DETAILED ACTION
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-9, and 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2016/0231716 (Johnson) (cited by Applicant) in view of
U.S. Patent Application Publication No. 2013/0317629 (Shapiro).
Claim 1:
The cited prior art describes a computer implemented method, comprising: (Johnson: “This application relates generally to the field of data processing and, in an example embodiment, to the optimization of industrial systems based on technical and business objectives and constraints.” Paragraph 0002; “FIG. 2 is a block diagram of a simulator/optimizer 200 configured to optimize various aspects of an industrial system (e.g., a power plant, such as the plant associated with FIG. 1) over one or more criteria for an economic lifecycle.” Paragraph 0036)
obtaining, using data from a set of sensors, a set of current operational characteristics for a plurality of plant devices in a manufacturing plant; (Johnson: see the state data 207 as illustrated in figure 2; “During operation of the power plant 205, various aspects or values of the operating power plant 205 that characterize current operating conditions or states with the plant 205 may be captured as state data 207. The state data 207 may be captured by sensors or gauges located within the plant 205 in some embodiments. The state data 207, in some examples, may include temperature readings, pressure readings, flow rate measurements, and other data. Further, the state data 207 may be read or captured periodically, continuously, or according to some other schedule over time.” Paragraph 0040; “Example industrial systems capable of being simulated and optimized in other embodiments include, but are not limited to, wind farms, distributed-generation electrical grids, rail operations, health delivery systems, air transportation networks, oil and gas extraction and production operations, manufacturing and supply chain systems” paragraph 0086)
simulating a plurality of manufacturing scenarios using different sets of operational characteristics for plant devices in which at least one operational characteristic from the set of current operational characteristics is varied; (Johnson: see the factor simulation engine 240 with an internal factor simulator 245 and an external factor simulator 250 and the optimization engine 270 as illustrated in figure 2; “A second component being supplied with any or all of the state data 207, internal factors 210, and the external factors 215 is the factor simulation engine 240, which includes an internal factor simulator 245 that may produce actual or simulated information describing the design, operations, reliability and other internal factor information associated with the power plant 205, such as temperature readings, pressure readings, flow rate measurements, and the like. The factor simulation engine 240, as shown in FIG. 2, may also include an external factor simulator 250 that generates actual or simulated information describing various physical, technical, and/or business external factors influencing the power plant 205, such as environmental regulations, actions of competitors, weather, long-term fuel costs, and the financial costs in the capital markets. In one example, internal factor simulator 245 may simulate the internal factors using random walks of factors ahead of real time.” Paragraph 0044; “The optimization engine 270 may then simulate the operation of the power plant 205 by executing multiple scenarios simulating the power plant 205 over some designated period of time, and over various values of the factors (e.g., system designs, maintenance periods, expected loads, etc.) to generate simulation/optimization results 275 of the overall performance of the plant 205, the useful life of the plant 205 consumed, the overall return of investment of the plant 205 (e.g., taking into account financing of the plant 205, fuel costs, the possible grid market pricing, and so on), and other information based on variations of the criteria received from the criteria module 255.” Paragraph 0047)
determining, from among the simulated manufacturing scenarios, a first manufacturing scenario that satisfies one or more overall operational parameters for one or more plant devices or for the manufacturing plant as a whole; and (Johnson: “Further, the optimization engine 270 may determine which design and operating choices, maintenance schedules, financing options, overall cash investment, and the like specified via the criteria are likely to provide better outcomes in terms of return on investment or other measures of economic value, or other types of value, such as, for example, compatibility of the power plant 205 design relative to other power plants 205 or systems working in tandem with the power plant 205. In some examples, the optimization engine 270 determines optimized results by comparing results of multiple executions over different criteria and by selecting one or more of those executions that represent increased measures of economic value mod/or some other value metric. In at least some example systems, the optimization engine 270 may trade off multiple criteria over one or more time intervals and enable the business-physical system of the power plant 205 to evolve over time, subject to the constraints of a given one or more periods.” Paragraph 0048)
Johnson does not explicitly describe settings as described below. However, Shapiro teaches the settings as described below.
