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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on -09/28/2023- is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 2-11, 13-22, 24-28, 30-34 objected to because of the following informalities: the above dependent claims 2-11, 13-22, 24-28, 30-34, are missing comma. The claims can be amended as example for claim 2. The method of claim 1, in which….
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.
Claims 1-34 are rejected under 35 U.S.C 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, natural phenomenon, or an abstract idea) without significantly more.
Specifically, claim 1 recites:
A method for determining spatial distribution of properties of formations in a region of interest in the subsurface using geophysical sensor signals detected proximate the region, the method comprising:
inversion processing an initial model of the spatial distribution, the inversion processing comprising calculating expected geophysical sensor signals using the initial model and comparing the expected geophysical sensor signals to the detected geophysical sensor signals, the inversion processing comprising at least second order optimization, the at least second order optimization comprising;
calculating a scalar and a sparsity-modified matrix using an adaptive gradient type scheme to estimate an inverse Hessian matrix, and using the estimated inverse Hessian matrix in a modified limited memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization; and
using the modified L-BFGS optimization to optimize the inversion processing, wherein an output of the optimized inversion processing comprises an updated model for which the expected geophysical sensor signals most closely match the detected geophysical sensor signals.
The claim limitations in the abstract idea have been highlighted in bold above.
Under the step 1 of the eligibility analysis, it is determined whether the claims are drawn to a statutory category by considering whether the claimed subject matter fall 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 the statutory category of (process).
Under the step 2A, prong one, it is considered 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 groupings of subject matter when recited as such in a claim limitation, that cover mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental process – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, a step of inversion processing an initial model of the spatial distribution, the inversion processing comprising calculating expected geophysical sensor signals using the initial model and comparing the expected geophysical sensor signals to the detected geophysical sensor signals, the inversion processing comprising at least second order optimization, the at least second order optimization comprising; (is considered a mathematical step)
calculating a scalar and a sparsity-modified matrix using an adaptive gradient type scheme to estimate an inverse Hessian matrix, and using the estimated inverse Hessian matrix in a modified limited memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization; (is considered a mathematical step) and
using the modified L-BFGS optimization to optimize the inversion processing, wherein an output of the optimized inversion processing comprises an updated model for which the expected geophysical sensor signals most closely match the detected geophysical sensor signals (is considered a mathematical step).
These mental steps represent that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
Similar limitations comprise the abstract ideas of the independent claims 12, 23 and 29.
Next, under the step 2A, prong two, it is considered whether the claim that recites a judicial exception is integrated into a practical application.
In this step, it is evaluated whether the claim recites meaningful additional elements that integrate the exception into a practical application of that exception.
In claim 1, The additional element in the preamble of “A method for determining…” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use.
In claim 23, the additional elements/steps recite the similar additional elements/steps as of claim 1. “A method for determining…” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the step 2B.
Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way.
The independent claims 1 and 23, therefore, are not patent eligible.
With regards to the dependent claims, the claims 2-11,24-28 comprise the analogous subject matter and also comprise additional features/steps which are the part of an expanded abstract idea of the independent claims 1 and 23 (additionally comprising mathematical relationship/mental process steps) and, therefore, the dependent claims are not eligible without additional elements that reflect a practical application and qualified for significantly more for substantially similar reason as discussed with regards to claim 1 and 23.
Regarding claim 12 and 29,
A computer program stored in a non-transitory computer readable medium, the program having logic operable to cause a programmable computer to perform actions for determining spatial distribution of properties of formations in a subsurface volume using geophysical sensor signals detected proximate the volume, the actions comprising: accepting as input to the computer the geophysical sensor signals; inversion processing an initial model of the spatial distribution, the inversion processing comprising calculating expected geophysical sensor signals using the initial model and comparing the expected geophysical sensor signals to the detected geophysical sensor signals, the inversion processing comprising at least second order optimizing, the at least second order optimizing comprising, calculating a scalar and a sparsity-modified matrix using an adaptive gradient type scheme to estimate an inverse Hessian matrix in limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization, using the scaled sparsity-modified matrix to improve the estimated inverse Hessian matrix in order to optimize the inversion processing; finalizing the model of the spatial distribution when a value of an objective function in the inversion processing is minimized; and generating an output representing the spatial distribution in the finalized model, wherein the output comprises the spatial distribution for which the calculated seismic signals most closely match the detected geophysical sensor signals.
