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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on January 12th, 2026 and February 10th, 2026 has been entered.
Claims 1, 3-12, and 14-19 remain pending in the application.
Response to Arguments
Applicant’s arguments and amendments, filed January 12th, 2026, with respect to the claim objections have been fully considered. The claim objections are withdrawn.
Applicant’s arguments, filed January 12th, 2026, with respect to the rejections under 35 U.S.C. 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 101
Claims 1, 3-12, and 14-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claims 1, 8, and 15 follows.
STEP 1
Regarding claims 1, 8, and 15, the claim recites a series of steps or acts and/or a series of structural elements including a system. Thus, the claims are directed to a process and/or a machine, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The steps of:
Partitioning the raw analyte data into a plurality of partitions;
Applying an adaptive filter separately to each of the plurality of partitions to generate rough filtered partitions having reduced noise;
Determining, for each of the plurality of partitions, a noise variance;
Constructing a noise variance profile based on the noise variance determined for each of the plurality of partitions;
And applying a smoothing algorithm across the rough filtered partitions to smooth the rough filtered partitions to generate a single smoothed signal trace.
set forth a judicial exception. These steps describe a concept performed in the human mind (including an observation, evaluation, judgment, opinion) (partitioning, determining, constructing) and/or Mathematical Concepts (applying an adaptive filter, constructing a noise variance profile, applying a smoothing algorithm). Thus, the claim is drawn to a Mental Process and/or Mathematical Concepts, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claims as a whole are analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claims 1, 8, 15 recites receiving raw analyte data corresponding to a noisy signal trace from an analyte sensor, generating a single smoothed signal trace, and displaying the smooth filtered data to the user, which is merely adding insignificant pre-solution and extra-solution activity to the judicial exception (MPEP 2106.05(g)). The received raw analyte data and generated/displayed single smoothed signal trace/smooth filtered data does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the raw analyte data or single smoothed signal trace/smooth filtered data, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. Besides the Abstract Idea, the claim recites additional steps of:
Receiving, by a processor, raw analyte data corresponding to a noisy signal trace from an analyte sensor;
Analyte sensor system
Memory
Processor
generating a single smoothed signal trace
displaying the smooth filtered data to the user
The receiving steps are well-understood, routine and conventional activities for those in the field of medical diagnostics. Further, the receiving, generating, and displaying steps are each recited at a high level of generality such that it amounts to insignificant pre-solution and extra-solution activity, e.g., mere data gathering and data-outputting steps necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the processor, memory, receiving, generating, and displaying do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claim 8, the system recited in the claim is a generic system comprising generic components configured to perform the abstract idea. The recited system and analyte sensor system are generic sensors configured to perform pre-solutional data gathering activity, and the processor/memory is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application. See the prior art of record: Sugimoto (US 5706423 A), in col. 1 lines 24-33, discloses a conventional data processor; Chang (US 20050114570 A1), in para. [0003-0004], discloses conventional data storage devices; Hasan (US 20160358439 A1) in para. [0028], discloses conventional non-transitory computer-readable media; Bohm (US 20120078071 A1) in para. [0013] discloses conventional continuous analyte sensor systems; Bidarahalli et al., "Noise Reduction in Continuous Blood Glucose Sensor using Physiology based Kalman Filter for Artificial Pancreatic System," 2018 3rd International Conference on Circuits, Control, Communication and Computing (14C), Bangalore, India, 2018, pp. 1-4, doi: 10.1109/CIMCA.2018.8739339.
The dependent claims also fail to add something more to the abstract independent claims. Claims 3-12, 14, and 16-19 are directed to more abstract ideas, which does not add anything significantly more. The steps recited in the independent claims maintain a high level of generality even when considered in combination with the dependent claims.
