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 .
DETAILED ACTION
Claims Status
Claims 1-20 are rejected under 35 U.S.C. 101 rejection.
Claims 1, 5 and 6 are rejected under 35 U.S.C. 102(a)(1) rejection.
Claims 2-4, and 7-20 are rejected under 35 U.S.C. 103 rejection.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more as addressed below.
The new 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (Vol. 84 No. 4, Jan 7, 2019 pp 50-57) has been applied and the claims are deemed as being patent ineligible.
The current 35 USC 101 analysis is based on the current guidance (Federal Register vol. 79, No. 241. pp. 74618-74633). The analysis follows several steps. Step 1 determines whether the claim belongs to a valid statutory class. Step 2A prong 1 identifies whether an abstract idea is claimed. Step 2A prong 2 determines whether an abstract idea is integrated into a practical application. If the abstract idea is integrated into a practical application the claim is patent eligible under 35 USC 101. Last, step 2B determines whether the claims contain something significantly more than the abstract idea. In most cases the existence of a practical application predicates the existence of an additional element that is significantly more.
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 below claims are considered to be in a statutory category (process).
Under Step 1 of the analysis, claims 1, 9, and 14 do belong to a statutory category, namely it is a process claim.
Under Step 2A Prong 1, the independent claim 1 includes abstract ideas as highlighted (using a bold font) below.
“1. A method for performing stochastic inversion on seismic data to estimate subsurface properties and their associated uncertainties, the method comprising:
(a) generating an initial subsurface model having an initial dimensionality and based at least in part on initial seismic data;
(b) compressing the initial model subsurface model to reduce a dimensionality of the initial subsurface model and form a compressed subsurface model having a compressed dimensionality that is less than the initial dimensionality;
(c) producing an initial plurality of particles from the compressed subsurface model at the compressed dimensionality;
(d) selecting particles from the initial plurality of particles;
(e) expanding the selected particles to return the selected particles to the initial dimensionality;
(f) iteratively updating a value of each particle of the selected particles utilizing synthetic seismic data produced from the initial subsurface model to generate a posterior set of particles; and
(g) outputting the posterior set of particles as a target distribution comprising a plurality of inverted subsurface models.”
“9. A method for performing stochastic inversion on seismic data to estimate subsurface properties and their associated uncertainties, the method comprising:
(a) generating an initial subsurface model based at least in part on initial seismic data;
(b) applying at least one of an image segmentation function and a geometric data compression function to the initial subsurface model to generate a compressed subsurface model;
(c) producing an initial plurality of particles from the compressed subsurface model;
(d) selecting particles from the initial plurality of particles;
(e) iteratively updating a value of each particle of the selected particles utilizing
synthetic seismic data produced from the initial subsurface model to generate a posterior set of particles; and
(f)outputting the posterior set of particles as a target distribution comprising a
plurality of inverted subsurface models.”
“14. A method for performing stochastic inversion on seismic data to estimate subsurface properties and their associated uncertainties, the method comprising:
(a) generating an initial subsurface model having an initial dimensionality and based at least in part on initial seismic data;
(b) compressing the initial subsurface model to reduce a dimensionality of the initial subsurface model and form a compressed subsurface model having a compressed dimensionality that is less than the initial dimensionality;
(c) perturbing the compressed subsurface model to produce a plurality of
compressed model perturbations at the compressed dimensionality;
(d) expanding the plurality of compressed model perturbations to the initial
dimensionality to produce a plurality of expanded model perturbations;
(e) combining the plurality of expanded model perturbations with the initial
subsurface model to produce an initial plurality of particles;
(f) selecting particles from the initial plurality of particles;
(g) iteratively updating a value of each particle of the selected particles utilizing
synthetic seismic data produced from the initial subsurface model to generate a posterior set of particles; and
(h) outputting the posterior set of particles as a target distribution comprising a
plurality of inverted subsurface models.”
The highlighted steps indicated as abstract idea limitations are considered to be equivalent to mathematical steps and fundamental aspect of mathematics or directed to mental processes performed in the human mind (including observation, evaluation and opinion).
