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 .
Status of Claims
This action is in response to the amendment filed on 04/23/2026 for application 18/312,193, in which:
Claims 1, 10, 11, and 12 are independent claims.
Claims 1-3 and 5-13 are currently amended.
Claim 4 is canceled.
Claim 15 is newly added.
Claims 1-3 and 5-15 are currently pending.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. DE10 2022 111 387.6, filed on 05/06/2022.
Prior Art References
Krishnan, S.R. and Seelamantula, C.S., 2012. On the selection of optimum Savitzky-Golay filters. IEEE transactions on signal processing, 61(2), pp.380-391. (Hereafter, “Krishnan”).
US 6801661 B1 - Method And System For Archival And Retrieval Of Images Based On The Shape Properties Of Identified Segments (Hereafter, “Sotak”).
US 20020180613 A1 - Digital signal receiver for measurement while drilling system having noise cancellation (Hereafter, “Shi”).
Wikipedia contributors, 'Electrocardiography', Wikipedia, The Free Encyclopedia, 28 December 2020, 16:58 UTC,
<https://en.wikipedia.org/w/index.php?title=Electrocardiography&oldid=996790421> [accessed 10 January 2026] (Hereafter, “Wiki”).
Response to Arguments
Applicant's arguments filed 04/23/2026 have been fully considered but they are not persuasive.
Regarding the 35 USC § 101 Rejections:
Applicant's arguments regarding the 35 U.S.C. 101 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant disagrees with the 101 rejections (Page 10). The Applicant supports the disagreements with noting that the independent claims include an adjustable filter having parameters set according to the data the filter is filtering; where the filter’s parameters are initially set based on training data as shown by “based on a frequency spectrum of the measured values of the training data". With using the initial filter parameters, measured values are then filtered and a fractal analysis of that filtered data is performed. These assertions are supported within the specification ([0088]).
Examiner respectfully disagrees. The previous and current rejections for the amended claims are directed to an abstract idea (Step 2A Prong 1) and do not integrate the abstract idea into a practical application (Step 2A Prong 2). The limitation recites “providing a filter having an adjustable filter strength” (which is being evaluated under MPEP 2106.05(f) as to merely provide an adjustable filter type is merely utilizing a computer as a tool to perform the abstract idea), ... setting the adjustable filtering strength to a predetermined initial filtering strength that is determined based on a frequency spectrum of the measured values of the training data (which is being evaluated under MPEP 2106.04 as a mental process as a human being can mentally perform evaluation and make a judgement to set an adjustable filter strength to a specific initial value based on a determination which is a mental step), filtering via the filter the measured values included in the training data and determining a fractal dimension of the filtered values provided by the filter (which is being evaluated under MPEP 2106.04 as a mental process as a human being can mentally perform evaluation and make a judgement to determine a fractal dimension where the limitation merely includes generic computing equipment). Eligibility must be assessed based on the claim as a whole. When viewed as an integrated whole, the claims limitation is further applying the abstract idea(s) and merely storing and/or retrieving information in memory. The additional elements recited within the independent claim only recites performance of an abstract idea within a computer, or data gathering for the abstract idea to a particular technological environment; thus, as the additional elements fall within MPEP 2106.05 they are unable to integrate the judicial exception as they are unable to provide significantly more. Examiner notes that “providing” the “filtering result” could be interpreted under a broadest reasonable interpretation as reading the values from memory.
Applicant further supports their assertion (Page 10), by noting that the filtering strength may be increased (by modifying the filter's parameters) and another fractal analysis of the data may be performed. This process of increasing the filtering strength and performing a fractal analysis is continued until the decay of the fractal dimensions drops below a threshold.
Examiner respectfully disagrees. The modification of filter parameters by iteratively repeating a process of a parameterizing by increasing filters is merely adjusting the abstract idea via evaluating the filter parameters and comparing of fractal dimension values that are determined; thus, the limitation is being evaluated under Step 2A Prong 1. The additional elements noted within Step 2A Prong 2 are unable to amount to significantly more than the judicial exception (when evaluated individually and holistically). The claims are not a technical solution to a technical problem as the independent claim is merely performing abstract ideas with computing tools and data gathering. Thus, the additional elements are not able to integrate the abstract ideas in a practical application. The claims are directed towards the improvement of an abstract idea. Therefore, the claims do not integrate the judicial exception into a practical application.
Applicant asserts (Page 10), that the method described is an improvement in the parameterizing of a filter. Applicant's initial setting of the filter parameters based on the frequency analysis and the subsequent tuning of these parameters using the fractal analysis is done “in an autonomous entirely data driven manner, that neither requires an expert analysis of the data nor any prior knowledge of the properties of the measured values and the properties of the noise” (Specification [0027]). Therefore, Applicant respectfully submits that the claimed parameterizing of a filter is patentable subject matter under § 101. Applicant respectfully submits the rejection of claims 1-14 under § 101 is therefore overcome and should be withdrawn.