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 1-4, 7-8, 10-11, 13-15, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kamath (US 20150289819 A1) in view of Helal (US 20190384848 A1), and further in view of Facchinetti (Facchinetti A., et al., "Online Denoising Method to Handle Intraindividual Variability of Signal-to-Noise Ratio in Continuous Glucose Monitoring," IEEE Trans Biomed Eng (BME), vol. 58(9), 2011, pp. 2664-2671 – cited by IDS filed 9/7/23).
Regarding claim 1, Kamath discloses a method for noise reduction in analyte (Abstract), data the method comprising: receiving, by a processor (processor module 22, para. [0197]), raw analyte data corresponding to a noisy signal trace from an analyte sensor (“receives sensor data”; “raw data stream from a glucose sensor”, para. [0286-0289, 0293, 0483], figs. 7B-7C & 31); partitioning, by the processor (processor module 22; “noise detection algorithm”, para. [0197, 0483]), the raw analyte data into a plurality of partitions (section 608, 610, 612, fig. 31; “raw data streams … prior to data smoothing … noise episodes … time periods”; “noise detection algorithm was applied to detect a noise episode … noise episode was declared”, para. [0286-0289, 0484-0486], sections 72a, 72b, 74a, 74b, figs. 7B-7C); applying, by the processor (processor module 22 comprising a digital filter, para. [0197-0198]), an adaptive filter separately to each of the plurality of partitions to generate rough filtered partitions having reduced noise (“adaptively filter the data stream according to the aberrancy”; “FIR filter … IIR-filter … switched”; “section 608 … filtered data was obtained by applying a 3-point moving average window to the raw data”; “noise episode was declared … more aggressive filter was applied … section 610 … filtered data was obtained by applying a 7-point moving average window to the raw data … section 612 … filtered data was obtained by applying a 3-point moving average window to the raw data”, para. [0295, 0374, 0484-0486]); and applying, by the processor (processor module 22, para. [0197]), a smoothing algorithm across the rough filtered partitions to smooth the rough filtered partitions to generate a single smoothed signal trace (“smoothed data … data that has been modified to make it smoother and more continuous and/or to remove or diminish outlying points”; “smoothing algorithm”; “create smoother transitions in the data between the raw and filtered data and avoided false detections of noise episodes”, para. [0100-0101, 0285, 0325, 0480, 0488], as seen in fig. 31).
Kamath does not disclose each of the plurality of partitions having a predetermined length.
However, Helal directed to methods associated with filtering large time series of data and applying mathematical filtering operation to glucose monitor signals (para. [0018, 0025]) discloses a large time series of data 110 (fig. 1), partitioning, by a processor (processors, para. [0020]), the time series of data into a plurality of partitions (“the input time series of data 110 can be partitioned into sub-arrays 112-1, 112-2, 112-3”, para. [0029]), each of the plurality of partitions having a predetermined length (“the sizes of the partitions 112-n can be the same … determined based on machine parameters such as memory size and/or processing speed”, para. [0030-0031]). Helal further discloses that the filtering methods described achieve a reduction in time by about 50% or more to obtain a filtered output signal (para. [0019-0020, 0044-0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath such that each of the plurality of partitions have a predetermined length, in view of the teachings of Helal, for the obvious advantage of reducing an overall computation time when filtering large time series of data by incorporating the parallelized filtering methods on partitions of data.
Kamath further discloses that one method of determining signal artifacts is based on residual/differential thresholding and that in an alternative embodiment of signal artifact detection a time series analysis can be used to measure variance over consecutive windows of the raw data stream and detects signal artifacts based on the variance in order to determine a level of signal artifacts (para. [0322-0324]). Kamath, as modified by Helal hereinabove, does not disclose determining, by the processor, for each of the plurality of partitions, a noise variance; and constructing a noise variance profile based on the noise variance determined for each of the plurality of partitions.
