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 § 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 1-19 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:
An imaging method for a dispersion energy spectrum of surface waves, wherein the method is executed by a processor and comprises:
obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation;
processing the first surface wave data to obtain second surface wave data, and the second surface wave data corresponding to a space-frequency domain representation; and
processing the second surface wave data based on a preset algorithm to obtain a first imaging result, the first imaging result corresponding to a slowness-frequency domain representation.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Step 1: 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).
Step 2A, Prong One: 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 exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
For example, the limitations of “processing the first surface wave data to obtain second surface wave data, and the second surface wave data corresponding to a space-frequency domain representation (see paras. [0114]: Fourier transform of instant application)” and “processing the second surface wave data based on a preset algorithm to obtain a first imaging result, the first imaging result corresponding to a slowness-frequency domain representation (see paras. [0114]: Fourier transform; paras. [0130]-[0134]: Radon transform of instant application)” are mathematical calculations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Similar limitations comprise the abstract ideas of Claims 18 and 19.
Step 2A, Prong Two: 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. This judicial exception is not integrated into a practical application. Therefore, none of the additional elements indicate a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B:
The above claims comprise the following additional elements:
In Claim 1: an imaging method for a dispersion energy spectrum of surface waves, wherein the method is executed by a processor (preamble); step of obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation;
In Claim 18: an electronic device, including a memory, a processor, and a computer program stored in the memory and operating on the processor (preamble); step of obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation;
In Claim 19: a non-transitory computer-readable storage medium, wherein the storage medium stores computer instructions, and a computer executes (preamble); step of obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation;
The additional elements such as a non-transitory computer-readable storage medium memory, memory, processor, and computer are recited at a high-level of generality (MPEP 2106.05(d)). Further, note that step of obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation is extra-solution (gathering data) activity (MPEP 2106.05(g)). Therefore, none of the additional elements indicate a practical application.
Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements/steps are well-understood, routine, and conventional in the relevant based on the prior art of record (Wang, Adler et al. (US 2004/0230389 A1). For example Wang and Adler teaches an imaging method for a dispersion energy spectrum of surface waves (page 4, lines 40-41 of Wang; paras. [0075]-[0076] of Adler), Further, Wang and Adler teach obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation (page 9, lines 5-7 of Wang; paras. [0056], [0088] of Adler).
Regarding claims 2-17,
All features recited in these claims are abstract ideas, as all features found in these claims are directed towards mathematical calculation steps. The explanation for the rejection of Claims 2-17 therefore are incorporated herein and applied to Claim 1. These claims therefore stand rejected for similar reasons as explained in above Claim 1.
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 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 1-2, 4-9 and 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (CN 112285767 A, hereinafter referred to as “Wang”) in view of Kinoshita et al. (US 2015/0036460 A1, hereinafter referred to as “Kinoshita”).
Regarding claim 1, Wang teaches an imaging method for a dispersion energy spectrum of surface waves (page 4, lines 40-41: a marine seismograph four-component marine surface wave multi-order frequency dispersion energy imaging system), wherein the method is executed by a processor (Fig. 2, data processing module 202) and comprises:
obtaining first surface wave data, and the first surface wave data corresponding to a space-time domain representation (page 8, lines 6-7: obtain three-component seismometer gather data in the gun line direction);
processing the first surface wave data (page 8, lines 6-7: obtain three-component seismometer gather data) to obtain second surface wave data (page 9, lines 5-7: calculating the dispersion energy spectrum of the ocean surface wave by the phase shift method is as follows. Common receiving point gather r of time-setting space-domain vertical component ocean surface wave Performing Fourier transform, note that the above feature of “calculating the dispersion energy spectrum of the ocean surface wave“ and “performing Fourier transform” reads on “processing the first surface wave data to obtain second surface wave data”), and the second surface wave data corresponding to a space-frequency domain representation (page 9, lines 5-7: page 9, lines 5-7: calculating the dispersion energy spectrum of the ocean surface wave by the phase shift method is as follows. Common receiving point gather r of time-setting space-domain vertical component ocean surface wave Performing Fourier transform, note that the above feature of “common receiving point gather r of time-setting space-domain vertical component ocean surface wave” and “performing Fourier transform” reads on “the second surface wave data corresponding to a space-frequency domain representation”); and
processing the second surface wave data (page 9, lines 5-7: see above) based on a preset algorithm to obtain a first imaging result (page 4, lines 40-41: a marine seismograph four-component marine surface wave multi-order frequency dispersion energy imaging system; page 8, lines 13-16: multi-order and multi-type marine surface wave frequency dispersion energy spectrum comprehensive imaging is realized by combining Schulter wave frequency dispersion energy spectrum imaging, acoustic guided wave frequency dispersion energy spectrum imaging and two types of marine surface wave frequency dispersion energy spectrum superposition).