Under Step 2A Prong 2, claims 1, 9 and 14 are not directed to any particular practical application. The claims taken as a whole do not integrate the abstract idea into a particular practical application. The step of “outputting the posterior set of particles …models” are just outputting the result of the calculations, which is insignificant extra-solution activity.
Under Step 2B, claims 1, 9, and 14 do not comprise any additional elements which would make the claim significantly more than the abstract idea, for the same reasons discussed above.
Claims 2-8, 10-13 and 15-20 additionally comprise the description of functions and merely extend the details of the abstract idea of mathematical concepts, more particularly mathematical calculations or mental steps. Therefore claims 2-8, 10-13 and 15-20 are similarly rejected under 35 U.S.C. 101.
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.
Claims 1, 5, and 6, are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fernandez (WO2011159255A2), hereinafter Fernandez.
Regarding Claim 1, Fernandez discloses a method for performing stochastic inversion on seismic data to estimate subsurface properties and their associated uncertainties (Para. [187], and [188]: "These techniques include, but are not limited to, seismic reflection techniques, gravimetric, magnetic or electromagnetic methods, electrical methods and tomography techniques. More particularly, seismic reflection techniques may be used to map the subsurface distribution and structure of terrains so as to delineate potential hydrocarbon accumulations. In a process known as seismic inversion, the seismic reflection data can be transformed into a quantitative rock property description of a reservoir"), the method comprising:
(a) generating an initial subsurface model having an initial dimensionality and based at least in part on initial seismic data (Para. [47]: "In one implementation, the data source 145 is a data acquisition system that includes a signal sensor, detector, receiver, source, scanner or any other suitable device that is operable to digitize observations (e.g., images) of a physical system (e.g., Earth, atmosphere, biological body, etc.). In geophysical exploration applications, the data acquisition system 145 may be adapted to collect geophysical data such as seismic reflection data that can be used to compute quantitative parameters (e.g., velocity structure, elastic parameters,
etc.) of rock terrains via seismic inversion techniques". Para. [94]: "In one implementation, the model parameter set m0is a 2-D image, such as the conductivity or the P-velocity field in a 2-D electromagnetic or seismic tomography problem");
(b) compressing the initial model subsurface model to reduce a dimensionality of the initial subsurface model and form a compressed subsurface model having a compressed dimensionality that is less than the initial dimensionality (Para. [55]: "the problem solver 120 computes a reduced base using the input model parameter set(s). This is performed in order to reduce the dimensionality of the correlated search space and create a reduced base associated with coefficients that are consistent with the structure of the input model parameter set and observed data. These coefficients represent the coordinates of any model parameter set located in the reduced base, and are much fewer than the number of model parameters in the input model parameter set, thereby rendering the subsequent search in the reduced base more efficient". Para. [57]: "The dimensionality of the reduced base is much lower, since the number a of coefficients a in the reduced base is much lower than the number n of the model parameters in the input model parameter set");
(c) producing an initial plurality of particles from the compressed subsurface
model at the compressed dimensionality (Para. [59]: "the problem solver 120 performs sampling in a parameter space associated with the reduced base to generate one or more output model parameter sets". Para. [132]: "Once the reduced base is built, sampling may be performed in the reduced- dimensional model parameter space associated with the reduced base using various sampling methods");
(d) selecting particles from the initial plurality of particles (Para. [59]: "sampling techniques may be employed to select model parameter sets in the reduced parameter space);
(e) expanding the selected particles to return the selected particles to the initial
Dimensionality (Para. [99]: "Alternatively, the 3-D image may be transformed into a new 2-D image by reshaping its different layers. The reduced base may be determined based on this new 2-D image and transformed back to the original 3-D space");
(f) iteratively updating a value of each particle of the selected particles utilizing
synthetic seismic data produced from the initial subsurface model to generate a posterior set of particles (Para. [54]: "The unique posterior model parameter set m0 may be derived by using local optimization methods that converge to one of these multiple solutions"); and
(g) outputting the posterior set of particles as a target distribution comprising a
plurality of inverted subsurface models (Para. [182]: "the output model parameter sets are analyzed at 208 to generate one or more measures of interest").