Examiner respectfully disagrees. As noted above, The claims are directed towards the improvement of an abstract idea. Therefore, the claims do not integrate the judicial exception into a practical application. The amended claim does clarify and add more detail of the measuring method for determining and providing results; however, the claims are directed towards the improvement of an abstract idea. Improvements to an abstract idea are still considered to an abstract idea. The extensiveness of numerical computations or amount of computations do not dictate judicial exception from being a mental process as merely invoking computers or machinery as a tool to perform abstract idea are mere instructions to apply it. The independent claim fails to recite the steps that achieve the improvement. The pending Claims are directed to a judicial exception due to reciting limitations which fall within the “mental processes” group of abstract ideas; where the judicial exception is unable to be directed to significantly more than the judicial exception due to the pending Claims not including additional elements that contribute to an “inventive concept”. The amended claims do not integrate the judicial exception into a practical application nor amount to significantly more. Although the Claims are interpreted in light of the specification, limitations from the specification are not read into the Claims.
MPEP 2106.05(a) recites:
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology … the claim must include the components or steps of the invention that provide the improvement described in the specification
…
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below.
Applicant fails to show how any alleged technical improvement would be provided by anything more than the judicial exception on its own. Additionally, applicant fails to show how the claim includes components or steps that would provide the alleged improvement described in the specification or by the cited case law. By MPEP 2106.05(f)(1), "the claim recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished". Moreover, the examiner maintains that the Claim does not impose any meaningful limits on the judicial exceptions. As noted in the rejection, due to the additional elements falling under MPEP 2106.05, the judicial exception is not integrated into a practical application. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1, similar independent claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. More specific details are discussed below within the 35 USC § 101 Rejections.
Regarding the 35 USC § 103 Rejections:
Applicant's arguments regarding the 35 U.S.C. 103 rejections of the previous office action have been fully considered, but are unpersuasive.
Applicant asserts (Pages 11-12), that due to specification supported amended claims 1, similar independent claims (12 and 13), and the newly added claim 15 should be considered; thus, the application is allowable. Applicant further support their assertions by noting Graham v. John Deere & KSR, and notes that the claim needs to be considered in its entirety and the initial burden is on the examiner to establish a prima facie case of obviousness. Applicant submits that the currently pending claims are patentable over the cited art for at least the following reasons.
Examiner respectfully disagrees. The office action establishes a proper and well-supported prima facie case as the claims are explained to be not patentable over the prior art(s) under the guidance of the MPEP; where each limitation is explicitly disclosed via combination of the references. More explained below within the response to arguments and within the office action. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant further support the claims being patentable/allowable (Page 12), as the amended claims recite “setting the adjustable filtering strength to a predetermined initial filtering strength that is determined based on a frequency spectrum of the measured values of the training data” which overcome the Krishnan and Sotak prior art references, whether considered separately or in combination, as the prior arts fail to disclose, teach or suggest all elements of independent claim 1.
Examiner respectfully disagrees. The “setting the adjustable filtering strength to a predetermined initial filtering strength [that is determined based on a frequency spectrum] of the measured values of the training data” is taught by Krishan. However, the newly amended limitation which recites “that is determined based on a frequency spectrum [of the measured values of the training data]” is not taught by Krishnan/Sotak. Nevertheless, further search and consideration, as required, was done by the examiner and the newly amended limitation is taught by the newly cited reference Shi. Necessitated by the amendment, the rejection has been updated. All elements of the independent claim 1 is taught by the combination of Krishnan/Sotak/Shi.
Applicant asserts (Page 12), that that the combination of Krishna and Sotak does not support a prima facie case of an obviousness rejection of Applicant's amended independent claim 1. Applicant respectfully submits that it is not at all obvious to use the fractal analysis of spatial data as taught in Sotak to perform a fractal analysis of time series data as recited in Applicant's amended claim 1; where Sotak teaches fractals calculated from a perimeter length of an identified shape in an image. Neither Krishnan's data nor Applicant's data are spatial and therefore have no perimeters or lengths from which to calculate such fractals. The fractal analysis of the time series data must therefore be different from that of the spatial data, and the Office Action has not demonstrated that the spatial fractals of Sotak may be applied to the fractal analysis of Krishnan' s or Applicant's time series data.
Examiner respectfully disagrees. Krishnan teaches using the “SURE objective (divergence term in higher dimensions) can be computed analytically only when the dependence of the denoising function on the parameters to be optimized is of a certain form” (Page 384) and also notes “that the index n here need not necessarily refer to time”. Sotak teaches spatial analysis as noted by Applicant. The fractal analysis of spatial data is more complex of Sotak; thus, performing fractal analysis on a lower dimensional data type (mathematically). Thus, the Sotak’s fractal analysis can be applied to the teaching of Krishnan.
Applicant asserts (Page 13), that the cited prior arts do not teach setting an initial filter strength "that is determined based on a frequency spectrum of the measured values of the training data" as recited in Applicant's amended independent claim 1, and therefore the combination of Krishnan and Sotak does not teach all limitations of Applicant's amended independent claim 1.
Examiner respectfully disagrees. Applicant’s arguments with respect to the independent claims(s) 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.
