Final Rejection
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 04/21/2026 has been entered. Claims 1-20 remain pending in the application.
Response to Arguments
Applicant’s amendments to the claims are sufficient to overcome the rejection under 35 U.S.C. 101 of claims 9-15. Accordingly, the rejection has been withdrawn.
Applicant's arguments filed 04/21/2026 have been fully considered but they are not persuasive.
Regarding applicants arguments to claim 1, applicant states “Wang's application of borehole data and geological data to a 3D coordinate system and slice- based processing does not teach or suggest any sampling, let alone "sampling values from at least one of (i) the obtained geological composition data or (ii) an image representing the geographical area that preserves first spatial data" (claim 1).”, examiner respectfully disagrees. Wang teaches the data related to prior stratigraphic information associated with the geographic site is representable as one or more (e.g., only one, only two, etc.) 2D images of stratigraphic pattern associated with the geographic site (See Paragraph 10 of Wang) and Wang further teaches applying the borehole data and the trusted geological data to a 3D coordinate system (See Paragraphs 14-15 of Wang). Thus, Wang properly teaches the newly amended claim limitation of “sampling values” as detailed below.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20230343029 A1) in view of Zhang (US 20210326721 A1).
Regarding claim 1, Wang teaches a method comprising: obtaining geological composition data from at least one drilled well within a geographical area (obtaining new borehole data of a plurality of boreholes at the geographic site and/or new trusted geological data associated with the geographic site). (Paragraphs 30, 102)
Wang also teaches generating preprocessed data by sampling values from at least one of (i) the obtained geological composition data (the processing in step 1602 includes applying the borehole data and the trusted geological data to a 3D coordinate system) or (ii) an image representing the geographical area that preserves first spatial data (the data related to prior stratigraphic information associated with the geographic site is representable as one or more (e.g., only one, only two, etc.) 2D images of stratigraphic pattern associated with the geographic site.). (Paragraphs 10, 17, 14-15, 19-20, 103-104, 122, 127)
Wang also teaches providing the preprocessed data to one or more machine learning models (machine-learning-based model), wherein the one or more machine learning models are trained to predict trend mappings of facies using second spatial data or location-based data (2D cross-sections of the slices are sequentially predicted) (As the spatial prediction proceeds, stratigraphic connectivity extracted from the 2D perpendicular training images are gradually added to the 3D point cloud). (Paragraphs 108, 120, 122, 137-138 Figs.16-17)
Wang does not explicitly teach controlling a drilling mechanism using the output of the one or more trained machine learning models.
Zhang teaches controlling a drilling mechanism using the output of the one or more trained machine learning models (generating an earth model based on output values from the candidate machine learning model, and adjusting a drilling operation according to the earth model). (Paragraphs 12-13, Figs.2A-2B)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Wang to incorporate controlling a drilling mechanism using the output of the one or more trained machine learning models in order to adjust a relative contribution of each input variable to the overall model and effectively use earth models that are updated during drilling, conventional and time-consuming processes to convert data between domains of time and depth can be avoided.
Regarding claim 2, Wang teaches wherein obtaining the geological composition data from the at least one drilled well within the geographical area comprises: obtaining data collected during or after drilling in the geographical area. (Paragraphs 15, 17, 102)
Regarding claim 3, Wang teaches wherein generating the preprocessed data comprises: generating a structured data container with two data dimensions, wherein the two dimensions match the dimensions of the image representing the geographical area. (Paragraphs 14-15, 104-108)
Wang also teaches sampling the values from at least one of (i) the obtained geological composition data or (ii) the image representing the geographical area. (Paragraphs 104-108, 10, 14-15)
Wang also teaches generating values for each of element of the generated structured data container using the sampled values from at least one of (i) the obtained geological composition data or (ii) the image representing the geographical area. (Paragraphs 111, 20, Claims 1, 15)
Regarding claim 4, Wang does not explicitly teach wherein generating the preprocessed data comprises: determining a quality of the image representing the geographical area; and adjusting, based on the determined quality, the image representing the geographical area.
Zhang teaches wherein generating the preprocessed data comprises: determining a quality of the image representing the geographical area; and adjusting, based on the determined quality, the image representing the geographical area. (Paragraph 86, Fig.6)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Wang to incorporate wherein generating the preprocessed data comprises: determining a quality of the image representing the geographical area; and adjusting, based on the determined quality, the image representing the geographical area in order to for the processing system to output the intended prediction.
Regarding claim 5, Wang does not explicitly teach wherein controlling the drilling mechanism using the output of the one or more trained machine learning models comprises: providing data indicating the output of the one or more trained machine learning models to one or more computers controlling the drilling mechanism.
Zhang teaches wherein controlling the drilling mechanism using the output of the one or more trained machine learning models comprises: providing data indicating the output of the one or more trained machine learning models to one or more computers controlling the drilling mechanism. (Paragraphs 89, 12-14, Fig.7)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Wang to incorporate wherein controlling the drilling mechanism using the output of the one or more trained machine learning models comprises: providing data indicating the output of the one or more trained machine learning models to one or more computers controlling the drilling mechanism in order to adjust a relative contribution of each input variable to the overall model and effectively use earth models that are updated during drilling, conventional and time-consuming processes to convert data between domains of time and depth can be avoided.