Wang does not specifically teach a slowness-frequency domain representation.
However, Kinoshita teaches a slowness-frequency domain representation.
Wang and Kinoshita are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the a slowness-frequency domain representation such as is described in Kinoshita into Wang, in order to require much less computing power and much less bandwidth for data transmission as compared to the conventional techniques (Kinoshita, para. [0003]).
Regarding claim 2, Wang in view of Kinoshita teaches all the limitation of claim 1, in addition, Wang teaches the processing the first surface wave data (page 9, lines 5-7: common receiving point gather r of time-setting space-domain vertical component) to obtain second surface wave data (page 9, lines 5-7: Fourier transformed data) includes:
performing a Fourier transform on the first surface wave data (page 9, lines 5-7: common receiving point gather r of time-setting space-domain vertical component) to obtain the second surface wave data (page 9, lines 5-7: In the above steps 103 and 104, the method for calculating the dispersion energy spectrum of the ocean surface wave by the phase shift method is as follows. Common receiving point gather r of time-setting space-domain vertical component ocean surface wave Performing Fourier transform).
Regarding claim 4, Wang in view of Kinoshita teaches all the limitation of claim 1, in addition, Wang teaches further comprising:
processing the first imaging result based on a preset transformation algorithm to obtain a second imaging result (page 4, lines 40-41: a block diagram of a marine seismograph four-component marine surface wave multi-order frequency dispersion energy imaging system; page 8, lines 13-16: multi-order and multi-type marine surface wave frequency dispersion energy spectrum comprehensive imaging is realized by combining Schulter wave frequency dispersion energy spectrum imaging, note that “Schulter wave frequency dispersion energy spectrum imaging,” reads on “first imaging result), and
the second imaging result including a dispersion energy spectrum (page 4, lines 40-41: a block diagram of a marine seismograph four-component marine surface wave multi-order frequency dispersion energy imaging system; page 8, lines 13-16: multi-order and multi-type marine surface wave frequency dispersion energy spectrum comprehensive imaging is realized by combining Schulter wave frequency dispersion energy spectrum imaging, note that “frequency dispersion energy spectrum comprehensive imaging” in page 8, lines 13-16 reads on “second imaging result”).
Regarding claim 5, Wang in view of Kinoshita teaches all the limitation of claim 4, in addition, Wang teaches that the preset transformation algorithm includes: obtaining the second imaging (page 8, lines 13-16: multi-order and multi-type marine surface wave frequency dispersion energy spectrum comprehensive imaging is realized by combining Schulter wave frequency dispersion energy spectrum imaging, note that “frequency dispersion energy spectrum comprehensive imaging” in page 8, lines 13-16 reads on “second imaging result”) result by transforming the first imaging (page 8, lines 13-16: multi-order and multi-type marine surface wave frequency dispersion energy spectrum comprehensive imaging is realized by combining Schulter wave frequency dispersion energy spectrum imaging, note that “Schulter wave frequency dispersion energy spectrum imaging” reads on “first imaging result).
Wang does not specifically teach a correlation between slowness and velocity.
