DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Application
The following is a Final Office Action. In response to Examiner's communication on 06/27/2025, Applicant on 10/27/2025, amended Claim 1, 8-9 and cancelled Claim 7. Claims 1-6, 8-10 are now pending in this application and have been rejected below.
Response to Amendment
Applicants’ amendments are insufficient to overcome the 35 USC 101 rejections set forth in the previous action. These actions have been updated to address the amendments and maintained below.
Applicants’ amendments are insufficient to overcome the 35 USC 103 rejections set forth in the previous action. Therefore, these rejections have been updated to address the amendments and maintained below.
Response to Arguments – 35 USC § 101
Applicant's arguments with respect to the 35 USC 101 rejections have been fully considered but are not found to be persuasive.
First, Applicant asserts that the Claims are not directed at an abstract idea, but rather a specific and tangible improvement in the operation of water supply infrastructure. Applicant notes that the claimed system employs a specific hardware configuration programmed to implement a unique technological process. Examiner respectfully disagrees.
As claimed, there is nothing in the language of the claims to indicate that we are physically changing the state of the hardware of the water supply pipe network, only that we generate a plan to optimally do so. Examiner notes that in accordance with the broadest reasonable interpretation of the claims, limitations are read in light of the specification, and without limitations explicitly providing for real-time administration of a water supply pipe network, as opposed to generating an optimal plan for administration, the invention as claimed can only be said to recite an abstract idea, without offering a specific technical improvement. Note that per MPEP 2106.05(a), “an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology”. While there may certainly be advantages distinct to applicant’s claimed method and system, these considerations do not render the claims as a whole subject matter eligible, as the claimed invention merely uses generic computing components to automate mental processes and mathematical relationships in implementing applicant’s claimed method and system. The same logic applies to Applicant’s assertion of introducing a novel human-machine collaboration; as claimed, the human does not specifically control the operation of the water supply pipe network. Further, even if the system were to be claimed as being particularly implemented to guide a human operator as stated in Remarks, that would fall within the purview of Certain Methods of Organizing Human Activity, namely that of Managing Personal Behavior or Relationships or Interactions between People, and would importantly still be insufficient to render the claim as a whole as integrated into a practical application or significantly more.
Accordingly, the rejections under 35 USC 101 have been updated to address amendments and maintained below.
Response to Arguments – 35 USC § 102 and 35 USC § 103
Applicant' s arguments with respect to the rejection of Claim 1-6, 8-10 under 35 USC 103 have been considered but found to be unpersuasive.
Applicant’s arguments under A. 1. and A. 2. assert that the Wenzel reference is directed to a fundamentally different approach, that of full automation and offline planning and design as opposed to Applicant’s human-in-the-loop and real-time operational scheduling paradigm. Applicant asserts that this difference means that one of ordinary skill in the art would not reasonably find Wenzel applicable. Examiner respectfully disagrees.
As claimed, there is nothing in the language of the claims to indicate that a human need be in the loop, or that operations be performed in real-time as opposed to offline planning. As noted above, Examiner notes that in accordance with the broadest reasonable interpretation of the claims, limitations are read in light of the specification, and without limitations explicitly providing for a human in the loop or real-time operational scheduling, such a difference cannot be said to preclude applicability of a given prior art reference. Further, from MPEP 2141.01(a), Analogous and Nonanalogous art, “A reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention)”. Even if a difference in operating paradigm between Wenzel and the claimed invention was apparent, the teachings of Wenzel are still applicable as Wenzel is clearly both from the same field of endeavor and addresses a reasonably pertinent problem.
Third, Applicant argues that Deb is a General-Purpose Algorithm Devoid of Any Specific Application Context, and that there is no guidance for applying the algorithm within the specific context of water supply.
