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 .
Response to Arguments
Applicant's arguments filed September 30, 2025 have been fully considered but they are not persuasive.
In response to Applicant's argument on page 8 pertaining to “Hencken (see Hencken, paragraphs [0019]-[0022]) discloses sequential acquisition of PRPD patterns at different RF measurement frequencies. Applicant respectfully submits the Examiner incorrectly characterizes Hencken's paragraph [0028] as teaching frequency-domain feature generation, ... This is not frequency-domain processing, but rather a two-dimensional histogram in the phase-charge space (see Hencken, paragraphs [0025]-[0028]).”. The Examiner respectfully disagrees.
The claims recite “a plurality of features from a frequency domain for each of the digital signals”. For Hencken to be able to acquire the partial discharge (PD) signals in different frequency ranges, Hencken will have to inherently perform frequency domain processing before grouping the signals in different frequencies (¶ 28 frequency range).
In response to Applicant's argument on page 8 pertaining to “Hencken processes accumulated PRPD patterns over consecutive AC cycles, not individual digital signals (see Hencken, paragraph [0019]: "group of patterns"). The reference entirely lacks disclosure of arithmetic coding, time-domain feature compression, or the mathematical relationship whereby N/2+ 1 frequency domain features are generated based on measured amplitudes. Hencken's "different frequency ranges" refer to RF measurement bands for acquiring patterns, not FFT frequency-domain analysis (see Hencken, paragraph [0025]).”. The Examiner respectfully disagrees.
The claims recite “digital signals that are converted from waveforms of signals collected from a source”. Hencken discloses, digital signals (Fig. 1, ¶ 63 analyzed signal into a digital signal) that are converted from waveforms of signals collected from a source (Fig. 1, ¶ 37 medium- or high voltage installation). The examiner does not rely on Hencken to disclose “arithmetic coding”. The examiner relies on Vos (Fig. 8, ¶ 39 These quantization indices can then be entropy coded with, for example, a Huffman or arithmetic coding). Furthermore, the claims do not recite “time-domain feature compression”. The examiner does not rely on Hencken to disclose the “FFT frequency-domain analysis”. The examiner relies on Huang (Fig. 1, ¶ 22 fast Fourier transform (FFT)).
In response to Applicant's argument on pages 8 – 9 pertaining to “Applicant respectfully submits the Examiner incorrectly characterised Leonard as teaching clustering of extracted features, since Leonard explicitly performs clustering on entire waveforms in the time domain, stating "clustering may be done over many hundred dimensions N, with each dimension corresponding to a signal time sample". No frequency-domain processing is disclosed (see Leonard, paragraphs [0022]-[0031]).”. The Examiner respectfully disagrees.
The clustering algorithm disclosed by Leonard works on any time of signal for example, time or frequency signals. The clustering algorithm is not limited to only one type of signal. Leonard discloses, identify a plurality of distinct clusters (Fig. 1, ¶ 29 clustering may be done over many hundred dimensions N).
In response to Applicant's argument on page 9 pertaining to “Additionally, Leonard addresses millisecond-scale power grid disturbances whilst partial discharge signals occur at nanosecond to microsecond scales (a temporal difference of three to six orders of magnitude). A skilled person would not consider Leonard's methods applicable to PD detection given this fundamental incompatibility. Furthermore, Leonard uses "a heuristic similar to the k-means algorithm" (see Leonard, paragraph [0029]), not the K-means clustering claimed.”. The Examiner respectfully disagrees.
The claims only recite PD signals. The claims don’t recite the time it takes for the PD signals to occur. Additionally, the claims only recite clustering. The claims do not recite K-means clustering. Leonard discloses, a plurality of distinct clusters (Fig. 1, ¶ 29 clustering may be done over many hundred dimensions N).
