DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The amendment filed on July 2, 2026 has been considered.
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.
Claims 1-3, 5, 8-10, 12, 15-17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Theyab (US 2022/0057539) in view of Jiang et al. (US 2021/0181362) and Shi et al. (US 2018/0336309).
Regarding claims 1, 8, and 15, Theyab discloses a computer-implemented method and system comprising
obtaining seismic data describing a subterranean surface (Abstract, lines 7-8);
generating, based on the seismic data, structural attributes for the subterranean surface (Abstract, lines 8-9);
providing the structural attributes as input to a smoothing algorithm (structural attribute is an input to the smoothing algorithm, paragraph 0007, lines 5-7) for identifying faults in the subterranean surface (seismic attributes that can indicate geological discontinuities include similarity, semblance, coherence, and fault likelihood attributes, paragraph 0046, lines 1-3), wherein an output of the smoothing algorithm comprises faulted areas (subsurface boundaries, such as faults and stratigraphic terminations, paragraph 0005, lines 6-8) and fault-free areas in the subterranean surface (grids representing subsurface properties, paragraph 0005, lines 5-6) (smoothing to reduce noise in grids representing subsurface properties while avoiding smoothing out subsurface boundaries, such as faults and stratigraphic terminations, paragraph 0005).
Regarding claims 8 and 15, Theyab further discloses one or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations (paragraph 0115).
Theyab does not disclose providing a neural network for identifying faults in the subterranean surface.
Jiang et al. discloses providing structural attributes as input to a neural network for identifying faults in the subterranean surface (paragraph 0004).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to provide Theyab with providing structural attributes as input to a neural network as disclosed by Jiang et al. for the purpose of identifying faults in the subterranean surface.
Theyab does not disclose identifying fault zones selected by the neural network from the faulted areas.
Jiang et al. discloses identifying fault zones (predicting faults, paragraph 0004) selected by the neural network (the prediction is performed by deep neural network model, paragraph 0004) from the faulted areas (the predicting faults predicts faults from the subterranean formation, paragraph 0004, where the subterranean formation includes areas containing faults or fault areas).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to provide Theyab with identifying fault zones selected by the neural network from the faulted areas as disclosed by Jiang et al. for the purpose of planning drilling to avoid subterranean hazards (paragraph 0003).
Theyab does not disclose generating one or more fault polygons by creating boundaries around the identified fault zones selected by the neural network in the subterranean surface, wherein the one or more fault polygons are graphical representations of the faulted areas on a map of the subterranean surface.
Shi et al. discloses generating one or more fault polygons (paragraph 0007; Fig. 4) by creating boundaries (boundaries generated by fault traces, e.g. 114, 126) around the identified fault zones (fault areas 132, 134) selected by the neural network (fault zones are predicted by DNN, paragraph 0004) in the subterranean surface (paragraph 0004), wherein the one or more fault polygons are graphical representations of the faulted areas on a map of the subterranean surface (polygon bounded by fault traces, Fig. 4; paragraph 0007).
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Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to provide Theyab with generating one or more fault polygons as disclosed by Shi et al. for the purpose of identifying faulted area.
Regarding claims 2, 9, and 16, Theyab discloses obtaining seismic data describing the subterranean surface comprises:
receiving a three-dimensional (3D) seismic volume of a subterranean formation (paragraph 0002, lines 5-6, 10-12); and
picking the subterranean surface from the 3D seismic volume of the subterranean formation (paragraph 0002, lines 5-6, 10-12).
Regarding claims 3, 10, and 17, Theyab discloses applying a surface conditioning algorithm (smoothing algorithm, paragraph 0007, lines 12-13) to the seismic data describing the subterranean surface (paragraph 0007, lines 12-14).
Regarding claims 5, 12, and 19, Theyab does not disclose performing a quality check on the one or more fault polygons.
Shi et al. discloses using trace (seismic) data to generate one or more fault polygons (geophysical interpretations) (Abstract, lines 8-9, 11-12).
Jiang et al. discloses using seismic attributes to analyze and enhance the quality of geological or geophysical interpretations (paragraph 0024, lines 1-4).
It would have been to use the trace (seismic) data to analyze of the quality of the fault polygons (geophysical interpretations).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to provide Theyab with using trace (seismic) data for the purpose of performing a quality check on the one or more fault polygons as suggested by Shi et al. and Jiang et al..
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Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Theyab in view of Jiang et al. and Shi et al. as applied to claims 1, 8, and 15 above, and further in view of Wang et al. (CN 112746837).
Regarding claims 4, 11, and 18, Theyab as modified by Jiang et al. and Shi et al. discloses the claimed limitations as discussed above except the neural network is an unsupervised neural network.
Wang et al. discloses a neural network is an unsupervised neural network (page 8, paragraph 2, lines 1-6) for indicating fault.
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to provide Theyab as modified with an unsupervised neural network as disclosed by Wang et al. for the purpose of indicating fault.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Theyab in view of Jiang et al. and Shi et al. as applied to claims 1, 8, and 15 above, and further in view of Salem (US 2023/0243991).
Regarding claims 6, 13, and 20, Theyab as modified by Jiang et al. and Shi et al. discloses the claimed limitations as discussed above except displaying, on a display device, the map of the subterranean surface and the one or more fault polygons imposed on the map of the subterranean surface.
Shi et al. discloses the map of the subterranean fault surface (Fig. 4) and the one or more fault polygons imposed on the map of the subterranean surface (polygon bounded by fault traces 1 and 2, Fig. 4; paragraph 0007).
Salem discloses displaying, on a display device, a visual map (paragraph 0048, lines 7-9).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to provide Theyab as modified with a display as disclosed by Salem for the purpose of displaying a subterranean surface map.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Theyab in view of Jiang et al. and Shi et al. as applied to claims 1 and 8 above, and further in view of Alam et al. (“Three Dimensional Fault Detection Using Dip and Azimuth”)
Regarding claims 7 and 14, Theyab as modified by Jiang et al. and Shi et al. discloses the claimed limitations as discussed above except the structural attributes comprise dip and azimuth attributes.
Alam et al. discloses structural attributes comprise dip and azimuth attributes (Abstract, lines 1-4) for structural interpretation.
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the invention was filed to provide Theyab as modified with dip and azimuth attributes as disclosed by Alam et al. for structural interpretation.
Response to Arguments
Applicant's arguments filed July 2, 2026 have been fully considered but they are not persuasive.
With respect to the rejections under 35 USC 103, Applicants argue “that Jiang's disclosure of computing fault probabilities does not teach or suggest identifying fault zones selected by the neural network from the faulted areas.”
Examiner’s position is that Jiang discloses a deep learning neural network model for predicting faults in subterranean formation (paragraph 0004). Examiner interprets the disclosure to read on “identifying fault zones selected by the neural network from the faulted areas.”
Applicants further argue “Shi's disclosure of fault polygons defined by fault traces does not teach or suggest generating fault polygons by creating boundaries around identified fault zones selected by the neural network.”
Examiner’s position, as discussed above, is that Shi teaches generating one or more fault polygons (paragraph 0007; Fig. 4) by creating boundaries (boundaries generated by fault traces, e.g. 114, 126) around the identified fault zones (fault areas 132, 134) selected by the neural network (fault zones are predicted by DNN, paragraph 0004) in the subterranean surface (paragraph 0004).
Applicant’s remaining arguments with respect to the rejections under 35 USC 103 have been considered but are traversed in view of the grounds of rejection discussed above.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Nghiem whose telephone number is (571) 272-2277. The examiner can normally be reached on M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MICHAEL P NGHIEM/Primary Examiner, Art Unit 2857 October 2, 2026
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