CTNF 18/414,685 CTNF 87724 /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. This action is responsive to communication filed on 01/17/2024. Claims 1-20 are pending. Drawings 06-22 AIA The drawings are objected to because Figures 2A, 2B and 8 fails to show reference number for corresponding elements shown in the drawing; there are labels present in Figures 2A, 2B and 8 with no corresponding reference number. All elements should contain reference numbers. Figures 2A, and 2B contain shades and are partially illegible. Graphs shown in Figure 2B corresponding to Radius (HGL) and T 2 (NMR) cannot be distinguished based on the graph legend illustrated. Graphs shown in Figure 5C corresponding to SNR=5, SNR=10, SNR=15 and SNR=20 cannot be distinguished based on the graph legend illustrated. Figure 5C fails to reference number for corresponding elements. Graphs shown in Figure 7A corresponding to Combined, Core Sample 1 and Core sample 2 cannot be distinguished based on the graph legend illustrated. Figure 7A fails to reference number for corresponding elements. Graphs shown in Figure 7B corresponding to SNR=5, SNR=10, SNR=15 and SNR=20 cannot be distinguished based on the graph legend illustrated. Figure 7B fails to reference number for corresponding elements. Figure 7C corresponding to SNR=5, SNR=10, SNR=15 and SNR=20 cannot be distinguished based on the graph legend illustrated. Figure 7C fails to reference number for corresponding elements. Graphs shown in Figures 12A, 12B, 14A, 14B, 16A, 16B contains a legend, corresponding graphs cannot be distinguished based on the graph legend illustrated . Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Claim Objections 07-29-01 AIA Claim 1 objected to because of the following informalities: the claim recites “determining a drilling path for a borehole or perforation interval based on the predicted pore throat size distribution, the one or more predicted pore throat size parameters, the predicted NMR T 2 distribution, and/or the one or more predicted NMR T 2 parameters” in last four lines of the claim. The examiner suggest the limitation to be amended as -- determining a drilling path for a borehole or perforation interval based on the predicted pore throat size distribution, the one or more predicted pore throat size parameters, the predicted NMR T 2 distribution, or and/or the one or more predicted NMR T 2 parameters --. Appropriate correction is required. 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. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. A subject matter eligibility analysis is set forth below. See MPEP 2106. Under Step 1 of the analysis, claim 1 , belongs to a statutory category namely a method. Under Step 2A, prong 1 : This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. The claim(s) 1 recite(s) concepts related to mathematical algorithms/concepts, and mental processes and concepts performed in the human mind e.g. observation, evaluation, judgment, opinion for “ transforming, … one or more input NMR measurements to a predicted pore throat size distribution or one or more predicted pore throat size parameters; transforming…, the predicted pore throat size distribution or the one or more predicted pore throat size parameters to a predicted NMR T 2 distribution or one or more predicted NMR T 2 parameters; modeling the predicted NMR T 2 distribution or the one or more predicted NMR T 2 parameters to one or more simulated NMR measurements; and determining a drilling path for a borehole or perforation interval based on the predicted pore throat size distribution, the one or more predicted pore throat size parameters, the predicted NMR T 2 distribution, and/or the one or more predicted NMR T 2 parameters” . The concepts discussed above can be considered to describe mental processes, namely concepts performed in the human mind or with pen and paper , and/or mathematical concepts, namely a series of calculations leading to one or more numerical results or answers (emphasis added). Although, the claim does not spell out any particular equation or formula being used, the lack of specific equations for individual steps merely points out that the claim would monopolize all possible calculations in performing the steps. These steps recited by the claims, therefore amount to a series of mental or mathematical steps, making these limitations amount to an abstract idea. Step 2A, prong 2 of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception(s) into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. This judicial exception is not integrated into a practical application because the abstract idea is not performed by using any particular device and because the “computer”, “multi-level machine learning model” recited in claims 1, amounts to the recitation of a general purpose computer used to apply the abstract idea; and because the recitation of “nuclear magnetic resonance (NMR) measurements of a sample” , is mere gathering recited at high level of generality and the results of the algorithm are merely output/stored as part of insignificant post-solution activity and are not used in any particular matter as to integrate the abstract idea in a practical application. Under Step 2B , the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as described above with respect to Step 2A Prong 2, merely amount to a general purpose computer , used to apply the abstract idea and mere data gathering/output recited at a high level of generality and insignificant extra-solution activity that when further analyzed under Step 2B is found to be well-understood, routine and conventional activities as evidenced by MPEP 2106.05(d)(II); and because the data of performing the algorithm must necessarily be “obtained” and the use of a general purpose computer to implement the abstract idea for performing the algorithm does not amount to significantly more than the recitation of the abstract idea itself. Therefore, claim 1 is rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more. Dependent claims 2-10 merely expand on the abstract idea by appending additional steps to