However, Facchinetti directed to online denoising method to handle intraindividual variability of signal-to-noise ratio in continuous glucose monitoring discloses a plurality of partitions (page 1, I. INTRODUCTION, “time intervals”; “gray areas”, as seen in fig. 1), determining for each of the plurality of partitions, a noise variance (page 3, II. METHOD, “an estimate of the noise variance valid for time t … time window … (average σ2 = 24.7 mg2/dL2)”; & page 4 “ˆσ2 is obtained on a sliding window of N samples … estimation of the “average” σ2 value of the last 6 h … compared also with the profile obtained by a moving average of the true σ2 values in the past 6 h”; & page 6 “SNR … time window … σ2 is large (around 13)”); and constructing a noise variance profile based on the noise variance determined for each of the plurality of partitions (pages 4 & 6, figs. 2 & 5, B. Qualitative Analysis “ˆσ2 profile estimated by the new method … σ2 profile used to generate the noise for the considered realization is displayed in Fig. 2(d)”; IV. ASSESSMENT ON REAL DATA “estimate of the measurement noise variance profile, shown in panel (c) of Fig. 5, is realistic”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath, as modified by Helal hereinabove, such that the method further comprises determining, by the processor, for each of the plurality of partitions, a noise variance; and constructing a noise variance profile based on the noise variance determined for each of the plurality of partitions, in view of the teachings of Facchinetti, as this would aid in handling/coping with the intraindividual variability of the SNR by incorporating the denoising procedure able to cope with the intraindividual variability of the SNR to improve signal quality.
Regarding claim 3, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the method of claim 1, wherein the adaptive filter is configured to implement a filtering aggressiveness determined individually for each of the plurality of partitions (“noise analysis … used to determine how to process sensor data … more aggressive … filter … selectively chosen”; “noise episode … more aggressive filter was applied”, para. [0479-0480, 0484]).
Regarding claim 4, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the method of claim 1, wherein the rough filtered partitions corresponding to one of the plurality of partitions is discontinuous from the rough filtered partitions corresponding to a neighboring partition of the plurality of partitions (unlabeled, but as seen in fig. 17 the filtered signals 176 and 178 are discontinuous, “normal signal noise … FIR-filtered data signal 1766 … IIR-filtered glucose signal 178” para. [0372-0373]).
Regarding claim 7, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the method of claim 1, wherein the smoothing algorithm (“smoothing algorithm”, para. [0325]) is configured to smooth over missing data in the raw analyte data to generate the single smoothed signal trace as continuous through the missing data (“smoothing … continuous”; “signal estimation … data smoothing … estimate glucose signal values based on present and historical data”; “replace some of or the entire data stream … smoothed”; “IIR-estimated signal 144 …smoothed”, para. [0101, 0331,0340-0342], figs. 12-14 & 31 (see also para. [0363-0364])).
Regarding claim 8, Kamath discloses a system for noise reduction in analyte data (Abstract), the system comprising: an analyte sensor system (“system”, para. [0002], glucose sensor 10, figs. 1A-2,) configured to generate raw analyte data for a user (figs. 7B-7C, “raw data streams from an implantable glucose sensor”, para. [0286]); a memory comprising executable instructions (“instructions that drive a computer”; “memory … programming”, para. [0094, 0197]); a processer in data communication with the memory and configured to execute the instructions (processor module 22 comprising memory storage components … programming, para. [0094, 0197]) to: receive the raw analyte data corresponding to a noisy signal trace from the analyte sensor system (“receives sensor data”; “raw data stream from a glucose sensor”, para. [0286-0289, 0293, 0483], figs. 7B-7C & 31); partition the raw analyte data into a plurality of partitions having a number of data samples (section 608, 610, 612, fig. 31; “raw data streams … prior to data smoothing … noise episodes … time periods”; “noise detection algorithm was applied to detect a noise episode … noise episode was declared”, para. [0286-0289, 0484-0486], sections 72a, 72b, 74a, 74b, figs. 7B-7C); apply an adaptive filter separately to each of the plurality of partitions to generate rough filtered partitions having reduced noise (“adaptively filter the data stream according to the aberrancy”; “FIR filter … IIR-filter … switched”; “section 608 … filtered data was obtained by applying a 3-point moving average window to the raw data”; “noise episode was declared … more aggressive filter was applied … section 610 … filtered data was obtained by applying a 7-point moving average window to the raw data … section 612 … filtered data was obtained by applying a 3-point moving average window to the raw data”, para. [0295, 0374, 0484-0486]); apply a smoothing algorithm across the rough filtered partitions to smooth the rough filtered partitions to generate smooth filtered data (“smoothed data … data that has been modified to make it smoother and more continuous and/or to remove or diminish outlying points”; “smoothing algorithm”; “create smoother transitions in the data between the raw and filtered data and avoided false detections of noise episodes”, para. [0100-0101, 0285, 0325, 0480, 0488], as seen in fig. 31); and display the smooth filtered data to the user (“real-time sensor data display”; “substantially continuously … updating … real-time graphical representation of glucose data”, para. [0489-0490]).
