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 .
Responsive to communications on 05/23/2024
Claims 1-20 pending
Claims 1-20 rejected
Priority
Application data sheet claims domestic priority to provisional application 63348342 effective filing date 06/02/2022. Application data sheet accepted by the examiner.
Information Disclosure Statement
Responsive to IDS received on 05/23/2024. All references considered except where lined through. IDS accepted by the examiner.
Drawings
Responsive to amended drawings submitted on 10/11/2023. Drawings convert the originally submitted drawings to black and white and contain no new matter. Drawings are accepted by the examiner.
Specification
Abstract received on 06/02/2023 is less than 150 words and contains no legal or implied phraseology. Abstract is accepted by the examiner.
Responsive to amended specification received on 10/11/2023. Specification amendments pertain to the removal of the colored drawings and contain no new matter. Specification is accepted by the examiner.
Claim Objections
Claim 2 and 4 states “of a road network” Claim 1 which it depends on states “the road network” The claim is objected to for clarity, since it is not clear if this is the same road network referenced in claim 1. The claim likely should state “of the road network”
Claim 7 and 11 state “a road network” this was likely meant to be written as “the road network”
Claim 19: “generate the plurality of synthetic infrastructure networks to include a water distribution network and a power distribution network. “Was likely meant to be written as “wherein the plurality of synthetic infrastructure networks include a water distribution network and a power distribution network. “
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6 and 12 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 6: The specification does not disclose a process where the invention “iteratively increase(s) a number of edges of the water network topology map based on a simulated hydraulic pressure .. with respect to the hydraulic threshold value” The specification recites in par 20: “The system 100 then uses an iterative approach, gradually increasing a pump's capacity to minimize the number of nodes with a hydraulic pressure below 40 psi and ensure that the network's maximum hydraulic pressure remains below 100 psi.” However this refers to the minimization of nodes in claim 1. The specification does not disclose a method which iteratively adds pipes/edges based on hydraulic pressure in the specifications.
Claim 12: The specification does not disclose a process, where a fitness function is used to iteratively improve the model by adding edges or modifying the power network topology map. As already stated, The specification does not disclose a method which adds pipes or edges to a model.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1- 17 are 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.
Claim 1 recites the limitation " the water network topology map data”. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation " the road network”. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “with respect to the one or more nodes and/or edges of the water topology map or the power network topology map” There is insufficient antecedent basis for this limitation in the claim. The claim did not previously outline nodes and/or edges present in the power topology map. Also the claim does not outline edges in either the water or power topology maps.
Claim 1 recites the limitation " the simulation water network topology map and/or the power network topology map”. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 recites the limitation “the set of nodes” There is insufficient antecedent basis for this limitation in the claim.
Claim 4 and 11 recites the limitation “the set of edges” There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation “parameters of the power network topology map” There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation “a clustering-based road network” The term “a clustering-based road network” in claim 13 is a relative term which renders the claim indefinite. The term is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 17 recites “modeling a direct physical connection between water pumps and power distribution by connecting the respective nodes of both networks. “There is insufficient antecedent basis for the terms “water pumps” and “the respective nodes of both networks”.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, an abstract idea, which has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception.
Claim 1
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites A system, comprising which is a machine.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 1 recites synthesize a water network topology map of a model infrastructure that minimizes a number of nodes with a hydraulic pressure below a hydraulic threshold value, the water network topology map data being indicative of one or more intersections that consume water and one or more water pumps that distribute water;
Synthesizing a network topology map is the creation of a nodal network. For example, nodes (1,1) (1,2) and edge list (01). This nodal network can be represented mathematically when applied by machine learning algorithms as shown above or drawn with a pen and paper by connecting dots. This is an abstract/representation of a water network.
The minimization of a number of nodes with hydraulic pressure below a threshold value is the understanding that nodes of a water network require pressure in order for the water to move through the network, and to build the network ensuring a minimum number of nodes below that value. One ordinarily skilled in the art can evaluate and determine hydraulic pressure requirements for nodes in a network given pressure values from other nodes to ensure that nodes are above a threshold value. It also needs to be considered that the threshold is not defined, meaning a person ordinarily skilled in the art can set the threshold to any arbitrary value (i.e.; zero).
The nodes being based on intersections which consume/distribute water is a further recitation of the abstract idea of building a nodal network and thus is a further recitation of the abstract idea.
The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ and MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.”
Because the claim limitation as identified above pertains to actions which can be performed in the human mind, such as evaluating a threshold of hydraulic pressure, and drawings nodes on a graph, the claim recites a mental process.
synthesize a power network topology map of the model infrastructure based on the road network that includes data indicative of a plurality of power substations and power transmission data with respect to the road network;
As stated above Synthesizing a power topology map is the creation of a nodal network. For example, nodes (1,1) (1,2) and edge list (01). This nodal network can be represented mathematically when applied by machine learning algorithms as shown above or drawn with a pen and paper by connecting dots. This is an abstract/representation of a power network.
The nodes being based on a road network which includes power substations and power transmission data is a further recitation of the abstract idea of building a nodal network and thus is a further recitation of the abstract idea.
The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ and MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.”
Because the claim limitation as identified above pertains to actions which can be performed in the human mind, such as evaluating a threshold of hydraulic pressure, and drawings nodes on a graph, the claim recites a mental process.
model a set of interconnections between the water network topology map and the power network topology map, the set of interconnections being indicative of a physical dependency between each respective water pump in the water network topology map and each respective power substation in the power network topology map;
This modeling step encompasses an observation of the created water network topology and power network topology maps, and then connecting the nodes which are evaluated by an individual to contain a physical dependency. For example, the node which correlates to the same street corner of a water pump is interconnected to the nearest power substation. This is an observation and evaluation of the two network maps to determine connections. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Therefore the claim recites a mental process.
iteratively simulate a failure of one or more components of the model infrastructure resulting in effect data indicative of a simulated effect of the failure with respect to one or more nodes and/or edges of the water network topology map or the power network topology map;
The simulation discussed as outlined by the specifications is a Monte Carlo simulation. This is a mathematical simulation process. Therefore, this simulation step which simulates failure is a mathematic process which occurs on a nodal network and performs mathematic calculations which determines which nodes or edges of the maps fail. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore the claim recites a mathematic calculation and recites an abstract idea.
and quantify a fitness of the model infrastructure including the simulation water network topology map and/or the power network topology map based on the effect data.
A fitness of a model infrastructure is a numeric value associated with the infrastructure based on its resilience to failure. This is a mathematic value determined through a fitness equation, where the term “quantify” is a textual replacement for the term “calculate.” The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore the claim recites a mathematic calculation and recites an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 1 additionally recites a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:
This is a recitation of generic machinery which performs the judicial exceptions recited above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore this claim limitation does not integrate a judicial exception into a practical application or provide significantly more.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, the claim does not contain additional elements that amount o significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 1 is not eligible under 35 USC 101.
Claim 13
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites A method of modeling synthetic infrastructure, comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. generating a plurality of synthetic infrastructure networks, including:
synthesizing a water distribution network topology leveraging a clustering-based road network including converting the road network to a graph such that links include roads and nodes define intersections,
and synthesizing a power distribution network including a set of substations;
As stated in claim 1, Synthesizing a power and water topology map is the creation of a nodal network. For example, nodes (1,1) (1,2) and edge list (01). This nodal network can be represented mathematically when applied by machine learning algorithms as shown above or drawn with a pen and paper by connecting dots. This is an abstract/representation of a power network.
The nodes being based on a road network which includes power substations and power transmission data as well as using links which include roads and nodes which define intersections is a further recitation of the abstract idea of building a nodal network and thus is a further recitation of the abstract idea.
The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ and MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.”
Because the claim limitation as identified above pertains to actions which can be performed in the human mind, such as drawings or calculating nodes on a graph, the claim recites a mental process.
modeling interdependencies between the plurality of synthetic infrastructure networks;
This modeling step encompasses an observation of the created water network topology and power network topology maps, and then connecting the nodes which are evaluated by an individual to contain a physical dependency. For example, the node which correlates to the same street corner of a water pump is interconnected to the nearest power substation. This is an observation and evaluation of the two network maps to determine connections. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Therefore the claim recites a mental process.
and simulating cascading failure for the plurality of synthetic infrastructure networks to estimate conditions corresponding to failures associated with the plurality of synthetic infrastructure networks.
