DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because they are directed to a system having no physical structure so as be classified as a machine. Claim 15 says the model is generated using a processing device, but it is the model that is part of the system and not the processing device. Claims 15-20 appear to be directed to software per se, see MPEP §2106.03(I), and accordingly are directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under step 1 of MPEP §2106’s subject matter eligibility guidelines, claims 1-14 fall within the category of a process and article of manufacture. For purposes of compact prosecution claims 15-20 are being addressed here as well in the event they are amended to fall within the statutory categories.
Under Step 2A, prong 1, the claim(s) recite(s) “(Claim 1) generating an equation-oriented model of a natural gas facility, the equation-oriented model comprising a plurality of equations corresponding to processing components of the natural gas facility; executing a script to automatically generate a plurality of cloned equation-oriented models of the natural gas facility; generating a plurality of input value sets each representing a different operating condition of the natural gas facility; and applying, in parallel, each of the plurality of input value sets to at least one of the plurality of cloned equation-oriented models to generate a plurality of natural gas facility performance predictions…(Claim 2) wherein the equation-oriented model of a natural gas facility further comprises controllable components of the natural gas facility…(Claim 3) wherein applying each of the plurality of input value sets to at least one of the plurality of cloned equation-oriented models generates one or more control recommendations for the controllable components of the natural gas facility…(Claim 4) manipulating the plant data based on one or more threshold values…(Claim 5) optimizing, based on a model constraint information, the equation-oriented model of the natural gas facility…(Claim 7) wherein applying each of the plurality of input value sets to at least one of the plurality of cloned equation-oriented models comprises simulating the plurality of cloned equation-oriented models for a determined nomination time period…(Claim 8) wherein natural gas facility is a liquified natural gas facility…(Claim 9) generating an equation-oriented model of a natural gas facility, the equation-oriented model comprising a plurality of equations corresponding to processing components of the natural gas facility; executing a script to automatically generate a plurality of cloned equation-oriented models of the natural gas facility; and applying, in parallel, one or more input value sets to at least one of the plurality of cloned equation-oriented models to generate one or more natural gas facility performance predictions...(Claim 10) wherein the instructions, when executed by the one or more processors, generate a plurality of input value sets representing a plurality of different operating condition of the natural gas facility…(Claim 11) wherein the equation-oriented model of a natural gas facility further comprises controllable components of the natural gas facility…(Claim 12) applying the one or more input value sets to at least one of the plurality of cloned equation-oriented models generates one or more control recommendations for controllable components of the natural gas facility…(Claim 13) manipulating the plant data based on one or more threshold values…(Claim 14) wherein applying the one or more input value sets to at least one of the plurality of cloned equation-oriented models comprises simulating the plurality of cloned equation-oriented models for a determined nomination time period…(Claim 15) an equation-oriented model of a natural gas facility the equation-oriented model comprising a plurality of equations corresponding to processing components of the natural gas facility; a script to, upon execution, automatically generate a plurality of cloned equation-oriented models of the natural gas facility; a plurality of input value sets generated to represent a plurality of different operating conditions of the natural gas facility; and applying, in parallel, each of the plurality of input value sets to at least one of the plurality of cloned equation-oriented models to generate a plurality of natural gas facility performance predictions…(Claim 16) an equation-oriented modeling system including a scriptor to automatically generate the plurality of cloned equation-oriented models, the equation-oriented modeling system generating the performance prediction of a modeled processing system…(claim 17) wherein the equation-oriented model of a natural gas facility further comprises controllable components of the natural gas facility…(claim 18) model constraint information for optimizing the equation-oriented model of the natural gas facility…(claim 20) wherein the natural gas facility is a liquified natural gas facility.” These claims fall within the judicial exception of mathematical concepts as articulated in MPEP §2106.04(a)(2)(I).
Under Step 2A, prong 2, the claims has the additional limitations of: (Claims 1 and 15) “a processing device” and (Claim 9) “One or more tangible non-transitory computer-readable storage media storing instructions which, when executed by one or more processors”. Regarding these limitations, "use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more." MPEP §2106.05(f). Claims 4, 6, 13, and 19 further recite “receiving plant data from a sensor of the natural gas facility” and “generating a report of the plurality of natural gas facility performance predictions and displaying the report on a display device of a user interface.” These data gathering and data outputting limitations constitute insignificant extra-solution activity. See MPEP §2106.05(g). Accordingly, the claims do not recite additional elements that integrate the judicial exception into a practical application.
Under Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As stated above in regard to Step 2A, prong 2, the claims recite the additional limitations of: (Claims 1 and 15) “a processing device” and (Claim 9) “One or more tangible non-transitory computer-readable storage media storing instructions which, when executed by one or more processors”. Regarding these limitations, "use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more." MPEP §2106.05(f). Claims 4, 6, 13, and 19 further recite “receiving plant data from a sensor of the natural gas facility” and “generating a report of the plurality of natural gas facility performance predictions and displaying the report on a display device of a user interface.” These data gathering and data outputting limitations constitute insignificant extra-solution activity. See MPEP §2106.05(g). Accordingly, the claims do not recite additional elements that are significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Apap et al. (US 2017/0308831) (hereinafter Apap) in view of Ghorayeb et al. (US 2024/0126959) (hereinafter Ghorayeb).
