DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a signal segment extractor, a signal segment noise calculator, and a signal segment filter in claims 1 and 18 with corresponding structure of machine readable instruction executed by a processor (see instant application paragraph [0004]).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of
matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the
conditions and requirements of this title.
Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“One or more non-transitory computer-readable media having data and machine readable instructions executable by a processor, the data comprising electroanatomical data characterizing an electrophysiological signal measured from a patient, the machine readable instructions comprising:
a signal segment extractor programmed to extract a signal segment of interest from the electrophysiological signal;
a signal segment noise calculator programmed to evaluate the extracted signal segment of interest to estimate a noise in the signal segment of interest; and
a signal segment filter programmed to determine a surrogate noise estimate for at least one remaining signal segment of the electrophysiological signal based on the estimated noise in the signal segment of interest, and filter the at least one remaining signal segment to remove noise therein based on the surrogate noise estimate, the at least one remaining signal segment being different from the extracted signal segment.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101:
Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (Abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations.
Similar limitations comprise the abstract ideas of Claims 18 and 22.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
• In Claim 1: One or more non-transitory computer-readable media having data and machine readable instructions executable by a processor, the data comprising electroanatomical data characterizing an electrophysiological signal measured from a patient;
• In Claim 18: A system comprising: at least one sensor configured to measure at least one electrophysiological signal from a location on tissue associated with a patient; memory configured to store machine readable instructions and data representing the measured at least one electrophysiological signal; at least one processor configured to
access the memory and configured to execute the machine readable instructions;
• In Claim 22: A method comprising: extracting a signal segment of interest from an electrophysiological signal measured from a patient; converting using a discrete Fourier transform (DFT) the signal segment of interest to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest.
With regards to Claim 1, the additional elements in the preambles are recited in generality and the recited computer-readable medium comprising software
code stored therein which, when executed by a processor, executes or initiates the execution of a signal segment extractor; a signal segment noise calculator; and a signal segment filter are examples of generic computer media (instructions) that are generally recited and, therefore, are not qualified as particular machines, they represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
With regards to Claim 18, the additional elements in the preambles are recited in generality and the recited control system comprising a processor are examples of generic computer equipment (components) that are generally recited and, therefore, are not qualified as particular machines, they represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
With regards to Claim 22, the additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record (Ferdosi, Matthiesen, Ramanathan and Garrett; these are cited below in the Claim Rejections under section 103).
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, Claims 2-17, 19-21, 23-24 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible either without additional elements that reflect a practical application and qualified for significantly more for substantially similar reasons as discussed with regards to Claims 1, 18, 22.
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.
Claims 1, 18-20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Nima Ferdosi et al. (US 20190183425), hereinafter ‘Ferdosi’, in view of Mads Emil Matthiesen et al. (US 20190261927), hereinafter ‘Matthiesen’.
Regarding Claim 1, Ferdosi discloses one or more non-transitory computer-readable media having data and machine readable instructions executable by a processor (e.g., The steps described herein may be implemented using any suitable controller or processor, and software application, which may be stored on any suitable storage location or calculator-readable medium. The software application provides instructions that enable the processor to perform the functions [0061]),
the data comprising electro-anatomical data characterizing an electrophysiological signal measured from a patient (e.g., a wireless sensor device with an embedded sensor and electrodes is attached to a user (i.e., a patient) to detect an electrocardiogram (ECG) signal (i.e., electro-anatomical data characterizing electrophysiological signal) (e.g., a single-lead, bipolar ECG signal) and other health conditions (e.g., posture) [0019]),
a signal segment extractor programmed to extract a signal segment of interest from the electrophysiological signal (e.g., a signal processing (i.e., signal segment extractor) unit of the wireless sensor device for the extraction of health related data (i.e., the data is a segment of interest) [0019] and The mid-beat ECG (i.e., electrophysiological signal) point typically occurs in the TP-segment (i.e., segment of interest) of the ECG signal and consists of signal artifacts such as baseline wander [0030]),
a signal segment noise calculator programmed to evaluate the extracted signal segment of interest to estimate a noise in the signal segment of interest (e.g., The low-distortion method (i.e., signal segment noise calculator) calculates baseline wander noise (i.e., to estimate a noise) during beats of the ECG (i.e., signal of interest) signal by interpolating between values of the TP segment (i.e., a segment of interest) of consecutive beats [0028]; Therefore, by interpolating consecutive mid-beat ECG samples, an estimate of the baseline waveform is obtained via step 504 [0030]).
Ferdosi does not explicitly disclose a signal segment filter programmed to determine a surrogate noise estimate for at least one remaining signal segment of the electrophysiological signal based on the estimated noise in the signal segment of interest, and to filter the at least one remaining signal segment to remove noise therein based on the surrogate noise estimate, the at least one remaining signal segment being different from the extracted signal segment.
Matthiesen discloses a signal segment filter programmed to determine a surrogate noise estimate for at least one remaining signal segment of the electrophysiological signal based on the estimated noise in the signal segment of interest (e.g.,… said processing comprises running an adaptive filter algorithm (i.e. a signal segment filter) on one or more intermediate signals (i.e., surrogate noise estimate and at least one remaining signal segment) which are based on the input signals to the processor (i.e., estimated noise in the signal segment of interest) [0016]; … this algorithm can also be implemented for real-time processing, where initialization is only done once, and where pre-processing and noise estimation (i.e., surrogate noise estimate) is done continuously or in small segments (i.e., a segment of interest) [0068]),
and filter the at least one remaining signal segment to remove noise therein based on the surrogate noise estimate, the at least one remaining signal segment being different from the extracted signal segment (e.g.,…said adaptive filter algorithm being arranged to calculate one or more estimated noise component(s) (i.e., surrogate noise estimate) of said one or more intermediate signals (i.e., at least one remaining signal segment) and being arranged to subtract said one or more estimated noise (i.e., remove noise) component(s) respectively from said one or more (i.e., different from extracted signal segment) intermediate signals [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention modify Ferdosi in view of Matthiesen for a signal segment filter programmed to determine a surrogate noise estimate for at least one remaining signal segment of the electrophysiological signal based on the estimated noise in the signal segment of interest, and to filter the at least one remaining signal segment to remove noise therein based on the surrogate noise estimate, the at least one remaining signal segment being different from the extracted signal segment “in this way, noise, especially noise due to mains interference, can be effectively removed from a cardiac signal without causing large distortions of the actual signal’, Matthiesen, [0017].