In response to Applicant's argument on page 9 pertaining to “the claimed invention lies in the synergistic combination of arithmetic-coded time-domain features with frequency-domain magnitude distributions. Hence, neither Henken nor Leonard discloses the claimed combination of one arithmetic-coded time-domain feature with N/2+1 frequency-domain features for dual-domain clustering, as claimed.”. The Examiner respectfully disagrees.
The examiner does not rely on Hencken or Leonard to disclose “arithmetic coding”. The examiner relies on Vos (Fig. 8, ¶ 39 These quantization indices can then be entropy coded with, for example, a Huffman or arithmetic coding). Furthermore, the claims do not recite “synergistic combination”, “dual-domain clustering”.
Therefore, applicant’s argument is not persuasive, and the rejection under 35 U.S.C § 103 of claims 1, 2, 8, 9 as being unpatentable over Hencken et al (EP 3 588 108 A1) (herein after Hencken) in view of Leonard (US 2014/0100821 A1) (herein after Leonard); the rejection under 35 U.S.C § 103 of claims 3 – 6, 10 – 13 as being unpatentable over Hencken et al (EP 3 588 108 A1) (herein after Hencken) in view of Leonard (US 2014/0100821 A1) (herein after Leonard) in view of Wood et al (US 2014/0310394 A1) (herein after Wood), and further in view of Vos et al (US 2008/0112632 A1) (herein after Vos); the rejection under 35 U.S.C § 103 of claims 7 and 14 as being unpatentable over Hencken et al (EP 3 588 108 A1) (herein after Hencken) in view of Leonard (US 2014/0100821 A1) (herein after Leonard) in view of Wood et al (US 2014/0310394 A1) (herein after Wood) in view of Vos et al (US 2008/0112632 A1) (herein after Vos), and further in view of Huang (US 2002/0099280 A1) (herein after Huang), is maintained below.
Claim Rejections - 35 USC § 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, 2, 8, 9 are rejected under 35 U.S.C. 103 as being unpatentable over Hencken et al (EP 3 588 108 A1) (herein after Hencken) in view of Leonard (US 2014/0100821 A1) (herein after Leonard)
Regarding claim 1, Hencken teaches, a method for separating partial discharge and noise signals (Fig. 1, ¶ 12 method for determining and processing phase resolved partial discharge patterns; ¶ 28 The PRPD of the latter type are only used for the removal of noise), the method comprises: receiving digital signals (Fig. 1, ¶ 63 analyzed signal into a digital signal) that are converted from waveforms of signals collected from a source (Fig. 1, ¶ 37 medium- or high voltage installation); generating a feature (Fig. 1, ¶ 19 group of patterns (e.g. acquired at one or several frequency intervals)); generating, using at least one processor (Fig 6. Processing 620), a feature (Fig. 1, ¶ 19 group of patterns (e.g. acquired at one or several frequency intervals)) from amplitudes in a time domain for each of the digital signals; generating, using the at least one processor, a plurality of features (Fig. 1, ¶ 47 "clouds" of counts 170, 180, 190) from a frequency domain (¶ 28 frequency range) for each of the digital signals, wherein the plurality of features from the frequency domain are dependent on a number of measured amplitudes per digital signal (¶ 27 partial discharges of that amplitude; ¶ 63 converting an acquired and/or processed and/or analyzed signal into a digital signal); — displaying, using the at least one processor, each distinct cluster on a Phase-Resolved Partial Discharge (PRPD) chart (Fig. 1, ¶ 47 PD counts), —.
Hencken fails to teach, — applying, using the at least one processor, clustering algorithm on the generated features for all the digital signals to identify a plurality of distinct clusters; — wherein at least one axis of the PRPD chart represents a voltage amplitude of each digital signal in the plurality of clusters; and separating, using the at least one processor and the distinct clusters, the partial discharge and noise signals from the digital signals.
