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 .
This action is responsive to the Preliminary Amendment filed on 4/7/2022.
Claims 16-35 are pending in the case.
Claim(s) 1-15 have been cancelled. Claims 16-35 are new.
Specification
The disclosure is objected to because of the following informalities:
In the abstract,
initialising and discretised,
should be initializing and discretized, respectively.
In the specification,
discretised, initialises, initialising, favourable, penalise, optimising, optimised , optimisation, analysed, utilised, behaviours and parameterized,
should be discretized, initializes, initializing, favorable, penalize, optimizing, optimized, optimization, analyzed, utilized, behaviors and parameterized, respectively
Appropriate correction is required.
Claim Objections
Claims 16, 25, 27, 32 and 35, are objected to because of the following informalities:
Claims 16, 32, and 35, "initialising" should be "initializing", and
Claims 16, 25, 27, 32 and 35, "discretised" should be "discretized".
Appropriate correction is required.
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.
Claim(s) 35 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding claim 35 this claim recites a “non-transient storage medium” comprising instructions that perform various functions. There is no structural component associated with the computer product and therefore the computer product can include transitory media. Therefore claim 35 is directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 26, 34, are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim(s) 26 recite(s) “the inverse discrete Fourier transform”. There is lack of antecedent basis for this limitation in these claim(s), rendering the claim(s) indefinite.
Claim(s) 34 recite(s) “the processing circuitry”. There is lack of antecedent basis for this limitation in these claim(s), rendering the claim(s) indefinite.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 16, 17, 23, 25, 27-30, are rejected under 35 U.S.C. 103 as being unpatentable over Valenzuela et al “LOW-PASS FILTERING AS BAYESIAN INFERENCE” dated 9 Feb 2019, and retrieved from arXiv:1902.03427v1 , in view of Liutkus et al “Gaussian Processes for Underdetermined Source Separation” IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol. 59, No. 7, 2 July 2011, pages 3155-3167, XP011356113, and Shaefer (US 20200221243 A1).
Valenzuela and Liutkus were cited in the IDS dated 4/7/2022.
Regarding claim 16, Valenzuela teaches a method comprising (Valenzuela Abstract):
obtaining input data comprising a plurality of measurements of a physical quantity sampled at regular spacings .... (Valenzuela Sec 1- 2nd and 3rd Paras, Sec 6- 2nd para, input may be monitored time series data for measurements for various physical quantities, sampling may be even or uneven);
... values of parameters of a kernel for a Gaussian process for modelling the data, wherein the parameters are associated with a mixture of spectral components representing a spectral density of the kernel (Valenzuela Secs 2.1-3.2, parameters may be for Gaussian kernel(s) for data modelling, parameters are associated with spectral components for spectral density);
determining a cut-off frequency for delimiting a low-frequency range and a high- frequency range (Valenzuela Sec 4, cutoff frequency may be set and low and high frequencies for input are based on the cutoff frequency);
performing a discrete Fourier transform on the input data to determine frequency domain data (Valenzuela Sec 2, discrete Fourier transform of time series data produces frequency domain data);
processing a portion of the frequency domain data within the low-frequency range to determine smoothed input data (Valenzuela Sec 2.2, low frequency data in domain data may be smoothed);
iteratively (Valenzuela Sec 2.1, process may be performed to optimize parameters):
determining a discretised power spectrum for the kernel (Valenzuela Secs 2.1 and 3.1, Fig. 1, Gaussian discretized power spectrum is determined);
generating a low-frequency covariance matrix from a portion of the discretised power spectrum within the low-frequency range; determining, using the smoothed input data and the low-frequency covariance matrix, a first log-likelihood component for the parameters given the smoothed input data (Valenzuela Sec 2.2 covariance matrix is determined using frequency domain data within the low-frequency range, Valenzuela Secs 2.2-3.2, log likelihood for parameters for low frequency information is determined using smoothed information and covariance matrix);
determining, using a portion of the discretised power spectrum within the high- frequency range, a second log-likelihood component for the parameters given a portion of the frequency domain data within the high-frequency range (Valenzuela Secs 2.2-3.2, log likelihood for high frequency information is determined); and
...setting... the values of the parameters to increase an objective function comprising the first log-likelihood component and the second log-likelihood component, wherein increasing the objective function increases a probability density associated with the parameters (Valenzuela Secs 2.2-3.2, parameters are determined using the high and low frequency log likelihoods, determination is based on optimization by increasing probability density); and
... predicting a value of a further measurement of the physical quantity, using the values of the parameters of the kernel after the iterative modifying of the values of the parameters (Valenzuela Sec 5.3, after parameters are determined, kernel may be used to predict future value, such as heart signal).
