DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the application 18/400,783 filed on 12/29/2023. Claims 21, 25-27, 31, 33-35, 37-38, and 40 were amended in the reply filed 2/2/2026. Claims 21, 31, and 38 were amended in the reply filed 4/2/2026. Claims 21-40 are pending. This action is non-final.
Response to Arguments
Regarding Applicant’s argument starting on page 8 regarding claims 21, 22, 28-33, and 36-38: Applicant’s arguments filed with respect to the 35 USC § 102 rejections made have been fully considered, but are moot. Claims 21-22, 28-32, and 36-38 are now rejected under 35 U.S.C. 103 as being unpatentable over Ganti (U.S. Pub. No. 2017/0364043) in view of Prokoski (U.S. Pub. No. 2002/0046038). Claims 23 and 33 are now rejected under 35 U.S.C. 103 as being unpatentable over Ganti (U.S. Pub. No. 2017/0364043) in view of Prokoski (U.S. Pub. No. 2002/0046038) in view of Chassin (U.S. Pub. No. 2010/0107173). Claims 24-27, 34-35, and 39-40 are not rejected under 35 USC § 102 or 103. These dependent claims are considered novel over the prior art.
Reasons for Patent Eligibility Under 35 U.S.C. § 101
Claims 21-40 are patent eligible under 35 USC § 101 because the claims integrate the abstract idea into a practical application and amount to “significantly more” than the abstract idea itself. The claims are directed to a control system, a method, and a controller for operation of a facility including equipment. The amendments to the independent claims now include operating the equipment of the facility according to the performed run time curve analysis. This direct control of equipment of the facility according to the data analysis integrates the abstract idea into a practical application and amounts to “significantly more” than the abstract idea itself. Therefore, claims 21-40 are patent eligible under 35 USC § 101.
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 24-27, 34-35, and 39-40 are not rejected under 35 USC § 102 or 103. These dependent claims are considered novel over the prior art. The reason for this is that they are written descriptions of specific and narrow mathematical functions which are not found in the art.
Claims 21-22, 28-32, and 36-38 are rejected under 35 U.S.C. 103 as being unpatentable over Ganti (U.S. Pub. No. 2017/0364043) in view of Prokoski (U.S. Pub. No. 2002/0046038).
Regarding claims 21, 31, and 38, Ganti discloses the following limitations:
A control system for operation of a facility including equipment, the control system comprising a non-transitory computer-readable medium encoded with instructions that are executable by one or more processors to cause the one or more processors to: [See [0008] The system may include a hardware processor and machine readable storage medium on which is stored a power plant model and instructions that cause the hardware processor to execute an optimization process related to optimizing the schedule during a selected operating period.]
perform an offline process to determine a run time curve comprising a cost per run time and estimated remaining time; [See [0108]; [0120]; Ganti teaches an estimate may be determined of a life cycle cost (LCC) of a power plant 12, such as with a LCC model 113. Ganti [0110] further teaches that an optimization problem for the power plant 12, or at least an objective function, may take into account such factors as power plant characteristics, site parameters, customer specifications, results from controls model 111, performance model 112, and/or LCC model 113, ambient condition, market condition, and/or process condition, as well as any additional information that might be suitable and/or desired. Such factors may be gathered into terms of an objective function, so that, for example, a LCC-based objective function (i.e., determine a run time curve) includes maintenance cost and operation cost represent over time (i.e., comprising a cost per run time), where time is a prediction horizon based on an estimated component service life (i.e., and estimated remaining time). Ganti [0113-0114] further teaches simulating the operation of a power plant via an offline model 124. Ganti further teaches that the offline model may be used to determine estimated values for cost (i.e., perform an offline process provide to determine a run time curve) of power production for each time interval in a prediction horizon and for various values of power output of the power plant to generate one or more offer curves.]
