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 .
Effective Filing Date
The effective filing date of 05/06/2022 is acknowledged.
Status of Claims
The present application is being examined under the claims filed on 05/04/2023.
Claim(s) 1-14 is/are rejected.
Claim(s) 1-14 is/are pending.
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”).
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”).
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) 1-14 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 1-11 recite methods.
Claims 12-14 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 included in the recorded data, parametrizing a filter having an adjustable filtering strength by:
setting the adjustable filtering strength to a predetermined initial filtering strength;
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 this process 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 process drops below a predetermined threshold;
putting the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration;
via the parametrized 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?
“recording data including measured values of the measurand 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.
“providing a filtering result including filtered values of the measured values of the measurand determined by the parametrized filter and/or a residue between the measured values and the filtered values determined by the parametrized 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 4.
“wherein the initial filtering strength is: predetermined based on the number of measured values included in the training data and/or based on a frequency spectrum of the measured values included in the training data, or set to a default value.”
which only provides further details regarding the initial filtering strength 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?
“via a measurement device, repeatedly or continuously determining [and providing] measured values of the measurand, wherein the measurement device is either: a physical device measuring the measurand at a measurement site, or a virtual device, a computer implemented device, or a soft sensor repeatedly or continuously determining and providing the measured values of the measurand based on data provided to it;”
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 included in the recorded data, parametrizing a filter having an adjustable filtering strength by:
setting the adjustable filtering strength to a predetermined initial filtering strength;
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 this process 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 process drops below a predetermined threshold;”
putting the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration;
via the parametrized filter, filtering the measured values of the measurand;”
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?
“via a measurement device, repeatedly or continuously [determining and] providing measured values of the measurand”
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.
“recording data including the measured values of the measurand 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.
“providing a filtering result including filtered values of the measured values of the measurand determined by the parametrized filter and/or a residue between the measured values and the filtered values determined by the parametrized filter; and”
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.
“[determining and] providing the measurement result of the measurand as or based on the filtering result determined by performing the filtering method, wherein the filtering result includes the filtered values or includes both the filtered values and the residue.”
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 10 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 device 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: repeatedly or continuously determine and provide the measured values of the measurand; wherein the measurement device is either:”
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 included in the recorded data, parametrize a filter having an adjustable filtering strength by:
setting the adjustable filtering strength to a predetermined initial filtering strength;
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 this process 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 process drops below a predetermined threshold;
put the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration;
via the parametrized filter, filter the measured values of the measurand;”
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 device 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: repeatedly or continuously determine and provide the measured values of the measurand; wherein the measurement device is either:”
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 the measured values of the measurand 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 a filtering result including filtered values of the measured values of the measurand determined by the parametrized filter and/or a residue between the measured values and the filtered values determined by the parametrized filter; and”
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.
“[determine and] provide the measurement result of the measurand as or based on the filtering result determined by performing the filtering method, wherein the filtering result includes the filtered values or includes both the filtered values and the residue.”
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 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 included in the recorded data, parametrize a filter having an adjustable filtering strength by:
setting the adjustable filtering strength to a predetermined initial filtering strength;
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 this process 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 process drops below a predetermined threshold;
put the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration;
via the parametrized filter, filter the measured values of the measurand;”
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 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 the measured values of the measurand 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 a filtering result including filtered values of the measured values of the measurand determined by the parametrized filter and/or a residue between the measured values and the filtered values determined by the parametrized filter; and”
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.
“[determine and] provide the measurement result of the measurand as or based on the filtering result determined by performing the filtering method, wherein the filtering result includes the filtered values or includes both the filtered values and the residue.”
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.
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) 1-10, 12-14 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 filtering method of filtering measured values of a measurand, the filtering method comprising:”
Krishnan teaches:
“recording data including measured values of the measurand and their time of determination;”
(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.”)
“based on training data included in the recorded data, parametrizing a filter having an adjustable filtering strength by:”
“setting the adjustable filtering strength to a predetermined initial filtering strength;”
(Krishnan Algorithm 1, “L ← Lmin”)
“L” teaches the “adjustable filtering strength”.
“Lmin” teaches the “predetermined initial filtering strength”.
“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 this process 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”.
(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
“putting the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration; 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.”)
“providing a filtering result including filtered values of the measured values of the measurand determined by the parametrized filter and/or a residue between the measured values and the filtered values determined by the parametrized filter.”
(Krishnan Algorithm 5(c), “Smoothed output obtained using Algorithm 1”)
PNG
media_image2.png
582
1190
media_image2.png
Greyscale
Sotak teaches:
“and determining a fractal dimension of the filtered values provided by the filter”; “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 process 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.”)
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.
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.
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.
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 filtering 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 filtering 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 4.
“4. The method according to claim 3,”
Krishnan teaches:
“wherein the initial filtering strength is: predetermined based on the number of measured values included in the training data and/or based on a frequency spectrum of the measured values included in the training data, or set to a default value.”
