CTNF 18/320,894 CTNF 91640 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. Drawings The drawings filed on 05/19/2023 are accepted. Information Disclosure Statement The references cited in the IDS, submitted on 05/19/2023, have been considered. Claim Rejections - 35 USC § 101 Non-Statutory 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, 3-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, Claim 1 recites: A method, comprising: obtaining first well log data for a first well; obtaining first surface production data for the first well based on a production operation, wherein the first well log data is acquired at the first well prior to the production operation being performed at the first well; obtaining a selection of a first depth interval among a plurality of depth intervals in the first well ; determining, by a computer processor, first predicted production data for the first depth interval in the first well using a first machine-learning model, the selection of the first depth interval, the first well log data, and the first surface production data ; and transmitting, by the computer processor, a first command to a first control system at the first well based on the first predicted production data. The claim limitations in the abstract idea have been highlighted in bold; the remaining limitations are “additional elements.” Similar limitations comprise the abstract ideas of claims 12 and 16. Under Step 1 of the analysis, claim 1 does belong to a statutory category, namely it is a process claim. Likewise, claim 12 is a process claim, claim 16 is an apparatus claim. Step 2A, Prong One: 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. Under Step 2A, Prong One, the broadest reasonable interpretation of the steps recited in Claim 1 include at least one judicial exception, that being a mathematical process. This can be seen in the claimed process step of “determining, by a computer processor, first predicted production data for the first depth interval in the first well using a first machine-learning model, the selection of the first depth interval, the first well log data, and the first surface production data…” (See, for example, FIGS. 1-5; ¶¶87-92, of the instant specification), which encompasses mathematical concepts requiring specific mathematical calculations (“ FIG. 5 provides an example of a random forest model for predicting water production flow at different depth intervals,” described in FIG. 5, ¶89 of the instant specification .) to perform the determining first predicted production data for the first depth interval, and therefore encompasses mathematical concepts. For example, when given the broadest reasonable interpretation in light of the specification, the step of “determining” is performed using one or more training algorithms ( random forest model(s) ). Claims 12 and 16 recite analogous judicial exceptions. Additionally, or alternatively, under Step 2A, Prong One, the broadest reasonable interpretation of the steps recited in Claim 12 include at least one judicial exception, that being a mathematical process. This can be seen in the claimed process step of “generating, by a computer processor, a trained machine-learning model…” (See, for example, FIGS. 1-5; ¶¶87-92, of the instant specification), and “the first machine-learning model is updated iteratively during a plurality of machine-learning epochs…” (See, for example, FIGS. 1-5; ¶¶87-92, 100, of the instant specification), each of which encompasses mathematical concepts requiring specific mathematical calculations (“ FIG. 5 provides an example of a random forest model for predicting water production flow at different depth intervals,” described in FIG. 5, ¶89 of the instant specification .) to perform the determining first predicted production data for the first depth interval, and therefore encompasses mathematical concepts. For example, when given the broadest reasonable interpretation in light of the specification, the steps of “generating” and “updated iteratively” are performed using one or more training algorithms ( random forest model(s) ). Claim 16 recites analogous judicial exceptions. Claim 1 additionally recites the claim limitation of “obtaining a selection of a first depth interval among a plurality of depth intervals in the first well…” (See, for example, FIGS. 1-4; ¶¶87-92, of the instant specification), which comprises the judicial exception of a mental process. Under the broadest reasonable interpretation, consistent with the specification, upon receipt of the first well log data and first surface production data, a human user would be capable of selecting a first depth interval among a plurality of depth intervals in the first well, by pen and paper. While such calculations by pen and paper may be time consuming, they fall in the “mental processes” abstract idea grouping. Noting MPEP 2106.04(a)(2)(III) “MENTAL PROCESSES,” “ The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea .” CyberSource Corp. v. Retail Decisions, Inc. , 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). “‘ [M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work.’ " (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook , 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). Claim 16 recites similar abstract ideas. In claim 1, the step of: “determining,” and in claim 12, the steps of “generating” and “updated iteratively” fall within the mathematical concepts grouping of abstract and the step of “selection,” recited in claims 1 and 16, falls within the mental concepts grouping of abstract ideas. The recited process steps are considered together as a single abstract idea for further analysis. Claims 12 and 16 recite similar abstract ideas. (Step 2A, Prong One: YES). Step 2A, Prong Two 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. 2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55. Each of the process steps “selection,” “determining,” “generating,” and “updated iteratively” fall within the mathematical concepts and/or mental concepts grouping of abstract ideas and are recited as being performed by a computer (“ FIG. 15 is a block diagram of a computer system (1502) used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures as described in the instant disclosure, according to an implementation. ” FIG. 15; ¶¶104-107, of the instant specification). The computer is recited at a high level of generality (“ computer system ”). The computer is used as a tool to perform the generic computer functions of collecting data and performing the recited process steps. The computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The recited process steps comprise an “insignificant extra-solution” activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,” Parker v. Flook , 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of the controller does not affect this analysis. See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,” Parker v. Flook , 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). Claim 1 recites the additional element(s) of using generic Artificial Intelligence/Machine-Learning (AI/ML) technology, i.e. “a first machine-learning model,” to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the “a first machine-learning model” merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of “a first machine-learning model” to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2. Claims 12 and 16 recite analogous additional element(s) of using generic Artificial Intelligence/Machine-Learning (AI/ML) technology. Claim 1 also recites the additional elements ( equipment ) of “a first well,” (See, for example, FIG. 1; ¶¶24-25, of the instant specification); and data comprising “first well log data,” “first surface production data,” “a first depth interval,” “a plurality of depth intervals,” (See, for example, FIG. 4; ¶¶81-87, of the instant specification). Claim 1 additionally recites “a production operation” (See, for example, FIG. 1; ¶¶24-25, of the instant specification), and the step of “transmitting…a first command to a first control system at the first well based on the first predicted production data” (See, for example, FIG. 4; ¶92, of the instant specification). However, these additional elements merely comprise generic conventional non-specific equipment, and computer hardware and software elements, and data/information, and is/are set forth at a highly generic level and each of which comprise an “insignificant extra-solution” activity(ies). Further, the step of “transmitting” comprises an “insignificant extra-solution” { post-solution } activity(ies). See MPEP 2106.05(g) “Insignificant Extra-Solution Activity,” Parker v. Flook , 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). Claims 12 and 16 recite analogous additional elements. The recited additional elements can also be viewed as nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer. Noting MPEP 2106.04(d)(I): “ It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point") ”. Thus, under Step 2A, Prong Two of the analysis, even when viewed in combination, these additional elements recited in claim 1, as well as claims 12 and 16, do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception. No specific practical application is associated with the claimed method. For instance, nothing is done once the first command is transmitted to the first control system at the first well. Under Step 2B, the claims 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 Two, merely amount to a general purpose computer system that attempts to apply the abstract idea in a technological environment, limiting the abstract idea to a particular field of use, and/or merely insignificant extra-solution activity ( Claims 1, 12, 16 ). Such insignificant extra-solution activity, e.g. data gathering and output, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional as evidenced by MPEP 2106.05(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, and electronically scanning or extracting data from a physical document). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that claim 1 , as well as claims 12 and 16, amount to significantly more than the abstract idea. Therefore, claim 1 , as well as claims 12 and 16, is not patent eligible under 101. With regards to the dependent claims, claims 3-11, 13-15, and 18-20 , provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims. Claim Rejections - 35 USC § 102 07-07 AIA 07-07-aia The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102(a)(1) that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1, 7, 9-12, 14, 16, and 20 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Maniar (U.S. Patent Publication 2020/0040719A1) . Regarding claim 1 , Maniar teaches a method (Maniar: Abstract. ), comprising: obtaining first well log data for a first well (Maniar: FIGS. 1.1-1.2; ¶¶14-15 [“Static data plot (108-3) is a logging trace, which is referred to as a well log. Production decline curve or graph (108-4) is a dynamic data plot of the fluid flow rate over time. Other data may also be collected…”]); obtaining first surface production data for the first well based on a production operation, wherein the first well log data is acquired at the first well prior to the production operation being performed at the first well (Maniar: FIGS. 1.1-1.2; ¶¶17-18 [“…the surface unit (112) is configured to send commands to the data acquisition tools (102-1), (102-2), (102-3), (102-4), and/or the wellsite systems and to receive data therefrom. In one or more embodiments, the surface unit (112) may be located at the wellsite system A (114-1), wellsite system B (114-2), wellsite system C (114-3), and/or remote locations. The surface unit (112) may be provided with computer facilities for receiving, storing, processing, and/or analyzing data from the data acquisition tools (102-1), (102-2), (102-3), (102-4), the wellsite system A (114-1), wellsite system B (114-2), wellsite system C (114-3), and/or other parts of the field (100).”]; FIG. 2; ¶¶17-18 [“…target well data set is obtained that specifies a target well to be drilled. In one or more embodiments, the target well data set is obtained prior to the drilling operation. For example, the target well data set may be obtained by gathering raw measurement data from seismic sensors and/or sensors of existing wells used in surveying operations.”]); obtaining a selection of a first depth interval among a plurality of depth intervals in the first well (Maniar: FIGS. 1.1-1.2, 2; ¶26 [“…the predicted ROP profile (237) is a prediction of one or more ROPs of the target well. As used herein, the term "ROP profile" refers to a collection of ROPs at different locations along a well. In other words, the ROP is the rate of penetration at a measured depth interval along the length of the well. Because the lithology and/or other operational characteristics of the subsurface changes, different measured depth intervals may have different ROPs that are efficient and productive for the measured depth interval.”]; FIG. 2; ¶¶17-18 [“…target well data set is obtained that specifies a target well to be drilled. In one or more embodiments, the target well data set is obtained prior to the drilling operation. For example, the target well data set may be obtained by gathering raw measurement data from seismic sensors and/or sensors of existing wells used in surveying operations.”]); determining, by a computer processor, first predicted production data for the first depth interval in the first well using a first machine-learning model, the selection of the first depth interval, the first well log data, and the first surface production data (Maniar: FIGS. 1.1-1.2, 2; ¶31 [“…the drilling model generator (223) is configured to generate, using a machine-learning algorithm and based on the training data set (236), the drilling model (224) that predicts the ROP profile of the target well. That is, the drilling model generator (223) generates the predicted ROP profile (237) based on the training data set (236). In one or more embodiments, the drilling model (224) describes a statistical relationship between the well data, the drilling parameters, the bit parameters, the well logs, the drilling fluid parameters, and the lithology parameters of the analog well data sets (e.g., analog well data set A (236-1), analog well data set B (236-2), etc.) in the training data set (236). In particular, the training data set (236) is used to validate results and model accuracy of the statistical relationship.”]; ¶36 [“…during drilling of the target well, the field task engine (231) may adjust the field control signal in response to the modeling engine (225) updating the predicted ROP profile (237))…”]; ¶46 [“…a drilling model that predicts the ROP profile of the target well is generated using a machine learning algorithm based on the training data set. In one or more embodiments, an ensemble method using tree-based weak-learners (e.g., Random-Forest, Least-Squares Boosting, etc.) is used as the machine-learning algorithm to generate the drilling model.”]); and transmitting, by the computer processor, a first command to a first control system at the first well based on the first predicted production data (Maniar: FIG. 2; ¶¶17-18 [“…the surface unit (112) is configured to send commands to the data acquisition tools (102-1), (102-2), (102-3), (102-4), and/or the wellsite systems and to receive data therefrom…surface unit (112) may then send command signals to the field (100) in response to data received, stored, processed, and/or analyzed