Applicant respectfully submits (Page 13), that independent claim 1 is patentable over Krishnan and Sotak because neither Krishnan nor Sotak, taken either separately or in combination, discloses, teaches or suggests all elements of claim 1. Therefore, the rejection of claim 1 under 35 U.S.C. § 103 as being obvious in view of Krishnan and Sotak is overcome and should be withdrawn. Consequently, Applicant respectfully requests withdrawal of the rejection of claim 1, and of claims 2-3 and 5-11 depending therefrom, and allowance of the same. Independent claims 12 and 13 have been amended in the same manner as that of independent claim 1. Therefore, Applicant respectfully submits independent claims 12 and 13 are patentable for at least the reasons supporting the patentability of independence claim 1. Consequently, Applicant respectfully requests withdrawal of the rejection of claims 12 and 13, and of claims 14-15 depending from claim 13, and allowance of the same. Response to
Examiner respectfully disagrees. As noted above and within the rejection, the combination of Krishan/Sotak/Shi teach all elements of the independent claims and is obvious under 35 U.S.C. § 103. As stated previously, the rejection follows the steps of the analysis as laid out in the MPEP which was followed for the previous and current examination (see MPEP 2106). The amended independent claim rejections have been updated with a new reference to explicitly teach elements of the newly added limitation. Applicant’s arguments regarding the other independent and dependent claims rely upon the same assertions as with respect to Claim 1, and are thus likewise unpersuasive. Therefore, for the reasons given above and in the updated rejections below, the rejection to all Claims (including Claim 1, analogous independent Claims, and all dependent Claims) are maintained and updated as necessitated by Claim amendments. More specific details are discussed below within the 35 USC § 103 Rejections.
Claim Rejections - 35 U.S.C. § 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.
Claim(s) 1-3 and 5-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. This judicial exception is not integrated into a practical application as outlined in the 2-step analyses for each claim that follows.
Combined Step 1 (Statutory Category) - Is the claim to a process, machine, manufacture or composition of matter?
Yes –
Claims 1-3 and 5-11 recite methods.
Claims 12-15 recite machines.
In reference to claim 1.
Step 2A Prong 1 (Recited Judicial Exception) - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“based on training data, including measured values measured by the measurement device during a training time interval, parameterizing the filter by:
setting the adjustable filtering strength to a predetermined initial filtering strength that is determined based on a frequency spectrum of the measured values of the training data;
filtering via the filter the measured values included in the training data and determining a fractal dimension of the filtered values provided by the filter; and
iteratively repeating the parameterizing of the filter by increasing the filtering strength of the filter to a higher filtering strength and by subsequently filtering the measured values and determining a fractal dimension of the filtered values determined by the filter having the higher filtering strength until a decay of the fractal dimensions determined at the end of each iteration of the parameterizing drops below a predetermined threshold;
determining the measurement result by removing noise included in the measured values by:
putting the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration of the parameterizing;
via the parameterize d filter, filtering the measured values of the measurand; and”
which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
Step 2A Prong 2 (Integration into a Practical Application) - Does the claim recite additional elements that integrate the judicial exception into a practical application? & Step 2B (Significantly More or Amounting to an Inventive Concept) - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“providing a measurement device configured to repeatedly or continuously measure measured values of the measurand; providing a filter having an adjustable filter strength;”
which merely recites the words apply it (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
“recording data including measured values of the measurand measured by the measurement device and their time of measurement;”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) iv. Storing and retrieving information in memory.
“providing the measurement result including filtered values of the measured values of the measurand determined by the parameterize d filter”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) iv. Storing and retrieving information in memory. Examiner notes that “providing” the “filtering result” could be interpreted under a broadest reasonable interpretation as reading the values from memory.
In reference to claim 2.
“wherein the filter is a parametrizable filter, a smoothing filter, a sliding window filter, a moving average filter, a Savitzky-Golay filter, a wavelet decomposition filter, an autoregressive filter (AR-filter), an autoregressive moving average filter (ARMA-filter), an autoregressive integrated moving average filter (ARIMA-filter), an autoregressive moving average filter (ARIMA filter) configured to filter the measured values (mv) based on an autoregressive integrated moving average model (ARIMA model), a seasonal autoregressive moving average filter (SARIMA-filter), a network filter, a neural network filter, or a neural network filter including a neural network, a recurrent neural network, a convolutional neural network or a Long short-term memory (LSTM).”
which only provides further details regarding the filtering algorithm to be used and thus is still a mental process based on the parent claim.
In reference to claim 3.
“wherein the filter is configured to operate based on parameter settings that are adjustable in a manner that enables for the filtering strength of the filter to be set to a number of different predetermined filtering strengths.”
which only provides further details regarding the parameterization of the filter and thus is still a mental process based on the parent claim.
In reference to claim 5.
“wherein the training data is unlabeled data and/or includes a predetermined number of measured values and/or measured values that have been measured during an initial and/or predetermined training time interval or an arbitrarily selected time interval of a predetermined duration.”
which only provides further details regarding the structure of the data and thus is still a mental process based on the parent claim.
In reference to claim 6.
“wherein each iteration includes a step of determining the decay of the fractal dimensions: as or based on a ratio of the fractal dimension of the filtered values determined during the respective iteration and a fractal dimension (do) of the unfiltered measured values included in training data, or as or based on a ratio of the fractal dimension of the filtered values determined during the respective iteration and the fractal dimension of the filtered values determined during the previous iteration, or based on three or more of the previously determined fractal dimensions and/or based on a property of a function fitted to several or all previously determined fractal dimensions.”
which only provides further details regarding the structure of the fractal dimension computation and thus is still a mental process based on the parent claim.
In reference to claim 7.
