Notice of Pre-AIA or AIA Status
Claims 1-9 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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202210186363.3, filed on 02/28/2022.
Specification objection
The disclosure is objected to because of the following informalities:
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The specification provides a formula for the "Minimum deviation nucleate boiling value" but does not define the variables used in the formula, a person skilled in the art might not be able to replicate the calculation, which could be considered a lack of clarity. Providing a formula without defining its components could make the disclosure insufficient to enable the invention to be carried out. The specification also mentions obtaining the "operation safety index" by calculating the "supercooling degree of reactor cooler outlet" and "thermal economic index" by calculating the "superheat degree of a steam outlet" but provides no calculation method or formula [0067, 0069]. A claim that is defined by a result to be achieved, but lacks the necessary detail to achieve that result, may be considered insufficient. A patent specification or technical document must provide a "written description" that is clear enough for someone with expertise in the field to understand and reproduce the invention or method described. Appropriate correction is required.
Claim Rejections - 35 USC § 112
Claim 2 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Claim 2 recites “wherein, the operation indexes at least comprise an operation safety index and a thermal economic index; the operation safety index is obtained by calculating a supercooling degree of a coolant reactor core outlet and a minimum deviation nucleate boiling value; and the thermal economic index is obtained by calculating a superheat degree of a steam outlet.”. The claim recites that both an "operation safety index" and a "thermal economic index" are obtained by calculating specific values. While the provided text includes a complex formula for the "minimum deviation nucleate boiling value" (qDNB), it does not define the variables or provide a description of how they are to be determined.
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For the "operation safety index" the claim states it is obtained by calculating the "supercooling degree of reactor cooler outlet" and for the "thermal economic index," the claim states it is obtained by calculating a "superheat degree of a steam outlet," but the specification provides no formula or method for this calculation see [0065-0069]. A core principle of the enablement requirement is that the public should not have to engage in "undue experimentation" to practice the claimed invention. The absence of definitions for variables in the (qDNB) formula and the complete lack of a method for calculating the "supercooling degree of reactor cooler outlet” and "superheat degree of a steam outlet" would likely require a person of ordinary skill in the art to engage in significant and potentially unpredictable trial and error to make and use the invention as claimed.
While the specification mentions calculating the "minimum deviation nucleate boiling value" as part of the "Operation safety index" and provides a complex formula for a variable identified as (qDNB), it fails to define the variables within this formula. The variables (p), (xe), (G), and (De) are not defined, nor are their units or significance explained, making the formula incomprehensible and non-enabling. The specification also does not explicitly state that the formula for (qDNB) is the formula for the "minimum deviation nucleate boiling value." For the "operation safety index" the claim states it is obtained by calculating the "supercooling degree of reactor cooler outlet" and for the "thermal economic index," the claim states it is obtained by calculating a "superheat degree of a steam outlet," but the specification provides no formula or method for this calculation see [0065-0069]. The absence of this information means the specification does not provide a written description of how to calculate the “Operation safety index” and "Thermal economic index," which is a required element of the claim.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The claim, as described, is not supported by the written description. The claim recites that both an "operation safety index" and a "thermal economic index" are obtained by calculating specific values. While the provided text includes a complex formula for the "minimum deviation nucleate boiling value" (qDNB), it does not define the variables or provide a description of how they are to be determined. For the "operation safety index" the claim states it is obtained by calculating the "supercooling degree of reactor cooler outlet" and for the "thermal economic index," the claim states it is obtained by calculating a "superheat degree of a steam outlet," but the specification provides no formula or method for this calculation see [0065-0069]. This lack of disclosure renders the scope of the claim unclear to a person skilled in the art.
For the examination purpose, claim 2 is rejected under “wherein, the operation indexes at least comprise an operation safety index and a thermal economic index; the operation safety index is obtained by
The claims have numerous issues with antecedent basis. The Examiner suggests amending the claims such that the first recitation of each distinct element uses articles such as “a”/”an”, later recitations referring back to the same distinct element uses articles such as “the”/”said”, to use disambiguating modifiers (e.g., first, second, etc.) when there are multiple distinct elements with the same base term, and that the use of modifiers for each distinct element is kept consistent. Below is a non-exhaustive list of examples of these issues:
Claim 4 recites the limitation “the actual demand” and “the optimization calculation”. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation “the optimization calculation”, “the objective function value” and “the information exchange”. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the operation result”, “the optimization scheme”, “the requirement” …". There is insufficient antecedent basis for this limitation in the claim.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These 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: 1-9 are directed to method or process that falls on one of statutory category.