operate the equipment of the facility using one or more first setpoints generated based at least in part on the cost per run time; [See [0108]; [0120]; Ganti teaches an estimate may be determined of a life cycle cost (LCC) of a power plant 12, such as with a LCC model 113. Ganti [0110] further teaches that an optimization problem for the power plant 12, or at least an objective function, may take into account such factors as power plant characteristics, site parameters, customer specifications, results from controls model 111, performance model 112, and/or LCC model 113, ambient condition, market condition, and/or process condition, as well as any additional information that might be suitable and/or desired. Such factors may be gathered into terms of an objective function, so that, for example, a LCC-based objective function (i.e., determine a run time curve) includes maintenance cost and operation cost represent over time (i.e., the cost per run time), where time is a prediction horizon based on an estimated component service life. Ganti [0110] further teaches that the objective function expressing LCC (life cycle cost) would be minimized to produce at least one operating parameter that may be used to run the power plant so as to keep LCC as low as feasible (i.e., operate the equipment of the facility ... based at least in part on the cost per run time). Ganti [0112] further teaches that initial setpoints determined may be adjusted responsive to and/or as part of the solution of the optimization problem to yield an enhanced or augmented or optimized setpoint (i.e., one or more first setpoints generated based at least in part on the cost per run time). In addition, iteration may be used with determining an initial setpoint, determining a value of a performance indicator, determining an estimated LCC cost, and enhancing or augmenting to refine results and/or better enhance or augment control setpoints of the power plant 12 (i.e., operate the equipment of the facility using one or more first setpoints generated based at least in part on the cost per run time).]
determine an actual remaining time based on the operation of the equipment; [See [0072] [0103]; [0229] Ganti teaches determining the remaining life of different parts of the power plant via an engineering model of a design model 71 (i.e., determine an actual remaining time) which determines the remaining life of the different parts based on sensor-collected data as input into the engineering model of the design model (i.e., based on the operation of the equipment).]
perform an online process to determine a second cost per run time for the actual remaining time ... (using the run time curve) ... and generate one or more second setpoints for the equipment; [See [0108]; [0120]; Ganti teaches an estimate may be determined of a life cycle cost (LCC) of a power plant 12, such as with a LCC model 113. Ganti [0110] further teaches that an optimization problem for the power plant 12, or at least an objective function, may take into account such factors as power plant characteristics, site parameters, customer specifications, results from controls model 111, performance model 112, and/or LCC model 113, ambient condition, market condition, and/or process condition, as well as any additional information that might be suitable and/or desired. Such factors may be gathered into terms of an objective function, so that, for example, a LCC-based objective function (i.e., a run time curve) includes maintenance cost and operation cost represent over time (i.e., a second cost per run time), where time is a prediction horizon based on an estimated component service life. [0097-0098]; [0120]; Ganti teaches computer models of power plants may be constructed and then used to control and optimize power plant operation. Such plant models may be dynamic and iteratively updated via ongoing comparison between actual (i.e., measured) operating parameters (i.e., perform an online process to determine a second cost per run time for the actual remaining time (i.e., ... (using the run time curve) ... ) versus those same parameters as predicted by the plant model. Ganti [0097-0098] further teaches that scripts may be generated for the assembled energy system components and their configuration. The generated scripts may include mathematical relationships within and/or among the energy system components, including economic and/or legal components, if used in the energy system component configuration. The computer system 80 then may solve mathematical relationships and show results of the solution on the display 81. Configurations in which signals may be transmitted from computer 80, the signals may be used to control an energy system in accordance with the results of the solution. Ganti [0063-0064] further teaches that optimized setpoints are thereby generated for the power plant equipment (i.e., generate one or more second setpoints for the equipment).]
operate the equipment of the facility using the one or more second setpoints. [See [0097-0098]; [0120]; Ganti teaches computer models of power plants may be constructed and then used to control and optimize power plant operation. Such plant models may be dynamic and iteratively updated via ongoing comparison between actual (i.e., measured) operating parameters versus those same parameters as predicted by the plant model. Ganti [0097-0098] further teaches that scripts may be generated for the assembled energy system components and their configuration. The generated scripts may include mathematical relationships within and/or among the energy system components, including economic and/or legal components, if used in the energy system component configuration. The computer system 80 then may solve mathematical relationships and show results of the solution on the display 81. Configurations in which signals may be transmitted from computer 80, the signals may be used to control an energy system in accordance with the results of the solution. Ganti [0063-0064] further teaches that optimized setpoints are thereby generated for the power plant equipment. Ganti [0064] further teaches that the plant controller may directly or automatically implement optimized setpoints without operator involvement (i.e., operate the equipment of the facility using the one or more second setpoints).]