Step 2A Prong 1 (Recited Judicial Exception) - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“at least once, periodically, or repeatedly updating the parametrization of the filter;”
which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer. Examiner notes that updating the filter parameters may be performed mentally.
“and subsequently determining [and providing] the filtering result with the filter operating based on the updated parametrization,”
which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“wherein each updated parametrization is determined by repeating the determination of the parametrization of the filter based on data included in the recorded data that includes at least one measured value of the measurand that has been determined and/or recorded after the previous parametrization of the filter has been determined.”
which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
Step 2A Prong 2 (Integration into a Practical Application) - Does the claim recite additional elements that integrate the judicial exception into a practical application? & Step 2B (Significantly More or Amounting to an Inventive Concept) - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“and subsequently [determining and] providing the filtering result with the filter operating based on the updated parametrization,”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) iv. Storing and retrieving information in memory. Examiner notes that “providing” the “filtering result” could be interpreted under a broadest reasonable interpretation as reading the values from memory.
In reference to claim 8.
“wherein each updated parametrization is determined based on data included in the recorded data that has been determined and/or recorded during a time interval of a predetermined duration preceding the point in time, when the respective updated parametrization is determined.”
which only provides further details regarding how the parameterization is updated and thus is still a mental process based on the parent claim.
In reference to claim 9.
“wherein the parametrization is updated: periodically after predetermined re-parametrization time intervals, after an event that may have an impact on properties of the measured values of the measurand and/or on properties of the noise included in the measured values has occurred, and/or when a given number larger or equal to one of measured values has been determined and/or recorded after the parametrization has last been determined.”
which only provides further details regarding how the parameterization is update and thus is still a mental process based on the parent claim.
In reference to claim 10.
Step 2A Prong 1 (Recited Judicial Exception) - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“includes determining [and providing] a residue between the measured values and the filtered values”
which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
Step 2A Prong 2 (Integration into a Practical Application) - Does the claim recite additional elements that integrate the judicial exception into a practical application? & Step 2B (Significantly More or Amounting to an Inventive Concept) - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“includes [determining and] providing a residue between the measured values and the filtered values”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) iv. Storing and retrieving information in memory.
In reference to claim 11.
Step 2A Prong 1 (Recited Judicial Exception) - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“performing the method of determining and providing the measurement result of the measurand according to claim 1 for two or more measurands;”
which recites the same mental process of the parent claim.
“monitoring, regulating and/or controlling the measurand or at least one of the measurands, monitoring, regulating and/or controlling an operation of a plant or facility and/or monitoring, regulating and/or controlling at least one step of a process performed at an application, where the measurement device is employed, based on the measurement result; and”
which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
Step 2A Prong 2 (Integration into a Practical Application) - Does the claim recite additional elements that integrate the judicial exception into a practical application? & Step 2B (Significantly More or Amounting to an Inventive Concept) - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“providing the measurement result of the measurand to a superordinate unit configured to monitor, to regulate and/or to control the respective measurand, an operation of a plant or facility, and/or at least one step of a process performed at the application, where the measurement device determining the measured values of the measurand is employed.”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) i. Receiving or transmitting data over a network.
In reference to claim 12.
Step 2A Prong 1 (Recited Judicial Exception) - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“a measurement unit configured to determine [and to provide] measured values of a measurand; a computing means, a memory associated with the computing means, and a computer program installed on the computing means which, when the computer program is executed by the computing means, causes the computing means to:”
which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“based on training data , including measured values measured by the measurement unit during a training time interval, parameterize a filter having an adjustable filtering strength by:
setting the adjustable filtering strength to a predetermined initial filtering strength that is determined based on a frequency spectrum of the measured values of the training data;
filtering via the filter the measured values included in the training data and determining a fractal dimension of the filtered values provided by the filter; and
iteratively repeating the parameterizing of the filter by increasing the filtering strength of the filter to a higher filtering strength and by subsequently filtering the measured values and determining the fractal dimension of the filtered values determined by the filter having the higher filtering strength until a decay of the fractal dimensions determined at the end of each iteration of the parameterizing drops below a predetermined threshold;
determine the measurement result by removing noise included in the measured values by:
putting the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration of the parameterizing; and
via the parameterize d filter, filtering the measured values of the measurand; and”
which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
Step 2A Prong 2 (Integration into a Practical Application) - Does the claim recite additional elements that integrate the judicial exception into a practical application? & Step 2B (Significantly More or Amounting to an Inventive Concept) - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“a measurement unit configured [to determine and] to provide measured values of a measurand; a computing means, a memory associated with the computing means, and a computer program installed on the computing means which, when the computer program is executed by the computing means, causes the computing means to:”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) iv. Storing and retrieving information in memory.
“record data including measured values of the measurand that are repeatedly or continuously measured and provided by the measurement unit and their time of determination;”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) iv. Storing and retrieving information in memory.
“provide the measurement result including the filtered values of the measured values of the measurand determined by the parameterized filter.”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) iv. Storing and retrieving information in memory.
In reference to claim 13.
