CTNF 18/633,950 CTNF 91535 DETAILED ACTION 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 Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claim(s) 20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim(s) are directed towards “One or more computer-readable storage media comprising processor-executable instructions to instruct a computing system to:…”. The claim(s) should be amended to read ‘ A non-transitory computer readable media having instructions stored thereon that, when executed by a processor, cause the processor to: … ’. Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more ( See 2019 Update: Eligibility Guidance ). Independent Claim(s) 1, 19, 20 recites receive petrophysics data acquired along a borehole in a subsurface region; generate test location recommendations along the borehole using the petrophysics data as input to a machine learning model; and output, based on the test location recommendations, selected locations for performing tests using a downhole tool disposed in the borehole [Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)] . In combination with Independent Claim(s) 1, Claim(s) 2-18 recite(s) the tests comprise reservoir tests. the machine learning model comprises a petro-reservoir machine learning model that receives the petrophysical data and outputs reservoir test location recommendations. the machine learning model comprises a trained machine learning that is trained using datasets from clastic subsurface regions. the machine learning model comprises a trained machine learning that is trained using datasets from carbonate subsurface regions. analyzing the petrophysics data to make a determination that the subsurface region is a clastic subsurface region or a carbonate subsurface region and, based on the determination, selecting the machine learning model from a collection of machine learning models that comprises a clastic subsurface region machine learning model and a carbonate subsurface region machine learning model. the tests comprise reservoir pressure tests. the test location recommendations comprise validity indicators with respect to measured depth along the borehole. the test location recommendations comprise probability of validity values with respect to measured depth along the borehole. the test location recommendations comprise mobility index values with respect to measured depth along the borehole. selecting the downhole tool based at least in part on the mobility index values and/or setting one or more operational parameters of the downhole tool based at least in part on the mobility index values. adjusting one or more of the selected locations in real-time while the downhole tool is disposed in the borehole responsive to information acquired by the downhole tool. the machine learning model comprises at least one tree structure. the machine learning model comprises a gradient boosted machine learning model. the gradient boosted machine learning model comprises an XGBoost machine learning model. training the machine learning model. tuning hyperparameters of the machine learning model. selecting a number of petrophysics data types from a group of more than 10 petrophysics data types, wherein the number of petrophysics data types is less than 10 [Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)] . This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)) (i.e. A method / A system / One or more computer-readable storage media comprising: one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: ); Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. generic data acquisition/output); or Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e. for performing tests using a downhole tool disposed in the borehole ). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure)). Claim Rejections - 35 USC § 102 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim(s) 1-3, 7, 10-13, 16, 19, 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by QUINTERO TUDARES ET AL. (US 20240319404 A1) (hereinafter “QUINTERO TUDARES”) . With respect to Claim(s) 1, 19, 20 , QUINTERO TUDARES teaches ‘Methods and systems for petrophysical modeling of a subterranean reservoir’ and the BRI of: A method / A system / One or more computer-readable storage media comprising: one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system ( See, e.g., ¶ 0043 ; See also, e.g., Fig(s). 6 ) to: receive petrophysics data acquired along a borehole in a subsurface region ( See, e.g., Fig(s). 3 ); generate test location recommendations along the borehole using the petrophysics data as input to a machine learning model ( See, e.g., ¶ 0030, 0043 ); and output, based on the test location recommendations, selected locations for performing tests using a downhole tool disposed in the borehole ( See, e.g., ¶ 0030, 0043 ). With respect to Claim(s) 2 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: the tests comprise reservoir tests ( See, e.g., ¶ 0036 ). With respect to Claim(s) 3 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: the machine learning model comprises a petro-reservoir machine learning model that receives the petrophysical data and outputs reservoir test location recommendations ( See, e.g., ¶ 0030, 0043 ). With respect to Claim(s) 7 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: the tests comprise reservoir pressure tests ( See, e.g., ¶ 0030, 0045, 0046, 0048, 0049 ). With respect to Claim(s) 10 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: the test location recommendations comprise mobility index values with respect to measured depth along the borehole ( See, e.g., ¶ 0030, 0046 ). With respect to Claim(s) 11 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: selecting the downhole tool based at least in part on the mobility index values and/ or setting one or more operational parameters of the downhole tool based at least in part on the mobility index values ( See, e.g., ¶ 0030, 0046 ). With respect to Claim(s) 12 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: adjusting one or more of the selected locations in real-time while the downhole tool is disposed in the borehole responsive to information acquired by the downhole tool ( See, e.g., ¶ 0031-0036 ; See also, e.g., Fig(s). 