Regarding claim 5, Fernandez disclose the method of claim 1, wherein (b) comprises applying a data compression function to the initial subsurface model to generate the compressed subsurface model defined by a lesser amount of data than the initial subsurface model (para. [55]: "the problem solver 120 computes a reduced base using the input model parameter set(s). This is performed in order to reduce the dimensionality of the correlated search space and create a reduced base associated with coefficients that are consistent with the structure of the input model parameter set and observed data. These coefficients represent the coordinates of any model parameter set located in the reduced base, and are much fewer than the number of model parameters in the input model parameter set, thereby rendering the subsequent search in the reduced base more efficient").
Regarding Claim 6, Fernandez disclose the method of claim 1 correspondently, wherein (b) comprises applying a data compression function to the initial subsurface model whereby at least some of a plurality of geometric features of the initial subsurface model are approximated by compressed geometric features having predefined geometric elements (para. [60]: "In one implementation, geometric sampling is
performed in the parameter space associated with the reduced base").
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 2 is rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of Denli et al., (US Pub.20200183041A1), hereinafter Denli.
Regarding claim 2, Fernandez disclose the method of claim 1 correspondently, wherein the plurality of inverted subsurface models comprises full waveform inversion (FWI) models.
Denli disclose the plurality of inverted subsurface models comprises full waveform inversion (FWI) models (para. [49]: "In practical applications, the present technological advancement may be used in conjunction with a seismic data analysis system (e.g., a high-speed computer) programmed in accordance with the disclosures herein. Preferably, in order to efficiently perform FWI").
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide full waveform inversion (FWI) models as taught by Denli into Fernandez in order to provide superior subsurface resolution, accurate velocity and property estimates, and improved imaging.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of Zhang (CN114114410), hereinafter Zhang.
Regarding claim 4, Fernandez disclose the method of claim 1, but does not disclose wherein (b) comprises applying a K-means image segmentation function to the initial subsurface model whereby pixels of the initial subsurface model are grouped into separate clusters.
Zhang disclose wherein (b) comprises applying a K-means image segmentation function to the initial subsurface model whereby pixels of the initial subsurface model are grouped into separate clusters (para [0095], where describing a reduction algorithm such as K-mean).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide applying a K-means image segmentation function, as taught by Zhang into Fernandez in order to provide simplifies complex images and reduces data size and computational load.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of LOMBARDO ROSARIA ET AL: "ADAPTIVE NON-LINER PRINCIPAL COMPONENT AND SURFACE ANALYSIS", hereinafter Lombardo.
Regarding Claim 7, Fernandez disclose the method of claims 6, but does not disclose correspondently, wherein the compressed geometric features comprise B-spline curves and the geometric elements comprise one or more control points and one or more knot vectors.
Lombardo disclose the compressed geometric features comprise B-spline curves and the geometric elements comprise one or more control points and one or more knot vectors (page 86, lines 16-28: Adaptive Principal Surfaces can be seen as a generalization of PCA for dimensionality reduction purpose, using adaptive non- linear transformation of principal components. In this work, B-splines have been considered as transformation functions for both variables and components).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide compressed geometric features comprise B-spline curves and the geometric elements, as taught by Lombardo into Fernandez in order to provide lossless compression geometric feature, which is highly effective for reduction data size.
Claims 3, 9, 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of Kimura “Dynamic-segmentation-Based Feature Dimension Reduction for Quick Audio-Video Searching”, hereinafter Kimura.
Regarding claim 3, Fernandez disclose the method of claim 1, but does not disclose wherein (b) comprises applying an image segmentation function to the initial subsurface model to generate a segmented subsurface model defined by a lesser amount of data than the initial subsurface model.
