DETAILED ACTION 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. This action is responsive to communication filed on 06/16/2023. Claims 1-25, and 27 are pending. Claim 26 is cancelled. Claims 3-5, 7, 9-10, 12-15, 17-18, 20-21, 23-25, and 27 have been amended. Entry of this amendment is accepted and made of record. Information Disclosure Statement The information disclosure statement (IDS) submitted on 07/31/2023 have been considered by the examiner. Drawings The drawings are objected to because figure 6b fails to show the labels for the graph axis an d figure 10 contains shadows that do not allow to clearly visualize/distinguish the different categories on the plot of experimental results . Figures should show respective titles, chart le g end and axis labels for the plots and graphs on figs 9-10. 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. Specification Applicant is reminded of the proper content of an abstract of the disclosure. A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art. If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives. Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps. Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length. See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts. The abstract of the disclosure is objected to because it exceeds 150 words in length. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b). Claim Rejections - 35 USC § 112 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 24 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term “ majority ” in claim 24 is a relative term which renders the claim indefinite. The term “ majority ” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear from the claim as to what will constitutes the “majority of the depths of the drill hole” since the a definition for what will constitute “ a majority of the depths ” have not been defined. Supposing that there are three groups “the majority” of the depths can be considered to be considered to be greater than 1/3 of the depths, if there are 2 groups the majority can be considered to be greater than 50% of the depths and if the amount of groups increases or decrease the value of what could constitute “ a majority ” will vary as it will depend on the amount of groups, therefore the term “a majority” is a relative term and renders the claim indefinite. Clarification and correction is required. For examination on the merits the claims are interpreted as best understood in light of the 35 USC 112(b) rejections above. 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 1-2 5 and 27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea FILLIN "Identify whether the claim(s) are directed to a law of nature; a natural phenomenon; or an abstract idea." \* MERGEFORMAT 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. Likely claims 21 , belongs to a statutory category, namely a method and claim 2 7 , belongs to a statutory category, namely it is a system (i.e. non-transient computer storage) . 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, 21 and 2 7 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 “ determining … a plurality of characteristic measures for each of the plurality of drilled holes, wherein the plurality of characteristic measures for each drilled hole comprise: at least one characteristic measure of a first type, wherein a characteristic measure of the first type is a measure based on a comparison of a distribution of a said at least one drilling variable for the drill hole with a distribution of a related or the same drilling variable across a plurality of the drilled holes; and at least one characteristic measure of a second type, wherein a characteristic measure of the second type is based on said at least one drilling variable across the plurality of depths of the drilled holes; applying … unsupervised learning to the plurality of characteristic measures, the unsupervised learning determining groups of the drilled holes ”, (claim 1); “ determining, by the one or more computing systems, a plurality of characteristic measures for the drill hole based on said at least one drilling variable across the plurality of depths of the drill hole; applying, by the one or more computing systems, the plurality of characteristic measures to a model, … determining, by the one or more computing systems, a plurality of characteristic measures for each of the plurality of drilled holes, wherein the plurality of characteristic measures for each drilled hole comprise: at least one characteristic measure of a first type, wherein a characteristic measure of the first type is a measure based on a comparison of a distribution of a said at least one drilling variable for the drill hole with a distribution of a related or the same drilling variable across a plurality of the drilled holes; and at least one characteristic measure of a second type, wherein a characteristic measure of the second type is based on said at least one drilling variable across the plurality of depths of the drilled holes; applying… unsupervised learning to the plurality of characteristic measures, the unsupervised learning determining groups of the drilled holes; and assigning at least one depth of the drill hole or the drill hole to a group of the model determined by the unsupervised learning ” ( claim 21 ) ; and “ determining, … a plurality of characteristic measures for each of the plurality of drilled holes, wherein the plurality of characteristic measures for each drilled hole comprise: at least one characteristic measure of a first type, wherein a characteristic measure of the first type is a measure based on a comparison of a distribution of a said at least one drilling variable for the drill hole with a distribution of a related or the same drilling variable across a plurality of the drilled holes; and at least one characteristic measure of a second type, wherein a characteristic measure of the second type is based on said at least one drilling variable across the plurality of depths of the drilled holes; applying … unsupervised learning to the plurality of characteristic measures, the unsupervised learning determining groups of the drilled hole s ” (claim 27). 