Kamath does not disclose partitioning the raw analyte data into a plurality of partitions having a predetermined number of data samples.
However, Helal directed to methods associated with filtering large time series of data and applying mathematical filtering operation to glucose monitor signals (para. [0018, 0025]) discloses a processor (processors, para. [0020]) configured to receive a large time series of data 110 (fig. 1) and partition the time series of data into a plurality of partitions (“the input time series of data 110 can be partitioned into sub-arrays 112-1, 112-2, 112-3”, para. [0029-0030]) having a predetermined number of data samples (“112-1 containing data values (x.sub.i, x.sub.i+, . . . x.sub.i+10) … the sizes of the partitions 112-n can be the same … determined based on machine parameters”, para. [0030-0031]). Helal further discloses that the filtering methods described achieve a reduction in time by about 50% or more to obtain a filtered output signal (para. [0019-0020, 0044-0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath such that the processor is further configured to partition the raw analyte data into a plurality of partitions having a predetermined number of data samples, in view of the teachings of Helal, for the obvious advantage of reducing an overall computation time when filtering large time series of data by incorporating the parallelized filtering methods on partitions of data.
Kamath further discloses that one method of determining signal artifacts is based on residual/differential thresholding and that in an alternative embodiment of signal artifact detection a time series analysis can be used to measure variance over consecutive windows of the raw data stream and detects signal artifacts based on the variance in order to determine a level of signal artifacts (para. [0322-0324]). Kamath, as modified by Helal hereinabove, does not disclose the processor configured to determine, for each of the plurality of partitions, a noise variance; and construct a noise variance profile based on the noise variance determined for each of the plurality of partitions.
However, Facchinetti directed to online denoising method to handle intraindividual variability of signal-to-noise ratio in continuous glucose monitoring discloses a plurality of partitions (page 1, I. INTRODUCTION, “time intervals”; “gray areas”, as seen in fig. 1), determining, for each of the plurality of partitions, a noise variance (page 3, II. METHOD, “an estimate of the noise variance valid for time t … time window … (average σ2 = 24.7 mg2/dL2)”; & page 4 “ˆσ2 is obtained on a sliding window of N samples … estimation of the “average” σ2 value of the last 6 h … compared also with the profile obtained by a moving average of the true σ2 values in the past 6 h”; & page 6 “SNR … time window … σ2 is large (around 13)”); and constructing a noise variance profile based on the noise variance determined for each of the plurality of partitions (pages 4 & 6, figs. 2 & 5, B. Qualitative Analysis “ˆσ2 profile estimated by the new method … σ2 profile used to generate the noise for the considered realization is displayed in Fig. 2(d)”; IV. ASSESSMENT ON REAL DATA “estimate of the measurement noise variance profile, shown in panel (c) of Fig. 5, is realistic”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath, as modified by Helal hereinabove, such that the processor is configured to determine, for each of the plurality of partitions, a noise variance; and construct a noise variance profile based on the noise variance determined for each of the plurality of partitions, in view of the teachings of Facchinetti, as this would aid in handling/coping with the intraindividual variability of the SNR by incorporating the denoising procedure able to cope with the intraindividual variability of the SNR to improve signal quality.