Examiner notes that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. The test for obviousness is not that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). As stated in the original Non-Final Rejection filed 06/27/2025, we apply the optimization method of Deb. et al to the system of Wenzel combined with Wang as it is applicable owing to its direct relationship to the multi-objective, Pareto-optimal formulation of Wang(p. 19). Finally, in response to applicant’s argument that the examiner’s conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant’s disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Applicant argues under B. The Proposed Combination Does Not Render the Claimed Invention Obvious that there is no apparent reason to combine references from distinct fields. Examiner respectfully disagrees and notes MPEP 2141.01(a), Analogous and Nonanalogous art, “A reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention (even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention)”. That the fields be disparate need not preclude the combination of the references as cited, as we are able to use the second standard of “reasonable pertinence”. Regarding the applicability of Wenzel to Wang, each reference discloses means for optimizing resource allocation. Extending the problem formulation as mentioned in Wang, is applicable to Wenzel as they pertain to the same problem of optimally managing resources and generated outputs; in [0166] of Wenzel, it is disclosed that “Asset allocator 402 is shown to include an optimization framework module 902. Optimization framework module 902 can be configured to define an optimization framework for the optimization problem solved by asset allocator 402.” While one such example is given with respect to a linear programming framework, the formulation of Wang would be equally applicable to this system. Regarding the applicability of Deb. et al to Wenzel combined with Wang, Deb. et al is applicable to the system of Wenzel combined with Wang owing to its direct relationship to the multi-objective, Pareto-optimal formulation of Wang.
Applicant further alleges that key claimed features are not taught:
The specific integration of a multi-objective Pareto front with a parallel coordinate visualization for the purpose of operational scheduling. Examiner respectfully disagrees.
Wang teaches, in [0081], "Furthermore, all the optimization solutions obtained by the above optimization algorithm are plotted in a multi-dimensional parallel coordinate diagram, the relationship between all the optimization solutions is analyzed, and selection is made according to actual needs to obtain an optimization plan for the transmission and distribution pattern of the multi-source water supply network system".
The application of a two-factor sorting method to screen scheduling schemes based on comprehensive benefit analysis. Examiner respectfully disagrees.
As Deb. et al teaches: The non-dominated genetic algorithm is well known in the art as it pertains to Pareto optimal solution sets. With respect to two-factor sorting, we cite on p.185, "That is, between two solutions with differing nondomination ranks, we prefer the solution with the lower (better) rank. Otherwise, if both solutions belong to the same front, then we prefer the solution that is located in a lesser crowded region.”
The output of a final scheduling scheme aimed at cooperatively reducing both energy consumption and maintenance costs. Examiner respectfully disagrees.
As Wenzel teaches regarding the formulation of the problem: In [0100], "For example, asset allocator 402 may consider the revenue generation potential of IBDR programs, the cost reduction potential of PBDR programs, and the equipment maintenance/replacement costs that would result from participating in various combinations of the IBDR programs and PBDR programs". In [0132], "In some embodiments, building status monitor 624 stores data regarding energy costs, such as pricing information available from sources 410 (energy charge, demand charge, etc.)". In [0170], "In some embodiments, the optimization problem generated by optimization problem constructor 910 includes an objective function. The objective function can include the sum of predicted utility usage costs over the horizon (i.e., the optimization period), the predicted demand charges, the total predicted incentive revenue over the prediction horizon, the sum of the predicted distribution costs, the sum of penalties on unmet and overmet loads over the prediction horizon, and/or the sum of the rate of change penalties over the prediction horizon (i.e., delta load penalties).
Wang discloses solving such a problem in [0079-0081], " S3: Select an optimization algorithm suitable for solving high-dimensional multi-objective optimization problems to solve the transmission and distribution pattern optimization model to obtain an optimized solution. S4: Analyze the relationship between the optimization solutions to obtain an optimization plan for the transmission and distribution pattern of the multi-source water supply network system".
Applicant further argues that the claimed invention provides unexpected technical advantages, namely that of enhanced decision-making and operational flexibility and computational efficiency. As cited in the original Non-Final Rejection, In [0079-0081] of Wang, " S3: Select an optimization algorithm suitable for solving high-dimensional multi-objective optimization problems to solve the transmission and distribution pattern optimization model to obtain an optimized solution. S4: Analyze the relationship between the optimization solutions to obtain an optimization plan for the transmission and distribution pattern of the multi-source water supply network system. Furthermore, all the optimization solutions obtained by the above optimization algorithm are plotted in a multi-dimensional parallel coordinate diagram, the relationship between all the optimization solutions is analyzed, and selection is made according to actual needs to obtain an optimization plan for the transmission and distribution pattern of the multi-source water supply network system.” This exactly provides for the visualization examination of trade-offs and selection of a scheme that balances physical objectives.