1 ). With respect to Claim(s) 13 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: the machine learning model comprises at least one tree structure ( See, e.g., ¶ 0043 ). With respect to Claim(s) 16 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: training the machine learning model ( See, e.g., ¶ 0043 ) . 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. 07-21-aia AIA Claim (s) 4-6, 8, 9, 17, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited reference(s) of the parent claim(s) in view of AKKURT ET AL. (US 20210110280 A1) (hereinafter “AKKURT”) . With respect to Claim(s) 4 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: the machine learning model comprises a trained machine learning that is trained using datasets from subsurface regions ( See, e.g., ¶ 0030, 0036, 0043 ). However, QUINTERO TUDARES is lacking the explicit language of: from clastic subsurface regions AKKURT teaches ‘A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups’ and the BRI of: a trained machine learning that is trained using datasets from clastic subsurface regions ( See, e.g., ¶ 0196 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify QUINTERO TUDARES to include a trained machine learning that is trained using datasets from clastic subsurface regions. One of ordinary skill in the art would have been motivated to modify QUINTERO TUDARES because it would be beneficial to integrate data to predict formation properties. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 5 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: the machine learning model comprises a trained machine learning that is trained using datasets from subsurface regions ( See, e.g., ¶ 0030, 0036, 0043 ). However, QUINTERO TUDARES is lacking the explicit language of: from carbonate subsurface regions AKKURT teaches ‘A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups’ and the BRI of: a trained machine learning that is trained using datasets from carbonate subsurface regions ( See, e.g., ¶ 0196 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify QUINTERO TUDARES to include a trained machine learning that is trained using datasets from carbonate subsurface regions. One of ordinary skill in the art would have been motivated to modify QUINTERO TUDARES because it would be beneficial to integrate data to predict formation properties. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 6 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: analyzing the petrophysics data to make a determination ( See, e.g., ¶ 0030, 0043 ). However, QUINTERO TUDARES is lacking the explicit language of: that the subsurface region is a clastic subsurface region or a carbonate subsurface region and, based on the determination, selecting the machine learning model from a collection of machine learning models that comprises a clastic subsurface region machine learning model and a carbonate subsurface region machine learning model. AKKURT teaches ‘A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups’ and the BRI of: that the subsurface region is a clastic subsurface region or a carbonate subsurface region and, based on the determination, selecting the machine learning model from a collection of machine learning models that comprises a clastic subsurface region machine learning model and a carbonate subsurface region machine learning model ( See, e.g., ¶ 0196 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify QUINTERO TUDARES to include that the subsurface region is a clastic subsurface region or a carbonate subsurface region and, based on the determination, selecting the machine learning model from a collection of machine learning models that comprises a clastic subsurface region machine learning model and a carbonate subsurface region machine learning model. One of ordinary skill in the art would have been motivated to modify QUINTERO TUDARES because it would be beneficial to integrate data to predict formation properties. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 8 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: the test location recommendations. However, QUINTERO TUDARES is lacking the explicit language of: validity indicators with respect to measured depth along the borehole. AKKURT teaches ‘A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups’ and the BRI of: validity indicators with respect to measured depth along the borehole ( See, e.g., ¶ 0103, 0105, 0109, 0168, 0181, 0192, 0238-0270, 0357 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify QUINTERO TUDARES to include validity indicators with respect to measured depth along the borehole. One of ordinary skill in the art would have been motivated to modify QUINTERO TUDARES because it would be beneficial to integrate data to predict formation properties. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 9 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: the test location recommendations. However, QUINTERO TUDARES is lacking the explicit language of: validity indicators with respect to measured depth along the borehole. AKKURT teaches ‘A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups’ and the BRI of: probability of validity values with respect to measured depth along the borehole ( See, e.g., ¶ 0103, 0105, 0109, 0168, 0181, 0192, 0238-0270, 0357 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify QUINTERO TUDARES to include probability of validity values with respect to measured depth along the borehole. One of ordinary skill in the art would have been motivated to modify QUINTERO TUDARES because it would be beneficial to integrate data to predict formation properties. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 17 , QUINTERO TUDARES teaches the BRI of the parent claim(s). However, QUINTERO TUDARES is lacking the explicit language of: tuning hyperparameters of the machine learning model. AKKURT teaches ‘A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups’ and the BRI of: tuning hyperparameters of the machine learning model ( See, e.g., ¶ 0054, 0219, 0222 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify QUINTERO TUDARES to include tuning hyperparameters of the machine learning model. One of ordinary skill in the art would have been motivated to modify QUINTERO TUDARES because it would be beneficial to integrate data to predict formation properties. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 18 , QUINTERO TUDARES teaches the BRI of the parent claim(s). However, QUINTERO TUDARES is lacking the explicit language of: selecting a number of petrophysics data types from a group of more than 10 petrophysics data types, wherein the number of petrophysics data types is less than 10. AKKURT teaches ‘A method includes receiving well log data for a plurality of wells. A flag is generated based at least partially on the well log data. The wells are sorted into groups based at least partially on the well log data, the flag, or both. A model is built for each of the wells based at least partially on the well log data, the flag, and the groups’ and the BRI of: selecting a number of petrophysics data types from a group of more than 10 petrophysics data types, wherein the number of petrophysics data types is less than 10 ( See, e.g., ¶ 0054, 0219, 0222 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify QUINTERO TUDARES to include selecting a number of petrophysics data types from a group of more than 10 petrophysics data types, wherein the number of petrophysics data types is less than 10. One of ordinary skill in the art would have been motivated to modify QUINTERO TUDARES because it would be beneficial to integrate data to predict formation properties. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results . 07-21-aia AIA Claim (s) 14, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited reference(s) of the parent claim(s) in view of RAWLINSON ET AL. (US 20210180439 A1) (hereinafter “RAWLINSON”) . With respect to Claim(s) 14 , QUINTERO TUDARES teaches the BRI of the parent claim(s). QUINTERO TUDARES further teaches the BRI of: the machine learning model. However, QUINTERO TUDARES is lacking the explicit language of: a gradient boosted machine learning model. RAWLINSON teaches ‘A method for dynamically constructing a well includes receiving inputs that specify characteristics of a proposed well. An indicator is generated for construction of the proposed well using the inputs and a machine learning model trained using data from actual wells, and the indicator is transmitted’ and the BRI of: a gradient boosted machine learning model ( See, e.g., ¶ 0206 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify QUINTERO TUDARES to include a gradient boosted machine learning model. One of ordinary skill in the art would have been motivated to modify QUINTERO TUDARES because it would be beneficial to build a dynamic well construction model. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. With respect to Claim(s) 15 , QUINTERO TUDARES, RAWLINSON teaches the BRI of the parent claim(s). RAWLINSON further teaches the BRI of: the gradient boosted machine learning model comprises an XGBoost machine learning model ( See, e.g., ¶ 0206 ). It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify QUINTERO TUDARES to include a gradient boosted machine learning model. One of ordinary skill in the art would have been motivated to modify QUINTERO TUDARES because it would be beneficial to build a dynamic well construction model. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYMOND NIMOX whose telephone number is (469)295-9226. The examiner can normally be reached Mon-Thu 10am-8pm CT. 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, 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. 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. RAYMOND NIMOX Primary Examiner Art Unit 2857 /RAYMOND L NIMOX/Primary Examiner, Art Unit Application/Control Number: 18/633,950 Page 2 Art Unit: 2857 Application/Control Number: 18/633,950 Page 3 Art Unit: 2857 Application/Control Number: 18/633,950 Page 4 Art Unit: 2857 Application/Control Number: 18/633,950 Page 5 Art Unit: 2857 Application/Control Number: 18/633,950 Page 6 Art Unit: 2857 Application/Control Number: 18/633,950 Page 7 Art Unit: 2857 Application/Control Number: 18/633,950 Page 8 Art Unit: 2857 Application/Control Number: 18/633,950 Page 9 Art Unit: 2857 Application/Control Number: 18/633,950 Page 10 Art Unit: 2857 Application/Control Number: 18/633,950 Page 11 Art Unit: 2857 Application/Control Number: 18/633,950 Page 12 Art Unit: 2857 Application/Control Number: 18/633,950 Page 13 Art Unit: 2857 Application/Control Number: 18/633,950 Page 14 Art Unit: 2857 Application/Control Number: 18/633,950 Page 15 Art Unit: 2857 Application/Control Number: 18/633,950 Page 16 Art Unit: 2857 Application/Control Number: 18/633,950 Page 17 Art Unit: 2857 Application/Control Number: 18/633,950 Page 18 Art Unit: 2857
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