Valenzuela does not specifically teach a computer-implemented method, within a finite sampling window, initialising values of parameters of a kernel, the cut-off frequency being an integer multiple of a fundamental frequency associated with a size of the finite sampling window, modifying the values of the parameters to increase an objective function
However Liutkus teaches input data comprising a plurality of ...input data points... sampled at regular spacings within a finite sampling window (Liutkus Sec 1- last Para, Secs IV.B, and IV.C, Abstract, input data may be sampled using regular sampling within definite frames, using overlapping framing allows frames to be considered independent for analysis),
initialising values of parameters of a kernel, modifying the values of the parameters to increase an objective function (Liutkus Sec V.B.1, Secs III.D and III.E, initializing and then optimizing parameters is a simple and computationally cheap solution (Also see Secs II and III)).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Liutkus of input data comprising a plurality of ...input data points... sampled at regular spacings within a finite sampling window, initialising values of parameters of a kernel, modifying the values of the parameters to increase an objective function, into the invention suggested by Valenzuela; since both inventions are directed towards determining parameters for a Gaussian kernel, and incorporating the teaching of Liutkus into the invention suggested by Valenzuela would provide the added advantage of using overlapping framing and therefore allowing frames to be considered independent for analysis and using a simple and computationally cheap solution for determining parameters, and the combination would perform with a reasonable expectation of success (Liutkus Sec 1- last Para, Secs IV.B, and IV.C, Abstract, Sec V.B.1, Secs III.D and III.E).
Valenzuela and Liutkus does not specifically teach a computer-implemented method, the cut-off frequency being an integer multiple of a fundamental frequency associated with a size of the finite sampling window
However Shaefer teaches a computer-implemented method (Shaefer [6]),
the cut-off frequency being an integer multiple of a fundamental frequency associated with a size of the finite sampling window (Shaefer [54, 92] cut off frequency to determine low and high frequency ranges may be based on an integer multiple of a fundamental frequency associated with sampling window, this allows cut off frequency (and therefore the frequency filtering) to be adapted to input data).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Shaefer of a computer-implemented method (Shaefer [6]),
the cut-off frequency being an integer multiple of a fundamental frequency associated with a size of the finite sampling window, into the invention suggested by Valenzuela and Liutkus; since both inventions are directed towards determining a cutoff frequency to filter frequencies, and incorporating the teaching of Shaefer into the invention suggested by Valenzuela and Liutkus would provide the added advantage of allowing cut off frequency (and therefore the frequency filtering) to be adapted to input data, and the combination would perform with a reasonable expectation of success (Shaefer [54, 61, 92].
Regarding claim 17, Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 16 above. Valenzuela further teaches wherein determining the second log-likelihood component comprises treating components of the frequency domain data within the high-frequency domain as independent Gaussian random variables (Valenzuela Secs 2.1, 3.1 and 3.2, log likelihood for high frequency range may be determined using independent Gaussian random variable analysis).