Step 2A Prong 1 (Recited Judicial Exception) - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“for each measurand, a measurement device configured to determine [and provide] measured values of the respective measurand; a computing means connected to and/or communicating with each measurement device and configured to receive the measured values of each measurand; a memory associated with the computing means; and a computer program installed on the computing means which, when the program is executed by the computing means, causes the computing means, for each measurand, to:”
which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
“based on training data, including measured values measured by the measurement unit during a training time interval, parameterize a filter having an adjustable filtering strength by:
setting the adjustable filtering strength to a predetermined initial filtering strength that is determined based on a frequency spectrum of the measured values of the training data;
filtering via the filter the measured values included in the training data and determining a fractal dimension of the filtered values provided by the filter; and
iteratively repeating the parameterizing of the filter by increasing the filtering strength of the filter to a higher filtering strength and by subsequently filtering the measured values and determining the fractal dimension of the filtered values determined by the filter having the higher filtering strength until a decay of the fractal dimensions determined at the end of each iteration of the parameterizing drops below a predetermined threshold;
determine the measurement result of the respective measurand by removing noise included in the measured values by:
putting the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration of the parameterizing; and
via the parameterized filter, filtering the measured values of the measurand; and”
which, but for the inclusion of generic computing equipment, is an evaluation that may be performed mentally by a human with the aid of pen and paper. Refer to MPEP 2106.04(a)(2)(III)(C) for more information about mental processes being performed on a computer.
Step 2A Prong 2 (Integration into a Practical Application) - Does the claim recite additional elements that integrate the judicial exception into a practical application? & Step 2B (Significantly More or Amounting to an Inventive Concept) - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“for each measurand, a measurement device configured [to determine] and provide measured values of the respective measurand; a computing means connected to and/or communicating with each measurement device and configured to receive the measured values of each measurand; a memory associated with the computing means; and a computer program installed on the computing means which, when the program is executed by the computing means, causes the computing means, for each measurand, to:”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) i. Receiving or transmitting data over a network and iv. Storing and retrieving information in memory.
“record data including measured values of the measurand that are repeatedly or continuously measured and provided by the respective measurement device and their time of determination;”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) iv. Storing and retrieving information in memory.
“provide the measurement result including the filtered values of the measured values of the measurand determined by the parameterized filter.”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) iv. Storing and retrieving information in memory.
In reference to claim 14.
Step 2A Prong 1 (Recited Judicial Exception) - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – the abstract idea of the parent claim.
Step 2A Prong 2 (Integration into a Practical Application) - Does the claim recite additional elements that integrate the judicial exception into a practical application? & Step 2B (Significantly More or Amounting to an Inventive Concept) - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“the computing means is located in an edge device, in a superordinate unit or in the cloud, and”
which merely recites the words apply it (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
“at least one or each measurement device is connected to and/or communicating with the computing means directly, via a superordinate unit, via an edge device located in the vicinity of the respective measurement device, and/or via the internet.”
which amounts to insignificant extra-solution activity per MPEP2106.05(g). This is well-understood, routine, conventional computer functionality as recognized by MPEP2106.05(d)(II) i. Receiving or transmitting data over a network and iv. Storing and retrieving information in memory.
In reference to claim 15.
“wherein the measurement result of the measurand or the measurement result of at least one of the measurands determined and provided by the measurement system further includes a residue between the measured values and the filtered values.”
which only provides further details regarding the determining/providing of the measurement system in terms of including a residue and the measured/filtered values; thus, is still a mental process based on the parent claim.
Claim Rejections - 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-10, and 12-15 is/are rejected under 35 U.S.C. 103 as being unpatentable
over Krishnan
in view of Sotok.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable
over Krishnan
in view of Sotok
in further view of Wiki.
In reference to claim 1.
“1. A measuring method for determining and providing a measurement result of a measurand, the measuring method comprising:”
Krishnan teaches:
“providing a measurement device configured to repeatedly or continuously measure measured values of the measurand;”
(Krishnan 380, “REAL-WORLD signals such as speech, electrocardiogram (ECG) and geophysical signals are time-varying in one or more properties ... second requirement stems from the fact that real-world measurements suffer from noise.”)
(Krishnan 397, Fig 7)
Figure 7(a) depicts the measurement device (ECG) to repeatedly/continuously measure the ECG measurement
“providing a filter having an adjustable filter strength;”
(Krishnan Algorithm 1, “L ← Lmin ... L ← L + 2”)
“L” teaches the “adjustable filtering strength”.
“Lmin” teaches the “predetermined initial filtering strength” which is then adjusted via “L ← L + 2”
“recording data including measured values of the measurand measured by the measurement device and their time of measurement;”
(Krishnan Abstract, “We consider the algorithm performance on real-world electrocardiogram (ECG) signals.”)
(Krishnan 380, “REAL-WORLD signals such as speech, electrocardiogram (ECG) and geophysical signals are time-varying in one or more properties.”)
(Krishnan 397, Fig 7)
Figure 7 depicts via graphs the measured by the measurement device and the time of measurement via the time index (x-axis)
“based on training data, including measured values measured by the measurement device during a training time interval, parameterizing the filter by:”
“setting the adjustable filtering strength to a predetermined initial filtering strength [that is determined based on a frequency spectrum] of the measured values of the training data”
(Krishnan Algorithm 1)
“L” teaches the “adjustable filtering strength”.