Regarding claim 10, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the system of claim 8, wherein the adaptive filter is configured to implement a filtering aggressiveness determined individually for each of the plurality of partitions (“noise analysis … used to determine how to process sensor data … more aggressive … filter … selectively chosen”; “noise episode … more aggressive filter was applied”, para. [0479-0480, 0484]).
Regarding claim 11, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the system of claim 8, wherein the rough filtered partitions corresponding to one of the plurality of partitions is discontinuous from the rough filtered partitions corresponding to a neighboring partition of the plurality of partitions (unlabeled, but as seen in fig. 17 the filtered signals 176 and 178 are discontinuous, “normal signal noise … FIR-filtered data signal 1766 … IIR-filtered glucose signal 178” para. [0372-0373]).
Regarding claim 14, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the system of claim 8, wherein the smoothing algorithm (“smoothing algorithm”, para. [0325]) is configured to smooth over missing data in the raw analyte data to form the smooth filtered data to be continuous (“smoothing … continuous”; “signal estimation … data smoothing … estimate glucose signal values based on present and historical data”; “replace some of or the entire data stream … smoothed”; “IIR-estimated signal 144 …smoothed”, para. [0101, 0331,0340-0342], figs. 12-14 & 31 (see also para. [0363-0364])).
Regarding claim 15, Kamath discloses a non-transitory computer-readable medium comprising instructions which, when executed by a processor (“instructions”; process module 22 comprising memory storage components, para. [0094, 0197]), cause the processor to perform a method for noise reduction in analyte data (“programming”, para. [0197], Abstract), the method comprising: receiving raw analyte data corresponding to a noisy signal trace from an analyte sensor (“receives sensor data”; “raw data stream from a glucose sensor”, para. [0286-0289, 0293, 0483], figs. 7B-7C & 31); partitioning the raw analyte data into a plurality of partitions (section 608, 610, 612, fig. 31; “raw data streams … prior to data smoothing … noise episodes … time periods”; “noise detection algorithm was applied to detect a noise episode … noise episode was declared”, para. [0286-0289, 0484-0486], sections 72a, 72b, 74a, 74b, figs. 7B-7C); applying an adaptive filter separately to each of the plurality of partitions to generate rough filtered partitions having reduced noise (“adaptively filter the data stream according to the aberrancy”; “FIR filter … IIR-filter … switched”; “section 608 … filtered data was obtained by applying a 3-point moving average window to the raw data”; “noise episode was declared … more aggressive filter was applied … section 610 … filtered data was obtained by applying a 7-point moving average window to the raw data … section 612 … filtered data was obtained by applying a 3-point moving average window to the raw data”, para. [0295, 0374, 0484-0486]); and applying, by the processor (processor module 22, para. [0197]); and applying a smoothing algorithm across the rough filtered partitions to smooth the rough filtered partitions to generate a single smoothed signal trace (“smoothed data … data that has been modified to make it smoother and more continuous and/or to remove or diminish outlying points”; “smoothing algorithm”; “create smoother transitions in the data between the raw and filtered data and avoided false detections of noise episodes”, para. [0100-0101, 0285, 0325, 0480, 0488], as seen in fig. 31).
Kamath does not disclose each of the plurality of partitions having a predetermined length.