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. 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 “one or more computing systems” respectively recited by claims 1, 21 and 2 7 , amounts to the recitation of a general purpose computer used to apply the abstract idea; the recitation of “receiving digital representations of peak locations”, “multi-element electroacoustic transducer array”, “receive, using first communication circuit” , 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 (i.e. non-transient computer storage, output indicating determined groups of drilled holes in claims 1, 24 and 27 ) and are not used in any particular matter as to integrate the abstract idea in a practical application. Under Step 2B , t he 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 “ computer system ”, 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 (i.e. storing data in a non-transient computer storage, claim 27 ) 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, claims 1, 24 and 27 are rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more. Dependent claims 2-20 merely expand on the abstract idea by appending additional steps to the mathematical algorithm on their respective independent claim 1. Dependent claims 22-25 merely expand on the abstract idea by appending additional steps to the mathematical algorithm on their respective independent claim 21. Dependent claims 2- 20 and 22-25 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 generally linking the abstract idea to a field of use (i.e. claims 2-6, 15- 16, 19, 22-23) and applied for performing the abstract idea i.e . d istribution of a related or the same drilling variable across a plurality of the drilled holes is divided into a plurality of groups and the at least one characteristic measure of the first type is a proportion of said observations of a drill hole that are within each group (claim 5); “ herein the plurality of groups are based on variation from a mean of the drilling variable across the plurality of drilled hole ” (claim 6); “ wherein the at least one characteristic measure of a second type comprises one or more of a minimum value, a median value, a mean value, a maximum value, a first quartile, a third quartile and one or more measures of variation ” (claim 7); “ wherein the one or more measures of variation comprise standard deviatio n” (claim 8); “ an average of increasing values, an average of decreasing values, a ratio of increasing values to all of the values and a ratio of decreasing values to all of the values ” (claim 9); “ he at least one characteristic measure of a second type comprises: at least one characteristic measure of central tendency of the drilling variable; and at least one characteristic measure of the distribution of the drilling variable ”(claim 10); “ wherein the at least one characteristic measure of a second type further comprises at least one of an average of increasing values and an average of decreasing values ” (claim 11); “ wherein the at least one characteristic measure of a second type further comprises at least one of a ratio of increasing values to all of the values and a ratio of decreasing values to all of the values. ” (claim 12); “ removing outliers from the data comprising at least one drilling variable prior to determining the plurality of characteristic measures ” (claim 13); “ removing observations from the data comprising at least one drilling variable if data comprising the observation is missing, prior to determining the plurality of characteristic measures ” (claim 14); “ wherein the process of applying unsupervised learning to the plurality of characteristic measures is configured to determine at least three groups of the drilled holes. ” (claim 17); “ assigning, by the one or more computing systems, each depth of the drill hole to one of the groups of the model; determining, by the one or more computing systems, a group of the model that corresponds to the majority of the depths of the drill hole; and assigning, by the one or more computing systems, the determined group that corresponds to the majority of the depths of the drill hole to the drill hole ” (claim 24); and “ adding the at least one depth of the drill hole or the drill hole to the group of the model determined from the unsupervised learning ” (claim 25). The claims 2- 20 and 22-25 do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for their respective independent claims 1 and 21. Claims 15, 18, 19 and 20 introduces additional elements describing the output indicating the determined groups of the drilled holes and providing the determined groups of the drilled holes to a controller ( claim 18 ), and elements generally linking the abstract idea to a field of use (i.e. mining apparatus comprising at least one of autonomous vehicle, concentrator, crusher and grinder, [ claims 18-19 ]) and “applying unsupervised learning” ( claim 17, 20 ) however, this merely amounts to insignificant extra-solution activity and the use of a general purpose computer as a tool to implement the abstract