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. Claim Interpretation The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are “an obtainment module configured to obtain…”, “a construction module configured to construct…”, and “a training module configured to train” in claim 8. Because this/these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 112 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claim 8 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The specification (see P0025-P0031) does not disclose sufficient corresponding structure for the claimed functions of an obtainment module, construction module, or training module (see MPEP 2181 (IV)). Thus, a person of ordinary skill in the art cannot determine how to perform the claimed functions, and the specification fails to demonstrate that the inventor was in possession of the claimed invention at the time of filing. The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the appl icant regards as his invention. Claim limitations “an obtainment module configured to obtain…”, “a construction module configured to construct…”, and “a training module configured to train” in claim 8 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. No association between the structure and the functions can be found in the specification (P0025-P0031). The specification fails to clearly link the claimed functions to disclosed structures, materials, or acts (see MPEP 2181 (III)). Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Applicant may: (a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph; (b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)). If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either: (a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or (b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181. Claim Rejections - 35 USC § 101 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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1 : The claims recite a method and apparatus, each of which are one of the four categories of eligible subject matter. Claims 1 and 8 Step 2A Prong 1 : The claims recite the following limitations: training the initial diagenetic parameter prediction model with the diagenesis samples until a loss between diagenetic parameter predict values obtained by the initial diagenetic parameter prediction model and the actual diagenetic parameters is within a preset loss range or the diagenetic parameter predict values reach a preset accuracy, so as to obtain a trained diagenetic parameter prediction model ( Mathematical Concept and Mental Process ) . In view of P0083-P0084 of the specification of the instant application, training the prediction model with the loss function requires the use of the loss function described in P0083. Furthermore, a human mind with the aid of a pen, paper, and data can practically determine when the output of a loss function is within a preset loss range or when a preset accuracy metric is reached. Accordingly, the claims recite an abstract idea. Step 2A Prong 2 : The judicial exceptions are not integrated into a practical application and the claim recites the following additional elements: obtaining a plurality of diagenesis samples each comprising diagenetic condition parameters and an actual diagenetic parameter evolved therefrom ; constructing an initial diagenetic parameter prediction model based on the diagenesis samples and a total dimension of the diagenetic condition parameters . Obtaining a plurality of diagenesis samples is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). Constructing an initial diagenetic parameter prediction model is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claims are directed towards an abstract idea. Step 2B : The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Obtaining a plurality of diagenesis samples is mere data gathering, which is an insignificant extra-solution activity as discussed in MPEP 2106.05(g). Constructing an initial diagenetic parameter prediction model is generally linking the abstract ideas to the technological environment of machine learning, as discussed in MPEP 2106.05(h). The claims are not patent eligible. Dependent Claims : Claims 4 and 6 : These claims recite further abstract ideas (mental processes and mathematical concepts) and thus are ineligible. Claims 2, 3, 5, and 7 : These claims recite further mere data gathering and generally linking the abstract ideas to the technological environment of machine learning and as explained above these do not provide a practical application or inventive concept and thus are ineligible. 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. Claims 1-3 , 5, and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over AlSinan et al (Pub. No.: US 20230097859 A1), hereafter AlSinan in view of Adelinet (Pub. No.: US 20180163516 A1), hereafter Adelinet . Regarding claim 8 , claim limitations “an obtainment module configured to obtain…”, “a construction module configured to construct…”, and “a training module configured to train” invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. These elements are interpreted under 35 U.S.C. 112(f) as processor(s) with the algorithm described in the specification (the algorithms to obtain a plurality of diagenesis samples, construct an initial diagenetic parameter prediction model, and train the diagenetic parameter prediction model ) that causes the processor(s) to perform the claimed function. Regarding claims 1 and 8 , A lSinan teaches obtaining a plurality of diagenesis samples each comprising diagenetic … parameters (see Abstract, discussing receiving fracture image data, which include diagenesis or various rock properties. See also [0058].) AlSinan