However, Helal directed to methods associated with filtering large time series of data and applying mathematical filtering operation to glucose monitor signals (para. [0018, 0025]) discloses a large time series of data 110 (fig. 1), partitioning, by a processor (processors, para. [0020]), the time series of data into a plurality of partitions (“the input time series of data 110 can be partitioned into sub-arrays 112-1, 112-2, 112-3”, para. [0029]), each of the plurality of partitions having a predetermined length (“the sizes of the partitions 112-n can be the same … determined based on machine parameters such as memory size and/or processing speed”, para. [0030-0031]). Helal further discloses that the filtering methods described achieve a reduction in time by about 50% or more to obtain a filtered output signal (para. [0019-0020, 0044-0046]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath such that each of the plurality of partitions have a predetermined length, in view of the teachings of Helal, for the obvious advantage of reducing an overall computation time when filtering large time series of data by incorporating the parallelized filtering methods on partitions of data.
Kamath further discloses that one method of determining signal artifacts is based on residual/differential thresholding and that in an alternative embodiment of signal artifact detection a time series analysis can be used to measure variance over consecutive windows of the raw data stream and detects signal artifacts based on the variance in order to determine a level of signal artifacts (para. [0322-0324]). Kamath, as modified by Helal hereinabove, does not disclose determining, for each of the plurality of partitions, a noise variance; and constructing a noise variance profile based on the noise variance determined for each of the plurality of partitions.
However, Facchinetti directed to online denoising method to handle intraindividual variability of signal-to-noise ratio in continuous glucose monitoring discloses a plurality of partitions (page 1, I. INTRODUCTION, “time intervals”; “gray areas”, as seen in fig. 1), determining, for each of the plurality of partitions, a noise variance (page 3, II. METHOD, “an estimate of the noise variance valid for time t … time window … (average σ2 = 24.7 mg2/dL2)”; & page 4 “ˆσ2 is obtained on a sliding window of N samples … estimation of the “average” σ2 value of the last 6 h … compared also with the profile obtained by a moving average of the true σ2 values in the past 6 h”; & page 6 “SNR … time window … σ2 is large (around 13)”); and constructing a noise variance profile based on the noise variance determined for each of the plurality of partitions (pages 4 & 6, figs. 2 & 5, B. Qualitative Analysis “ˆσ2 profile estimated by the new method … σ2 profile used to generate the noise for the considered realization is displayed in Fig. 2(d)”; IV. ASSESSMENT ON REAL DATA “estimate of the measurement noise variance profile, shown in panel (c) of Fig. 5, is realistic”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath, as modified by Helal hereinabove, such that the method further comprises determining, for each of the plurality of partitions, a noise variance; and constructing a noise variance profile based on the noise variance determined for each of the plurality of partitions, in view of the teachings of Facchinetti, as this would aid in handling/coping with the intraindividual variability of the SNR by incorporating the denoising procedure able to cope with the intraindividual variability of the SNR to improve signal quality.
Regarding claim 17, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the non-transitory computer-readable medium of claim 15, wherein the adaptive filter is configured to implement a filtering aggressiveness determined individually for each of the plurality of partitions (“noise analysis … used to determine how to process sensor data … more aggressive … filter … selectively chosen”; “noise episode … more aggressive filter was applied”, para. [0479-0480, 0484]).
Regarding claim 18, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the non-transitory computer-readable medium of claim 15, wherein the rough filtered partitions corresponding to one of the plurality of partitions is discontinuous from the rough filtered partitions corresponding to a neighboring partition of the plurality of partitions (unlabeled, but as seen in fig. 17 the filtered signals 176 and 178 are discontinuous, “normal signal noise … FIR-filtered data signal 1766 … IIR-filtered glucose signal 178” para. [0372-0373]).
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kamath in view of Helal and Facchinetti, as applied to claims 1, 8, and 15 above, and further in view of Liu (CN 114938951 A English Translation).
Regarding claim 5, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the method of claim 1. Kamath, as modified by Helal and Facchinetti hereinabove, does not disclose mirroring, by the processor, a portion of the raw analyte data corresponding to at least one of a beginning of the raw analyte data or an end of the raw analyte data to form mirrored data at the beginning or end of the raw analyte data for generation of the single smoothed signal trace; and removing, by the processor, a portion of the single smoothed signal trace corresponding to the mirrored data.