idea, respectively, and the results are not used in any particular matter as to integrate the abstract idea in a practical application . Therefore, for similar reasons as described above, the additional elements fail to integrate the recited abstract idea into a practical application or amount to significantly more than the abstract idea itself. The claim(s) 2- 20 and 22-25 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements are 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 claims 1-2 5 and 27 are rejected under 35 USC 101 as being directed to non-statutory subject matter. Claim Rejections - 35 USC § 102 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 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 – (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. (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. Claim(s) 1- 8, 10- 11, 13-25, and 2 7 is/are rejected under 35 U.S.C. 102 (a)(1) as being FILLIN "Insert either—clearly anticipated—or—anticipated—with an explanation at the end of the paragraph." \d "[ 3 ]" anticipated by FILLIN "Insert the prior art relied upon." \d "[ 4 ]" Kristjansson et al. US 20170328181 A1 (hereinafter Kristjansson ) . Regarding claim 1 , Kristjansson disclose a method, comprising: receiving or generating, at one or more computing systems (para. 0021, 0026) , data comprising at least one drilling variable for a plurality of drilled holes across observations at a plurality of depths, wherein the at least one drilling variable is a variable affected by at least one physical characteristic of rock in the plurality of drilled holes ( abstract, para. 0016 - 0017, 0026- 0027, 0029, wherein historic drilling data from a group of drilled wells is disclosed and wherein at least two drilling variables is disclosed , wherein the variables can comprise one or both of mechanical specific energy (MSE) and on-bottom rate of penetration (ROP) , wherein rock hardness, porosity, ‘ drillability ’, type of formation, etc. is disclosed ) ; determining, by the one or more computing systems, a plurality of characteristic measures for each of the plurality of drilled holes (see para. 0016-0017, 0025- 0029) , wherein the plurality of characteristic measures for each drilled hole comprise: at least one characteristic measure of a first type, wherein a characteristic measure of the first type is a measure based on a comparison of a distribution of a said at least one drilling variable for the drill hole with a distribution of a related or the same drilling variable across a plurality of the drilled holes ( see para. 0016-0017, 0019, 0022, 0024-0025, 0034, 0036-0040 wherein multivariate distribution is disclosed ; para. 0050-0054, claim 5, wherein comparisons are disclosed ) ; and at least one characteristic measure of a second type, wherein a characteristic measure of the second type is based on said at least one drilling variable across the plurality of depths of the drilled holes ( see para. 0019- 002 2 , 0024-0025 , 0042 ) ; applying, by the one or more computing systems, unsupervised learning to the plurality of characteristic measures, the unsupervised learning determining groups of the drilled holes (see abstract, para. 0019 , 0025, 0040, wherein unsupervised learning is applied ) ; and generating, by the one or more computing systems, an output indicating the determined groups of the drilled holes ( see abstract, para. 0015, 0019-0020, 0030, 0040 wherein the output resides in the extracted data from the group of drilled wells ) . Regarding claim 2 , Kristjansson further disclose that the at least one drilling variable for a plurality of drilled holes across a plurality of depths comprises a measure of mechanical specific energy (MSE) ( see para. 0003, 0016-0017, 0022-0025 ) . Regarding claim 3 , Kristjansson further disclose wherein the at least one characteristic measure of the first type is based on mechanical specific energy (MSE) ( see para. 0003, 0016-0017, 0022-0025 , claim 3 ). Regarding claim 4 , Kristjansson further disclose , wherein the at least one characteristic measure of the second type is based on mechanical specific energy (MSE) ( see para. 0003, 0016-0017, 0022-0025 , claim 3 ). . Regarding claim 5 , Kristjansson further disclose that, the distribution of a related or the same drilling variable across a plurality of the drilled holes is divided into a plurality of groups and the at least one characteristic measure of the first type is a proportion of said observations of a drill hole that are within each group ( see abstract, para. 0015, 0019-0020, 0026-0028, 0035, 0043, wherein multivariate distribution is disclosed and wherein cluster analysis recognize individual sets of more normally shaped distributions that form patterns in the drilling variables , claim 6 ) . Regarding claim 6 , Kristjansson further disclose wherein the plurality of groups are based on variation from a mean of the drilling variable across the plurality of drilled holes ( see para. 0038, wherein model parameters include a vector of mean drilling variable values; 0047, wherein a mean value of an indicator is disclosed ) . Regarding claim 7 , Kristjansson further disclose , wherein the at least one characteristic measure of a second type comprises one or more of a minimum value, a median value, a mean value, a maximum value, a first quartile, a third quartile and one or more measures of variation ( see para. 0016, wherein minimum MSE is disclosed; 0038, wherein model parameters include a vector of mean drilling variable values; 0047, wherein a mean value of an indicator is disclosed; see para. 0016, 0048, wherein maximum and minimum value