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 21-40 are pending. This action is final.
Response to Arguments
Regarding Applicant’s argument starting on page 8 regarding claims 21, 31, and 38: Applicant’s arguments filed with respect to the objections made have been fully considered, and are persuasive. The objections have been withdrawn.
Regarding Applicant’s argument starting on page 8 regarding claim 33: Applicant’s arguments filed with respect to the double patenting rejections made have been fully considered, and are persuasive. The double patenting rejections have been withdrawn.
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 not persuasive.
Applicant first argues that Ganti does not disclose “perform an offline process to determine a runtime curve comprising a cost per run time and estimated remaining time” and “perform an online process to determine a second cost per run time for the actual remaining time using the run time curve to generate one or more second setpoints for the equipment.” Examiner respectfully disagrees. As described in the citations of the rejection below, Ganti teaches generating optimized setpoints for different parts of its power plant equipment in two ways: (1) an LCC model used to simulate the operation and life cycles of the different parts of its power plant equipment (i.e., “offline”), and (2) receiving dynamic feedback from the different parts of its power plant equipment in real time (i.e., “online”). Both of these methods are used to determine optimized setpoints for different parts of its power plant equipment.
Applicant further argues that Ganti does not disclose “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,” “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” and “operate the equipment of the facility using the one or more second setpoints.” Examiner respectfully disagrees. Specifically, Applicant argues that Ganti does not teach or suggest that the “parameters” are an “actual remaining time.” However, Ganti [0072] and [0103], for example, teaches that one of the determined parameters for a part of the power plant equipment is the remaining life of that part of the power plant equipment. Also, Ganti [0097] 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.” The disclosure of Ganti [0110] teaches determining a predicted life cycle of a component via a model and also Ganti [0072] [0103] [0229] teaches determining the remaining actual life of different parts of the power plant.
Applicant’s arguments regarding 35 USC § 103 are not persuasive for the same reasons. See rejections below for more detail.
Regarding Applicant’s argument starting on page 12 regarding claims 21-40: Applicant’s arguments filed with respect to the 35 USC § 101 rejections made have been fully considered, and are persuasive. The rejections made under 35 USC § 101 have been withdrawn.
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 § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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. 102(a)(2) as being anticipated by Ganti (U.S. Pub. No. 2017/0364043).
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 [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 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).]
Regarding claims 22 and 32 Ganti 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 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 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 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 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).]
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 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 Chassin (U.S. Pub. No. 2010/0107173).
Regarding claims 23 and 33, Ganti discloses all claim 21, 22, 31, and 32 limitations. Ganti 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 with the electrical contracts of Chassin. By making this combination, the electrical contracts of Ganti would be able to better optimize the cost/benefit analysis of producing and selling electricity at different rates. For example, a user of Ganti 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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