However, Kinoshita teaches a correlation between slowness and velocity (para. [0043]: the slowness values at the limited number of discrete frequencies are optionally estimated from the transformed sonic data (e.g., sonic data in the frequency-wave number domain) based on a relationship between slowness, wave number and frequency, note that the above feature of “relationship between slowness and frequency” reads on “a correlation between slowness and velocity” because the frequency value is related to velocity based on V =f𝛌, where 𝛌 is given waveform”).
Wang and Kinoshita are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the correlation between slowness and velocity such as is described in Kinoshita into Wang, in order to require much less computing power and much less bandwidth for data transmission as compared to the conventional techniques (Kinoshita, para. [0003]).
Regarding claim 17, Wang in view of Kinoshita teaches all the limitation of claim 1, in addition, Wang teaches further comprising:
performing preprocessing (page 7, lines 3-4: step 102) on the first surface wave data before performing transformation processing on the first surface wave data (page 8, lines 6-7: see claim 1 above; page 9, lines 5-7: see claim 1 above), wherein the preprocessing includes selection processing and filtering processing (page 7, lines 3-4: step 102: channel equalization processing and band-pass filtering processing, outputting a result and storing the result in an SU or SEGY format).
Regarding claim 18, it is an apparatus type claim having similar limitations as of claim 1 above. Therefore, it is rejected under the same rational as of claim 1 above.
Regarding claim 19, it is non-transitory computer-readable storage medium having similar limitations as of claim 1 above. Therefore, it is rejected under the same rational as of claim 1 above.
Claims 3, 6-9, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Kinoshita and Ma et al. (US 2018/0364383 A1, hereinafter referred to as “Ma”).
Regarding claim 3, Wang in view of Kinoshita teaches all the limitation of claim 1. Wang does not specifically teach that the preset algorithm includes a high-resolution Radon transform based on an iterative shrinkage threshold algorithm.
However, Ma teaches that the preset algorithm includes a high-resolution Radon transform based on an iterative shrinkage threshold algorithm (para. [0027]: sparse radon transform algorithms can bring new problems, including large computation times, the introduction of artifacts, and the difficulty to set up the inversion parameters. Some implementations can use a sparse time-invariant radon transform in the time-frequency domain based on iterative radon model shrinkage).
Wang and Ma are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the preset algorithm s including a high-resolution Radon transform such as is described in Ma into Wang, in order to perform a super-resolution radon transform on wave data (Ma, para. [0003]).
Regarding claim 6, Wang in view of Kinoshita teaches all the limitation of claim 1, in addition, Wang teaches the processing the second surface wave data (page 9, lines 5-7: see claim 1 above) based on a preset algorithm to obtain a first imaging result (page 4, lines 40-41: a block diagram of a marine seismograph four-component marine surface wave multi-order frequency dispersion energy imaging system; page 8, lines 13-16: multi-order and multi-type marine surface wave frequency dispersion energy spectrum comprehensive imaging is realized by combining Schulter wave frequency dispersion energy spectrum imaging, note that “Schulter wave frequency dispersion energy spectrum imaging” reads on “first imaging result) and second wave data (page 9, lines 5-7: see claim1 above).
Wang does not specifically teach constructing an objective function; and solving the objective function using a preset solving algorithm, wherein a solution of the objective function is the first imaging result, and the preset solving algorithm is a steepest descent algorithm.
However, Ma teaches constructing an objective function (para. [0025]: cost function, equation 1; para. [0026]: equation 2); and
solving the objective function using a preset solving algorithm (para. [0025]: cost function, equation 1; para. [0026]: equation 2), wherein a solution of the objective function is the first imaging result (para. [0028]: the task can be cast as an inverse equation of recovering the original high-resolution image with fine details from coarse scale information), and the preset solving algorithm is a steepest descent algorithm (para. [0026]: the direct representation of sparsity is to minimize the L0 norm of the model m, equation 2; para. [0027]: Equation 2 can be solved by an iteratively re-weighted least squares (IRLS) algorithm; para. [0037]: equation (4) is an extension of the classical gradient method, note that “minimize the L0 norm of the model m, equation 2” in para. [0026], “iteratively re-weighted least squares (IRLS) algorithm” in para. [0027], and para. [0037] reads on “the preset solving algorithm is a steepest descent algorithm”).