Regarding claim 6, Wang does not explicitly teach wherein controlling the drilling mechanism using the output of the one or more trained machine learning models comprises: adjusting a steering direction or operation of the drilling mechanism using the output of the one or more trained machine learning models.
Zhang teaches wherein controlling the drilling mechanism using the output of the one or more trained machine learning models comprises: adjusting a steering direction or operation of the drilling mechanism using the output of the one or more trained machine learning models. (Paragraphs 89, 12-14, 28, 42, Fig.7)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Wang to incorporate wherein controlling the drilling mechanism using the output of the one or more trained machine learning models comprises: adjusting a steering direction or operation of the drilling mechanism using the output of the one or more trained machine learning models in order to adjust a relative contribution of each input variable to the overall model and effectively use earth models that are updated during drilling, conventional and time-consuming processes to convert data between domains of time and depth can be avoided.
Regarding claim 7, Wang does not explicitly teach training the one or more machine learning models to predict trend mappings of facies using ground truth data generated by object modeling.
Zhang teaches training the one or more machine learning models to predict trend mappings of facies using ground truth data generated by object modeling. (Paragraph 86, Fig.6)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Wang to incorporate training the one or more machine learning models to predict trend mappings of facies using ground truth data generated by object modeling in order to for the processing system to output the intended prediction.
Regarding claim 8, Wang teaches wherein the second spatial data includes the first spatial data. (Paragraphs 16-17, 138, Claims 10-11)
Regarding claim 9, Wang teaches One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising: obtaining geological composition data from at least one drilled well within a geographical area (obtaining new borehole data of a plurality of boreholes at the geographic site and/or new trusted geological data associated with the geographic site). (Abstract, Paragraphs 30, 32, 102)
Wang also teaches generating preprocessed data by sampling values from at least one of (i) the obtained geological composition data (the processing in step 1602 includes applying the borehole data and the trusted geological data to a 3D coordinate system) or (ii) an image representing the geographical area that preserves first spatial data (the data related to prior stratigraphic information associated with the geographic site is representable as one or more (e.g., only one, only two, etc.) 2D images of stratigraphic pattern associated with the geographic site.). (Paragraphs 10, 17, 14-15, 19-20, 103-104, 122, 1272)
Wang also teaches providing the preprocessed data to one or more machine learning models (machine-learning-based model), wherein the one or more machine learning models are trained to predict trend mappings of facies using second spatial data or location-based data (2D cross-sections of the slices are sequentially predicted) (As the spatial prediction proceeds, stratigraphic connectivity extracted from the 2D perpendicular training images are gradually added to the 3D point cloud). (Paragraphs 108, 120, 122, 137-138 Figs.16-17)
Wang does not explicitly teach controlling a drilling mechanism using the output of the one or more trained machine learning models.
Zhang teaches controlling a drilling mechanism using the output of the one or more trained machine learning models (generating an earth model based on output values from the candidate machine learning model, and adjusting a drilling operation according to the earth model). (Paragraphs 12-13, Figs.2A-2B)
It would have been obvious to one having ordinary skill in the art before the effective filling date to have modified Wang to incorporate controlling a drilling mechanism using the output of the one or more trained machine learning models in order to adjust a relative contribution of each input variable to the overall model and effectively use earth models that are updated during drilling, conventional and time-consuming processes to convert data between domains of time and depth can be avoided.
Regarding claims 10 and 17, the claims disclose substantially the same limitations, as claim 2. All limitations as recited have been analyzed and rejected with respect to claims 10 and 17, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claims 10 and 17 are rejected for the same rational over the prior art cited in claim 2.
Regarding claims 11 and 18, the claims disclose substantially the same limitations, as claim 3. 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 3.
Regarding claims 12 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 12 and 19, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claims 12 and 19 are rejected for the same rational over the prior art cited in claim 4.
Regarding claims 13 and 20, the claims disclose substantially the same limitations, as claim 5. All limitations as recited have been analyzed and rejected with respect to claims 13 and 20, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claims 13 and 20 are rejected for the same rational over the prior art cited in claim 5.
Regarding claim 14, the claim discloses substantially the same limitations, as claim 6. All limitations as recited have been analyzed and rejected with respect to claim 14, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 14 is rejected for the same rational over the prior art cited in claim 6.
Regarding claim 15, the claim discloses substantially the same limitations, as claim 7. All limitations as recited have been analyzed and rejected with respect to claim 15, and do not introduce any additional narrowing of the scopes of the claims as analyzed. Therefore, claim 15 is rejected for the same rational over the prior art cited in claim 7.
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.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDALLAH ABULABAN whose telephone number is (571)272-4755. The examiner can normally be reached Monday - Friday 7:00am-3:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Isam Alsomiri can be reached at 571-272-6970. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDALLAH ABULABAN/Primary Examiner, Art Unit 3645
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