Kimura disclose wherein (b) comprises applying an image segmentation function to the initial subsurface model to generate a segmented subsurface model defined by a lesser amount of data than the initial subsurface model (page 1, column 1, line 26 - line 38, Col. 2, lines 1-4, where the algorithm are piecewise linear representation of sequential feature trajectories (called segment-based PCA) and efficient pruning of the search space (called distance bounding). In the dimension reduction technique, segment-based PCA was carried out by dividing trajectories into equal-length segments and doing KL transform in every segment. However, it is expected that allowing the segments to have variable lengths would improve dimension reduction performance. Here, we .introduce dynamic segmentation. Dynamic segmentation refers to partitioning feature trajectories dynamically so as to minimize the average dimensionality. However, finding optimal partitioning requires a huge amount of calculation).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide full waveform inversion (FWI) models as taught by Kimura into Fernandez in order to reduces the average dimensionality and accelerates the search, it requires huge amount of calculation(see Abstract).
Regarding Claim 9, Fernandez disclose a method for performing stochastic inversion on seismic data to estimate subsurface properties and their associated uncertainties(Para. [187], [188]: "These techniques include, but are not limited to, seismic reflection techniques, gravimetric, magnetic or electromagnetic methods, electrical methods and tomography techniques. More particularly, seismic reflection techniques may be used to map the subsurface distribution and structure of terrains so as to delineate potential hydrocarbon accumulations. In a process known as seismic inversion, the seismic reflection data can be transformed into a quantitative rock property description of a reservoir"), the method comprising:
(a) generating an initial subsurface model based at least in part on initial seismic
data (Para. [47]: "In one implementation, the data source 145 is a data acquisition system that includes a signal sensor, detector, receiver, source, scanner or any other suitable device that is operable to digitize observations (e.g., images) of a physical system (e.g., Earth, atmosphere, biological body, etc.). In geophysical exploration applications, the data acquisition system 145 may be adapted to collect geophysical data such as seismic reflection data that can be used to compute quantitative parameters (e.g., velocity structure, elastic parameters, etc.) of rock terrains via seismic inversion techniques". Par. 94: "In one implementation, the model parameter set m0is a 2-D image, such as the conductivity or the P-velocity field in a 2-D electromagnetic or seismic tomography problem");
(b) applying data compression function to the initial subsurface model to generate a compressed subsurface model (Para. [55]: "the problem solver 120 computes a reduced base using the input model parameter set(s). This is performed in order to reduce the dimensionality of the correlated search space and create a reduced base associated with coefficients that are consistent with the structure of the input model parameter set and observed data. These coefficients represent the coordinates of any model parameter set located in the reduced base, and are much fewer than the number of model parameters in the input model parameter set, thereby rendering the subsequent search in the reduced base more efficient". Para. [57]: "The dimensionality of the reduced base is much lower, since the number a of coefficients a in the reduced base is much lower than the number n of the model parameters in the input model parameter set");
(c) producing an initial plurality of particles from the compressed subsurface
model (Par. 59: "the problem solver 120 performs sampling in a parameter space associated with the reduced base to generate one or more output model parameter sets". Par. 132: "Once the reduced base is built, sampling may be performed in the reduced- dimensional model parameter space associated with the reduced base using various sampling methods");
(d) selecting particles from the initial plurality of particles(Para. [59]: "sampling techniques may be employed to select model parameter sets in the reduced parameter space);
(e) iteratively updating a value of each particle of the selected particles utilizing
synthetic seismic data produced from the initial subsurface model to generate a posterior set of particles (Para. [54]: "The unique posterior model parameter set mo may be derived by using local optimization methods that converge to one of these multiple solutions"); and
(f) outputting the posterior set of particles as a target distribution comprising a
plurality of inverted subsurface models (Para. [182]: "the output model parameter sets are analyzed at 208 to generate one or more measures of interest").
Fernandez does not disclose at least one of an image segmentation function and a geometric data.