Wang and Ma are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the constructing an objective function and solving the objective function using a preset solving algorithm such as are described in Ma into Wang, in order to perform a super-resolution radon transform on wave data (Ma, para. [0003]).
Regarding claim 7, Wang in view of Kinoshita and Ma teaches all the limitation of claim 6.
Wang and Kinoshita do not specifically teach that the objective function is constructed by a following formula:
Ф
=
d
-
L
×
m
2
+
β
m
1
wherein d denotes the second surface wave data; L denotes a positive transformation operator; β denotes a regularization parameter; and m denotes a Radon model.
However, Ma teaches that the objective function is constructed by a following formula:
Ф
=
d
-
L
×
m
2
+
β
m
1
wherein d denotes the second surface wave data; L denotes a positive transformation operator; β denotes a regularization parameter (para. [0025]: cost function, equation 1; para. [0026]: equation 2); and m denotes a Radon model (para. [0027]: Some implementations can use a sparse time-invariant radon transform in the time-frequency domain based on iterative radon model shrinkage).
Wang and Ma are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the objective function such as is described in Ma into Wang, in order to perform a super-resolution radon transform on wave data (Ma, para. [0003]).
Regarding claim 8, Wang in view of Kinoshita and Ma teaches all the limitation of claim 6.
Wang and Kinoshita do not specifically teach the solving the objective function using a preset solving algorithm includes: determining a gradient direction of the objective function; determining an iteration step size in the gradient direction; and
updating a Radon model through at least one round of iteration based on the gradient direction and the iteration step size, stopping the at least one round of iteration until an iteration end condition is met, and obtaining an optimal solution of the objective function.
However, Ma teaches that the solving the objective function using a preset solving algorithm (para. [0025]: cost function, equation 1; para. [0026]: equation 2; para. [0027: see claim 7 above) includes:
determining a gradient direction of the objective function (para. [0026]: the direct representation of sparsity is to minimize the L0 norm of the model m, equation 2; para. [0027]: Equation 2 can be solved by an iteratively re-weighted least squares (IRLS) algorithm; para. [0037]: equation (4) is an extension of the classical gradient method, note that “minimize the L0 norm of the model m, equation 2” in para. [0026], “iteratively re-weighted least squares (IRLS) algorithm” in para. [0027], and para. [0037] reads on “the preset solving algorithm is a steepest descent algorithm”);
determining an iteration step size in the gradient direction (para. [0025]: equation 1; para. [0026]: the direct representation of sparsity is to minimize the L0 norm of the model m, equation 2; para. [0027]: Equation 2 can be solved by an iteratively re-weighted least squares (IRLS) algorithm; para. [0037]: equation (4) is an extension of the classical gradient method, note that the above feature of paras. [0025]-[0026] and “minimize the L0 norm of the model m, equation 2” in para. [0026], “iteratively re-weighted least squares (IRLS) algorithm” in para. [0027], and para. [0037] reads on “the preset solving algorithm is a steepest descent algorithm”); and
updating a Radon model through at least one round of iteration based on the gradient direction and the iteration step size ( para. [0025]: equation 1; para. [0026]: the direct representation of sparsity is to minimize the L0 norm of the model m, equation 2; para. [0027]: Equation 2 can be solved by an iteratively re-weighted least squares (IRLS) algorithm; para. [0037]: equation (4) is an extension of the classical gradient method),
stopping the at least one round of iteration until an iteration end condition is met, and obtaining an optimal solution of the objective function (para [0034}: Then an iterative hard thresholding algorithm (IHT) is applied to solve the l_0 norm regularization Equation (3) from the warm start obtained in the first Mage. These two operations are then repeated until the sparse representation m fits the data d).
Wang and Ma are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the solving the objective function such as is described in Ma into Wang, in order to perform a super-resolution radon transform on wave data (Ma, para. [0003]).
