaro/Shao because each of the inventions concern physiological predictions from data. The motivation to do so is that the Mastrototaro/Shao combination teaches prediction with an ensemble of predictive models, and Dinney teaches that these predictive models can be used “to improve accuracy” (Dinney, [0011]).
Regarding Claim 5, the Mastrototaro/Shao/Dobra/Dinney combination of Claim 4 teaches the method of Claim 4 (and thus the rejection of Claim 4 is incorporated). The combination has already been shown to teach, through Shao (for the ensemble/fused SG) and Mastrototaro (for application to glucose), to teach fusing said first and second sensor glucose values to obtain a fused SG, wherein said final sensor glucose value is determined based on said fused SG (Shao, pg. 1664, 2nd column, 1st paragraph, “Ensemble modeling is a statistical technique that combines the results of multiple individual models to produce a single prediction”).
Claims 6-12 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, in view of Shao, Dobra, and Dinney, and further in view of Telson, US PG Pub 2011/0040163.
Regarding Claim 6, the Mastrototaro/Shao/Dobra/Dinney combination of Claim 5 teaches the method of Claim 5 (and thus the rejection of Claim 5 is incorporated). Mastrototaro does not teach, but Telson teaches, an electrochemical impedance spectroscopy (EIS) procedure for said working electrode (Telson, Abstract, “electrical impedance spectroscopy to adjust calibration settings in … an in vivo continuous glucose monitoring system”) to obtain a plurality of values of an EIS-based parameter for said electrode, wherein … SG is further calculated based on said values of the impedance-based parameter (Telson, [0013], “obtaining a references parameter value … performing EIS on the in vivo sensor to obtain an in vivo parameter value, and comparing the in vivo parameter value and the reference parameter value to identify the particular characteristics of the in vivo sensor” which are used in calibration, thus used, by Mastrototaro and Shao, to calculated said fused SG). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate EIS for glucose sensing, as does Telson, in the invention of Mastrototaro, because both inventions use electrode sensor current values to calculate and calibrate glucose sensors. The motivation to do so is that “the adjustments can compensate for the condition of the sensor membrane in vivo” (Telson, Abstract).
Regarding Claim 7, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 6 teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). The EIS procedure of Telson uses the EIS-based parameter of capacitance (Telson, [0014], “the reference parameter value and the in vivo parameter value may be capacitance values”) which is imaginary impedance (impedance is a complex value with imaginary and real parts of capacitance and resistance, respectively, which are both used in, for example, [0046-00047]).
Regarding Claim 8, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 6 teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). The EIS procedure of Telson uses the EIS-based parameter of resistance (Telson, [0043], “there are two parameters that may be extracted from the EIS measurement, R and C”) which is imaginary impedance (impedance is a complex value with imaginary and real parts of capacitance and resistance, respectively, which are both used in, for example, [0046-00047]).
Regarding Claim 9, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 6 teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). Mastrototaro further teaches smoothing said values of any of the measurements that are taken (Mastrototaro, column 10, lines 21-25, “In further alternatives, clipping may be applied to the sampled values, interval values, memory values, calculated glucose values, estimated values of a measured characteristic, or any combination of the values” where “clipping” is an example of smoothing) thus smoothing said values of the EIS-based parameter prior to calculating said fused SG would be obvious in light of the cited references because any measured or estimated values might be noisy. The motivation to do so is “to limit the signal magnitude variation from one value to the next thereby reducing the effects of extraneous data, outlying data points, or transients” (Mastrototaro, column 9, lines 7-9).
Regarding Claim 10, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 6 teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). The combination, through Mastrototaro and Shao (for fused) further teaches calculation of the fused SG is repeated periodically to generate a plurality of fused SG values over time (Mastrototaro, column 6, lines 44-47, “The glucose monitor might contain the necessary software to calibrate glucose sensor signals, display a real-time blood glucose value, show blood glucose trends, activate alarms and the like” where “trends” denotes to generate a plurality of … SG values over time).
Regarding Claim 11, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 6 teaches the method of Claim 6 (and thus the rejection of Claim 6 is incorporated). The combination, through Mastrototaro and Shao (for fused) further teaches wherein calculation of said fused SG is repeated continuously to generate a stream of fused SG values over time (Mastrototaro, column 4, lines 62-52, “a glucose monitor that is coupled to a sensor set to provide continuous data recording of readings of glucose levels from a sensor for a period of time” & column 6, lines 44-47, “The glucose monitor might … show blood glucose trends” where “trends” denotes a stream).