the mathematical algorithm on their respective independent claim 1. Dependent claims 2-20 merely expands on the abstract idea by reciting additional steps related to mathematical algorithms/concepts, and mental processes and concepts performed in the human mind e.g. observation, evaluation, judgment, opinion and mere characterization of the data acquired and applied for performing the abstract idea and data characterization. Dependent claims 2-20 , do not set forth further additional elements that integrate the rejected abstract idea into a practical application or amount to significantly more than the abstract idea itself. Therefore, these claims are found to be ineligible for the reasons described with respect to independent claim 1 from which they depend. With respect to dependent claim 2 , the claim further recites aspects of the abstract idea and introduces the additional element of “ the one or more input NMR measurements are recorded in a time domain as echo trains ” however this merely amounts mere data gathering recited at a high level of generality and generally linking the abstract idea to a field of use and to insignificant extra-solution activity. With respect to dependent claim 3 , the claim further recites aspects of the abstract idea and introduces the additional element of “ the one or more simulated NMR measurements are NMR response in a time domain as echo trains ” however this merely amounts mere data gathering recited at a high level of generality and generally linking the abstract idea to a field of use and to insignificant extra-solution activity. With respect to dependent claim 4 , the claim further recites aspects of the abstract idea and introduces the additional element of “ the one or more simulated NMR measurements are compared to the one or more input NMR measurements ” however this merely amounts mere data gathering recited at a high level of generality and generally linking the abstract idea to a field of use and to insignificant extra-solution activity. With respect to dependent claim 6 , the claim further recites aspects of the abstract idea and introduces the additional element of “ a measured pore throat size distribution ” however this merely amounts mere data gathering recited at a high level of generality and generally linking the abstract idea to a field of use and to insignificant extra-solution activity. With respect to dependent claim 9 , the claim further recites aspects of the abstract idea of “ training the multi-level machine learning model, and wherein training the multi-level machine learning model comprises augmenting the one or more input NMR measurements with one or more noise contamination levels ” since the steps set forth mathematical calculations; and the claim do not set forth further additional elements that integrate the rejected abstract idea into a practical application or amount to significantly more than the abstract idea itself. Therefore, these claims are found to be ineligible for the reasons described with respect to independent claim 1 from which they depend. With respect to dependent claim 12 , the claim further recites aspects of the abstract idea of “ training the multi-level machine learning model, and wherein training the multi-level machine learning model comprises randomly selecting and linearly combining one or more input variables with random ratios and linearly combining one or more target variables with the random ratios ” since the steps set forth mathematical calculations in which machine learning amounts to the recitation of the abstract idea on a generic computer and also merely indicate a field of use or technological environment in which the judicial exception is performed, this type of limitation merely confines the abstract idea to a particular technological environment and thus fails to add inventive concept to the claims see MPEP 2106.5(h) and the claim do not set forth further additional elements that integrate the rejected abstract idea into a practical application or amount to significantly more than the abstract idea itself. Therefore, these claims are found to be ineligible for the reasons described with respect to independent claim 1 from which they depend. Therefore, for similar reasons as discussed above, the additional elements disclosed by dependent c laims 2-4, 6, 9 and 12 fail to integrate the recited abstract idea into a practical application or amount to significantly more than the abstract idea itself. Therefore claims 1-20 are rejected under 35 USC 101 as being directed to non-statutory subject matter. Reasons for Overcoming the Prior Art The closest prior art of record cited in for PTOL-892 is considered pertinent to applicant’s disclosure. a. Chen et al. WO 2016137472 A1 disclose systems, methods, and software for predicting a pore throat size distribution; obtaining a nuclear magnetic resonance (NMR) relaxation-time distribution data set; training a radial basis function (RBF) model based on the NMR relaxation-time distribution data set and a measured pore throat size distribution data set. Chen further disclose obtaining a subsequent NMR relaxation-time distribution data set, employing the trained RBF model to predict a pore throat size distribution data set based at least in part on the subsequent NMR relaxation-time distribution data set, storing or displaying a predicted pore throat size distribution corresponding to the predicted pore throat size distribution data set (see abstract; page 3, ll. 15-32; page 5, ll. 7-24; page 6, ll. 18-27). b. AlSinan et al. US 20240201413 A1 disclose generating digital models of core samples taken from one or more reservoir formations based on corresponding images of the core samples; determining a pore throat size distribution of the core samples based on the generated digital models; determining corresponding capillary pressure curves and corresponding NMR value distributions of the core samples with one or more numerical simulations; generating one or more machine-learning (ML) models based on the pore throat size distribution, corresponding capillary pressure curves, and