In analogous art, Leonard teaches, — applying, using the at least one processor, clustering algorithm (Fig. 1, ¶ 29 A heuristic similar to the k-means algorithm) on the generated features for all the digital signals to identify a plurality of distinct clusters (Fig. 1, ¶ 29 clustering may be done over many hundred dimensions N); — wherein at least one axis of the PRPD chart represents a voltage amplitude (Fig. 8, ¶ 61 PRPD diagrams) of each digital signal in the plurality of clusters; and separating, using the at least one processor and the distinct clusters, the partial discharge and noise signals from the digital signals (Fig. 1, ¶ 54 the cluster signatures SNR is high, PD cluster signature and a noise signature; Examiner interpretation: SNR is high because the partial discharge (PD) and noise signals are separated). —
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken by combining the method of separating partial discharge and noise signals taught by Hencken with a method of separating partial discharge and noise signals by; applying, using the at least one processor, clustering algorithm on the generated features for all the digital signals to identify a plurality of distinct clusters; wherein at least one axis of the PRPD chart represents a voltage amplitude of each digital signal in the plurality of clusters; and separating, using the at least one processor and the distinct clusters, the partial discharge and noise signals from the digital signals; taught by Leonard for the benefit of separating partial discharge and noise signals using reduced computation time and producing precise results [Leonard: ¶ 5].
Regarding claim 2, Hencken in view of Leonard teaches the limitations of claim 1, which this claim depends on.
Hencken further teaches, the method according to claim 1 wherein the source is data collection module comprising a sensor (Fig. 1, ¶ 32 different sensors) for measuring a wideband of electromagnetic signals (Fig. 1, ¶ 6 wideband signals as real PD) in an environment and an acquisition device (Fig. 1, ¶ 6 wideband signals as real PD) for recording and translating the electromagnetic signals measured from the sensor to the digital signals (Fig. 1, ¶ 63 analyzed signal into a digital signal).
Regarding claim 8, Hencken teaches, a system (Fig. 1, measurement system) for separating partial discharge and noise signals (Fig. 1, ¶ 12 method for determining and processing phase resolved partial discharge patterns; ¶ 28 The PRPD of the latter type are only used for the removal of noise) comprising: a data collecting module (Fig. 6, determining 610; Note: Fig 6 is a block diagram to implement Fig 1, see ¶ 12) configured to recording and translating electromagnetic signals (Fig. 1, ¶ 6 wideband signals as real PD) measured from a sensor to digital signals (Fig. 1, ¶ 32 different sensors; Fig. 1, ¶ 63 analyzed signal into a digital signal); and a data processing module (Fig 6. Processing 620) comprising one or more computing processors executing instructions, wherein the instructions are configured to: receive digital signals (Fig. 1, ¶ 63 analyzed signal into a digital signal) from the data collecting module; generate a feature (Fig. 1, ¶ 19 group of patterns (e.g. acquired at one or several frequency intervals)) from amplitudes in a time domain for each of the digital signals; generate a plurality of features (Fig. 1, ¶ 47 "clouds" of counts 170, 180, 190) from a frequency domain (¶ 28 frequency range) for each of the digital signals, wherein the plurality of features from the frequency domain are dependent on a number of measured amplitudes per digital signal (¶ 27 partial discharges of that amplitude; ¶ 63 converting an acquired and/or processed and/or analyzed signal into a digital signal); — display each distinct cluster on a Phase-Resolved Partial Discharge (PRPD) chart (Fig. 1, ¶ 47 PD counts), —.
Hencken fails to teach, — apply clustering algorithm on the generated features for all the digital signals to identify a plurality of distinct clusters; — wherein at least one axis of the PRPD chart represents a voltage amplitude of each digital signal in the plurality of clusters; and separate, using the distinct clusters, the partial discharge and noise signals from the digital signals.