However, Liu directed to the field of data processing, discloses mirroring a portion of the raw data corresponding to at least one of a beginning of the raw data or an end of the raw data to form mirrored data at the beginning or end of the raw data (“mirroring and extending … data”; “sliding window … data needs to be mirrored and extended … starting from the sampling point 0 … size required to supplement the sliding window … completely traverse the … data”, para. [0034-0035, 0085-0086]) for generation of the single smoothed signal trace (“filtering and denoising the original one-dimensional data”; “data smoothing and filtering”, para. [0044, 0059]); and removing a portion of the single smoothed signal trace corresponding to the mirrored data (“after traversing … data that have not been mirrored and extended are retained as the actual”, para. [0102], fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath, as modified by Helal and Facchinetti hereinabove, such that the method further comprises mirroring, by the processor, a portion of the raw analyte data corresponding to at least one of a beginning of the raw analyte data or an end of the raw analyte data to form mirrored data at the beginning or end of the raw analyte data for generation of the single smoothed signal trace; and removing, by the processor, a portion of the single smoothed signal trace corresponding to the mirrored data, in view of the teachings of Liu, in order to prevent the situation that the sliding window cannot completely traverse the data and retaining data that has not been mirrored or extended as the actual data.
Regarding claim 12, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the system of claim 8. Kamath, as modified by Helal and Facchinetti hereinabove, does not expressly disclose wherein the processor is further configured to: mirror a portion of the raw analyte data corresponding to at least one of a beginning of the raw analyte data or an end of the raw analyte data to form mirrored data at the beginning or end of the raw analyte data for generation of the smooth filtered data; and remove a portion of the smooth filtered data corresponding to the mirrored data.
However, Liu directed to the field of data processing, discloses mirroring a portion of the raw data corresponding to at least one of a beginning of the raw data or an end of the raw data to form mirrored data at the beginning or end of the raw data (“mirroring and extending … data”; “sliding window … data needs to be mirrored and extended … starting from the sampling point 0 … size required to supplement the sliding window … completely traverse the … data”, para. [0034-0035, 0085-0086]) for generation of the smooth filtered data (“filtering and denoising the original one-dimensional data”; “data smoothing and filtering”, para. [0044, 0059]); and removing a portion of the smooth filtered data corresponding to the mirrored data (“after traversing … data that have not been mirrored and extended are retained as the actual”, para. [0102], fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath, as modified by Helal and Facchinetti hereinabove, such that the processor is further configured to: mirror a portion of the raw analyte data corresponding to at least one of a beginning of the raw analyte data or an end of the raw analyte data to form mirrored data at the beginning or end of the raw analyte data for generation of the smooth filtered data; and remove a portion of the smooth filtered data corresponding to the mirrored data, in view of the teachings of Liu, in order to prevent the situation that the sliding window cannot completely traverse the data and retaining data that has not been mirrored or extended as the actual data.
Regarding claim 19, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the non-transitory computer-readable medium of claim 15. Kamath, as modified by Helal and Facchinetti hereinabove, does not expressly disclose mirroring a portion of the raw analyte data corresponding to at least one of a beginning of the raw analyte data or an end of the raw analyte data to form mirrored data at the beginning or end of the raw analyte data for generation of the single smoothed signal trace; and removing a portion of the signal smoothed signal trace corresponding to the mirrored data.