corresponding NMR value distributions of the core samples; adjusting the one or more ML models with one or more reservoir data of the one or more reservoir formations; generating adjusted capillary pressure curves and NMR value distributions from the one or more adjusted ML models; and determining a reservoir formation specific pore throat size distribution from at least one of the adjusted capillary pressure curves or the adjusted NMR value distributions (see abstract). AlSinan disclose calibrating an NMR response and predicting pore throat size distribution from NMR T 2 logs, for any reservoir of interest in order to enhance petrophysical rock typing; and further discloses a ML model that can be a combination of DNN and LSSTM models and that calculated PTSD and numerically simulated Pc curves and T2 data can be fed into one or more ML models in order to map non-linear relationship between PTSD and T2. (see para. 0015, 0029, 0052-0053). AlSinan further disclose determining a reservoir formation specific pore throat size distribution from at least one of the adjusted capillary pressure curves or the adjusted NMR value distributions and that from NMR T2 data from the adjusted ML model a petrophysical rock type dependent PTSD can be predicted (see para. 0061). c. Gao et al. US 20230243728 A1 disclose, drilling a core sample out of the reservoir, determining a porosity distribution along the core sample, obtaining T2 distribution at different saturation levels of the core sample, determining a pore throat size distribution along a core sample, determining the pore throat size distribution along the core sample, determining first porosities from the T2 distributions that correspond to second porosities of the pore throat size distribution for each saturation level, determining T2 distributions at the first porosities from the T2 distributions, determining pore throat sizes at the second porosities from the pore throat size distributions, plotting the pore throat sizes as function of the relaxation times T2 to obtain the surface relaxation, and determining the pore size distribution of the reservoir (see abstract; para. 0049-0055). Gao disclose a reservoir simulator 160 to train and apply a regression model to predict NMR t2 based on multifractal parameters (see para. 0040) and further disclose the reservoir simulator applies a linear regression model to determine a pore size based temperature correction of NMR T2 distribution data based on the temperature dependence of overall shape shift of NMR T2 distribution (see para. 0041). d. Shao et al. US2020/0319369A1 disclose a method generating temperature corrected nuclear magnetic resonance measurement derived value corresponding to a target temperature using at least one of a dimension reduction operation or a parameter correlation operation based on a difference between the target temperature and a sample temperature and further discloses a formation property prediction comprising an NMR relaxation time distribution such as a T1 and/or T2 distribution and wherein the device or system can apply a correlation between pore throat size and a geometric man of the T2 distribution to determine a pore throat size distribution and related pore throat size values (see abstract, para. 0056, 0069, 0079, 0094). e. Chen et al. US2016/0370492 (hereinafter Chen) disclose systems, methods, and software for predicting a pore throat size distribution; the method also includes employing the trained RBI: model to predict a pore throat size distribution data set based at least in part on the subsequent NMR relaxation-time distribution data set. The method also includes storing or displaying a predicted pore throat size distribution corresponding to the predicted pore throat size distribution data set. The predicted pore throat size distribution is associated with a rock sample or subsurface formation volume (see abstract; para 0022-0024, 0033-0035). Regarding claims 1-20 , currently rejected under 35 USC 101 the closest prior art of made of record either in singularly or in combination fails to teach, disclose or suggest the orderly combination of steps set forth by independent claim 1, without the use of impermissible hindsight in particular the step of transforming, via a second neural network, the predicted pore throat size distribution or the one or more predicted pore throat size parameters to a predicted NMR T2 distribution or one or more predicted NMR T2 parameters; modeling the predicted NMR T2 distribution or the one or more predicted NMR T2 parameters to one or more simulated NMR measurements determining a drilling path for a borehole or perforation interval based on the predicted pore throat size distribution, the one or more predicted pore throat size parameters, the predicted NMR T2 distribution, and/or the one or more predicted NMR T2 parameters. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to YARITZA H PEREZ BERMUDEZ whose telephone number is (571)270-1520. The examiner can normally be reached Monday-Friday. 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, Shelby A Turner can be reached at (571) 272-6334. 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. /YARITZA H. PEREZ BERMUDEZ/ Examiner Art Unit 2857 /SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857 Application/Control Number: 18/414,685 Page 2 Art Unit: 2857 Application/Control Number: 18/414,685 Page 3 Art Unit: 2857 Application/Control Number: 18/414,685 Page 4 Art Unit: 2857 Application/Control Number: 18/414,685 Page 5 Art Unit: 2857 Application/Control Number: 18/414,685 Page 6 Art Unit: 2857 Application/Control Number: 18/414,685 Page 7 Art Unit: 2857 Application/Control Number: 18/414,685 Page 8 Art Unit: 2857 Application/Control Number: 18/414,685 Page 9 Art Unit: 2857 Application/Control Number: 18/414,685 Page 10 Art Unit: 2857 Application/Control Number: 18/414,685 Page 11 Art Unit: 2857 Application/Control Number: 18/414,685 Page 12 Art Unit: 2857 Application/Control Number: 18/414,685 Page 13 Art Unit: 2857 Application/Control Number: 18/414,685 Page 14 Art Unit: 2857
Prosecution Insights