Although Ganti teaches using a LCC-based objective function which includes maintenance cost and operation cost represent over time (i.e., a second cost per run time), Ganti does not explicitly teach that the method of determining an actual remaining life of the equipment includes selecting a remaining time on in a run time curve associated with the actual remaining life. Therefore, Ganti does not, however Prokoski does, disclose the following limitations:
... selecting ... (an output) ... from using the run time curve at a point associated with the actual remaining time [See [0047] Prokoski teaches producing a valuation curve for the lifetime of a property, and selecting a point on the curve corresponding to the current stage in the overall life in order to generate a corresponding output valuation.]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the LCC-based objective function of Ganti with the curve output process of Prokoski. By making this combination, a user of Ganti would be able to graphically select a point on a graphical representation of the LCC-based objective function in order to receive an output indicating the cost per run time of a piece of equipment. This graphical representation of the LCC-based objective function as a curved line will help the user visualize the lifespan of a piece of equipment and its variable maintenance and operation costs during that lifespan.
Regarding claims 22 and 32 Ganti in view of Prokoski discloses all claim 21 and 31 limitations. Ganti further discloses the following limitations:
wherein the offline process receives contract information. [See [0070] Ganti teaches that power plants have constraints which cover a vast array of possibilities including contract terms. Ganti [0091]; [0120]; further teaches that an offline optimizer module 218 may be used to minimize a cost function subject to a set of constraints such as the contract term constraints described in [0070].]
Regarding claims 28 and 36, Ganti in view of Prokoski discloses all claim 21 limitations. Ganti further discloses the following limitations:
wherein the offline process: sets a rate variable to each of a plurality of different values; [See [0062] Ganti teaches that the optimized operating mode may be determined by the optimizer 64 based on one or more defined cost functions. Such cost functions, for example, may regard a cost to produce power, profitability, efficiency, or some other criteria as defined by the operator 39. Ganti [0063] further teaches that to determine costs and profitability, the plant controller 22 may include or be in communication with an economic model 63 that tracks the price of power and certain other variable costs, such as the costs of the fuel used in the gas turbine system, the inlet conditioning system, and HRSG duct firing system (i.e., wherein the offline process: sets a rate variable to each of a plurality of different values).]
and performs a plurality of offline optimizations of a cost function, each offline optimization using a different version of the cost function in which the rate variable is set to one of the plurality of different values. [See [0062] Ganti teaches that the optimized operating mode may be determined by the optimizer 64 based on one or more defined cost functions. Such cost functions, for example, may regard a cost to produce power, profitability, efficiency, or some other criteria as defined by the operator 39. Ganti [0063] further teaches that to determine costs and profitability, the plant controller 22 may include or be in communication with an economic model 63 that tracks the price of power and certain other variable costs, such as the costs of the fuel used in the gas turbine system, the inlet conditioning system, and HRSG duct firing system. Ganti [0063] further teaches that the optimizer 64 can perform cost-based optimizations of equipment setpoints based on the collected plurality of cost data (e.g., certain other variable costs, such as the costs of the fuel used in the gas turbine system, the inlet conditioning system, and HRSG duct firing system) (i.e., each offline optimization using a different version of the cost function in which the rate variable is set to one of the plurality of different values). This optimization process may be repeatedly performed with different data in order to provide an up-to-date optimization of the equipment setpoints (i.e., performs a plurality of offline optimizations of a cost function).]
Regarding claim 29, Ganti in view of Prokoski discloses all claim 21 limitations. Ganti further discloses the following limitations:
wherein the online process: determines the actual remaining time from the run time curve. [See [0108] Ganti teaches that optimizing the costs of a power plant includes determining and optimizing the service life of the power plant, which may be expressed in hours of operation before the end of the life of the power plant (i.e., determines the actual remaining time from the run time curve).]
Regarding claim 30, Ganti in view of Prokoski discloses all claim 21 and 28 limitations. Ganti further discloses the following limitations:
wherein the rate variable is an hourly cost. [See [0062] Ganti teaches that the optimized operating mode may be determined by the optimizer 64 based on one or more defined cost functions. Such cost functions, for example, may regard a cost to produce power, profitability, efficiency, or some other criteria as defined by the operator 39. Ganti [0063] further teaches that to determine costs and profitability, the plant controller 22 may include or be in communication with an economic model 63 that tracks the price of power and certain other variable costs, such as the costs of the fuel used in the gas turbine system, the inlet conditioning system, and HRSG duct firing system. Ganti [0063] further teaches that the optimizer 64 can perform cost-based optimizations of equipment setpoints based on the collected plurality of cost data (e.g., certain other variable costs, such as the costs of the fuel used in the gas turbine system, the inlet conditioning system, and HRSG duct firing system). This optimization process may be repeatedly performed with different data in order to provide an up-to-date optimization of the equipment setpoints. Ganti [0002] teaches that an average variable cost curve may represent a cumulative cost divided by a cumulative power output for a given point, and an incremental variable cost curve may represent a change in cost divided by a change in power output. An incremental variable cost curve may be obtained, for example, by taking a first derivative of an input-output curve of the power plant that represents cost per hour versus power generated. This combination of citations of Ganti shows that the collected plurality of cost data (e.g., certain other variable costs, such as the costs of the fuel used in the gas turbine system, the inlet conditioning system, and HRSG duct firing system) may be represented as a cost per hour (i.e., wherein the rate variable is an hourly cost).]
Regarding claim 37, Ganti in view of Prokoski discloses all claim 31 limitations. Ganti further discloses the following limitations:
wherein the online process: determines the actual remaining time from the run time curve. [See [0108] Ganti teaches that optimizing the costs of a power plant includes determining and optimizing the service life of the power plant, which may be expressed in hours of operation before the end of the life of the power plant (i.e., determines the actual remaining time from the run time curve).]
Claims 23 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Ganti (U.S. Pub. No. 2017/0364043) in view of Prokoski (U.S. Pub. No. 2002/0046038) in view of Chassin (U.S. Pub. No. 2010/0107173).
Regarding claims 23 and 33, Ganti in view of Prokoski discloses all claim 21, 22, 31, and 32 limitations. Ganti in view of Prokoski does not, however Chassin does, explicitly disclose the following limitations:
wherein the contract information comprises a marginal cost rate. [See [0514]; [0563] Chassin teaches electricity contracts comprising marginal prices for electricity.]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical contracts of Ganti in view of Prokoski with the electrical contracts of Chassin. By making this combination, the electrical contracts of Ganti in view of Prokoski would be able to better optimize the cost/benefit analysis of producing and selling electricity at different rates. For example, a user of Ganti in view of Prokoski would now be able to sell electricity at marginal prices in certain instances in which it made sense from an optimization perspective.
Prior Art
The following prior art is relevant to the invention but was not used in prior art rejections:
Greiner (U.S. Pub. No. 2008/0082345) – System and method for evaluating risks associated with delaying machine maintenance
Hummon (U.S. Pub. No. 2017/0288401) – Orchestrated energy
Menon (U.S. Pub. No. 2018/0284707) – Gas turbine dispatch optimizer real-time command and operations
Hayashi (U.S. Pub. No. 2002/0120412) – Operation and maintenance planning aiding system for power generation installation
Pop (U.S. Pub. No. 2009/0292574) – Method to analyze economics of asset management solutions for nuclear steam generators
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRIS GOMEZ whose telephone number is (571) 272-0926. The examiner can normally be reached Mon-Fri 7-4 CDT.
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/CHRISTOPHER GOMEZ/
Examiner, Art Unit 3628
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