Regarding Claim 18, Ferdosi discloses a system comprising at least one sensor configured to measure at least one electrophysiological signal from a location on tissue associated with a patient (e.g., …during detection/recording of an ECG signal by a wireless sensor device attached to the user, the position of the user's heart relative to the electrodes of the wireless sensor device… [0039]),
memory configured to store machine readable instructions and data representing the measured at least one electrophysiological signal (e.g., …the memory device includes an application that, when executed by the processor, causes the processor to determine at least one beat of the ECG signal… [Abstract]),
at least one processor configured to access the memory and configured to execute the machine readable instructions (e.g., …a processor coupled to the sensor, wherein the processor includes a beat selection logic unit, and a memory device coupled to the processor…[Abstract]),
a signal segment extractor programmed to evaluate a signal morphology of the at least one electrophysiological signal to identify a signal segment of interest of the at least one electrophysiological signal (e.g., The analog ECG signal is filtered by a fixed analog anti-aliasing filter before the analog ECG signal is sampled and converted to a digital domain through an Analog to Digital converter (ADC) and passed to a signal processing unit of the wireless sensor device for the extraction (i.e., signal segment extractor) of health related data including but not limited to a RR interval, heart rate, heart rate variability and other ECG signal features (i.e., identify a signal segment of interest of the at least one electrophysiological signal) [0019]; The filter parameters are varied so that the ECG signal is denoised while preserving most of the morphological features (i.e., evaluate a signal morphology of a electrophysiological signal)… [0051]),
a signal segment noise calculator and to estimate a noise in the signal segment of interest (e.g., The low-distortion method (i.e., signal segment noise calculator) calculates baseline wander noise (i.e., estimates a noise) during beats of the ECG signal by interpolating between values of the TP segment (i.e., signal segment of interest) of consecutive beats [0028]).
Ferdosi does not specifically disclose to convert the signal segment of interest to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest, and evaluate the frequency domain data to estimate a noise in the signal segment of interest, and a signal segment filter programmed to compute a surrogate noise estimate for at least one remaining signal segment of the at least one electrophysiological signal based on the estimated noise in the signal segment of interest, and remove a noise in the at least one remaining signal segment based on the surrogate noise estimate and remove the noise in the signal segment of interest based on the estimated noise to provide a noise filtered version of the at least one electrophysiological signal.
Matthiesen discloses to convert the signal segment of interest to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest (e.g., The relative level of noise from the different harmonics can be estimated by an FFT or more efficiently by the Goertzel algorithm, which computes the DFT (convert the signal segment of interest to corresponding frequency domain data) for only a specific set of frequencies (i.e., discrete frequency bins for signals in the signal segment of interest) [0073]),
to evaluate the frequency domain data to estimate a noise in the signal segment of interest (e.g., The relative level of noise from the different harmonics can be estimated (i.e., estimate a noise in the signal segment of interest) by an FFT or more efficiently by the Goertzel algorithm (i.e., evaluate frequency domain data), which computes the DFT for only a specific set of frequencies [0073]),
a signal segment filter programmed to compute a surrogate noise estimate for at least one remaining signal segment of the at least one electrophysiological signal based on the estimated noise in the signal segment of interest (e.g., …said processing comprises running an adaptive filter algorithm (i.e., signal segment filter) on one or more intermediate signals (i.e., surrogate noise estimate and at least one remaining signal segment) which are based on the input signals to the processor (i.e., one remaining signal of interest and estimated noise) [0016]; … this algorithm can also be implemented for real-time processing, where initialization is only done once, and where pre-processing and noise estimation (i.e., surrogate noise estimate) is done continuously or in small segments (i.e., a segment of interest) [0068]),
to remove a noise in the at least one remaining signal segment based on the surrogate noise estimate and remove the noise in the signal segment of interest based on the estimated noise to provide a noise filtered version of the at least one electrophysiological signal (e.g., The adaptive filter algorithm is arranged to calculate estimated noise component(s) (i.e., surrogate noise estimate) of the signals and arranged to subtract the estimated noise component(s) (i.e., remove the noise to provide a noise filtered version) from the signals [Abstract]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen to convert the signal segment of interest to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest, and evaluate the frequency domain data to estimate a noise in the signal segment of interest to know “around which frequencies the noise will be expected…output can additionally be used to determine which harmonics need to be filtered, and which are of so low amplitude that they are negligible”, Matthiesen, [0073].
This would give the advantage that noise, especially noise due to mains interference, can be effectively removed from a cardiac signal without causing large distortions of the actual signal, (Matthiesen, [0017]).
Regarding Claim 19, Ferdosi and Matthiesen disclose the limitations of Claim 18.
Ferdosi further discloses including amplitudes for a signal (e.g., …additional inputs to the beat selection logic unit include polarity changes, irregular RR intervals, irregular QRS amplitudes, and statistical changes [0040]).
Ferdosi does not explicitly disclose to compute a set of DFT coefficients that include a phase and an amplitude for each signal in the signal segment of interest to convert the sampled signal segment of interest to the corresponding frequency domain data.
Matthiesen discloses to compute a set of DFT coefficients that include a phase and an amplitude for each signal in the signal segment of interest to convert the sampled signal segment of interest to the corresponding frequency domain data (e.g., …the one or more estimated noise components of said adaptive filter algorithm (i.e., to compute a set of DFT coefficients) could be estimated as one or more sinusoidal waves where the amplitude, the phase shift and the frequency are estimated by the adaptive filter algorithm (i.e., to convert the sampled signal segment of interest to the corresponding frequency domain data) [0020]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen to compute a set of DFT coefficients that include a phase and an amplitude for each signal in the signal segment of interest to convert the sampled signal segment of interest to the corresponding frequency domain data to know “around which frequencies the noise will be expected…output can additionally be used to determine which harmonics need to be filtered, and which are of so low amplitude that they are negligible”, Matthiesen, [0073].
Regarding Claim 20, Ferdosi and Matthiesen disclose the limitations of Claim 19.
Ferdosi further discloses to evaluate the corresponding data to identify a frequency of a noise signal among respective signals in the signal segment of interest (e.g., The ECG signal is filtered to remove low-frequency noise sources including but not limited to baseline wander and high frequency noise sources including but not limited to motion artifacts and power-line noise [0026]).
Ferdosi does not explicitly disclose to evaluate the corresponding frequency domain data to identify a frequency of a noise signal among respective signals in the signal segment of interest, to select a DFT coefficient of the set of DFT coefficients for the noise signal based on the identified frequency, and estimate the noise in the signal segment of interest based on the selected DFT coefficient.