Regarding claim 23, Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 16 above. Valenzuela does not specifically teach wherein the cut-off frequency is an integer multiple of a power of two times the fundamental frequency
However Shaefer teaches wherein the cut-off frequency is an integer multiple of a power of two times the fundamental frequency (Shaefer [54] cut off frequency may be equal to fundamental frequency associated with sampling window (multiplied by two to the power of zero)).
Regarding claim 25 Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 16 above. Claim 16 further discloses regular spacings within the finite sampling window
Valenzuela further teaches wherein determining the discretised power spectrum of the kernel comprises: generating a covariance structure comprising evaluations of the kernel at the regular spacings ...; and performing an inverse discrete Fourier transform on data comprising the covariance structure (Valenzuela Secs 2.1 and 2.2, covariance matrix is determined using frequency domain data within the low-frequency range, inverse discrete Fourier transform may be performed using the covariance matrix, Valenzuela Sec 1- 2nd and 3rd Paras, Sec 6- 2nd para, input may be monitored time series data for measurements for various physical quantities, sampling may be even or uneven).
Regarding claim 27, Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 23 above.
Valenzuela further teaches wherein generating the low- frequency covariance matrix comprises performing a discrete Fourier transform on a portion of the discretised power spectrum within the low-frequency range, to determine a low-frequency covariance structure; and
arranging elements of the low-frequency covariance structure into a matrix (Valenzuela Secs 2.2, 3.2 and 4, discrete Fourier transform may be performed on low frequency range of power spectrum, covariance may be a covariance matrix).
Regarding claim 28, Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 16 above. Valenzuela further teaches wherein the input data is time- series data, and the regular spacings correspond to a series of equal temporal intervals (Valenzuela Sec 5.1, 2nd para, input may be time series data which is evenly sampled).
Regarding claim 29 Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 28 above. Valenzuela further teaches wherein the input data comprises any one of audio data ... (Valenzuela Sec 1, 1st Para, data may be audio data).
Regarding claim 30 Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 28 above. Valenzuela does not specifically teach receiving the input data as a data stream; and processing the input data as the data stream is received by moving the finite sampling window over the input data and modifying values of the respective sets of parameters sequentially for different positions of the finite sampling window.
However Liutkus teaches receiving the input data as a data stream; and
processing the input data as the data stream is received by moving the finite sampling window over the input data and modifying values of the respective sets of parameters sequentially for different positions of the finite sampling window (Liutkus Secs IV.B and IV.G, input may be data stream, moving sampling window (frames) may be used and parameters determined based on current frame (Also see all of Sec IV)).
Claim 18, is rejected under 35 U.S.C. 103 as being unpatentable over Valenzuela et al, in view of Liutkus et al and Shaefer (US 20200221243 A1), and further in view of Huang (US 20150181372 A1).
Regarding claim 18, Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 16 above. Valenzuela does not specifically teach wherein the second log- likelihood component is a log-density of a Rayleigh distribution truncated to exclude terms within the low-frequency range
However Huang teaches wherein the second log- likelihood component is a log-density of a Rayleigh distribution truncated to exclude terms within the low-frequency range (Huang [35, 36, 51] log likelihood may be determined from Rayleigh distribution of ranges, to simply operations (ranges exclude other range terms))
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Huang of wherein the second log- likelihood component is a log-density of a Rayleigh distribution truncated to exclude terms within the low-frequency range, into the invention suggested by Valenzuela, Liutkus and Shaefer; since both inventions are directed towards determining log likelihoods based on power spectrums, and incorporating the teaching of Huang into the invention suggested by Valenzuela, Liutkus and Shaefer would provide the added advantage of simplifying operations, and the combination would perform with a reasonable expectation of success (Huang [35, 36, 51]).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Valenzuela et al, in view of Liutkus et al and Shaefer (US 20200221243 A1), and further in view of Turner et al “Time-Frequency Analysis as Probabilistic Inference” from IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 62, NO. 23, DECEMBER 1, 2014.