Regarding claims 1,9, and 15, Apap teaches a method, non-transitory storage medium having instructions to perform the method, and a system that used a processing device (fig. 7, computer having storage 726 and memory 727) for generating a performance prediction of a processing plant, the method comprising:
generating, using a processing device, an equation-oriented model of a gas facility, the equation-oriented model comprising a plurality of equations corresponding to processing components of the natural gas facility (ph. [0035], “The present invention must consider the risks (i.e., uncertainties) related to the rundown blending operations 200 when making robust feedstock selection decisions for the refinery or petrochemical plant.”; ph. [0043], “The method 420 begins at step 505 by generating multiple simulation cases for determining probabilistic information regarding feedstock procurement on long-term contracts. In the embodiments of FIG. 5A, method 420 generates each simulation case as a different and independent instance of the same mixed-integer, non-linear optimization problem (MINLP) model. The general form of an MINLP model, which may contain thousands of equations and variables”);
executing a script to automatically generate a plurality of cloned equation-oriented models of the gas facility and generating a plurality of input value sets each representing a different operating condition of the natural gas facility (ph. [0045], “In method 420, the MINLP model includes uncertain input parameters (e.g., some elements in coefficient vectors c and/or d in Equation (1) above, and/or coefficients or scalar terms in Equation (2), such as demand D) that represent uncertainty related to selecting feedstocks to procure on long-term contracts. This uncertainty includes market uncertainty, such as benchmark-crude prices and crack spreads, and operations uncertainty, such as key equipment availability. The method 420 generates each simulation case (instance of the MINLP model) using different realization data for the uncertain parameters, so as to model different uncertain outcomes.”); and
applying, in parallel, each of the plurality of input value sets to at least one of the plurality of cloned equation-oriented models to generate a plurality of gas facility performance predictions (ph. [0049], “the method 420, at step 510, loads each generated simulation case (e.g., via a Solver Engine 150 of process modeling system 100, such as PIMS-AO) with no restrictions on the available capacity. Note, the method 420 uses computationally-tractable problem instances (i.e., simulation cases) that do not require complex decomposition strategies. The method 420 then rigorously solves (e.g., via Solver Engine 150 of the process modeling system 100) the loaded simulation cases in parallel to obtain optimal outcomes (results) for each simulation case (based on the configured realization values for the uncertain input parameters). As such, by solving multiple instances of the model with different realizations for the uncertain input parameters (i.e., the simulation cases), the method 420 characterizes the uncertainty of the model in the optimal outcomes. These optimal outcomes (for the given simulation cases) include the optimal feedstocks, respective feedstock volumes, and operating conditions with respect to each given simulation case. Note that the simulation cases may be solved in parallel because the different instances of the MINLP model are independent of one another.”).
While Apap teaches that it’s for a refinery or petrochemical plant, Apap does not explicitly teach it’s for a natural gas facility. However, Ghorayeb teaches it’s for a natural gas facility (ph. [0117], “the model can provide reasonable accuracy near the critical point, particularly for calculations of the compressibility factor and liquid density; mixing rules can be formulated to not employ more than a single binary interaction parameter, which can be independent of temperature, pressure and composition; and the equation can be applicable to calculations of fluid properties in natural gas processes.”). One of ordinary skill in the art before the effective filing date would have been motivated to apply the processes of Apap in the field of natural gas as taught by Ghorayeb in order to optimize natural gas blending in the same manner.
Regarding claim 2, 11, and 17, the Apap/Ghorayeb combination teaches the method of claim 1, media of claim 9 and system of claim 15. The combination further teaches wherein the equation-oriented model of a natural gas facility further comprises controllable components of the natural gas facility (Apap, ph. [0036], “For example, the present invention may determine the selected robust feedstocks must be optimally mixed and processed in a process unit 210 (e.g., separation with a particular temperature profile), splitter 270 (e.g., removing sulfur content in a hydro-treating operation), further processing in other process units 280 (e.g., chemically altering this stream), and subsequently blended into a particular product in blending operations (blender) 285 (e.g., with a particular compositional recipe). These optimal operating conditions are provided to refinery or plant applications, such as a blending control system, a plant process control system, and any other such control system, to program the rundown blending operations 200, including process unit 210, splitter 270, and blender 285, to process the procured feedstocks into the particular blended products.”; Ghorayeb, ph. [0117], “the equation can be applicable to calculations of fluid properties in natural gas processes.”).
Regarding claims 3 and 12, the Apap/Ghorayeb combination teaches the method of claim 2 and media of claim 9. The combination further teaches applying each of the plurality of input value sets to at least one of the plurality of cloned equation-oriented models generates one or more control recommendations for the controllable components of the natural gas facility (Apap, ph. [0036], “the optimal operating conditions are provided from the process modeling system 100 of FIG. 1A and programmed (automatically or by a user) at one or more plant or refinery control systems.”; Ghorayeb, ph. [0117], “the equation can be applicable to calculations of fluid properties in natural gas processes.”).
