CTNF 18/391,313 CTNF 92068 DETAILED ACTION This office action is responsive to the above identified application filed 12/20/2023. The application contains claims 1-20, all examined and rejected. 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. Information Disclosure Statement The Information Disclosure Statement with references submitted 12/20/2023, has been considered and entered into the file. Claim Rejections - 35 USC § 112 07-30-03 AIA 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. Claim 1 recites the limitation “controller,” coupled with functional language without reciting sufficient structure to achieve the function. Claim 9 recites the limitation “controller,” coupled with functional language without reciting sufficient structure to achieve the function. Claim 13 recites the limitation “controller,” coupled with functional language without reciting sufficient structure to achieve the function. Claim 14 recites the limitation “controller,” coupled with functional language without reciting sufficient structure to achieve the function. Claim 15 recites the limitation “controller,” coupled with functional language without reciting sufficient structure to achieve the function. Since these claim limitations invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA), sixth paragraph, claims 1-20 are interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof. A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA), sixth paragraph limitation: Paragraph [0043] states, “The devices may be of various control types, such as a programmable logic controller (PLC) or a remote terminal unit (RTU). For example, a programmable logic controller (PLC) may control valve states, pipe pressures, warning alarms, and/or pressure releases throughout the oil and gas field. In particular, a programmable logic controller (PLC) may be a ruggedized computer system with functionality to withstand vibrations, extreme temperatures, wet conditions, and/or dusty conditions, for example, around a well system (106). With respect to an RTU, an RTU may include hardware and/or software, such as a microprocessor, that connects sensors and/or actuators using network connections to perform various processes in the automation system. As such, a distributed control system may include various autonomous controllers (such as remote terminal units)” Based on the guidelines announced from Federal Register Vol. 76, No. 27, this has been interpreted as encompassing a hardware or hardware in combination with software implementation of the module, but not a pure software implementation. If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action. Claimed modules also trigger interpretation of the claim language under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA), sixth paragraph since they are considered a place holder for a corresponding structure in the specification. If applicant does not wish to have the claim limitation treated under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA), sixth paragraph, applicant may amend the claim so that it will clearly not invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA), sixth paragraph, or present a sufficient showing that the claim recites sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA), sixth paragraph. For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance with 35 U.S.C. § 112 and for Treatment of Related Issues in Patent Applications , 76 FR 7162, 7167 (Feb. 9, 2011). Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. While independent claims 1, 9 and 15 are each directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process, mathematical concept). Claims 1-20 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below. When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG) STEP 1. Per Step 1, the claims are determined to include process, machine, and manufacture as in independent Claim 1, 9, and 15, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category. At step 2A, prong 1, The invention is directed to identifying features within received data that could be an indication of the probability of occurrence of a machine failure based on analyzed historic data which is akin to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are: “determining, a first and second predicted pressure gradient of the multiphase mixture in the pipeline, respectively, based on the flow data” (Mental process, observation, evaluation and judgment) “forming an aggregate pressure gradient from the first predicted pressure gradient and the second predicted pressure gradient” (Mental process, observation, evaluation and judgment); “adjusting, the set of operation parameters based on, at least, the aggregate pressure gradient” (Mental process, observation, evaluation and judgment). The claim recites additional elements as “obtaining flow data from a pipeline conveying a multiphase mixture of, at least, oil and water; obtaining a set of operation parameters related to a flow of the multiphase mixture in the pipeline” (insignificant extra-solution activity, MPEP 2106.05(g)); “a first artificial intelligence model and a second artificial intelligence model”, “a pipeline controller” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)). This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract. STEP 2B. Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts. The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s). When taken the steps individually, these steps are: “obtaining flow data from a pipeline conveying a multiphase mixture of, at least, oil and water; obtaining a set of operation parameters related to a flow of the multiphase mixture in the pipeline” (well-understood, routine, or conventional, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); “a first artificial intelligence model and a second artificial intelligence model”, “a pipeline controller” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)). In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed. In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves. Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts. Further, note that the limitations, in the instant claims, are done by the generically recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions. Claim 9 recites a system comprising “ a pipeline that conveys a multiphase mixture of, at least, oil and water; and a pipeline controller that can configure one or more configurable parameters of the pipeline … controller” configured to perform the same method as set forth in claim 1, the added element of “ a pipeline that conveys a multiphase mixture of, at least, oil and water; and a pipeline controller that can configure one or more configurable parameters of the pipeline … controller” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer and merely indicates a field of use or technological environment in which the judicial exception is performed. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer using a controller is tantamount to a mere instruction to apply the judicial exception to a computer. Claim 9 is therefore rejected according to the same findings and rationale as provided above. Claim 15 recites a system comprising “ A non-transitory computer-readable memory comprising computer-executable instructions stored thereon that, when executed on a processor” configured to perform the same method as set forth in claim 1, the added element of “ A non-transitory computer-readable memory comprising computer-executable instructions stored thereon that, when executed on a processor” do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer. Claim 15 is therefore rejected according to the same findings and rationale as provided above. Independent claims 9 and 15 are the same analogy and rejected using similar analysis as claim 1. CONCLUSION It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well- understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish). The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim. claims 2 disclose “the multiphase mixture is produced by a well, wherein the set of operation parameters comprises: a set of well control parameters defining an operation of the well; and a set of pipeline parameters governing the flow of the multiphase mixture in the pipeline” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose “wherein the set of operation parameters comprises: a set of pipeline parameters governing the flow of the multiphase mixture in the pipeline” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 4 disclose “wherein the flow data comprises: an oil and water slip velocity relating the velocity of the oil and the velocity of the water of the multiphase mixture; a diameter of the pipeline; a roughness of the pipeline; and a viscosity of the oil of the multiphase mixture” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea; claim 5 disclose “wherein the first artificial intelligence model is a least squares support vector machine, wherein the second artificial intelligence model is a radial basis function neural network” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claim 6 disclose “determining, with an optimizer, a set of optimal operation parameters based on the aggregate pressure gradient, wherein the set of optimal operation parameters maximize a production of oil” (Mental process, Mathematical concept), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claim 7 disclose “acquiring sensor data” (insignificant extra-solution activity, MPEP 2106.05(g) and well-understood, routine, or conventional, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), “with at least one sensor” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), “the sensor data comprising at least one of a pressure difference between two locations on the pipeline and a production metric” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)); and “determining, based on the sensor data and the aggregate pressure gradient, a blockage or leak in the pipeline” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claim 8 disclose “wherein the first and second predicted pressure gradients, determined with the first and second artificial intelligence models, respectively, are further based on the set of operation parameters” (data description, which is directed to generally linking the use of a judicial exception to a particular technological environment or type or source of data or field of use MPEP 2106.05(h)), “wherein the method further comprises: iteratively adjusting the set of operation parameters to identify a set of optimal operation parameters that result in a desired aggregate pressure gradient” (mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed. For at least these reasons, the claimed inventions of each of dependent claims 2-8, 10-14, and 16-20,are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. 