Regarding claim 9, Wang in view of Kinoshita and Ma teaches all the limitation of claim 8.
Wang and Kinoshita do not specifically teach that the at least one round of iteration is performed at least based on an iterative shrinkage threshold algorithm.
However, Ma teaches that the at least one round of iteration is performed at least based on an iterative shrinkage threshold algorithm (para. [0037]: one of the most popular methods for solving the L1 regularization Equation (2) is the iterative shrinkage-thresholding algorithm (ISTA)).
Wang and Ma are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the at least one round of iteration performed at least based on an iterative shrinkage threshold algorithm such as is described in Ma into Wang, in order to perform a super-resolution radon transform on wave data (Ma, para. [0003]).
Regarding claim 13, Wang in view of Kinoshita and Ma teaches all the limitation of claim 8.
Wand and Kinoshita do not specifically teach further comprising: solving the objective function using a steepest descent algorithm with a preferred parameter; and the preferred parameter including at least one of a preferred iteration step size and a preferred count of rounds of iteration.
However, Ma teaches further comprising: solving the objective function using a steepest descent algorithm (para. [0025]: equation 1; para. [0026]: the direct representation of sparsity is to minimize the L0 norm of the model m, equation 2; para. [0027]: Equation 2 can be solved by an iteratively re-weighted least squares (IRLS) algorithm; para. [0037]: equation (4) is an extension of the classical gradient method) with a preferred parameter and a preferred count of rounds of iteration (para. [0037]: τ is an appropriate step size; para. [0044]: there are two parameters remaining to be determined in the shrinkage and hard thresholding iterations, that is, the regularizing parameter μ and the step length τ. Several methods differ in the strategies to pick the parameters in each iteration, note that the above feature of “the regularizing parameter μ and the step length τ” and “Several methods differ in the strategies to pick the parameters in each iteration” in para. [0044] reads on “a preferred parameter”); and
the preferred parameter including at least one of a preferred iteration step size and a preferred count of rounds of iteration (para. [0037]: τ is an appropriate step size; para. [0044]: there are two parameters remaining to be determined in the shrinkage and hard thresholding iterations, that is, the regularizing parameter μ and the step length τ. Several methods differ in the strategies to pick the parameters in each iteration, note that the above feature of “step length τ” and “Several methods differ in the strategies to pick the parameters in each iteration” reads on “a preferred count of rounds of iteration”).
Wang and Ma are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the solving the objective function and preferred parameter such as are described in Ma into Wang, in order to perform a super-resolution radon transform on wave data (Ma, para. [0003]).
Regarding claim 14, Wang in view of Kinoshita and Ma teaches all the limitation of claim 13.
Wang and Kinoshita do not specifically teaches that that the preferred parameter is determined through a process includes: determining the preferred parameter by processing at least one of the first surface wave data, the second surface wave data, and the third surface wave data based on a parameter determination model; and the parameter determination model being a machine learning model.
However, Ma teaches that the preferred parameter (paras. [0037], [0044]: see claim 13 above) is determined through a process includes: determining the preferred parameter by processing at least one of the first surface wave data, the second surface wave data, and the third surface wave data (para. [0023]: seismic data; para. [0025]: field data; para. [0037]: τ is an appropriate step size; para. [0044]: there are two parameters remaining to be determined in the shrinkage and hard thresholding iterations, that is, the regularizing parameter μ and the step length τ. Several methods differ in the strategies to pick the parameters in each iteration) based on a parameter determination model (para. [0049]: It is known that the traditional reweighted least-square (IRLS)-based sparse radon transform algorithms can require a matrix inverse operation at each iteration due to the update of the reweighted matrix); and
the parameter determination model being a machine learning model (para. [0049]: It is known that the traditional reweighted least-square (IRLS)-based sparse radon transform algorithms can require a matrix inverse operation at each iteration due to the update of the reweighted matrix).
Wang and Ma are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the preferred parameter such as is described in Ma into Wang, in order to perform a super-resolution radon transform on wave data (Ma, para. [0003]).