“Lmin” teaches the “predetermined initial filtering strength” which is then adjusted via “L ← L + 2”
“filtering via the filter the measured values included in the training data [and determining a fractal dimension of the filtered values provided by the filter]; and”
(Krishnan Algorithm 1, “Employ LS-fit over
[
-
L
T
2
,
L
T
2
]
”)
“iteratively repeating the parameterizing of the filter by increasing the filtering strength of the filter to a higher filtering strength and by subsequently filtering the measured values”
(Krishnan Algorithm 1, “while
L
≤
L
m
a
x
do”)
The “while” loop teaches iteration.
(Krishnan Algorithm 1, “L ← L + 2”)
The incrementing of “L” teaches increasing the “filtering strength of the filter” where the filter length L is a filter parameter; thus this iteratively repeating process is parameterizing the filter.
(Krishnan 383, “As L increases, the bias increases, whereas variance decreases, and vice versa. This is intuitive because, if the filter length is increased, we will not be able to capture the finer variations of the signal (implying greater bias), whereas the noise becomes better smoothed out (implying lesser variance).”)
The increase of “L” teaches increasing the “filtering strength of the filter” because a more smoothed out curve is achieved.
PNG
media_image1.png
624
766
media_image1.png
Greyscale
“determining the measurement result by removing noise included in the measured values by:”
“putting the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration of the parameterizing; and via the parametrized filter, filtering the measured values of the measurand; and”
(Krishnan Algorithm 1, “
L
o
p
t
←
a
r
g
m
i
n
e
~
(
L
)
do”)
(Krishnan 385, “We next test the algorithm detailed in the previous section using both synthesized and real data.”)
(Krishnan 383, “if the filter length is increased ... whereas the noise becomes better smoothed out (implying lesser variance)”)
Thus, removing noise via smoothing out the noise.
“providing the measurement result including filtered values of the measured values of the measurand determined by the parametrized filter”
(Krishnan Algorithm 5(c), “Smoothed output obtained using Algorithm 1”)
PNG
media_image2.png
451
925
media_image2.png
Greyscale
Sotak teaches:
“and determining a fractal dimension of the filtered values provided by the filter”; “and determining a fractal dimension of the filtered values determined by the filter having the higher filtering strength until a decay of the fractal dimensions determined at the end of each iteration of the parameterizing drops below a predetermined threshold;”
(Sotak [0015], “The adaptive morphological filter then consists of measuring the fractal dimension of the original boundary, applying a size 1 open-close filter, and measuring the fractal dimension of the resulting boundary. If the difference between the two measures is greater than some threshold, then a size 2 filter is applied. The resulting boundary's fractal dimension is compared to that of the size 1 filter. If this comparison is greater than the threshold, then repeat the process with the size 3 filter. This process is repeated until either the change in the fractal dimension is less than the threshold or the specified size limit is reached.”)
Shi teaches:
“that is determined based on a frequency spectrum [of the measured values of the training data]”
(Shi [0064], “... The input samples 401 pass through a filter 408 that allows a range of frequencies, ... to pass through while rejecting noise at frequencies outside of the selected frequency range. In this example, it is desirable to track the IP harmonic 906, and therefore the filter 408 allows the 6th harmonic 906 to pass through while suppressing other frequencies. Spectral analysis of the signals measured by pressure transducers (130,132 in FIG.1) can be used to estimate the approximate fundamental frequency and the harmonic frequencies of the mud pump noise so that the tracking algorithms can be set up to track the noise and generate the appropriate noise reference waveforms ...” )
Shi teaches the determinations based on a frequency spectrum via spectral analysis for input samples (measured values) through filters for reducing noise in a signal measured by transducers.
Motivation to combine Krishnan, Sotak.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Krishnan, Sotak, Shi.
Krishnan discloses a selection procedure for optimal Savitzy-Golay filters in the context of ECG signals.
Sotak discloses a method for representing an image in terms of the shape properties of its identified segments.
Shi discloses a method for reducing noise in a measured telemetry signal via spectral analysis of the signals measured by transducers.
One would be motivated to combine these references because the signal data of Krishnan is readily applicable to the image segmentation techniques of Sotak. Krishnan is concerned with fitting filter parameters to subsegments of ECG signals and the fractal dimension taught in Sotak teaches an evaluative measure that can be integrated into Krishnan. Krishnan/Sotak signal segmentation and analysis is applicable to the teaching of the spectral analysis which is evaluating and determining via signals analysis based on a frequency spectrum to reduce noise.
Further, MPEP § 2143(I) EXAMPLES OF RATIONALES sets forth the Supreme Court rationales for obviousness, including:
(A) Combining prior art elements according to known methods to yield predictable results;
(B) Simple substitution of one known element for another to obtain predictable results;
(D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results;
(F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art;
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
In reference to claim 2.
“2. The measuring method according to claim 1,”
Krishnan teaches:
“wherein the filter is a parametrizable filter, a smoothing filter, a sliding window filter, a moving average filter, a Savitzky-Golay filter, a wavelet decomposition filter, an autoregressive filter (AR-filter), an autoregressive moving average filter (ARMA-filter), an autoregressive integrated moving average filter (ARIMA-filter), an autoregressive moving average filter (ARIMA filter) configured to filter the measured values (mv) based on an autoregressive integrated moving average model (ARIMA model), a seasonal autoregressive moving average filter (SARIMA-filter), a network filter, a neural network filter, or a neural network filter including a neural network, a recurrent neural network, a convolutional neural network or a Long short-term memory (LSTM).”
