CTNF 18/162,592 CTNF 79281 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Continued Examination Under 37 CFR 1.114 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/06/25 has been entered. Response to Arguments Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot in view of the new grounds of rejection necessitated by the applicant’s amendments to the claims. Although the same art was applied, a new interpretation (with new citations) and new reasoning was given. Information Disclosure Statement The IDS of 03/24/26 has been considered. Drawings As discussed previously, the drawings filed on 01/31/23 are accepted. Examiner’s Note - 35 USC § 101 For previously discussed reasons, claims 1-20 qualify as eligible subject matter under 35 U.S.C. 101. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hong et al (WO2021150929A1) in view of Katterbauer (WO2021252350A1) . With respect to claim 1, Hong et al discloses: A method (abstract) estimating physical parameters from a training dataset comprising remote sensing data (paragraph 0003 states, “A geoscientist may interact with a computerized system that can render at least a portion of an earth model … the horizon can be assigned a particular physical character …”; paragraph 0042 discloses remote technology; paragraph 0029 states, “a bore in material such as rock …”) preprocessing the estimated physical parameters to determine a signal-to-noise ratio, quantify an uncertainty, and remove outliers (paragraph 00140 states, “For example, a downhole tool can have a signal-to-noise ratio that helps to reduce uncertainty as to bed boundary detection …”; paragraph 0040 states, “A workflow may include quantitative interpretation, which may include pre- and poststack seismic data conditioning, inversion.”) training a first neural network to convert the estimated physical parameters to rock characteristics (Paragraph 0040 states, “As an example, a workflow may aim to output rock properties based at least in part on processing of seismic data.” Paragraph 00120 states, “As to types of machine learning models, consider one or more of … a neural network (NN) model …”) training a second neural network to convert the rock characteristics to the estimated physical parameters (The closest support for this limitation appears to be paragraphs 0048-0049 of the applicant’s original specification, which states, “Each geophysical data set of training data is first converted to the corresponding estimated physical parameters by an inversion procedure … the first CNN (228) is trained to map from the former to the latter … a second CNN (229) is trained on the same data in the opposite direction …” This concept of inversion is taught throughout the disclosure of Hong et al. Inversion is taught in paragraphs 0031, 0038, 0040, 0063, 0071, 0079, 0081, 0084-0085, 0093, 0099, 0102, 0104, and 0106 (non-exhaustive list). As discussed above, paragraph 0040 states, “As an example, a workflow may aim to output rock properties based at least in part on processing of seismic data.” This is an example of a first neural network converting estimated physical parameters to rock characteristics. Paragraph 0099 states, “a model automatically output that can be utilized for inversion … for outputting model parameters (e.g., earth model parameters).” This is an example of a second neural network performing inversion operations.) converting the estimated physical parameters into the rock characteristics with the first neural network (paragraph 0040) converting the rock characteristics from the first neural network into reconciled physical parameters with the second neural network (suggested by inversion operations disclosed by Hong et al, as discussed above) obtaining new remote sensing data (paragraph 0045 states, “Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuity.” New remote sensing data is suggested by disclosure of real-time operations (paragraph 00108).) estimating new estimated physical parameters from the new remote sensing data (suggested by applying the principles of Hong et al discussed above to new data, which is suggested by disclosure of real-time operations (paragraph 00108).) performing geosteering of a well based on a subsurface geology (paragraph 00137 states, “As an example, data may, alone or as inverted facilitate geosteering.”) With respect to claim 1, Hong et al differs from the claimed invention in that it does not explicitly disclose: training a third neural network to convert the estimated physical parameters to updated reconciled physical parameters, using the reconciled physical parameters from the second neural network as targets converting new estimated physical parameters into new reconciled physical parameters with the third neural network and performing geosteering of a well based on a subsurface geology interpreted from the new reconciled physical parameters With respect to claim 1, Katterbauer et al discloses: training a third neural network to convert the estimated physical parameters to updated reconciled physical parameters, using the reconciled physical parameters from the second neural network as targets (paragraphs 0017-0018 state, “The well logs of the target well can be reconciled with each other. In some implementations, the well logs of the target well are estimated by the computing system using a second AI network with well data of the target well … reconciled well logs of each of the existing wells are obtained by the computing system using the second AI network with well data of the existing well.” The examiner broadly interprets the initial conversion/inversion operations taught by Hong et al to represent the claimed first and second neural network and the “target well” for Katterbauer’s base AI network, while the “second AI network” in Katterbauer et al serves as the claimed third neural network.) converting new estimated physical parameters into new reconciled physical parameters with the third neural network (obvious implication of combination) and performing geosteering of a well based on a subsurface geology interpreted from the new reconciled physical parameters (obvious implication of combination) With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Katterbauer et al into the invention of Hong et al. The motivation for the skilled artisan in doing so is to gain the benefit of ensuring accurate data processing. Independent claims 10 and 18 represent variations of claim 1. They are rejected for similar reasons as those given with respect to claim 1 above. With respect to claims 2 and 11, Hong et al, as modified, discloses: wherein the remote sensing data comprises at least one selected from the group consisting of: logging while drilling (LWD) data and deep remote sensing data (Hong paragraph 00209 discloses LWD module 1454) With respect to claims 3 and 12, Hong et al, as modified, discloses: wherein the deep remote sensing data is at least one selected from the group consisting of: a deep seismic data set and a deep electromagnetic (EM) data set (Hong paragraph 0040 discloses electromagnetic data; see also paragraphs 0051, 0076-0077, and 00130) With respect to claims 4 and 13, Hong et al, as modified, discloses: wherein the rock characteristics comprise a saturation (Hong paragraphs 0065, 00247, and 00258) With respect to claims 5 and 14, Hong et al, as modified, discloses: wherein the LWD data comprises at least one selected from the group consisting of: neutron porosity data, borehole caliper data, nuclear magnetic resonance data, gamma ray data, weight on bit data, rate of penetration data, inclination data, measured depth data, true vertical depth data, bearing data, temperature data, and pressure data (Hong paragraph 00242) With respect to claims 6 and 15, Hong et al, as modified, discloses: wherein the first neural network, the second neural network, and the third neural network are convolutional neural networks (obvious in view of CNN teachings in Hong paragraph 00120) With respect to claims 7, 16, and 19, Hong et al, as modified, discloses: further comprising incorporating expert information in the first neural network, in the second neural network, and in the third neural network (Hong paragraph 00185; obvious in view of broad machine learning teachings of Hong et al) With respect to claims 8, 17, and 20, Hong et al, as modified, discloses: wherein the expert information comprises an uncertainty value (obvious in view of Hong’s recognition of uncertainty, such as in paragraph 00140, as well as the role uncertainty plays in the broad frameworks disclosed by Hong) With respect to claim 9, Hong et al, as modified, discloses: wherein the training dataset comes from a nearby well (Hong paragraph 0026 discloses well/logging data) Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Bush (US Pat 6236942) discloses a system and method for delineating spatially dependent objects, such as hydrocarbon accumulations from seismic data. Van Camp (US PgPub 20210079752) discloses machine learning control for automatic kick detection and blowout prevention. AlSinan et al (US PgPub 20230097859) discloses a method and system for determining coarsened grid models using machine-learning models and fracture models. Alnaimi et al (US PgPub 20230220957) discloses digitalization and automation of corrosion coupon analysis with a predictive element. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM. 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, ARLEEN M VAZQUEZ can be reached on (571)272-2619. 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. /LEONARD S LIANG/Examiner, Art Unit 2857 05/30/26 Application/Control Number: 18/162,592 Page 2 Art Unit: 2857 Application/Control Number: 18/162,592 Page 3 Art Unit: 2857 Application/Control Number: 18/162,592 Page 4 Art Unit: 2857 Application/Control Number: 18/162,592 Page 5 Art Unit: 2857 Application/Control Number: 18/162,592 Page 6 Art Unit: 2857 Application/Control Number: 18/162,592 Page 7 Art Unit: 2857 Application/Control Number: 18/162,592 Page 8 Art Unit: 2857 Application/Control Number: 18/162,592 Page 9 Art Unit: 2857 Application/Control Number: 18/162,592 Page 10 Art Unit: 2857 Application/Control Number: 18/162,592 Page 11 Art Unit: 2857
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