Step 2A Prong 1
Claim 1 recites
A reactor operation optimization method based on an improved multi-population genetic algorithm, comprising:
S1: defining an operating condition, and further designing an operating scheme according to the operating condition; (Defining conditions and planning schemes are core mental processes. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas.)
S2: obtaining operating data of a reactor system of the operating scheme through numerical simulation research, and obtaining operation indexes by calculating the operating data; (Under the broadest reasonable interpretation, these limitations are by mathematical calculation see [0088] are “within the realm of abstract ideas. So, it falls under the mathematical concepts of abstract ideas. See also claim 2-3) and
S3: optimizing the operation indexes based on an improved multi-population genetic algorithm to obtain an optimization result; The concept of a "multi-population" is an abstract, theoretical construction. The evolution of separate sub-populations and the migration of individuals between them are governed by mathematical rules (e.g., how often and how many individuals migrate). Under the broadest reasonable interpretation, these limitations are by mathematical calculation (i.e., algorithm) are “within the realm of abstract ideas. So, it falls under the mathematical concepts of abstract ideas.)
obtaining an optimal operating parameter setting under the operating condition according to the optimization result. (A user must interpret the abstract optimization result and mentally map it to the operating parameters of a machine or system. This involves a cognitive step of translating numbers and equations into physical actions and settings. For instance, a person must mentally translate the optimal pressure value into the specific setting on a control panel. This act of "translating" an abstract idea into a physical action is a core part of a mental process. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping 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. Claim do no recite the additional elements that integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
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. Claim do no recite the additional elements that integrate the judicial exception into a practical application or amount to significantly more than the judicial exception. Thus, claim 1 is not patent eligible and directed to abstract idea.
Claim 2 further recites wherein, the operation indexes at least comprise an operation safety index and a thermal economic index; the operation safety index is obtained by calculating a supercooling degree of a coolant reactor core outlet and a minimum deviation nucleate boiling value; and the thermal economic index is obtained by calculating a superheat degree of a steam outlet. Under the broadest reasonable interpretation, these limitations are by mathematical calculation see [0067-0069] are “within the realm of abstract ideas. So, it falls under the mathematical concepts of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 3 further recites wherein, the operation indexes comprise dynamic response indexes; the dynamic response indexes at least comprise a stationarity index, a rapidity index and a steady-state performance index; the stationarity index is obtained by calculating a overshoot; the rapidity index is obtained by calculating an adjustment time; and the steady-state performance index is obtained by calculating a steady-state error. Under the broadest reasonable interpretation, these limitations are by mathematical calculation see [0071-0076] are “within the realm of abstract ideas. So, it falls under the mathematical concepts of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 4 further recites before optimizing the operation indexes based on the improved multi-population genetic algorithm, further comprising: determining the operation indexes to be optimized according to the actual demand; determining optimization variables and feasible regions, and carrying out the optimization calculation of the operation indexes to be optimized by using the improved multi- population genetic algorithm. The "actual demand" and "operation indexes" that need to be optimized are abstract concepts determined by a mental analysis of a real-world problem. For example, a business may have an abstract goal to "increase efficiency" or "reduce cost," and these goals must be mentally defined and translated into measurable indexes. Researchers use a mental process to define "optimization variables" and "feasible regions". The core of a genetic algorithm is a mathematical metaheuristic inspired by the biological processes of natural selection and genetics. It uses abstract concepts like "population," "selection," "mutation," and "crossover" to evolve towards an optimal solution for a problem. Under the broadest reasonable interpretation, these limitations are by mathematical calculation and mental process are “within the realm of abstract ideas. So, it falls under the combination of mental process and mathematical concepts of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 5 further recites wherein, the optimization variables are constant operating parameters required by a control strategy in the operating scheme; and the feasible regions are determined by a sensitivity analysis method of a single variable. It is a mental construct that can be applied to thousands of different physical processes. The described process, which involves determining feasible regions through sensitivity analysis, can be performed entirely in the human mind using a pencil and paper. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 6 further recites wherein, the process of carrying out the optimization calculation of the operation indexes to be optimized by using the improved multi-population genetic algorithm comprises, providing operating parameters, wherein the multi-population genetic algorithm creates discrete random population according to parameter settings of the operating parameters, calculates the objective function value of initial population after chromosome coding, and performs evolutionary operations on the initial population; a migration operator introduces a best individual into other populations every definite evolutionary algebra to replace a worst individual in a target population and realize the information exchange of the target population, and ends the calculation when a genetic algebra reaches a maximum value. The algorithm uses concepts from discrete mathematics, such as sets and finite sequences. A population is a finite set of individuals, and a chromosome is a discrete sequence of bits or other values. The random nature of genetic operators like mutation and crossover is governed by probability. The migration operator, which introduces a "best individual," can be implemented probabilistically or deterministically. These probabilistic and statistical frameworks guide the search process. The core purpose of the genetic algorithm is rooted in the mathematical concept of function optimization. The "objective function" or "fitness function" is a mathematical tool for evaluating the quality of a solution, and the algorithm's entire process is structured around maximizing or minimizing this function. This highly abstract, mathematical framework provides a theoretical basis for the algorithm's search effectiveness. Under the broadest reasonable interpretation, these limitations are by mathematical algorithm are “within the realm of abstract ideas. So, it falls under the mathematical concepts of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 7 further recites wherein, the operating parameters at least comprise a population number, an individual number, a variable dimension, a generation gap value and maximum genetic algebra. The mental process of abstraction involves recognizing a common feature or relationship across many individual examples and then forming a concept based on that commonality. Under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an evaluation or judgment that could be performed in the human mind or with the aid of pencil and paper. If a claim, under its broadest reasonable interpretation, covers a mental process, then it falls within the “Mental Process” grouping of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 8 further recites wherein, the evolutionary operations on the initial population at least comprise a selection operation, a crossover operation and a mutation operation; and the crossover operation and mutation operation are based on adaptive strategies, and the crossover operator and mutation operator change from fixed values to changes with the fitness of the population. The EA operationalizes this abstract idea by representing potential solutions as "individuals" and assigning each a "fitness" score via a well-defined mathematical function. The algorithm then systematically searches for the individual with the highest (or lowest) fitness. Under the broadest reasonable interpretation, these limitations are by mathematical algorithm are “within the realm of abstract ideas. So, it falls under the mathematical concepts of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim 9 further recites wherein obtaining the optimal operating parameter setting under the operating condition comprises: comparing the operation result of the optimized scheme with the operation result of the designed operating scheme, and if the requirements are not met, returning to adjust the feasible region and recalculating; if the requirements are met, obtaining the optimal operating parameter setting under the operating condition based on the operation result of the optimization scheme. The process begins by comparing an operational result to a set of requirements. This is a classic mental step that humans perform every day. An observer could see the output of a system, measure it against a standard, and determine if it meets requirements. Based on the comparison, a decision is made to either return to a previous step ("adjust the feasible region") or proceed ("obtain the optimal operating parameter setting"). Such "if-then" logic is a fundamental mental process and a basic part of human decision-making. The concept of recalculating based on a change in variables is a mathematical principle. Mathematical concepts are considered an unpatentable abstract idea. Under the broadest reasonable interpretation, these limitations are by mathematical calculation and mental process are “within the realm of abstract ideas. So, it falls under the combination of mental process and mathematical concepts of abstract ideas. Claim therefore, when taken as a whole, still does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Claim recites unpatentable ineligible subject matter for the same reasoning and analysis as mentioned for claim 1.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-6 and 9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xu, et al. ("Optimization of forced circulation to natural circulation transition characteristics of IPWR." Annals of Nuclear Energy 157 (2021): 108249.)
Regarding claim 1
Xu teaches a reactor operation optimization method based on an improved multi-population genetic algorithm, (see abstract- For nuclear power plants, the operating characteristics of the forced circulation to natural circulation are particularly critical. In this paper, a simulation-based optimization method integrating the RELAP5 code and a multi-population genetic algorithm, is applied to investigate the characteristics of transition from forced circulation to natural circulation of the integrated pressurized water reactor) comprising:
S1: defining an operating condition, and further designing an operating scheme according to the operating condition; (see fig 1 and table 3 and page 2-The schematic diagram of the control system for IPWR is shown in Fig. 1. The primary average coolant temperature (Tav) and steam pressure (Ps) are restrained in constant values through PID control principle while the plant load varying. Through adjusting the reactor power, the primary average coolant temperature are controlled to be stable. Moreover, the steam pressure can be kept by changing the feedwater flow.)
