CTNF 18/591,065 CTNF 82020 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. Claim Objections 07-29-01 AIA Claim s 1, 6, and 11 objected to because of the following informalities: The indented clause "b. bootstrapping..." or "b. bootstrap..." is unclear and appears to misstate the intended limitation. The term "synthetic angle gather" is apparently used both for the input and output of the bootstrapping process. Furthermore, the language suggests that a *single* individual angle selection (random selection of angles) is applied to *each* synthetic angle gather (input) resulting in a single output sampling. However, based on Examiner's understanding of the specification (paragraph 44, for example) and of the bootstrapping method in general, Examiner believes there are supposed to be *multiple* random samplings for each input synthetic angle gather . Appropriate correction is required. In view of the foregoing, Examiner has interpreted and suggested amending the claims as shown below. The suggested deletion is to avoid redundancy. The suggested amendment to step c is for consistency. Examiner has interpreted and suggests amending the claims as follows, with additions underlined, deletions double-bracketed or struck through, and all changes in boldface: 1. A computer-implemented method for seismic inversion with uncertainty quantification, comprising: a. receiving well logs and a recorded seismic dataset; b. bootstrapping a plurality of synthetic angle gathers generated by forward seismic modeling with the well logs, each of the plurality of bootstrapped synthetic angle gathers having an individual angle configuration of a random selection of angles to generate bootstrapped angle gathers ; c. training neural networks using the well logs and the bootstrapped synthetic angle gathers as training pairs to build an ensemble of neural networks; d. preparing seismic angle gathers from the recorded seismic dataset using each of the individual angle configurations to create an ensemble of seismic angle gathers; e. presenting each of the ensemble of seismic angle gathers to a member of the ensemble of neural networks that was trained with a matching individual angle configuration to generate an ensemble of inversion results; and f. quantifying uncertainty in the ensemble of inversion results. 6. A computer system, comprising: one or more processors; memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions that when executed by the one or more processors cause the system to: a. receive well logs and a recorded seismic dataset; b. bootstrap a plurality of synthetic angle gathers generated by forward seismic modeling with the well logs, each of the plurality of bootstrapped synthetic angle gathers having an individual angle configuration of a random selection of angles to generate bootstrapped angle gathers ; c. train neural networks using the well logs and the bootstrapped synthetic angle gathers as training pairs to build an ensemble of neural networks; d. prepare seismic angle gathers from the recorded seismic dataset using each of the individual angle configurations to create an ensemble of seismic angle gathers; e. present each of the ensemble of seismic angle gathers to a member of the ensemble of neural networks that was trained with a matching individual angle configuration to generate an ensemble of inversion results; and f. quantify uncertainty in the ensemble of inversion results. 11. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device with one or more processors and memory, cause the device to a. receive well logs and a recorded seismic dataset; b. bootstrap a plurality of synthetic angle gathers generated by forward seismic modeling with the well logs, each of the plurality of bootstrapped synthetic angle gathers having an individual angle configuration of a random selection of angles to generate bootstrapped angle gathers ; c. train neural networks using the well logs and the bootstrapped synthetic angle gathers as training pairs to build an ensemble of neural networks; d. prepare seismic angle gathers from the recorded seismic dataset using each of the individual angle configurations to create an ensemble of seismic angle gathers; e. present each of the ensemble of seismic angle gathers to a member of the ensemble of neural networks that was trained with a matching individual angle configuration to generate an ensemble of inversion results; and f. quantify uncertainty in the ensemble of inversion results. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 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. Note that, in the following rejections, the highlighting indicates differences from the exact claim language, or items involved in an obviousness argument. 07-21-aia AIA Claim (s) 1-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Roy et al. (2021/0311223) in view of Lim et al. (NPL; “Sensitivity and uncertainty of PP- and PS-Joint Zoeppritz AVO inversion: Eagle Ford case study”; copy submitted by Applicant 2/29/24) further in view of Hong et al. (WO 2021/130512 A1) further in view of Daly (2023/0358917) . Regarding claim 1, Roy et al. disclose a computer-implemented method (see paragraph 48) for seismic inversion with uncertainty quantification (method of seismic inversion; see paragraph 30) , comprising: a. receiving well logs (well logs; see paragraph 30) and a recorded seismic dataset (data included in the well logs; see paragraph 30; and/or the seismic dataset; see paragraph 35) ; ... ; c. training neural networks using the well logs (see paragraph 33) ... ; d. preparing seismic angle gathers (seismic angle gathers; see paragraph 32) from the recorded seismic dataset ... ; e. presenting each of the ... seismic angle gathers to a neural network (see paragraph 33) ... ; and ... . Roy et al. do not disclose the highlighted limitations: a. receiving well logs and a recorded seismic dataset; b. bootstrapping a plurality of synthetic angle gathers generated by forward seismic modeling with the well logs, each of the plurality of synthetic angle gathers having an individual angle configuration of a random selection of angles to generate bootstrapped angle gathers; c. training neural networks using the well logs and the bootstrapped angle gathers as training pairs to build an ensemble of neural networks; d. preparing seismic angle gathers from the recorded seismic dataset using each of the individual angle configurations to create an ensemble of seismic angle gathers; e. presenting each of the ensemble of seismic angle gathers to a member of the ensemble of neural networks that was trained with a matching individual angle configuration to generate an ensemble of inversion results; and f. quantifying uncertainty in the ensemble of inversion