The claim limitations with the non-statutory subject matter and abstract idea have been highlighted in bold above.
Under the step 1 of the eligibility analysis, it is determined whether the claims are drawn to a statutory or non-statutory category. In the above claim, the highlighted portion (A computer program) constitutes a non-statutory subject matter under a broadest reasonable interpretation.
The claim does/do not fall within at least one of the four categories of patent eligible subject matter because under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into groupings of subject matter when recited as such in a claim limitation, that cover “products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations”. Refer to MPEP 2106.03.
Since, the claim 12 fails the patent eligibility under patent eligibility step 2A, prong one, further analysis under step 2A, prong two is ineligible. Although, the claim 20 recites the same abstract ideas (inversion processing… and generating…) as claim 1 and the recited abstract ideas do not appear to be integrated into a practical application for the same reasons as discussed in claim 1 above.
Therefore, the independent claim 12 is not patent eligible. Claim 29 is also not patent eligible as discussed for claim 12.
Applicant is suggested to amend the claims 12 and 29 as:
A non- transitory computer-readable medium storing instruction that, when executed by the computer cause the computer to perform actions for determining spatial…
Similarly amend their dependent claims 13-22 and 30-34 (A non-transitory computer-readable medium).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 10, 11, 12, 21, 22, 23, 24, 26, 27, 29, 30, 32, 33 is/are rejected under 35 U.S.C. 102(a) (1)/(a) (2) as being anticipated by Warner et al (US 20160238729 A1) herein after “Warner”
Regarding claim 1, Warner teaches a method for determining spatial distribution of properties of formations in a region of interest in the subsurface using geophysical sensor signals detected proximate the region (abstract: A method of subsurface exploration includes generating a representation of a portional volume of the Earth from a seismic measurement of a physical parameter.), the method comprising:
inversion processing an initial model of the spatial distribution (para [0125] FIG. 6 a) to f) show examples of starting models and the resulting final models obtained from conventional full waveform inversion modelling; [0154] At step 102, an initial starting model of the specified subsurface portion of the Earth is provided.),
Here starting or initial model of the subsurface portion of earth (i.e., the spatial distribution) is provided by inversion processing. the inversion processing comprising calculating expected geophysical sensor signals using the initial model (para [0159] Once the model has been generated, the method then proceeds to step 104.[0160] Step 104: Generate Predicted Data Set).
The inversion processing with initial model is used to calculate a predicted (expected) geophysical sensor signal. and comparing the expected geophysical sensor signals to the detected geophysical sensor signals (para [0161] At step 104, a predicted seismic data set is generated. The predicted data is required to correspond to the same source-receiver location data positions as the actual measured trace data so that the modelled and observed data can be compared. In other words, the predicted data set corresponds discrete point to discrete point to the observed dataset. The predicted data set is generated for the same measurement parameter(s) at the same frequency or frequencies.),
Here the measured seismic data (i.e., detected geophysical sensor signal) is compared to the predicted seismic data.
the inversion processing comprising at least second order optimization, the at least second order optimization comprising (para [0037] For a scalar function of a single scalar variable, the Taylor series can be used, truncated to second order.)