Matthiesen discloses to evaluate the corresponding frequency domain data to identify a frequency of a noise signal among respective signals in the signal segment of interest (e.g., …the processor could be adapted to analyze the frequency spectrum of one or more of the one or more cardiac signals to determine the frequency or the frequencies which contribute(s) the most to the noise… In this case, band pass filters could be used as described above centered around the frequency of interest [0028]),
to select a DFT coefficient of the set of DFT coefficients for the noise signal based on the identified frequency (e.g., …the frequency analysis (i.e., for the noise signal) could be performed via a Goertzel algorithm (i.e., to select a DFT coefficient of the set of DFT coefficients) at the expected mains frequency and at relevant harmonics of the expected mains frequency (i.e., based on the identified frequency) [0030]),
to estimate the noise in the signal segment of interest based on the selected DFT coefficient (e.g., In this way, it is possible to find the contribution to the overall noise (i.e., to estimate the noise) of the different frequencies. By using a Goertzel algorithm (i.e., based on the selected DFT coefficient) …it is already known at which frequencies it is most likely to find mains interference noise [0030]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen to evaluate the corresponding frequency domain data to identify a frequency of a noise signal among respective signals in the signal segment of interest, to select a DFT coefficient of the set of DFT coefficients for the noise signal based on the identified frequency, and estimate the noise in the signal segment of interest based on the selected DFT coefficient to know “around which frequencies the noise will be expected…output can additionally be used to determine which harmonics need to be filtered, and which are of so low amplitude that they are negligible”, Matthiesen, [0073].
Regarding Claim 22,
Ferdosi discloses a method comprising extracting a signal segment of interest from an electrophysiological signal measured from a patient (e.g., …a wireless sensor device with an embedded sensor and electrodes is attached to a user (i.e., patient) to detect (i.e., measure) an electrocardiogram (ECG) signal (i.e., electrophysiological signal of interest) … [0019]).
Ferdosi does not explicitly disclose converting using a discrete Fourier transform (DFT) the signal segment of interest to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest, the converting comprises computing a set of DFT coefficients for each of the signals of the signal segment of interest, evaluating the frequency domain data to identify a frequency of a noise signal among the signals in the signal segment of interest; selecting a DFT coefficient of the set of DFT coefficients for the noise signal based on the identified frequency to estimate a noise in the signal segment of interest, and determining a surrogate noise estimate for at least one remaining signal segment of the electrophysiological signal based on the estimated noise in the signal segment of interest, and filtering the at least one remaining signal segment to remove noise therein based on the surrogate noise estimate.
Matthiesen discloses converting using a discrete Fourier transform (DFT) the signal segment of interest to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest (e.g., The relative level of noise from the different harmonics can be estimated by an FFT or more efficiently by the Goertzel algorithm (i.e., converting using a DFT), which computes the DFT for only a specific set of frequencies (i.e., to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest) [0073]),
the converting comprises computing a set of DFT coefficients for each of the signals of the signal segment of interest (e.g., …the one or more estimated noise components of said adaptive filter algorithm could be estimated as one or more sinusoidal waves where the amplitude, the phase shift and the frequency are estimated (i.e., computing a set of DFT coefficients) by the adaptive filter algorithm (i.e., for each of the signals of interest) [0020]),
evaluating the frequency domain data to identify a frequency of a noise signal among the signals in the signal segment of interest, selecting a DFT coefficient of the set of DFT coefficients for the noise signal based on the identified frequency to estimate a noise in the signal segment of interest (e.g., …the processor could be adapted to analyze the frequency spectrum of one or more of the one or more cardiac signals to determine the frequency or the frequencies which contribute(s) the most to the noise (i.e., evaluating the frequency domain data to identify a frequency of a noise signal among the signals in the signal segment of interest)… In this case, band pass filters could be used as described above centered around the frequency of interest [0028]; …the frequency analysis could be performed via a Goertzel algorithm (i.e., selecting a DFT coefficient of the set of DFT coefficients) at the expected mains frequency and at relevant harmonics of the expected mains frequency [0030]),
determining a surrogate noise estimate for at least one remaining signal segment of the electrophysiological signal based on the estimated noise in the signal segment of interest, and filtering the at least one remaining signal segment to remove noise therein based on the surrogate noise estimate ( e.g., …said adaptive filter algorithm being arranged to calculate one or more estimated noise component(s) (i.e., surrogate noise estimate) of said one or more intermediate signals (i.e., at least one remaining signal segment) and being arranged to subtract said one or more estimated noise component(s) (i.e., remove noise) respectively from said one or more intermediate signals [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen for converting using a discrete Fourier transform (DFT) the signal segment of interest to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest, the converting comprises computing a set of DFT coefficients for each of the signals of the signal segment of interest, evaluating the frequency domain data to identify a frequency of a noise signal among the signals in the signal segment of interest; selecting a DFT coefficient of the set of DFT coefficients for the noise signal based on the identified frequency to estimate a noise in the signal segment of interest, and determining a surrogate noise estimate for at least one remaining signal segment of the electrophysiological signal based on the estimated noise in the signal segment of interest, and filtering the at least one remaining signal segment to remove noise therein based on the surrogate noise estimate because “use of the Goertzel algorithm is especially relevant when filtering for mains interference since it is known around which frequencies the noise will be expected. So, an additional element in the algorithm is to first determine the most appropriate order of frequencies to filter”, Matthiesen, [0073], and to know “around which frequencies the noise will be expected…output can additionally be used to determine which harmonics need to be filtered, and which are of so low amplitude that they are negligible”, Matthiesen, [0073].
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ferdosi and Matthiesen in view of Charulatha Ramanathan et al. (US 20120101398), hereinafter ‘Ramanathan’.
Regarding Claim 2, Ferdosi and Matthiesen disclose the limitations of Claim 1.
Ferdosi further discloses sampling and evaluating the signal morphology (e.g., …using a beat selection (e.g., sample) logic and denoising the at least one beat using at least one ensemble averaging filter [Abstract]; The filter parameters are varied so that the ECG signal is denoised while preserving most of the morphological features… [0051]).
However, Ferdosi and Matthiesen does not explicitly disclose to apply a moving window function to sample a portion of the electrophysiological signal and evaluate a signal morphology of the sampled portion of the electrophysiological signal to determine whether the portion of the electrophysiological signal is to be identified as the signal segment of interest.