07-21-aia AIA Claim s 1-4, 6, 8-11, 13, 15-17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over “Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms” Published July 24, 2021 disclosed in IDS submitted 12/20/2023 [hereinafter D1] in view of McCann et al. [US 2019/0316943 A1, hereinafter D2] With regard to Claim 1, D1 disclose a method, comprising: obtaining flow data from a pipeline conveying a multiphase mixture of, at least, oil and water (P. 2, Col. 2, ¶2, “oil and water flowed from separate tanks into a pipeline”, P. 2, Col. 1-2, “dataset includes thirteen variables including … oil viscosity”, “water viscosity”, “oil density” “water density”, “water velocity”, “oil velocity”, “water surface tension”, “oil surface tension”, “water-oil surface tension”, “surface roughness”, “input diameter”, “flow pattern (FP)”) ; obtaining a set of operation parameters related to a flow of the multiphase mixture in the pipeline (P. 2, Col. 2, ¶2, “oil and water flowed from separate tanks into a pipeline. The liquids’ volumetric flow rates were regulated either by varying the number of revolutions of the centrifugal pumps or by a set of control valves. The liquids’ viscosities were varied by changing the temperature of the liquid phases through heat exchangers”, P. 2, Col. 1-2, “dataset includes thirteen variables including … oil viscosity”, “water viscosity”, “oil density” “water density”, “water velocity”, “oil velocity”, “water surface tension”, “oil surface tension”, “water-oil surface tension”, “surface roughness”, “input diameter”, “flow pattern (FP)”) ; determining, with a first artificial intelligence model and a second artificial intelligence model, a first and second predicted pressure gradient of the multiphase mixture in the pipeline, respectively, based on the flow data (P. 2, 2.2., “five robust MLs, namely, Support Vector Machine (SVM) (Vapnik, 2013), Gaussian Process (GP) (Williams and Rasmussen, 1996), Random Forest (RF) (Breiman, 2001), Artificial Neural Network (ANN) (Rosenblatt, 1958), and k Nearest Neighbor (kNN) (Altman, 1992) were employed to predict the pressure gradients in oil-water flow”) ; forming an aggregate pressure gradient from the first predicted pressure gradient and the second predicted pressure gradient (P. 2, 2.2., “The fusion of all these MLs has also been investigated using their average and weighted average prediction values”) ; and D1 does not explicitly teach adjusting, with a pipeline controller, the set of operation parameters based on, at least, the aggregate pressure gradient. D2 teach adjusting, with a pipeline controller, the set of operation parameters based on, at least, the aggregate pressure gradient (Fig. 21, ¶71, “workflow could be; 1) measure flow parameters; 2) input these into a real-time flow model (OLGA); 3) vary flow rates, choke positions, gas lift parameters, ESP (electrical submersible pump) speed etc.; 4) Iterate back to step 1) changing system parameters 3) until the production is optimized”, ¶167, “ the combination of the spatial and temporal distributions of multiphase flow attributes … with the calibrated mechanistic multiphase flow model … to control process parameters in order to improve production performance of an oil/gas well or group of oil/gas wells or oilfield”, ¶174, “model uses the pressure gradient in the system including in the wellbore, which is highly dependent on the gas bubble distribution in the wellbore. The distribution values computed in step 211 can be used to provide a real input to this calculation”, ¶182, “The surface control system 208 will be used to control valves and chokes that exist in the wells or on the seabed where the produced multiphase flow enters the subsea flowlines 209”, ¶183, “such predictions can be used to determine the system control input parameters that result in improved or optimized production”) . D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of monitoring, modeling, and optimization of multiphase fluid flow in oil and gas production and pipelines. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 pressure gradient framework as described above t to incorporate pressure gradient based optimization and control techniques to allow the predicted aggregate pressure gradient to be used to automatically adjust operational parameters in order to improve pipeline and well performance. Such modification would improve production optimization because real-time determination of these different and changing flow regimes would offer reservoir and production engineers a deeper insight into production and so allow improved optimization (D2, ¶11) . With regard to Claim 2, D1-D2 teach the method of claim 1, wherein the multiphase mixture is produced by a well (D2, ¶7, “oil well starts life producing mainly oil, but as the oil depressurizes along the flow line gas is liberated, so at the wellhead there is almost always some gas present. In addition, most wells produce some water”, ¶9, “ both oil and gas wells generate multiphase flows with gas, oil and water”, ¶¶50-53, “oil well system including a plurality of oil wells, a plurality of collection pipelines” ¶¶74-75, “oil, water and gas from a well or group of wells”) , wherein the set of operation parameters comprises: a set of well control parameters defining an operation of the well (D2, Fig. 2, 214, ¶53, “using the calibrated or modified multiphase fluid flow model to monitor and/or control the operation of the oil well system”, ¶71, “ vary flow rates, choke positions, gas lift parameters, ESP (electrical submersible pump) speed etc.” ; and a set of pipeline parameters governing the flow of the multiphase mixture in the pipeline (D1, P. 2, Col. 1-2, “dataset includes thirteen variables including … oil viscosity”, “water viscosity”, “oil density” “water density”, “water velocity”, “oil velocity”, “water surface tension”, “oil surface tension”, “water-oil surface tension”, “surface roughness”, “input diameter”, “flow pattern (FP)”, D2, Fig. 18, 183, ¶¶50-53, “output data are processed to determine, at at least one position across the cross-section of the pipe, (a) the individual concentration of at least one of natural gas, mineral oil and/or water, (b) the individual density of at least one of natural gas, mineral oil and/or water and (c) the individual velocity of at least one of natural gas, mineral oil and/or water”, ¶154) . With regard to Claim 3, D1-D2 teach the method of claim 1, wherein the set of operation parameters comprises: a set of pipeline parameters governing the flow of the multiphase mixture in the pipeline (D1, P. 2, Col. 1-2, “dataset includes thirteen variables including … oil viscosity”, “water viscosity”, “oil density” “water density”, “water velocity”, “oil velocity”, “water surface tension”, “oil surface tension”, “water-oil surface tension”, “surface roughness”, “input diameter”, “flow pattern (FP)”, D2, Fig. 18, ¶¶50-53, “(a) the individual concentration of at least one of natural gas, mineral oil and/or water, (b) the individual density of at least one of natural gas, mineral oil and/or water and (c) the individual velocity of at least one of natural gas, mineral oil and/or water””) . With regard to Claim 4, D1-D2 teach the method of claim 1, wherein the flow data comprises: an oil and water slip velocity relating the velocity of the oil and the velocity of the water of the multiphase mixture (D2, Fig. 18, 183) ; a diameter of the pipeline (D1, P. 2, Col. 1-2, “input diameter”) ; a roughness of the pipeline ( D1, P. 2, Col. 1-2, “surface roughness”) ; and a viscosity of the oil of the multiphase mixture (D1, P. 2, Col. 1-2, “dataset includes thirteen variables including … oil viscosity”) . With regard to Claim 6, D1-D2 teach the method of claim 1, further comprising: determining, with an optimizer, a set of optimal operation parameters based on the aggregate pressure gradient (D1, Abstract, “The MLs include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and fusions of these five MLs”, P. 2, ¶4, “The algorithms include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k Nearest Neighbor (kNN), and fusion of all these algorithms”, P. 2, 2.2., “The fusion of all these MLs has also been investigated using their average and weighted average prediction values”, D2, ¶71, “vary flow rates, choke positions, gas lift parameters, ESP (electrical submersible pump) speed etc.”) , wherein the set of optimal operation parameters maximize a production of oil (D2, ¶167, “control process parameters in order to improve production performance of an oil/gas well or group of oil/gas wells or oilfield”). With regard to Claim 8, D1-D2 teach the method of claim 1, wherein the first and second predicted pressure gradients, determined with the first and second artificial intelligence models, respectively, are further based on the set of operation parameters (D1, P. 2, Col. 2, ¶2, “oil and water flowed from separate tanks into a pipeline”, P. 2, Col. 1-2, “dataset includes thirteen variables including … oil viscosity”, “water viscosity”, “oil density” “water density”, “water velocity”, “oil velocity”, “water surface tension”, “oil surface tension”, “water-oil surface tension”, “surface roughness”, “input diameter”, “flow pattern (FP)”) , wherein the method further comprises: iteratively adjusting the set of operation parameters to identify a set of optimal operation parameters that result in a desired aggregate pressure gradient (D1, Fig. 2, P. 4-5, 2.2.4, Abstract, “The MLs include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and