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Examiner note: Fig 1 shows a control scheme designed for an ideal steady-state operating condition of an Integrated Pressurized Water Reactor (IPWR). The diagram illustrates how the system maintains a steady state by regulating key parameters. The operating condition is an "ideal steady-state”. This is a state where the system's variables, such as temperature, pressure, and power, are not changing over time. The control scheme is designed to maintain this balance. The diagram shows how the system is designed to achieve and maintain this steady state. It uses feedback loops to adjust the reactor's power and the flow of feedwater to the steam generator.
S2: obtaining operating data of a reactor system of the operating scheme through numerical simulation research, (see table 3 and see page 7- The results obtained from single objective optimization are presented in Table 3. Three optimization variables include the initial steady state power level, the primary coolant average temperature, and main pumps shutdown interval were selected to combine the optimal solution for the transition of IP200 under the load tracking control mode with constant primary coolant average temperature and secondary steam pressure. The solution Ⅰ is the optimal result calculated by the high-power group, and its corresponding objective function value (transition time, Dt) is 408 s. Although the solution Ⅱ is more time-consuming than the first option (its objective function value, Dt = 478 s), it still has some merits. And it’s worth noting that all simulations are based on steady-state conditions, which means that the program first maintains a low power level and runs for 500 s before the four main pumps shut down. The control strategy including the reactor power control system and the feedwater control system will ensure reactor safety and rapid response during the transition process)
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Examiner note: Optimization variables" such as initial power level, coolant temperature, and pump shutdown interval. It also provides "results" for two solutions (Solution I and Solution II) with corresponding "objective function value (transition time, Δt)." These variables and results represent the operating data and performance metrics obtained from the simulation.
obtaining operation indexes by calculating the operation data; (see page 6- The reactor power also plays a significant role in the transition. The power here refers to the load level of the reactor before the transition, that is, the initial power. The Fig. 6 reveals the relationship between the initial power and the objective function (transition time, Dt). It seems that the higher the initial power, the faster the transition can be completed. But hidden behind it is a threat to the safety of the reactor core. The subcooling degree of the core outlet coolant decreases as the power increases, which resulted in a reduction in the safety margin of the core.)
Examiner note: Subcooling degree of the core outlet coolant—a safety indicator—is related to reactor power, which is a type of operating data. The degree of subcooling is a quantifiable metric that is reduced as reactor power increases. Because the degree of subcooling can be considered an "operation index" and its value is "calculated" or determined from the operating data (power).
S3: optimizing the operation indexes based on an improved multi-population genetic algorithm to obtain an optimization result; (see section 2.2.2, see fig 3, 4 and table 3- In this paper, a multi-population genetic algorithm (multiple population GA, MPGA) is selected and employed to optimize the decision variables. MPGA is based on the standard genetic algorithm whose primary calculation process is shown in Fig. 3. It is widely acknowledged that genetic algorithm (GA) is a highly parallel, random, and self-adaptive global optimization probabilistic search algorithm. The specific operating rule is to replace the worst individual in the target population with the best individual in the source population. see page 7-The Fig. 9 refers to the trend of the optimal value at each generation. The x-axis represents generation, and the y-axis represents the objective function value of the best individual (that is, the transition time, Dt). For the high-power group, the best solution is quickly obtained (the corresponding objective function value is 408). And the optimal value of the low-power group also stabilize after the second generation. Due to the algorithm shows good convergence under different feasible regions of decision variables, it can be inferred that the multi-population genetic algorithm is suitable for the optimization problem studied in our work.)
obtaining an optimal operating parameter setting under the operating condition according to the optimization result. (see fig 4)
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Examiner note: The Optimization Algorithm proposes a "candidate solution" (a set of parameters). These parameters are used to run a Simulation. The simulation's "output" is a "quality value" or "objective function value." This value is fed back to the Optimization Algorithm, which uses it to evaluate the quality of the candidate solution. The algorithm then proposes a new, improved candidate solution, and the cycle repeats until an optimal or near-optimal solution is found. This iterative process aims to find the best possible parameter settings to achieve a desired outcome.)