Ramanathan discloses to apply a moving window function to sample a portion of the electrophysiological signal (e.g., Additionally or alternatively, the cursor itself can also take on the form of the selected virtual electrode construct, such as while it moves across a window (i.e., a moving window function) in which the organ model is being displayed (i.e., to sample a portion of the electrophysiological signal) [0074]),
to evaluate a signal morphology of the sampled portion of the electrophysiological signal to determine whether the portion of the electrophysiological signal is to be identified as the signal segment of interest (e.g., The shape and morphology of the electrogram can be analyzed and the classified into known or typical morphologies [0138] (i.e., evaluate a signal morphology of the sampled portion of the electrophysiological signal); GUI controls 372 can be provided in an adjacent window, such as to control the color, size and method utilized to mark each region of interest with the virtual electrodes (i.e., determine whether the portion of the electrophysiological signal is to be identified as the signal segment of interest) [0132]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi, Matthiesen with Ramanathan to apply a moving window function to sample a portion of the electrophysiological signal and evaluate a signal morphology of the sampled portion of the electrophysiological signal to determine whether the portion of the electrophysiological signal is to be identified as the signal segment of interest “to allow a user to select an interval within the time period. Responsive to the user selection of the interval, a visual representation of physiological information is generated for the user selected interval by applying at least one method to the electroanatomic data”, Ramanathan, [0028].
Claims 3-8, 10-14, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ferdosi, Matthiessen and Ramanathan in view of Michael Garrett et al. (US 20160384757), hereinafter ‘Garrett’.
Regarding Claim 3, Ferdosi, Matthiesen and Ramanathan disclose the limitations of Claim 2.
Ferdosi does not explicitly disclose the moving window function includes a Hamming window.
Garret discloses the moving window function includes a Hamming window (e.g., …for each window, step 312 includes creating a Hamming window data set centered at a middle portion of the window [0128]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi, Matthiesen with Garrett for the moving window function to include a Hamming window because “the window function enhances, in some embodiments, the ability of the FFT operation to extract spectral data from signals by reducing the effects of leakage that may occur during an FFT operation of the data. Put any way, the window function can attenuate or remove high frequency components that result from discontinuities in the discretization of the data and the analysis using window”, Garrett, [0128].
Regarding Claim 4, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 3.
Ferdosi further discloses converting the sampled signal (e.g., The analog ECG signal is filtered by a fixed analog anti-aliasing filter before the analog ECG signal is sampled and converted to a digital domain through an Analog to Digital converter (ADC) and passed to a signal processing unit… [0019]).
Ferdosi does not explicitly disclose a transform function programmed to convert the sampled signal segment of interest to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest.
Matthiesen discloses a transform function programmed to convert the sampled signal segment of interest to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest (e.g., The relative level of noise from the different harmonics can be estimated by an FFT or more efficiently by the Goertzel algorithm (i.e., a transform function programmed to convert the sampled signal segment of interest to corresponding frequency domain data), which computes the DFT for only a specific set of frequencies (i.e., discrete frequency bins for signals in the signal segment of interest) [0073]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen for a transform function programmed to convert the sampled signal segment of interest to corresponding frequency domain data having discrete frequency bins for signals in the signal segment of interest because “use of the Goertzel algorithm is especially relevant when filtering for mains interference since it is known around which frequencies the noise will be expected. So, an additional element in the algorithm is to first determine the most appropriate order of frequencies to filter”, Matthiesen, [0073].
Regarding Claim 5, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 4.
Ferdosi does not explicitly disclose the transform function is programmed to apply discrete Fourier transform (DFT) to the sampled signal segment of interest to convert the sampled signal segment of interest to the corresponding frequency domain data.
Matthiesen discloses the transform function is programmed to apply discrete Fourier transform (DFT) to the sampled signal segment of interest to convert the sampled signal segment of interest to the corresponding frequency domain data (e.g., The relative level of noise from the different harmonics can be estimated by an FFT or more efficiently by the Goertzel algorithm (i.e., transform function programmed to apply (DFT) to the sampled signal segment of interest), which computes the DFT for only a specific set of frequencies (i.e., corresponding frequency domain data) [0073]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen the transform function to be programmed to apply discrete Fourier transform (DFT) to the sampled signal segment of interest to convert the sampled signal segment of interest to the corresponding frequency domain data because “use of the Goertzel algorithm is especially relevant when filtering for mains interference since it is known around which frequencies the noise will be expected. So, an additional element in the algorithm is to first determine the most appropriate order of frequencies to filter”, Matthiesen, [0073].
Regarding Claim 6, Ferdosi, Matthiesen, Ramanathan and Garret disclose the limitations of Claim 5.
Ferdosi further discloses including amplitudes for a signal (e.g., …additional inputs to the beat selection logic unit include polarity changes, irregular RR intervals, irregular QRS amplitudes, and statistical changes [0040]).
Ferdosi does not explicitly disclose to compute a set of DFT coefficients that include a phase and an amplitude for each signal in the signal segment of interest to convert the sampled signal segment of interest to the corresponding frequency domain data.
Matthiesen discloses to compute a set of DFT coefficients that include a phase and an amplitude for each signal in the signal segment of interest to convert the sampled signal segment of interest to the corresponding frequency domain data (e.g., …the one or more estimated noise components of said adaptive filter algorithm could be estimated as one or more sinusoidal waves where the amplitude, the phase shift (i.e., to compute a set of DFT coefficients that include a phase and an amplitude for each signal in the signal segment of interest) and the frequency are estimated by the adaptive filter algorithm (i.e., to the corresponding frequency domain data) [0020]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen to compute a set of DFT coefficients that include a phase and an amplitude for each signal in the signal segment of interest to convert the sampled signal segment of interest to the corresponding frequency domain data to know “around which frequencies the noise will be expected…output can additionally be used to determine which harmonics need to be filtered, and which are of so low amplitude that they are negligible”, Matthiesen, [0073].
Regarding Claim 7, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 6.
Ferdosi further discloses to evaluate the corresponding data to identify a frequency of a noise signal among respective signals in the signal segment of interest (e.g., The ECG signal is filtered to remove low-frequency noise sources including but not limited to baseline wander and high frequency noise sources including but not limited to motion artifacts and power-line noise [0026]).
Ferdosi does not explicitly disclose to evaluate the corresponding frequency domain data to identify a frequency of a noise signal among respective signals in the signal segment of interest, to select a DFT coefficient of the set of DFT coefficients for the noise signal based on the identified frequency, and estimate the noise in the signal segment of interest based on the selected DFT coefficient.