to, for example, control and/or optimize the various field operations…”]). Regarding Claims 12 and 16 , each claim recites limitations found within Claim 1, and is rejected under the same rationale applied to the rejection of Claim 1. Additionally regarding claim 12 , Maniar additionally, or alternatively, discloses obtaining acquired production logging tool (PLT) data for a plurality of depth intervals in the plurality of wells (Maniar: FIGS. 1.1; ¶16 [“…each of the wellsite system A (114-1), wellsite system B (114-2), and wellsite system C (114-3) is associated with a rig, a wellbore, and other wellsite equipment configured to perform wellbore operations, such as logging, drilling, fracturing, production, or other applicable operations. For example, the wellsite system A (114-1) is associated with a rig (101), a wellbore (103), and drilling equipment to perform drilling operations. Similarly, the wellsite system B (114-2) and wellsite system C (114-3) are associated with respective rigs, wellbores, and other wellsite equipment, such as production equipment to perform production operations and logging equipment to perform logging operations…”]), and wherein the first machine-learning model is updated iteratively during a plurality of machine-learning epochs based on a comparison between a portion of the acquired PLT data and second predicted production data that are generated by the first machine-learning model in a respective machine-learning epoch among the plurality of machine-learning epochs (Maniar: FIG. 2; ¶¶33-35 [“…an inference engine, with a knowledge base such as the training data set (236), may form an expert system. In an expert system, the knowledge base stores facts and the inference engine applies logical rules to the knowledge base to deduce new knowledge. This process may iterate as each new fact in the knowledge base triggers additional rules in the inference engine.”]). Additionally regarding claim 16 , Maniar additionally, or alternatively, discloses a reservoir simulator comprising a computer processor, wherein the reservoir simulator is coupled to the first well control system (Maniar: FIG. 2; ¶¶31-32 [“…the drilling model generator (223) is configured to generate, using a machine-learning algorithm and based on the training data set (236), the drilling model (224) that predicts the ROP profile of the target well. That is, the drilling model generator (223) generates the predicted ROP profile (237) based on the training data set (236). drilling model (224) is an approximation based at least in part on the sensor data. The greater the accuracy of the drilling model (224), the more efficient and productive the drilling and other field operations for gathering hydrocarbons and other valuable assets from the subterranean formation may be. One or more embodiments improve the accuracy of the drilling model (224), and thereby improve the field operations performed.”]). Regarding claim 7 , Maniar teaches all the limitations of the parent claim 1 as shown above. Maniar additionally discloses the first machine-learning model is a random forest model comprising a plurality of decision tree nodes coupled using an ensemble method, and wherein the first machine-learning model is trained using a bootstrap and aggregation operation (Maniar: FIGS. 1.1-1.2, 2; ¶31, ¶36; ¶46 [“…a drilling model that predicts the ROP profile of the target well is generated using a machine learning algorithm based on the training data set. In one or more embodiments, an ensemble method using tree-based weak-learners (e.g., Random-Forest, Least-Squares Boosting, etc.) is used as the machine-learning algorithm to generate the drilling model.”]; { See above .}). Regarding claim 14 , the claim recites limitations found within Claim 7, and is rejected under the same rationale applied to the rejection of Claim 7. Regarding claim 9 , Maniar teaches all the limitations of the parent claim 1 as shown above. Maniar additionally discloses obtaining training data comprising second well log data for a plurality of training wells, second surface production data for the plurality of training wells, and acquired production logging tool (PLT) data for a second plurality of depth intervals for the plurality of training wells, wherein the acquired PLT data is acquired using a plurality of production logging tools in the plurality of training wells; and performing a training operation of an initial model using the training data to produce the first machine-learning model (Maniar: FIG. 2; ¶¶33-35 { See above .