Regarding claim 19, Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 16 above. Valenzuela further teaches wherein the mixture of spectral components comprises a mixture of Gaussian components, and the parameters comprise ...a respective mean, and a respective variance for each of the Gaussian components in the mixture (Valenzuela Sec 4, parameters may be mean and variance, Valenzuela Secs 2.1-3.2, parameters may be for Gaussian kernel(s) for data modelling, parameters are associated with spectral components for spectral density).
Valenzuela does not specifically teach the parameters comprise a respective amplitude
However Turner teaches the parameters comprise a respective amplitude (Turner Sec II and III, gaussian sub band likelihoods may be determined for parameters, parameters can include amplitude which facilitate using envelopes).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Turner of the parameters comprise a respective amplitude, into the invention suggested by Valenzuela, Liutkus and Shaefer; since both inventions are directed towards determining gaussian sub band likelihoods for parameters, and incorporating the teaching of Turner into the invention suggested by Valenzuela, Liutkus and Shaefer would provide the added advantage of facilitating using envelopes, and the combination would perform with a reasonable expectation of success (Turner Sec II and III).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Valenzuela et al, in view of Liutkus et al, Shaefer (US 20200221243 A1) and Turner et al, and further in view of Wilson et al, “Gaussian Process Kernels for Pattern Discovery and Extrapolation”, PROCEEDINGS OF THE 30™ INTERNATIONAL CONFERENCE ON MACHINE LEARNING, Atlanta, GA, 31 December 2013, 10 pages, cited in the IDS dated 4/7/2022.
Regarding claim 20 Valenzuela, Liutkus, Shaefer and Turner teach the invention as claimed in claim 19 above. Valenzuela further teaches the probability density associated with the parameters is a posterior probability density for the parameters given the input data (Valenzuela Secs I and IV, probability density for parameters may be posterior probability density based on input data).
Valenzuela does not specifically teach the objective function comprises a log-prior density for the parameters, the log-prior density corresponding to a uniform logarithmic prior mapped onto a folded domain to take account of aliasing of frequencies above a Nyquist frequency associated with the regular spacings within the finite sampling window.
However Wilson teaches the objective function comprises a log-prior density for the parameters, the log-prior density corresponding to a uniform logarithmic prior mapped onto a folded domain to take account of aliasing of frequencies above a Nyquist frequency associated with the regular spacings within the finite sampling window (Wilson Secs 4, 4.1, Fig. 1, parameters may be optimized based on log-prior density for uniform logarithmic prior mapped to a folded domain for aliasing of frequencies above a Nyquist frequency associated with the regular spacings within the finite sampling window, this allows for long range extrapolation).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Wilson of the objective function comprises a log-prior density for the parameters, the log-prior density corresponding to a uniform logarithmic prior mapped onto a folded domain to take account of aliasing of frequencies above a Nyquist frequency associated with the regular spacings within the finite sampling window, into the invention suggested by Valenzuela, Liutkus, Shaefer and Turner; since both inventions are directed towards parameters optimized based on log-prior density, and incorporating the teaching of Wilson into the invention suggested by Valenzuela, Liutkus, Shaefer and Turner would provide the added advantage of allowing long range extrapolation, and the combination would perform with a reasonable expectation of success (Wilson Secs 4, 4.1, Fig. 1).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Valenzuela et al, in view of Liutkus et al and Shaefer (US 20200221243 A1), and further in view of Hazeyama (US 20140063112 A1).
Regarding claim 21, Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 16 above. Valenzuela does not specifically teach receiving user input, wherein the determining of the cut-off frequency is based on the received user input
However Hazeyama teaches receiving user input, wherein the determining of the cut-off frequency is based on the received user input (Hazeyama [128, 129] designer may provide cut off frequency for determining frequency bands).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Hazeyama of receiving user input, wherein the determining of the cut-off frequency is based on the received user input, into the invention suggested by Valenzuela, Liutkus and Shaefer; since both inventions are directed towards using cut off frequency to determine frequency bands, and incorporating the teaching of Hazeyama into the invention suggested by Valenzuela, Liutkus and Shaefer would provide the added advantage of allowing a designer to control the appropriate frequency for determining frequency bands, and the combination would perform with a reasonable expectation of success (Hazeyama [128, 129]).