Matthiesen discloses to evaluate the corresponding frequency domain data to identify a frequency of a noise signal among respective signals in the signal segment of interest (e.g., …the processor could be adapted to analyze the frequency spectrum of one or more of the one or more cardiac signals to determine the frequency or the frequencies which contribute(s) the most to the noise… In this case, band pass filters could be used as described above centered around the frequency of interest [0028]),
to select a DFT coefficient of the set of DFT coefficients for the noise signal based on the identified frequency (e.g., …the frequency analysis could be performed via a Goertzel algorithm (i.e., to select a DFT coefficient of the set of DFT coefficients) at the expected mains frequency and at relevant harmonics of the expected mains frequency (i.e., for the noise signal based on the identified frequency) [0030]),
to estimate the noise in the signal segment of interest based on the selected DFT coefficient (e.g., In this way, it is possible to find the contribution to the overall noise (i.e., estimate the noise) of the different frequencies. By using a Goertzel algorithm (i.e., based on the selected DFT coefficient) …it is already known at which frequencies (i.e., in the signal segment of interest) it is most likely to find mains interference noise [0030]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen to evaluate the corresponding frequency domain data to identify a frequency of a noise signal among respective signals in the signal segment of interest, to select a DFT coefficient of the set of DFT coefficients for the noise signal based on the identified frequency, and estimate the noise in the signal segment of interest based on the selected DFT coefficient to know “around which frequencies the noise will be expected…output can additionally be used to determine which harmonics need to be filtered, and which are of so low amplitude that they are negligible”, Matthiesen, [0073].
Regarding Claim 8, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 7.
Ferdosi discloses a noise estimation function programmed to interpolate the estimated noise in the signal segment of interest to the at least one remaining signal segment (e.g., The low-distortion method (i.e., a noise estimation function) calculates baseline wander noise (i.e., estimated noise) during beats of the ECG signal (i.e., signal segment of interest) by interpolating between values of the TP segment (i.e., remaining signal segment) of consecutive beats [0028]).
Ferdosi does not explicitly disclose to provide the surrogate noise estimate for the at least one remaining signal segment based on the selected DFT coefficient for the noise signal.
Matthiesen discloses to provide the surrogate noise estimate for the at least one remaining signal segment based on the selected DFT coefficient for the noise signal (e.g., In one embodiment of the filter algorithm, the signal or signals to be filtered could be filtered by one or more band pass filter(s) on the one or more cardiac signal(s), said band pass filter(s) being centered around the expected mains frequency and/or the expected harmonics (i.e., based on selected DFT coefficient) of the mains frequency. In this way, the noise (i.e., surrogate noise estimate) due to the mains interference can be essentially isolated so that it is easier to make an estimation of the mains interference noise [0026]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen, to provide the surrogate noise estimate for the at least one remaining signal segment based on the selected DFT coefficient for the noise signal to know “around which frequencies the noise will be expected…output can additionally be used to determine which harmonics need to be filtered, and which are of so low amplitude that they are negligible”, Matthiesen, [0073].
Ferdosi, Matthiesen, and Ramanathan do not explicitly disclose an extended window function programmed to sample the at least one remaining signal segment of the electrophysiological signal.
Garrett discloses an extended window function programmed to sample the at least one remaining signal segment of the electrophysiological signal (e.g., When the Hamming window data set is not placed at the exact middle sample of the signal data set, then the Hamming window data set is placed in asymmetric relation to the full signal data set so that samples that are equal distances away from the middle of the windows have an equal value in the Hamming window data set [0128]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi, Matthiesen, Ramanathan with Garrett for an extended window function programmed to sample the at least one remaining signal segment of the electrophysiological signal because “the window function can attenuate or remove high frequency components that result from discontinuities in the discretization of the data and the analysis using window”, Garrett, [0128].
Regarding Claim 10, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 8.
Ferdosi does not explicitly disclose a segment filter function programmed to subtract the noise in the at least one remaining signal segment from the surrogate noise estimate for the at least one remaining signal segment to filter the electrophysiological signal for the noise.
Matthiesen discloses a segment filter function programmed to subtract the noise in the at least one remaining signal segment from the surrogate noise estimate for the at least one remaining signal segment to filter the electrophysiological signal for the noise (e.g., …said adaptive filter algorithm (i.e., a segment filter function) being arranged to calculate one or more estimated noise component(s) of said one or more intermediate signals and being arranged to subtract said one or more estimated noise component(s) (i.e., noise in the at least one remaining signal segment) respectively from said one or more intermediate signals (i.e., surrogate noise estimate) [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen for a segment filter function programmed to subtract the noise in the at least one remaining signal segment from the surrogate noise estimate for the at least one remaining signal segment to filter the electrophysiological signal for the noise “in this way, noise, especially noise due to mains interference, can be effectively removed from a cardiac signal without causing large distortions of the actual signal’, Matthiesen, [0017].
Regarding Claim 11, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 10.
Ferdosi further discloses the signal having line noise and to detect peaks and QRS complex (e.g., …the QRS detection via step 202 comprises detecting at least one QRS complex peak from an original ECG signal detected… The ECG signal is filtered to remove low-frequency noise sources including but not limited to baseline wander and high frequency noise sources including but not limited to motion artifacts and power-line noise [0026]).
Ferdosi does not explicitly disclose the noise in the electrophysiological signal is line noise having a frequency of one of 50 Hertz (Hz) and 60 Hz, and the signal segment of interest does not comprise one of a spike.
Matthiesen discloses the noise in the electrophysiological signal is line noise having a frequency of one of 50 Hertz (Hz) and 60 Hz (e.g., Amongst the main causes for such noise is in particular the prominent mains interference at around 50 Hz or around 60 Hz [0008]),
the signal segment of interest does not comprise one of a spike (e.g., …the system could experience spikes in the measurement signals which are due to external influences, the process could include a spike detection algorithm and the processor could be arranged to pause the adaptation of the noise estimation for a specific amount of time when a spike is detected… [0031]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen for the noise in the electrophysiological signal is line noise having a frequency of one of 50 Hertz (Hz) and 60 Hz because “A major challenge in the recording of cardiac electrophysiological signals is the interference from electrical noise sources, which is picked up by the highly sensitive apparatus”, Matthiesen, [0008]. Additionally, the signal segment of interest has the advantage that it does not comprise one of a spike “in this way, the spikes will not contribute to distorting and corrupting the estimation of the noise signal”, Matthiesen, [0031].
Ferdosi and Matthiesen do not explicitly discloses the signal segment of interest does not comprise one of a QRS complex.