is disclosed; para. 0061, wherein median ROP values is disclosed ) . Regarding claim 8 , Kristjansson further disclose , wherein the one or more measures of variation comprise standard deviation ( see para. 0031-0032, wherein standard deviation is disclosed ) . Regarding claim 10 , Kristjansson further disclose, wherein the at least one characteristic measure of a second type comprises: at least one characteristic measure of central tendency of the drilling variable ( see para. 0031-0032 ; 0061 ) ; and at least one characteristic measure of the distribution of the drilling variable ( see para. 0031 - 0034 ) . Regarding claim 11 , Kristjansson further disclose wherein the at least one characteristic measure of a second type further comprises at least one of an average of increasing values and an average of decreasing values (see para. 0024, 0042, 0058) . Regarding claim 13 , Kristjansson further disclose removing outliers from the data comprising at least one drilling variable prior to determining the plurality of characteristic measures ( see para. 0018, 0031 ) . Regarding claim 14 , Kristjansson further disclose removing observations from the data comprising at least one drilling variable if data comprising the observation is missing, prior to determining the plurality of characteristic measures ( para. 0018, 0031-0032 ). Regarding claim 15 , Kristjansson further disclose wherein the output indicating the determined groups of the drilled holes further indicates the at least one physical characteristic of rock, based on the determined groups ( see para. 0027, 0029 ) . Regarding claim 16 , Kristjansson further disclose wherein the at least one physical characteristic of rock comprises rock hardness ( see para. 0029 , wherein rock hardness is disclosed ) . Regarding claim 17 , Kristjansson further disclose, wherein the process of applying unsupervised learning to the plurality of characteristic measures is configured to determine at least three groups of the drilled holes ( see para. 0019, 0025, 0040, 0043-0044, 0058 ) . Regarding claim 18 , Kristjansson further disclose causing the determined groups of the drilled holes to be provided to a controller of at least one mining apparatus operating in relation to the drilled holes ( see para. 0017, 0021, 0023, 0025, 0026, 0030, wherein a processor is disclosed, and wherein drilling parameters settings can be applied to drill, and wherein a drill bit is disclosed ) . Regarding claim 19 , Kristjansson further disclose wherein the mining apparatus comprises at least one of an autonomous vehicle, concentrator, crusher and grinder ( see para. 0017, 0021, 0023, 0025, 0026, 0030, wherein a processor is disclosed, and wherein drilling parameters settings can be applied to drill, and wherein a drill bit is disclosed, it is implied that drill bits when penetrating the subsurface acts as both grinders and crushers, therefore meets the claimed limitation ) . Regarding claim 20 , Kristjansson further disclose wherein the output indicating the determined groups of the drilled holes is generated from an unsupervised learning process (see abstract, para. 0019, 0025, 0040, wherein unsupervised learning is applied ) . Regarding claim 21 , Kristjansson a method comprising: receiving or generating, at one or more computing systems (para. 0021, 0026) , data comprising at least one drilling variable for a drill hole at a plurality of depths , wherein the at least one drilling variable is a variable affected by at least one physical characteristic of rock in the drill hole ( abstract, para. 0016-0017, 0021, 0025- 0029, wherein historic drilling data from a group of drilled wells is disclosed and wherein at least two drilling variables is disclosed at a plurality of depths , wherein the variables can comprise one or both of mechanical specific energy (MSE) and on-bottom rate of penetration (ROP) , wherein rock hardness, porosity, ‘ drillability ’, type of formation, etc. is disclosed ); determining, by the one or more computing systems, a plurality of characteristic measures for the drill hole based on said at least one drilling variable across the plurality of depths of the drill hole (see para. 001 5 -0017, 0019, 0025- 0029) ; applying, by the one or more computing systems, the plurality of characteristic measures to a model, wherein the model is determined from the unsupervised learning (see abstract, para. 0019, 0025, 0040, wherein unsupervised learning is applied ) of the a method comprising: receiving or generating, at one or more computing systems, data comprising at least one drilling variable for a plurality of drilled holes across observations at a plurality of depths, wherein the at least one drilling variable is a variable affected by at least one physical characteristic of rock in the plurality of drilled holes ( abstract, para. 0016-0017, 0026-0027, 0029, wherein historic drilling data from a group of drilled wells is disclosed and wherein at least two drilling variables is disclosed, wherein the variables can comprise one or both of mechanical specific energy (MSE) and on-bottom rate of penetration (ROP) , wherein rock hardness, porosity, ‘ drillability ’, type of formation, etc. is disclosed ); ; determining, by the one or more computing systems, a plurality of characteristic measures for each of the plurality of drilled holes (see para. 0016-0017, 0025-0029) , wherein the plurality of characteristic measures for each drilled hole comprise: at least one characteristic measure of a first type, wherein a characteristic measure of the first type is a measure based on a comparison of a distribution of a said at least one