DETAILED ACTION
Acknowledgement
This final office action is in response to the amendment filed on 05/26/2026.
Status of Claims
Claims 2-4 have been cancelled.
Claims 1 and 5 have been amended.
Claims 1 and 5 are now pending.
Response to Arguments
The 35 U.S.C. 112(b) rejection of claims 1-3 is withdrawn in light of amendments.
Applicant's arguments filed on 05/26/2026 regarding the 35 U.S.C. 101 rejection of claims have been fully considered. The Applicant argues, in summary, that (i) the claims do not recite an abstract idea, (ii) the claimed subject matter is integrated into a practical application, and (iii) the claims recite significantly more than an abstract idea. The claims are directed to a computer-implemented framework for coordinated optimization in a water network using integrated machine learning processing, which cannot practically be performed mentally by a human or through mere pen and paper analysis. The claims provide a specific technical solution for improving coordinated operation, intelligent scheduling, and comprehensive simulation of the water network system through integration of water resources quantities, ecological environment factors, and economic society factors into a unified computational framework.
The Examiner respectfully disagrees. The Examiner maintains the position that the claims are directed to the abstract ideas of Mental Processes and Mathematical Concepts because the claims describes a process of collecting and analyzing historical data via models and mathematical calculations in order to predict values (e.g. population, GDP, etc.), obtain water resource quantities, obtain coordination indicators, and obtain regulation schemes, which can practically be performed in the human mind with pen and paper. The claims explicitly recite mathematical processes and methods that can be performed without a computer. The Examiner notes that claims 1 and 5 methods steps do not explicitly recite performance by a computer or other technological components. As per MPEP 2106.04(a), a claim recites a judicial exception when the judicial exception is “set forth” or “described” in the claim. The Examiner is not stating that the human mind can perform machine learning, as machine learning is recognized as a computer-based technology and is listed as an additional element in Steps 2A(2) and 2B. However, a claim that recites the use of machine learning technology can still be directed towards an abstract idea as in Eligibility Example 47 claim 2.
The Examiner also maintains the position that the additional elements recited in the claims and listed in Steps 2A(2) and 2B do not integrate the abstract idea into a practical application nor provide significantly more. The additional elements reflect the use of machine learning technology to perform and improve abstract processes of data analysis, prediction, and scheduling operations. The Applicant acknowledges that the claims reflect a computational framework directed to improving coordinated optimization and scheduling of the water network system. Per MPEP 2106.05(a)(III), an improvement in the abstract idea itself, is not an improvement in technology. There is no direct improvement in the functioning of a computer or in machine learning technology beyond its original capabilities as a results of implementing claims 1 and 5 method steps. Therefore, amended claims 1 and 5 do not overcome the 35 U.S.C. 101 rejection.
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 1 and 5 are rejected under 35 U.S.C. 101 because the claimed invention, “Method For Coordinated Optimization of Water Resources, Ecological Environment, and Socioeconomic System in Water Network System”, is directed to an abstract idea, specifically Mental Processes and Mathematical Concepts, without significantly more. The claims as a whole do not include additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the abstract idea because the additional elements individually or in combination provide mere instructions to implement the abstract idea on a computer.
Step 1: Claims 1 and 5 are directed to a statutory category, namely a process.
Step 2A (1): Claims 1 and 5 are directed to an abstract idea of Mental Processes and Mathematical Concepts, based on the following claim limitations: “step S1, collecting historical rainfall, historical evapotranspiration and historical water resources quantity data of a study area, and dividing the data into a training set, a validation set, and a test set according to a ratio of 80/10/10,…,fitting marginal distributions of rainfall and evapotranspiration, constructing a joint distribution of the rainfall and the evapotranspiration, randomly sampling to generate massive scenarios and inputting the massive scenarios into a the… model to obtain water resources quantities corresponding to the massive scenarios; step S2, collecting historical population and historical gross domestic product (GDP) data of the study area, constructing an economic society scale prediction model, using a rolling prediction method to obtain population prediction values and GDP prediction values, and combining to obtain economic society scale prediction samples; wherein the step S2 further comprises: step S21, constructing the economic society scale prediction model based on the machine learning model, calibrating parameters of the economic society scale prediction model based on the historical population and the historical GDP data, and step S22, combining m population prediction values and n GDP prediction values to obtain mxn economic society scale prediction samples, wherein m and n are positive integers; step S3, inputting massive scenario samples into a pre-built multi-objective optimization model to obtain water resources-ecological environment-economic society coordination indicators, constructing a system dynamics model and calibrating model parameters, randomly combining the water resources quantities corresponding to the massive scenarios with the mxn economic society scale prediction samples to obtain the massive scenario samples, and sequentially inputting into the system dynamics model to obtain water resources-ecological environment-economic society coordination indicator values corresponding to the massive scenario samples; and step S4, reducing dimensionality of the water resources-ecological environment-economic society coordination indicators by using a maximum entropy projection pursuit method to obtain comprehensive coordination indicators and calculate corresponding comprehensive coordination indicator values, constructing regulation schemes, calculating transition degrees between the comprehensive coordination indicator values under different regulation schemes for each of the massive scenario samples, and screening to obtain an optimal scheme for the water resources-ecological environment-economic society coordination in the water network system, wherein the step S3 further comprises: step S31, constructing a multi-objective optimization model with an objective function of maximizing a degree of the water resources-ecological environment-economic society coordination; step S32, inputting the massive scenario samples into the multi-objective optimization model to obtain the water resources-ecological environment-economic society coordination indicators in the water network system corresponding to the massive scenario samples; step S33, collecting data of the water resources quantities, economic society, and ecological environment of the study area, constructing the system dynamics model based on relationships among the water resources quantities, the economic society, and the ecological environment, and calibrating parameters of the system dynamics model based on the data of the water resources quantities, the economic society, and the ecological environment of the study area; step S34, extracting the water resources quantities corresponding to the massive scenarios and the mxn economic society scale prediction samples, randomly combining the two to obtain the massive scenario samples; and step S35, sequentially inputting the massive scenario samples into the system dynamics model to obtain the water resources-ecological environment-economic society coordination indicator values corresponding to the massive scenario samples, wherein the step S33 further comprises: step S33a, determining system elements based on the relationships among the water resources quantities, the economic society, and the ecological environment, and dividing into three categories: water system connectivity, natural functions, and social functions; step S33b, based on the three categories of the system elements: the water system connectivity, the natural functions, and the social functions, constructing multiple system causal loops, comprising m positive feedback loops and n negative feedback loops, and constructing a system causal loop diagram, wherein the m and the n are positive integers; step S33c, determining levels and rates based on the system causal loop diagram to obtain three subsystems of the water system connectivity, the natural functions, and the social functions, and merging the three subsystems to construct the system dynamics model; and step S33d, calibrating the parameters of the system dynamics model using a sensitivity analysis method and historical validation based on the data of the water resources quantities, the economic society, and the ecological environment of the study area, wherein the step S4 further comprises: step S41, using the maximum entropy projection pursuit method to reduce the dimensionality of the water resources-ecological environment-economic society coordination indicators and obtain the comprehensive coordination indicators; step S42, calculating respectively to obtain the comprehensive coordination indicator values corresponding to the massive scenario samples based on the water resources- ecological environment-economic society coordination indicator values corresponding to the massive scenario samples; and step S43, constructing the regulation schemes, calculating the transition degrees between the comprehensive coordination indicator values under the different regulation schemes for each of the massive scenario samples, and selecting a regulation scheme with a highest transition degree as a most improved regulation scheme, namely, the optimal scheme for the water resources- ecological environment-economic society coordination in the water network system corresponding to the massive scenario samples, wherein the step S41 further comprises: step S41a, constructing a projection function; step S41b, using entropy of projection values as a projection indicator function, and using the projection indicator function to measure information content of the projection values; and step S41c, using a maximum entropy method to determine an optimal projection direction, and constructing an optimization model; step S41d, inputting data of the water resources-ecological environment-economic society coordination indicators into the optimization model, and calculating to obtain one- dimensional projection vectors, namely, the comprehensive coordination indicators after dimensionality reduction, wherein the step S43 further comprises: step S43a, sequentially inputting the massive scenario samples into the multi-objective optimization model, setting A regulation measures, and setting B different regulation levels for each of the regulation measures to obtain AxB regulation schemes, wherein A and B are integers greater than 2; step S43b, sequentially modifying parameters and boundary conditions of the multi- objective optimization model based on the AxB regulation schemes, and solving the model to obtain the comprehensive coordination indicator values corresponding to each of the regulation schemes for each of the massive scenario samples; and step S43c, using Markov one-step transition probability to sequentially calculate the transition degrees between original comprehensive coordination indicator values of each of the massive scenario samples and the comprehensive coordination indicator values after regulation under all the regulation schemes, and selecting the regulation scheme with the highest transition degree as the most improved regulation scheme, namely, the optimal scheme for the water resources-ecological environment-economic society coordination in the water network system corresponding to the massive scenario samples (claim 1); and wherein the step S22 further comprises: step S22a, using first four-fifths of the historical population and the historical GDP data of the study area as a prediction set and remaining one-fifth as the validation set; step S22b, inputting the prediction set into the economic society scale prediction model, using a moving average method to shift backward, and obtaining prediction values for the remaining one-fifth; step S22c, comparing the prediction values for the remaining one-fifth with the validation set and optimizing the economic society scale prediction model; and step S22d, inputting the historical population and the historical GDP data of the study area into the economic society scale prediction model after optimization to obtain the m population prediction values and the n GDP prediction values, and combining to obtain the mxn economic society scale prediction samples (claim 5)”. These claim limitations describe a process of collecting and analyzing historical data via models and mathematical calculations in order to predict values (e.g. population, GDP, etc.), obtain water resource quantities, coordination indicators, and regulation schemes, which can practically be performed in the human mind with pen and paper. Therefore, these limitations, under the broadest reasonable interpretation, fall within the abstract groupings of Mental Processes which include concepts performed in the human mind such as observations, evaluations, judgments, and opinions and Mathematical Concepts which encompasses mathematical relationships, mathematical formulas or equations, and mathematical calculations. Mental Processes include claims directed to collecting information, analyzing it, and displaying certain results of the collection and analysis even if they are claimed as being performed on a computer. Therefore, claims 1 and 5 are directed to an abstract idea and are not patent eligible.
Step 2A (2): The claims as a whole do not integrate this abstract idea into a practical application. In particular, claim 1 recite additional elements of “constructing a machine learning model, wherein the machine learning model corresponds to a gradient boosting tree machine learning model, and training the machine learning model using the training set, and optimizing hyperparameters of the machine learning model based on the historical rainfall, the historical evapotranspiration, and the historical water resources quantity data using a Bayesian optimization method, evaluating the machine learning model after hyperparameter optimization using the test set, and validating using the validation set; inputting the massive scenarios into the machine learning model;”. The Examiner evaluated the claims in light of the Applicant’s specification and determined that the additional elements do not integrate the abstract idea into a practical application because the claims do not recite (a) an improvement to another technology or technical field and (b) an improvement to the functioning of the computer itself and (c) implementing the abstract idea with or by use of a particular machine, (d) effecting a particular transformation or reduction of an article, or (e) applying the judicial exception in some other meaningful way beyond generally linking the use of an abstract idea to a particular technological environment. These additional elements evaluated individually and in combination are viewed as a computing components that are used to perform the abstract process identified in Step 2A(1). The use of trained machine learning models are considered instructions to apply or implement a model on a computer. Training involves fitting a particular model to a dataset and is an integral part of the machine learning process. Training is a computational process to generate the model by discovering patterns in the training data. Limitations that recite mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea are not indicative of integration into a practical application (see MPEP 2106.05(f)). Therefore, claims 1 and 5 as a whole do not include individual or a combination of additional elements that integrate the abstract idea into a practical application and thus are not patent eligible.
Step 2B: The claims as a whole do not include additional elements that are sufficient to amount to significantly more than the abstract idea. Claims 1 recite additional elements of “constructing a machine learning model, wherein the machine learning model corresponds to a gradient boosting tree machine learning model, and training the machine learning model using the training set, and optimizing hyperparameters of the machine learning model based on the historical rainfall, the historical evapotranspiration, and the historical water resources quantity data using a Bayesian optimization method, evaluating the machine learning model after hyperparameter optimization using the test set, and validating using the validation set; inputting the massive scenarios into the machine learning model;”. These additional elements evaluated individually and in combination are viewed as mere instructions to apply or implement the abstract idea on a computer. Applying an abstract idea on a computer does not integrate a judicial exception into a practical application or provide an inventive concept (see MPEP 2106.05(f)). Therefore, claims 1 and 5 as a whole do not include individual or a combination of additional elements that are sufficient to amount to significantly more than the abstract idea and thus are not patent eligible.
Conclusion
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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/A.M./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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