Here the Taylor series truncated to second order is viewed as second order optimization.
calculating a scalar and a sparsity-modified matrix using an adaptive gradient type scheme to estimate an inverse Hessian matrix ([0038] If the model has M parameters, then the gradient is a column vector of length M and the Hessian is an M×M symmetric matrix. [0039] If the number of model parameters M is large, then calculating the Hessian is computationally demanding, and inverting the Hessian exactly is normally computationally intractable. Consequently, the method that is typically used is to replace the inverse of the Hessian in equation (9) by a simple scalar a (referred to as the step length)… Equation 9) is then pre-multiplied by the matrix D to extract the wavefield only at the points where data exists [0044] 5. Iterate from step 2 using the new model until the objective function is minimized. [0051] The method outlined above for performing FWI is commonly extended and modified in a variety of ways to improve both its efficiency and effectiveness. [0052] For example, various improved approximations to the Hessian matrix H and its inverse can be made including use of Newton, Gauss-Newton, quasi-Newton and Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) methods.)),
Here an inverse Hessian matrix is estimated by calculating a scalar and a sparsity- modified matrix (i.e., mulitplied matrix) of full wave by using a gradient type scheme.
and using the estimated inverse Hessian matrix in a modified limited memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization (para [0052]); and using the modified L-BFGS optimization to optimize the inversion processing ([0052].)
wherein an output of the optimized inversion processing comprises an updated model for which the expected geophysical sensor signals most closely match the detected geophysical sensor signals. (para 0187] For any useful convolutional filter, and for any useful measure of misfit or similarity, as the convolutional filter moves towards the reference filter, the predicted seismic data set will move towards the observed seismic data set. Thus, the starting model will move towards the true model. [0190] At step 116 it is determined whether convergence criteria have been met. For example, when the method is deemed to have reached convergence when the difference between the data sets reaches a threshold percentage or other value. If the criteria as set out above have been met, then the method proceeds to step 118 and finishes with the resultant Earth model generated. If the criteria have not been met, then the method proceeds back to repeat steps 104 to 114 as discussed above.[0191] Step 118: Finish [0192] When, at step 118, it has been determined that the convergence criteria has been met, the method finishes and the modelled subsurface portion of the Earth is deemed to be sufficiently accurate to be used for subsurface exploration.
Here examiner views the optimized inversion process has an updated model when the predicted earth sensor signal (i.e., geophysical signal) matches the observed or detected sensor signals.
Regarding claim 10, Warner teaches, the method of claim 1, wherein the adaptive gradient type scheme is regularized and/or constrained (para 0053] Various modifications upon simple steepest-descent are known in the art; for example conjugate gradient methods. The predicted data, observed data, data residuals, forward wavefield, backward-propagated wavefield, gradient and Hessian may all be pre-processed in various ways.
[0054] Further, additional constraints may be placed upon model updates and/or recovered model. Sources and other sub-sets of the data may be selected, modified and/or combined in various ways, and these used in the inversion in preference to using the original physical sources).
Here the gradient type is adjusted or adaptive based on model updates with constraints.
Claim 21 is rejected as claim 10 having same claim limitation.
Regarding claim 11, Warner teaches, the method of claim 1 wherein an improved estimated inverse Hessian is obtained using a combination of data obtained at previous iteration steps (para [0050] Note that iteration is necessary because the problem to be solved is non-linear and the inverse problem has been linearized in particular stages… [0052] For example, various improved approximations to the Hessian matrix H and its inverse can be made including use of Newton, Gauss-Newton, quasi-Newton and Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) methods.).
Claim 22 is rejected as claim 11 having same claim limitation.
Regarding Claim 12, Warner teaches a computer program stored in a non-transitory computer readable medium, the program having logic operable to cause a programmable computer to perform actions for determining spatial distribution of properties of formations in a subsurface volume using geophysical sensor signals detected proximate the volume, the actions comprising:
accepting as input to the computer the geophysical sensor signals (para [0066] a) providing an observed seismic data set comprising at least three distinct non-zero data values derived from at least three distinct non-zero seismic measured values of said portion of the volume of the Earth;);
inversion processing an initial model of the spatial distribution (para [0125] FIG. 6 a) to f) show examples of starting models and the resulting final models obtained from conventional full waveform inversion modelling; [0154] At step 102, an initial starting model of the specified subsurface portion of the Earth is provided.),
Here starting or initial model of the subsurface portion of earth (i.e., the spatial distribution) is provided by inversion processing. the inversion processing comprising calculating expected geophysical sensor signals using the initial model (para [0159] Once the model has been generated, the method then proceeds to step 104.[0160] Step 104: Generate Predicted Data Set).