Ramanathan discloses the signal segment of interest does not comprise one of a QRS complex (e.g., Frequency analysis of electrograms can be performed real time and continuously by using techniques for removal of intermittent ventricular activity (‘QRS subtraction`) [0119]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi, Matthiesen, with Ramanathan for the signal segment of interest to not comprise one of a QRS complex as it is known in the art as a “baseline correction technique”, Ramanathan, [0118] and a way “of extracting and analyzing the frequency spectrum of complex cardiac electrical activity, Ramanathan, [0117].
Regarding Claim 12, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 11.
Ferdosi further discloses the electroanatomical data comprises a plurality of electrophysiological signals measured from the patient via a set of sensors, and the electrophysiological signal correspond to a given electrophysiological signal (e.g., …a wireless sensor device with an embedded sensor and electrodes is attached to a user to detect an electrocardiogram (ECG) signal (e.g., a single-lead, bipolar ECG signal) and other health conditions [0019]).
Ferdosi does not explicitly disclose the noise estimation function is programmed to provide a surrogate noise estimate for remaining electrophysiological signals of the plurality of electrophysiological signals based on the estimated noise in the signal segment of interest of the given electrophysiological signal, and the segment filter function is programmed subtract a noise in the remaining electrophysiological signals from the surrogate noise estimate for the remaining electrophysiological signals to filter the remaining electrophysiological signals for the noise.
Matthiesen discloses the noise estimation function is programmed to provide a surrogate noise estimate for remaining electrophysiological signals of the plurality of electrophysiological signals based on the estimated noise in the signal segment of interest of the given electrophysiological signal (e.g., Since the partially isolated noise signal 104 also contains part of the physiological signal, the adaptation process 102 (i.e., noise estimation function) uses the partially isolated noise signal 104 (i.e., the estimated noise in the signal segment of interest) to generate an estimation of the noise signal (i.e., surrogate noise estimate) as a pure sine wave 105 [0051]),
the segment filter function is programmed subtract a noise in the remaining electrophysiological signals from the surrogate noise estimate for the remaining electrophysiological signals to filter the remaining electrophysiological signals for the noise (e.g., …said adaptive filter algorithm (i.e., the segment filter function) being arranged to calculate one or more estimated noise component(s) of said one or more intermediate signals and being arranged to subtract said one or more estimated noise component(s) (i.e., a noise in the remaining signal) respectively from said one or more intermediate signals (i.e., the surrogate noise estimate) [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen so that the noise estimation function is programmed to provide a surrogate noise estimate for remaining electrophysiological signals of the plurality of electrophysiological signals based on the estimated noise in the signal segment of interest of the given electrophysiological signal, and the segment filter function is programmed subtract a noise in the remaining electrophysiological signals from the surrogate noise estimate for the remaining electrophysiological signals to filter the remaining electrophysiological signals for the noise “in this way, noise, especially noise due to mains interference, can be effectively removed from a cardiac signal without causing large distortions of the actual signal’, Matthiesen, [0017].
Regarding Claim 13, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 12.
Ferdosi further discloses the signal segment extractor (e.g., …a signal processing unit of the wireless sensor device for the extraction of health related data… [0019]; The mid-beat ECG point typically occurs in the TP-segment of the ECG signal and consists of signal artifacts such as baseline wander [0030]),
the signal segment noise calculator (e.g., The low-distortion method calculates baseline wander noise during beats of the ECG signal by interpolating between values of the TP segment of consecutive beats [0028]).
Ferdosi does not explicitly disclose a plurality of noise filtering systems respectively comprising the signal segment filter, the set of sensors are arranged in a plurality of spatial zones, and wherein a respective noise filtering system of the plurality of noise filtering systems is adapted to be employed for each spatial zone, and configured to estimate a noise in a signal segment of interest of a respective electrophysiological signal measured by a sensor of a respective spatial zone of the plurality of spatial zones, the respective electrophysiological signal corresponding to the given electrophysiological signal, compute a surrogate noise estimate for electrophysiological signals measured by remaining sensors of the respective spatial zone based on the estimated noise in the signal segment of interest of the respective electrophysiological, and subtract a noise in the electrophysiological signals measured by the remaining sensors of the respective spatial zone from the surrogate noise estimate for the electrophysiological signals to filter the electrophysiological signals for the noise.
Matthiesen discloses a plurality of noise filtering systems respectively comprising the signal segment filter (e.g., …said processing comprises running an adaptive filter algorithm on one or more intermediate signals (i.e., a plurality of noise filtering systems) which are based on the input signals to the processor [0016]; …this algorithm can also be implemented for real-time processing, where initialization is only done once, and where pre-processing and noise estimation is done continuously or in small segments (i.e., the signal segment filter) [0068]),
the set of sensors are arranged in a plurality of spatial zones, and wherein a respective noise filtering system of the plurality of noise filtering systems is adapted to be employed for each spatial zone (e.g., …the present invention relates to a system for filtering cardiac signals, the system comprising: a plurality of cardiac terminals adapted to collect cardiac electrophysiological potentials from a plurality of cardiac electrodes placed at respective cardiac locations in or on an individual… [0001]),
to estimate a noise in a signal segment of interest of a respective electrophysiological signal measured by a sensor of a respective spatial zone of the plurality of spatial zones, the respective electrophysiological signal corresponding to the given electrophysiological signal (e.g., …the processor could be adapted to calculate an average of more than one of the one or more cardiac signal(s) and/or the one or more indifferent signal(s) and in that said calculated average is used by the adaptive filter algorithm to provide a first estimate of the frequency of the noise components [0025]; …a processor device adapted to process one or more input signals which are based on the collected electrophysiological potentials [0001]),
to compute a surrogate noise estimate for electrophysiological signals measured by remaining sensors of the respective spatial zone based on the estimated noise in the signal segment of interest of the respective electrophysiological (e.g., …said processing comprises running an adaptive filter algorithm on one or more intermediate signals which are based on the input signals (i.e., electrophysiological signals measured by remaining sensor of the respective spatial zone) to the processor, said adaptive filter algorithm being arranged to calculate one or more estimated noise component(s) (i.e., surrogate noise estimate) of said one or more intermediate signals (i.e., estimated noise in the signal segment of interest) … [0016]),
to subtract a noise in the electrophysiological signals measured by the remaining sensors of the respective spatial zone from the surrogate noise estimate for the electrophysiological signals to filter the electrophysiological signals for the noise (e.g., … an estimate of the sine wave for the largest contributing frequency is run first, and then this estimated noise (i.e., noise in the electrophysiological signals) is subtracted from the input signal (i.e., surrogate noise estimate) … [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen for a plurality of noise filtering systems respectively comprising the signal segment filter, the set of sensors are arranged in a plurality of spatial zones, and wherein a respective noise filtering system of the plurality of noise filtering systems is adapted to be employed for each spatial zone “for filtering cardiac signals representing electrophysiological potentials from a plurality of electrodes at respective cardiac locations”, Matthiesen, [0001]. This would give the advantage that noise, especially noise due to mains interference, can be effectively removed from a cardiac signal without causing large distortions of the actual signal, (Matthiesen, [0017]).