}; FIGS. 1.1-1.2; ¶¶22-28 [“…the content stored in the data repository (238) includes a collection of existing well data sets (e.g., existing well data set A (233), existing well data set B (234-1), existing well data set C (234-2), etc.), a target well data set (235), a training data set (236), and a predicted ROP profile (237). In one or more embodiments, an existing well data set is a collection of data that describes or otherwise is associated with an existing well. As used herein, the term "existing well" refers to a well that is already drilled, such as that corresponding to the wellsite A (114-1), wellsite B (114-2), wellsite C (114-3), etc. as depicted in FIG. 1.1. For example, the existing well data set A (233), existing well data set B (234-1), and existing well data set C (234-2) may correspond to the wellsite A (114-1), wellsite B (114-2), and wellsite C (114-3), respectively, as depicted in FIG. 1.1. Further, each of the existing well data set A (233), existing well data set B (234-1), and existing well data set C (234-2) may include one or more of well data (e.g., well data A (233-1)), drilling parameters (e.g., drilling parameter A (233-2)), bit parameters ( e.g., bit parameter A (233-3)), well logs ( e.g., well log A (233-4)), drilling fluid parameters (e.g., drilling fluid parameter A (233-5)), lithology parameters (e.g., lithology parameter A (233-6)), etc.”]). Regarding claim 20 , the claim recites limitations found within Claim 9, and is rejected under the same rationale applied to the rejection of Claim 9. Regarding claim 10 , Maniar teaches all the limitations of the parent claim 1 as shown above. Maniar additionally discloses acquiring, using a logging system coupled to the first well, the first well log data (Maniar: FIGS. 1.1-1.2; ¶¶14-15 { See above .}). Regarding claim 11 , Maniar teaches all the limitations of the parent claim 1 as shown above. Maniar additionally discloses the first control system is coupled to a wellhead assembly (Maniar: FIGS. 1.1-1.2; ¶¶14-15 { See above .}), and wherein the first command adjusts one or more production parameters in the wellhead assembly (Maniar: FIG. 2; ¶36 [“…the field operation control signal may be used to control the drilling equipment of the target well. In one or more embodiments, during drilling of the target well, the field task engine (231) may adjust the field control signal in response to the modeling engine (225) updating the predicted ROP profile (237)…”] { See above .}) . Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of the appropriate paragraphs of AIA 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 2-3, 5-6, 8, 13, 15, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Maniar; in view of Thigpen (U.S. Patent Publication 2008/0262735 A1) . Regarding claim 2 , Maniar teaches all the limitations of the parent claim 1 as shown above. Maniar additionally discloses determining second predicted production data for a second depth interval among the plurality of depth intervals (Maniar: FIG. 2; ¶¶33-35; FIGS. 1.1-1.2; ¶¶22-28 { See above .}), and transmitting a second command to a second control system (Maniar: FIG. 2; ¶¶17-18 { See above .}). However, Maniar fails to explicitly teach determining whether a second depth interval is experiencing a water breakthrough based on the second predicted production data, and implementing a remediation operation in response to determining that the second depth interval is experiencing the water breakthrough. Thigpen, in an analogous art, is directed to estimating an occurrence of a water breakthrough in a production well (Thigpen: Abstract ). Therein, Thigpen teaches determining whether a second depth interval is experiencing a water breakthrough based on second predicted production data (Thigpen: FIGS. 1A-1B, 2; ¶14 [“FIG. 1A shows a well 50 formed in a formation 55 that is producing formation fluid 56a and 56b from two exemplary production zones 52a (upper production zone) and 52b (lower production zone) respectively.”]; FIG. 2; ¶22 [“FIG. 2 shows a functional diagram of a production well system 200 that may be utilized to implement the various functions and method relating to detection and prediction of water breakthrough, determining actions that may be taken to mitigate the effects of an occurrence of a water breakthrough condition, for taking certain actions in response thereto…”]; ¶27 [“Once the central controller 150 using the well performance analyzer determines an actual or potential water breakthrough, it determines the actions to be taken to mitigate or eliminate the effects of the water breakthrough and may send messages, alarm conditions, water breakthrough parameters, the actions for the operator to take, the actions that are automatically taken by the controller 150 etc. as shown at 260, which messages are displayed at a suitable display 262 located at one or more locations, including at the well site and/or a remote control unit 185.”]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to implement the features of determining whether a second depth interval is experiencing a water breakthrough based on the second predicted production data, and implementing a remediation operation in response to determining that the second depth interval is experiencing the water breakthrough, disclosed by Thigpen, into Maniar, with the motivation and expected benefit of determining whether a depth interval is experiencing a water breakthrough based on predicted production data, and implementing a remediation operation in response to determining that the depth interval is experiencing the water breakthrough. This method for improving Maniar was within the ordinary ability of one of ordinary skill in the art based on the teachings of Thigpen. Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Maniar and Thigpen to obtain the invention as specified in claim 2. Regarding claim 17 , the claim recites limitations found within claim 2, and is rejected under the same rationale applied to the rejection of claim 2. Regarding claim 3 , Maniar teaches all the limitations of the parent claim 1 as shown above. Thigpen additionally discloses determining, using the first well log data, rock matrix porosity data, and fracture porosity data for the first depth interval in the first well, determining, using the first well log data, rock matrix porosity data, and fracture porosity data for the first depth interval in the first well, and determining a petrophysical rock type for the first depth interval, wherein the rock matrix porosity data, the fracture porosity data, the rock matrix permeability data, the fracture permeability data, and the petrophysical rock type are used by the first machine-learning model to determine the first predicted production data for the first depth interval, wherein the rock matrix porosity data, the fracture porosity data, the rock matrix permeability data, the fracture permeability data, and the petrophysical rock type are used by the first machine-learning model to determine the first predicted production data for the first depth interval (Thigpen: FIG. 2; ¶¶23-26 [“…database 230 that is accessible to the processors 152, which database may include well completion data and information, such as: types and locations of sensors in the well; sensor parameters; types of devices and their parameters, such as choke sizes, choke positions, valve sizes, valve positions, etc; formation parameters, such as rock type for various formation layers, porosity, permeability, mobility, depth of each layer and each production zone; sand screen parameters; tracer information; ESP parameters…The models/algorithms may use information relating to the formation parameters 230; well completion data 230; test data 224 on the well 50; and other information to predict the occurrence of the water breakthrough and/or the source of such breakthrough. For example, the processor may predict an occurrence of a water breakthrough using four dimensional seismic maps in view of the position of the water front relating to a particular producing zone or from formation fractures associated with the producing zone…”]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to implement the features of determining, using the first well log data, rock matrix porosity data, and fracture porosity data for the first depth interval in the first well, determining, using the first well log data, rock matrix permeability data, and fracture permeability data for the first depth interval in the first well, and determining a petrophysical rock type for the first depth interval, wherein the rock matrix porosity data, the fracture porosity data, the rock matrix permeability data, the fracture permeability data, and the petrophysical rock type are used by the first machine-learning model to determine the first predicted production data for the first depth interval, disclosed by Thigpen, into Maniar, with the motivation and expected benefit of determining whether a depth interval is experiencing a water breakthrough based on predicted production data, and implementing a remediation operation in response to determining that the depth interval is experiencing the water breakthrough. This method for improving Maniar was within the ordinary ability of one of ordinary skill in the art based on the teachings of Thigpen. Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Maniar and Thigpen to obtain the invention as specified in claim 3. Regarding claims 13 and 18 , each claim recites limitations found within claim 3, and is rejected under the same rationale applied to the rejection of claim 3. Regarding claim 5 , Maniar teaches all the limitations of the parent claim 1 as shown above. Thigpen additionally discloses the first predicted production data corresponds to a water entry at the first depth interval in the first well (Thigpen: FIG. 2; ¶¶23-26 [“The water measure or water content in the formation fluid may also be estimated from: the downhole sensors (such as resistivity or density sensors); analysis of tracers present in the produced fluid downhole or at the surface; density measurements; or from any other suitable sensor measurements. The water content may also be calculated in whole or in part downhole by a suitable processor and transmitted to the central controller 150 via a suitable link or wireless telemetry method, including acoustic and electromagnetic telemetry methods. The central controller 150, in one aspect, may utilize one or more programs, models and/or algorithms to estimate whether the water breakthrough already has occurred or when the water breakthrough may occur, i.e., predict the occurrence of a water breakthrough. The models/algorithms may use information relating to the formation parameters 230…”]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to implement the feature of providing first predicted production data which corresponds to a water entry at the first depth interval in the first well, disclosed by Thigpen, into Maniar, with the motivation and expected benefit of determining whether a depth interval is experiencing a water breakthrough based on predicted production data, and implementing a remediation operation in response to determining that the depth interval is experiencing the water breakthrough. This method for improving Maniar was within the ordinary ability of one of ordinary skill in the art based on the teachings of Thigpen. Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Maniar and Thigpen to obtain the invention as specified in claim 5. Regarding claim 6 , Maniar teaches all the limitations of the parent claim 1 as shown above. Thigpen additionally discloses the first surface production data comprise oil rate data, water rate data, and total flow rate data, and the first surface production data are acquired using a flow meter (Thigpen: FIG. 2; ¶¶19-21 [“A suitable flow meter 120, which may be a high-precision, low-flow, flow meter (such as gear-type meter or a nutating meter), measures the flow rate through lines 21 and 22, and provides signals representative of the corresponding flow rates.”]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to implement the feature of providing first surface production data comprise oil rate data, water rate data, and total flow rate data, and the first surface production data are acquired using a flow meter, disclosed by Thigpen, into Maniar, with the motivation and expected benefit of determining whether a depth interval is experiencing a water breakthrough based on predicted production data, and implementing a remediation operation in response to determining that the depth interval is experiencing the water breakthrough. This method for improving Maniar was within the ordinary ability of one of ordinary skill in the art based on the teachings of Thigpen. Examiner takes official notice that while Maniar, in view of Thigpen, doesn’t explicitly recite “multiphase flow meter,” numerous different flow meters are notoriously well-known, routine, and conventional in the art to which Applicant’s invention relates, prior to the date of invention. Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Maniar and Thigpen to obtain the invention as specified in claim 6. Regarding claim 8 , Maniar teaches all the limitations of the parent claim 1 as shown above. Maniar additionally discloses obtaining a selection of a second depth interval and a third depth interval among the plurality of depth intervals in the first well, determining, using the first machine-learning model, the first well log data, and second surface production data, first predicted oil rate data at the second depth interval, and determining, using the first machine-learning model, the first well log data, and the second surface production data, second predicted oil rate data at the third depth interval, wherein the first predicted oil rate data is greater than the second predicted oil rate data (Maniar: FIGS. 1.1-1.2; ¶¶22-28; FIG. 2; ¶¶33-35 { See above .}). Thigpen additionally discloses obtaining a selection of a second depth interval and a third depth interval among the plurality of depth intervals in the first well, wherein the second depth interval is higher in the first well than the third depth interval (Thigpen: FIGS. 1A-1B; ¶14 [“FIG. 1A shows a well 50 formed in a formation 55 that is producing formation fluid 56a and 56b from two exemplary production zones 52a (upper production zone) and 52b (lower production zone) respectively. The well 50 is shown lined with a casing 57 that has perforations 54a adjacent the upper production zone 52a and perforations 54b adjacent the lower production zone 52b. A packer 64, which may be a retrievable packer, positioned above or uphole of the lower production zone perforations 54a isolates the lower production zone 52b from the upper production zone 52a…”]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to implement the features of FIGS. 1.1-1.2; ¶¶22-28; FIG. 2; ¶¶33-35 { See above .}). Thigpen additionally discloses obtaining a selection of a second depth interval and a third depth interval among the plurality of depth intervals in the first well, wherein the second depth interval is higher in the first well than the third depth interval, disclosed by Thigpen, into Maniar, with the motivation and expected benefit of determining whether a depth interval is experiencing a water breakthrough based on predicted production data, and implementing a remediation operation in response to determining that the depth interval is experiencing the water breakthrough. This method for improving Maniar was within the ordinary ability of one of ordinary skill in the art based on the teachings of Thigpen. Examiner takes official notice that while Maniar, in view of Thigpen, doesn’t explicitly recite “multiphase flow meter,” numerous different flow meters are notoriously well-known, routine, and conventional in the art to which Applicant’s invention relates, prior to the date of invention. Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Maniar and Thigpen to obtain the invention as specified in claim 8. Regarding claim 15 , Maniar teaches all the limitations of the parent claim 12 as shown above. Thigpen additionally discloses determining whether predetermined depth interval is experiencing a water breakthrough (Thigpen: FIGS. 1A-1B, 2; ¶14; FIG. 2; ¶22, ¶27 { See above .