The inversion processing with initial model is used to calculate a predicted (expected) geophysical sensor signal. and comparing the expected geophysical sensor signals to the detected geophysical sensor signals (para [0161] At step 104, a predicted seismic data set is generated. The predicted data is required to correspond to the same source-receiver location data positions as the actual measured trace data so that the modelled and observed data can be compared. In other words, the predicted data set corresponds discrete point to discrete point to the observed dataset. The predicted data set is generated for the same measurement parameter(s) at the same frequency or frequencies.),
Here the measured seismic data (i.e., detected geophysical sensor signal) is compared to the predicted seismic data.
the inversion processing comprising at least second order optimization, the at least second order optimization comprising (para [0037] For a scalar function of a single scalar variable, the Taylor series can be used, truncated to second order.)
Here the Taylor series truncated to second order is viewed as second order optimization.
calculating a scalar and a sparsity-modified matrix using an adaptive gradient type scheme to estimate an inverse Hessian matrix ([0038] If the model has M parameters, then the gradient is a column vector of length M and the Hessian is an M×M symmetric matrix. [0039] If the number of model parameters M is large, then calculating the Hessian is computationally demanding, and inverting the Hessian exactly is normally computationally intractable. Consequently, the method that is typically used is to replace the inverse of the Hessian in equation (9) by a simple scalar a (referred to as the step length)… Equation 9) is then pre-multiplied by the matrix D to extract the wavefield only at the points where data exists [0044] 5. Iterate from step 2 using the new model until the objective function is minimized. [0051] The method outlined above for performing FWI is commonly extended and modified in a variety of ways to improve both its efficiency and effectiveness. [0052] For example, various improved approximations to the Hessian matrix H and its inverse can be made including use of Newton, Gauss-Newton, quasi-Newton and Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) methods.)),
Here an inverse Hessian matrix is estimated by calculating a scalar and a sparsity- modified matrix (i.e., multiplied matrix) of full wave by using a gradient type scheme.
and using the estimated inverse Hessian matrix in a modified limited memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization (para [0052]); and using the modified L-BFGS optimization to optimize the inversion processing ([0052].)
using the scaled sparsity-modified matrix to improve the estimated inverse Hessian matrix in order to optimize the inversion processing ([0039] If the number of model parameters M is large, then calculating the Hessian is computationally demanding, and inverting the Hessian exactly is normally computationally intractable. Consequently the method that is typically used is to replace the inverse of the Hessian in equation (9) by a simple scalar a (referred to as the step length)…. Equation 9) is then pre-multiplied by the matrix D to extract the wavefield only at the points where data exists. [0052] For example, various improved approximations to the Hessian matrix H and its inverse can be made including use of Newton, Gauss-Newton, quasi-Newton and Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) methods.));
Here the multiplying matrix is viewed as scaling the sparsity matrix improve the estimated inverse Hessian matrix in order to optimize the inversion processing.
finalizing the model of the spatial distribution when a value of an objective function in the inversion processing is minimized ([0172] Step 110: Construct Misfit Function
[0173] At step 110, a misfit (or objective) function is configured. In one example, the misfit function (or objective function) is configured to measure the dis-similarity between the actual filter coefficients and reference filter coefficients.
[0179] Step 112: Minimize or Maximize the Misfit Function:); (para [0190] At step 116 it is determined whether convergence criteria have been met. For example, when the method is deemed to have reached convergence when the difference between the data sets reaches a threshold percentage or other value. If the criteria as set out above have been met, then the method proceeds to step 118 and finishes with the resultant Earth model generated.