drilling variable for the drill hole with a distribution of a related or the same drilling variable across a plurality of the drilled holes ( see para. 0016-0017, 0019, 0022, 0024-0025, 0034, 0036-0040 wherein multivariate distribution is disclosed; para. 0050-0054, claim 5, wherein comparisons are disclosed ) ; and at least one characteristic measure of a second type, wherein a characteristic measure of the second type is based on said at least one drilling variable across the plurality of depths of the drilled holes ( see para. 0019-0022, 0024-0025, 0042 ) ; applying, by the one or more computing systems, unsupervised learning to the plurality of characteristic measures, the unsupervised learning determining groups of the drilled holes (see abstract, para. 0019, 0025, 0040, wherein unsupervised learning is applied ) ; and generating, by the one or more computing systems, an output indicating the determined groups of the drilled holes ( see abstract, para. 0015, 0019-0020, 0030 , 0040 wherein the output resides in the extracted data from the group of drilled wells ) ; and assigning at least one depth of the drill hole or the drill hole to a group of the model determined by the unsupervised learning ( see abstract, para. 0015, 0017, 0019-0020, 0024, 0028, 0040, wherein the wherein group of drilled wells and variables at a specified drilled depth is disclosed and wherein pattern recognition model identifies and extracts archetype drilling mode patterns, and assigns each incremental instance of historic drilling data to belong to one of these archetypes , and wherein unsupervised learning is disclosed ) . Regarding claim 2 2 , Kristjansson further disclose wherein the model indicates a plurality of groups and each group indicates at least one physical characteristic of rock ( see para. 0027, 0029 , 0040 ) . Regarding claim 2 3 , Kristjansson further disclose that the at least one physical characteristic of rock comprises rock hardness ( see para. 0029, wherein rock hardness is disclosed ) . R e gardin g claim 2 4 , Kristjansson further disclose wherein assigning the drill hole to the group of the model determined by the unsupervised learning (see para. 0019, 0035 , 0040 ), comprises: assigning, by the one or more computing systems, each depth of the drill hole to one of the groups of the model ( see abstract, para. 0015, 0017, 0019 - 0021 , 0024, 0028, wherein the wherein group of drilled wells and variables at a specified drilled depth is disclosed and wherein pattern recognition model identifies and extracts archetype drilling mode patterns /groups of the model , and assigns each incremental instance of historic drilling data to belong to one of these archetypes ) ; determining, by the one or more computing systems, a group of the model that corresponds to the majority of the depths of the drill hole ( see para. 0040, wherein number of drilling modes is disclosed and data from a depth or time interval with the highest probability of belonging to an archetype pattern will be assigned to that archetype ) ; and assigning, by the one or more computing systems, the determined group that corresponds to the majority of the depths of the drill hole to the drill hole ( see para. 0040, wherein number of drilling modes is disclosed, and data from a depth or time interval with the highest probability belonging to an archetype pattern will be assigned to that archetype and wherein the drilling modes will be used to quantify learning information within each well and from one well to the next ) . Regarding claim 2 5 , Kristjansson further disclose adding the at least one depth of the drill hole or the drill hole to the group of the model determined from the unsupervised learning (see abstract, para. 0019-0020, 0025, 0040 ) . Regarding claim 2 7 , Kristjansson further disclose n on-transient computer storage (para. 0026) comprising instructions that, when executed by a computing system, cause the computing system to perform the a method comprising: receiving or generating, at one or more computing systems, data comprising at least one drilling variable for a plurality of drilled holes across observations at a plurality of depths, wherein the at least one drilling variable is a variable affected by at least one physical characteristic of rock in the plurality of drilled holes ( abstract, para. 0016-0017, 0026-0027, 0029, wherein historic drilling data from a group of drilled wells is disclosed and wherein at least two drilling variables is disclosed, wherein the variables can comprise one or both of mechanical specific energy (MSE) and on-bottom rate of penetration (ROP) , wherein rock hardness, porosity, ‘ drillability ’, type of formation, etc. is disclosed ) ; determining, by the one or more computing systems, a plurality of characteristic measures for each of the plurality of drilled holes (see para. 0016-0017, 0025-0029) , wherein the plurality of characteristic measures for each drilled hole comprise: at least one characteristic measure of a first type, wherein a characteristic measure of the first type is a measure based on a comparison of a distribution of a said at least one drilling variable for the drill hole with a distribution of a related or the same drilling variable across a plurality of the drilled holes ( see para. 0016-0017, 0019, 0022, 0024-0025, 0034, 0036-0040 wherein multivariate distribution is disclosed; para. 0050-0054, claim 5, wherein comparisons are disclosed ) ; and at least one characteristic measure of a second type, wherein a characteristic measure of the second type is based on said at least one drilling variable across the plurality of depths of the drilled holes ( see para. 0019-0022, 0024-0025, 0042 ) ; applying, by the one or more computing systems, unsupervised learning to the plurality of characteristic measures, the unsupervised learning determining groups of the drilled holes (see abstract, para. 0019, 0025, 0040, wherein unsupervised learning is applied ) ; and generating, by the one or more computing systems, an output indicating the determined groups of the drilled holes ( see abstract, para. 0015, 0019-0020, 0040, wherein the output resides in the extracted data from the group of drilled wells ) . 