Regarding claim 2
Xu further teaches wherein, the operation indexes at least comprise an operation safety index, the operation safety index is obtained by calculating a supercooling degree of a coolant reactor core outlet ( see section 2.2.1-The maneuverability and safety of the nuclear power plant during the conversion process is one of the important indicators for evaluating the natural circulation capacity of the marine nuclear power plant. see page 5-6-The temperature boundary at different loads can be determined, as shown in Fig. 5. Based on thermal hydraulic safety criteria, the highest limit value of primary coolant average temperature (shown in red) can be determined to ensure that reactor core outlet coolant subcooling degree is greater than 10 K. Similarly, the lowest limit of primary coolant average temperature (shown in black) can be determined to ensure that steam superheated degree is greater than 30 K. It’s obviously that the stability area of the primary coolant average temperature shrinks as the power of natural circulation increases, indicating that the setting of the average temperature of the primary circuit is also affected by the reactor power. The reactor power also plays a significant role in the transition. The power here refers to the load level of the reactor before the transition, that is, the initial power. The Fig. 6 reveals the relationship between the initial power and the objective function (transition time, Dt). It seems that the higher the initial power, the faster the transition can be completed. But hidden behind it is a threat to the safety of the reactor core. The subcooling degree of the core outlet coolant decreases as the power increases, which resulted in a reduction in the safety margin of the core.)
operation safety index is obtained by calculating a minimum deviation nucleate boiling value; (see page 7 and fig 10(f)-In addition, the W3 formula is used to calculate the change trend of the core DNBR (Fig. 10 f). During the transition process, the core DNBR is always greater than 1.3, which indicates that the reactor always has high safety.)
a thermal economic index, the thermal economic index is obtained by calculating a superheat degree of a steam outlet. (see fig 10 (e) and table 2 and page 8- Based on the results, the solution with higher initial power level always transitions to natural circulation conditions faster. The transition time required for solution I with higher initial power saves about 14% compared with the result of low power operation (solution II).)
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Regarding claim 4
Xu further teaches before optimizing the operation indexes based on the improved multi-population genetic algorithm, further comprising: determining the operation indexes to be optimized according to the actual demand; determining optimization variables (see section 2.2.1-This study is to use single objective optimization technique to investigate optimum characteristics of the transition process. Single objective optimization generally refers to finding a set of variables that make the objective function (only one) reach the maximum or minimum. In this way, the decision variables, objective functions and the optimization algorithm should be defined. The maneuverability and safety of the nuclear power plant during the conversion process is one of the important indicators for evaluating the natural circulation capacity of the marine nuclear power plant. Thus, an ideal transition from forced circulation to natural circulation should be rapid, stable and safe. That is, the reactor power can automatically track the load demand when the reactor power and flow mismatch caused by the sudden shutdown of the main pump, and the steam flow and feed water flow can be matched in a short time. Besides, the main thermal parameters of the reactor are required to can reestablish a steady state to avoid pressure oscillation. And it’s imperative to make the reactor meet the requirements of thermal safety during the transition. Therefore, the primary coolant average temperature (Tav ), the initial power (P), and main pump shutdown interval (d) are considered as decision variables)
Examiner note: In this context, the "operation indexes" are the desired performance goals (e.g., maneuverability, safety, stability), while the decision variables are the parameters adjusted by the optimization algorithm to achieve those goals.
and feasible regions, (see page 7-For the high-power group, the best solution is quickly obtained (the corresponding objective function value is 408). And the optimal value of the low-power group also stabilize after the second generation. Due to the algorithm shows good convergence under different feasible regions of decision variables, it can be inferred that the multi-population genetic algorithm is suitable for the optimization problem studied in our work)and
Examiner note: It shows the stability of the optimal value within a specific feasible region (the "low-power group"). The multi-population genetic algorithm (MPGA) operates within this space of possible solutions. The results discuss using a modified GA to find "feasible regions," suggesting that the algorithm and the concept of feasible regions are directly connected.