Regarding Claim 12, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 11 teaches the method of Claim 11 (and thus the rejection of Claim 11 is incorporated). The combination, through Mastrototaro and Shao (for fused) further teaches including smoothing one or more segments of said stream of fused SG values (Mastrototaro, column 10, lines 21-25, “In further alternatives, clipping may be applied to the sampled values, interval values, memory values, calculated glucose values” where “clipping” is an example of smoothing; also see column 17, lines 60-61, “readings are processed (filtered, smoothed, clipped, averaged, and the like)”).
Regarding Claim 14, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 11 teaches the method of Claim 11 (and thus the rejection of Claim 11 is incorporated). Mastrototaro (in light of Shao for fused) further teaches blanking one or more portions of said stream of fused SG values (Mastrototaro, column 9, lines 13-15, “interval values that are outside of the clipping limits are ignored” with column 10, lines 21-24, “clipping may be applied to the … calculated glucose values” shows that blanking/ignoring predicted SG values is envisioned by Mastrototaro, in particular values outside of the clipping limits can either be smoothed, i.e. replaces with the clipping limit value, or blanked, i.e. ignored and not used).
Regarding Claim 15, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 14 teaches the method of Claim 14 (and thus the rejection of Claim 14 is incorporated). Mastrototaro (in light of Shao for fused) further teaches wherein said blanking is based on a level of noise in said stream of fused SG values (Mastrototaro, column 9, lines 13-15, “interval values that are outside of the clipping limits are ignored” with column 9, lines 23-26, “the level that the clipping limits are set to is dependent on an acceptable amount of change … which is affected by the sensor sensitivity, signal noise, signal drift, and the like”).
Regarding Claim 16, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 14 teaches the method of Claim 14 (and thus the rejection of Claim 14 is incorporated). Mastrototaro (in light of Shao for fused) further teaches wherein said blanking is based on representative values of one or more of Isig, a counter electrode voltage (Vcntr), and said EIS-based parameter (Mastrototaro, column 10, lines 50-61, “each memory storage value is considered valid (Valid ISIG value) unless one of the following calibration cancellation events occurs … once a calibration cancellation event occurs, the successive memory storage values are not valid, and therefore not used to calculate blood glucose” that is, the fused SG values are blanked based on Isig values being invalid).
Regarding Claim 17, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 14 teaches the method of Claim 14 (and thus the rejection of Claim 14 is incorporated). Mastrototaro (in light of Shao for fused) further teaches wherein fused SG and said final SG are calculated in real time (Mastrototaro, column 6, line 28, “The glucose monitor takes raw glucose sensor data from the glucose sensor and assesses it during real-time”).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, in view of Shao, Dobra, Dinney, and Telson, and further in view of Abeyratne, US PG Pub 2011/0301487.
Regarding Claim 13, the Mastrototaro/Shao/Dobra/Dinney/Telson combination of Claim 12 teaches the method of Claim 12 (and thus the rejection of Claim 12 is incorporated). Mastrototaro teaches smoothing and filtering the data results (column 17, lines 60-61, “readings are processed (filtered, smoothed, clipped, averaged, and the like)”) but is silent regarding smoothing with a low-pass filter. Abeyratne teaches smoothing, i.e. removing noise, with a low-pass filter ([0174], “The digital signal … is passed to a band-pass digital filter to remove out-of-band noise” where “band-pass filter” is a low-pass filter in combination with a high-pass filter to remove both high and low frequency noise). It would have been obvious to one or ordinary skill in the art before the effective filing date to use a low-pass filter, like Abeyratne, to smooth the data of Mastrototaro, because both inventions need to smooth the data in their calculations. The motivation to do so is “to remove out-of-band noise” (Abeyratne, [0174]).
Claims 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mastrototaro, in view of Shao and Dobra, and further in view of Abeyratne.
Regarding Claim 19, the Mastrototaro/Shao/Dobra combination of Claim 18 teaches the method of Claim 18 (and thus the rejection of Claim 18 is incorporated). Mastrototaro teaches wherein the preprocessed Isig values are smoothed (column 10, lines 6-8, “Fig. 8 shows interval value R’, which is calculated by averaging sampled values N through Q”, see Fig. 8, sampled values are clipped and averaged, which results in smoothing) but does not do so using a polynomial model for local regression with weighted linear least squares. However, Abeyratne teaches this limitation (Abeyratne, [0137], “to reveal the slow changes and remove outliers [the] Loess Smoothening Method is used, which is based on a local regression using weighted linear least squares and a 2nd degree polynomial model”). It would have been obvious to one of ordinary skill in the art to incorporate this feature from Abeyratne into the invention of Mastrototaro because both inventions desire to smooth sensor data, i.e. “to reveal slow changes and remove outliers.” The motivation to do so is “in order to improve the performance … and to remove outliers” (Abeyratne, [0137]).