}). Maniar additionally discloses the first predicted production data corresponds to a categorical variable based on a predetermined depth interval (Maniar: FIGS. 3.1-3.2; ¶¶52-55 [“…analyzed data in different measurement types may be used as continuous variables and/or as categorical variables, which are either inherently categorized or categorized through the process of discretization, during the machine-learning process.”]). It would have been obvious to one of ordinary skill in the art at the time the invention was made to implement the features of determining whether first predicted production data corresponds to a categorical variable based on whether the predetermined depth interval is experiencing a water breakthrough, disclosed by Maniar and Thigpen, into Maniar, with the motivation and expected benefit of determining whether a depth interval is experiencing a water breakthrough based on predicted production data, and implementing a remediation operation in response to determining that the depth interval is experiencing the water breakthrough. This method for improving Maniar was within the ordinary ability of one of ordinary skill in the art based on the teachings of Maniar and Thigpen. Therefore, it would have been obvious to one of ordinary skill in the art to combine the teachings of Maniar and Thigpen to obtain the invention as specified in claim 15 . Allowable Subject Matter Claims 4 and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and to overcome the outstanding section 101 rejections. The primary reason for the indicated allowability of dependent claim 4 , is that, in combination with the other claim elements, determining a height value above free water level of the first depth interval, determining first water saturation data of the first depth interval prior to the production operation, determining second water saturation data for the first depth interval during the production operation, and determining, using a water cut sensor, water cut data during the production operation, where the height value, the first water saturation data, the second water saturation data, and the water cut data are used by the first machine-learning model to determine the first predicted production data for the first depth interval. Therefore, dependent claim 4 would be allowable over the prior art of record if rewritten in independent form including all of the limitations of the base claim and any intervening claims and to overcome the outstanding section 101 rejections. Regarding claim 19 , the claim recites limitations found within claim 4, and would be allowable under the same rationale applied to the indicated allowability of claim 4. Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. U.S. Patent 11,435,499 B1, to Peredriy et al. , discloses automatically identifying tops of geological layers identified using machine-learning techniques. U.S. Patent Publication 2008/0208476 A1, to Karami , discloses controlling a production/injection operation for an oilfield, the oilfield having a first wellsite with a producing well advanced into subterranean formations with geological structures and reservoirs therein. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEFFREY P AIELLO whose telephone number is (303) 297-4216. The examiner can normally be reached on 8 AM - 4:30 PM EST. 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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. /JEFFREY P AIELLO/Primary Examiner, Art Unit 2857 Application/Control Number: 18/320,894 Page 2 Art Unit: 2857 Application/Control Number: 18/320,894 Page 3 Art Unit: 2857 Application/Control Number: 18/320,894 Page 4 Art Unit: 2857 Application/Control Number: 18/320,894 Page 5 Art Unit: 2857 Application/Control Number: 18/320,894 Page 6 Art Unit: 2857 Application/Control Number: 18/320,894 Page 7 Art Unit: 2857 Application/Control Number: 18/320,894 Page 8 Art Unit: 2857 Application/Control Number: 18/320,894 Page 9 Art Unit: 2857 Application/Control Number: 18/320,894 Page 10 Art Unit: 2857 Application/Control Number: 18/320,894 Page 11 Art Unit: 2857 Application/Control Number: 18/320,894 Page 12 Art Unit: 2857 Application/Control Number: 18/320,894 Page 13 Art Unit: 2857 Application/Control Number: 18/320,894 Page 14 Art Unit: 2857 Application/Control Number: 18/320,894 Page 15 Art Unit: 2857 Application/Control Number: 18/320,894 Page 16 Art Unit: 2857 Application/Control Number: 18/320,894 Page 17 Art Unit: 2857 Application/Control Number: 18/320,894 Page 18 Art Unit: 2857 Application/Control Number: 18/320,894 Page 19 Art Unit: 2857 Application/Control Number: 18/320,894 Page 20 Art Unit: 2857 Application/Control Number: 18/320,894 Page 21 Art Unit: 2857 Application/Control Number: 18/320,894 Page 22 Art Unit: 2857 Application/Control Number: 18/320,894 Page 23 Art Unit: 2857 Application/Control Number: 18/320,894 Page 24 Art Unit: 2857 Application/Control Number: 18/320,894 Page 25 Art Unit: 2857 Application/Control Number: 18/320,894 Page 26 Art Unit: 2857