carrying out the optimization calculation of the operation indexes to be optimized by using the improved multi- population genetic algorithm. (see section 2.2.2, see fig 3, 4 and table 3- In this paper, a multi-population genetic algorithm (multiple population GA, MPGA) is selected and employed to optimize the decision variables. MPGA is based on the standard genetic algorithm whose primary calculation process is shown in Fig. 3. It is widely acknowledged that genetic algorithm (GA) is a highly parallel, random, and self-adaptive global optimization probabilistic search algorithm. The specific operating rule is to replace the worst individual in the target population with the best individual in the source population. see page 7-The Fig. 9 refers to the trend of the optimal value at each generation. The x-axis represents generation, and the y-axis represents the objective function value of the best individual (that is, the transition time, Dt). For the high-power group, the best solution is quickly obtained (the corresponding objective function value is 408). And the optimal value of the low-power group also stabilize after the second generation. Due to the algorithm shows good convergence under different feasible regions of decision variables, it can be inferred that the multi-population genetic algorithm is suitable for the optimization problem studied in our work.)
Regarding claim 5
Xu further teaches wherein, the optimization variables are constant operating parameters required by a control strategy in the operating scheme; (see section 2.1 and fig 1- The primary average coolant temperature (Tav) and steam pressure (Ps) are restrained in constant values through PID control principle while the plant load varying. Through adjusting the reactor power, the primary average coolant temperature are controlled to be stable. Moreover, the steam pressure can be kept by changing the feedwater flow. see fig 1 and section 2.2.1- the primary coolant average temperature (Tav ), the initial power (P), and main pump shutdown interval (d) are considered as decision variables. As the reactor requires that both the primary coolant average temperature and the secondary loop vapor pressure remain constant during steady-state operation, the reactor power will change with the primary coolant average temperature. In this way, due to the coolant flow through the core is greatly reduced when the pumps are shut down, the temperature of the coolant at the core outlet increases, resulting in a rise in the primary coolant average temperature.)
and the feasible regions are determined by a sensitivity analysis method of a single variable. (see page 4-vector of design variables which opted due to the high sensitivity of the transition performance to their values. see page 7-The Fig. 9 refers to the trend of the optimal value at each generation. The x-axis represents generation, and the y-axis represents the objective function value of the best individual (that is, the transition time, Dt). For the high-power group, the best solution is quickly obtained (the corresponding objective function value is 408). And the optimal value of the low-power group also stabilize after the second generation. Due to the algorithm shows good convergence under different feasible regions of decision variables, it can be inferred that the multi-population genetic algorithm is suitable for the optimization problem studied in our work.)
Regarding claim 6
Xu further teaches wherein, the process of carrying out the optimization calculation of the operation indexes to be optimized by using the improved multi-population genetic algorithm comprises, providing operating parameters, wherein the multi-population genetic algorithm creates discrete random population according to parameter settings of the operating parameters, calculates the objective function value of initial population after chromosome coding, and performs evolutionary operations on the initial population; a migration operator introduces a best individual into other populations every definite evolutionary algebra to replace a worst individual in a target population and realize the information exchange of the target population, and ends the calculation when a genetic algebra reaches a maximum value. (see fig 3-4 and section 2.2.1-In this paper, a multi-population genetic algorithm (multiple population GA, MPGA) is selected and employed to optimize the decision variables. MPGA is based on the standard genetic algorithm whose primary calculation process The principle of the multi-population genetic algorithm is to randomly create m populations in the search space, and each population contains n0 individuals. Each population is relatively independent, and they are connected with each other through immigration operators. The migration operator introduces the optimal individuals that appear in various groups in the evolution process into other populations periodically (every certain evolutionary generation) to realize the information exchange between the populations. The specific operating rule is to replace the worst individual in the target population with the best individual in the source population. At the same time, the elite population is also the basis for judging the termination of the algorithm. The crossover operator is mainly used for generating new individuals and have a critical impact on the global search capability of the algorithm. While the mutation operator is just an auxiliary operator and determines the local search capability of the genetic algorithm.)