adjusting one or more settings of one or more plant devices based on a set of operational characteristics corresponding to the first manufacturing scenario. (Shapiro: see the set points 315 sent to the plan control system 21 from the plant optimization and scheduling system 26 as illustrated in figures 2, 3; “The system utilizes modeled information (described in detail below), real time and historical data to perform optimization and planning functions, and sends set point information to logic controllers.” Paragraph 0036) (Johnson: “Based on these comparisons, the optimization engine 270 may identify at least one, and possibly several, of the simulation scenarios for employment in the power plant 205 (operation 410) and may be configured to manually or automatically implement them in the control or operations system(s) of the plant or within the administrative business processes or outage work scope.” Paragraph 0063)
One of ordinary skill in the art would have recognized that applying the known technique of Johnson, namely, an optimization and control system for an industrial system, with the known techniques of Shapiro, namely, process optimization and planning for multi-unit plants, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Johnson to determine optimized aspects for industrial systems using simulation scenarios with the teachings of Shapiro to determine optimal operations using process simulation would have been recognized by those of ordinary skill in the art as resulting in an improved control system (i.e., the combination of the references provides for process control system using simulations to determine and adjust settings based on the teachings of a control system using simulations in Johnson and the teachings of a control system using simulations to adjust settings in Shapiro).
Claim 2:
The cited prior art describes the computer implemented method of claim 1, further comprising: generating a visualization that presents the set of operational characteristics corresponding to the first manufacturing scenario. (Johnson: “The simulation/optimization results 275 may include the physical and business-oriented aspects discussed above (e.g., overall performance, useful life, overall return on investment), along with the input data that define the scenarios used to perform the various simulations and optimizations. In one example, the simulation/optimization results 275 may be made available for viewing on the user devices 201 via the user interface 260.” Paragraph 0049; “Based on these comparisons, the optimization engine 270 may identify at least one, and possibly several, of the simulation scenarios for employment in the power plant 205 (operation 410) and may be configured to manually or automatically implement them in the control or operations system(s) of the plant or within the administrative business processes or outage work scope.” Paragraph 0063)
Claim 3:
The cited prior art describes the computer implemented method of claim 1, wherein simulating the plurality of manufacturing scenarios is based on one or more factors comprising:
a plant operational capacity; (Johnson: “The internal factors 210 may include technical, physical, or business factors, such as, for example, the power plant 205 design, operations, availability, lineups, upgrades, maintenance, dispatch, capital equipment purchases, and so on.” Paragraph 0038)
operational ranges for plant devices; (Johnson: “The control system model 308 may be configured to provide parameters, limitations, and the like regarding the operation of particular subsystems (e.g., gas turbine) or components of the power plant 205. For example, the control system model 308 may provide information regarding allowable inlet schedules and other parameters for ramp up of a component in response to increasing load, how much remaining useful life may be consumed by overfiring a component by a specific period of time, and so forth. Such information may be useful in determining whether operating the component such a manner may be useful in generating additional revenue.” Paragraph 0055)
actual and expected degradations in plant device efficiency; or
actual and expected manufacturing plant or plant device shutdowns. (Johnson: “In like framework, the simulator/optimizer may track other causal factors, such as component shutdowns, trips and starts, air cleanliness with respect to particles and chemical concentration, and metal temperature, from direct measure and/or virtual sensing of these factors as the plant is operated. Additional factors indicative of RUL 172, such as repair records, original equipment manufacturer (OEM), or OEM lot, may be operationally tracked and employed in the simulation or post processing.” Paragraph 0032)
Claim 4:
