Notice of Pre-AIA or AIA Status
Claims 1 and 9 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 07/05/2026 has been entered and considered by the examiner. By the amendment, claims 1 and 9 are amended and claims 2-8 and 10 are cancelled. In view of amendment made, the previous 112 rejection of the claim are withdrawn. Following Applicants arguments and amendments made, Examiner modify the prior art rejections. And, the 101 rejection is still maintained. See office action for detail.
Applicant arguments on 101 rejections
The final step of the method is not merely to display or store these graphs, but to actively apply them for concrete operational purposes: daily management and maintenance of the pipe network, enhanced disinfection of water quality, energy conservation and consumption reduction, or formulation of water quality maintenance plans. That is, the output of the simulation is not an end in itself but serves as a control or decision-making basis for physical operations performed on the actual water supply infrastructure. This integration of simulation results into specific, tangible physical-world operations is precisely the kind of practical application that transforms an abstract idea into patent-eligible subject matter. Accordingly, the claimed invention as a whole is not directed to an abstract idea. It is directed to a specific, practical application of integrated water quality simulation for real-world water supply system management.
Examiner response
Examiner respectfully disagrees. The final claim limitation of “using the time-series graphs for daily management and maintenance of the pipe network, enhanced disinfection of water quality, energy conservation and consumption reduction, or for formulating water quality maintenance plans” falls under the combination of mental process or methods of organizing human activity of abstract ideas. Reviewing graphs to make business, managerial, or operational decisions ("formulating maintenance plans," "energy conservation") falls squarely under methods of organizing human activity and mental evaluation of data. A manager reviewing a hand-drawn chart to decide when to flush a pipe or add chlorine performs a pure mental decision-making step. Claim do not positively recite or require specific, concrete structural transformations or active hardware control steps. The operations recited ("daily management," "disinfection," "formulating maintenance plans") are stated at a high, result-oriented level of generality and do not tie the mental and mathematical abstract results to an improved, specific, and unconventional physical machine or transformation step. Thus, the 101 rejection is still maintained.
Applicant arguments on 103 rejections
Amended claim 1 contains several limitations that are neither expressly nor inherently disclosed in Luan. First, Luan does not describe a "multi-species water quality simulation method" for the pipe network.
Examiner response
In view of amended claims, Examiner cited the new reference Shang et al. ("EPANET multi-species extension user’s manual." Risk Reduction Engineering Laboratory, US Environmental Protection Agency, Cincinnati, Ohio (2008).) See office action for detail.
Applicant arguments
Applicant argues Luan does not disclose the use of an automated script to sequentially execute the 2D river simulation, data interface conversion, and 1 D pipe network simulation to generate time-series graphs for specific nodes or pipe segments. Luan's model runs a single integrated simulation, but it does not describe an automated, script-driven workflow that orchestrates separate components in a sequential manner. The claimed method's use of an automated script to sequentially execute the 2D river simulation, the data interface conversion, and the 1D pipe network simulation, and to parse the output and generate time-series graphs, is a specific teleological implementation not found in Luan.
Examiner response
Examiner respectfully disagrees. Luan further teaches the use of an automated script (see fig 1 Luan) to sequentially execute the 2D river simulation, data interface conversion, and 1 D pipe network simulation to generate time-series graphs for specific nodes or pipe segments. (see fig 1 model framework execution-left side (2D river hydrodynamic and water quality simulation), 1D-2D water quantity and water quality exchanges (center), 1D underground drainage (pipe) network hydrodynamic and water quality simulation (right). The figure 1 of Luan is workflow/model framework that illustrates the sequence of operations by the coupled simulation system. See also section 2.2.2- The SWMM provides the advantage of addressing various hydraulic structures (such as pumping stations, sluices, and weirs). The code is open source. Thus, fig 1 can be understood as depicting the automatic execution of computer executable instructions (i.e., script or program code) that orchestrate the sequential execution of 2D model, the 1D model and data exchange interface to generate simulation outputs. See simulation results fig 19-generating time-series graph.
Applicant arguments
Applicant argues the combination of Luan and Rossman lacks the specific data interface step wherein "the data interface converts the water quality concentration data output from the 2D river model into relative coefficients by dividing each concentration value by the initial concentration, and replaces the original time pattern in the pipe network prediction model with those relative coefficients."
Examiner response
Examiner respectfully disagrees. Luan teaches water quality concentration data at the water intake output from the 2D river hydrodynamic and water quality model; (see section 2.2.3 and fig 1- Since the amount of water entering the node equals the amount of water lost in the corresponding cell, the concentration Cw of pollutants entering the rainwater node is the concentration of pollutants in the corresponding cell). Luan does not teach “converts the water quality concentration data output from the 2D river model into relative coefficients by dividing each concentration value by the initial concentration, and replaces the original time pattern in the pipe network prediction model with those relative coefficients," for which Rossman was bought. Rossman teaches calculated normalized concentration ratios (Ct/C0), which are dimensionless values relative to the initial concentration. Rossman further teaches representing time-varying values using dimensionless pattern multipliers. It would have been obvious to employ the normalized concentration ratios as the pattern multipliers because both are relative scaling values references to a baseline. See office action for detail analysis.
Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 9 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 1-8 is directed to method or process that falls on one of statutory category.
Claim 9 is a non-transitory computer computer-readable storage medium storing a computer program, which falls into manufacture.
Step 2A Prong 1
Claim 1 and 9 recites
An integrated hydrodynamic and water quality simulation method for water supply systems of water treatment plants, pipe networks, and rivers, comprising the following steps: S1: combining collected river topographic data and river shoreline data to construct a two-dimensional (2D) river hydrodynamic and water quality model, the model being configured with boundary hydrodynamic and water quality parameters to simulate the hydrodynamic and water quality conditions of the river water source; (The core of the claim—"constructing a 2D model," "configuring boundary parameters," and "simulating hydrodynamics/water quality"—describes a mathematical simulation process rather than a physical machine. It is simulating "cause and effect" on a computer (i.e., collecting data, simulating conditions). The steps described (collecting topographical data, configuring boundary parameters based on observed conditions, and simulating water quality) can often be performed mentally or with basic tools (e.g., paper/pencil) in a conceptual sense. The claim is directed to an abstract idea, specifically a mathematical concept (mathematical modeling, algorithms, and simulation) [MPEP 2106.04(a)(2)(I)].))
S2: monitoring and analyzing the temporal variation of water quality parameters at a water intake by using the 2D river hydrodynamic and water quality model, thereby obtaining time-series data of the water quality parameters; (The claim phrase "analyzing the temporal variation of water quality parameters" describes an activity that can be performed in the human mind (observation, evaluation, judgment, or analysis). "Using the 2D river hydrodynamic and water quality model" refers to mathematical relationships, algorithms, and equations (2D governing equations for velocity, convection-diffusion for quality) The step constitutes an abstract idea (mental process/math).)
S3: constructing a water supply pipe network water quality prediction model that integrates multi-water source inputs, using a multi-species water quality simulation method for simulating at least one chemical species' oxidation, adsorption, decay or generation reaction in the water supply pipe network, and the water treatment plant being represented as a treatment node in the pipeline system to simulate its water quality treatment effects; (The claim describes "constructing... a model" that "integrates inputs" and "represented a treatment plant as a treatment node." These actions describe intellectual, analytical, and mathematical steps—collecting data, establishing relationships, and modeling—that can be performed in the human mind, by a human using pen and paper, or using a general-purpose computer to perform mental calculations. Specifically, the limitations recite mental acts of modeling, calculating chemical reactions (oxidation, adsorption, decay, generation), and abstracting a physical water plant into a theoretical node. This constitutes a "mental process" and "mathematical concept," which are enumerated abstract ideas under MPEP 2106.04(a).)
S4: establishing a data interface to realize data exchange and synchronization between the 2D river
hydrodynamic and water quality model and the water supply pipe network water quality prediction model; (The act of "establishing a data interface" to "exchange and synchronize data" between two models is a functional description of data manipulation. It describes what needs to be done (exchange data) rather than how it is done technically. This is a mental process or a fundamental administrative activity (managing data flow) that could be done by a human, often termed "organizing information." A hydrologist deciding to link two separate reports or manually carrying a clipboard of numbers from one desk to another.)
wherein the data interface converts water quality concentration data at the water intake output from the 2D river hydrodynamic and water quality model into relative coefficients by dividing each concentration value at each time point by the initial concentration value, and replaces an original time pattern in the water supply pipe network water quality prediction model with the relative coefficients as a time pattern to reflect an influence of the temporal variation of the water quality parameters at the water intake on water quality in the water supply pipe network;(claim recites mathematical calculations (division/ratios) and data conversion operations are abstract ideas that can be done manually via pen and paper or mentally. Evaluating and substituting a variable value into an equation or model represents mental work. So, it falls under the combination of mental process and mathematical concepts of abstract idea)
S5: the water supply pipe network water quality prediction model performing one-dimensional (1D) hydrodynamic and water quality simulation of the water supply pipe network based on the data obtained from the 2D river hydrodynamic and water quality model, wherein the water quality and hydrodynamic parameters provided by the 2D river hydrodynamic and water quality model are input into the water supply pipe network water quality prediction model as boundary conditions or initial conditions to predict the water quality changes and hydraulic behaviors in the water supply pipe network; The method performs "one-dimensional (1D) hydrodynamic and water quality simulation." Simulation, modeling, and calculation steps are considered mathematical concepts. The steps describe "inputting," "processing," and "predicting" changes based on data. The fundamental steps of modeling water quality—treating parameters as boundary conditions to calculate future states—can be performed by a person using pencil and paper, which is the definition of a mental process. So, it falls under the combination of mental process and mathematical concepts of abstract idea)
S6: integrating the river hydrodynamic and water quality model and the water supply pipe network water quality prediction model into a numerical model for the integrated water supply system of water treatment plants, pipe networks, and rivers. (A human operator (e.g., a hydrologist or water utility manager) can simulate water quality by manually calculating river flow, water treatment capacity, and pipe network pressure. The step of "integrating... models into a numerical model" is a mathematical manipulation.)
using an automated script to sequentially execute the 2D river hydrodynamic and water quality simulation, the data interface conversion, and the 1D pipe network simulation, (A human analyst can mentally conceptualize or write down step-by-step differential equations on paper, manually compute inputs/outputs, and convert data parameters from a river model sheet to a pipe network sheet. So, it falls under the combination of mental process and mathematical of abstract ideas. According to MPEP 2106.05(a), using a generic, known computer tool to automate a known process (modeling) without creating a specific, technical improvement to the tool itself is simply using a computer as a tool to perform an abstract idea. The "automated" of the simulation is a generic function performed by computer))
generating time-series graphs of water quality parameters for specific nodes or pipe segments, and using the time-series graphs for daily management and maintenance of the pipe network, enhanced disinfection of water quality, energy conservation and consumption reduction, or for formulating water quality maintenance plans. (A person can tabulate time versus concentration values on a sheet of paper and manually sketch a line graph showing how water quality changes. Reviewing a graph to make a decision or plan an action (e.g., deciding to add more disinfectant or schedule pipe flushing) falls mental evaluation, and a mental process of professional judgment. So, it falls under the mental process of abstract ideas)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular, the additional elements of a non-transitory computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the integrated hydrodynamic and water quality simulation method for water supply systems of water treatment plants, pipe networks, and rivers according to claim 1 in claim 9 which are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); The claim is directed to an abstract idea. To avoid the 101 rejection, the claim must be drafted to show an "improvement to the functioning of a computer, or an improvement to another technology or technical field".
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In view of Step 2B, the claim as a whole does not amount to significantly more than the recited exception,
i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. The additional elements of a non-transitory computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the integrated hydrodynamic and water quality simulation method for water supply systems of water treatment plants, pipe networks, and rivers according to claim 1 in claim 9 which are mere instructions to implement an abstract idea on a computer, or merely using a generic computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f); Thus, claim 1 and 9 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4. Claim(s) 1 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Luan et al., (“A 1D-2D dynamic bidirectional coupling model for high-resolution simulation of urban water environments based on GPU acceleration techniques." Journal of Cleaner Production 428 (2023): 139494) in view of Rakesh ("Modeling of subsurface horizontal flow constructed wetlands using OpenFOAM®." Modeling Earth Systems and Environment 2.2 (2016)) in view of Rossman et al. ("EPANET 2: user’s manual." (2000): 1-200.) and further in view of Shang et al. ("EPANET multi-species extension user’s manual." Risk Reduction Engineering Laboratory, US Environmental Protection Agency, Cincinnati, Ohio (2008).)
Regarding claim 1
Luan teaches an integrated hydrodynamic and water quality simulation method for water supply systems of water treatment plants, pipe networks, and rivers, (see abstract- An integrated model that can be employed to efficiently and accurately simulate the whole system of urban surfaces, underground drainage systems and river and lake water environments is an important tool for comprehensively treating urban water pollution. See introduction-Therefore, it is very important to establish an efficient and high-precision numerical model of the urban water environment that can be employed to simulate the entire water environment, including urban surfaces, rivers and underground drainage systems, to explore the mechanism of urban point source pollution and nonpoint source pollution, sponge city construction, black and odorous water treatment, water purification efficiency enhancement of sewage and the reduction of treatment cost. See page 2- The 1D hydrological, hydrodynamics and water quality coupling module are operated by the SWMM, including the simulation of rainfall runoff, pollutant accumulation and wash-off processes, LID processes, 1D pipe networks and river networks hydrodynamics and water quality transport processes) comprising the following steps:
S1: combining collected river topographic data and river shoreline data to construct a two-dimensional (2D) river hydrodynamic and water quality model, (see page 10-The basic data required for the urban water environment numerical model constructed in this paper mainly include topographic, drainage pipe networks, land use, rainfall and infiltration data. The digital surface model (DSM), pipe networks data and image data of the 2D simulation area are shown in Fig. 14. The DSM data are terrain data with a 0.25-m resolution for 2022 provided by the Changzhi City Housing and Urban-Rural Bureau. See page 11-The GAST-GCSE model and the pure SWMM were constructed according to the basic data. Therefore, the GAST was model was used for high-resolution simulation of the 2D river channel. The 2D region was defined with the open boundaries, and the Courant-Friedrichs-Lewy (CFL) number is 0.4) the model being configured with boundary hydrodynamic and water quality parameters to simulate the hydrodynamic and water quality conditions of the river water source; (see page 11-Regarding the construction of the GAST-GCSE model, pollutant cumulation and wash-off and the hydrodynamic and water quality transport processes of the river networks in the upper reaches of the study area were calculated in the SWMM. “The surface water dynamic and water quality transport process in the 2D area were simulated by the GAST model with a high resolution. Therefore, the GAST was model was used for high-resolution simulation of the 2D river channel.” The 2D region was defined with the open boundaries. See page 6 col1- In the SWMM, the water level of the 2D model is employed as the lower boundary condition. water level boundary condition of the river at the current moment, boundary condition of the pollutant concentration in the river channel at the next time step)
S2: monitoring and analyzing the temporal variation of water quality parameters at a water intake by using the 2D river hydrodynamic and water quality model, thereby obtaining time-series data of the water quality parameters;(see page 12-13 Fig. 18 shows the simulation results at three monitoring waterlogging points. Table 3 provides a comparison between the simulated and measured values at the peak waterlogging time. See page 11 and fig 19 - The NH4+-N concentration was monitored twice a month, 3 samples were collected each time and the monitoring results were averaged. A total of 12 final NH4+-N concentration data points were obtained from January to June 2022 for the lower reaches of the Shizi River. According to the monitoring results, the NH4+-N concentration fluctuated by approximately 1.5 mg/L. Therefore, the NH4+-N concentration in the upstream tributaries of the Shizi River and Heishui River was set to 1.5 mg/L.to better simulate the transport trend of overflow pollutants at the rainwater nodes, rainfall production and confluence of the road and the surface water dynamic and water quality transport process in the 2D area were simulated by the GAST model with a high resolution. Fig 19-shows generated time series water quality parameters)
S3: constructing a water supply pipe network water quality prediction model that integrates multi-water source inputs,(see abstract-An integrated model that can be employed to efficiently and accurately simulate the whole system of urban surfaces, underground drainage systems and river and lake water environments is an important tool for comprehensively treating urban water pollution. see page 2 and fig 1-The 1D hydrological, hydrodynamics and water quality coupling module are operated by the SWMM, including the simulation of rainfall runoff, pollutant accumulation and wash-off processes, LID processes, 1D pipe networks and river networks hydrodynamics and water quality transport processes. See page 11- The basic domestic sewage flow in the pipeline is distributed across the rain and sewage confluence area according to the inlet flow of the downstream sewage treatment plant in the form of node inflow and population distribution. Therefore, the SWMM was used to calculate rainfall runoff and pollutant accumulation and wash-off and simulate the underground drainage system in the building community. the SWMM generated a large amount of dynamic water quantity and water quality exchange at the rainwater nodes, overflow outlets, and 1D and 2D river coupling sections See also fig 4)
the water treatment plant being represented as a treatment node in the pipeline system to simulate its water quality treatment effects; (see abstract-An integrated model that can be employed to efficiently and accurately simulate the whole system of urban surfaces, underground drainage systems and river and lake water environments is an important tool for comprehensively treating urban water pollution. See page 6-In side exchange of the water quantity and water quality, a connection is established with the 2D river channel through the rainwater outlets, overflow outlets and tail water outlets of sewage treatment plants. In the model, the water quantity and water quality exchange methods (free outflow/submerged outflow/one-way flow considering jacking) are set in the SWMM. In the calculation of water quantity and water quality exchange, by extracting flow and pollutant concentration data of the SWMM outlet, water quantity and water quality discharge data of the SWMM outlet are exchanged. See page 11- In the simulation process of the coupling model, the GAST model and the SWMM generated a large amount of dynamic water quantity and water quality exchange at the rainwater nodes, overflow outlets, and 1D and 2D river coupling sections. The pure SWMM could maintain the conservation of the water quantity and water quality.)
S4: establishing a data interface to realize data exchange and synchronization between the 2D river hydrodynamic and water quality model and the water supply pipe network water quality prediction model; (see page 2 and fig 1-The coupling model includes three modules: a 2D surface hydrology, hydrodynamics and water quality coupling module; a 1D hydrology, hydrodynamics and water quality coupling module; and a 1D-2D water quantity and water quality exchange module. see section 2.2.3- When the water level in the river channel is high, a backing effect is imposed on the overflow or rainwater outlet, resulting in pipeline blockage, causing a large amount of mixed sewage to overflow to the surface, and producing serious waterlogging. Therefore, the real-time speed and accuracy of water quantity and water quality exchange are very important in numerical modeling of the urban water environment. In this paper, water quantity and water quality exchange between the 2D surface and underground drainage system was divided into three modes: vertical exchange, forward exchange and side exchange. See also page 11- n the simulation process of the coupling model, the GAST model and the SWMM generated a large amount of dynamic water quantity and water quality exchange at the rainwater nodes, overflow outlets, and 1D and 2D river coupling sections.)
water quality concentration data at the water intake output from the 2D river hydrodynamic and water quality model; (see section 2.2.3 and fig 1- Since the amount of water entering the node equals the amount of water lost in the corresponding cell, the concentration Cw of pollutants entering the rainwater node is the concentration of pollutants in the corresponding cell.)
S5: the water supply pipe network water quality prediction model performing one-dimensional (1D) hydrodynamic and water quality simulation of the water supply pipe network based on the data obtained from the 2D river hydrodynamic and water quality model, wherein the water quality and hydrodynamic parameters provided by the 2D river hydrodynamic and water quality model are input into the water supply pipe network water quality prediction model as boundary conditions or initial conditions to predict the water quality changes and hydraulic behaviors in the water supply pipe network; (see page 11- Therefore, the SWMM was used to calculate rainfall runoff and pollutant accumulation and wash-off and simulate the underground drainage system in the building community. Regarding the construction of the GAST-GCSE model, pollutant cumulation and wash-off and the hydrodynamic and water quality transport processes of the river networks in the upper reaches of the study area were calculated in the SWMM. “The surface water dynamic and water quality transport process in the 2D area were simulated by the GAST model with a high resolution. Therefore, the GAST was model was used for high-resolution simulation of the 2D river channel.” See page 6 col1- In the SWMM, the water level of the 2D model is employed as the lower boundary condition. water level boundary condition of the river at the current moment, boundary condition of the pollutant concentration in the river channel at the next time step. See also section 2.2.4 and fig 1-First, the input file required for the calculation of the SWMM (input file) was read in the 2D model and the correctness of the input data was assessed. Node attribute information of the SWMM was obtained to establish a connection with the 2D model. Then, the SWMM is initialized, and the time step of the SWMM was set to be consistent with the calculation time step of the GAST model. Then the SWMM was run, and time step was updated. Information such as the node water level, outlet flow and pollutant concentration calculated by the SWMM, was exchanged with the GAST model. Then, the 2D model was operated to advance the simulated time. According to the 1D and 2D water quantity and water quality calculation results at each node in the current time step, water quantity and water quality exchange between the surface and the node was calculated, and the calculated water quantity and water quality exchange data were than fed back to the SWMM.)
S6: integrating the river hydrodynamic and water quality model and the water supply pipe network water quality prediction model into a numerical model for the integrated water supply system of water treatment plants, pipe networks, and rivers, (see page 2 col 2-The purpose of this study was to develop a 1D-2D dynamic bidirectional coupling model for high-resolution simulation of the urban water environment based on GPU acceleration techniques to reveal the multi process mutual feedback mechanism of the water environment. In this model, the 2D hydrodynamics and water quality coupling GPU accelerated surface water flow and transport (GAST) model, which can be used to simulate large-scale rivers in complex terrain with high efficiency and precision, is dynamically coupled with the SWMM, which comprises hydrological, hydrodynamic and water quality and low impact development (LID) modules. The performance of the model was evaluated in two typical test cases. Then, by establishing a large-scale urban integrated water environment numerical model including 2D rivers, roads, and houses and 1D rivers and underground drainage systems, the multi process evolution and mutual feedback mechanism of the water environment in the study area were examined. The 1D hydrological, hydrodynamics and water quality coupling module are operated by the SWMM, including the simulation of rainfall runoff, pollutant accumulation and wash-off processes, LID processes, 1D pipe networks and river networks hydrodynamics and water quality transport processes.)
and using an automated script to sequentially execute the 2D river hydrodynamic and water quality simulation, the data interface conversion, and the 1D pipe network simulation, (see fig 1 model framework-left side (2D river hydrodynamic and water quality simulation), 1D-2D water quantity and water quality exchanges (center), 1D underground drainage (pipe) network hydrodynamic and water quality simulation (right) and see section 2.1-A 1D-2D dynamic bidirectional coupling model for high-resolution simulation of the urban water environment based on GPU acceleration techniques was proposed, which comprises the GAST model and SWMM. The purpose was to better understand the various water pollution mechanisms that may occur in cities. The model framework is shown in Fig.1. See also section 2.2.2- The SWMM provides the advantage of addressing various hydraulic structures (such as pumping stations, sluices, and weirs). The code is open source.)
generating time-series graphs of water quality parameters for specific nodes or pipe segments, (see section 3.1.1 Model setting- The pipe networks system consists of 6 nodes and 6 pipelines. The 2D surface is a square closed plain area with a side length of 200 m, including nodes 2, 3, 4 and 5, and overflow and backflow are allowed at these nodes. The water quantity and water quality in the pipeline can be exchanged with those at the surface through these four nodes. see fig 19, 21 and fig 12-13-The simulation results of the NH 4+-N concentration in sections 1, 2 and 3 (Fig. 14) obtained by the GAST-GCSE model and the pure SWMM are shown in Fig. 19. Fig. 21 shows that at 1 h, the NH4+-N concentration on the surface and in the drainage pipe networks around the river was high, mostly above 10 mg/L.)
using the time-series graphs for daily management and maintenance of the pipe network, enhanced disinfection of water quality, energy conservation and consumption reduction, or for formulating water quality maintenance plans. (See abstract- An integrated model that can be employed to efficiently and accurately simulate the whole system of urban surfaces, underground drainage systems and river and lake water environments is an important tool for comprehensively treating urban water pollution. See para 16- The model could be used as an effective tool for integrated simulation of the entire urban scale water environment and could provide technical support for comprehensive management of the urban water environment. According to the simulation results of the water environment in the main urban area of Changzhi city, notable real-time interaction occurred between the urban river channel and its sur rounding 2D surface and underground drainage system during rainfall, and the multiple processes of the water environment interacted with each other. Integrated simulation is necessary to explain this process. This study provides new insights, ideas and tools for the study of urban water environment mechanisms.)
Luan does not teach converts water quality concentration data at the water intake output from the 2D river hydrodynamic and water quality model into relative coefficients by dividing each concentration value at each time point by the initial concentration value. and replaces an original time pattern in the water supply pipe network water quality prediction model with the relative coefficients as a time pattern to reflect an influence of the temporal variation of the water quality parameters at the water intake on water quality in the water supply pipe network;
In the related field of invention, Rossman teaches converts water quality concentration data at the water intake output from the 2D river hydrodynamic and water quality model into relative coefficients by dividing each concentration value at each time point by the initial concentration value, (see page 44-The Kb for first-order reactions can be estimated by placing a sample of water in a series of non-reacting glass bottles and analyzing the contents of each bottle at different points in time. If the reaction is first-order, then plotting the natural log (Ct/Co) against time should result in a straight line, where Ct is concentration at time t and Co is concentration at time zero. Kb would then be estimated as the slope of this line.)
Examiner note: Examiner consider the Ct/Co (normalized concentration ratio) as the relative coefficient.
replaces an original time pattern in the water supply pipe network water quality prediction model with the relative coefficients as a time pattern to reflect an influence of the temporal variation of the water quality parameters at the water intake on water quality in the water supply pipe network; (see page 22- A new Pattern 1 will be created and the Pattern Editor dialog should appear (see Figure 2.9). Enter the multiplier values 0.5, 1.3, 1.0, 1.2 for the time periods 1 to 4 that will give our pattern a duration of 24 hours. The multipliers are used to modify the demand from its base level in each time period. Since we are making a run of 72 hours, the pattern will wrap around to the start after each 24-hour interval of time. See page 153- The DEMAND MULTIPLIER is used to adjust the values of baseline demands for all junctions and all demand categories. For example, a value of 2 doubles all baseline demands, while a value of 0.5 would halve them. The default value is 1.0.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the an integrated hydrodynamic and water quality simulation method as disclosed by Luan to include converts water quality concentration data at the water intake output from the 2D river hydrodynamic and water quality model into relative coefficients by dividing each concentration value at each time point by the initial concentration value and replaces an original time pattern in the water supply pipe network water quality prediction model with the relative coefficients as a time pattern to reflect an influence of the temporal variation of the water quality parameters at the water intake on water quality in the water supply pipe network as taught by Rossman in the system of Luan for improving our understanding of the movement and fate of drinking water constituents within distribution systems. It can be used for many different kinds of applications in distribution systems analysis. Sampling program design, hydraulic model calibration, chlorine residual analysis, and consumer exposure assessment are some examples. (See section 1.1, Rossman)
The combination of Luan and Rossman does not teach using a multi-species water quality simulation method for simulating at least one chemical species' oxidation, adsorption, decay or generation reaction in the water supply pipe network.
In the related field of invention, Shang teaches using a multi-species water quality simulation method for simulating at least one chemical species' oxidation, adsorption, decay or generation reaction in the water supply pipe network, (see section 5 and page 53-61-This section demonstrates how several different multi-species reaction systems of interest can be modeled with EPANET-MSX. Oxidation, Mass Transfer, and Adsorption)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the an integrated hydrodynamic and water quality simulation method as disclosed by Luan and Rossman to include using a multi-species water quality simulation method for simulating at least one chemical species' oxidation, adsorption, decay or generation reaction in the water supply pipe network as taught by Shang in the system of Luan and Rossman for modeling the hydraulic and water quality behavior of drinking water distribution systems and describes an extension to the original EPANET that allows it to model any system of multiple, interacting chemical species. (See section 1, Introduction)
Regarding claim 9
The combination of Luan, Rossman and Shang further teaches a non-transitory computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the integrated hydrodynamic and water quality simulation method for water supply systems of water treatment plants, pipe networks, and rivers according to claim 1. (See abstract and fig 1-An integrated model that can be employed to efficiently and accurately simulate the whole system of urban surfaces, underground drainage systems and river and lake water environments is an important tool for comprehensively treating urban water pollution. Therefore, we developed a 1D-2D dynamic bidirectional coupling model for high-resolution simulation of the entire urban water environment (GAST-GCSE) comprising the graphics processing unit (GPU)-accelerated surface water flow and transport (GAST) model and the storm water management model (SWMM). The SWMM and GAST model were used for coupled simulation of hydrological and hydrodynamic quantities and water quality in 1D and 2D regions, respectively. The time steps of the two models are the same. Compute Unified Device Architecture (CUDA) parallel architecture programming and advanced model algorithms were adopted to improve the simulation accuracy and efficiency of the GAST model.)
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jun et al. KR 20220149031 A
i. Discussing the method for simulating a cyber-physical system for urban water resources through the connection of water supply and sewage. The method comprises a step (a) of selecting a water supply or sewer pipe network data file; a step (b) of automatically parsing the pipe network data file into an EPANET or SWMM model by a controller in a city water resources application program; a step (c) of displaying the automatically parsed syntax in a graphic viewer within the city water resources application program; a step (d) of outputting a first pipe network analysis result by executing a simulation in which the urban water resources simulator analyzes a water pipe network by using the EPANET model; a step (e) of outputting a second pipe network analysis result by executing a simulation in which the urban water resources simulator analyzes the sewage pipe network by using the first pipe network analysis result as input data of the SWMM model; and a step (f) of displaying the simulated first and second pipe network analysis results in connection with each other by the graphic viewer.
6. All claims 1 and 9 are rejected.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PURSOTTAM GIRI/
Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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