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 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 5 and 15 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 5, the claim recites the limitation “generating the preliminary source spectra estimation and the estimated locations of the first acoustic source and the second acoustic source by applying the acoustic data to a beamformer that does not use a noise spatial correlation matrix”, which renders the claim indefinite. The method of claim 1, from which claim 5 depends, requires “applying the initial noise spatial correlation matrix estimation to a beamformer to generate a first source spectra estimation for the first acoustic source and the second acoustic source”, so it is unclear as to how the method of claim 5 can simultaneously require the use of a noise spatial correlation matrix, and preclude the use of noise spatial correlation matrix. Therefore the claim is unclear and thus indefinite.
Regarding claim 15, the claim is a system claim corresponding to claim 5 and is therefore rejected for the same reasons.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 9-14, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ang et al. (US 20210238989 A1, “Ang’) in view of Ang et al. (US 20170184751 A1, “Ang 2”).
Regarding claim 1, Ang discloses a method comprising: receiving, at a hydrophone array, acoustic data from a first acoustic source and a second acoustic source in a downhole environment ([0027], tool includes a plurality of hydrophones configured to receive audio signals from an acoustic source within a cased borehole); generating an initial noise spatial correlation matrix estimation based on the acoustic data ([0035]-[0039], expected value of hydrophone correlation matrix provided by EQ (3). By subbing values of an interference correlation matrix and noise correlation matrix into the equation defining the source correlation matrix, all three signal components may be accounted for, as represented in EQ 5); applying the initial noise spatial correlation matrix estimation to a beamformer to generate a first source spectra estimation for the first acoustic source and the second acoustic source ([0059], one or more beamformer weights are adjusted based on the noise correlation matrices to detect leaks within the wellbore)([0048], by finding the optimal weight, the beamformer may find the spatial power spectrum to identify the location of wellbore leakages with noise being suppressed);
Ang may not explicitly teach generating a revised noise spatial correlation matrix estimation based on the first source spectra estimation; and applying the revised noise spatial correlation matrix estimation to the beamformer to generate a second source spectra estimation for the first acoustic source and the second acoustic source in the downhole environment based on the first source spectra estimation.
Ang 2 teaches generating a revised noise spatial correlation matrix estimation based on the first source spectra estimation; and applying the revised noise spatial correlation matrix estimation to the beamformer to generate a second source spectra estimation for the first acoustic source and the second acoustic source in the downhole environment based on the first source spectra estimation ([0039]-[0040], applying a sample covariance matrix to each of the decomposed frames converts each of the decomposed frames to the sample covariance matrix. Applying the Capon spatial spectrum construction operation to any sample covariance matrix yields a Capon spatial spectrum response for each of the frames which can then be searched for the peak signals corresponding to source locations).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of sound source localization, before the effective filing date of the claimed invention, to modify the method of Ang, to include the matrix and spectra revision of Ang 2 with a reasonable expectation of success, with the motivation of identifying correlation peaks indicated of leak locations within a wellbore [0039]-[0040].
Regarding claim 2, Ang, as modified in view of Ang 2 teaches the method of claim 1. Ang further comprising: estimating locations of the first acoustic source and the second acoustic source relative to the hydrophone array based on the acoustic data; and generating the revised noise spatial correlation matrix estimation based on the estimated locations of the first acoustic source and the second acoustic source([0025] leak detector can use beamforming techniques to identify localized peaks corresponding to locations of leaks, derived from the acoustic data)([0059], one or more beamformer weights are adjusted based on the noise correlation matrices to detect leaks within the wellbore)([0048], by finding the optimal weight, the beamformer may find the spatial power spectrum to identify the location of wellbore leakages with noise being suppressed).
Regarding claim 3, Ang, as modified in view of Ang 2 teaches the method of claim 2. Ang further teaches generating a preliminary source spectra estimation based on the acoustic data; and generating the initial noise spatial correlation matrix estimation based on the preliminary source spectra estimation and the estimated locations of the first acoustic source and the second acoustic source([0035]-[0039], expected value of hydrophone correlation matrix provided by EQ (3). By subbing values of an interference correlation matrix and noise correlation matrix into the equation defining the source correlation matrix, all three signal components may be accounted for, as represented in EQ 5).
Regarding claim 4, Ang, as modified in view of Ang 2, teaches the method of claim 3. Ang further teaches the initial noise spatial correlation matrix estimation is generated by applying the preliminary source spectra estimation and the estimated locations of the first acoustic source and the second acoustic source to a propagation model ([0036]-[0039], measured pressure field contains both the leakage location, road noise, and nose. Equation 1 can be modelled using the source transfer function from the source location to the array as well as the transfer function of the roadnoise in order to capture the spatial and temporal nose. Substituting uncorrelated source, interference and noise into equation 3 generates the model of the hydrophone correlation matrix, which includes the source correlation matrix, the interference correlation matrix, and the noise correlation matrix)(it is the examiner’s interpretation that the transfer functions are equivalent to propagation models).