fusions of these five MLs”, P. 2, ¶4, “The algorithms include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k Nearest Neighbor (kNN), and fusion of all these algorithms”, P. 2, 2.2., “The fusion of all these MLs has also been investigated using their average and weighted average prediction values” D2, Fig. 21, ¶71, “workflow could be; 1) measure flow parameters; 2) input these into a real-time flow model (OLGA); 3) vary flow rates, choke positions, gas lift parameters, ESP (electrical submersible pump) speed etc.; 4) Iterate back to step 1) changing system parameters 3) until the production is optimized”, , ¶183, “such predictions can be used to determine the system control input parameters that result in improved or optimized production”, ¶167, “ the combination of the spatial and temporal distributions of multiphase flow attributes … with the calibrated mechanistic multiphase flow model … to control process parameters in order to improve production performance of an oil/gas well or group of oil/gas wells or oilfield”, ¶174, “model uses the pressure gradient in the system including in the wellbore, which is highly dependent on the gas bubble distribution in the wellbore. The distribution values computed in step 211 can be used to provide a real input to this calculation”, ¶182, “The surface control system 208 will be used to control valves and chokes that exist in the wells or on the seabed where the produced multiphase flow enters the subsea flowlines 209”) . With regard to Claim 9, Claim 9 is similar in scope to claim 1; therefore it is rejected under similar rationale. Further D2 teach system, comprising: a pipeline controller that can configure one or more configurable parameters of the pipeline (¶182, “ surface control system 208 will be used to control valves and chokes that exist in the wells or on the seabed where the produced multiphase flow enters the subsea flowlines “) , the one or more configurable parameters comprised by a set of operation parameters, the pipeline controller configured to: obtain flow data from the pipeline (Fig. 21, ¶71, “ 1) measure flow parameters; 2) input these into a real-time flow model (OLGA); 3) vary flow rates, choke positions, gas lift parameters, ESP (electrical submersible pump) speed etc.; 4) Iterate back to step 1) changing system parameters 3) until the production is optimized”). With regard to Claim 15, Claim 15 is similar in scope to claim 1; therefore it is rejected under similar rationale. D1-D2 further teach A non-transitory computer-readable memory comprising computer-executable instructions stored thereon that, when executed on a processor, cause the processor to perform steps (D1, Table 2, Table 4-5, P. 6, 2.6, “All model development and statistical calculations were performed using Matlab”, P. 7. 3.1, “hyperparameters of both RF and SVM models were optimized during the training step”, D2, ¶68, “preferred embodiments of the present invention can provide that software or processing is provided”, ¶71, “calibrate, validate and characterize multiphase flow modelling software, for example, OLGA”, ¶165, “ Bayesian approach provides several advantages that make it well suited to applications where model computations can be expensive in computer resources to run”) . With regard to Claim 10, Claim 10 is similar in scope to claim 2; therefore it is rejected under similar rationale. With regard to Claim 11, Claim 11 is similar in scope to claim 4; therefore it is rejected under similar rationale. With regard to Claim 13, Claim 13 is similar in scope to claim 6; therefore it is rejected under similar rationale. With regard to Claim 16, Claim 16 is similar in scope to claim 2; therefore it is rejected under similar rationale. With regard to Claim 17, Claim 17 is similar in scope to claim 4; therefore it is rejected under similar rationale. With regard to Claim 19, Claim 19 is similar in scope to claim 6; therefore it is rejected under similar rationale . 07-21-aia AIA Claim s 5, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over “Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms” Published July 24, 2021 disclosed in IDS submitted 12/20/2023 [hereinafter D1] in view of McCann et al. [US 2019/0316943 A1, hereinafter D2] in view of Gao et al. [US 20220097809 A1, hereinafter Gao] . With regard to Claim 5, D1-D2 teach the method of claim 1: wherein the first artificial intelligence model is a least squares support vector machine, wherein the second artificial intelligence model is a radial basis function neural network. D1-D2 teach the method of claim 1: wherein the first artificial intelligence model is a support vector machine, wherein the second artificial intelligence model is a neural network (D1, Abstract, “ The MLs include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and fusions of these five MLs”, P. 2, ¶4, “The algorithms include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k Nearest Neighbor (kNN), and fusion of all these algorithms”) . D1-D2 does not explicitly teach a least squares support vector machine, a radial basis function neural network. Gao teach wherein the first artificial intelligence model is a least squares support vector machine (¶6, “ a least squares support vector machine (LS-SVM) classifier is employed“, ¶8, “employing a least squares support vector machine (LS-SVM) to calculate a classification plane”, ¶44, “The present disclosure classifies the working conditions … by employing a least squares support vector machine (LS-SVM) algorithm“, ¶47, “employing a radial basis function as a kernel function of an LS-SVM algorithm”) , wherein the second artificial intelligence model is a radial basis function neural network (¶6, “a radial basis function neural network method optimized by a genetic algorithm”, ¶19, “Construct a three-layer forward radial basis function neural network”, ¶55, “Construct a three-layer forward radial basis function neural network, composed of an input layer, a hidden layer and an output layer”) . D1-D2 and Gao are analogous art to the claimed invention because they are from a similar field of endeavor of using machine learning models on operational input data to predict a target operating variable and improve prediction accuracy. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by Gao with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-D2 as described above D1-D2 teach using multiple machine learning (ML) models to predict pressure gradient and the fusing their outputs. Gao teach least squares support vector machine (LS-SVM) and radial basis function neural network (RBF neural network) as known predictive models and uses them together for forecasting. A person of ordinary skill in the art would recognize LS-SVM and RBF neural network as an alternative predictive models that could be substituted for D1-D2 SVM and ANN models LS-SVM and RBF neural network model as RBF neural network often outperform ANNs in small to medium-sized problems with fewer training samples, especially when the data is well-clustered. They are efficient for function approximation, time series prediction, and classification and LS-SVM is generally faster to train due to linear equation solving (e.g., conjugate gradient) vs. quadratic programming (QP) problem solvers providing better speed, scalability. This simply combining prior art elements according to known methods to yield predictable results, simple substitution of one known element for another to obtain predictable results, use of known technique to improve similar devices (methods, or products) in the same way, and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143). With regard to Claim 12, Claim 12 is similar in scope to claim 5; therefore it is rejected under similar rationale. With regard to Claim 18, Claim 18 is similar in scope to claim 5; therefore it is rejected under similar rationale . 07-21-aia AIA Claim s 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over “Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms” Published July 24, 2021 disclosed in IDS submitted 12/20/2023 [hereinafter D1] in view of McCann et al. [US 2019/0316943 A1, hereinafter D2] in view of Chen et al. [US 20230332976 A1, hereinafter Chen] . With regard to Claim 7, D1-D2 teach the method of claim 1, further comprising: acquiring sensor data, with at least one sensor (D2, ¶51, “determine , at at least one position across the cross - section of the pipe , ( a ) the individual concentration of at least one of natural gas , mineral oil and / or water , ( b ) the individual density of at least one of natural gas , mineral oil and / or water and ( c ) the individual velocity of at least one of natural gas , mineral oil and / or water”) , the sensor data comprising a production metric (D2, ¶74, “Quantify the mass/volume flowrate of different constituents of the flow: oil, water and gas from a well or group of wells “, ¶167, “control process parameters in order to improve production performance of an oil/gas well or group of oil/gas wells or oilfield”) ; and determining the aggregate pressure gradient (D1, Abstract, “The MLs include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and fusions of these five MLs”, P. 2, ¶4, “The algorithms include Support Vector Machine (SVM), Gaussian Process (GP), Random Forest (RF), Artificial Neural Network (ANN), k Nearest Neighbor (kNN), and fusion of all these algorithms”, P. 2, 2.2., “The fusion of all these MLs has also been investigated using their average and weighted average prediction values”) . D1-D2 does not teach the sensor data comprising at least one of a pressure difference between two locations on the pipeline; determining, based on the sensor data a blockage or leak in the pipeline. Chen teach acquiring sensor data, with at least one sensor (Fig. 1, Abstract, “a plurality of sensor sites”, ¶11, “ identifying a pipeline pressure surge by applying a trained convolutional neural network (CNN) model for classifying pipeline pressure measurement images on each sensor site of a plurality of sensor sites”, “plurality of sensor sites collecting pipeline pressure measurement data”, ¶200, “use adjacent sensor sites to identify a pipeline leak”) , the sensor data comprising