To the point of Operational Flexibility and Computational Efficiency by virtue of leveraging human judgement, Examiner respectfully notes that as outlined above, such human judgement is not explicitly present in the language of the claims.
The rejections have been updated to address the amendments and maintained below.
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.
Claim 1-6, 8-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
The claims are directed to a method and apparatus. Therefore, the claim is directed to at least one of the four statutory categories.
101 Analysis – Step 2A
Regarding Prong 1 of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether they recite subject matter that is directed to a judicial expectation, namely a law of nature, a natural phenomenon, or one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 1 includes limitations that recite an abstract idea and will henceforth be used as a representative claim for the 101 rejection until otherwise noted. Claim 1 recites:
A scheduling method for uniformly reducing energy and maintenance costs of a water supply pipe network system, performed by a scheduling system comprising one or more sensors and a processor, comprising the following steps of: S1: acquiring, via the one or more sensors, a topological structure and operation data of the water supply pipe network system, constructing, by the processor, a high-dimensional multi-objective optimization model for water pump scheduling of the water supply pipe network system, and setting, by the processor, a decision variable, an objective function and a constraint condition of the model, wherein the high-dimensional multi-objective optimization model takes an energy cost and a maintenance cost of each water pump in the water supply pipe network system as independent optimization objectives; S2: solving, by the processor, the high-dimensional multi-objective optimization model by applying an optimization algorithm to obtain a Pareto optimal solution set; and S3: based on the Pareto optimal solution set, screening and outputting, by the processor, a scheduling scheme for cooperatively reducing the energy cost and the maintenance cost of the water pump by drawing a parallel coordinate graph for visual comparison and using a two-factor sorting method, wherein the step S3 specifically comprises the following step of: drawing the Pareto optimal solution in the parallel coordinate graph, screening an energy-saving scheduling scheme with a comprehensive benefit advantage as the scheduling scheme for cooperatively reducing the energy cost and the maintenance cost of the water pump through the two-factor sorting method and the visual comparison analysis, and outputting, by the processor, the scheduling scheme.
The examiner submits that the foregoing bolded limitation(s) constitute an abstract idea because under its broadest reasonable interpretation, the claim covers a mental process, mathematical concept, and certain method of organizing human activity. “uniformly reducing energy and maintenance costs” pertain to cost optimization, a fundamental economic practice. “constructing a high-dimensional multi-objective optimization model”, “setting a decision variable, an objective function and a constraint condition of the model”, taking “energy cost and a maintenance cost of each water pump…as independent optimization objectives”, “solving the high-dimensional multi-objective optimization model by applying an optimization algorithm to obtain a Pareto optimal solution set”, “drawing a parallel coordinate graph for visual comparison and using a two-factor sorting method” recite mathematical concepts because, in view of Applicant's Specification, the elements recite mathematical calculations and relationships". Further, “acquiring a topological structure and operation data of the water supply pipe network system”, as well as “screening and outputting a scheduling scheme…by drawing a parallel coordinate graph for visual comparison and using a two-factor sorting method”, recite mental processes because the elements recite observations or evaluations that can be practically performed in the mind or by a human using pen and paper". Accordingly, the claim recites at least one abstract idea.
Claims 8 and 10 recite abstract ideas by analogous reasoning.
Dependent Claims 2-6, 9 recite abstract ideas by virtue of their dependency from independent Claim 1.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into practical application. As noted in the MPEP, it must be determined whether any additional elements in the claim beyond the judicial exception integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements, such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”), as it pertains to representative Claims :
Claim 1 recites “performed by a scheduling system comprising one or more sensors and a processor”. ”. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements are generic computing components that are merely used as a tool to perform the recited abstract idea and/or do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use under Step 2A Prong Two.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing an abstract idea.
Claim 8 recites additional elements of implementing the method as “a system”, with “a water supply pipe network data acquisition module, a water pump scheduling optimization module and a scheduling scheme screening module”. Claim 10 recites a “scheduling device, comprising a memory and a processor”.