The cited prior art describes the computer implemented method of claim 1, wherein determining, from among the simulated manufacturing scenarios, the first manufacturing scenario, comprises: determining, from among the simulated manufacturing scenarios, the first manufacturing scenario that achieves a plant manufacturing output that is greater than plant manufacturing outputs achieved by other simulated manufacturing scenarios. (Johnson: “Further, the optimization engine 270 may determine which design and operating choices, maintenance schedules, financing options, overall cash investment, and the like specified via the criteria are likely to provide better outcomes in terms of return on investment or other measures of economic value, or other types of value, such as, for example, compatibility of the power plant 205 design relative to other power plants 205 or systems working in tandem with the power plant 205. In some examples, the optimization engine 270 determines optimized results by comparing results of multiple executions over different criteria and by selecting one or more of those executions that represent increased measures of economic value mod/or some other value metric. In at least some example systems, the optimization engine 270 may trade off multiple criteria over one or more time intervals and enable the business-physical system of the power plant 205 to evolve over time, subject to the constraints of a given one or more periods.” Paragraph 0048)
Claim 6:
The cited prior art describes a system, comprising: (Johnson: “This application relates generally to the field of data processing and, in an example embodiment, to the optimization of industrial systems based on technical and business objectives and constraints.” Paragraph 0002; “FIG. 2 is a block diagram of a simulator/optimizer 200 configured to optimize various aspects of an industrial system (e.g., a power plant, such as the plant associated with FIG. 1) over one or more criteria for an economic lifecycle.” Paragraph 0036)
one or more memory devices storing instructions; and (Johnson: “The disk drive unit 1216 (a type of non-volatile memory storage) includes a machine-readable medium 1222 on which is stored one or more sets of data structures and instructions 1224 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The data structures and instructions 1224 may also reside, completely or at least partially, within the main memory 1204, the static memory 1206, and/or the processor 1202 during execution thereof by processing system 1200, with the main memory 1204, the static memory 1206, and the processor 1202 also constituting machine-readable, tangible media.” Paragraph 0127)
one or more data processing apparatus that are configured to interact with the one or more memory devices, and upon execution of the instructions, perform operations including: (Johnson: “The example of the processing system 1200 includes a processor 1202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory 1204 (e.g., random access memory), and static memory 1206 (e.g., static random-access memory), which communicate with each other via bus 1208.” Paragraph 0126)
obtaining, using data from a set of sensors, a set of current operational characteristics for a plurality of plant devices in a manufacturing plant; (Johnson: see the state data 207 as illustrated in figure 2; “During operation of the power plant 205, various aspects or values of the operating power plant 205 that characterize current operating conditions or states with the plant 205 may be captured as state data 207. The state data 207 may be captured by sensors or gauges located within the plant 205 in some embodiments. The state data 207, in some examples, may include temperature readings, pressure readings, flow rate measurements, and other data. Further, the state data 207 may be read or captured periodically, continuously, or according to some other schedule over time.” Paragraph 0040; “Example industrial systems capable of being simulated and optimized in other embodiments include, but are not limited to, wind farms, distributed-generation electrical grids, rail operations, health delivery systems, air transportation networks, oil and gas extraction and production operations, manufacturing and supply chain systems” paragraph 0086)
simulating a plurality of manufacturing scenarios using different sets of operational characteristics for plant devices in which at least one operational characteristic from the set of current operational characteristics is varied; (Johnson: see the factor simulation engine 240 with an internal factor simulator 245 and an external factor simulator 250 and the optimization engine 270 as illustrated in figure 2; “A second component being supplied with any or all of the state data 207, internal factors 210, and the external factors 215 is the factor simulation engine 240, which includes an internal factor simulator 245 that may produce actual or simulated information describing the design, operations, reliability and other internal factor information associated with the power plant 205, such as temperature readings, pressure readings, flow rate measurements, and the like. The factor simulation engine 240, as shown in FIG. 2, may also include an external factor simulator 250 that generates actual or simulated information describing various physical, technical, and/or business external factors influencing the power plant 205, such as environmental regulations, actions of competitors, weather, long-term fuel costs, and the financial costs in the capital markets. In one example, internal factor simulator 245 may simulate the internal factors using random walks of factors ahead of real time.” Paragraph 0044; “The optimization engine 270 may then simulate the operation of the power plant 205 by executing multiple scenarios simulating the power plant 205 over some designated period of time, and over various values of the factors (e.g., system designs, maintenance periods, expected loads, etc.) to generate simulation/optimization results 275 of the overall performance of the plant 205, the useful life of the plant 205 consumed, the overall return of investment of the plant 205 (e.g., taking into account financing of the plant 205, fuel costs, the possible grid market pricing, and so on), and other information based on variations of the criteria received from the criteria module 255.” Paragraph 0047)