further teaches constructing an initial diagenetic parameter prediction model based on the diagenesis samples and a total dimension of the diagenetic condition parameters ( a multiphase simulation model is generated using a deep artificial neural network that predicts upscaled multiphase data. The model is constructed based on fracture image data, P0017. Fracture image data may include diagenesis or various rock properties, P0058 ) ; and training the initial diagenetic parameter prediction model with the diagenesis samples until a loss between diagenetic parameter predict values obtained by the initial diagenetic parameter prediction model and the actual diagenetic parameters is within a preset loss range or the diagenetic parameter predict values reach a preset accuracy, so as to obtain a trained diagenetic parameter prediction model ( The multiphase simulation model is trained with trial-error analysis using fracture image data until the trained model satisfies a predetermined level of prediction accuracy. Based on error data from the trial-error analysis, weights and biases within an artificial neural network may be adjusted to optimize the artificial neural network architecture and the related model parameters, P0017 ). A lSinan does not appear to explicitly teach that the diagenesis samples each compris [e] diagenetic condition parameters and an actual diagenetic parameter evolved therefrom ” . Adelinet teaches obtaining a plurality of diagenesis samples each comprising diagenetic condition parameters ( Mechanical conditions for at least one rock sample are determined, P0019-P0023. Mechanical conditions include porosity, permeability, and mineralogical composition, P0016, P0030-P0031 ) and an actual diagenetic parameter evolved therefrom ( Measurements of porosity , permeability, and mineralogical composition are identified from geological time t to current time indicating how the parameters may have evolved over time, P0019-P0021 ). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of A lSinan and Adelinet before them, to include Adelinet’s specific teaching of collecting measurements of porosity, permeability, and mineralogical composition of rocks over time in A lSinan ’s system of Determining Coarsened Grid Models Using Machine-Learning Models and Fracture Models . One would have been motivated to make such a combination of collecting measurements of porosity, permeability, and mineralogical composition of rocks over time (see Adelinet P0019-P0023) and collecting data in regard to diagenesis or various rock properties to determine fractures in rocks using an artificial neural network (see A lSinan P0058-P0059 ). Regarding claim 2 , A lSinan in view of Adelinet teaches the limitations of claim 1 as outlined above. Adelinet further teaches wherein the diagenetic condition parameters comprise a diagenesis prediction period, and at least further comprise one or combinations of an ion concentration, a mineral content, temperature and pressure conditions, an acidity-basicity, and a porosity ( Mechanical conditions for at least one rock sample are determined for each diagenetic stage , P0019-P0023. Mechanical conditions include porosity, permeability, and mineralogical composition, P0016, P0030-P0031 ) ; the actual diagenetic parameter at least comprise one or more of the ion concentration, the mineral content, the temperature and pressure conditions, the acidity-basicity and the porosity after an evolution time elapses by the diagenesis prediction period ( Measurements of porosity permeability, and mineralogical composition are identified from geological time t to current time indicating how the parameters may have evolved over time, P0019-P0021 ) . Regarding claim 3 , A lSinan in view of Adelinet teaches the limitations of claim 2 as outlined above. Adelinet further teaches wherein the ion concentration, the mineral content, the temperature and pressure conditions, the acidity-basicity, and the porosity at least comprised in the diagenetic condition parameters are measured values obtained at one or more observation moments ( Measurements of porosity permeability, and mineralogical composition are identified from geological time t to current time indicating how the parameters may have evolved over time, P0019-P0021 ) . Regarding claim 5 , A lSinan in view of Adelinet teaches the limitations of claim 3 as outlined above. A lSinan further teaches wherein the step of constructing an initial diagenetic parameter prediction model based on the diagenesis samples and a total dimension of the diagenetic condition parameters further comprises constructing a machine learning model based on the diagenesis samples when the total dimension of the diagenetic condition parameters is less than a preset dimension threshold , and taking the machine teaming model as the initial diagenetic parameter prediction model; and constructing a deep learning network model based on the diagenesis samples when the total dimension of the diagenetic condition parameters is greater than or equal to the preset dimension threshold , and taking the deep learning network model as the initial diagenetic parameter prediction model ( A deep learning network model being constructed regardless of the threshold being met results in a machine learning model being constructed in the case the threshold is not met and a deep learning network model being constructed in the case the threshold is met . A deep learning CNN model is constructed to evaluate 100m x 100m dimensional porous media, P0089-P0090 ) . Regarding claim 7 , A lSinan in view of Adelinet teaches the limitations of claim 1 as outlined above. Adelinet further teaches collecting diagenetic condition parameter s ( Mechanical conditions for at least one rock sample are determined, P0019-P0023. Mechanical conditions include porosity, permeability, and mineralogical composition, P0016, P0030-P0031 ). A lSinan further teaches inputting the diagenetic condition parameters into the diagenetic parameter prediction model to obtain diagenetic parameters predicted based on the diagenetic condition parameters ( predicted transmissibility data is generated using an artificial neural network with fracture image data as the input, P0083. Transmissibility predictions are interpreted as a diagenetic parameter under the broadest reasonable interpretation because diagenetic conditions include pressure conditions as described in P0011 of the specification of the instant application. Fracture image data may include diagenesis or various rock properties, P0058 ) . Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over AlSinan in Adelinet and further in view of Li et al (Pub. No.: US 20180347354 A1), hereafter Li . Regarding claim 4 , AlSinan in view of Adelinet teaches the limitations of claim 3 as outlined above. AlSinan in view of Adelinet does not appear to explicitly teach “ carrying out a feature selection on the diagenetic condition parameters, and removing a parameter with an influence coefficient less than a preset value, among the diagenetic condition parameters ”. Li teaches carrying out a feature selection on the diagenetic condition parameters, and removing a parameter with an influence coefficient less than a preset value, among the diagenetic condition parameters ( Feature selection is performed on rock samples. If the theta value is too low for frequency bands associated with a feature, weights associated with the feature are reduced. If bands are above the minimal theta value, the bands associated with the feature are selected, P0096 ) . Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of A lSinan , Adelinet , and Li before them, to include Li’s specific teaching of performing feature selection on rock samples in A lSinan ’s system of Determining Coarsened Grid Models Using Machine-Learning Models and Fracture Models. One would have been motivated to make such a combination of performing feature selection on rock samples (see Li P0096) and collecting data in regard to diagenesis or various rock properties to determine fractures in rocks using an artificial neural network (see A lSinan P0058-P0059). Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over AlSinan in Adelinet and further in view of Wolfe et al (Pub. No.: US 20180279563 A1), hereafter Wolfe and Hellinga et al (Pub. No.: US 20200284811 A1 ), hereafter Hellinga . Regarding claim 6 , AlSinan in view of Adelinet does not appear to explicitly teach “c lassifying the diagenesis samples into a training set and a test set in a preset ratio using a random sampling method or a stratified sampling metho d”. Wolfe teaches classifying the diagenesis samples into a training set and a test set in a preset ratio using a random sampling method or a stratified sampling method ( Stratified random sampling may be used to divide samples into a preset ratio of a training dataset and a testing dataset, P0204. The data is regarding light and temperature data , P0185-P0186 ). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of A lSinan , Adelinet , and Wolfe before them, to include Wolfe’s specific teaching of using stratified random sampling in A lSinan ’s system of Determining Coarsened Grid Models Using Machine-Learning Models and Fracture Models. One would have been motivated to make such a combination of using stratified random sampling to divide samples of light and temperature data into a preset ratio of a training dataset and a testing dataset (see Wolfe P0185-P0186, P0204 ) and collecting data in regard to diagenesis or various rock properties to determine fractures in rocks using an artificial neural network (see A lSinan P0058-P0059). AlSinan in view of Adelinet and Wolfe does not appear to explicitly teach . Hellinga teaches ( Z-score normalization is applied to data regarding porosity of rock materials. The Z-scores are determined by dividing the difference between values of a dataset and the mean with the standard deviation, P0024, P0033 ). Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of A lSinan , Adelinet , Wolfe , and Hellinga before them, to include Hellinga’s specific teaching of applying Z-score normalization to data regarding porosity of rock materials in A lSinan ’s system of Determining Coarsened Grid Models Using Machine-Learning Models and Fracture Models. One would have been motivated to make such a combination of applying Z-score normalization to data regarding porosity of rock materials (see Hellinga P0024, P0033 ) and collecting data in regard to diagenesis or various rock properties to determine fractures in rocks using an artificial neural network (see A lSinan P0058-P0059). Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20240012174 A1 (Al- Firisi et al) teaches a system including applying deep learning algorithms to predict heterogenous rock permeability. US 20200073373 A1 ( Ramanath et al) teaches calculating Z-scores by dividing the difference between specific data points and the mean with the standard deviation . Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT ISHAN MOUNDI whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (703)756-1547 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 8:30 A.M. - 5 P.M. . 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. 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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. /I.M./ Examiner, Art Unit 2141 /MATTHEW ELL/ Supervisory Patent Examiner, Art Unit 2141