Regarding claim 9, Ang, as modified in view of Ang 2 teaches the method of claim 1. Ang further teaches the revised noise spatial correlation matrix estimation is generated by applying the first source spectra estimation to a propagation model([0036]-[0039], measured pressure field contains both the leakage location, road noise, and nose. Equation 1 can be modelled using the source transfer function from the source location to the array as well as the transfer function of the roadnoise in order to capture the spatial and temporal nose. Substituting uncorrelated source, interference and noise into equation 3 generates the model of the hydrophone correlation matrix, which includes the source correlation matrix, the interference correlation matrix, and the noise correlation matrix)([0059], beamformer weights are adjusted based on the roadnoise and leakage detection matrices in order to eliminate noise components while performing leak detection in the wellbore)(it is the examiner’s interpretation that adjusting the beamformer weights associated with the roadnoise matrix is equivalent to revising the matrix).
Regarding claim 10, Ang, as modified in view of Ang 2 teaches the method of claim 1. Ang 2 further teaches determining an initial estimate of locations of the first acoustic source and the second acoustic source with respect to the hydrophone array; generating the first source spectra estimation based on the initial estimate of the locations of the first acoustic source and the second acoustic source with respect to the hydrophone array; determining a refined estimate of the locations of the first acoustic source and the second acoustic source with respect to the hydrophone array; and generating another source spectra estimation based on the refined estimate of the locations of the first acoustic source and the second acoustic source with respect to the hydrophone array ([0032]-[0042], localization of sensor array starts with constructing a spatial spectrum and searching for the peaks. Peaks are used to determine likelihood of a sources location. Initial estimates are used to determine where the spatial scanning is to be performed. For each location, a fixed time independent steering vector is computed before estimating the Capon spatial spectrum. To compensate for movement of the sensors causing mismatch, acquisition is shorted to preserve the stationary condition. Recorded acquisition may be split into a plurality of frames in which the stationary condition is preserved, which then have a sample covariance matrix applied to them. Capon spatial spectrum construction is then performed for any single sample covariance matrix, allowing the capon spatial spectrum response for each of the frames to be produced which allows for peak searching to determine source locations. To reduce the instances of falsely identified source locations, a Capon spatial spectrum that undergoes summation and normalization is constructed.)
Regarding claim 11, claim is a system claim corresponding to claim 1 and is therefore rejected for the same reasons.
Regarding claim 12, the claim is a system claim corresponding to claim 2 and is therefore rejected for the same reasons.
Regarding claim 13, the claim is a system claim corresponding to claim 3 and is therefore rejected for the same reasons.
Regarding claim 14, the claim is a system claim corresponding to claim 4 and is therefore rejected for the same reasons.
Regarding claim 19, the claim is a system claim corresponding to claim 9 and is therefore rejected for the same reasons
Regarding claim 20, the claim is a CRM claim corresponding to claim 1 and is therefore rejected for the same reasons.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ang in view of Ang 2 and Jagannathan et al. (US 20170269243 A1, “Jagannathan”).
Regarding claim 5, Ang, as modified in view of Ang 2 teaches the method of claim 3. Ang, as modified in view of Ang 2 may not explicitly teach generating the preliminary source spectra estimation and the estimated locations of the first acoustic source and the second acoustic source by applying the acoustic data to a beamformer that does not use a noise spatial correlation matrix.
Jagannathan teaches generating the preliminary source spectra estimation and the estimated locations of the first acoustic source and the second acoustic source by applying the acoustic data to a beamformer that does not use a noise spatial correlation matrix([0030], beamforming process exploits the phase relationship of the signals received at the receiver locations to determine specific source locations by calculating circular time delay profiles for the signals. Knowledge of the free-space propagation can then be used to derive source locations).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of sound source localization, before the effective filing date of the claimed invention, to modify the method of Ang, as modified in view of Ang 2 to include the source locating without noise correlation matrix of Jagannathan with a reasonable expectation of success, with the motivation of leveraging unique time-delay profiles between the source and receivers in order to determine source locations [0030].
Regarding claim 15, the claim is a system claim corresponding to claim 5 and is therefore rejected for the same reasons.