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. Claim (s) 9 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over FILLIN "Insert the prior art relied upon." \d "[ 4 ]" Kristjansson et al. US 20170328181 A1 (hereinafter Kristjansson ) in view of Strachan et al. US20110174541 (hereinafter Strachan) . Regarding claim 9 , Kristjansson further disclose , wherein an average level of a drilling variable is disclosed and wherein and average ROP may yield of at least 6.85m/ hr faster than the fastest average, which can suggest an average increasing (see para. 0024, 0042, 0058, 0062). Kristjansson further disclose, wherein the at least one characteristic measure of a second type further comprises at least one of a ratio ( see para. 0030, wherein a flow ratio is disclosed, and wherein maximum rated differential pressure and torque is disclosed; para. 0039, wherein a ratio of within chain to between chain variance is disclosed; 0022 wherein low MSE, high ROP is disclosed; 0016, 0048, 0052, wherein maximum or minimum values is disclosed ) . However it does not expressly or explicitly wherein the at least one characteristic measure of a second type comprises one or more of: an average of increasing values, an average of decreasing values, a ratio of increasing values to all of the values and a ratio of decreasing values to all of the values . Strachan disclose a system for drilling a well where measured drilling parameter s are analyzed (see abstract, 0011, 0047, 0088, 0096, 0586, 00590), wherein the at least one characteristic measure of a second type further comprises at least one of a ratio of increasing values to all of the values and a ratio of decreasing values to all of the values ( see para. 0597, Figs 26-27; “the percentage of frictional torque-increases (in greater proportion than for the sharp bit) and the percentage of cutting torque decreases (in greater proportion than for the sharp bit) with respect to a given total torque as WOB increases” ) . Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of Strachan to configure the system of Kristjansson so that at least one characteristic measure of a second type further comprises at least one of a ratio of increasing values to all of the values and a ratio of decreasing values to all of the values for the benefit of providing an enhanced system capable of evaluating a drill condition in order to ensure proper drilling operation ensuring optimum drilling operations (see para. 0595, 0597). Regarding claim 12 , Kristjansson further disclose, wherein the at least one characteristic measure of a second type further comprises at least one of a ratio ( see para. 0030, wherein a flow ratio is disclosed, and wherein maximum rated differential pressure and torque is disclosed; para. 0039, wherein a ratio of within chain to between chain variance is disclosed; 0022 wherein low MSE, high ROP is disclosed; 0016, 0048, 0052, wherein maximum or minimum values is disclosed ) . However, Kristjansson do not expressly or explicitly disclose wherein the at least one characteristic measure of a second type further comprises at least one of a ratio of increasing values to all of the values and a ratio of decreasing values to all of the values . Strachan disclose a system for drilling a well where measured drilling parameter s are analyzed (see abstract, 0011, 0047, 0088, 0096, 0586, 00590 ) , wherein the at least one characteristic measure of a second type further comprises at least one of a ratio of increasing values to all of the values and a ratio of decreasing values to all of the values ( see para. 0597 , Figs 26-27; “the percentage of frictional torque-increases (in greater proportion than for the sharp bit) and the percentage of cutting torque decreases (in greater proportion than for the sharp bit) with respect to a given total torque as WOB increases” ) . Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention given the teachings of Strachan to configure the system of Kristjansson so that at least one characteristic measure of a second type further comprises at least one of a ratio of increasing values to all of the values and a ratio of decreasing values to all of the values for the benefit of providing an enhanced system capable of evaluating a drill condition in order to ensure proper drilling operation ensuring optimum drilling operations (see para. 0595, 0597). Conclusion The prior art made of record cited in form PTOL-892 and not relied upon is considered pertinent to applicant's disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT YARITZA H PEREZ BERMUDEZ whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1520 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 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, FILLIN "SPE Name?" \* MERGEFORMAT Shelby A Turner can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (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. FILLIN "Examiner Stamp" \* MERGEFORMAT /YARITZA H. PEREZ BERMUDEZ/ Examiner Art Unit 2857 /SHELBY A TURNER/ Supervisory Patent Examiner, Art Unit 2857