(Krishnan 386, “In this section, we provide results on ECG signals with two variants of the SURE-optimal S-G filter selection algorithm, the first being that based on adaptive filter length as discussed so far in the paper.”)
“S-G” filter teaches the “Savitzky-Golay filter”.
In reference to claim 3.
“3. The measuring method according to claim 1,”
Krishnan teaches:
”wherein the filter is configured to operate based on parameter settings that are adjustable in a manner that enables for the filtering strength of the filter to be set to a number of different predetermined filtering strengths.”
(Krishnan 383, “As L increases, the bias increases, whereas variance decreases, and vice versa. This is intuitive because, if the filter length is increased, we will not be able to capture the finer variations of the signal (implying greater bias), whereas the noise becomes better smoothed out (implying lesser variance).”)
“L” teaches the adjustable parameter.
In reference to claim 5.
“5. The measuring method according to claim 1,”
Krishnan teaches:
“wherein the training data is unlabeled data and/or includes a predetermined number of measured values and/or measured values that have been measured during an initial and/or predetermined training time interval or an arbitrarily selected time interval of a predetermined duration.”
(Krishnan Abstract, “We consider the algorithm performance on real-world electrocardiogram (ECG) signals.”)
(Krishnan 380, “REAL-WORLD signals such as speech, electrocardiogram (ECG) and geophysical signals are time-varying in one or more properties.”)
(Krishnan Algorithm 1, “Employ LS-fit over
[
-
L
T
2
,
L
T
2
]
”)
“
[
-
L
T
2
,
L
T
2
]
” teaches “an arbitrarily selected time interval of predetermined duration”.
In reference to claim 6.
Sotak teaches:
“6. The measuring method according to claim 5, wherein each iteration includes a step of determining the decay of the fractal dimensions:”
“as or based on a ratio of the fractal dimension of the filtered values determined during the respective iteration and a fractal dimension (do) of the unfiltered measured values included in training data, or as or based on a ratio of the fractal dimension of the filtered values determined during the respective iteration and the fractal dimension of the filtered values determined during the previous iteration, or based on three or more of the previously determined fractal dimensions and/or based on a property of a function fitted to several or all previously determined fractal dimensions.”
(Sotak [0015], “The adaptive morphological filter then consists of measuring the fractal dimension of the original boundary, applying a size 1 open-close filter, and measuring the fractal dimension of the resulting boundary. If the difference between the two measures is greater than some threshold, then a size 2 filter is applied. The resulting boundary's fractal dimension is compared to that of the size 1 filter. If this comparison is greater than the threshold, then repeat the process with the size 3 filter. This process is repeated until either the change in the fractal dimension is less than the threshold or the specified size limit is reached.”)
In reference to claim 7.
“7. The measuring method according to claim 6, further comprising:”
Krishnan teaches:
“at least once, periodically, or repeatedly updating the parametrization of the filter; and”
(Krishnan Algorithm 1, “L ← L + 2”)
The incrementing of “L” teaches increasing the “updating the parametrization of the filter”.
“subsequently determining and providing the filtering result with the filter operating based on the updated parametrization, wherein each updated parametrization is determined by repeating the determination of the parametrization of the filter based on data included in the recorded data that includes at least one measured value of the measurand that has been determined and/or recorded after the previous parametrization of the filter has been determined.”
(Krishnan Algorithm 1, “Employ LS-fit over
[
-
L
T
2
,
L
T
2
]
”)
In reference to claim 8.
“8. The measuring method according to claim 7,”
Krishnan teaches:
“wherein each updated parametrization is determined based on data included in the recorded data that has been determined and/or recorded during a time interval of a predetermined duration preceding the point in time, when the respective updated parametrization is determined.”
(Krishnan Algorithm 1, “Employ LS-fit over
[
-
L
T
2
,
L
T
2
]
”)
In reference to claim 9.
“9. The measuring method according to claim 8, wherein the parametrization is updated:”
Krishnan teaches:
“periodically after predetermined re-parametrization time intervals,”
(Krishnan Algorithm 1, “L ← L + 2”)
The incrementing of “L” teaches “periodically” updating the parameterization.
(Krishnan Algorithm 1, “Employ LS-fit over
[
-
L
T
2
,
L
T
2
]
”)
“
[
-
L
T
2
,
L
T
2
]
” teaches “re-parameterization time intervals”.
“after an event that may have an impact on properties of the measured values of the measurand and/or on properties of the noise included in the measured values has occurred, and/or when a given number larger or equal to one of measured values has been determined and/or recorded after the parametrization has last been determined.”
(Krishnan Algorithm 1, “Evaluate
e
~
with this L”)
Evaluating “e ̃” teaches “an event that may have and impact […] on properties of the noise included in the measured values has occurred”. “e ̃” is influenced by the noise of the “measured values” per (Krishnan 383, “As L increases, the bias increases, whereas variance decreases, and vice versa. This is intuitive because, if the filter length is increased, we will not be able to capture the finer variations of the signal (implying greater bias), whereas the noise becomes better smoothed out (implying lesser variance).”)
In reference to claim 10.
“10. The measuring method according to claim 1”
Shi teaches:
“wherein the determining and the providing of the measurement result includes determining and providing a residue between the measured values and filter values.”
(Shi [0016], “... The pump signature is then subtracted from the incoming signal to leave a residual that should contain mostly telemetry signal ...”)