In analogous art, Leonard teaches, — apply clustering algorithm (Fig. 1, ¶ 29 A heuristic similar to the k-means algorithm) on the generated features for all the digital signals to identify a plurality of distinct clusters (Fig. 1, ¶ 29 clustering may be done over many hundred dimensions N); — wherein at least one axis of the PRPD chart represents a voltage amplitude (Fig. 8, ¶ 61 PRPD diagrams) of each digital signal in the plurality of clusters; and separate, using the distinct clusters, the partial discharge and noise signals from the digital signals (Fig. 1, ¶ 54 the cluster signatures SNR is high, PD cluster signature and a noise signature; Examiner interpretation: SNR is high because the partial discharge (PD) and noise signals are separated).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken by combining the system of separating partial discharge and noise signals taught by Hencken with a system of separating partial discharge and noise signals by; applying a clustering algorithm on the generated features for all the digital signals to identify a plurality of distinct clusters; wherein at least one axis of the PRPD chart represents a voltage amplitude of each digital signal in the plurality of clusters; and separate, using the distinct clusters, the partial discharge and noise signals from the digital signals; taught by Leonard for the benefit of separating partial discharge and noise signals using reduced computation time and producing precise results [Leonard: ¶ 5].
Regarding claim 9, Hencken in view of Leonard teaches the limitations of claim 8, which this claim depends on.
Hencken further teaches, the system according to claim 8 wherein the data collecting module comprises: a sensor (Fig. 1, ¶ 32 different sensors) for measuring the electromagnetic signals; and an acquisition device (Fig. 1, ¶ 32 patterns acquired with different measurement methods) for recording and translating the electromagnetic signals measured from the sensor to the digital signals (Fig. 1, ¶ 63 analyzed signal into a digital signal).
Claim(s) 3 – 6, 10 – 13 are rejected under 35 U.S.C. 103 as being unpatentable over Hencken et al (EP 3 588 108 A1) (herein after Hencken) in view of Leonard (US 2014/0100821 A1) (herein after Leonard) in view of Wood et al (US 2014/0310394 A1) (herein after Wood), and further in view of Vos et al (US 2008/0112632 A1) (herein after Vos).
Regarding claim 3, Hencken in view of Leonard teaches the limitations of claim 1, which this claim depends on.
Hencken in view of Leonard fail to teach, the method according to claim 1 wherein the step of generating the feature from the time domain for each of the digital signals comprises: determining histogram bin width and the number of bins for all measured amplitudes in the digital signals; associating all measured amplitudes with their respective bin values; building a global probability table; and encoding information in the global probability table to generate the feature.
In analogous art, Wood teaches, the method according to claim 1 wherein the step of generating the feature from the time domain for each of the digital signals comprises: determining histogram bin width and the number of bins (Fig. 5, ¶ 30 each bin) for all measured amplitudes in the digital signals (Fig. 5, ¶ 30 each bin is used as a binary amplitude signal); associating all measured amplitudes with their respective bin values (Fig. 5, ¶ 32 peaks of clustered frequencies); —.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard by combining the generating of a feature taught by Hencken in view of Leonard with generating of a feature, wherein the step of generating the feature from a time domain for each of digital signals comprises: determining histogram bin width and a number of bins for all measured amplitudes in the digital signals; associating all measured amplitudes with their respective bin values; taught by Wood for the benefit of identifying a feature by matching a known Fourier pattern with an unknown Fourier pattern. [Wood: ¶ 6]
Hencken in view of Leonard in view of Wood fail to teach, — building a global probability table; and encoding information in the global probability table to generate the feature.
In analogous art, Vos teaches, — building a global probability table (Fig. 8, ¶ 28 probability density function (pdf) — a generic PDF could be a Gaussian density); and encoding information in the global probability table to generate the feature (Fig. 8, ¶ 28 (an envelope) that needs only two parameters, the amplitude and the decay constant; Examiner interpretation: the pdf is used to generate the envelope).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard in view of Wood by combining the generating of a feature, taught by Hencken in view of Leonard in view of Wood with building a global probability table; and encoding information in the global probability table to generate a feature; taught by Vos for the benefit of encoding a probability data with lossless arithmetic encoding while taking into consideration the spread in the transform domain. [Vos: ¶ 9 – 10]
Regarding claim 4, Hencken in view of Leonard in view of Wood in view of Vos teaches the limitations of claim 3, which this claim depends on.