Kimura disclose at least one of an image segmentation function and a geometric data (Kimura describes a method to compress data, wherein, page 1, col. 1, lines 26 - 38: "In coping with the high-dimensionality problem, it is natural to think of dimension reduction. Previously, we proposed a quick and accurate search algorithm for multimedia signals based on dimension reduction. The main techniques in the algorithm are piecewise linear representation of sequential feature trajectories (called segment-based PCA) and efficient pruning of the search space (called distance bounding). In the dimension reduction technique, segment-based PCA was carried out by dividing trajectories into equal-length segments and doing KL transform in every segment. However, it is expected that allowing the segments to have variable lengths would improve dimension reduction performance". Kimura further defines the method as suitable for seismic data, see page 1, col. 2, line 18).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide image segmentation function and a geometric data as taught by Kimura into Fernandez in order to reduces the average dimensionality and accelerates the search, it requires huge amount of calculation(see Abstract).
Regarding Claim 11, Fernandez and Kimura disclose the method of claim 9, but Fernandez does not disclose wherein (b) comprises applying the image segmentation function to the initial subsurface model to generate a segmented subsurface model having a reduced dimensionality with respect to a dimensionality of the initial subsurface model.
Kimura disclose wherein (b) comprises applying an image segmentation function to the initial subsurface model to generate a segmented subsurface model having a reduced dimensionality with respect to a dimensionality of the initial subsurface model (page 1, column 1, line 26 - line 38, Col. 2, lines 1-4, where the algorithm are piecewise linear representation of sequential feature trajectories (called segment-based PCA) and efficient pruning of the search space (called distance bounding). In the dimension reduction technique, segment-based PCA was carried out by dividing trajectories into equal-length segments and doing KL transform in every segment. However, it is expected that allowing the segments to have variable lengths would improve dimension reduction performance. Here, we .introduce dynamic segmentation. Dynamic segmentation refers to partitioning feature trajectories dynamically so as to minimize the average dimensionality. However, finding optimal partitioning requires a huge amount of calculation).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide full waveform inversion (FWI) models as taught by Kimura into Fernandez in order to reduces the average dimensionality and accelerates the search, it requires huge amount of calculation(see Abstract).
Regarding Claim 12, Fernandez and Kimura disclose the method of claim 9, further Fernandez disclose wherein (b) comprises applying the data compression function to the initial subsurface model to generate the compressed subsurface model having a reduced dimensionality with respect to the initial subsurface model (para. [55]: "the problem solver 120 computes a reduced base using the input model parameter set(s). This is performed in order to reduce the dimensionality of the correlated search space and create a reduced base associated with coefficients that are consistent with the structure of the input model parameter set and observed data. These coefficients represent the coordinates of any model parameter set located in the reduced base, and are much fewer than the number of model parameters in the input model parameter set, thereby rendering the subsequent search in the reduced base more efficient").
Claims 8 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of Kimura and Park et al., (KR20210103339A), hereinafter Park.
Regarding Claims 8 and 13, Fernandez disclose the method of claims 1 and 9 correspondently, but Fernandez does not disclose wherein (b) comprises:
(b1) applying an image segmentation function to the initial subsurface model to
generate a segmented subsurface model defined by a lesser amount of data than the initial subsurface model; and
(b2) applying a data compression function to the segmented subsurface model to
generate the compressed subsurface model defined by a lesser amount of data than the segmented subsurface model.
Kimura disclose wherein (b1) applying an image segmentation function to the initial subsurface model to generate a segmented subsurface model defined by a lesser amount of data than the initial subsurface model (page 1, column 1, line 26 - line 38, Col. 2, lines 1-4, where the algorithm are piecewise linear representation of sequential feature trajectories (called segment-based PCA) and efficient pruning of the search space (called distance bounding). In the dimension reduction technique, segment-based PCA was carried out by dividing trajectories into equal-length segments and doing KL transform in every segment. However, it is expected that allowing the segments to have variable lengths would improve dimension reduction performance. Here, we .introduce dynamic segmentation. Dynamic segmentation refers to partitioning feature trajectories dynamically so as to minimize the average dimensionality. However, finding optimal partitioning requires a huge amount of calculation).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide full waveform inversion (FWI) models as taught by Kimura into Fernandez in order to reduces the average dimensionality and accelerates the search, it requires huge amount of calculation(see Abstract).