determining, from among the simulated manufacturing scenarios, a first manufacturing scenario that satisfies one or more overall operational parameters for one or more plant devices or for the manufacturing plant as a whole; and (Johnson: “Further, the optimization engine 270 may determine which design and operating choices, maintenance schedules, financing options, overall cash investment, and the like specified via the criteria are likely to provide better outcomes in terms of return on investment or other measures of economic value, or other types of value, such as, for example, compatibility of the power plant 205 design relative to other power plants 205 or systems working in tandem with the power plant 205. In some examples, the optimization engine 270 determines optimized results by comparing results of multiple executions over different criteria and by selecting one or more of those executions that represent increased measures of economic value mod/or some other value metric. In at least some example systems, the optimization engine 270 may trade off multiple criteria over one or more time intervals and enable the business-physical system of the power plant 205 to evolve over time, subject to the constraints of a given one or more periods.” Paragraph 0048)
Johnson does not explicitly describe settings as described below. However, Shapiro teaches the settings as described below.
adjusting one or more settings of one or more plant devices based on a set of operational characteristics corresponding to the first manufacturing scenario. (Shapiro: see the set points 315 sent to the plan control system 21 from the plant optimization and scheduling system 26 as illustrated in figures 2, 3; “The system utilizes modeled information (described in detail below), real time and historical data to perform optimization and planning functions, and sends set point information to logic controllers.” Paragraph 0036) (Johnson: “Based on these comparisons, the optimization engine 270 may identify at least one, and possibly several, of the simulation scenarios for employment in the power plant 205 (operation 410) and may be configured to manually or automatically implement them in the control or operations system(s) of the plant or within the administrative business processes or outage work scope.” Paragraph 0063)
Johnson and Shapiro are combinable for the same rationale as set forth above with respect to claim 1.
Claim 7:
The cited prior art describes the system of claim 6, wherein the one or more data processing apparatus are configured to perform operations further comprising: generating a visualization that presents the set of operational characteristics corresponding to the first manufacturing scenario. (Johnson: “The simulation/optimization results 275 may include the physical and business-oriented aspects discussed above (e.g., overall performance, useful life, overall return on investment), along with the input data that define the scenarios used to perform the various simulations and optimizations. In one example, the simulation/optimization results 275 may be made available for viewing on the user devices 201 via the user interface 260.” Paragraph 0049; “Based on these comparisons, the optimization engine 270 may identify at least one, and possibly several, of the simulation scenarios for employment in the power plant 205 (operation 410) and may be configured to manually or automatically implement them in the control or operations system(s) of the plant or within the administrative business processes or outage work scope.” Paragraph 0063)
Claim 8:
The cited prior art describes the system of claim 6, wherein simulating the plurality of manufacturing scenarios is based on one or more factors comprising:
a plant operational capacity; (Johnson: “The internal factors 210 may include technical, physical, or business factors, such as, for example, the power plant 205 design, operations, availability, lineups, upgrades, maintenance, dispatch, capital equipment purchases, and so on.” Paragraph 0038)
operational ranges for plant devices; (Johnson: “The control system model 308 may be configured to provide parameters, limitations, and the like regarding the operation of particular subsystems (e.g., gas turbine) or components of the power plant 205. For example, the control system model 308 may provide information regarding allowable inlet schedules and other parameters for ramp up of a component in response to increasing load, how much remaining useful life may be consumed by overfiring a component by a specific period of time, and so forth. Such information may be useful in determining whether operating the component such a manner may be useful in generating additional revenue.” Paragraph 0055)