Allowable Subject Matter
Claims 6-8 and 16-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 6, Ang, as modified in view of Ang 2 teaches the method of claim 3. Ang, as modified in view of Ang 2 may not explicitly teach the first source spectra estimation corresponds to the preliminary source spectra estimation, the method further comprising: determining whether the first source spectra estimation and the preliminary source spectra estimation converge; determining whether to further change the first source spectra estimation based on whether the first source spectra estimation and the preliminary source spectra estimation converge; generating the revised noise spatial correlation matrix estimation in response to a determination to further change the first source spectra estimation; and applying the revised noise spatial correlation matrix estimation to generate the second source spectra estimation in response to a determination to further change the first source spectra estimation.
Nichols et al. ("Use of noise correlation matrices to interpret ocean ambient noise." The Journal of the Acoustical Society of America 145.4 (2019): 2337-2349., “Nichols”) teaches the first source spectra estimation corresponds to the preliminary source spectra estimation, the method further comprising: determining whether the first source spectra estimation and the preliminary source spectra estimation converge ([pg. 8], SCM of the spectrograms starts by rearranging the spectrograms in order of decreasing correlation with respect to the first spectrogram. In order to be included in a cluster, a correlation value between the spectrograms must be greater than 0.5)([pg. 11], characteristic spectra and the measured spectra are compared to determine a correlation coefficient)(it is the examiner’s interpretation that the correlation coefficient is equivalent to a measure of convergence); determining whether to further change the first source spectra estimation based on whether the first source spectra estimation and the preliminary source spectra estimation converge ([pg. 8], spectrograms are iteratively rearranged and reconstructed in order to maximize the correlation); generating the revised noise spatial correlation matrix estimation in response to a determination to further change the first source spectra estimation; and applying the revised noise spatial correlation matrix estimation to generate the second source spectra estimation in response to a determination to further change the first source spectra estimation ([pg. 11], characteristic spectra are used to reconstruct the measured spectrogram. A source is called present if its demeaned replica correlates to the measured spectrogram with a correlation greater than 0.3. wind noise is applied to each of the spectrograms by finding the best match of the eight wind condition spectra. Each measured spectrum is then reconstructed by computing the mean of all present sources)(it is the examiner’s interpretation that this means spectrogram are determined for each of multiple sources, a minimum threshold correlation is computed or else the spectrogram is changed, then an best fit (or revised) noise spectra is applied to all sources present and a correlation of the reconstructed spectrograms is calculated) (However Nichols teaches the spectrogram correlation and reconstruction are performed in the frequency domain rather than the spatial domain. Additionally, Nichols fails to teach the required limitation that the second source spectra are changed in response to determining that the first source spectra requires further changes. No other identified prior art teaches the required limitations, nor does any other identified prior art teach the required limitations in part with sufficient motivation to combine).
Regarding claim 7, Ang, as modified in view of Ang 2 teaches the method of claim 1. Ang, as modified in view of Ang 2 may not explicitly teach determining whether the first source spectra estimation and the second source spectra estimation converge; determining whether to further change the second source spectra estimation based on whether the first source spectra estimation and the second source spectra estimation converge; generating the revised noise spatial correlation matrix estimation in response to a determination to further change the second source spectra estimation; and applying the revised noise spatial correlation matrix estimation to generate the second source spectra estimation in response to a determination to further change the first source spectra estimation. (The claim is equivalent to claim 6 with the exception that the determination to change the second source spectra determines whether the first source spectra, as opposed to the determination to change the first source spectra determines whether to change the second source spectra. Therefore the claim is indicated as containing allowable subject matter for the same reasons as applied to claim 6, above)
Regarding claim 8, the claim is indicated as containing allowable subject matter due to its respective dependency upon a claim indicated as containing allowable subject matter.
Regarding claim 16, the claim is a system claim corresponding to claim 6 and is therefore indicated as containing allowable subject matter for the same reasons.
Regarding claim 17, the claim is a system claim corresponding to claim 7 and is therefore indicated as containing allowable subject matter for the same reasons.
Regarding claim 18, the claim is indicated as containing allowable subject matter due to its respective dependency upon a claim indicated as containing allowable subject matter.
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Xu et al. ("Performance analysis of matched-field source localization under spatially correlated noise field." IEEE Journal of Oceanic Engineering 36.2 (2011): 273-284., “Xu”) which discloses an acoustic beamforming system for estimating source locations
Huang et al. ("Multiple source localization in a shallow water waveguide exploiting subarray beamforming and deep neural networks." Sensors 19.21 (2019): 4768., “Huang”) which discloses sound source localization using hydrophone arrays and beamforming
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/CHRISTOPHER RICHARD WALKER/ Examiner, Art Unit 3645
/YUQING XIAO/ Supervisory Patent Examiner, Art Unit 3645