Park disclose(b2) applying a data compression function to the segmented subsurface model to generate the compressed subsurface model defined by a lesser amount of data than the segmented subsurface model (Page 7, lines 28-37, where described a four compression step of segmented image :
“Referring back to FIG. 8, in operation S840, the image interpolation apparatus 200 may reduce the dimensions of the first elastic wave image and the second elastic wave image by using the generated encoder. That is, the image interpolation apparatus 200 may respectively input the first segmented elastic image and the second segmented elastic image to the detected encoder. Here, the second segmented elastic image is an image fragment generated by dividing the second elastic image according to the segmentation method, and may be generated in the same manner as the method in which the first segmented elastic image is generated. The first segmented elastic image processed and output by the encoder will have a lower dimension, which is referred to as a first reduced elastic wave image. Similarly, the second segmented elastic image processed and output by the encoder will have a lower dimension, which is referred to as a second reduced elastic wave image.”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide data compression function to the segmented subsurface model, as taught by Park into Fernandez in order to reduces the resource requirement and improves accuracy on sparse datasets.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of Kimura, as applied above, and further in view of Denli et al., (US Pub.2020183041A1), hereinafter Denli.
Regarding claim 10, Fernandez and Kimura disclose the method of claim 9 correspondently, wherein the plurality of inverted subsurface models comprises full waveform inversion (FWI) models.
Denli disclose the plurality of inverted subsurface models comprises full waveform inversion (FWI) models (para. [49]: "In practical applications, the present technological advancement may be used in conjunction with a seismic data analysis system (e.g., a high-speed computer) programmed in accordance with the disclosures herein. Preferably, in order to efficiently perform FWI").
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide full waveform inversion (FWI) models as taught by Denli in combination of Fernandez and Kimura in order to provide superior subsurface resolution, accurate velocity and property estimates, and improved imaging.
Claims 14, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of Ciucivara et al., (US Pub.20140358503), hereinafter Ciucivara.
Regarding Claim 14, Fernandez disclose a method for performing stochastic inversion on seismic data to estimate subsurface properties and their associated uncertainties (Para. [187], [188]: "These techniques include, but are not limited to, seismic reflection techniques, gravimetric, magnetic or electromagnetic methods, electrical methods and tomography techniques. More particularly, seismic reflection techniques may be used to map the subsurface distribution and structure of terrains so as to delineate potential hydrocarbon accumulations. In a process known as seismic inversion, the seismic reflection data can be transformed into a quantitative rock property description of a reservoir"), the method comprising:
(a) generating an initial subsurface model having an initial dimensionality and
based at least in part on initial seismic data (Para. [47]: "In one implementation, the data source 145 is a data acquisition system that includes a signal sensor, detector, receiver, source, scanner or any other suitable device that is operable to digitize observations (e.g., images) of a physical system (e.g., Earth, atmosphere, biological body, etc.). In geophysical exploration applications, the data acquisition system 145 may be adapted to collect geophysical data such as seismic reflection data that can be used to compute quantitative parameters (e.g., velocity structure, elastic parameters,
etc.) of rock terrains via seismic inversion techniques". Para. [94]: "In one implementation, the model parameter set m0is a 2-D image, such as the conductivity or the P-velocity field in a 2-D electromagnetic or seismic tomography problem");
(b) compressing the initial subsurface model to reduce a dimensionality of the
initial subsurface model and form a compressed subsurface model having a compressed dimensionality that is less than the initial dimensionality (Para. [55]: "the problem solver 120 computes a reduced base using the input model parameter set(s). This is performed in order to reduce the dimensionality of the correlated search space and create a reduced base associated with coefficients that are consistent with the structure of the input model parameter set and observed data. These coefficients represent the coordinates of any model parameter set located in the reduced base, and are much fewer than the number of model parameters in the input model parameter set, thereby rendering the subsequent search in the reduced base more efficient". Para. [57]: "The dimensionality of the reduced base is much lower, since the number a of coefficients a in the reduced base is much lower than the number n of the model parameters in the input model parameter set");