Regarding Claim 14, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 13.
Ferdosi does not explicitly disclose the signal segment of interest is a first signal segment of interest, the surrogate noise is a first surrogate noise, and the data comprises electrical signal data characterizing a signal generated by a therapeutic device or a navigation system, the signal segment extractor is programmed to extract a second signal segment of interest from the signal, the signal segment noise calculator is programmed to evaluate the second signal segment of interest to estimate the noise in the second signal segment of interest, and the signal segment filter is programmed to determine a second surrogate noise estimate for at least one remaining signal segment of the signal and filter the at least one remaining signal segment of the signal to remove noise therein based on the second surrogate noise estimate.
Matthiesen discloses the signal segment of interest is a first signal segment of interest, the surrogate noise is a first surrogate noise, and the data comprises electrical signal data characterizing a signal generated by a therapeutic device or a navigation system (e.g., …starting with the frequency which has the greatest contribution to the noise (i.e., first signal segment of interest)…, an estimate of the sine wave (i.e., surrogate noise) for the largest contributing frequency is run first [0029]; … cardiac electrophysiological potentials from a plurality of cardiac electrodes placed at respective cardiac locations in or on an individual (i.e., data comprises electrical signal data characterizing a signal generated by a therapeutic device)… [0001]),
to extract a second signal segment of interest from the signal, the signal segment noise calculator is programmed to evaluate the second signal segment of interest to estimate the noise in the second signal segment of interest (e.g., …the adaptive filter algorithm could also be run multiple times (i.e., to extract a second signal segment of interest from the signal) …and repeated at relevant harmonics (i.e., to evaluate the second signal segment of interest to estimate the noise in the second signal segment of interest) [0029]),
to determine a second surrogate noise estimate for at least one remaining signal segment of the signal and filter the at least one remaining signal segment of the signal to remove noise therein based on the second surrogate noise estimate (e.g., …then this estimated noise (i.e., second surrogate noise estimate) is subtracted from the input signal (i.e., remove noise therein based on the second surrogate noise estimate), after which the process is repeated for the next frequency (i.e., one remaining signal segment of the signal and filter the at least one remaining signal segment of the signal) … [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen for the signal segment of interest is a first signal segment of interest, the surrogate noise is a first surrogate noise, and the data comprises electrical signal data characterizing a signal generated by a therapeutic device or a navigation system, the signal segment extractor is programmed to extract a second signal segment of interest from the signal, the signal segment noise calculator is programmed to evaluate the second signal segment of interest to estimate the noise in the second signal segment of interest, and the signal segment filter is programmed to determine a second surrogate noise estimate for at least one remaining signal segment of the signal and filter the at least one remaining signal segment of the signal to remove noise therein based on the second surrogate noise estimate because “the mains interference can occur at the mains frequency and at different harmonics of the mains frequency, the processor could be adapted to analyze the frequency spectrum of one or more of the one or more cardiac signals to determine the frequency or the frequencies which contribute(s) the most to the noise”, Matthiesen, [0028].
Regarding Claim 16, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 13.
Ferdosi further discloses a mapping system programmed to generate a first graphical map of electroanatomic activity based on electrophysiological signals provided by respective sensors (e.g., Aside from being used to measure heart related data, the wireless sensor device also records a digitized ECG waveform… [0019]; The diagram 800 includes a first graph 802 depicting an EGG signal before denoising… [0036]),
to generate a second graphical map of electroanatomic activity based on electrophysiological signals provided by respective sensors (e.g., The diagram 800 includes a second graph 804 (i.e., generate a second graphical map) depicting the ECG signal (i.e., electrophysiological signal provided by respective sensors) after denoising… [0036]),
to evaluate the first and second graphical maps of the electroanatomic activity to determine a quality of noise filtering (e.g., …using a method and system in accordance with the present invention. The ECG signal after denoising is smoother and less noisy [0036]).
Ferdosi does not explicitly disclose electroanatomic activity based on electrophysiological signals provided by respective sensors of one or more first spatial zones of the plurality of spatial zones, electroanatomic activity based on electrophysiological signals provided by respective sensors of one or more second spatial zones of the plurality of spatial zones.
Matthiesen discloses electroanatomic activity based on electrophysiological signals provided by respective sensors of one or more first spatial zones of the plurality of spatial zones (e.g., …the present invention relates to a system for filtering cardiac signals, the system comprising: a plurality of cardiac terminals adapted to collect cardiac electrophysiological potentials from a plurality of cardiac electrodes placed at respective cardiac locations in or on an individual… [0001]),
electroanatomic activity based on electrophysiological signals provided by respective sensors of one or more second spatial zones of the plurality of spatial zones (e.g., …a processor device adapted to process one or more input signals which are based on the collected electrophysiological potentials [0001]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen to evaluate electroanatomic activity based on electrophysiological signals provided by respective sensors of one or more first spatial zones of the plurality of spatial zones, electroanatomic activity based on electrophysiological signals provided by respective sensors of one or more second spatial zones of the plurality of spatial zones “for filtering cardiac signals representing electrophysiological potentials from a plurality of electrodes at respective cardiac locations”, Matthiesen, [0001].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ferdosi, Matthiesen, Ramanathan and Garrett in view of Budimir S. Drakulic et al. (US 20220249006), hereinafter ‘Drakulic’.
Regarding Claim 9, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 8.
Ferdosi discloses to scale the noised signal (e.g., Note that the denoised beat is time-scaled back to the length of the original beat by the wireless sensor device if the denoising process is performed on the beat [0054]).
Ferdosi, Matthiesen, Ramanathan and Garrett does not explicitly disclose to scale the selected DFT coefficient to scale the estimated noise in the signal segment of interest noise estimation in the at least one remaining signal segment.
Drakulic discloses to scale the selected DFT coefficient to scale the estimated noise in the signal segment of interest noise estimation in the at least one remaining signal segment (e.g., Two buttons can be below this window to allow the user to change the time 7016 and amplitude scale 7018 (i.e., to scale the selected DFT coefficient) on the displayed signals in order to accurately view the signals' shape details (i.e., to scale the estimated noise). If the selected time 7016 or amplitude scale 7018 make the signal partially visible, scroll bars can be automatically displayed to allow the user to access any part of the signals (i.e., the signal segment of interest noise estimation) [0468]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi, Matthiesen, Ramanathan and Garrett with Drakulic to scale the selected DFT coefficient to scale the estimated noise in the signal segment of interest noise estimation in the at least one remaining signal segment because “this can allow the user to easily identify the section of signal shown in the detail view”, Drakulic, [0469].