(Krishnan Algorithm 1, “L ← Lmin”)
“Lmin” teaches the initial filtering strength set to a “default value”.
In reference to claim 5.
“5. The filtering method according to claim 4,”
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 filtering 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 filtering 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 filtering 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 filtering 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. A method of determining and providing a measurement result of a measurand, the method comprising:”
Krishnan teaches:
“via a measurement device, repeatedly or continuously determining and providing measured values of the measurand, wherein the measurement device is either: a physical device measuring the measurand at a measurement site, or a virtual device, a computer implemented device, or a soft sensor repeatedly or continuously determining and providing the measured values of the measurand based on data provided to it; recording data including the measured values of the measurand and their time of determination;”
(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.”)
“based on training data included in the recorded data, parametrizing a filter having an adjustable filtering strength by:”
“setting the adjustable filtering strength to a predetermined initial filtering strength;”
(Krishnan Algorithm 1, “L ← Lmin”)
“L” teaches the “adjustable filtering strength”.
“Lmin” teaches the “predetermined initial filtering strength”.
“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 this process 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”.
(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.
“putting the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration; via the parametrized filter, filtering the measured values of the measurand;”
(Krishnan Algorithm 1, “Lopt←arg min(e) ̃(L) do”)
(Krishnan 385, “We next test the algorithm detailed in the previous section using both synthesized and real data.”)
“providing a filtering result including filtered values of the measured values of the measurand determined by the parametrized filter and/or a residue between the measured values and the filtered values determined by the parametrized filter; and”
(Krishnan Algorithm 5(c))
“determining and providing the measurement result of the measurand as or based on the filtering result determined by performing the filtering method, wherein the filtering result includes the filtered values or includes both the filtered values and the residue.”
(Krishnan Algorithm 5(c))
Sotak teaches:
“and determining a fractal dimension of the filtered values provided by the filter”; “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 process 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.”)
In reference to claim 11.
“11. The method according to claim 10, 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 10 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, Wiki.
Krishnan, Sotak discloses optimizing a filter for ECG 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.
“12. A measurement device, comprising:”
Krishnan teaches:
“a measurement device 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: repeatedly or continuously determine and provide the measured values of the measurand; wherein the measurement device is either: record data including the measured values of the measurand and their time of determination;”
(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.”)
“based on training data included in the recorded data, parametrize a filter having an adjustable filtering strength by:”
“setting the adjustable filtering strength to a predetermined initial filtering strength;”
(Krishnan Algorithm 1, “L ← Lmin”)
“L” teaches the “adjustable filtering strength”.
“Lmin” teaches the “predetermined initial filtering strength”.
“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 this process 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”.
(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.
“put the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration; via the parametrized filter, filter the measured values of the measurand;”
(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.”)
“provide a filtering result including filtered values of the measured values of the measurand determined by the parametrized filter and/or a residue between the measured values and the filtered values determined by the parametrized filter; and”
(Krishnan Algorithm 5(c))
“determine and provide the measurement result of the measurand as or based on the filtering result determined by performing the filtering method, wherein the filtering result includes the filtered values or includes both the filtered values and the residue.”
(Krishnan Algorithm 5(c))
Sotak teaches:
“data and determining a fractal dimension of the filtered values provided by the filter”; “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 process 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.”)
In reference to claim 13.
“13. A measurement system configured to determine a measurement result for at least one measurand, the measurement system comprising:”
Krishnan teaches:
“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 to: record data including the measured values of the measurand and their time of determination;”
(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.”)
“based on training data included in the recorded data, parametrize a filter having an adjustable filtering strength by:”
“setting the adjustable filtering strength to a predetermined initial filtering strength;”
(Krishnan Algorithm 1, “L ← Lmin”)
“L” teaches the “adjustable filtering strength”.
“Lmin” teaches the “predetermined initial filtering strength”.
“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 this process 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”.
(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.
“put the filter into operation based on a parametrization corresponding to the filtering strength employed in the last iteration; via the parametrized filter, filter the measured values of the measurand;”
(Krishnan Algorithm 1, “L_opt←arg min( e) ̃(L) do”)
(Krishnan 385, “We next test the algorithm detailed in the previous section using both synthesized and real data.”)
“provide a filtering result including filtered values of the measured values of the measurand determined by the parametrized filter and/or a residue between the measured values and the filtered values determined by the parametrized filter; and”
(Krishnan Algorithm 5(c))
“determine and provide the measurement result of the measurand as or based on the filtering result determined by performing the filtering method, wherein the filtering result includes the filtered values or includes both the filtered values and the residue.”
(Krishnan Algorithm 5(c))
Sotak teaches:
“data and determining a fractal dimension of the filtered values provided by the filter”; “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 process 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.”)
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.
Conclusion
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/CODY RYAN GILLESPIE/Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151