(d) expanding the plurality of compressed model to the initial dimensionality to produce a plurality of expanded model (para [0099], "Alternatively, the 3-D image may be transformed into a new 2-D image by reshaping its different layers. The reduced base may be determined based on this new 2-D image and transformed back to the original 3-D space");
(e) produce an initial plurality of particles (Para. [59]: "the problem solver 120 performs sampling in a parameter space associated with the reduced base to generate one or more output model parameter sets", e.g., sampling in a parameter space is corresponds to particles);
(f) selecting particles from the initial plurality of particles (Para. [59]: "the problem solver 120 performs sampling in a parameter space associated with the reduced base to generate one or more output model parameter sets". Para. [132]: "Once the reduced base is built, sampling may be performed in the reduced- dimensional model parameter space associated with the reduced base using various sampling methods");
(g) iteratively updating a value of each particle of the selected particles utilizing synthetic seismic data produced from the initial subsurface model to generate a posterior set of particles (Para. [54]: "The unique posterior model parameter set mo may be derived by using local optimization methods that converge to one of these multiple solutions"); and
(h) outputting the posterior set of particles as a target distribution comprising a plurality of inverted subsurface models (Para. [182]: "the output model parameter sets are analyzed at 208 to generate one or more measures of interest").
Fernandez does not disclose: (c) perturbing the compressed subsurface model to produce a plurality of compressed model perturbations at the compressed dimensionality;
(e) combining the plurality of expanded model perturbations with the initial subsurface model.
Ciucivara disclose(c) perturbing the compressed subsurface model to produce a plurality of compressed model perturbations at the compressed dimensionality(Claim 1, where reducing the principal component domain to an N-dimensional reduced domain by selecting N, less than all, of the principal components, based on a criterion favoring large principal components over small principal components; defining an D-dimensional perturbation space within the reduced domain, where D<N; generating one or more perturbed models by perturbing the model within the perturbation space).;
(e) combining the plurality of expanded model perturbations with the initial subsurface model(Claim 1, where reducing the principal component domain to an N-dimensional reduced domain by selecting N, less than all, of the principal components, based on a criterion favoring large principal components over small principal components; defining an D-dimensional perturbation space within the reduced domain, where D<N; generating one or more perturbed models by perturbing the model within the perturbation space).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide perturbing the compressed subsurface model, as taught by Ciucivara into expending model to the initial dimensionality of Fernandez in order to improve low-frequency information recovery, anti-noise interference and robustness.
Regarding claim 18, Fernandez disclose the method of claim 14, wherein (b) comprises applying a data compression function to the initial subsurface model to generate the compressed subsurface model defined by a lesser amount of data than the initial subsurface model (para. [55]: "the problem solver 120 computes a reduced base using the input model parameter set(s). This is performed in order to reduce the dimensionality of the correlated search space and create a reduced base associated with coefficients that are consistent with the structure of the input model parameter set and observed data. These coefficients represent the coordinates of any model parameter set located in the reduced base, and are much fewer than the number of model parameters in the input model parameter set, thereby rendering the subsequent search in the reduced base more efficient").
Regarding Claim 19, Fernandez disclose the method of claim 14 correspondently, wherein (b) comprises applying a data compression function to the initial subsurface model whereby at least some of a plurality of geometric features of the initial subsurface model are approximated by compressed geometric features having predefined geometric elements (par. 60: "In one implementation, geometric sampling is
performed in the parameter space associated with the reduced base").
Claim 15 are rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of Ciucivara, as applied above, and further in view of Denli et al., (US Pub.20200183041A1), hereinafter Denli.
Regarding claim 15, Fernandez and Ciucivara disclose the method of claim 14 correspondently, wherein the plurality of inverted subsurface models comprises full waveform inversion (FWI) models.