Regarding claim 15, Wang in view of Kinoshita and Ma teaches all the limitation of claim 14.
Wang does not specifically teach that an input of the parameter determination model further includes a frequency, and slowness.
However, Kinoshita teaches that an input of the parameter determination model further includes a frequency, and slowness (para. [0033]: to compute slowness values at a limited number of discrete frequencies, the measured sonic data is transformed from the time-space domain into the frequency-wave number domain).
Wang and Kinoshita are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the an input of the parameter determination model including a frequency and slowness such as is described in Kinoshita into Wang, in order to require much less computing power and much less bandwidth for data transmission as compared to the conventional techniques (Kinoshita, para. [0003]).
Wang and Kinoshita do not specifically teach an offset.
However, Ma teaches an offset (para. [0025]: obtaining the radon transform in the time-offset, frequency-offset).
Wang and Ma are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the offset such as is described in Ma into Wang, in order to perform a super-resolution radon transform on wave data (Ma, para. [0003]).
Regarding claim 16, Wang in view of Kinoshita and Ma teaches all the limitation of claim 8.
Wang and Kinoshita do not specifically teach the solving the objective function using a preset solving algorithm includes: adjusting an iteration step size in each round of iteration in different solving stages, including: determining an adjusted iteration step size of a current round of iteration based on a current count of processed rounds of iteration and a gradient direction consistency.
However, Ma teaches the solving the objective function using a preset solving algorithm (para. [0025]: cost function, equation 1; para. [0026]: equation 2; para. [0027]: Some implementations can use a sparse time-invariant radon transform in the time-frequency domain based on iterative radon model shrinkage) includes: adjusting an iteration step size in each round of iteration in different solving stages (para. [0037]: τ is an appropriate step size; para. [0044]: there are two parameters remaining to be determined in the shrinkage and hard thresholding iterations, that is, the regularizing parameter μ and the step length τ. Several methods differ in the strategies to pick the parameters in each iteration),
including: determining an adjusted iteration step size of a current round of iteration based on a current count of processed rounds of iteration and a gradient direction consistency (para. [0027]: Equation (2) can be solved by an iteratively re-weighted least squares (IRLS) algorithm; para. [0037]: Equation (4) is an extension of the classical gradient method… where τ is an appropriate step size; para. [0044]: see claim 13 above; para. [0046]: Since the parameter τ has the same function in IHT the Barzilai-Borwein method can also be used to choose the step size τ. Different techniques can be used for stability control of IHT).
Wang and Ma are both considered to be analogous art to the claimed invention because they are in the similar filed of processing waves data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the solving the objective function such as is described in Ma into Wang, in order to perform a super-resolution radon transform on wave data (Ma, para. [0003]).
Although there are no prior art rejections for Claims 10-12, the Examiner cannot
comment on their allowability until the rejections under 35 U.S.C 101 are satisfactorily
addressed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. (US 2017/0115413 A1) teaches a technique includes receiving data representing time domain waveforms acquired by receivers of a drilling string-disposed acoustic measurement tool in response to energy emitted by at least one dipole source of the tool. The technique includes processing the data to determine slowness values associated with a plurality of acoustic modes, including a formation flexural acoustic mode and a tool flexural acoustic mode.
Wang et al. (US 12339410 B2) teaches a method for determining a shear slowness of a subterranean formation includes receiving waveforms data acquired by receivers in an acoustic measurement tool in response to energy emitted by at least one dipole source. The waveforms are processed to extract a formation flexural acoustic mode and a tool flexural acoustic mode.
Xu et al. (CN 114415234 A) teaches a method for determining shallow surface transverse wave velocity based on active source surface wave frequency dispersion and H/V, comprising the following steps: separating from the active source seismic record by using the high resolution linear Landon transformation technology to obtain the base-order mode Raylera surface wave.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANGKYUNG LEE whose telephone number is (571)272-3669. The examiner can normally be reached on Monday-Friday 8:30am-4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lee Rodak can be reached on (571)270-5628. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SANGKYUNG LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858