Prosecution Insights
Last updated: October 26, 2026
Application No. 18/362,402

MACHINE-LEARNING BASED GEOBODY PREDICTION WITH SPARSE INPUT

Non-Final OA §103
Filed
Jul 31, 2023
Examiner
BECK, LERON
Art Unit
2487
Tech Center
2400 — Computer Networks
Assignee
Landmark Graphics Corporation
OA Round
3 (Non-Final)
80%
Grant Probability
Favorable
3-4
OA Rounds
0m
Est. Remaining
91%
With Interview

Examiner Intelligence

Grants 80% — above average
80%
Career Allowance Rate
717 granted / 893 resolved
+22.3% vs TC avg
Moderate +11% lift
Without
With
+10.8%
Interview Lift
resolved cases with interview
Typical timeline
2y 7m
Avg Prosecution
38 currently pending
Career history
950
Total Applications
across all art units

Statute-Specific Performance

§101
8.8%
-31.2% vs TC avg
§103
52.3%
+12.3% vs TC avg
§102
12.5%
-27.5% vs TC avg
§112
12.3%
-27.7% vs TC avg
Black line = Tech Center average estimate • Based on career data from 893 resolved cases

Office Action

§103
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 . Continued Examination Under 37 CFR 1.114 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 8/27/2026 has been entered. Response to Arguments Applicant’s arguments have been considered but are not persuasive. Applicant argues that Salman's assigned labels are training targets used before prediction. Salman does not disclose providing those labels, or another guide input tile containing known presence-or-absence information, to the trained model during prediction. According to Salman’s claim 8, the model trains, during a training phase and based at least on the label and the training seismic volume, a machine learning model), Salman disloses in ([0071], a guide input tile is nothing more than patches that provide information to the ML; ([0073], wherein labels are assigned to each patch, and ([0048], wherein if the he assigned label indicates the locations where the known fault(s) intersects the patch, then it would be obvious that the unassigned labels represents no indication about presence or absence of the geobody). Therefore, rejection is maintained. 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. Claim(s) 1-2, 8, 11-12, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over 20190162868 A1-Salman et al (Hereinafter referred to “Salman”) Regarding claim 1, Salman disclose a method for detecting, by a learning machine, a geobody in a seismic volume (Fig. 2), the method comprising: Receiving, during prediction by the trained learning machine, a first seismic input tile representing first seismic data from the seismic volume ([0039], wherein the input receiver is configured to obtain the training seismic volume); Receiving, during the prediction by the trained learning machine, a first guide input tile ([0071], a guide input tile is nothing more than patches that provide information to the ML; According to Salman’s claim 8, the model trains, during a training phase and based at least on the label and the training seismic volume, a machine learning model) including first labels that indicate presence of the geobody in a respective region in the seismic volume or absence of the geobody in the respective region ([0073], wherein labels are assigned to each patch), and one or more unlabeled regions that make no indication about presence or absence of the geobody ([0048], wherein if the he assigned label indicates the locations where the known fault(s) intersects the patch, then it would be obvious that the unassigned labels repsresnts no indication about presencr or absence of the geobody) ; and determining, during the prediction by the trained learning machine and based on the first seismic input tile and the first guide input tile, a first prediction about geobody presence or absence in the seismic volume ([0036]; According to Salman’s claim 8, the model trains, during a training phase and based at least on the label and the training seismic volume, a machine learning model.). Regarding claim 2, Salman discloses the method of claim 1, the method further comprising: generating an output tile that graphically illustrates the first prediction (Fig 3). Regarding claim 8, Salman discloses the method of claim 1, wherein the first seismic input tile and the first guide input tile are represented in one or more computerized graphical formats ([0023]). Regarding claim 11, analyses are analogous to those presented for claim 1 and are applicable for claim 11. Regarding claim 12, analyses are analogous to those presented for claim 2 and are applicable for claim 12. Regarding claim 17, analyses are analogous to those presented for claim 1 and are applicable for claim 17. Regarding claim 18, analyses are analogous to those presented for claim 2 and are applicable for claim 18. Claim(s) 9 rejected under 35 U.S.C. 103 as being unpatentable over 20190162868 A1-Salman et al (Hereinafter referred to as “Salman”), in view of US 20230258838 A1-Roy et al (Hereinafter referred toas “Roy”). Regarding claim 9, Salman fails to disclose the method of claim 1 (See claim 1), Salman fails to disclose wherein the first prediction about the geobody in the seismic volume indicates that the geobody is absent from the seismic volume. However, in the same field of endeavor, Roy discloses wherein the first prediction about the geobody in the seismic volume indicates that the geobody is absent from the seismic volume ([0044], wherein predict presence and/or absence of particular materials (e.g., hydrocarbons) in a subsurface region. For example, seismic attributes may include amplitude attributes (including amplitude variation with angle/offset attributes), time/horizon attributes, frequency attributes, and/or other seismic attributes). Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the method disclosed by Salman to disclose wherein the first prediction about the geobody in the seismic volume indicates that the geobody is absent from the seismic volume as taught by Roy , to provide accurate results, by providing sufficient amount of high quality training data ([0027], Roy). Allowable Subject Matter Claims 3-7, 10, 13-16, 20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to LERON BECK whose telephone number is (571)270-1175. The examiner can normally be reached M-F 8 am-5pm. 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, David Czekaj can be reached at (571) 272-7327. 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. LERON . BECK Examiner Art Unit 2487 /LERON BECK/Primary Examiner, Art Unit 2487
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Prosecution Timeline

Jul 31, 2023
Application Filed
Mar 02, 2026
Non-Final Rejection mailed — §103
Mar 25, 2026
Response Filed
Jun 11, 2026
Final Rejection mailed — §103
Aug 11, 2026
Response after Non-Final Action
Aug 27, 2026
Request for Continued Examination
Aug 30, 2026
Response after Non-Final Action
Oct 05, 2026
Non-Final Rejection mailed — §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
80%
Grant Probability
91%
With Interview (+10.8%)
2y 7m (~0m remaining)
Median Time to Grant
High
PTA Risk
Based on 893 resolved cases by this examiner. Grant probability derived from career allowance rate.

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