These additional elements do not integrate the abstract idea into a practical application by analogous reasoning as above.
Claims 2-6, 9 do not recite additional elements beyond those found in Claims from which they dependent and therefore do not integrate the recited abstract ideas into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the MPEP, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to well-understood and routine activities that are conventional in the art.
Claim 8 recites additional elements of implementing the method as “a system”, with “a water supply pipe network data acquisition module, a water pump scheduling optimization module and a scheduling scheme screening module”. Claim 10 recites a “scheduling device, comprising a memory and a processor”.
These additional elements do not integrate the abstract idea into a practical application by analogous reasoning as above.
Claims 2-6, 9 do not recite additional elements beyond those found in Claims from which they dependent and therefore do not integrate the recited abstract ideas into a practical application.
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.
Claims 1-3, 5-6, 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Wenzel(US 20200042918 A1) in view of Wang(CN112926164A) in further view of Deb. et al(“A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II”).
Claim 1, 8, 10
As to Claim 1,
Wenzel teaches:
A scheduling method for uniformly reducing energy and maintenance costs of a water supply pipe network system, performed by a scheduling system comprising one or more sensors and a processor, comprising the following steps of:
In [0005], "The central plant also includes an asset allocator configured to determine an optimal allocation of the energy loads across the central plant equipment by identifying one or more sources configured to supply the input resources, one or more subplants configured to convert the input resources to the output resources, and one or more sinks configured to consume the output resources; generating a cost function including a cost of purchasing the input resources from the sources; generating a resource balance constraint that requires balance between a total amount of each resource supplied by the sources and the subplants and a total amount of each resource consumed by the subplants and the sinks; and solving an optimization problem to determine the optimal allocation of the energy loads across the central plant equipment. Solving the optimization problem includes optimizing the cost function subject to the resource balance constraint. The asset allocator is configured to control the central plant equipment to achieve the optimal allocation of the energy loads." In [0123], “In some embodiments, BMS 606 is the same or similar to the BMS described with reference to FIG. 1. BMS 606 may be configured to monitor conditions within a controlled building or building zone. For example, BMS 606 may receive input from various sensors (e.g., temperature sensors, humidity sensors, airflow sensors, voltage sensors, etc.) distributed throughout the building and may report building conditions to central plant controller 600. Building conditions may include, for example, a temperature of the building or a zone of the building, a power consumption (e.g., electric load) of the building, a state of one or more actuators configured to affect a controlled state within the building, or other types of information relating to the controlled building”.
S1: acquiring, via the one or more sensors, a topological structure and operation data of the water supply pipe network system,
In [0052], "The asset allocator can be configured to manage energy assets such as central plant equipment, battery storage, and other types of equipment configured to serve the energy loads of a building. The asset allocator can determine an optimal distribution of heating, cooling, electricity, and energy loads across different subplants (i.e., equipment groups) of the central plant capable of producing that type of energy". In [0131], "Memory 610 is shown to include a building status monitor 624. Central plant controller 600 may receive data regarding the overall building or building space to be heated or cooled by system 400 via building status monitor 624. In an exemplary embodiment, building status monitor 624 may include a graphical user interface component configured to provide graphical user interfaces to a user for selecting building requirements (e.g., overall temperature parameters, selecting schedules for the building, selecting different temperature levels for different building zones, etc.)". In [0123], “In some embodiments, BMS 606 is the same or similar to the BMS described with reference to FIG. 1. BMS 606 may be configured to monitor conditions within a controlled building or building zone. For example, BMS 606 may receive input from various sensors (e.g., temperature sensors, humidity sensors, airflow sensors, voltage sensors, etc.) distributed throughout the building and may report building conditions to central plant controller 600”.
wherein the high-dimensional multi-objective optimization model takes an energy cost and a maintenance cost of each water pump in the water supply pipe network system
In [0100], "For example, asset allocator 402 may consider the revenue generation potential of IBDR programs, the cost reduction potential of PBDR programs, and the equipment maintenance/replacement costs that would result from participating in various combinations of the IBDR programs and PBDR programs". In [0132], "In some embodiments, building status monitor 624 stores data regarding energy costs, such as pricing information available from sources 410 (energy charge, demand charge, etc.)". In [0004], “Some central plants include energy storage. Energy storage may be a tank of water that stores hot water for campus heating, an ice tank for campus cooling, and/or battery storage”.