actual and expected degradations in plant device efficiency; or
actual and expected manufacturing plant or plant device shutdowns. (Johnson: “In like framework, the simulator/optimizer may track other causal factors, such as component shutdowns, trips and starts, air cleanliness with respect to particles and chemical concentration, and metal temperature, from direct measure and/or virtual sensing of these factors as the plant is operated. Additional factors indicative of RUL 172, such as repair records, original equipment manufacturer (OEM), or OEM lot, may be operationally tracked and employed in the simulation or post processing.” Paragraph 0032)
Claim 9:
The cited prior art describes the system of claim 6, wherein determining, from among the simulated manufacturing scenarios, the first manufacturing scenario, comprises: determining, from among the simulated manufacturing scenarios, the first manufacturing scenario that achieves a plant manufacturing output that is greater than plant manufacturing outputs achieved by other simulated manufacturing scenarios. (Johnson: “Further, the optimization engine 270 may determine which design and operating choices, maintenance schedules, financing options, overall cash investment, and the like specified via the criteria are likely to provide better outcomes in terms of return on investment or other measures of economic value, or other types of value, such as, for example, compatibility of the power plant 205 design relative to other power plants 205 or systems working in tandem with the power plant 205. In some examples, the optimization engine 270 determines optimized results by comparing results of multiple executions over different criteria and by selecting one or more of those executions that represent increased measures of economic value mod/or some other value metric. In at least some example systems, the optimization engine 270 may trade off multiple criteria over one or more time intervals and enable the business-physical system of the power plant 205 to evolve over time, subject to the constraints of a given one or more periods.” Paragraph 0048)
Claim 11:
The cited prior art describes a non-transitory computer readable medium storing instructions that, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations comprising: (Johnson: “This application relates generally to the field of data processing and, in an example embodiment, to the optimization of industrial systems based on technical and business objectives and constraints.” Paragraph 0002; “FIG. 2 is a block diagram of a simulator/optimizer 200 configured to optimize various aspects of an industrial system (e.g., a power plant, such as the plant associated with FIG. 1) over one or more criteria for an economic lifecycle.” Paragraph 0036; “The disk drive unit 1216 (a type of non-volatile memory storage) includes a machine-readable medium 1222 on which is stored one or more sets of data structures and instructions 1224 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The data structures and instructions 1224 may also reside, completely or at least partially, within the main memory 1204, the static memory 1206, and/or the processor 1202 during execution thereof by processing system 1200, with the main memory 1204, the static memory 1206, and the processor 1202 also constituting machine-readable, tangible media.” Paragraph 0127)
obtaining, using data from a set of sensors, a set of current operational characteristics for a plurality of plant devices in a manufacturing plant; (Johnson: see the state data 207 as illustrated in figure 2; “During operation of the power plant 205, various aspects or values of the operating power plant 205 that characterize current operating conditions or states with the plant 205 may be captured as state data 207. The state data 207 may be captured by sensors or gauges located within the plant 205 in some embodiments. The state data 207, in some examples, may include temperature readings, pressure readings, flow rate measurements, and other data. Further, the state data 207 may be read or captured periodically, continuously, or according to some other schedule over time.” Paragraph 0040; “Example industrial systems capable of being simulated and optimized in other embodiments include, but are not limited to, wind farms, distributed-generation electrical grids, rail operations, health delivery systems, air transportation networks, oil and gas extraction and production operations, manufacturing and supply chain systems” paragraph 0086)
simulating a plurality of manufacturing scenarios using different sets of operational characteristics for plant devices in which at least one operational characteristic from the set of current operational characteristics is varied; (Johnson: see the factor simulation engine 240 with an internal factor simulator 245 and an external factor simulator 250 and the optimization engine 270 as illustrated in figure 2; “A second component being supplied with any or all of the state data 207, internal factors 210, and the external factors 215 is the factor simulation engine 240, which includes an internal factor simulator 245 that may produce actual or simulated information describing the design, operations, reliability and other internal factor information associated with the power plant 205, such as temperature