Here the earth model (i.e, model of spatial distribution) is finalized with the objection function value in the inversion processing is minimized by satisfying a convergence criteria.
and generating an output representing the spatial distribution in the finalized model (para [0270] For any useful convolutional filter, and for any useful measure of difference or similarity, as the convolutional filter moves towards the reference filter, the predicted seismic data set will move towards the observed seismic data set. Thus, the starting model will move towards the true model. para [0273] At step 320 it is determined whether convergence criteria have been met. For example, when the method is deemed to have reached convergence when the difference between the data sets reaches a threshold percentage. If the criteria as set out above have been met, then the method proceeds to step 322 and finishes with the resultant Earth model generated). [0274] Step 322: Finish [0275] When, at step 322, it has been determined that the convergence criteria has been met, the method finishes and the modelled subsurface portion of the Earth is deemed to be sufficiently accurate to be used for subsurface exploration. This may involve the direct interpretation of the recovered model, and/or involve the process of depth-migration to generate a subsurface reflectivity image to be used for the identification of subsurface features such as cavities or channels which may contain natural resources such as hydrocarbons. Examples of such hydrocarbons are oil and natural gas.)
Here, the final model generated represent the spatial distribution of hydrocarbons.
wherein the output comprises the spatial distribution for which the calculated seismic signals most closely match the detected geophysical sensor signals (in above paragraphs 270-275 when the predicted or calculated seismic signal match or meet the observed or measured/detected signal, the spatial distribution is finalized.
Regarding Claim 23, Warner teaches a method for determining spatial distribution of properties of formations in a region of interest in the subsurface using geophysical sensor signals detected proximate the region, the method comprising:
inversion processing an initial model of the spatial distribution (para [0125] FIG. 6 a) to f) show examples of starting models and the resulting final models obtained from conventional full waveform inversion modelling; [0154] At step 102, an initial starting model of the specified subsurface portion of the Earth is provided.),
Here starting or initial model of the subsurface portion of earth (i.e., the spatial distribution) is provided by inversion processing. the inversion processing comprising calculating expected geophysical sensor signals using the initial model (para [0159] Once the model has been generated, the method then proceeds to step 104.[0160] Step 104: Generate Predicted Data Set).
The inversion processing with initial model is used to calculate a predicted (expected) geophysical sensor signal. and comparing the expected geophysical sensor signals to the detected geophysical sensor signals (para [0161] At step 104, a predicted seismic data set is generated. The predicted data is required to correspond to the same source-receiver location data positions as the actual measured trace data so that the modelled and observed data can be compared. In other words, the predicted data set corresponds discrete point to discrete point to the observed dataset. The predicted data set is generated for the same measurement parameter(s) at the same frequency or frequencies.),
Here the measured seismic data (i.e., detected geophysical sensor signal) is compared to the predicted seismic data.
the inversion processing comprising at least second order optimization, the at least second order optimization comprising (para [0037] For a scalar function of a single scalar variable, the Taylor series can be used, truncated to second order.)
Here the Taylor series truncated to second order is viewed as second order optimization.
calculating an estimate of an inverse Hessian as a convolutional operator (C) ( para [0052] For example, various improved approximations to the Hessian matrix H and its inverse can be made including use of Newton, Gauss-Newton, quasi-Newton and Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) methods. [0166] The convolutional filter may comprise any generalized convolutional operation that can be described by a finite set of parameters that depend upon both the predicted d.sub.pred(r,s) and observed d.sub.obs(r,s) data, such that when the convolution and associated operations are applied to all or part of the predicted data d.sub.pred(r,s) an accurate or generally approximate model of the observed data is generated. para [0187] For any useful convolutional filter, and for any useful measure of misfit or similarity, as the convolutional filter moves towards the reference filter, the predicted seismic data set will move towards the observed seismic data set. Thus, the starting model will move towards the true model.),
Here an inverse Hessian inverse of Hessian is estimated as a convolutional operator (filter).
