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
The amendment filed 01/08/2026 has been entered. Claims 1-7, 9-14 and 16-20 remain pending in the application.
Response to Arguments
Applicant' s arguments with respect to claim(s) 1, 10, 17 and all subsequent dependent claims have been considered but are moot in view of the references cited in the most current rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7, 9-14 and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Liu (US 20230074047 A1) in view of Di (US 20230109902 A1) and Zhang (CN 110163114 A, all citations provided from machine translation attached).
Regarding claim 1, Liu teaches a method for generating seismic images of a subsurface formation (acquiring seismic images representative of geological formations within a subsurface region 26 of the Earth), the method comprising: obtaining seismic data (36) representing the subsurface formation. (Paragraph 31, Fig.2)
Liu also teaches generating velocity models for the subsurface formation based on the seismic data (receiving a velocity model corresponding to at least one attribute of seismic data, receiving source wavelet data corresponding to the seismic data). (Claim 1, Fig.5)
Liu also teaches predicting Green's functions for the subsurface formation using a first neural network to extract features from the velocity models (velocity model 511) and a neural network to generate wavefields (source wavelet 510). (Paragraphs 11, 37, 56-57, 54, Fig.5)
Liu also teaches where inputs to the first neural network comprise the velocity models (Green's functions directly from a velocity model in conjunction with the aforementioned machine learning, deep learning, and/or neural networks), and the first neural network comprises features of locations of source and receiver pairs, and outputs of the neural network comprise the predicted Green's functions. (Paragraphs 11, 37, 56-57, Fig.5)
Liu does not explicitly teach using a second neural network to output a predicted function and inputting to the second neural network the features extracted by the first neural network.
Di teaches using a second neural network to output a predicted function. (Abstract, Paragraphs 4-6, Claims 1, 3)
Zhang teaches inputting to the second neural network the features extracted by the first neural network (when training the second neural network model, using the previously trained the first neural network model). (Page.6, Last paragraph)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Liu to incorporate using a second neural network to output a predicted function as taught by Di in order to generate seismic images and identify reflections in the seismic image and to attenuate the low frequency noise and further modify Liu to incorporate inputting to the second neural network the features extracted by the first neural network as taught by Zhang in order to improve image quality and improve the accuracy of subsequent analysis and prediction.
Regarding claim 2, Liu teaches determining one or more locations to drill wells in the subsurface formation based on the generated seismic images; and controlling drilling equipment to drill the wells in the one or more locations. (Paragraphs 49, 27)
Regarding claim 3, Liu teaches wherein generating the velocity models comprises generating the velocity models by applying a seismic tomography technique to the seismic data. (Paragraphs 56-57, 59, 61)
Regarding claim 4, Liu teaches training the first neural network based on a training data set comprising velocity models labeled with corresponding Green's functions. (Abstract, Paragraphs 11, 37, 56-57, 61, Claims 1-2)
Liu does not explicitly teach using a second neural network.
Di teaches using a second neural network. (Abstract, Paragraphs 4-6, Claims 1, 3)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Liu to incorporate using a second neural network in order to generate seismic images and identify reflections in the seismic image and to attenuate the low frequency noise.
Regarding claim 5, Liu teaches wherein the training data set comprises synthetically generated velocity models and corresponding Green's functions. (Paragraphs 60-61, 67)
Regarding claim 6, Liu teaches wherein the synthetically generated velocity models are derived from a geological model, and the corresponding Green's functions are determined based on a simulation of seismic wave propagation from a source location to a receiver location. (Paragraphs 60-61, 67)
Regarding claim 7, Liu teaches wherein the first neural network comprises a transformer network or a U-network. (Paragraphs 37, 50, 56-57, Fig.5)
Regarding claim 9, Liu teaches wherein the first neural network comprises a U- network. (Paragraphs 37, 50, 56-57, Fig.5)
Liu does not explicitly teach the second neural network comprises a transformer network.
Di teaches the second neural network comprises a transformer network. (Abstract, Paragraphs 4-6, Claims 1, 3, 10, 17)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Liu to incorporate the second neural network comprises a transformer network in order to generate seismic images and identify reflections in the seismic image and to attenuate the low frequency noise.
Regarding claim 10, Liu teaches a system (64) for generating seismic images of a subsurface formation, the system comprising: at least one processor (64) and a memory (66) storing instructions that when executed by the at least one processor cause the at least one processor to perform operations comprising: obtaining seismic data (36) representing the subsurface formation. (Abstract, Paragraphs 31, 36, 39, Figs.2, 4)
Liu also teaches generating velocity models for the subsurface formation based on the seismic data (receiving a velocity model corresponding to at least one attribute of seismic data, receiving source wavelet data corresponding to the seismic data). (Claim 1, Fig.5)
Liu also teaches predicting Green's functions for the subsurface formation using a first neural network to extract features from the velocity models and a neural network to generate wavefields, where inputs to the first neural network comprise the velocity models and locations of source and receiver pairs, and outputs of the neural network comprise the predicted Green's functions (Green's functions directly from a velocity model in conjunction with the aforementioned machine learning, deep learning, and/or neural networks). (Paragraphs 11, 56-57, Fig.5)
Liu also teaches generating seismic images of the subsurface formation based on the seismic data and the predicted Green's functions. (Paragraphs 11, 52, 54, 67)
Liu does not explicitly teach using a second neural network to output a predicted function and inputting to the second neural network the features extracted by the first neural network.