The simulation discussed as outlined by the specifications is a Monte Carlo simulation. This is a mathematical simulation process. Therefore, this simulation step which simulates failure is a mathematic process which occurs on a nodal network and performs mathematic calculations which determines which nodes or edges of the maps fail. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore the claim recites a mathematic calculation and recites an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 13 does not recite additional elements that integrate the judicial exception into a practical application?
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, Claim 13 does not recite additional elements that amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 13 is not eligible under 35 USC 101.
Claim 18
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites. A non-transitory, computer-readable medium storing instructions that when executed by one or more processors cause the one or more processors to: which is a product of manufacture.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 18 recites: generate a plurality of synthetic infrastructure networks;
As stated in claim 1, Synthesizing an infrastructure network is the creation of a nodal network. For example, nodes (1,1) (1,2) and edge list (01). This nodal network can be represented mathematically when applied by machine learning algorithms as shown above or drawn with a pen and paper by connecting dots.
The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ and MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.”
Because the claim limitation as identified above pertains to actions which can be performed in the human mind, such as drawings or calculating nodes on a graph, the claim recites a mental process.
model interdependencies between the plurality of synthetic infrastructure networks;
This modeling step encompasses an observation of the created network topology maps, and then connecting the nodes which are evaluated by an individual to contain a physical dependency. For example, the node which correlates to the same street corner of a water pump is interconnected to the nearest power substation. This is an observation and evaluation of the two network maps to determine connections. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Therefore the claim recites a mental process.
and simulate cascading failure for the plurality of synthetic infrastructure networks to estimate conditions corresponding to failures associated with the plurality of synthetic infrastructure networks.
The simulation discussed as outlined by the specifications is a Monte Carlo simulation. This is a mathematical simulation process. Therefore, this simulation step which simulates failure is a mathematic process which occurs on a nodal network and performs mathematic calculations which determines which nodes or edges of the maps fail. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore the claim recites a mathematic calculation and recites an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 18 additionally recites . A non-transitory, computer-readable medium storing instructions that when executed by one or more processors cause the one or more processors to:
This is a recitation of generic machinery which performs the judicial exceptions recited above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore this claim limitation does not integrate a judicial exception into a practical application or provide significantly more.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, the additional elements do not amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 18 is not eligible under 35 USC 101.
Claim 2:The system of claim 1, wherein a node of the set of nodes of the water network topology map is indicative of an intersection of a road network and includes data indicative of local water demand associated with the intersection.
This claim limitation pertains to the nodal network which was determined to be an abstract idea. Including data indicative of local water demand is giving the node data. For example, the hydraulic pressure already outlined in claim 1. Therefore this claim is a further recitation of the abstract idea.
Claim 3:The system of claim 2, wherein the data indicative of local water demand includes at least one of: a hydraulic pressure value and a water use rate.
This claim limitation pertains to the data information of the nodal network which was determined to be an abstract idea. This claim specifies the data to, For example, the hydraulic pressure already outlined in claim 1. Therefore, this claim is a further recitation of the abstract idea.
Claim 4:
The system of claim 1, where an edge of the set of edges of the water network topology map is a linkage between a first node indicative of a first intersection of a road network and a second node indicative of a second intersection of a road network, the edge including data indicative of water transfer by a water pump between the first intersection and the second intersection.
This claim pertains to the representation of nodes and edges in the water topology network map which was determined to recite an abstract idea. Therefore this claim is a further recitation of the abstract idea of creating a water network topology map, and is a further recitation of the abstract idea.
Claim 5:The system of claim 4, wherein the data indicative of water transfer includes at least one of: a pipe diameter and a flow rate.
This claim pertains to the data associated with the edges of the water network topology map which was determined to recite an abstract idea. Therefore this claim is a further recitation of the abstract idea of creating a water network topology map, and is a further recitation of the abstract idea.
Claim 6:
The system of claim 1, wherein the memory includes instructions, which, when executed, further cause the processor to:
iteratively increase a number of edges of the water network topology map based on a simulated hydraulic pressure of each respective node of the water network topology map with respect to the hydraulic threshold value.
Increasing a number of edges is a modification of the water network topology map. The simulated hydraulic pressure is the mathematic values associated with the map. Increasing the number of edges based on the hydraulic pressure is to increase edges, either to increase or decrease a pressure as understood by one ordinarily skilled in the art (the addition of pipes to a pump etc). Therefore the claim is a further recitation of the abstract idea of creating a water topology map and further recites an abstract idea.
Claim 7:
The system of claim 1, wherein a node of a set of nodes of the power network topology map is indicative of an intersection of a road network and includes data indicative of local power demand associated with the intersection.
This claim pertains to the representation of nodes and edges in the power topology network map which was determined to recite an abstract idea. Therefore this claim is a further recitation of the abstract idea of creating a power network topology map, and is a further recitation of the abstract idea.
Claim 8:
The system of claim 7, wherein the data indicative of local power demand includes at least one of: a voltage value at the intersection and a power use rate.
This claim pertains to the data associated with the nodes of the power network topology map which was determined to recite an abstract idea. Therefore this claim is a further recitation of the abstract idea of creating a power network topology map, and is a further recitation of the abstract idea.
Claim 9:
The system of claim 1, wherein the memory includes instructions, which, when executed, further cause the processor to:
generate a plurality of Voroni polygons enclosing a plurality of reference points, where each reference point is indicative of a geometric position of a power substation and where each Voroni polygon is indicative of a geographic area covered by each respective reference point with respect to the road network
Generating Voronoi polygons is generating a boundary for reference points using a mathematical algorithm to ensure that the center of the points is closest to the reference point. This is done to ensure the power substations are closest to the demand nodes. This is a mental process performed by one ordinarily skilled in the art using geometry, and is a further recitation of an abstract idea.
Claim 10:
The system of claim 9, wherein each reference point is associated with a power output value.
This claim pertains to what the reference points represents for the Voronoi polygons, and is thus a further recitation of the abstract idea of generating Voronoi polygons for a map.
Claim 11:
The system of claim 1, where an edge of the set of edges of the power network topology map is a linkage between a first node indicative of a first intersection of a road network and a second node indicative of a second intersection of a road network, the edge including data indicative of power transfer between the first intersection and the second intersection.
This claim pertains to the representation of nodes and edges in the power topology network map which was determined to recite an abstract idea. Therefore this claim is a further recitation of the abstract idea of creating a power network topology map, and is a further recitation of the abstract idea.
Claim 12:
The system of claim 1, wherein the memory includes instructions, which, when executed, further cause the processor to:
iteratively update one or more parameters of the power network topology map and/or a number of edges of the water topology map based on the fitness of the model infrastructure.
This claim is a modification of the network or power network topology map based on the calculated fitness value. Therefore, this claim is a further recitation of the abstract idea of creating/modifying a power/water network topology map, and is a further recitation of the abstract idea.
Claim 14:The method of claim 13, further comprising:
modeling a probable location of pumps including capacity and power requirements for the water distribution network.
This claim pertains to the generation of the water distribution network topology where it further describes the nodes created. Therefore this claim is a further recitation of the abstract idea.
Claim 15:
The method of claim 13, further comprising:
establishing substation service regions for the power distribution network using Voronoi polygons including an estimated geometric area that consists of all the nearest points to a reference point in a plane.
Generating Voronoi polygons is generating a boundary for reference points using a mathematical algorithm to ensure that the center of the points is closest to the reference point. This is done to ensure the power substations are closest to the demand nodes. This is a mental process performed by one ordinarily skilled in the art using geometry, and is a further recitation of an abstract idea.
Claim 16:
The method of claim 15, wherein each Voronoi polygon has one substation that provides power to the entire polygon.
This claim pertains to what the reference points represents for the Voronoi polygons, and is thus a further recitation of the abstract idea of generating Voronoi polygons for a map.
Claim 17:The method of claim 13, further comprising:
modeling a direct physical connection between water pumps and power distribution by connecting the respective nodes of both networks.
This claim pertains to the interconnections between the respective nodes of the networks. This claim further specifies what the connection is between. As stated previously, This is an observation and evaluation of the two network maps to determine connections. The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Therefore the claim recites a mental process.
Claim 19:The non-transitory, computer-readable medium of claim 18 storing further instructions that when executed by the one or more processors cause the one or more processors to:
generate the plurality of synthetic infrastructure networks to include a water distribution network and a power distribution network.
This pertains to the generation of networks which was determined to be an abstract idea. Therefore this claim limitation is a further recitation of the abstract idea.
Claim 20:
The non-transitory, computer-readable medium of claim 19 storing further instructions that when executed by the one or more processors cause the one or more processors to:
simulate a substation failure associated with the power distribution network, the power distribution network including substations connected through a transmission network, wherein a failure of a given substation results in failures to connected substations.
This refers to the simulation of failures of nodes which was determined to be a mathematic idea such as Monte Carlo Simulation. This claim states that the simulation of the nodes pertains to a substation failure where the node represents a substation. Therefore, this claim is a further recitation of the abstract idea of simulating the failure of the nodes.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, and 7-11 are rejected under 35 U.S.C. 103 as being unpatentable over Wang_2021 (“Generating Synthetic Systems of Interdependent Critical Infrastructure Networks”) , Wu_2012 (US 8175859 B1), Shuang_2020 (“Optimization of Water Distribution Network Design for Resisting Cascading Failures”), Ahmad_2020 (“A synthetic water distribution network model for urban resilience”) and Meyur_2020 (“Creating Realistic Synthetic Power Distribution Networks based on Interdependent Road Infrastructure”)
Claim 1:
Wang_2021 makes obvious
synthesize a water network topology map (Fig. 2. Simulated gas–power–water system of Shelby County. The nodes in green, red, and blue represent facilities in gas, power, and water networks.) of a model infrastructure (page 11 conclusion: “SICIN is the first method to consider the simultaneous network flow optimization of multiple infrastructure networks to synthesize interdependent links.”)
the water network topology map data page 3 col 2 par 4: “The facilities in each of the three networks are classified into three types: supply, transmission, and demand. Each type is illustrated using different shapes of the nodes as shown in Fig. 1.” See figure 1 which depicts pumping stations (examiner note: water pumps) as well as delivery substations. (examiner note demands nodes which consume water)
synthesize a power network topology map (Fig. 2. Simulated gas–power–water system of Shelby County. The nodes in green, red, and blue represent facilities in gas, power, and water networks.) of the model infrastructure (page 11 conclusion: “SICIN is the first method to consider the simultaneous network flow optimization of multiple infrastructure networks to synthesize interdependent links.”) See figures 1 and 2., “Fig. 1. Interdependencies across power, water, and gas networks at the county or city level” as well as “transmission substation” in figure 1. See Figure 2 lines being understood as transmission data connecting a plurality of “12kv substations.”)
model a set of interconnections between the water network topology map and the power network topology map, (See figure 2 which depicts interconnections between the two topology maps) the set of interconnections being indicative of a physical dependency between each respective water pump in the water network topology map and each respective power substation in the power network topology map; ((page 5 section A Interdependent Links: “The physical links for dependencies Gd → Ps,Wd → Ps ,Pd → Gpipe, and Pd → Wpipe illustrated in Section II are added based on geographic proximity”) page 6 col 2 par 2: “Pumping stations depend on the electricity provided by the power demand nodes to extract water from nearby rivers and then transport the water through water pipelines to storage tanks and end-users. Examiner note: Where the examiner understands this process to make obvious a physical connection/dependency between the nodes corresponding to the water pump and power substations where the power substations are understood to be power demand nodes which supply power to the pumps.
Wang_2021 does not expressly recite A system, comprising a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to:
that minimizes a number of nodes with a hydraulic pressure below a hydraulic threshold value,(
being indicative of one or more intersections that
based on the road network … with respect to the road network;
iteratively simulate a failure of one or more components of the model infrastructure resulting in effect data indicative of a simulated effect of the failure with respect to one or more nodes and/or edges of the water network topology map or the power network topology map;
and quantify a fitness of the model infrastructure including the simulation water network topology map and/or the power network topology map based on the effect data.
Wu_2012 however makes obvious A system, comprising a processor in communication with a memory, the memory including instructions, which, when executed, cause the processor to: (Claim 21. A non-transitory computer readable media storing computer software that when executed is operable to)
Wang_2021 and Wu_2012 are analogous art to the claimed invention because they are from the same field of endeavor called infrastructure modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wang_2021 and Wu_2012. The rational for doing so would have been “Obvious to try." Both the prior arts of Wang_2021 and Wu_2012 build a water distribution network. Wu_2012 builds this using software on non-transitory computer readable medium. The prior art of Wang_2021 is silent that their method is applied using software on non-transitory compute readable medium. One ordinarily skilled in the art would have pursued using software on a computer to implement the method of Wu_2012 with a reasonable expectation of success and would have found it obvious to try doing so. Therefore, it would have been obvious to try the methodology and workflow of Wang_2021 with the usage of non-transitory computer readable media/software to obtain the invention as specified in the claims.
Shuang_2020 however makes obvious that minimizes a number of nodes with a hydraulic pressure below a hydraulic threshold value, ( page 3 col 1 par 1: “Eq. (2) describes the second objective
Function the minimization of the total hydraulic pressure deficit.” .. page 2 problem formulation: “Minimum pressure constraint: Pressure of each node should not less than the minimum pressure (Examiner note: Threshold).”)
iteratively simulate a failure of one or more components of the model infrastructure resulting in effect data indicative of a simulated effect of the failure with respect to one or more nodes and/or edges of the water network topology map or the power network topology map; (page 7 col 2 par 2: “They are used to simulate the cascading failure process, and then calculate the cost and head deficit. Each pipe is selected as an initial target for attack. The head deficit and cost are calculated after the WDN returns to a stable state. All pipes are selected as initial attack targets. The head deficit is the average value of the whole WDN simulated with the failure of each pipe”)
and quantify a fitness of the model infrastructure including the simulation water network topology map and/or the power network topology map based on the effect data. (page 7 col 2 par 2: “Particles randomly generate the pipe diameter. Only the pipe diameter solutions that meet the minimum nodal pressure requirements can be further tested for CFS simulation. They are used to simulate the cascading failure process, and then calculate the cost and head deficit. Each pipe is selected as an initial target for attack. The head deficit and cost are calculated after the WDN returns to a stable state. All pipes are selected as initial attack targets. The head deficit is the average value of the whole WDN simulated with the failure of each pipe.”) Examiner note: Where the examiner interprets the combination of head deficit and cost to be a fitness score corresponding to the model infrastructure.
Wang_2021 and Shuang_2020 are analogous art to the claimed invention because they are from the same field of endeavor called infrastructure modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wang_2021 and Shuang_2020.
Shuang_2020 abstract states “However, WDNs are highly sensitive and vulnerable to disasters. The aim of this study is to mitigate the catastrophic consequences of cascading failures in WDNs. A _ow-based WDN cascading failure model is built. The extended multi-objective particle swarm optimization model is developed to resist cascading failures and improve resilience. This model takes pipe diameter as the decision variable to minimize cost and maximize pressure deficit. Water balance, pressure, and standard pipe diameter are the constraints. The classical optimal scenario (COS) and the cascading failure scenario (CFS) are simulated”
The rational for doing so would have been to follow a teaching or motivation proposed in the prior art.
Wang_2021 models infrastructure with a purpose of See Wang_2021 conclusion “The lack of data on interdependent infrastructure systems has limited the ability to understand and model these systems and their interactions to evaluate their vulnerability and resilience. This study proposes an approach, SICIN, to simulate synthetic ICI networks.” Shuang_2020 provides an approach to evaluate vulnerability and resilience, which includes simulation of cascading failures, as well as minimizing pressure deficit so that the water is pumped properly. Shuang_2020 abstract states “However, WDNs are highly sensitive and vulnerable to disasters. The aim of this study is to mitigate the catastrophic consequences of cascading failures in WDNs. A _ow-based WDN cascading failure model is built. The extended multi-objective particle swarm optimization model is developed to resist cascading failures and improve resilience. This model takes pipe diameter as the decision variable to minimize cost and maximize pressure deficit. Water balance, pressure, and standard pipe diameter are the constraints. The classical optimal scenario (COS) and the cascading failure scenario (CFS) are simulated” Therefore it would have been obvious to combine the simulation workflow of Wang_2021 with cascade error simulation and pressure analysis by Shuang_2020 for the benefit of evaluating the vulnerability and resilience of water networks to obtain the invention as specified in the claims.
Ahmad_2020 however makes obvious [the water network] being indicative of one or more intersections that [consume water] (page 3 col 2 par 2: “A Python library named OSMnx (Boeing, 20 l 7) is used to retrieve the road network from OpenStreetMap and to convert that network to a graph, which symbolizes roads as links and intersections as nodes. Each node represents the aggregated water demand from buildings that receive water from the node.”)
Wang_2021 and Ahmad_2020 are analogous art to the claimed invention because they are from the same field of endeavor called infrastructure modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wang_2021 and Ahmad_2020. The rational for doing so would have been to follow a teaching and motivation proposed in the prior art. Both Wang_2021 and Ahmad_2020 model infrastructure networks in order to be used to estimate vulnerabilities, see Ahmad_2020 col 2 “How vulnerable are water components from internal or external causes.” Ahmad_2020 page 2 col 1 par 3 states “The shortage of primary data on the locations of components along with their characteristics (e.g., pipe diameter, power requirement of a pump) of water networks can be partially overcome by estimating synthetic networks. Approximate and estimated network geometry can provide much of the information needed to characterize network vulnerability and resilience. Publicly available data on other infrastructure networks such as roads and power lines, combined with engineering principles of water system design and hydraulics, along with resource usage context in the form of census data, can be used to estimate 'synthetic networks.' This is made possible because water distribution network (WDN) topology and demand have strong correlations with street networks and population density,” Therefore it would have been obvious to combine the water network modeling of Wang_2021 with the use of road networks and intersections by Ahmad_2020 for the benefit of overcoming a lack of data through the use of publicly available data to build a WDN to obtain the invention as specified in the claims.
Meyur_2020 however, makes obvious [power network topology map of the model infrastructure] based on the road network page 2 col 2 par 2: “Roads The road network represented in the form of a graph R = (VR,LR), where VR and LR are respectively the sets of nodes and links of the network. Each road link l ∈ LR is represented as an unordered pair of terminal nodes (u, v) (Examiner note: As in the links are roads with the end of the roads being the nodes, which implies intersections of the roads.) with u, v ∈ VR. Each road node has a spatial embedding in form of longitude and latitude. Therefore each node v ∈ VR can be represented in two dimensional space as pv ∈ R2. Similarly, a road link l = (u, v) can be represented as a vector pu−pv.”)… [includes data indicative of a plurality of power substations] with respect to the road network. (page 5 col 1 par 7: “the road network nodes and transformers are clustered so that each node (road network or transformer is mapped to the nearest substation.)
Wang_2021 and Meyru_2020 are analogous art to the claimed invention because they are from the same field of endeavor called network infrastructure simulation. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wang_2021 and Meyru_2020. The rational for doing so would have been to follow a teaching and motivation proposed in the prior art. Meyru_2021 conclusion States “This paper proposes a methodology to generate synthetic distribution networks for a particular geographical location based on available road network data. The generated network connects individual residential customers to substations while maintaining a radial configuration. Additionally, the network is created such that the overall length of overhead lines is minimized which is similar to planning methodologies undertaken by distribution companies. This ensures that generated synthetic distribution network is realistic and can be used to represent the network of the geographic location accurately.” Therefore it would have been obvious to combine the power network mapping of Wang_2021 with the power network mapping based on road network data by Meyru_2020 for the benefit of generating a realistic synthetic distribution network to map costumer use to substations accurately to obtain the invention as specified in the claims.
Claim 2:Wang_2021 makes obvious The system of claim 1, wherein a node of the set of nodes of the water network topology map “page 6 col 2 par 5: “Constraints (12) and (13) ensure the flow conservation at water transmission nodes NW t and water demand nodes NW d . Specifically, for water demand nodes, residents’ water demand zW,t i is incorporated as the extra sink in the flow conservation”)
Wang_2021 does not expressly recite
Ahmad_2020 however makes obvious [set of nodes of water network topology map] is indicative of an intersection of a road network … [local water demands associated] with the intersection. page 3 col 2 par 2: “A Python library named OSMnx (Boeing, 20 l 7) is used to retrieve the road network from OpenStreetMap and to convert that network to a graph, which symbolizes roads as links and intersections as nodes. Each node represents the aggregated water demand from buildings that receive water from the node.”)
Whereas stated previously it would have been obvious to combine the water network modeling of Wang_2021 with the use of road networks and intersections by Ahmad_2020 for the benefit of overcoming a lack of data through the use of publicly available data to build a WDN to obtain the invention as specified in the claims.
Claim 3:
The system of claim 2,
Wang_2021 makes further obvious wherein the data indicative of local water demand includes at least one of: a hydraulic pressure value and a water use rate. (page 8 col 1 par 2: “The amount of services required for water zW,t i , gas zG,t i , and power zP,t i at the demand nodes is assumed to be proportional to the population around each demand node.”) Examiner note: implies a water use rate
Ahmad_2020 also makes further obvious wherein the data indicative of local water demand includes at least one of: a hydraulic pressure value and a water use rate (page 7 col 1 par 3: “To compute the hydraulic head for each node, it is assumed that water is delivered from the water supply system (e.g. WTP) at a pressure equivalent to a piezometric head of 75 psi. The hydraulic head or available pressure for each node is calculated as,”)
Whereas stated previously it would have been obvious to combine the water network modeling of Wang_2021 with the use of road networks and intersections by Ahmad_2020 for the benefit of overcoming a lack of data through the use of publicly available data to build a WDN to obtain the invention as specified in the claims. Where Ahmad_2020 further states page 7 col 2 par 1: “As water flows from the primary source (e.g., WTP) to the water main, nodes on the water main are secondary sources for the synthesized WDN and must have adequate hydraulic head to ensure required pressure for other nodes of the network. Therefore, nodes with an insufficient hydraulic head are identified and pumps are added to ensure a minimum pressure of 40 psi for the entire network.” Where one ordinarily skilled in the art would be further motivated to track hydraulic pressure value at the nodes to ensure minimum pressure is identified to accurately simulate the network.
Claim 4:Wang_2021 makes further obvious The system of claim 1, where an edge of the set of edges of the water network topology map is a linkage between a first node page 5 col 1 par 1: “The adoption of the tripartite graph structure derives from the fact that 1) nodes in most infrastructure networks are categorized into supply, transmission, and demand nodes depending on the function of the facilities represented by the nodes, and 2) edges represent pipelines or power lines that transport resources between nodes of different types.” Page 3193 col 2 par 3: “The water network requires electricity to distribute water through the use of pumping stations and storage tanks” … (page 10 col 2 par 2: “For water networks, the water flow in all pipelines from spring to winter is less than 0.13 m3/s which is below the capacity, 1.13 m3/s, of water pipelines of diameter 0.6 m [55].” Page 6 col 2 par 5: “Constraints (12) and (13) ensure the flow conservation at water transmission nodes NW t and water demand nodes NW d . Specifically, for water demand nodes, residents’ water demand zW,t i is incorporated as the extra sink in the flow conservation” Examiner note: Where this is water transfer between water nodes. See equations (12) and (13)
Wang_2021 does not expressly recite [a first node] indicative of a first intersection of a road network [and a second node] indicative of a second intersection of a road network
Ahmad_2020 however makes obvious [a first node] indicative of a first intersection of a road network [and a second node] indicative of a second intersection of a road network page 3 col 2 par 2: “A Python library named OSMnx (Boeing, 20 l 7) is used to retrieve the road network from OpenStreetMap and to convert that network to a graph, which symbolizes roads as links and intersections as nodes. Each node represents the aggregated water demand from buildings that receive water from the node.”)
Whereas stated previously it would have been obvious to combine the water network modeling of Wang_2021 with the use of road networks and intersections by Ahmad_2020 for the benefit of overcoming a lack of data through the use of publicly available data to build a WDN to obtain the invention as specified in the claims.
Claim 5:
Wang_2021 makes further obvious The system of claim 4, wherein the data indicative of water transfer includes at least one of: a pipe diameter and a flow rate. (page 10 col 2 par 2: “For water networks, the water flow in all pipelines from spring to winter is less than 0.13 m3/s which is below the capacity, 1.13 m3/s, of water pipelines of diameter 0.6 m [55].”
Claim 7:
Wang_2021 makes further obvious The system of claim 1, wherein a node of a set of nodes of the power network topology map page 7 col 1 par 2: “For each power demand node i ∈ NP d , constraint (17) considers all sources of power consumption to calculate the total power load, including power for transporting natural gas [see (6) and (7)], power for transporting water [see (8)–(10)], and power to serve residents’ demand zP,t i .”)
Wang_2021 does not expressly recite is indicative of an intersection of a road network and includes … with the intersection
Meyur_2020 however makes obvious is indicative of an intersection of a road network and includes … with the intersection (page 2 col 2 par 2: “Roads The road network represented in the form of a graph R = (VR,LR), where VR and LR are respectively the sets of nodes and links of the network. Each road link l ∈ LR is represented as an unordered pair of terminal nodes (u, v) (Examiner note: As in the links are roads with the end of the roads being the nodes, which implies intersections of the roads.) with u, v ∈ VR. Each road node has a spatial embedding in form of longitude and latitude. Therefore each node v ∈ VR can be represented in two dimensional space as pv ∈ R2. Similarly, a road link l = (u, v) can be represented as a vector pu−pv.” … page 6 col 1 Node variables: “Node variables Vs comprises of nt transformer and nr road nodes. Let pi denote the power consumption at the ith transformer node. This is obtained by summing up the total load demand of residences connected to the transformer.”)
Wang_2021 and Meyru_2020 are analogous art to the claimed invention because they are from the same field of endeavor called network infrastructure simulation. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wang_2021 and Meyru_2020. The rational for doing so would have been to follow a teaching and motivation proposed in the prior art. Meyru_2021 conclusion States “This paper proposes a methodology to generate synthetic distribution networks for a particular geographical location based on available road network data. The generated network connects individual residential customers to substations while maintaining a radial configuration. Additionally, the network is created such that the overall length of overhead lines is minimized which is similar to planning methodologies undertaken by distribution companies. This ensures that generated synthetic distribution network is realistic and can be used to represent the network of the geographic location accurately.” Therefore it would have been obvious to combine the power network mapping of Wang_2021 with the power network mapping based on road network data by Meyru_2020 for the benefit of generating a realistic synthetic distribution network to map costumer use to substations accurately to obtain the invention as specified in the claims.
Claim 8:
Wang_2021 makes further obvious The system of claim 7, wherein the data indicative of local power demand includes at least one of: a voltage value at the intersection and a power use rate. (See figure 2: “23kv substation and 12kv substation” in reference to the power network)
Claim 9:
Meyur_2020 makes further obvious The system of claim 1, wherein the memory includes instructions, which, when executed, further cause the processor to:
generate a plurality of Voroni polygons enclosing a plurality of reference points, where each reference point is indicative of a geometric position of a power substation and where each Voroni polygon is indicative of a geographic area covered by each respective reference point with respect to the road network (page 5 col 2 par 1: “The graph GP(VP, EP) is partitioned into M subgraphs {Gs1 , Gs2 , ・ ・ ・ , GsM} corresponding to each of the substation. This is depicted in Fig 1 where each color represents a partition of road and transformer nodes. These partitions are known as Voronoi cells which are centered at the substation location. The partitioning is done based on the shortest path distance metric which ensures that each node is mapped to the nearest substation and each induced subgraph Gs(Vs, Es) corresponding to substation s has a single connected component.”)
Whereas previously stated, it would have been obvious to combine the power network mapping of Wang_2021 with the power network mapping based on road network data by Meyru_2020 for the benefit of generating a realistic synthetic distribution network to map costumer use to substations accurately to obtain the invention as specified in the claims.
Claim 10:
Meyur_2020 makes further obvious The system of claim 9, wherein each reference point is associated with a power output value. (page 6 col 1 par 2: “Node variables Vs comprises of nt transformer and nr road nodes. Let pi denote the power consumption at the ith transformer node. This is obtained by summing up the total load demand of residences connected to the transformer. These power consumptions can be stacked in a nt-length vector p. Let vi represent the voltage at the node i. The nodal voltages at all nodes can be stacked in nt + nr length vectors v.”)
Whereas previously stated, it would have been obvious to combine the power network mapping of Wang_2021 with the power network mapping based on road network data by Meyru_2020 for the benefit of generating a realistic synthetic distribution network to map costumer use to substations accurately to obtain the invention as specified in the claims.
Claim 11:
Wang_2021 makes further obvious The system of claim 1, where an edge of the set of edges of the power network topology map is a linkage between a first node
(page 5 col 1 par 1: “The adoption of the tripartite graph structure derives from the fact that 1) nodes in most infrastructure networks are categorized into supply, transmission, and demand nodes depending on the function of the facilities represented by the nodes, and 2) edges represent pipelines or power lines that transport resources between nodes of different types. .. page 3 col 2 par 3: “In power grids, the electricity generated from natural gas is stepped up to a high voltage by transformers and transported to transmission substations where the electric power is then stepped down to a distribution-level voltage” .. page 7 col 1 par 3: “To obtain the power load, the power flow along power lines ft ij is usually evaluated using the voltage angle θ calculated by multiplying l by the inverse of the susceptance matrix. However, this calculation is omitted since the power flow is solved independently of the flow optimization. More details about the dc power flow model can be found in [18].” Examiner note: Where the term usually makes obvious that this is routinely done and can be applied.
Wang_2021 does not expressly recite indicative of a first intersection of a road network and a second node indicative of a second intersection of a road network, … the first intersection and the second intersection.
Meyur_2020 however makes obvious indicative of a first intersection of a road network and a second node indicative of a second intersection of a road network, … the first intersection and the second intersection. (page 2 col 2 par 2: “Roads The road network represented in the form of a graph R = (VR,LR), where VR and LR are respectively the sets of nodes and links of the network. Each road link l ∈ LR is represented as an unordered pair of terminal nodes (u, v) (Examiner note: As in the links are roads with the end of the roads being the nodes, which implies intersections of the roads.) with u, v ∈ VR. Each road node has a spatial embedding in form of longitude and latitude. Therefore each node v ∈ VR can be represented in two dimensional space as pv ∈ R2. Similarly, a road link l = (u, v) can be represented as a vector pu−pv.” … page 6 col 1 Node variables: “Node variables Vs comprises of nt transformer and nr road nodes. Let pi denote the power consumption at the ith transformer node. This is obtained by summing up the total demand of residences connected to the transformer. … page 6 edge variables “Each edge e = (i, j) is assigned a flow variable fe directed from the source node i to destination node j”)
Where it would have been obvious to combine the power network mapping of Wang_2021 with the power network mapping based on road network data by Meyru_2020 for the benefit of generating a realistic synthetic distribution network to map costumer use to substations accurately to obtain the invention as specified in the claims.
Claims 6 is rejected under 35 U.S.C. 103 as being unpatentable over Wang_2021, Wu_2012, Shuang_2020, Ahmad_2020, Meyur_2020, Huzsvar_2021 (“Increasing the capacity of water distribution networks using fitness function transformation”), and Sugishita_2021 (“A growth model for water distribution networks with loops”)
Claim 6:
The system of claim 1, wherein the memory includes instructions, which, when executed, further cause the processor to:
Wang_2021 does not expressly recite iteratively increase a number of edges of the water network topology map based on a simulated hydraulic pressure of each respective node of the water network topology map with respect to the hydraulic threshold value.
Huzsvar_2021 however makes obvious the water network topology map (page 5 section 2.7: “Fig. 5 describes the process of the topology extension. The optimization process searches for the new pipeline (Examiner note: a new edge) between two existing nodes, which causes the highest possible capacity increment. “) based on a simulated (page 10 col 2:” Table 6 contains the number of function calls, showing that the optimal solutions were found with a relatively low number of hydraulic simulations (compared, e.g., to a full evaluation of all possible pipe connections). “ ) each respective node of the water network topology map with respect to [pressure sensitivity value] (Abstract: “To determine the optimal solution, a parameter, namely pressure sensitivity, is defined, which can localize nodes with local capacity problems computationally efficiently.”)
Wang_2021 and Huzsvar_2021 are analogous art to the claimed invention because they are from the same field of endeavor called infrastructure network modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wang_2021 and Huzsvar_2021. The rational for doing so would have been to follow a teaching and motivation proposed in the prior art. Wang_2021 generates a water network topology to be later used for optimization see abstract “research progress in modeling and optimizing the system performance” Wang_2021 when generating the WDN optimizes for the demand of the nodes as well, see page 6-7 sections “flow constraints for individual networks and System level Optimization” Huzscar_2021 is a methodology which optimizes system performance of the WDN by determining where to add a next pipe. Huzscar_2021 abstract states “Even the most carefully designed water distribution network (WDN) can suffer from local capacity deficiencies as a result of the quick and unpredictable growth of the urbanization of new industrial sites. To solve this problem, this paper focuses on the identification of the best possible location for a new pipeline within an existing WDN, which maximizes the node-wise capacity.” When optimizing system performance for an existing system optimized through the method of Wang_2021 for modeling interdependencies, one ordinarily skilled in the art would use the method of Huzscar_2021 for optimizing the system by learning where to add a new pipe, for both ensuring demands for the local community are met and to also ensure interdependencies are modeled.
Therefore it would have been obvious to combine the modeling workflow of Wang_2021 with the optimization of adding a new pipe by Huzscar_2021 for the benefit of optimizing system performance in a growing community and to ensure interdependencies to obtain the invention as specified in the claims.
Huzsvar_2021 and Wang_2021 do not expressly recite iteratively … hydraulic pressure … the hydraulic threshold value.
Sugishita_2021 however makes obvious iteratively (page 5 section Growth model: “In this section ,we propose a growth model for WDNs. The proposed model is classified as a greedy model where edges are added one by one based on a local optimization criterion[2,3]. Our model generates networks with loops and is capable of generating networks with multiple root nodes.”)
Huzsvar_2021, Wang_2021, and Sughishita_2021 are analogous art to the claimed invention because they are from the same field of endeavor called infrastructure network modeling.
Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Huzsvar_2021, Wang_2021, and Sughishita_2021.
The rational for doing so would have been to follow a teaching and motivation proposed in the prior art. Huzsvar_2021 teaches a method where a WDN is modified to add an edge based on a pressure sensitivity value. Huzsvar_2021 teaches doing this once to improve a network. Sughishita_2021 teaches an iterative approach doing it multiple times to grow a network. Huzsvar_2021 page 12 col 2 par 1 states “as for future plans, a natural next step would be to extend the methodology towards adding multiple pipelines simultaneously, increasing the applicability of the method towards larger networks.”
Therefore, it would have been obvious to combine the addition of edges based on pressure sensitivity of Huzsvar_2021 with the iterative addition of multiple edges of Sughishita_2021 for the benefit of increasing the applicability of the method to larger networks obtain the invention as specified in the claims.
Wang_2021 Huzsvar_2021 and Sugishita_2021 do not expressly recite hydraulic pressure … the hydraulic threshold value.
Wu_2013 however makes obvious hydraulic pressure (par 10: “ This formulation can be used to solve for the demand (q), but it is valid only if the hydraulic pressures at all nodes are adequate so that the demand is independent of pressure”) … the hydraulic threshold value. (par 23: it is believed that a junction demand that is not affected by pressure or water consumption will be kept constant if the pressure is above a threshold that is referred to as the "pressure threshold" above which demand is no longer sensitive to pressure. This pressure threshold must be greater than or equal to the reference pressure at which the target demand is met.)
Huzsvar_2021, Wang_2021, and Wu_2013 are analogous art to the claimed invention because they are from the same field of endeavor called infrastructure network modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Huzsvar_2021, Wang_2021, and Wu_2013. The rational for doing so would have been to use a simple substitution of known techniques in the art. Huzsvar_2021 teaches a method where a WDN is modified to add an edge based on a pressure sensitivity value. Huzsvar_2021 does this in order to solve for “local capacity” of nodes (see abstract) Huzsvar_2021 does not explicitly use hydraulic pressure with a threshold value. However Huzsvar_2021 does identify its use to solve for the similar problem (page 2 col 2 par 2:” The hydraulic reliability approach tries to describe a threshold (in terms of specific parameters) within which the WDN can still fulfill the needs of the inhabitants” Huzsvar_2021 identifies that using hydraulic thresholds is an alternative method to perform the local capacity analysis of Huzsvar_2021. Therefore, it would have been obvious to substitute the pressure sensitivity analysis of Huzsvar_2021 with the use of hydraulic pressure and thresholds to determine if node capacity is met of Wu_2013.
(3) Claims 12 is rejected under 35 U.S.C. 103 as being unpatentable over Wang_2021, Wu_2012, Shuang_2020, Ahmad_2020, Meyur_2020, Huzsvar_2021 as further evidenced by Suribabu_2016 (“Resilience Enhancement Methods for Water Distribution Networks”)
Claim 12:
The system of claim 1, wherein the memory includes instructions, which, when executed, further cause the processor to:
Wang_2021 does not expressly recite iteratively update one or more parameters of the power network topology map and/or a number of edges of the water topology map based on the fitness of the model infrastructure.
Shuang_2020 however makes obvious iteratively update one or more parameters of the power network topology map and/
or
PNG
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See figure 1. Specifically k = k + 1 (iterative) , particle swarm optimization (fitness) and “anti-cascade reliability”
Shuang_2020 does not expressly recite a number of edges
Huzsvar_2021 however makes obvious a number of edges (section 2.7: “Fig. 5 describes the process of the topology extension. The optimization process searches for the new pipeline (examiner note: an increase in the number of edges) between two existing nodes, which causes the highest possible capacity increment.”)
Wu_2021, Shuang_2020 and Huszvar_2021 are analogous art to the claimed invention because they are from the same field of endeavor called infrastructure modeling.
Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wu_2021, Shuang_2020 and Huszvar_2021.
The rational for doing so would have been to follow a teaching and motivation proposed in the prior art. Both Shuang_2020 and Huszvar_2021 discuss WDN optimization, especially in relation to increased demands. See Shuang_2020 section 3 par 1: “Urban expansion may be one cause of cascading failures.
For example, the water demand has increased because of the sharp rise in population and industries” and huszvar_2021 abstract “Even the most carefully designed water distribution network (WDN) can suffer from local capacity deficiencies as a result of the quick and unpredictable growth of the urbanization of new industrial sites.” Shuang_2020 teaches the updating of different parameters in the PSO. See page 5 col 1 “Diameters of the pipes in the WDN are the decision variables. The decision interval is the commercial standard pipe diameter. The diameter of each pipe is set as a solution with particles to search.” Shuang_2020 does this to improve WDN optimization in relation to urban expansion. Husvar_2021 provides a known method of combating this problem, which is to add a new pipe to the network where needed. Husvar_2021 implies that both these methods, (changing diameter or adding a new pipe) are functionally equivalent /interchangeable in the real world. See page 11-12 “In the second case with the shorter length limits, the method suggested a new pipe parallel to an existing one, which translates to a diameter increase in real-life.” Suribabu_2016 provides a motivation to modify the PSO algorithm of Shuang_2020 to work on edge addition. Suribabu_2016 states page 222 parallel piping approach “Parallel piping is another method to improve the resilience of the network in a cost-effective manner. Similar to the change in diameter method” and page 230: “Unlike increase in pipe size method, parallel piping method makes meagre increase in cost as very few pipes are installed additionally to the network, but it results in a notable increase in efficiency. Thus, parallel piping system is more reliable and efficient for networks experiencing high velocity”
Therefore it would have been obvious to combine the iterative fitness calculative modification PSO algorithm of Shuang_2020 which modifies diameters with optimization of edge addition by Husvar_2021 for the benefit of improving existing WDN networks against failures due to growth to improve the resilience of the model in a cost effective manner and to obtain the invention as specified in the claims.
Claims 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Wang_2021 Zhang_2016 (“MODELING AND SIMULATION OF THE VULNERABILITY OF INTERDEPENDENT POWER-WATER INFRASTRUCTURE NETWORKS TO CASCADING FAILURES”), and Meyur_2020
Claim 13:
Wang_2021 makes obvious A method (page 4 col 1 par 2: “the method is illustrated using a power–gas–water network”) of modeling synthetic infrastructure, comprising: “page 3 par 1 contributions: “We address the aforementioned limitations of state-of-the-art approaches by proposing a new method to generate synthetic interdependent critical infrastructure networks (SICIN). The outcome includes 1) a fully characterized system of interdependent power, water, and gas networks that can be used to validate and demonstrate models about infrastructure networks,”)
generating a plurality of synthetic infrastructure networks, including: (page 3 par 1 contributions “2) the corresponding algorithm that can either be applied or adapted to generate synthetic infrastructure networks.”)
synthesizing a water distribution network topology (See “Fig. 2. Simulated gas–power–water system of Shelby County. The nodes in green, red, and blue represent facilities in gas, power, and water networks.” )
and synthesizing a power distribution network including a set of substations; (See “Fig. 2. Simulated gas–power–water system of Shelby County. The nodes in green, red, and blue represent facilities in gas, power, and water networks.” Where figure 2 contains multiple nodes for “12kv Substation”)
modeling interdependencies between the plurality of synthetic infrastructure networks; (Fig 1 description: “Interdependencies across power, water, and gas networks at the county or city level.”)
Wang_2021 does not expressly recite leveraging a clustering-based road network including converting the road network to a graph such that links include roads and nodes define intersections,
and simulating cascading failure for the plurality of synthetic infrastructure networks to estimate conditions corresponding to failures associated with the plurality of synthetic infrastructure networks.
Zhang_2016 however makes obvious and simulating cascading failure (page 108 section 4 par 1: “Before exploring the vulnerability of interdependent power-water networks to cascading failures through numerical simulation, the values of some parameters in the simulation model must be fixed “) for the plurality of synthetic infrastructure networks (page 115 conclusion: “This study takes power and water networks as an example of interdependent infrastructure networks, and then develops a more realistic simulation model that considers the dynamic redistribution of load in power network to explore the vulnerability of interdependent networks through numerical simulation. In this model, fictional power and water networks are generated based on the spatial proximity among nodes, and their interdependences across the two networks are built based on the physical dependency. Moreover, critical tolerance threshold is originally proposed to indicate the vulnerability of the network to cascading failures. Finally, the vulnerability of interdependent networks under some key parameters is explored through numerical simulation. *β”) to estimate conditions corresponding to failures associated with the plurality of synthetic infrastructure networks. (page 114 par 1: “The simulation results in subsection 4.1 reveal that EA (EB) of the power (water) network will fall rapidly to 0 after fA increases to the critical fraction fA* under a relatively small value of , but will decreases smoothly along with the increase of fA when the value of is large enough. Here, we will study the impact of three attack strategies on this relationship, which is shown in Figure 9. This figure illustrates that the critical fraction fA* under high-load attack is always the least one under the three attack strategies when the value of is relatively small. For example, when =0.5, fA*=2% under high-load attack, fA*=5% under high-degree attack and fA*=6% under random attack. In other words, only 2% of power nodes with the highest loads can trigger cascading failures in interdependent networks under high-load attack when =0.5. ββ β ββ”)
Wang_2021 and Zhang_2016 are analogous art to the claimed invention because they are from the same field of endeavor called network infrastructure modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wang_2021 and Zhang_2016.
The rational for doing so would have been to follow a teaching and motivation proposed In the prior art. Both Wang_2021 and Zhang_2016 have interconnected power and water distribution networks in order to understand the interactions between them. See Wang_2021 conclusion “The lack of data on interdependent infrastructure systems has limited the ability to understand and model these systems and their interactions to evaluate their vulnerability and resilience. This study proposes an approach, SICIN, to simulate synthetic ICI networks.” Zhang_2016 uses a similar model to Wang_2021 for the goal of evaluating vulnerability, see abstract “ Critical infrastructures are becoming increasingly interdependent and vulnerable to cascading failures. Existing studies have analyzed the vulnerability of interdependent networks to cascading failures from the static perspective of network topology structure. This paper develops a more realistic cascading failures model that considers the dynamic redistribution of load in power network to explore the vulnerability of interdependent power-water networks. “Therefore, it would have been obvious to combine the simulation models of Wang_2021 with the simulation to test for vulnerabilities by Zhang_2016 for the benefit of evaluating the vulnerability of the interactions in interdependent infrastructure systems to obtain the invention as specified in the claims.
Wang_2021 and Zhang_2016 do not expressly recite leveraging a clustering-based road network including converting the road network to a graph such that links include roads and nodes define intersections,
Meyru_2020 makes obvious leveraging a clustering-based road network (page 5 col 1 par 7: “the road network nodes and transformers are clustered so that each node (road network or transformer is mapped to the nearest substation.) including converting the road network to a graph such that links include roads and nodes define intersections, (page 2 col 2 par 2: “Roads The road network represented in the form of a graph R = (VR,LR), where VR and LR are respectively the sets of nodes and links of the network. Each road link l ∈ LR is represented as an unordered pair of terminal nodes (u, v) (Examiner note: As in the links are roads with the end of the roads being the nodes, which implies intersections of the roads.) with u, v ∈ VR. Each road node has a spatial embedding in form of longitude and latitude. Therefore each node v ∈ VR can be represented in two dimensional space as pv ∈ R2. Similarly, a road link l = (u, v) can be represented as a vector pu−pv.”)
Wang_2021 and Meyru_2020 are analogous art to the claimed invention because they are from the same field of endeavor called network infrastructure simulation. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wang_2021 and Meyru_2020. The rational for doing so would have been to follow a teaching and motivation proposed in the prior art. Meyru_2021 conclusion States “This paper proposes a methodology to generate synthetic distribution networks for a particular geographical location based on available road network data. The generated network connects individual residential customers to substations while maintaining a radial configuration. Additionally, the network is created such that the overall length of overhead lines is minimized which is similar to planning methodologies undertaken by distribution companies. This ensures that generated synthetic distribution network is realistic and can be used to represent the network of the geographic location accurately.” Therefore it would have been obvious to combine the power network mapping of Wang_2021 with the power network mapping based on road network data by Meyru_2020 for the benefit of generating a realistic synthetic distribution network to map costumer use to substations accurately to obtain the invention as specified in the claims.
Claim 14:
Wang_2021 makes further obvious The method of claim 13, further comprising:
modeling a probable location of pumps (page 8 col 2 par 3: “For example, in the solved-out optimal distribution plan, a pumping station might be built far from the river or a gate station is planned to be built on sites designed for other purposes. The total distance given by our method will increase and get close to real networks when we consider additional constraints.” See also figure 2 which models probable locations of pumps) including capacity (page 5 col 2 par 2: “We propose to generate interdependent links along with their corresponding capacity given the network topology and flow initialization” page 3201 par 2: “For water networks, the water flow in all pipelines from spring to winter is less than 0.13 m3/s which is below the capacity, 1.13 m3/s, of water pipelines of diameter 0.6 m [55].”) and power requirements (page 6 col 2 par 2: “Pumping stations depend on the electricity provided by the power demand nodes to extract water from nearby rivers and then transport the water through water pipelines to storage tanks and end-users.”
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Examiner note: this is a calculation of power requirements. ) for the water distribution network. See “Fig. 2. Simulated gas–power–water system of Shelby County. The nodes in green, red, and blue represent facilities in gas, power, and water networks.”
Claim 15:
Meyur_2020 makes further obvious The method of claim 13, further comprising:
establishing substation service regions for the power distribution network using Voronoi polygons including an estimated geometric area that consists of all the nearest points to a reference point in a plane. (page 5 col 2 par 1: “The graph GP(VP, EP) is partitioned into M subgraphs {Gs1 , Gs2 , ・ ・ ・ , GsM} corresponding to each of the substation. This is depicted in Fig 1 where each color represents a partition of road and transformer nodes. These partitions are known as Voronoi cells which are centered at the substation location. The partitioning is done based on the shortest path distance metric which ensures that each node is mapped to the nearest substation and each induced subgraph Gs(Vs, Es) corresponding to substation s has a single connected component.”)
Whereas previously stated, it would have been obvious to combine the power network mapping of Wang_2021 with the power network mapping based on road network data by Meyru_2020 for the benefit of generating a realistic synthetic distribution network to map costumer use to substations accurately to obtain the invention as specified in the claims.
Claim 16:
Meyur_2020 makes further obvious The method of claim 15, wherein each Voronoi polygon has one substation that provides power to the entire polygon. (“The graph GP(VP, EP) is partitioned into M subgraphs {Gs1 , Gs2 , ・ ・ ・ , GsM} corresponding to each of the substation. This is depicted in Fig 1 where each color represents a partition of road and transformer nodes. These partitions are known as Voronoi cells which are centered at the substation location. The partitioning is done based on the shortest path distance metric which ensures that each node is mapped to the nearest substation and each induced subgraph Gs(Vs, Es) corresponding to substation s has a single connected component.”)
Whereas previously stated, it would have been obvious to combine the power network mapping of Wang_2021 with the power network mapping based on road network data by Meyru_2020 for the benefit of generating a realistic synthetic distribution network to map costumer use to substations accurately to obtain the invention as specified in the claims.