However, Liu directed to the field of data processing, discloses mirroring a portion of the raw data corresponding to at least one of a beginning of the raw data or an end of the raw data to form mirrored data at the beginning or end of the raw data (“mirroring and extending … data”; “sliding window … data needs to be mirrored and extended … starting from the sampling point 0 … size required to supplement the sliding window … completely traverse the … data”, para. [0034-0035, 0085-0086]) for generation of the single smoothed signal trace (“filtering and denoising the original one-dimensional data”; “data smoothing and filtering”, para. [0044, 0059]); and removing a portion of the single smoothed signal trace corresponding to the mirrored data (“after traversing … data that have not been mirrored and extended are retained as the actual”, para. [0102], fig. 6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath, as modified by Helal and Facchinetti hereinabove, such that the method further comprises: mirroring a portion of the raw analyte data corresponding to at least one of a beginning of the raw analyte data or an end of the raw analyte data to form mirrored data at the beginning or end of the raw analyte data for generation of the single smoothed signal trace; and removing a portion of the signal smoothed signal trace corresponding to the mirrored data, in view of the teachings of Liu, in order to prevent the situation that the sliding window cannot completely traverse the data and retaining data that has not been mirrored or extended as the actual data.
Claims 6, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kamath in view of Helal and Facchinetti, as applied to claims 1, 8, and 15 above, and further in view of Beck-Nielsen (US 20090287107 A1).
Regarding claim 6, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the method of claim 1. Kamath, as modified by Helal and Facchinetti hereinabove, does not expressly disclose at least one of the plurality of partitions overlaps another of the plurality of partitions.
However, Beck-Nielsen directed to the analysis of EEG signals to detect hypoglycaemia discloses wherein at least one of the plurality of partitions overlaps another of the plurality of partitions (“individual optimization … duration of the time segment or of the overlap of one time segment with the next”; “time segments may overlap so that data from the end of one time period is re-used in the next”, para. [0026, 0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath, as modified by Helal and Facchinetti hereinabove, such that at least one of the plurality of partitions overlaps another of the plurality of partitions, in view of the teachings of Beck-Nielson, as such a modification would have been the result of routine optimization of the duration of the time segments such that data from the end of one time period is re-used in the next.
Regarding claim 9, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the system of claim 8. Kamath, as modified by Helal and Facchinetti hereinabove, does not expressly disclose at least one of the plurality of partitions overlaps another of the plurality of partitions.
However, Beck-Nielsen directed to the analysis of EEG signals to detect hypoglycaemia discloses wherein at least one of the plurality of partitions overlaps another of the plurality of partitions (“individual optimization … duration of the time segment or of the overlap of one time segment with the next”; “time segments may overlap so that data from the end of one time period is re-used in the next”, para. [0026, 0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath, as modified by Helal and Facchinetti hereinabove, such that at least one of the plurality of partitions overlaps another of the plurality of partitions, in view of the teachings of Beck-Nielson, as such a modification would have been the result of routine optimization of the duration of the time segments such that data from the end of one time period is re-used in the next.
Regarding claim 16, Kamath, as modified by Helal and Facchinetti hereinabove, discloses the non-transitory computer-readable medium of claim 15. Kamath, as modified by Helal and Facchinetti hereinabove, does not expressly disclose at least one of the plurality of partitions overlaps another of the plurality of partitions.
However, Beck-Nielsen directed to the analysis of EEG signals to detect hypoglycaemia discloses wherein at least one of the plurality of partitions overlaps another of the plurality of partitions (“individual optimization … duration of the time segment or of the overlap of one time segment with the next”; “time segments may overlap so that data from the end of one time period is re-used in the next”, para. [0026, 0056]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kamath, as modified by Helal and Facchinetti hereinabove, such that at least one of the plurality of partitions overlaps another of the plurality of partitions, in view of the teachings of Beck-Nielson, as such a modification would have been the result of routine optimization of the duration of the time segments such that data from the end of one time period is re-used in the next.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Kruse (US 20220192544 A1) directed to a process to continuously determine a current glucose level in blood of an organism and discloses a process for predicting the noise variance of the measurement noise and regularly adjusting the variance of the measurement noise based on the level of the sum of the horizon of the Moving Horizon Estimation method and the number of the previous measurements to calculate the estimation of the measurement noise and/or the process noise (para. [0037-0038, 0080-0096]).
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/A.E.H./Examiner, Art Unit 3791
/AURELIE H TU/Primary Examiner, Art Unit 3791