The incoming signal is subtracted from the pump signature; thus, incoming signal - pump signature = residual value which is interpreted by the examiner as teaching a residue between measured and filtered values.
In reference to claim 11.
“11. The measuring method according to claim 1, further comprising at least one of the steps of:”
Krishnan teaches:
“providing the measurement result of the measurand to a superordinate unit configured to monitor, to regulate and/or to control the respective measurand, an operation of a plant or facility, and/or at least one step of a process performed at the application, where the measurement device determining the measured values of the measurand is employed.”
(Krishnan 390, “We next present the average execution times in MATLAB on a Macintosh machine having a 2 2.4 GHz quad-core Intel Xeon processor, along with the average times taken by the other benchmarked methods considered”)
Examiner notes that it remains unclear with the context of the claims and specification what a “superordinate unit” is. Under a broadest reasonable interpretation of “superordinate”, a “superordinate unit” may be any system composed of subsystems, e.g., the quoted Macintosh running MATLAB.
Wiki teaches:
“performing the method of determining and providing the measurement result of the measurand according to claim 1 for two or more measurands; monitoring, regulating and/or controlling the measurand or at least one of the measurands,”
(Wiki Electrodes and leads, “Commonly, 10 electrodes attached to the body are used to form 12 ECG leads, with each lead measuring a specific electrical potential difference (as listed in the table below).”)
“monitoring, regulating and/or controlling an operation of a plant or facility and/or monitoring, regulating and/or controlling at least one step of a process performed at an application, where the measurement device is employed, based on the measurement result; and”
(Wiki Interpretation, “Interpretation of the ECG is fundamentally about understanding the electrical conduction system of the heart.”)
“Understanding the electrical conduction system of the heart” teaches “monitoring […] at least one step of a process performed an application, where the measurement device is employed”.
Motivation to combine Krishnan, Sotak, Wiki.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Krishnan, Sotak, Shi, Wiki.
Krishnan, Sotak, Shi discloses optimizing a filter for signal data.
Wiki discloses a survey of the state of ECG technology.
One would be motivated to combine these references because the disclosure of Wiki adds context to how the ECG signals of Krishnan function and would allow one of ordinary skill in the art to navigate the complexities of ECG data.
Further, MPEP § 2143(I) EXAMPLES OF RATIONALES sets forth the Supreme Court rationales for obviousness, including:
(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
In reference to claim 12.
Claim 12 incorporates substantively all the limitations of Claim 1 in a “measurement device ... comprising: a measurement unit configured to determine and to provide measured values of a measurand; a computing means, a memory associated with the computing means, and a computer program installed on the computing means which, when the computer program is executed by the computing means, causes the computing means to” (Krishnan Abstract, “We consider the algorithm performance on real-world electrocardiogram (ECG) signals”; Krishnan 380, “REAL-WORLD signals such as speech, electrocardiogram (ECG) and geophysical signals are time-varying in one or more properties”); thus, Claim 12 is rejected for reasons set forth in the rejections of Claim 1.
In reference to claim 13.
Claim 13 incorporates substantively all the limitations of Claim 1 in a “measurement system comprising: for each measurand, a measurement device configured to determine and provide measured values of the respective measurand; a computing means connected to and/or communicating with each measurement device and configured to receive the measured values of each measurand; a memory associated with the computing means; and a computer program installed on the computing means which, when the program 1s executed by the computing means, causes the computing means, for each measurand, to” (Krishnan Abstract, “We consider the algorithm performance on real-world electrocardiogram (ECG) signals”; Krishnan 380, “REAL-WORLD signals such as speech, electrocardiogram (ECG) and geophysical signals are time-varying in one or more properties”); thus, Claim 13 is rejected for reasons set forth in the rejections of Claim 1.
In reference to claim 14.
“14. The measurement system according to claim 13, wherein:”
Krishnan teaches:
“the computing means is located in an edge device, in a superordinate unit or in the cloud, and”
(Krishnan 390, “We next present the average execution times in MATLAB on a Macintosh machine having a 2 2.4 GHz quad-core Intel Xeon processor, along with the average times taken by the other benchmarked methods considered”)
Examiner notes that it remains unclear with the context of the claims and specification what a “superordinate unit” is. Under a broadest reasonable interpretation of “superordinate”, a “superordinate unit” may be any system composed of subsystems, e.g., the quoted Macintosh running MATLAB.
“at least one or each measurement device is connected to and/or communicating with the computing means directly, via a superordinate unit, via an edge device located in the vicinity of the respective measurement device, and/or via the internet.”
(Krishnan Abstract, “We consider the algorithm performance on real-world electrocardiogram (ECG) signals.”)
The ECG teaches the measurement device that is necessarily connected to the system of Krishnan either physically or over a network.
In reference to claim 15.
“15. The measurement system according to claim 13, wherein:”
Shi teaches:
the measurement result of the measurand or the measurement result of at least one of the measurands determined and provided by the measurement system further includes a residue between the measured values and the filtered values.
(Shi [0016], “... The pump signature is then subtracted from the incoming signal to leave a residual that should contain mostly telemetry signal ...”)
The incoming signal is subtracted from the pump signature; thus, incoming signal - pump signature = residual value which is interpreted by the examiner as teaching a residue between measured and filtered values.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/I.R./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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