scheme for cooperatively reducing the energy cost and the maintenance cost of the water pump
In [0170], ""In some embodiments, the optimization problem generated by optimization problem constructor 910 includes an objective function. The objective function can include the sum of predicted utility usage costs over the horizon (i.e., the optimization period), the predicted demand charges, the total predicted incentive revenue over the prediction horizon, the sum of the predicted distribution costs, the sum of penalties on unmet and overmet loads over the prediction horizon, and/or the sum of the rate of change penalties over the prediction horizon (i.e., delta load penalties). All of these terms may add to the total cost, with the exception of the total predicted incentive revenue. The predicted incentive revenue may subtract from the total cost. For example, the objective function generated by optimization problem constructor 910 may have the following form"". In [0172], ""The optimization problem generated by optimization problem constructor 910 can be considered a finite-horizon optimal control problem. The optimization problem may take the form:
minimize J(x)
subject to resource balances, operational domains for subplants 420 (e.g., subplant curves), constraints to predict the SOC of storage 430, storage capacity constraints, subplant/storage box constraints (e.g., capacity constraints and discharge/charge rate constraints), demand/peak usage constraints, auxiliary constraints for rate of change variables, auxiliary constraints for demand charges, and site specific constraints”. In [0004], “Some central plants include energy storage. Energy storage may be a tank of water that stores hot water for campus heating, an ice tank for campus cooling, and/or battery storage”.
Wenzel does not disclose the remaining limitations.
However, Wang teaches:
constructing, by the processor, a high-dimensional multi-objective optimization model for water pump scheduling of the water supply pipe network system, and setting, by the processor, a decision variable, an objective function and a constraint condition of the model,
In [0008], "S2: constructing a transmission and distribution pattern optimization model, and setting decision variables, objective functions and constraints of the transmission and distribution pattern optimization model". In [0001], "The present invention relates to the technical field of water supply pipe network systems, and more specifically, to a method for optimizing the transmission and distribution pattern of a multi-source water supply pipe network system". The presence of a computer processor to facilitate computations is implicit here.
as independent optimization objectives;
In [0022], "As a preferred solution, the objectives of the transmission and distribution pattern optimization model include minimizing the leakage percentage, pressure balance, water age, maximum water age of nodes, and number of valves of the multi-source water supply network system, while maximizing the reliability of the multi-source water supply network system and the percentage increase of water volume of the weak water source; the expression formula of the objective function is as follows". See Page 3 of the attached Original Document for a statement of the objective function – here we can clearly see the specification of multiple objectives. While there certainly might be some degree of relatedness, in line with the Broadest Reasonable Interpretation of the claim, we understand independent here to signify corresponding to separate terms in the objective function.
S2: solving, by the processor, the high-dimensional multi-objective optimization model by applying an optimization algorithm to obtain a Pareto optimal solution set; and S3: based on the Pareto optimal solution set, screening and outputting, by the processor, a scheduling scheme
In [0079-0081], " S3: Select an optimization algorithm suitable for solving high-dimensional multi-objective optimization problems to solve the transmission and distribution pattern optimization model to obtain an optimized solution. S4: Analyze the relationship between the optimization solutions to obtain an optimization plan for the transmission and distribution pattern of the multi-source water supply network system.