Di teaches using a second neural network to output a predicted function. (Abstract, Paragraphs 4-6, Claims 1, 3)
Zhang teaches inputting to the second neural network the features extracted by the first neural network (when training the second neural network model, using the previously trained the first neural network model). (Page.6, Last paragraph)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Liu to incorporate using a second neural network to output a predicted function as taught by Di in order to generate seismic images and identify reflections in the seismic image and to attenuate the low frequency noise and further modify Liu to incorporate inputting to the second neural network the features extracted by the first neural network as taught by Zhang in order to improve image quality and improve the accuracy of subsequent analysis and prediction.
Regarding claims 11 and 18, the claims disclose substantially the same limitations, as claim 2. All limitations as recited have been analyzed and rejected with respect to claims 11 and 18, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claims 11 and 18 are rejected for the same rational over the prior art cited in claim 2.
Regarding claim 12, the claim discloses substantially the same limitations, as claim 3. All limitations as recited have been analyzed and rejected with respect to claim 12, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 12 is rejected for the same rational over the prior art cited in claim 3.
Regarding claims 13 and 19, the claims disclose substantially the same limitations, as claim 4. All limitations as recited have been analyzed and rejected with respect to claims 13 and 19, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claims 13 and 19 are rejected for the same rational over the prior art cited in claim 4.
Regarding claim 14, Liu teaches wherein the training data set comprises: synthetically generated velocity models derived from a geological model, and corresponding Green's functions determined based on a simulation of seismic wave propagation from a source location to a receiver location. (Paragraphs 54, 45, 60-61, 67)
Regarding claim 16, the claim discloses substantially the same limitations, as claim 9. All limitations as recited have been analyzed and rejected with respect to claim 16, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 16 is rejected for the same rational over the prior art cited in claim 9.
Regarding claim 17, Liu teaches one or more non-transitory, machine-readable storage devices storing instructions for generating seismic images of a subsurface formation, the instructions being executable by one or more processors, to cause performance of operations comprising: obtaining seismic data (36) representing the subsurface formation. (Abstract, Paragraphs 31, 36, 39, Claims 13-18, Figs.2, 4)
Liu also teaches generating velocity models for the subsurface formation based on the seismic data (receiving a velocity model corresponding to at least one attribute of seismic data, receiving source wavelet data corresponding to the seismic data). (Claim 1, Fig.5)
Liu also teaches predicting Green's functions for the subsurface formation using a first neural network to extract features from the velocity models (velocity model 511) and a neural network to generate wavefields (source wavelet 510). (Paragraphs 11, 37, 56-57, 54, Fig.5)
Liu also teaches where inputs to the first neural network comprise the velocity models (Green's functions directly from a velocity model in conjunction with the aforementioned machine learning, deep learning, and/or neural networks), and the first neural network comprises features of locations of source and receiver pairs, and outputs of the neural network comprise the predicted Green's functions. (Paragraphs 11, 37, 56-57, Fig.5)
Liu also teaches generating seismic images of the subsurface formation based on the seismic data and the predicted Green's functions. (Paragraphs 11, 52, 54, 67)
Liu does not explicitly teach using a second neural network to output a predicted function and inputting to the second neural network the features extracted by the first neural network.
Di teaches using a second neural network to output a predicted function. (Abstract, Paragraphs 4-6, Claims 1, 3)
Zhang teaches inputting to the second neural network the features extracted by the first neural network (when training the second neural network model, using the previously trained the first neural network model). (Page.6, Last paragraph)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Liu to incorporate using a second neural network to output a predicted function as taught by Di in order to generate seismic images and identify reflections in the seismic image and to attenuate the low frequency noise and further modify Liu to incorporate inputting to the second neural network the features extracted by the first neural network as taught by Zhang in order to improve image quality and improve the accuracy of subsequent analysis and prediction.
Regarding claim 20, the claim discloses substantially the same limitations, as claim 14. All limitations as recited have been analyzed and rejected with respect to claim 20, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 20 is rejected for the same rational over the prior art cited in claim 14.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gashawbeza (US 20220413172 A1), which is directed to generating, based on the P-wave velocity model and the velocity ratio data, an initial S-wave velocity model regarding the geological region of interest
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/ABDALLAH ABULABAN/Primary Examiner, Art Unit 3645