Claim 17:
Wang_2021 makes further obvious The method of claim 13, further comprising:
modeling a direct physical connection between water pumps and power distribution (page 5 section A Interdependent Links: “The physical links for dependencies Gd → Ps,Wd → Ps ,Pd → Gpipe, and Pd → Wpipe illustrated in Section II are added based on geographic proximity”) by connecting the respective nodes of both networks.
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Please see figure 2 pasted above. Note the longitude and latitude, pumping stations and power substations. Examiner note: Where the above figure makes obvious modeling a connection between pumps and power units where the pumps are dependent on the power as discussed above.
Zhang_2016 also further makes obvious by connecting the respective nodes of both networks. (page 104 section 2.1 “Two types of nodes are considered for the two networks, namely source nodes (i.e., supply nodes) and demand nodes (i.e., sink nodes). For the electric power network, we take generators that produce power as source nodes, substations that deliver power to users or other systems as demand nodes, and electric wires as edges. For the water distribution network, we take pumps that supply water as source nodes, storage tanks that deliver water to users or other systems as demand nodes, and water pipelines as edges” ,,, page 105 section 2.2 par 2: “For interdependent power-water networks, we assume that each pump in the water network depends on the geographically nearest substation in the power network to supply and deliver water to storage tanks, and each storage tank in the water network depends on the geographically nearest substation to deliver water to the ultimate users.”)
Whereas previously stated, would have been obvious to combine the simulation models of Wang_2021 with the simulation to test for vulnerabilities by Zhang_2016 for the benefit of evaluating the vulnerability of the interactions in interdependent infrastructure systems to obtain the invention as specified in the claims.
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang_2021, Zhang_2016, and Wu_2012
Claim 18:
Wang_2021 makes obvious
generate a plurality of synthetic infrastructure networks; (page 3193 contributions par 1 “2) the corresponding algorithm that can either be applied or adapted to generate synthetic infrastructure networks.”)
model interdependencies between the plurality of synthetic infrastructure networks; (page 3 contribution bullet 2: “We devise a pseudo-tripartite graph algorithm to generate infrastructure networks, which ensures the connectivity of generated networks, guarantees the conformity of the associative degree distribution to real networks, and characterizes the supply-transmission-demand level of infrastructure facilities.” .. Fig 1 description: “Interdependencies across power, water, and gas networks at the county or city level.”
Wang_2021 does not expressly recite A non-transitory, computer-readable medium storing instructions that when executed by one or more processors cause the one or more processors to:
and simulate cascading failure for the plurality of synthetic infrastructure networks to estimate conditions corresponding to failures associated with the plurality of synthetic infrastructure networks.
Zhang_2016 however makes obvious and simulating cascading failure (page 108 section 4 par 1: “Before exploring the vulnerability of interdependent power-water networks to cascading failures through numerical simulation, the values of some parameters in the simulation model must be fixed “) for the plurality of synthetic infrastructure networks (page 115 conclusion: “This study takes power and water networks as an example of interdependent infrastructure networks, and then develops a more realistic simulation model that considers the dynamic redistribution of load in power network to explore the vulnerability of interdependent networks through numerical simulation. In this model, fictional power and water networks are generated based on the spatial proximity among nodes, and their interdependences across the two networks are built based on the physical dependency. Moreover, critical tolerance threshold is originally proposed to indicate the vulnerability of the network to cascading failures. Finally, the vulnerability of interdependent networks under some key parameters is explored through numerical simulation. *β”) to estimate conditions corresponding to failures associated with the plurality of synthetic infrastructure networks. (page 114 par 1: “The simulation results in subsection 4.1 reveal that EA (EB) of the power (water) network will fall rapidly to 0 after fA increases to the critical fraction fA* under a relatively small value of , but will decreases smoothly along with the increase of fA when the value of is large enough. Here, we will study the impact of three attack strategies on this relationship, which is shown in Figure 9. This figure illustrates that the critical fraction fA* under high-load attack is always the least one under the three attack strategies when the value of is relatively small. For example, when =0.5, fA*=2% under high-load attack, fA*=5% under high-degree attack and fA*=6% under random attack. In other words, only 2% of power nodes with the highest loads can trigger cascading failures in interdependent networks under high-load attack when =0.5. ββ β ββ”)
Wang_2021 and Zhang_2016 are analogous art to the claimed invention because they are from the same field of endeavor called network infrastructure modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wang_2021 and Zhang_2016.
The rational for doing so would have been to follow a teaching and motivation proposed In the prior art. Both Wang_2021 and Zhang_2016 have interconnected power and water distribution networks in order to understand the interactions between them. See Wang_2021 conclusion “The lack of data on interdependent infrastructure systems has limited the ability to understand and model these systems and their interactions to evaluate their vulnerability and resilience. This study proposes an approach, SICIN, to simulate synthetic ICI networks.” Zhang_2016 uses a similar model to Wang_2021 for the goal of evaluating vulnerability, see abstract “ Critical infrastructures are becoming increasingly interdependent and vulnerable to cascading failures. Existing studies have analyzed the vulnerability of interdependent networks to cascading failures from the static perspective of network topology structure. This paper develops a more realistic cascading failures model that considers the dynamic redistribution of load in power network to explore the vulnerability of interdependent power-water networks. “Therefore, it would have been obvious to combine the simulation models of Wang_2021 with the simulation to test for vulnerabilities by Zhang_2016 for the benefit of evaluating the vulnerability of the interactions in interdependent infrastructure systems to obtain the invention as specified in the claims.
Wang_2021 and Zhang_2016 do not expressly recite A non-transitory, computer-readable medium storing instructions that when executed by one or more processors cause the one or more processors to:
Wu_2012 however makes obvious A non-transitory, computer-readable medium storing instructions that when executed by one or more processors cause the one or more processors to: (see claim 21: “A non-transitory computer readable media storing computer software that when executed is operable to”)
Wang_2021 and Wu_2012 are analogous art to the claimed invention because they are from the same field of endeavor called infrastructure modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Wang_2021 and Wu_2012. The rational for doing so would have been “Obvious to try." Both the prior arts of Wang_2021 and Wu_2012 build a water distribution network. Wu_2012 builds this using software on non-transitory computer readable medium. The prior art of Wang_2021 is silent that their method is applied using software on non-transitory compute readable medium. One ordinarily skilled in the art would have pursued using software on a computer to implement the method of Wu_2012 with a reasonable expectation of success, and would have found it obvious to try doing so. Therefore, it would have been obvious to try the methodology and workflow of Wang_2021 with the usage of non-transitory computer readable media/software to obtain the invention as specified in the claims.
Claim 19:
Wang_2021 makes further obvious The non-transitory, computer-readable medium of claim 18 storing further instructions that when executed by the one or more processors cause the one or more processors to:
generate the plurality of synthetic infrastructure networks to include a water distribution network and a power distribution network. (Fig 1 description: “Interdependencies across power, water, and gas networks at the county or city level.”)
Claim 20:
Zhang_2016 makes further obvious The non-transitory, computer-readable medium of claim 19 storing further instructions that when executed by the one or more processors cause the one or more processors to:
simulate a substation failure (page 5 section 3: “Then, the failures of these attacked power nodes can spread within the power network and potentially cause other substations to malfunction )associated with the power distribution network, the power distribution network including substations connected through a transmission network, (page 6 col 1 par 2: “Next, we will explore the cascading failures mechanism within the power network. We assume that cascading failures originate from an attack on some power nodes. Here, the initial attack on the power network is interpreted as the removal of those attacked nodes from the network (Albert et al. 2000, Holme et al. 2002). The loads of removed nodes will then be redistributed to their neighboring nodes based on the following rule, “) wherein a failure of a given substation results in failures to connected substations. (page 5 section 3: “Then, the failures of these attacked power nodes can spread within the power network and potentially cause other substations to malfunction” … page 6 col 2 par 2: “where is the set of neighboring failed nodes of node j at the end of time t. Next, if the load Lj(t+1) of node j at time t+1 is larger than its capacity, this load Lj(t+1) will then be distributed to its neighboring operating nodes and potentially cause them to fail ()jtΨ”)
Where it would have been obvious to combine the simulation models of Wang_2021 with the simulation to test for vulnerabilities by Zhang_2016 for the benefit of evaluating the vulnerability of the interactions in interdependent infrastructure systems to obtain the invention as specified in the claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMAD HUSSAM SHALABY whose telephone number is (571)272-7414. The examiner can normally be reached Mon-Fri 7:30am - 5pm.
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/A.H.S./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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