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Ferdosi in view of Matthiesen, Ramanathan and Garrett in view of Shahabedin Shahdoostfard et al. (US 20190274568), hereinafter ‘Shahdoostfard’.
Regarding Claim 15, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 13.
Ferdosi discloses the data comprises electrical signal data characterizing measured electrical signals applied to a body of the patient (e.g., …a wireless sensor device with an embedded sensor and electrodes is attached to a user to detect an electrocardiogram (ECG) signal… [0019]).
Ferdosi does not explicitly disclose to evaluate the measured electrical signals to determine a common noise in the measured electrical signals.
Matthiesen discloses to evaluate the measured electrical signals to determine a common noise in the measured electrical signals (e.g., The noise may include noise components common to all signals (common mode noise) and noise components that vary from signal to signal [0008]; … the average of all signals is computed to generate an estimate of the common-mode signal [0075]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen to evaluate the measured electrical signals to determine a common noise in the measured electrical signals because “in most actual situations, there will be multiple signals which need to be filtered. However, it can be assumed that the frequency of the mains interference of all the signals will be essentially the same”, Matthiesen, [0075].
Ferdosi, Matthiesen, Ramanathan and Garrett does not explicitly disclose to extract the signal segment of interest based on the determined common noise in the measured electrical signals.
Shahdoostfard discloses to extract the signal segment of interest based on the determined common noise in the measured electrical signals (e.g., …the signals of interest in the examples disclosed herein include (i.e., to extract the signal segment of interest) the line noise as a common mode signal (i.e., based on the determined common noise in the measured electrical signals) [0040]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi, Matthiesen, Ramanathan and Garrett with Shahdoostfard to extract the signal segment of interest based on the determined common noise in the measured electrical signals because as it known in the art “by utilizing the line noise signal as a common mode signal for the system 100, no additional input signals need to be injected into the system to detect channel integrity, Shahdoostfard, [0027].
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Ferdosi, Matthiesen, Ramanathan and Garrett in view of Lingzhi Hu (US 20210161498), hereinafter ‘Hu’.
Regarding Claim 17, Ferdosi, Matthiesen, Ramanathan and Garrett disclose the limitations of Claim 13.
Ferdosi, Matthiesen, Ramanathan and Garrett does not explicitly disclose a machine learning algorithm programmed to refine a noise filtering of the respective noise filtering system based on historical noise filtering data.
Hu discloses the respective noise filtering system further comprises a machine learning algorithm programmed to refine a noise filtering of the respective noise filtering system based on historical noise filtering data (e.g., In some embodiments, the target noise prediction model may be determined by training a preliminary machine learning model based on multiple groups of training data using a model training algorithm; The multiple groups of training data (also referred to as training set) may include historical first noise signals, historical second noise signals and historical excitation signals sampled from historical operations… [0095]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi, Matthiesen, Ramanathan and Garrett with Hu for a machine learning algorithm programmed to refine a noise filtering of the respective noise filtering system based on historical noise filtering data because “the predicted noise signals may be used to measure a noise distribution (or a noise field) of the noises arriving at the target position. In some embodiments, the target noise prediction model may be determined by training a preliminary machine learning model based on multiple groups of training data using a model training algorithm, Hu, [0095].
Claim(s) 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Ferdosi, Matthiesen and Garrett.
Regarding Claim 21 and Claim 23, Ferdosi and Matthiesen disclose the system as discussed above in Claim 20 and Claim 22, and Ferdosi, Matthiesen and Garrett disclose the claimed limitations as discussed above in Claim 8.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Ferdosi, Matthiesen and Garrett in view of Qingguo Zeng (US 20140200823), hereinafter ‘Zeng’.
Regarding Claim 24, Ferdosi, Matthiesen and Garrett disclose the limitations Claim 23.
Ferdosi discloses subtracting the noise in the signal segment of interest from the estimated noise to provide a filtered signal segment of interest (e.g., Therefore, by interpolating consecutive mid-beat ECG samples, an estimate of the baseline waveform is obtained via step 504. The estimate of the baseline waveform (i.e., estimated noise) obtained via step 504 is cancelled by subtraction (i.e., subtracting the noise to provide a filtered signal) from the original ECG signal via step 506 (i.e., signal segment of interest) [0030]).
Ferdosi does not explicitly disclose filtering the at least one remaining signal segment comprises subtracting the noise in the at least one remaining signal segment from the surrogate noise estimate to provide at least one filtered remaining signal segment.
Matthiesen discloses filtering the at least one remaining signal segment comprises subtracting the noise in the at least one remaining signal segment from the surrogate noise estimate to provide at least one filtered remaining signal segment (e.g., …said adaptive filter algorithm (i.e., filtering the at least one remaining signal segment) being arranged to calculate one or more estimated noise component(s) of said one or more intermediate signals and being arranged to subtract said one or more estimated noise component(s) (i.e., subtracting the noise in the at least one remaining signal segment) respectively from said one or more intermediate signals (i.e., from the surrogate noise estimate to provide at least one filtered remaining signal segment) [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi with Matthiesen for filtering the at least one remaining signal segment comprises subtracting the noise in the at least one remaining signal segment from the surrogate noise estimate to provide at least one filtered remaining signal segment “in this way, noise, especially noise due to mains interference, can be effectively removed from a cardiac signal without causing large distortions of the actual signal’, Matthiesen, [0017].
Ferdosi, Matthiesen and Garrett does not explicitly disclose combining the at least one filtered remaining signal segment and the filtered signal segment of interest to provide a noise filtered electrophysiological signal.
Zeng discloses combining the at least one filtered remaining signal segment and the filtered signal segment of interest to provide a noise filtered electrophysiological signal (e.g., The method can also include combining the first recovered signal (i.e., the at least one filtered remaining signal segment) with the feature signal (i.e., the filtered signal segment of interest) to provide a filtered version of the given electrophysiological signal [0005]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ferdosi, Matthiesen and Garrett with Zeng for combining the at least one filtered remaining signal segment and the filtered signal segment of interest “to provide a filtered version of the given electrophysiological signal”, Zeng, [0005].
Conclusion
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/Agustin R Campozano/Examiner, Art Unit 2863
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2863
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