and using the estimated inverse Hessian in a modified limited memory Broyden–Fletcher– Goldfarb–Shanno (L-BFGS) optimization, using the modified L-BFGS optimization to optimize the inversion processing ([0052] For example, various improved approximations to the Hessian matrix H and its inverse can be made including use of Newton, Gauss-Newton, quasi-Newton and Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) methods.); and
and generating an output of the inversion processing representing an optimized model of the spatial distribution wherein the calculated geophysical sensor signals most closely match the detected geophysical sensor signals (para 0187] For any useful convolutional filter, and for any useful measure of misfit or similarity, as the convolutional filter moves towards the reference filter, the predicted seismic data set will move towards the observed seismic data set. Thus, the starting model will move towards the true model. [0190] At step 116 it is determined whether convergence criteria have been met. For example, when the method is deemed to have reached convergence when the difference between the data sets reaches a threshold percentage or other value. If the criteria as set out above have been met, then the method proceeds to step 118 and finishes with the resultant Earth model generated. If the criteria have not been met, then the method proceeds back to repeat steps 104 to 114 as discussed above.[0191] Step 118: Finish [0192] When, at step 118, it has been determined that the convergence criteria has been met, the method finishes and the modelled subsurface portion of the Earth is deemed to be sufficiently accurate to be used for subsurface exploration.
Here examiner views the optimized inversion process has an updated model when the predicted earth sensor signal (i.e., geophysical signal) matches the observed or detected sensor signals.
Claims 24, 30 is rejected as claim 5 having same claim limitation. Please see below.
Claims 26, 32 is rejected as claim 7 having same claim limitation. Please see below.
Claim 27, 33 is rejected as claim 8 having same claim limitation. Please see below.
Regarding Claim 29 Warner teaches a method for determining spatial distribution of properties of formations in a region of interest in the subsurface using geophysical sensor signals detected proximate the region, the method comprising:
inversion processing an initial model of the spatial distribution (para [0125] FIG. 6 a) to f) show examples of starting models and the resulting final models obtained from conventional full waveform inversion modelling; [0154] At step 102, an initial starting model of the specified subsurface portion of the Earth is provided.),
Here starting or initial model of the subsurface portion of earth (i.e., the spatial distribution) is provided by inversion processing. the inversion processing comprising calculating expected geophysical sensor signals using the initial model (para [0159] Once the model has been generated, the method then proceeds to step 104.[0160] Step 104: Generate Predicted Data Set).
The inversion processing with initial model is used to calculate a predicted (expected) geophysical sensor signal. and comparing the expected geophysical sensor signals to the detected geophysical sensor signals (para [0161] At step 104, a predicted seismic data set is generated. The predicted data is required to correspond to the same source-receiver location data positions as the actual measured trace data so that the modelled and observed data can be compared. In other words, the predicted data set corresponds discrete point to discrete point to the observed dataset. The predicted data set is generated for the same measurement parameter(s) at the same frequency or frequencies.),
Here the measured seismic data (i.e., detected geophysical sensor signal) is compared to the predicted seismic data.
the inversion processing comprising at least second order optimization, the at least second order optimization comprising (para [0037] For a scalar function of a single scalar variable, the Taylor series can be used, truncated to second order.)
Here the Taylor series truncated to second order is viewed as second order optimization.
calculating an estimate of an inverse Hessian as a convolutional operator (C) ( para [0052] For example, various improved approximations to the Hessian matrix H and its inverse can be made including use of Newton, Gauss-Newton, quasi-Newton and Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) methods. [0166] The convolutional filter may comprise any generalized convolutional operation that can be described by a finite set of parameters that depend upon both the predicted d.sub.pred(r,s) and observed d.sub.obs(r,s) data, such that when the convolution and associated operations are applied to all or part of the predicted data d.sub.pred(r,s) an accurate or generally approximate model of the observed data is generated. para [0187] For any useful convolutional filter, and for any useful measure of misfit or similarity, as the convolutional filter moves towards the reference filter, the predicted seismic data set will move towards the observed seismic data set. Thus, the starting model will move towards the true model.),
Here an inverse Hessian inverse of Hessian is estimated as a convolutional operator (filter).
and using the estimated inverse Hessian in a modified limited memory Broyden–Fletcher– Goldfarb–Shanno (L-BFGS) optimization, using the modified L-BFGS optimization to optimize the inversion processing ([0052] For example, various improved approximations to the Hessian matrix H and its inverse can be made including use of Newton, Gauss-Newton, quasi-Newton and Limited Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) methods.); and
finalizing the model of the spatial distribution when a value of an objective function in the inversion processing is minimized ([0172] Step 110: Construct Misfit Function
[0173] At step 110, a misfit (or objective) function is configured. In one example, the misfit function (or objective function) is configured to measure the dis-similarity between the actual filter coefficients and reference filter coefficients.