Denli disclose the plurality of inverted subsurface models comprises full waveform inversion (FWI) models (para. [49]: "In practical applications, the present technological advancement may be used in conjunction with a seismic data analysis system (e.g., a high-speed computer) programmed in accordance with the disclosures herein. Preferably, in order to efficiently perform FWI").
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide full waveform inversion (FWI) models as taught by Denli in combination of Fernandez and Ciucivara in order to provide superior subsurface resolution, accurate velocity and property estimates, and improved imaging.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of Ciucivara, as applied above and further in view of Kimura.
Regarding claim 16, Fernandez disclose the method of claim 14, but does not disclose wherein (b) comprises applying an image segmentation function to the initial subsurface model to generate a segmented subsurface model defined by a lesser amount of data than the initial subsurface model.
Kimura disclose wherein (b) comprises applying an image segmentation function to the initial subsurface model to generate a segmented subsurface model defined by a lesser amount of data than the initial subsurface model (page 1, column 1, line 26 - line 38, Col. 2, lines 1-4, where the algorithm are piecewise linear representation of sequential feature trajectories (called segment-based PCA) and efficient pruning of the search space (called distance bounding). In the dimension reduction technique, segment-based PCA was carried out by dividing trajectories into equal-length segments and doing KL transform in every segment. However, it is expected that allowing the segments to have variable lengths would improve dimension reduction performance. Here, we .introduce dynamic segmentation. Dynamic segmentation refers to partitioning feature trajectories dynamically so as to minimize the average dimensionality. However, finding optimal partitioning requires a huge amount of calculation).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide full waveform inversion (FWI) models as taught by Kimura into Fernandez in order to reduces the average dimensionality and accelerates the search, it requires huge amount of calculation(see Abstract).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of Ciucivara, as applied above and further in view of Zhang (CN114114410-A), hereinafter Zhang.
Regarding claim 17, Fernandez disclose the method of claim 14, but does not disclose wherein (b) comprises applying a K-means image segmentation function to the initial subsurface model whereby pixels of the initial subsurface model are grouped into separate clusters.
Zhang disclose wherein (b) comprises applying a K-means image segmentation function to the initial subsurface model whereby pixels of the initial subsurface model are grouped into separate clusters (para [0095], where describing a reduction algorithm such as K-mean).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide applying a K-means image segmentation function, as taught by Zhang into Fernandez in order to provide simplifies complex images and reduces data size and computational load.
Claims 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fernandez in view of Ciucivara , as applied above and further in view of LOMBARDO ROSARIA ET AL: "ADAPTIVE NON-LINER PRINCIPAL COMPONENT AND SURFACE ANALYSIS", hereinafter Lombardo.
Regarding Claim 20, Fernandez and Ciucivara disclose the method of claim 19 but do not disclose correspondently, wherein the compressed geometric features comprise B-spline curves and the geometric elements comprise one or more control points and one or more knot vectors.
Lombardo disclose the compressed geometric features comprise B-spline curves and the geometric elements comprise one or more control points and one or more knot vectors (page 86, lines 16-28: Adaptive Principal Surfaces can be seen as a generalization of PCA for dimensionality reduction purpose, using adaptive non- linear transformation of principal components. In this work, B-splines have been considered as transformation functions for both variables and components).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the applicants' invention was made to provide compressed geometric features comprise B-spline curves and the geometric elements, as taught by Lombardo in combination of Fernandez and Ciucivara in order to provide lossless compression geometric feature, which is highly effective for reduction data size.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zheng (US Pub.20230251395A1) disclose Fig. 5, # 100 Run SVGD, para [0047] disclose “ computing system 60 running an efficient particle-based inference algorithm known as Stein Variational Gradient Descent (SVGD). To perform Bayesian optimization (e.g., a Bayesian inversion) with SVGD, probability distributions are represented by sets of particles instead of probability density functions”.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KALERIA KNOX whose telephone number is (571)270-5971. The examiner can normally be reached M-F 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571)2722302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KALERIA KNOX/
Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights