DETAILED ACTIONS
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 statements (IDS) submitted on 05/28/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections- 35 USC §101
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 therefore, 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 judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The following analysis is based on the claims,
Regarding Claim 1,
A method comprising:
receiving continuous source seismic data from a marine seismic survey of a geologic region.
breaking up the continuous source seismic data into portions.
performing a simulation for each of the portions to generate simulated seismic data; and
generating an image of the geologic region using the portions of the continuous source seismic data and the simulated seismic data.
The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “breaking up the continuous source seismic data into portions;” and represents mathematical concepts/functions/formula to break up the seismic data into subset/portions of data see (Specification [0125]-[0126]). The step of “performing a simulation for each of the portions to generate simulated seismic data;” and “generating an image of the geologic region using the portions of the continuous source seismic data and the simulated seismic data represents mathematical manipulation using known simulation framework /model see (Specification [0027]-[0029], [0109], [0149],[0152]- [0167]). These steps represent a process (a mathematical manipulation) that, under its broadest reasonable interpretation encompasses grouping/evaluation/generating/judgement based on the mathematical manipulation and are abstract ideas.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 1 recites additional elements
“receiving continuous source seismic data from a marine seismic survey of a geologic region”; are data gathering steps for the particular technological environment or field of use. Obtaining seismic data under certain conditions and environment represents mere data gathering steps and only adds an insignificant extra-solution activity to the judicial exception. 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, do not disclose the practical implementation of the result by a user, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed at a judicial exception and require further analysis under Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data from external factors such as pipe environmental and dimensional data. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
claims 2-18 are rejected under 35 U.S.C. 101 because claims depend on
claim 1, therefore, has the abstract idea of claim 1 and also has the routine and conventional structure above of claim 1. In addition, claims 2-18 further recite the elements which are simply more standard computational, mathematical calculation to data gathering /generate data and/ or a model, and. Furthermore, claims 2-18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 19,
A system comprising:
a processor; memory accessible by the processor; and processor-executable instructions stored in the memory that are executable to instruct the system to:
receive continuous source seismic data from a marine seismic survey of a geologic region;
break up the continuous source seismic data into portions;
perform a simulation for each of the portions to generate simulated seismic data; and generate an image of the geologic region using the portions of the continuous source seismic data and the simulated seismic data.
The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “a processor; memory accessible by the processor; and processor-executable instructions stored in the memory that are executable to instruct the system” “break up the continuous source seismic data into portions;” and represents mathematical concepts/functions/formula to break up the seismic data into subset/portions of data see (Specification [0125]-[0126]). The step of “perform a simulation for each of the portions to generate simulated seismic data;” and “generate an image of the geologic region using the portions of the continuous source seismic data and the simulated seismic data;” represents mathematical manipulation using known simulation framework /model see (Specification [0027]-[0029], [0109], [0149],[0152]- [0167]). These steps represent a process (a mathematical manipulation) that, under its broadest reasonable interpretation encompasses grouping/evaluation/generating/judgement based on the mathematical manipulation and are abstract ideas.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 19 recites additional elements
“receive continuous source seismic data from a marine seismic survey of a geologic region;” are data gathering steps for the particular technological environment or field of use. Obtaining seismic data under certain conditions and environment represents mere data gathering steps and only adds an insignificant extra-solution activity to the judicial exception. 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, do not disclose the practical implementation of the result by a user, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed at a judicial exception and require further analysis under Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data from external factors such as pipe environmental and dimensional data. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
Regarding Claim 20,
One or more computer-readable storage media comprising computer-executable instructions executable to instruct a computer to:
receive continuous source seismic data from a marine seismic survey of a geologic region;
break up the continuous source seismic data into portions;
perform a simulation for each of the portions to generate simulated seismic data;
generate an image of the geologic region using the portions of the continuous source seismic data and the simulated seismic data.
The claim limitations underlined above is abstract idea, and the remaining limitations are “additional elements”.
Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., mathematical manipulation.
Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes.
In the above claim, the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation).
For example, steps of “One or more computer-readable storage media comprising computer-executable instructions executable to instruct a computer” “break up the continuous source seismic data into portions;” and represents mathematical concepts/functions/formula to break up the seismic data into subset/portions of data see (Specification [0125]-[0126]). The step of “perform a simulation for each of the portions to generate simulated seismic data;” and “generate an image of the geologic region using the portions of the continuous source seismic data and the simulated seismic data;” represents mathematical manipulation using known simulation framework /model see (Specification [0027]-[0029], [0109], [0149],[0152]- [0167]). These steps represent a process (a mathematical manipulation) that, under its broadest reasonable interpretation encompasses grouping/evaluation/generating/judgement based on the mathematical manipulation and are abstract ideas.
Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No.
Claim 20 recites additional elements
“receive continuous source seismic data from a marine seismic survey of a geologic region;” are data gathering steps for the particular technological environment or field of use. Obtaining seismic data under certain conditions and environment represents mere data gathering steps and only adds an insignificant extra-solution activity to the judicial exception. 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, do not disclose the practical implementation of the result by a user, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed at a judicial exception and require further analysis under Step 2B.
Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring data from external factors such as pipe environmental and dimensional data. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 1-2, 10-15, and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang et al. (US 2017/0108602 A1, hereinafter Yang, IDS reference).
Regarding Claim 1, Yang teaches,
A method comprising: receiving continuous source seismic data from a marine seismic survey of a geologic region (Yang, Figure 1, [0008] “obtaining a seismic dataset” [0032] “FIG. 1 illustrates an exemplary method for generating FWI AVA stacks. In step 101, processed data is obtained, which can be a single shot gather generated from the collected seismic data”) breaking up the continuous source seismic data into portions [0008] “obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges”);
performing a simulation for each of the portions to generate simulated seismic data; (Yang, Figure 1, [0008] “performing with a computer, an acoustic full wavefield inversion process on each of the subsets”), and
generating an image of the geologic region using the portions of the continuous source seismic data and the simulated seismic data. (Yang, Figure 1, [0019] generating acoustic impedances for each of the subsets, as a function of reflection angle, using the respective density models; and transforming, using a computer, the acoustic impedances for each of the subsets into reflectivity sections. Figures 3-4, [0040] “five data subsets are generated (see step 102) as shown in FIG. 3B. Starting from the same velocity model, acoustic FWI was performed (see step 103) using each of the subsets respectively. The resulting acoustic impedances (see step 104) after the inversion are "shaped" and "stretched" (see step 105) into reflectivity sections (see step 106) and shown in the panels in FIG. 4A”).
Regarding Claim 2, Yang teaches the method of claim 1,
Yang further teaches comprising performing an iterative inversion using the portions and the simulated seismic data. (Yang, Figure 1, [0008] “performing with a computer, an acoustic full wavefield inversion process on each of the subsets”).
Regarding Claim 10, Yang teaches the method of claim 1,
Yang further teaches wherein the performing a simulation for each of the portions to generate simulated seismic data performs simulations in parallel. (Yang, Figure 1, Step 102, [0042] “Preferably, in order to efficiently perform FWI, the computer is a high performance computer (HPC), known as to those skilled in the art, Such high performance computers typically involve clusters of nodes, each node having multiple CPU's and computer memory that allow parallel computation”).
Regarding Claim 11, Yang teaches the method of claim 1,
Yang teaches wherein the generating the image of the geologic region using the portions of the continuous source seismic data and the simulated seismic data is computationally more efficient than generating the image without breaking up the continuous source seismic data into the portions. (Yang, [0002] Exemplary embodiments described herein pertain to the field of geophysical prospecting, and more particularly to geophysical data processing. Specifically, embodiments described herein relate to a method for more efficiently generating FWI model domain angle stacks. [0008] “obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges”).
Regarding Claim 12, Yang teaches the method of claim 11,
Yang further teaches wherein the generating is computationally more efficient via a reduction in memory utilization. (Yang, [0042] 0042] In all practical applications, the present technological advancement must be used in conjunction with a computer, programmed in accordance with the disclosures herein. Preferably, in order to efficiently perform FWI, the computer is a high performance computer (HPC), known as to those skilled in the art, Such high performance computers
typically involve clusters of nodes, each node having multiple CPU's and computer memory that allow parallel computation”).
Regarding Claim 13, Yang teaches the method of claim 1,
Yang further teaches wherein the breaking up the continuous source seismic data into the portions is performed in a model-domain. (Yang, Figure 1, [0030] “embodiments described herein relate to a method for more efficiently generating FWI model domain angle stacks”. Figure 4, [0040]. “The AVA in FIGS. 4A and 4B appears to be consistent. For a closer scrutiny, five vertical lines 402a, 403a, 404a, 405a, and 406a from the model domain stacks”).
Regarding Claim 14, Yang teaches the method of claim 13,
Yang further teaches wherein the portions correspond to a set of point-source responses in the model-domain (Yang, Figure 1, [0029] “The proposed FWI model
domain angle stacks can be generated by inverting the datasets of different angle ranges for different acoustic models” [0032]” In step 102, the shot gather is divided into several subsets. Each subset is within a relatively small range of reflection angles. This can be achieved by using carefully designed data masks which include the information of reflector dipping angles and P-wave velocity. With the dipping angles and P-wave velocity, a ray-tracing method can be used to find the time and offset of the reflected wave in the data from each of the subsurface points at each reflection angle”).
Regarding Claim 15, Yang teaches the method of claim 1,
Yang further teaches wherein the breaking up the continuous source seismic data into the portions is performed in a data-domain. [0008] “obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges”. [0029] “The proposed FWI model domain angle stacks can be generated by inverting the datasets of different angle ranges for different acoustic models”).
Regarding Claim 17, Yang teaches the method of claim 1,
Yang further teaches wherein the image indicates hydrocarbons in the geologic region. (Yang, Figure 1, [0002] Exemplary embodiments described herein pertain to the field of geophysical prospecting, and more particularly to geophysical data processing. Specifically, embodiments described herein relate to a method for more efficiently generating FWI model domain angle stacks. 0005] Full Wavefield Inversion (FWI) is a geophysical method which is used to estimate subsurface properties (such as velocity or density).
Regarding Claim 18, Yang teaches the method of claim 1,
Yang further teaches, wherein the marine seismic survey utilizes a continuous source towed by a vessel along a line. (Yang, [0004] “Seismic prospecting is facilitated by obtaining raw seismic data during performance of a seismic survey [0032] In step 101, processed data is obtained, which can be a single shot gather generated from the collected seismic data”).
Regarding Claim 19, Yang teaches,
A system comprising: a processor; memory accessible by the processor; and processor-executable instructions stored in the memory that are executable to instruct the system to (Yang, [0019] A non-transitory computer readable storage medium encoded with instructions, which when executed by a computer cause the computer to implement a method including: obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges; performing, with a computer, an acoustic full wavefield inversion process on each of the subsets, respectively, to invert for density and generate respective density model”).
receive continuous source seismic data from a marine seismic survey of a geologic region; break up the continuous source seismic data into portions (Yang, Figure 1, [0008] “obtaining a seismic dataset” [0032] “FIG. 1 illustrates an exemplary method for generating FWI AVA stacks. In step 101, processed data is obtained, which can be a single shot gather generated from the collected seismic data”) breaking up the continuous source seismic data into portions [0008] “obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges”);
performing a simulation for each of the portions to generate simulated seismic data; (Yang, Figure 1, [0008] “performing with a computer, an acoustic full wavefield inversion process on each of the subsets”) and generating an image of the geologic region using the portions of the continuous source seismic data and the simulated seismic data. (Yang, Figure 1, [0019] generating acoustic impedances for each of the subsets, as a function of reflection angle, using the respective density models; and transforming, using a computer, the acoustic impedances for each of the subsets into reflectivity sections. Figures 3-4, [0040] “five data subsets are generated (see step 102) as shown in FIG. 3B. Starting from the same velocity model, acoustic FWI was performed (see step 103) using each of the subsets respectively. The resulting acoustic impedances (see step 104) after the inversion are "shaped" and "stretched" (see step 105) into reflectivity sections (see step 106) and shown in the panels in FIG. 4A”).
Regarding Claim 20, Yang teaches,
One or more computer-readable storage media comprising computer-executable instructions executable to instruct a computer(Yang, [0019] A non-transitory computer readable storage medium encoded with instructions, which when executed by a computer cause the computer to implement a method including: obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges; performing, with a computer, an acoustic full wavefield inversion process on each of the subsets, respectively, to invert for density and generate respective density model”) to:
receive continuous source seismic data from a marine seismic survey of a geologic region; break up the continuous source seismic data into portions(Yang, Figure 1, [0008] “obtaining a seismic dataset” [0032] “FIG. 1 illustrates an exemplary method for generating FWI AVA stacks. In step 101, processed data is obtained, which can be a single shot gather generated from the collected seismic data”) breaking up the continuous source seismic data into portions [0008] “obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges”).
; perform a simulation for each of the portions to generate simulated seismic data; (Yang, Figure 1, [0008] “performing with a computer, an acoustic full wavefield inversion process on each of the subsets”) and generate an image of the geologic region using the portions of the continuous source seismic data and the simulated seismic data. (Yang, Figure 1, [0019] generating acoustic impedances for each of the subsets, as a function of reflection angle, using the respective density models; and transforming, using a computer, the acoustic impedances for each of the subsets into reflectivity sections. Figures 3-4, [0040] “five data subsets are generated (see step 102) as shown in FIG. 3B. Starting from the same velocity model, acoustic FWI was performed (see step 103) using each of the subsets respectively. The resulting acoustic impedances (see step 104) after the inversion are "shaped" and "stretched" (see step 105) into reflectivity sections (see step 106) and shown in the panels in FIG. 4A”).
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.
Claims 3-9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yang and in view of Wu et al. (US 2021/0149066 A1, hereinafter Wu).
Regarding Claim 3, Yang teaches the method of claim 1,
Yang further teaches wherein the portions represent decomposed portions of a wavefield represented by the continuous source seismic data (Yang, Figure 1, [0029] “The proposed FWI model domain angle stacks can be generated by inverting the datasets of different angle ranges for different acoustic models” [0032]” In step 102, the shot gather is divided into several subsets. Each subset is within a relatively small range of reflection angles. This can be achieved by using carefully designed data masks which include the information of reflector dipping angles and P-wave velocity. With the dipping angles and P-wave velocity, a ray-tracing method can be used to find the time and offset of the reflected wave in the data from each of the subsurface points at each reflection angle”).
Yang is silent on wherein the portions define a set of point-source responses and a residual.
However, Wu teaches wherein the portions define a set of point-source responses and a residual. (Wu, Figure 19, [0223] For example, the framework 1900 can extend geophysics data processing into reservoir modelling by integrating with the PETREL® framework via the Earth Model Building (EMB) tools, which enable a variety of depth imaging workflows, including model building, editing
and updating, depth-tomography QC, residual moveout analysis, and volumetric common-image-point (CIP) pick QC. Such functionalities, in conjunction with the framework's depth tomography and migration algorithms”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Yang’s method to incorporate a method of analyzing seismic data using reflection seismology model Framework method as taught by Wu and obtain an accurate seismic image of subsurface region with the benefit of obtaining accurate subterranean environment formation image and characterization (Wu, [0002]). It would have been obvious to a person of ordinary skill to include the well-known reflection seismology method of computational Framework with the other machine learning Models, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 4, combination of Yang and Wu teaches the method of claim 3,
Yang is silent on wherein generating the image includes windowing the residual into windowed portions and associating each of the windowed portions with the portions of the continuous source seismic data.
However, Wu teaches wherein generating the image includes windowing (Wu, [0053] As an example, an approach can include least squares simultaneous subtraction. Such a process performs adaptive matching and subtraction of one or more noise models by using least-squares-derived temporal filters to simultaneously match the models to the input seismic data. Matching filters are calculated in different time and space windows within a given input gather”) the residual into windowed portions and associating each of the windowed portions with the portions of the continuous source seismic data(Wu, Figure 19, [0223] For example, the framework 1900 can extend geophysics data processing into reservoir modelling by integrating with the PETREL® framework via the Earth Model Building (EMB) tools, which enable a variety of depth imaging workflows, including model building, editing and updating, depth-tomography QC, residual moveout analysis, and volumetric common-image-point (CIP) pick QC. Such functionalities, in conjunction with the framework's depth tomography and migration algorithms”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Yang’s method to incorporate a method of analyzing seismic data using reflection seismology model Framework method as taught by Wu and obtain an accurate seismic image of subsurface region with the benefit of obtaining accurate subterranean environment formation image and characterization (Wu, [0002]). It would have been obvious to a person of ordinary skill to include the well-known reflection seismology method of computational Framework with the other machine learning Models, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 5, combination of Yang and Wu teaches the method of claim 3,
Yang is silent on comprising separating signal and coherent noise components of the set of point-source responses.
However, Wu teaches comprising separating signal and coherent noise components of the set of point-source responses (Wu. [0143] an adaptive subtraction method may be applied to separate artifacts followed by simultaneous adaptive subtraction using the separated models to get a final output. Simultaneous adaptive subtraction provides an ability to match each model in a window where results can take into account quality of each multiple model. As an example, a workflow can include data preconditioning, for example, to address one or more types of noise, to determine whether a seismic trace includes a sufficient amount of information (e.g., signal, etc.), etc”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Yang’s method to incorporate a method of analyzing seismic data using reflection seismology model Framework method and attenuate noise from data as taught by Wu and obtain an accurate seismic image of subsurface region with the benefit of obtaining accurate subterranean environment formation image and characterization (Wu, [0002]). It would have been obvious to a person of ordinary skill to include the well-known reflection seismology method of computational Framework with the other machine learning Models, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 6, combination of Yang and Wu teaches the method of claim 5,
Yang is silent on wherein generating the image does not utilize the coherent noise components to generate the image with reduced noise.
However, Wu teaches on wherein generating the image does not utilize the coherent noise components to generate the image with reduced noise. (Wu, 0102] As an example, where a seismic survey generates multiples, a workflow may aim to attenuate the presence of those multiples in seismic survey data. Such a workflow can include generation of multiple attenuated seismic data. Such
data may be considered to have a lesser noise than acquired data, for example, where primaries are desirable for analysis to characterize a subterranean environment. As an example, consider primaries being signal and multiples being a type of noise that can obscure the signal. In such an example, a multiple attenuation workflow that receives seismic data and that de-noises the seismic data to generate "cleaner" seismic data ( e.g., with a higher signal to noise ratio) can facilitate reservoir characterization ( e.g., as to lithology, as to reflector
locations, as to geobodies”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Yang’s method to incorporate a method of analyzing seismic data using reflection seismology model Framework method and attenuate noise from data as taught by Wu and obtain an accurate seismic image of subsurface region with the benefit of obtaining accurate subterranean environment formation image and characterization (Wu, [0002]). It would have been obvious to a person of ordinary skill to include the well-known reflection seismology method of computational Framework with the other machine learning Models, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 7, Yang teaches the method of claim 1,
Yang is silent on wherein the portions represent localized portions of the continuous source seismic data defined by a time window function that includes a start time and an end time.
However, Wu teaches wherein the portions represent localized portions of the continuous source seismic data defined by a time window function that includes a start time and an end time. (Wu, [0129] As an example, another approach, referred to as a fast approach, can process a triplet combination in a manner that offers improvement in computation run time and ability to increase crossline computation grid area allowing capturing of 3D effect improving multiple prediction timing. [0130] As an example, a method can include identifying a
grid of uniformly spaced surface points (related to a subsurface Downward Reflection Point (DRP)) centered at a target trace midpoint location where such a grid can vary with each target trace as it is shaped depending on, for example, target trace midpoint location ( center of the grid); target trace offset ( define grid extend along trace azimuth in junction with aperture value); and/or target trace azimuth (define grid azimuth from north)”.
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Yang’s method to incorporate a method of analyzing seismic data using reflection seismology model Framework method and attenuate noise from data as taught by Wu and obtain an accurate seismic image of subsurface region with the benefit of obtaining accurate subterranean environment formation image and characterization (Wu, [0002]). It would have been obvious to a person of ordinary skill to include the well-known reflection seismology method of computational Framework with the other machine learning Models, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 8, combination of Yang and Wu teaches the method of claim 7,
Yang is silent on wherein the simulation utilizes a commencement time prior to the start time.
However, Wu teaches wherein the simulation utilizes a commencement time prior to the start time. (Wu, [0143] “Simultaneous adaptive subtraction provides an ability to match each model in a window where results can take into account quality of each multiple model. As an example, a workflow can include data reconditioning, for example, to address one or more types of noise, to determine whether a seismic trace includes a sufficient amount of information (e.g., signal, etc.), etc”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Yang’s method to incorporate a method of analyzing seismic data using reflection seismology model Framework method and attenuate noise from data as taught by Wu and obtain an accurate seismic image of subsurface region with the benefit of obtaining accurate subterranean environment formation image and characterization (Wu, [0002]). It would have been obvious to a person of ordinary skill to include the well-known reflection seismology method of computational Framework with the other machine learning Models, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 9, combination of Yang and Wu teaches the method of claim 8,
Yang is silent on wherein the commencement prior to the start time improves accuracy of modeling of energy in each of the localized portions
However, Wu teaches wherein the commencement prior to the start time improves accuracy of modeling of energy in each of the localized portions (Wu, [0129] As an example, another approach, referred to as a fast approach, can process a triplet combination in a manner that offers improvement in computation run time and ability to increase crossline computation grid area allowing capturing of 3D effect improving multiple prediction timing. [0130] As an example, a method can include identifying a grid of uniformly spaced surface points (related to a subsurface Downward Reflection Point (DRP)) centered at a target trace midpoint location where such a grid can vary with each target trace as it is shaped depending on, for example, target trace midpoint location ( center of the grid); target trace offset ( define grid extend along trace azimuth in junction with aperture value);”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Yang’s method to incorporate a method of analyzing seismic data using reflection seismology model Framework method and attenuate noise from data as taught by Wu and obtain an accurate seismic image of subsurface region with the benefit of obtaining accurate subterranean environment formation image and characterization (Wu, [0002]). It would have been obvious to a person of ordinary skill to include the well-known reflection seismology method of computational Framework with the other machine learning Models, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Regarding Claim 16, Yang teaches the method of claim 15,
Yang is silent on wherein the portions correspond to time windowed portions in the data-domain.
However, Wu teaches wherein the portions correspond to time windowed portions in the data-domain.(Wu, [0053]” As an example, an approach can include least squares simultaneous subtraction. Such a process performs adaptive matching and subtraction of one or more noise models by using least-squares-derived temporal filters to simultaneously match the models to the input seismic data. Matching filters are calculated in different time and space windows within a given input gather”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Yang’s method to incorporate a method of analyzing seismic data using reflection seismology model Framework method and attenuate noise from data as taught by Wu and obtain an accurate seismic image of subsurface region with the benefit of obtaining accurate subterranean environment formation image and characterization (Wu, [0002]). It would have been obvious to a person of ordinary skill to include the well-known reflection seismology method of computational Framework with the other machine learning Models, in order to yield the predicted results of generating accurate seismic image or geographic map, yet with higher accuracy (KSR).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
DOUGLAS SPENCER SASSEN (US 2016/0116620 A1) recites “A method and system of processing seismic data is presented. The method may include, for each of a plurality of seismic traces, generating a respective intermediate set of reflectivity coefficients and a partial deconvolution of an estimated wavelet from the respective seismic trace. The method may also include decomposing a model into a plurality of orthogonal components, and projecting each of a plurality of eigenvectors corresponding to one of the orthogonal components onto intermediate reflectivity coefficients corresponding with all of the plurality of seismic traces at each of a plurality of times to generate a plurality of eigen-coefficients associated with each of the plurality of times. The eigen-coefficients may be used to generate a plurality of basis coefficients, which may then be used to generate a respective updated set of reflectivity coefficients for each of the seismic traces” (abstract).
Li et al. (US 2022/0099855 A1) The invention provides “A method can include receiving a first trained machine model trained via unsupervised learning using unlabeled seismic image data; receiving labeled seismic image data acquired via an interactive interpretation process; and building a second trained machine model, as initialized from the first trained machine model, via supervised learning using the received labels, where the second trained machine model predicts stratigraphy of a geologic region from seismic image data of the geologic region” (abstract)
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/DILARA SULTANA/Examiner, Art Unit 2858
09/03/2026
/SON T LE/Primary Examiner, Art Unit 2858
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