readings, pressure readings, flow rate measurements, and the like. The factor simulation engine 240, as shown in FIG. 2, may also include an external factor simulator 250 that generates actual or simulated information describing various physical, technical, and/or business external factors influencing the power plant 205, such as environmental regulations, actions of competitors, weather, long-term fuel costs, and the financial costs in the capital markets. In one example, internal factor simulator 245 may simulate the internal factors using random walks of factors ahead of real time.” Paragraph 0044; “The optimization engine 270 may then simulate the operation of the power plant 205 by executing multiple scenarios simulating the power plant 205 over some designated period of time, and over various values of the factors (e.g., system designs, maintenance periods, expected loads, etc.) to generate simulation/optimization results 275 of the overall performance of the plant 205, the useful life of the plant 205 consumed, the overall return of investment of the plant 205 (e.g., taking into account financing of the plant 205, fuel costs, the possible grid market pricing, and so on), and other information based on variations of the criteria received from the criteria module 255.” Paragraph 0047)
determining, from among the simulated manufacturing scenarios, a first manufacturing scenario that satisfies one or more overall operational parameters for one or more plant devices or for the manufacturing plant as a whole; and (Johnson: “Further, the optimization engine 270 may determine which design and operating choices, maintenance schedules, financing options, overall cash investment, and the like specified via the criteria are likely to provide better outcomes in terms of return on investment or other measures of economic value, or other types of value, such as, for example, compatibility of the power plant 205 design relative to other power plants 205 or systems working in tandem with the power plant 205. In some examples, the optimization engine 270 determines optimized results by comparing results of multiple executions over different criteria and by selecting one or more of those executions that represent increased measures of economic value mod/or some other value metric. In at least some example systems, the optimization engine 270 may trade off multiple criteria over one or more time intervals and enable the business-physical system of the power plant 205 to evolve over time, subject to the constraints of a given one or more periods.” Paragraph 0048)
Johnson does not explicitly describe settings as described below. However, Shapiro teaches the settings as described below.
adjusting one or more settings of one or more plant devices based on a set of operational characteristics corresponding to the first manufacturing scenario. (Shapiro: see the set points 315 sent to the plan control system 21 from the plant optimization and scheduling system 26 as illustrated in figures 2, 3; “The system utilizes modeled information (described in detail below), real time and historical data to perform optimization and planning functions, and sends set point information to logic controllers.” Paragraph 0036) (Johnson: “Based on these comparisons, the optimization engine 270 may identify at least one, and possibly several, of the simulation scenarios for employment in the power plant 205 (operation 410) and may be configured to manually or automatically implement them in the control or operations system(s) of the plant or within the administrative business processes or outage work scope.” Paragraph 0063)
Johnson and Shapiro are combinable for the same rationale as set forth above with respect to claim 1.
Claim 12:
The cited prior art describes the non-transitory computer readable medium of claim 11, wherein the instructions cause the one or more data processing apparatus to perform operations comprising: generating a visualization that presents the set of operational characteristics corresponding to the first manufacturing scenario. (Johnson: “The simulation/optimization results 275 may include the physical and business-oriented aspects discussed above (e.g., overall performance, useful life, overall return on investment), along with the input data that define the scenarios used to perform the various simulations and optimizations. In one example, the simulation/optimization results 275 may be made available for viewing on the user devices 201 via the user interface 260.” Paragraph 0049; “Based on these comparisons, the optimization engine 270 may identify at least one, and possibly several, of the simulation scenarios for employment in the power plant 205 (operation 410) and may be configured to manually or automatically implement them in the control or operations system(s) of the plant or within the administrative business processes or outage work scope.” Paragraph 0063)
Claim 13:
The cited prior art describes the non-transitory computer readable medium of claim 11, wherein simulating the plurality of manufacturing scenarios is based on one or more factors comprising:
a plant operational capacity; (Johnson: “The internal factors 210 may include technical, physical, or business factors, such as, for example, the power plant 205 design, operations, availability, lineups, upgrades, maintenance, dispatch, capital equipment purchases, and so on.” Paragraph 0038)