at least one of a pressure difference between two locations on the pipeline (¶20, “, the recorded historic pressure surge data inputs can be calculated from an adjacent sensor pair of the plurality of sensor sites”, “ratio parameter value of a pressure drop (DP) over a distance between the adjacent sensor pair”, ¶258, “The gradient of the measurement ratio can be used with a DT gradient as analytical inputs to build the logics (i.e., training the models) in differentiating simulated leaks from other pressure surges. DT can represent the difference between the timestamps of the adjacent sensor pair. The DT can be used to estimate the speed of sound and accurately determine the leak location. DT gradient can represent the DT divided by the distance between the adjacent sensor pair”), and a production metric; and determining, based on the sensor data and the aggregate pressure gradient, a blockage or leak in the pipeline (¶258, “The gradient of the measurement ratio can be used with a DT gradient as analytical inputs to build the logics (i.e., training the models ) in differentiating simulated leaks from other pressure surges . DT can represent the difference between the timestamps of the adjacent sensor pair. The DT can be used to estimate the speed of sound and accurately determine the leak location. DT gradient can represent the DT divided by the distance between the adjacent sensor pair”, ¶11, “determining whether the identified pressure surge is a pipeline leak at the cloud site using the pressure surge information”, ¶15, “determining whether the identified pressure surge is a pipeline leak can include differentiating the pipeline leak from other pressure surges by using an adaptive neuro-fuzzy inference system (ANFIS) model”) . D1-D2 and Chen are analogous art to the claimed invention because they are from a similar field of endeavor of monitoring and analyzing fluid flow conditions within pipelines. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by Chen with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1-D2 as described above to provide robust and reliable leak detection that is crucial for pipeline operations (e.g., oil and gas) to mitigate environmental pollution, minimize financial loss, and secure compliance with regulatory requirements (Chen, ¶4) . With regard to Claim 14, Claim 14 is similar in scope to claim 7; therefore it is rejected under similar rationale. With regard to Claim 20, Claim 20 is similar in scope to claim 7; therefore it is rejected under similar rationale. Conclusion 07-96 The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure. US Patent Application Publication No. 20240003245 filed by Havre et al. that disclose the ability to detect leakage in pipelines using sensor data and gradient pressure See at least ¶237 Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck , 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) ( quoting In re Lemelson , 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT. 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, Michelle Bechtold can be reached at (571) 431-0762. 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. /MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148 Application/Control Number: 18/391,313 Page 2 Art Unit: 2148 Application/Control Number: 18/391,313 Page 3 Art Unit: 2148 Application/Control Number: 18/391,313 Page 4 Art Unit: 2148 Application/Control Number: 18/391,313 Page 5 Art Unit: 2148 Application/Control Number: 18/391,313 Page 6 Art Unit: 2148 Application/Control Number: 18/391,313 Page 7 Art Unit: 2148 Application/Control Number: 18/391,313 Page 8 Art Unit: 2148 Application/Control Number: 18/391,313 Page 9 Art Unit: 2148 Application/Control Number: 18/391,313 Page 10 Art Unit: 2148 Application/Control Number: 18/391,313 Page 11 Art Unit: 2148 Application/Control Number: 18/391,313 Page 12 Art Unit: 2148 Application/Control Number: 18/391,313 Page 13 Art Unit: 2148 Application/Control Number: 18/391,313 Page 14 Art Unit: 2148 Application/Control Number: 18/391,313 Page 15 Art Unit: 2148 Application/Control Number: 18/391,313 Page 16 Art Unit: 2148 Application/Control Number: 18/391,313 Page 17 Art Unit: 2148 Application/Control Number: 18/391,313 Page 18 Art Unit: 2148 Application/Control Number: 18/391,313 Page 19 Art Unit: 2148 Application/Control Number: 18/391,313 Page 20 Art Unit: 2148 Application/Control Number: 18/391,313 Page 21 Art Unit: 2148 Application/Control Number: 18/391,313 Page 22 Art Unit: 2148 Application/Control Number: 18/391,313 Page 23 Art Unit: 2148 Application/Control Number: 18/391,313 Page 24 Art Unit: 2148 Application/Control Number: 18/391,313 Page 25 Art Unit: 2148 Application/Control Number: 18/391,313 Page 26 Art Unit: 2148 Application/Control Number: 18/391,313 Page 27 Art Unit: 2148 Application/Control Number: 18/391,313 Page 28 Art Unit: 2148 Application/Control Number: 18/391,313 Page 29 Art Unit: 2148 Application/Control Number: 18/391,313 Page 30 Art Unit: 2148 Application/Control Number: 18/391,313 Page 31 Art Unit: 2148
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