[0179] Step 112: Minimize or Maximize the Misfit Function:); (para [0190] At step 116 it is determined whether convergence criteria have been met. For example, when the method is deemed to have reached convergence when the difference between the data sets reaches a threshold percentage or other value. If the criteria as set out above have been met, then the method proceeds to step 118 and finishes with the resultant Earth model generated,
Here the earth model (i.e, model of spatial distribution ) is finalized with the objection function value in the inversion processing is minimized by satisfying a convergence criteria.
the finalized model representing the spatial distribution for which the expected geophysical sensor signals most closely match the detected geophysical sensor signals. (para [0270] For any useful convolutional filter, and for any useful measure of difference or similarity, as the convolutional filter moves towards the reference filter, the predicted seismic data set will move towards the observed seismic data set. Thus, the starting model will move towards the true model. para [0273] At step 320 it is determined whether convergence criteria have been met. For example, when the method is deemed to have reached convergence when the difference between the data sets reaches a threshold percentage. If the criteria as set out above have been met, then the method proceeds to step 322 and finishes with the resultant Earth model generated). [0274] Step 322: Finish [0275] When, at step 322, it has been determined that the convergence criteria has been met, the method finishes and the modelled subsurface portion of the Earth is deemed to be sufficiently accurate to be used for subsurface exploration. This may involve the direct interpretation of the recovered model, and/or involve the process of depth-migration to generate a subsurface reflectivity image to be used for the identification of subsurface features such as cavities or channels which may contain natural resources such as hydrocarbons. Examples of such hydrocarbons are oil and natural gas.)
Here, the final model generated represent the spatial distribution of hydrocarbons. in above paragraphs 270-275 when the predicted or expected seismic signal match or meet the observed or measured/detected signal, the spatial distribution is finalized.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3-5, 7, 8, 14-16, 18, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Warner in view of Guo et al (Image domain least-squares…) herein after “Guo”
Regarding claim 3, Warner teaches, the method of claim 1, Warner does not clearly teach wherein diagonal terms and off-diagonal terms of the estimated inverse Hessian matrix are approximated by using a non-stationary convolution operator to produce an estimate of the inverse Hessian matrix.
Guo teaches wherein diagonal terms and off-diagonal terms of the estimated inverse Hessian matrix are approximated by using a non-stationary convolution operator to produce an estimate of the inverse Hessian matrix (page 2, left col. line 41. The inverse of the diagonal Hessian can help to recover the true amplitude of the migration image by eliminating the geometric spreading effects during wave propagation while balancing the uneven illumination caused by the imperfect acquisition system and complex overburden velocity. Therefore, it is also applied in seismic inversion for reconstructing deep structures (Wang & Rao 2009). However, the off diagonal values of the Hessian matrix need to be considered to improve the resolution of the migration images… page 2 right col. line 15. The inverse of the Hessian can also be approximated with a bank of non-stationary matching filters (Guitton 2004, 2017))
Here the diagonal and off diagonal terms of the inverse Hessian matrix are approximated using a non-stationary filters (i.e., convolution operators) to estimate the inverse Hessian matrix for image resolution improvement.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Guo into Warner for the purpose of estimating the inverse hessian matrix’s diagonal and off-diagonal terms using non-stationary filters so that the image resolutions can be improved.
Claim 14 is rejected as claim 3 having same or similar limitations/elements.
Regarding claim 4, the combination of Warner and Guo teaches, the method of claim 3 Warner teaches wherein a plurality of convolution operators is used in products or in linear combinations, or in both products and linear combinations (para [0083] In one embodiment, a plurality of convolutional filters is provided. In one embodiment, a plurality of further convolutional filters is provided, [0103] In one embodiment, the secondary objective function consists of a norm of the unweighted convolutional filter coefficients divided by a norm of the weighted filter coefficients.).