Regarding claims 4 and 13, the Apap/Ghorayeb combination teaches the method of claim 1 and media of claim 9. Apap further teaches receiving plant data from a sensor of the natural gas facility (ph. [0085], “Connected to the bus 725 is an input/output device interface 728 for connecting various input and output devices such as a keyboard, mouse, display, touch screen overlay, speakers, camera, sensor feeds…”); and manipulating the plant data based on one or more threshold values (ph. [0008], “the selecting of the robust feedstocks further includes applying a threshold probability to the probabilistic feed slate distribution to select a subset of the distributed feedstocks as robust feedstocks.”).
Regarding claims 5 and 18, the Apap/Ghorayeb combination teaches the method of claim 1 and system of claim 15. The combination further teaches further optimizing, based on a model constraint information, the equation-oriented model of the natural gas facility (Apap, ph. [0030], “Specifically, the Modeler Engine 140 provides parameters for a user or system to define feedstock selection planning as a non-linear model (e.g., an MINLP), including conservation equations (mass and such), functions (e.g., objective functions), variables, constraints (variable, equation, and function upper and lower limits), and the like.”, ph. [0033], “The Solver Engine 150 loads and optimally solves the model instances generated by the Modeler Engine 150 to provide a modeled outcome for each model instance.”; Ghorayeb, ph. [0117], “the equation can be applicable to calculations of fluid properties in natural gas processes.”).
Regarding claims 6 and 19, the Apap/Ghorayeb combination teaches the method of claim 1 and system of claim 15. Apap further teaches generating a report of the plurality of natural gas facility performance predictions and displaying the report on a display device of a user interface (ph. [0034], “he Solution Analyzer 160 then provides the results from analyzing the modeled outcomes, such as the selected contract feedstocks, to a user (e.g., as an economic priority ranking or other ordering in a spreadsheet or on the user interface display screen 110), or to other refinery or plant systems (applications), such as a blending control system, a plant process control system, and any other such control system, to program the refinery or plant operations.”; see fig. 1C-I, output from 160).
Regarding claims 7 and 14, the Apap/Ghorayeb combination teaches the method of claim 1 and media of claim 9. Apap further teaches applying each of the plurality of input value sets to at least one of the plurality of cloned equation-oriented models comprises simulating the plurality of cloned equation-oriented models for a determined nomination time period (ph. [0037]-[0038], Timescales for Evaluating Feedstock Selection).
Regarding claims 8 and 20, the Apap/Ghorayeb combination teaches the method of claim 1 and system of claim 15. Ghorayeb further teaches wherein natural gas facility is a liquified natural gas facility (ph. [0117], “the equation can be applicable to calculations of fluid properties in natural gas processes.”).
Regarding claim 10, the Apap/Ghorayeb combination teaches the media of claim 9. Apap further teaches generate a plurality of input value sets representing a plurality of different operating condition of the natural gas facility (ph. [0045], “In method 420, the MINLP model includes uncertain input parameters (e.g., some elements in coefficient vectors c and/or d in Equation (1) above, and/or coefficients or scalar terms in Equation (2), such as demand D) that represent uncertainty related to selecting feedstocks to procure on long-term contracts. This uncertainty includes market uncertainty, such as benchmark-crude prices and crack spreads, and operations uncertainty, such as key equipment availability. The method 420 generates each simulation case (instance of the MINLP model) using different realization data for the uncertain parameters, so as to model different uncertain outcomes.”).
Regarding claim 16, the Apap/Ghorayeb combination teaches the system of claim 15. Apap further teaches an equation-oriented modeling system including a scriptor to automatically generate the plurality of cloned equation-oriented models, the equation-oriented modeling system generating the performance prediction of a modeled processing system (ph. [0035], “The present invention must consider the risks (i.e., uncertainties) related to the rundown blending operations 200 when making robust feedstock selection decisions for the refinery or petrochemical plant.”; ph. [0043], “The method 420 begins at step 505 by generating multiple simulation cases for determining probabilistic information regarding feedstock procurement on long-term contracts. In the embodiments of FIG. 5A, method 420 generates each simulation case as a different and independent instance of the same mixed-integer, non-linear optimization problem (MINLP) model. The general form of an MINLP model, which may contain thousands of equations and variables”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Almarhoon et al. (US 2023/0265753) teaches a gas blend optimizer.
Hu et al. (US 2021/0150933) teaches a simulation method for gas reservoir exploitation.
Notz et al. (US 2022/0051756) teaches modelling of operating and/or dimensioning parameters of a gas treatment plant.
Bharathi (US 2021/0294655) teaches prioritizing execution of multiple groups of analytic models.
Bleackley et al. (US 2011/0264415) teaches configuration engine for a process simulator of an oil, gas, LNG refinery.
Ye, Z. et al. “MINLP Model for Operational Optimization of LNG Terminals” teaches modeling of LNG Terminals.
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BRIAN W. WATHEN
Primary Examiner
Art Unit 2151
/BRIAN W WATHEN/ Primary Examiner, Art Unit 2151
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