results. Lim et al. disclose a. ... a recorded seismic dataset (AVO reflections; see page 1778, right column, lines 2-11) ; b. bootstrapping (see page 1778, left column, last paragraph, bridging onto right column) a plurality of synthetic angle gathers (input AVO data; see page 1777, right column, line 21-24) generated by forward seismic modeling with provided data (see page 1777, left column, all) , each of the plurality of synthetic angle gathers having an individual angle configuration (realized by selecting angle pairs; see page 1778, right column, lines 12-20) of a random selection of angles to generate bootstrapped angle gathers (see page 1778, right column, lines 16-20) ; ... ; d. preparing ... angle gathers (reflectivities in certain AVO range; see page 1778, right column, lines 20-22) from the recorded seismic dataset using each of the individual angle configurations to create an ensemble of seismic angle gathers (300 different bootstrapping realizations; see page 1778, right column, lines 20-22) ; e. ... to generate an ensemble of inversion results (AVO inversion results from repeated bootstrapping realizations, error distributions for target parameters; see page 1778, right column, lines 5-8, 20-27) ; and f. quantifying uncertainty in the ensemble of inversion results (see page 1778, right column, 2 nd paragraph) . It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of Roy et et al. to incorporate b. bootstrapping a plurality of synthetic angle gathers generated by forward seismic modeling with the well logs, each of the plurality of synthetic angle gathers having an individual angle configuration of a random selection of angles to generate bootstrapped angle gathers; ...; d. preparing seismic angle gathers from the recorded seismic dataset using each of the individual angle configurations to create an ensemble of seismic angle gathers; e. ... to generate an ensemble of inversion results; and f. quantifying uncertainty in the ensemble of inversion results, similarly to the invention of Lim et al., in order to reduce uncertainty in inversion results, as suggested by Lim et al. (see page 1778, Conclusions) . Hong discloses c. training neural networks using ... well logs (see page 12, lines 20-22) and ... bootstrapped data as training pairs to build an ensemble of neural networks (see page 9, line 25, through page 10, line 5; page 12, lines 10-14, and page 14, line 20, through page 15, line 14) ; and presenting data to a member of the ensemble of neural networks (base learners; see page 15, lines 8-14) . It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to further modify the combination to include training neural networks using the well logs and the bootstrapped angle gathers as training pairs to build an ensemble of neural networks and e. presenting each of the ensemble of seismic angle gathers to a member of the ensemble of neural networks ... to generate an ensemble of inversion results, in order to achieve a better-trained model, as suggested by Hong (see page 9, line 25, through page 10, line 5) . Daly discloses matching a randomization scheme applied to data for training of a model to one used when applying the model to data of interest (see paragraph 46) . It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to further modify the combination such that each of the ensemble of seismic angle gathers were presented to a member of the ensemble of neural networks that was trained with a matching individual angle configuration, as suggested by the invention of Daly, because such a modification would have combined prior art elements according to known methods to yield predictable results. KSR Int'l Co. v. Teleflex Inc., 550 U.S. at 416, 82 USPQ2d at 1395. Regarding claim 2, this combination of references further teaches the method of claim 1 wherein the well logs include at least one of P-wave velocity (Vp), S-wave velocity (Vs) (see Roy et al., paragraph 30) , density (ρ), and acoustic impedance (AI). Regarding claim 3, this combination of references further teaches the method of claim 1 wherein the uncertainty is quantified by finding an average inversion result and a standard deviation of the ensemble of inversion results (see Lim et al., page 1778, 2 nd paragraph) . Regarding claim 4, this combination of references further teaches the method of claim 1 wherein the inversion results are at least one of P-wave velocity (Vp), S-wave velocity (Vs) (see Lim et al., page 1776, Summary) , density (ρ), acoustic impedance (AI), V p /V s ratio, Young's Modulus (E), and Poisson's ratio (ν). Regarding claim 5, this combination of references further teaches the method of claim 1 wherein the inversion results are estimated wavelets (see Roy et al., paragraph 29) . Regarding claim 6, see the foregoing rejection of claim 1, for all limitations except the following. Roy et al. further disclose a computer system, comprising: one or more processors (see paragraph 48) ; memory (see paragraph 48) ; and one or more programs (see paragraph 48) , wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors ( supra ) , the one or more programs including instructions that when executed by the one or more processors cause the system ( supra ) to: ... (limitations similar to those of claim 1) . Regarding claims 7-10, see the foregoing rejections of claims 2-5, respectively. Regarding claim 11, see the foregoing rejection of claim 1, for all limitations except the following. Roy et al. further disclose a non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device with one or more processors and memory, cause the device (see paragraph 48) to ... (limitations similar to those of claim 1) . Regarding claims 12-15, see the foregoing rejections of claims 2-5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEOFFREY T EVANS whose telephone number is (571)272-2369. The examiner can normally be reached M-F, 9 AM - 5:30 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, Walter Lindsay can be reached at (571) 272-1674. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /WALTER L LINDSAY JR/Supervisory Patent Examiner, Art Unit 2852 /GEOFFREY T EVANS/Examiner, Art Unit 2852 Application/Control Number: 18/591,065 Page 2 Art Unit: 2852 Application/Control Number: 18/591,065 Page 3 Art Unit: 2852 Application/Control Number: 18/591,065 Page 4 Art Unit: 2852 Application/Control Number: 18/591,065 Page 5 Art Unit: 2852 Application/Control Number: 18/591,065 Page 6 Art Unit: 2852 Application/Control Number: 18/591,065 Page 7 Art Unit: 2852 Application/Control Number: 18/591,065 Page 8 Art Unit: 2852 Application/Control Number: 18/591,065 Page 9 Art Unit: 2852 Application/Control Number: 18/591,065 Page 10 Art Unit: 2852
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