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Valenzuela et al, in view of Liutkus et al and Shaefer (US 20200221243 A1), and further in view of Zhang (US 20190158340 A1).
Regarding claim 22 Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 16 above. Valenzuela does not specifically teach wherein determining the cut-off frequency comprises determining the cut-off frequency as an integer multiple of the fundamental frequency, wherein the integer multiple is given by [C(N log N)1/3], where N is the number of samples in the input data and C is a predetermined constant.
However Zhang teaches wherein determining the cut-off frequency comprises determining the cut-off frequency as an integer multiple of the fundamental frequency, wherein the integer multiple is given by [C(N log N)1/3], where N is the number of samples in the input data and C is a predetermined constant (Zhang [138, 195, 329, 376, 618] frequency sub-bands may be determined based on determined frequency, determined frequency may be based on number of factors affecting determination and accordingly taking a root value of input values)
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Zhang of wherein determining the cut-off frequency comprises determining the cut-off frequency as an integer multiple of the fundamental frequency, wherein the integer multiple is given by [C(N log N)1/3], where N is the number of samples in the input data and C is a predetermined constant, into the invention suggested by Valenzuela, Liutkus and Shaefer; since both inventions are directed towards determining frequency to determine frequency bands, and incorporating the teaching of Zhang into the invention suggested by Valenzuela, Liutkus and Shaefer would provide the added advantage of taking into the number of factors that affect the determination in order to determine the frequency, and the combination would perform with a reasonable expectation of success (Zhang [138, 195, 329, 376, 618]).
Claim(s) 24, 32-35, is/are rejected under 35 U.S.C. 103 as being unpatentable over Valenzuela et al, in view of Liutkus et al and Shaefer (US 20200221243 A1), and further in view of Mukherjee (US 20100280827 A1).
Regarding claim 24, Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 16 above. Valenzuela does not specifically teach wherein performing the discrete Fourier transform comprises performing a fast Fourier transform
However Mukherjee teaches wherein performing the discrete Fourier transform comprises performing a fast Fourier transform (Mukherjee [41, 42] frequency domain input data may be based on fast Fourier transform (FFT), which allows similar input data to be compared and matched)
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Mukherjee of wherein performing the discrete Fourier transform comprises performing a fast Fourier transform, into the invention suggested by Valenzuela, Liutkus and Shaefer; since both inventions are directed towards determining frequency domain based on input data, and incorporating the teaching of Mukherjee into the invention suggested by Valenzuela, Liutkus and Shaefer would provide the added advantage of allowing similar input data to be compared and matched,
and the combination would perform with a reasonable expectation of success (Mukherjee [41, 42]).
Claim 32 is directed towards a system executing instructions similar in scope to the instructions performed by the method of claim 16 and is rejected under the same rationale.
Valenzuela further teaches obtaining input data comprising a plurality of samples arranged on a regular grid ... (Valenzuela Secs 4 and 5.1, Fig. 2, input data may be two dimensional time series data, Valenzuela Sec 1- 2nd and 3rd Paras, Sec 6- 2nd para, input may be monitored time series data for measurements for various physical quantities, sampling may be even or uneven).
Valenzuela does not specifically teach system comprising: one or more processors; and a non-transient storage medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations.
However Mukherjee teaches system comprising: one or more processors; and a non-transient storage medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising (Mukherjee [25, 26, 64, 94] system with processor executing instructions stored in memory, parameters may be determined for Gaussian Mixture Model using time series input data).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Mukherjee of system comprising: one or more processors; and a non-transient storage medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising, into the invention suggested by Valenzuela, Liutkus and Shaefer; since both inventions are directed towards determining frequency domain based on input data, and incorporating the teaching of Mukherjee into the invention suggested by Valenzuela, Liutkus and Shaefer would provide the added advantage of allowing generic computer components to be used to implement the method, and the combination would perform with a reasonable expectation of success (Mukherjee [25, 26, 64, 94]).