Wood further teaches, the method according to claim 3 wherein the histogram bin width and the number of bins are determined via Freedman-Diaconis (FD) rule (Fig. 5, ¶ 29 Freedman-Diaconis bin-width selection algorithms).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard in view of Wood in view of Vos by combining the method of determining a histogram bin width taught by Hencken in view of Leonard in view of Wood in view of Vos with a method wherein the histogram bin width and number of bins are determined via Freedman-Diaconis (FD) rule; taught by Wood for the benefit of identifying a feature by matching a known Fourier pattern with an unknown Fourier pattern. [Wood: ¶ 6]
Regarding claim 5, Hencken in view of Leonard in view of Wood in view of Vos teaches the limitations of claim 4, which this claim depends on.
Vos further teaches, the method according to claim 4 wherein the step of building the global probability table comprises: determining the probability of the measured amplitudes in each bin with respect to the rest of the measured amplitudes (Fig. 8, ¶ 28 the amplitude and the decay constant) to build the global probability table (Fig. 8, ¶ 28 probability density function (pdf) — a generic PDF could be a Gaussian density).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard in view of Wood in view of Vos by combining a step of building a global probability table taught by Hencken in view of Leonard in view of Wood in view of Vos with a step of building the global probability table, wherein the step of building the global probability table comprises: determining the probability of the measured amplitudes in each bin with respect to the rest of the measured amplitudes to build the global probability table; taught by Vos for the benefit of encoding a probability data with lossless arithmetic encoding while taking into consideration the spread in the transform domain. [Vos: ¶ 9 – 10]
Regarding claim 6, Hencken in view of Leonard in view of Wood in view of Vos teaches the limitations of claim 5, which this claim depends on.
Vos further teaches, the method according to claim 5 wherein the information in the global probability table is encoded via Arithmetic Coding (Fig. 8, ¶ 39 The envelope parameters are encoded — These quantization indices can then be entropy coded with, for example, a Huffman or arithmetic coding).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard in view of Wood in view of Vos by combining a method of building a global probability table taught by Hencken in view of Leonard in view of Wood in view of Vos with a method wherein, the information in the global probability table is encoded via Arithmetic Coding; taught by Vos for the benefit of encoding a probability data with lossless arithmetic encoding while taking into consideration the spread in the transform domain. [Vos: ¶ 9 – 10]
Regarding claim 10, Hencken in view of Leonard teaches the limitations of claim 8, which this claim depends on.
Hencken in view of Leonard fail to teach, the system according to claim 8 wherein the data processing module is configured to generate the feature from the time domain for each of the digital signals by: determining histogram bin width and the number of bins for all measured amplitudes in the digital signals; associating all measured amplitudes with their respective bin values; building a global probability table; and encoding information in the global probability table to generate the feature.
In analogous art, Wood teaches, the system according to claim 8 wherein the data processing module is configured to generate the feature from the time domain for each of the digital signals by: determining histogram bin width and the number of bins (Fig. 5, ¶ 30 each bin) for all measured amplitudes in the digital signals (Fig. 5, ¶ 30 each bin is used as a binary amplitude signal); associating all measured amplitudes with their respective bin values (Fig. 5, ¶ 32 peaks of clustered frequencies);—.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard by combining the generating of a feature taught by Hencken in view of Leonard with generating of a feature, wherein generating the feature from a time domain for each of digital signals comprises: determining histogram bin width and a number of bins for all measured amplitudes in the digital signals; associating all measured amplitudes with their respective bin values; taught by Wood for the benefit of identifying a feature by matching a known Fourier pattern with an unknown Fourier pattern. [Wood: ¶ 6]
Hencken in view of Leonard in view of Wood fail to teach, — building a global probability table; and encoding information in the global probability table to generate the feature.
In analogous art, Vos teaches, — building a global probability table (Fig. 8, ¶ 28 probability density function (pdf) — a generic PDF could be a Gaussian density); and encoding information in the global probability table to generate the feature (Fig. 8, ¶ 28 (an envelope) that needs only two parameters, the amplitude and the decay constant; Examiner interpretation: the pdf is used to generate the envelope).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard in view of Wood by combining the generating of a feature, taught by Hencken in view of Leonard in view of Wood with building a global probability table; and encoding information in the global probability table to generate a feature; taught by Vos for the benefit of encoding a probability data with lossless arithmetic encoding while taking into consideration the spread in the transform domain. [Vos: ¶ 9 – 10]
Regarding claim 11, Hencken in view of Leonard in view of Wood in view of Vos teaches the limitations of claim 10, which this claim depends on.
Wood further teaches, the system according to claim 10 wherein the histogram bin width and the number of bins are determined via Freedman-Diaconis (FD) rule (Fig. 5, ¶ 29 Freedman-Diaconis bin-width selection algorithms).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard in view of Wood in view of Vos by combining the system of determining a histogram bin width taught by Hencken in view of Leonard in view of Wood in view of Vos with determining a histogram bin width and number of bins via Freedman-Diaconis (FD) rule; taught by Wood for the benefit of identifying a feature by matching a known Fourier pattern with an unknown Fourier pattern. [Wood: ¶ 6]
Regarding claim 12, Hencken in view of Leonard in view of Wood in view of Vos teaches the limitations of claim 11, which this claim depends on.
Vos further teaches, the system according to claim 11 wherein the data processing module is configured to build the global probability table by: determining the probability of the measured amplitudes in each bin with respect to the rest of the measured amplitudes (Fig. 8, ¶ 28 the amplitude and the decay constant) to build the global probability table (Fig. 8, ¶ 28 probability density function (pdf) — a generic PDF could be a Gaussian density).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard in view of Wood in view of Vos by combining system of building a global probability table taught by Hencken in view of Leonard in view of Wood in view of Vos with a system of building a global probability table, wherein the system of building the global probability table comprises: determining the probability of the measured amplitudes in each bin with respect to the rest of the measured amplitudes to build the global probability table; taught by Vos for the benefit of encoding a probability data with lossless arithmetic encoding while taking into consideration the spread in the transform domain. [Vos: ¶ 9 – 10]
Regarding claim 13, Hencken in view of Leonard in view of Wood in view of Vos teaches the limitations of claim 12, which this claim depends on.
Vos further teaches, the system according to claim 12 wherein the information in the global probability table is encoded via Arithmetic Coding (Fig. 8, ¶ 39 The envelope parameters are encoded — These quantization indices can then be entropy coded with, for example, a Huffman or arithmetic coding).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard in view of Wood in view of Vos by combining a system of building a global probability table taught by Hencken in view of Leonard in view of Wood in view of Vos with a system wherein, the information in the global probability table is encoded via Arithmetic Coding; taught by Vos for the benefit of encoding a probability data with lossless arithmetic encoding while taking into consideration the spread in the transform domain. [Vos: ¶ 9 – 10]
Claim(s) 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hencken et al (EP 3 588 108 A1) (herein after Hencken) in view of Leonard (US 2014/0100821 A1) (herein after Leonard) in view of Wood et al (US 2014/0310394 A1) (herein after Wood) in view of Vos et al (US 2008/0112632 A1) (herein after Vos), and further in view of Huang (US 2002/0099280 A1) (herein after Huang).
Regarding claim 7, Hencken in view of Leonard in view of Wood in view of Vos teaches the limitations of claim 6, which this claim depends on.
Hencken in view of Leonard in view of Wood in view of Vos fail to teach, the method according to claim 6 wherein the step of generating a plurality of features from the frequency domain for each of the digital signals comprises: applying a Fast Fourier Transform (FFT) for each digital signal to transform the digital signal from the Time Domain of N measured amplitudes to the Frequency Domain of (N/2)+1 magnitudes; grouping the magnitudes according to respective frequency; standardizing the magnitudes in each frequency to bring the magnitudes into a uniform format; normalizing the standardized magnitudes based on global maximum and global minimum magnitude values; determining histogram bin width and the number of bins for all normalized magnitudes in each frequency; associating all normalized magnitudes with their respective bin values; and scaling magnitudes in each bin are scaled to be between 0 and 1 to generate (N/2)+1 features.
In analogous art, Huang teaches, the method according to claim 6 wherein the step of generating a plurality of features from the frequency domain for each of the digital signals comprises: applying a Fast Fourier Transform (FFT) (Fig. 1, ¶ 22 fast Fourier transform (FFT)) for each digital signal to transform the digital signal from the Time Domain of N measured amplitudes to the Frequency Domain of (N/2)+1 magnitudes (Fig. 1, ¶ 28 In function block 114, magnitudes and frequency locations of peaks; Examiner interpretation: N time domain samples are transformed into (N/2)+1 frequency samples in a Fourier transform); grouping the magnitudes according to respective frequency (Fig. 2, ¶ 27 a height representing the magnitude of the FFT at that frequency; Note: Fig. 2 is an FFT plot generated by Fig 1, see ¶ 27); standardizing the magnitudes in each frequency to bring the magnitudes into a uniform format (Fig. 2, ¶ 29 a list of magnitudes and spectral locations of the identified IR peaks is generated); normalizing the standardized magnitudes based on global maximum and global minimum magnitude values (Fig. 2, ¶ 28 maximum magnitude (illustrated at the lowest frequency location)); determining histogram bin width and the number of bins for all normalized magnitudes in each frequency (Fig. 2, ¶ 27 frequency locations spaced every 0.01 Hz; Examiner interpretation: the frequency locations are the widths); associating all normalized magnitudes with their respective bin values (Fig. 2, ¶ 28 maximum magnitude (illustrated at the lowest frequency location); Examiner interpretation: the magnitudes are associated with locations); and scaling magnitudes in each bin are scaled to be between 0 and 1 to generate (N/2)+1 features (Fig. 2, ¶ 28 the maximum magnitude in the FFT spectrum is set to the predetermined value, and the remainder scaled appropriately).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard in view of Wood in view of Vos by combining the method of generating a plurality of features from a frequency domain for digital signals taught by Hencken in view of Leonard in view of Wood in view of Vos with a step of generating a plurality of features from a frequency domain for digital signals, wherein the step of generating the plurality of features from the frequency domain for each of the digital signals comprises: applying a Fast Fourier Transform (FFT) for each digital signal to transform the digital signal from a Time Domain of N measured amplitudes to the Frequency Domain of (N/2)+1 magnitudes; grouping the magnitudes according to respective frequency; standardizing the magnitudes in each frequency to bring the magnitudes into a uniform format; normalizing the standardized magnitudes based on global maximum and global minimum magnitude values; determining histogram bin width and the number of bins for all normalized magnitudes in each frequency; associating all normalized magnitudes with their respective bin values; and scaling magnitudes in each bin are scaled to be between 0 and 1 to generate (N/2)+1 features; taught by Huang for the benefit of transforming a signal from a time domain to a frequency domain optimally using different techniques to identify desired frequencies. [Huang: ¶ 10]
Regarding claim 14, Hencken in view of Leonard in view of Wood in view of Vos teaches the limitations of claim 13, which this claim depends on.
Hencken in view of Leonard in view of Wood in view of Vos fail to teach, the system according to claim 13 wherein the data processing module is configured to generate the plurality of features from the frequency domain for each of the digital signals by: applying a Fast Fourier Transform (FFT) for each digital signal to transform the digital signal from the Time Domain of N measured amplitudes to the Frequency Domain of (N/2)+1 magnitudes; grouping the magnitudes according to respective frequency; standardizing the magnitudes in each frequency to bring the magnitudes into a uniform format; normalizing the standardized magnitudes based on global maximum and global minimum magnitude values; determining histogram bin width and the number of bins for all normalized magnitudes in each frequency; associating all normalized magnitudes with their respective bin values; and scaling magnitudes in each bin are scaled to be between 0 and 1 to generate (N/2)+1 features.
In analogous art, Huang teaches, the system according to claim 13 wherein the data processing module is configured to generate the plurality of features from the frequency domain for each of the digital signals by: applying a Fast Fourier Transform (FFT) (Fig. 1, ¶ 22 fast Fourier transform (FFT)) for each digital signal to transform the digital signal from the Time Domain of N measured amplitudes to the Frequency Domain of (N/2)+1 magnitudes (Fig. 1, ¶ 28 In function block 114, magnitudes and frequency locations of peaks; Examiner interpretation: N time domain samples are transformed into (N/2)+1 frequency samples in a Fourier transform); grouping the magnitudes according to respective frequency (Fig. 2, ¶ 27 a height representing the magnitude of the FFT at that frequency; Note: Fig. 2 is an FFT plot generated by Fig 1, see ¶ 27); standardizing the magnitudes in each frequency to bring the magnitudes into a uniform format (Fig. 2, ¶ 29 a list of magnitudes and spectral locations of the identified IR peaks is generated); normalizing the standardized magnitudes based on global maximum and global minimum magnitude values (Fig. 2, ¶ 28 maximum magnitude (illustrated at the lowest frequency location)); determining histogram bin width and the number of bins for all normalized magnitudes in each frequency (Fig. 2, ¶ 27 frequency locations spaced every 0.01 Hz; Examiner interpretation: the frequency locations are the widths); associating all normalized magnitudes with their respective bin values (Fig. 2, ¶ 28 maximum magnitude (illustrated at the lowest frequency location); Examiner interpretation: the magnitudes are associated with locations); and scaling magnitudes in each bin are scaled to be between 0 and 1 to generate (N/2)+1 features (Fig. 2, ¶ 28 the maximum magnitude in the FFT spectrum is set to the predetermined value, and the remainder scaled appropriately).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hencken in view of Leonard in view of Wood in view of Vos by combining the system of generating a plurality of features from a frequency domain for digital signals taught by Hencken in view of Leonard in view of Wood in view of Vos with a system of generating a plurality of features from a frequency domain for digital signals, wherein the system of generates the plurality of features from the frequency domain for each of the digital signals by: applying a Fast Fourier Transform (FFT) for each digital signal to transform the digital signal from a Time Domain of N measured amplitudes to the Frequency Domain of (N/2)+1 magnitudes; grouping the magnitudes according to respective frequency; standardizing the magnitudes in each frequency to bring the magnitudes into a uniform format; normalizing the standardized magnitudes based on global maximum and global minimum magnitude values; determining histogram bin width and the number of bins for all normalized magnitudes in each frequency; associating all normalized magnitudes with their respective bin values; and scaling magnitudes in each bin are scaled to be between 0 and 1 to generate (N/2)+1 features; taught by Huang for the benefit of transforming a signal from a time domain to a frequency domain optimally using different techniques to identify desired frequencies. [Huang: ¶ 10]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chattopadhyay et al (US 2020/0311559 A1) teaches, The method according to claim 3 wherein the histogram bin width and the number of bins (Fig. 5A, ¶ 27 determining the optimal bin size or bin width) are determined via Freedman-Diaconis (FD) rule (Fig. 5A, ¶ 28 bin size could be determined using certain formulas, such as the Freedman-Diaconis formula).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/JOSEPH O. NYAMOGO/
Examiner
Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 1/9/2026