operational ranges for plant devices; (Johnson: “The control system model 308 may be configured to provide parameters, limitations, and the like regarding the operation of particular subsystems (e.g., gas turbine) or components of the power plant 205. For example, the control system model 308 may provide information regarding allowable inlet schedules and other parameters for ramp up of a component in response to increasing load, how much remaining useful life may be consumed by overfiring a component by a specific period of time, and so forth. Such information may be useful in determining whether operating the component such a manner may be useful in generating additional revenue.” Paragraph 0055)
actual and expected degradations in plant device efficiency; or
actual and expected manufacturing plant or plant device shutdowns. (Johnson: “In like framework, the simulator/optimizer may track other causal factors, such as component shutdowns, trips and starts, air cleanliness with respect to particles and chemical concentration, and metal temperature, from direct measure and/or virtual sensing of these factors as the plant is operated. Additional factors indicative of RUL 172, such as repair records, original equipment manufacturer (OEM), or OEM lot, may be operationally tracked and employed in the simulation or post processing.” Paragraph 0032)
Claim 14:
The cited prior art describes the non-transitory computer readable medium of claim 11, wherein determining, from among the simulated manufacturing scenarios, the first manufacturing scenario, comprises: determining, from among the simulated manufacturing scenarios, the first manufacturing scenario that achieves a plant manufacturing output that is greater than plant manufacturing outputs achieved by other simulated manufacturing scenarios. (Johnson: “Further, the optimization engine 270 may determine which design and operating choices, maintenance schedules, financing options, overall cash investment, and the like specified via the criteria are likely to provide better outcomes in terms of return on investment or other measures of economic value, or other types of value, such as, for example, compatibility of the power plant 205 design relative to other power plants 205 or systems working in tandem with the power plant 205. In some examples, the optimization engine 270 determines optimized results by comparing results of multiple executions over different criteria and by selecting one or more of those executions that represent increased measures of economic value mod/or some other value metric. In at least some example systems, the optimization engine 270 may trade off multiple criteria over one or more time intervals and enable the business-physical system of the power plant 205 to evolve over time, subject to the constraints of a given one or more periods.” Paragraph 0048)
Claims 5, 10, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2016/0231716 (Johnson) (cited by Applicant) in view of
U.S. Patent Application Publication No. 2013/0317629 (Shapiro) and further in view of
U.S. Patent Application Publication No. 2013/0116802 (Friman).
Claim 5:
The cited prior art describes the computer implemented method of claim 1, further comprising:
transmitting information regarding the first manufacturing scenario that achieves the target plant manufacturing output and the one or more settings of the one or more plant devices to a plant operator device; (Johnson: “In one example, the simulation/optimization results 275 may be made available for viewing on the user devices 201 via the user interface 260.” Paragraph 0049; “The optimization engine 270 may then simulate the operation of the power plant 205 by executing multiple scenarios simulating the power plant 205 over some designated period of time, and over various values of the factors (e.g., system designs, maintenance periods, expected loads, etc.) to generate simulation/optimization results 275 of the overall performance of the plant 205, the useful life of the plant 205 consumed, the overall return of investment of the plant 205 (e.g., taking into account financing of the plant 205, fuel costs, the possible grid market pricing, and so on), and other information based on variations of the criteria received from the criteria module 255.” Paragraph 0047; “Based on these comparisons, the optimization engine 270 may identify at least one, and possibly several, of the simulation scenarios for employment in the power plant 205 (operation 410) and may be configured to manually or automatically implement them in the control or operations system(s) of the plant or within the administrative business processes or outage work scope.” Paragraph 0063) (Shapiro: see the set points 315 sent to the plan control system 21 from the plant optimization and scheduling system 26 as illustrated in figures 2, 3; “The system utilizes modeled information (described in detail below), real time and historical data to perform optimization and planning functions, and sends set point information to logic controllers.” Paragraph 0036)
Johnson and Shapiro do not explicitly describe a confirmation as described below. However, Friman teaches the confirmation as described below.