Regarding claim 33, Valenzuela, Liutkus, Shaefer and Mukherjee teach the invention as claimed in claim 32 above. Valenzuela further teaches a receiver for receiving the data comprising the plurality of samples (Valenzuela Sec 1- 2nd and 3rd Paras, Sec 6- 2nd para, input may be monitored time series data for measurements for various physical quantities).
Claim(s) 34, is/are dependent on claim 32 above, is/are directed towards a system executing instructions similar in scope to the instructions performed by the method of claim(s) 24 respectively, and is/are rejected under the same rationale.
Claim 35 is directed towards a medium storing instructions similar in scope to the instructions executed by the system of claim 32, and is rejected under the same rationale. Mukherjee further teaches a non-transient storage medium comprising instructions which, when executed by the one or more processors, cause the one or more processors to perform operations (Mukherjee [25, 26, 64, 94] system with processor executing instructions stored in memory).
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Valenzuela et al, in view of Liutkus et al and Shaefer (US 20200221243 A1), and further in view of Wang (US 20200099434 A1).
Regarding claim 26, Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 23 above. Valenzuela does not specifically teach wherein performing the inverse discrete Fourier transform comprises performing an inverse fast Fourier transform
However Wang teaches wherein performing the inverse discrete Fourier transform comprises performing an inverse fast Fourier transform (Wang [86-88, 95] inverse fast Fourier transform may be used, different transforms are replaceable with one another).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Wang of wherein performing the inverse discrete Fourier transform comprises performing an inverse fast Fourier transform, into the invention suggested by Valenzuela, Liutkus and Shaefer; since both inventions are directed towards performing Fourier transforms, and incorporating the teaching of Wang into the invention suggested by Valenzuela, Liutkus and Shaefer would provide the added advantage of using a methodology with reduced complexity, and the combination would perform with a reasonable expectation of success (Wang [86-88, 95]).
Claim(s) 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Valenzuela et al, in view of Liutkus et al and Shaefer (US 20200221243 A1), and further in view of Hollender (US 20190073609 A1).
Regarding claim 31 Valenzuela, Liutkus and Shaefer teach the invention as claimed in claim 28 above.
Valenzuela further teaches using the Gaussian process after the iterative modifying of the values of the parameters to predict a given event occurring at a time later than a period corresponding to the finite sampling window (Valenzuela Sec 5.3 after parameters are set, process may be used to predict likelihood of congestive heart failure).
Valenzuela does not specifically teach responsive to predicting the given event occurring, triggering an alarm warning and/or a control signal
However Hollender teaches responsive to predicting the given event occurring, triggering an alarm warning... (Hollender [6, 24, 55, 56, 81, 82] Gaussian process predicts likelihood of triggering event occurring, the event is predicted- an alarm may be triggered to allow user to view alarm information to determine which ones to act upon).
It would have been obvious to one of an ordinary skill in the art before the effective filing date of the claimed invention, to have incorporated the concept taught by Hollender of responsive to predicting the given event occurring, triggering an alarm warning, into the invention suggested by Valenzuela, Liutkus and Shaefer; since both inventions are directed towards Gaussian process to predict likelihood of triggering event occurring, and incorporating the teaching of Hollender into the invention suggested by Valenzuela, Liutkus and Shaefer would provide the added advantage of allowing user to view alarm information to determine which ones to act upon, and the combination would perform with a reasonable expectation of success (Hollender [6, 24, 55, 56, 81, 82]).
Conclusion
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SANCHITA . ROY
Primary Examiner
Art Unit 2146
/SANCHITA ROY/Primary Examiner, Art Unit 2146