Regarding Claim 20, the Mastrototaro/Shao/Dobra/Abeyratne combination of Claim 19 teaches the method of Claim 19 (and thus the rejection of Claim 19 is incorporated). Mastrototaro further teaches calculating signal noise for said smoothed Isig values (Mastrototaro, column 10, lines 39-49, “unstable signal alarm limits are set to detect when memory storage values” i.e. smoothed Isig values “change too much … in essence, the glucose monitor has detected too much noise in the ISIG from the glucose sensor” denotes that signal noise for said smoothed Isig values has been calculated to determine if the noise is so great that the sensor needs to be re-calibrated or replaced).
Response to Arguments
Applicant’s arguments filed June 8th, 2026 have been fully considered, but are not fully persuasive.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection of the previous office action have been fully considered, but are not fully persuasive.
Applicant first argues that the rejection has failed to address the limitation of a glucose sensor including physical sensor electronics. The rejection does not mention those items; however, note that these elements are only recited in the preamble of the claim, as intended use of the method, and are not positively recited. Even if the glucose sensor were positively recited, it would merely represent extra-solution activity of collecting the data used in the mental process analysis in the claims, which can surely be demonstrated to be well-understood, routine, and conventional (e.g. collecting current data via a physical glucose sensor).
Applicant next states that “the office has not properly performed an analysis under Step 2A Prong Two to determine whether the claim as a whole integrates the alleged abstract idea into a practical application.” This analysis is, however, clearly performed in the previous and current rejections – additional elements a)-d) were analyzed to determine whether they integrate the abstract idea into a practical application, and it was determined (with rationales provided by MPEP 2106.05) that they do not and that the claim as a whole is directed to the recited abstract idea. This is a proper application of Step 2A Prong 2 of the subject-matter eligibility guidance.
Applicant finally argues that “the claim as a whole ‘improves other technology or technical field.” Applicant’s argument is unpersuasive – first, in that “retrospective calibration” is only recited in the preamble, and as such mere intended use cannot provide a practical application. Further, the statement “retrospective sensor systems have the ability to have the entire traces of raw signals available for use by an algorithm before processing and converting to glucose values” which describes merely a mental or mathematical process of collecting data, analyzing it, and presenting the results of the analysis, which is clearly recognized as subject-matter ineligible material (i.e. lacking integration into a practical application or significantly more than the abstract idea itself). Similarly, the argument that “the discrete wavelet composition is a used as a valuable tool” points to an improvement in the abstract idea of analyzing glucose measurement data itself, and not to an improvement in technology. Applicant further argues that the generic computer components “are not merely well-understood and routine elements, but instead, integrate any alleged abstract idea into a practical application” but this analysis appears to be conflating Step 2A Prong 2 of the guidance with Step 2B. Applicant’s argument regarding “without additional finger stick data” appears disconnected from the actual claim language of the claimed invention, which, in summary, appears to collect data, analyze it using mental and mathematical processes, and report the outcome of the analysis.
Applicant’s arguments regarding the 35 U.S.C. 103 rejections of the claims have been fully considered, and are alternatively unpersuasive or moot.
Applicant’s argument that “Shao does not disclose or suggest ‘… by using the discrete wavelet decomposition to select a model for determining the final SG value’” is moot, because new reference Dobra is used to teach a decision tree for selecting a model based on predictor attributes (where Isig and the discrete wavelet decomposition values are identified as predictor attributes for the regression target, in Mastrototaro and Shao).
Applicant’s argument that “Shao is in an entirely different technical field of analyzing near infrared spectroscopy … one of ordinary skill in the art would not look to Shao to utilize discrete wavelet decomposition in conjunction with determining sensor glucose values” is unpersuasive. Shao is indeed analogous art to the claimed invention – analyzing spectroscopy data to measure analytes in samples (i.e. glucose in blood is the measured analyte) - see Claim 6 of the claimed invention, to note that spectroscopy data is the data to be analyzed in the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Robinson, US PG Pub 2016/0058308 also teaches that values obtained by discrete wavelet decomposition are useful features in machine learning models for predicting characteristics associated with diabetes and blood glucose (Robinson, [0118]).
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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/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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