Here the multiple convolution filter are used in weighted filter coefficients (i.e., in products).
Claim 15 is rejected as claim 4 having same claim limitation.
Regarding claim 5, the combination of Warner and Guo teaches, the method of claim 3 Warner teaches wherein at least one of the plurality of convolution operators comprises match filtering (para [0285] Firstly, whilst the above embodiments are illustrated with regard to one dimensional Wiener filters, multidimensional filters could also be used. In the simple one-dimensional scheme above, the filter for each source-receiver pair is designed only using data from that source-receiver pair. The scheme is also implemented in time—the two sequences that are being matched represent data that varies in time, and the Wiener filter has its coefficients arranged by temporal lag.).
Here the two sequence of convolution filters are matching.
Claim 16, is rejected as claim 5 having same claim limitation.
Regarding claim 7, the combination of Warner and Guo teaches, the method of claim 5 Warner teaches wherein the match filtering is performed in a transformed domain comprising at least one of curvelet, Fourier, radon and wavelet domain (para 0210] A Wiener filter is a convolutional filter of finite length that is operable to convert an input wavelet into a desired output wavelet in a least-squares manner.).
Claim 18, is rejected as claim 7 having same claim limitation.
Regarding claim 8, the combination of Warner and Guo teaches, the method of claim 5 Warner teaches wherein the match filtering is applied in at least one dimension ([0285] Firstly, whilst the above embodiments are illustrated with regard to one dimensional Wiener filters,)
Claim 19 is rejected as claim 8 having same claim limitation.
Claim(s) 6 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable the combination of Warner and Guo in view of Szeliski (US 20080025633 A1)
Regarding claim 6, the combination of Warner and Guo teaches, the method of claim 3 the combination does not clearly teach wherein the off-diagonal terms are modified to preserve a positive definite property.
Szeliski teaches wherein the off-diagonal terms are modified to preserve a positive definite property (para [0093] The solution of sparse positive definite (SPD) linear systems of equations generally breaks down into two major classes of algorithms: direct and iterative.
Para [0138] For example, conventional ILUM techniques traditionally simply drop coefficient matrix entries that are of small magnitude relative to other entries in a given row. Conventionally, this is known as ILU with thresholding, or "ILUT." However, in stark contrast to such techniques, rather than dropping such terms, the Finite Element Preconditioner re-distributes these entries to other off-diagonal and on-diagonal entries in a way that preserves a good finite-element approximation to the original variational problem.).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Szeliski into Warner for the purpose of modifying the off diagonal terms in inversion processing to preserve a positive property so that the solutions can be accurately calculated.
Claims 17 are rejected as claim 6 having same limitation.
Claim(s) 25, 31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Warner in view of Szeliski (US 20080025633 A1)
Claims 25, 31 are rejected as claim 6 having same limitation.
Claim(s) 9, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of the combination of Warner and Guo in view of Li (CN 105334537 A1)
Regarding claim 9, the combination of Warner and Guo teaches, the method of claim 5 the combination does not clearly teach wherein the match filtering is applied in overlapping windows.
Li teaches wherein the match filtering is applied in overlapping windows (page 2, line 19, based on multiple-wave adaptive subtraction method 3D of matched filters in the 3D data window are overlapped,).
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have incorporated Li into Warner for the purpose of matching filters in an overlapping window for having an optimized filtering of waves by reducing the number of iterations.
Claims 20 is rejected as claim 9 having same limitation.
Claim(s) 28, 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Warner in view of Li (CN 105334537 A1)
Claims 28, 34 are rejected as claim 9 having same limitation.
Allowable Subject Matter
There are no prior art rejections for claims 2, 13. However, examiner cannot comment on their allowability until the rejections under USC 101 are adequately addressed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Routh et al US 20150012256 A1 discusses inversion processing of seismic data.
Tang et al US 20130311149 A1 discuss inversion processing of full wave.
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/SHARAD TIMILSINA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857