Furthermore, all the optimization solutions obtained by the above optimization algorithm are plotted in a multi-dimensional parallel coordinate diagram, the relationship between all the optimization solutions is analyzed, and selection is made according to actual needs to obtain an optimization plan for the transmission and distribution pattern of the multi-source water supply network system.”
by drawing a parallel coordinate graph for visual comparison
In [0081], "Furthermore, all the optimization solutions obtained by the above optimization algorithm are plotted in a multi-dimensional parallel coordinate diagram, the relationship between all the optimization solutions is analyzed, and selection is made according to actual needs to obtain an optimization plan for the transmission and distribution pattern of the multi-source water supply network system".
wherein the step S3 specifically comprises the following step of: drawing the Pareto optimal solution in the parallel coordinate graph, screening an energy-saving scheduling scheme with a comprehensive benefit advantage as the scheduling scheme for cooperatively reducing the energy cost and the maintenance cost of the water pump through the …
In [0081], "Furthermore, all the optimization solutions obtained by the above optimization algorithm are plotted in a multi-dimensional parallel coordinate diagram, the relationship between all the optimization solutions is analyzed, and selection is made according to actual needs to obtain an optimization plan for the transmission and distribution pattern of the multi-source water supply network system".
and the visual comparison analysis, and outputting, by the processor, the scheduling scheme.
In [0081], "Furthermore, all the optimization solutions obtained by the above optimization algorithm are plotted in a multi-dimensional parallel coordinate diagram, the relationship between all the optimization solutions is analyzed, and selection is made according to actual needs to obtain an optimization plan for the transmission and distribution pattern of the multi-source water supply network system".
Wenzel discloses a system for optimizing the operations of a power plant, interfacing with subplants and subcomponents. Wang discloses a system meant to manage the supply and distribution of a network of pipes transporting water. Each reference discloses means for optimizing resource allocation. Extending the problem formulation as mentioned in Wang, is applicable to Wenzel as they pertain to the same problem of optimally managing resources and generated outputs; in [0166] of Wenzel, it is disclosed that “Asset allocator 402 is shown to include an optimization framework module 902. Optimization framework module 902 can be configured to define an optimization framework for the optimization problem solved by asset allocator 402.” While one such example is given with respect to a linear programming framework, the formulation of Wang would be equally applicable to this system.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the problem formulation and solving taught in Wang and apply that to the system as taught in Wenzel. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit that adopting the formulation would enable users to optimize for a plurality of objectives, in this case both maintenance and energy cost.
Wenzel combined with Wang do not disclose the remaining limitations.
However, Deb. et al teaches:
and using a two-factor sorting method; two-factor sorting methodThe non-dominated genetic algorithm is well known in the art as it pertains to Pareto optimal solution sets. With respect to two-factor sorting, we cite on p.185, "That is, between two solutions with differing nondomination ranks, we prefer the solution with the lower (better) rank. Otherwise, if both solutions belong to the same front, then we prefer the solution that is located in a lesser crowded region.”
Deb. et al is applicable to the system of Wenzel combined with Wang owing to its direct relationship to the multi-objective, Pareto-optimal formulation of Wang.
It would have been obvious to one of ordinary skill in the art to apply the optimization methodology of Deb. et al to the system of Wenzel combined with Wang. The algorithm taught by Deb. et al merely enables us to compute and analyze solutions with respect to the formulated problem, and so provides the benefit of supporting the multi-objective optimization approach of Wenzel combined with Wang.
Claims 8 and 10 are rejected as presenting substantially similar limitations as Claim 1. While Claim 8 does disclose the notion of a “water pump scheduling optimization module”, a “water supply pipe network data acquisition model”, and a “scheduling scheme screening module”, we have fully disclosed the ability to perform the functions of these modules. In [0079], "Still referring to FIG. 3, airside system 300 is shown to include a building management system (BMS) controller 366 and a client device 368. BMS controller 366 can include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.) that serve as system level controllers, application or data servers, head nodes, or master controllers for airside system 300, central plant 200, HVAC system 100, and/or other controllable systems that serve building 10. BMS controller 366 may communicate with multiple downstream building systems or subsystems (e.g., HVAC system 100, a security system, a lighting system, central plant 200, etc.) via a communications link 370 according to like or disparate protocols (e.g., LON, BACnet, etc.). In various embodiments, AHU controller 330 and BMS controller 366 can be separate (as shown in FIG. 3) or integrated. In an integrated implementation, AHU controller 330 can be a software module configured for execution by a processor of BMS controller 366". While we are not specifically provided the claimed modules, we consider the functionalities as well as their compatibility with various subsystems to disclose the limitations. With respect to Claim 10, in [0129], "Still referring to FIG. 6, processing circuit 607 is shown to include a processor 608 and memory 610. Processor 608 may be a general purpose or specific purpose processor, an application specific integrated circuit (ASIC), one or more field programmable gate arrays (FPGAs), a group of processing components, or other suitable processing components. Processor 608 may be configured to execute computer code or instructions stored in memory 610 or received from other computer readable media (e.g., CDROM, network storage, a remote server, etc.)".
Claim 2
As to Claim 2, Wenzel combined with Wang and Deb. et al teaches all the limitations of Claim 1 as discussed above.
Wenzel teaches:
The scheduling method according to claim 1, wherein the operation data of the water supply pipe network system comprises operation conditions and actual peak-valley price periods of each water pump and each water tank, and an operation requirement of the water supply pipe network system.
In [0088], "In some embodiments, storage 430 is used by asset allocation system 400 to take advantage of price-based demand response (PBDR) programs. PBDR programs encourage consumers to reduce consumption when generation, transmission, and distribution costs are high. PBDR programs are typically implemented (e.g., by sources 410) in the form of energy prices that vary as a function of time. For example, some utilities may increase the price per unit of electricity during peak usage hours to encourage customers to reduce electricity consumption during peak times. Some utilities also charge consumers a separate demand charge based on the maximum rate of electricity consumption at any time during a predetermined demand charge period". In [0177], "Similarly, subplant models 936 may define the input resources of each subplant 420, the output resources of each subplant 420, relationships between the input and output variables of each subplant 420 (i.e., the operational domain of each subplant 420), and optimization constraints associated with each of subplants 420". In [0132], "Central plant controller 600 may determine on/off configurations and operating setpoints to satisfy the building requirements received from building status monitor 624. In some embodiments, building status monitor 624 receives, collects, stores, and/or transmits cooling load requirements, building temperature setpoints, occupancy data, weather data, energy data, schedule data, and other building parameters. In some embodiments, building status monitor 624 stores data regarding energy costs, such as pricing information available from sources 410 (energy charge, demand charge, etc.)." In [0002], “The present disclosure relates generally to a central plant or central energy facility configured to serve the energy loads of a building or campus. The present disclosure relates more particular to a central plant with an asset allocator configured to determine an optimal distribution of the energy loads across various subplants of the central plant”. In [0004], “Some central plants include energy storage. Energy storage may be a tank of water that stores hot water for campus heating, an ice tank for campus cooling, and/or battery storage”.
Claim 3, 9
As to Claim 3, Wenzel combined with Wang and Deb. et al teaches all the limitations of Claim 2 as discussed above.
Wenzel teaches:
The scheduling method according to claim 2, wherein the step S1 specifically comprises the following steps of: S11: acquiring the topological structure of the water supply pipe network system,
In [0052], "The asset allocator can be configured to manage energy assets such as central plant equipment, battery storage, and other types of equipment configured to serve the energy loads of a building. The asset allocator can determine an optimal distribution of heating, cooling, electricity, and energy loads across different subplants (i.e., equipment groups) of the central plant capable of producing that type of energy". In [0131], "Memory 610 is shown to include a building status monitor 624. Central plant controller 600 may receive data regarding the overall building or building space to be heated or cooled by system 400 via building status monitor 624. In an exemplary embodiment, building status monitor 624 may include a graphical user interface component configured to provide graphical user interfaces to a user for selecting building requirements (e.g., overall temperature parameters, selecting schedules for the building, selecting different temperature levels for different building zones, etc.)". In [0002], “The present disclosure relates generally to a central plant or central energy facility configured to serve the energy loads of a building or campus. The present disclosure relates more particular to a central plant with an asset allocator configured to determine an optimal distribution of the energy loads across various subplants of the central plant”. In [0004], “Some central plants include energy storage. Energy storage may be a tank of water that stores hot water for campus heating, an ice tank for campus cooling, and/or battery storage”.
determining a control correlation between each water pump and each water tank,
In [0098], "Each of subplants 420 and storage 430 may include equipment that can be controlled by asset allocator 402 to optimize the performance of asset allocation system 400. Subplant equipment may include, for example, heating devices, chillers, heat recovery heat exchangers, cooling towers, energy storage devices, pumps, valves, and/or other devices of subplants 420 and storage 430. Individual devices of subplants 420 can be turned on or off to adjust the resource production of each subplant 420. In some embodiments, individual devices of subplants 420 can be operated at variable capacities (e.g., operating a chiller at 10% capacity or 60% capacity) according to an operating setpoint received from asset allocator 402. Asset allocator 402 can control the equipment of subplants 420 and storage 430 to adjust the amount of each resource purchased, consumed, and/or produced by system 400". In [0004], “Some central plants include energy storage. Energy storage may be a tank of water that stores hot water for campus heating, an ice tank for campus cooling, and/or battery storage”.
S12: determining calculation methods of the energy cost and the maintenance cost of the water pump according to the actual peak-valley price period for setting the objective function of the scheduling model;
In [0132], "In some embodiments, building status monitor 624 receives, collects, stores, and/or transmits cooling load requirements, building temperature setpoints, occupancy data, weather data, energy data, schedule data, and other building parameters. In some embodiments, building status monitor 624 stores data regarding energy costs, such as pricing information available from sources 410 (energy charge, demand charge, etc.)". In [0100], "For example, asset allocator 402 may consider the revenue generation potential of IBDR programs, the cost reduction potential of PBDR programs, and the equipment maintenance/replacement costs that would result from participating in various combinations of the IBDR programs and PBDR programs". In [0004], “Some central plants include energy storage. Energy storage may be a tank of water that stores hot water for campus heating, an ice tank for campus cooling, and/or battery storage”.
and S13: setting the constraint condition of the scheduling model according to the operation requirement of the water supply pipe network system.
In [0015], "The method further includes generating a cost function including a cost of purchasing the input resources from the sources and generating a resource balance constraint. The resource balance constraint requires balance between a total amount of each resource supplied by the sources and the subplants and a total amount of each resource consumed by the subplants and the sinks. The method further includes solving an optimization problem to determine an optimal allocation of the energy loads across the central plant equipment. Solving the optimization problem includes optimizing the cost function subject to the resource balance constraint". In [0002], “The present disclosure relates generally to a central plant or central energy facility configured to serve the energy loads of a building or campus. The present disclosure relates more particular to a central plant with an asset allocator configured to determine an optimal distribution of the energy loads across various subplants of the central plant”. In [0004], “Some central plants include energy storage. Energy storage may be a tank of water that stores hot water for campus heating, an ice tank for campus cooling, and/or battery storage”.
Wenzel does not disclose the remaining elements.
However, Wang teaches:
and setting the decision variable of the scheduling model according to a logical control rule of each water pump;
In [0021], "As a preferred solution, the decision variables in the transmission and distribution pattern optimization model include the key position of the valve and the valve opening; when optimizing the transmission and distribution pattern optimization model, the valve opening is simulated and regulated by changing the local head loss coefficient of the pipeline".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the problem formulation and solving taught in Wang, and the final solution analysis of Deb et. al and apply that to the system as taught in Wenzel. Motivation to do some come from the same reasoning as outlined above with respect to Claim 1.
While Claim 9 does additionally disclose the notion of a “one or more sensors” acquiring structure and operation data, we have fully disclosed this functionality, in [0123], “In some embodiments, BMS 606 is the same or similar to the BMS described with reference to FIG. 1. BMS 606 may be configured to monitor conditions within a controlled building or building zone. For example, BMS 606 may receive input from various sensors (e.g., temperature sensors, humidity sensors, airflow sensors, voltage sensors, etc.) distributed throughout the building and may report building conditions to central plant controller 600. Building conditions may include, for example, a temperature of the building or a zone of the building, a power consumption (e.g., electric load) of the building, a state of one or more actuators configured to affect a controlled state within the building, or other types of information relating to the controlled building”.
Claim 9 is rejected as disclosing substantially similar limitations as Claim 3.
Claim 5
As to Claim 5, Wenzel combined with Wang and Deb. et al teaches all the limitations of Claim 3 as discussed above.
Wenzel also teaches:
The scheduling method according to claim 3, wherein the objective function of the high-dimensional multi-objective optimization model comprises minimizing the energy c