receiving, from the plant operator device, a message confirming that the one or more settings of the one or more plant devices can be adjusted; and (Friman: “However, the results need to be accepted by the user before proposed tuning parameters are downloaded to the PI/PID controller. No changes are made to the online controller without confirmation. . . . A user can select the target speed and simulate set point changes with different target speed choices, because the fastest tuning is not always the best one. The proposed tuning parameters will be downloaded onto the online controller once the user accepts them by clicking the "Download to Controller" button. The user gets a printed one-page report of the controller tuning operation.” Paragraph 0067)
wherein adjusting one or more settings of one or more plant devices is only performed in response to the message confirming that the one or more settings of plant devices can be adjusted. (Friman: “However, the results need to be accepted by the user before proposed tuning parameters are downloaded to the PI/PID controller. No changes are made to the online controller without confirmation. . . . A user can select the target speed and simulate set point changes with different target speed choices, because the fastest tuning is not always the best one. The proposed tuning parameters will be downloaded onto the online controller once the user accepts them by clicking the "Download to Controller" button. The user gets a printed one-page report of the controller tuning operation.” Paragraph 0067) (Shapiro: see the set points 315 sent to the plan control system 21 from the plant optimization and scheduling system 26 as illustrated in figures 2, 3; “The system utilizes modeled information (described in detail below), real time and historical data to perform optimization and planning functions, and sends set point information to logic controllers.” Paragraph 0036) (Johnson: “Based on these comparisons, the optimization engine 270 may identify at least one, and possibly several, of the simulation scenarios for employment in the power plant 205 (operation 410) and may be configured to manually or automatically implement them in the control or operations system(s) of the plant or within the administrative business processes or outage work scope.” Paragraph 0063)
One of ordinary skill in the art would have recognized that applying the known technique of Johnson, namely, an optimization and control system for an industrial system, with the known techniques of Shapiro, namely, process optimization and planning for multi-unit plants, and the known techniques of Friman, namely, a simulation system for an industrial process, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Johnson to determine optimized aspects for industrial systems using simulation scenarios with the teachings of Shapiro to determine optimal operations using process simulation and the teachings of Friman to use a simulation to determine parameters would have been recognized by those of ordinary skill in the art as resulting in an improved control system (i.e., the combination of the references provides for process control system using simulations to determine and adjust and confirm settings based on the teachings of a control system using simulations in Johnson and the teachings of a control system using simulations to adjust settings in Shapiro and the teachings of a control system with operator setting adjustment in Friman).
Claim 10:
Claim 10 is substantially similar to claim 5 and is rejected based on the same reasons and rationale.
10. The system of claim 6, wherein the one or more data processing apparatus are configured to perform operations further comprising:
transmitting information regarding the first manufacturing scenario that achieves the target plant manufacturing output and the one or more settings of the one or more plant devices to a plant operator device;
receiving, from the plant operator device, a message confirming that the one or more settings of the one or more plant devices can be adjusted; and
wherein adjusting one or more settings of one or more plant devices is only performed in response to the message confirming that the one or more settings of plant devices can be adjusted.
Claim 15:
Claim 15 is substantially similar to claim 5 and is rejected based on the same reasons and rationale.
15. The non-transitory computer readable medium of claim 11, wherein the instructions cause the one or more data processing apparatus to perform operations comprising:
transmitting information regarding the first manufacturing scenario that achieves the target plant manufacturing output and the one or more settings of the one or more plant devices to a plant operator device;
receiving, from the plant operator device, a message confirming that the one or more settings of the one or more plant devices can be adjusted; and
wherein adjusting one or more settings of one or more plant devices is only performed in response to the message confirming that the one or more settings of plant devices can be adjusted.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2009/0089032 describes enhanced simulation models for automation.
U.S. Patent Application Publication No. 2020/0241488 describes controller optimization for a control system.
U.S. Patent Application Publication No. 2009/0089234 describes automated code generation for simulators.
U.S. Patent Application Publication No. 2009/0089227 describes automated recommendations from simulation.
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/Christopher E. Everett/Primary Examiner, Art Unit 2117