Examiner note: The process of "randomly create m populations in the search space." This clearly describes creating discrete random populations. Genetic algorithms (GAs) have well-known parameters, including population size and number of populations. It mentions population size ("n0 individuals") and number of populations ("m populations"), which are standard operating parameters. The "elite population is also the basis for judging the termination of the algorithm". The termination condition for a genetic algorithm is reaching a maximum number of generations or "algebra"
Regarding claim 9
Xu further teaches wherein obtaining the optimal operating parameter setting under the operating condition comprises: comparing the operation result of the optimized scheme with the operation result of the designed operating scheme, (see table 3 solution I and II) and, obtaining the optimal operating parameter setting under the operating condition based on the operation result of the optimization scheme. see fig 4)
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Examiner note: Table 3 presents a comparison of two different solutions, labeled "I" and "II". The solutions that represent two distinct operating schemes, each with different parameters such as initial power, pump shutdown interval, and steam superheat. The objective function value for each scheme is also listed, which allows for a direct comparison of their performance.
if the requirements are not met, returning to adjust the feasible region and recalculating; if the requirements are met. (see fig 3 and page 7- Due to the algorithm shows good convergence under different feasible regions of decision variables, it can be inferred that the multi-population genetic algorithm is suitable for the optimization problem studied in our work.)
Examiner note: The flowchart checks if the "convergence condition" is satisfied. This condition represents a set of requirements or constraints for the problem. If the convergence condition is "no," the process loops back to the "Immigration operator" stage. This step introduces new individuals (solutions) into the populations, allowing the algorithm to explore different parts of the solution space and adjust the set of possible solutions in a new iteration. The cycle continues until the convergence condition is met, indicating that a satisfactory solution has been found. If the requirement is met, the objective function value recalculating as shown in fig 4.
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.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu, et al. ("Optimization of forced circulation to natural circulation transition characteristics of IPWR." Annals of Nuclear Energy 157 (2021): 108249.) in view of Lin et al. ("GA-based multiobjective PID control for a linear brushless DC motor." IEEE/ASME transactions on mechatronics 8.1 (2003): 56-65.)
Regarding claim 3
Xu does not teach wherein, the operation indexes comprise dynamic response indexes; the dynamic response indexes at least comprise a stationarity index, a rapidity index and a steady-state performance index; the stationarity index is obtained by calculating a overshoot; the rapidity index is obtained by calculating an adjustment time; and the steady-state performance index is obtained by calculating a steady-state error.
In the related field of invention, Lin teaches wherein, the operation indexes comprise dynamic response indexes; the dynamic response indexes at least comprise a stationarity index, a rapidity index and a steady-state performance index; the stationarity index is obtained by calculating a overshoot; the rapidity index is obtained by calculating an adjustment time; and the steady-state performance index is obtained by calculating a steady-state error. (see section C)
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Examiner note: In the above section, normalized maximum overshoot: This corresponds to the "stationarity index" obtained by calculating an overshoot. Normalized rise time: This corresponds to the "rapidity index" obtained by calculating a adjustment time. Normalized steady-state error: This corresponds to the "steady-state performance index" obtained by calculating a steady-state error.
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 optimization method based on improved multi-population genetic algorithm as disclosed by Xu to include wherein, the operation indexes comprise dynamic response indexes; the dynamic response indexes at least comprise a stationarity index, a rapidity index and a steady-state performance index; the stationarity index is obtained by calculating a overshoot; the rapidity index is obtained by calculating an adjustment time; and the steady-state performance index is obtained by calculating a steady-state error as taught by Lin in the system of Xu in order to provide an effective way to implement simple but robust solutions covering a wide range of plant perturbation and, in addition, provides excellent tracking performance without resorting to excessive control. (see abstract, Lin)
Claim(s) 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu, et al. ("Optimization of forced circulation to natural circulation transition characteristics of IPWR." Annals of Nuclear Energy 157 (2021): 108249.) in view of Hassanat, Ahmad, et al. "Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach." Information 10.12 (2019): 390.
Regarding claim 7
Xu further teaches wherein, the operating parameters at least comprise a population number, an individual number, a variable dimension, a generation gap value. (See page 3 col 1 and table 1-And it’s worth mentioning that the impacts of different parameters are also analyzed on the transition performance in the next section. The ranges of the variables are listed in Table 1 which are determined based on the thermal and hydraulic restrictions of reactors see section 2.2.2 and table 2- a multi-population genetic algorithm (multiple population GA, MPGA) is selected and employed to optimize the decision variables. MPGA is based on the standard genetic algorithm whose primary calculation process is shown in Fig. 3. The optimal individuals in each evolutionary generation of various groups are