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 Office Action is in response to Applicant’s amendment filed on 1/22/2026.
Claims 1-20 are pending.
Response to Amendment
Applicant’s amendments have fixed the deficiencies set forth in the previous Office Action hence the respective rejections/objections have been withdrawn, except for those rejections/objections if still maintained or newly added in this Office Action.
The amendments of claims 3 and 13 widen the scope of the claims. Therefore, claims 3 and 13 are rejected in view of new grounds of rejection necessitated by the amendments.
Response to Arguments
Regarding Applicant’s arguments about the rejections for claims 1-21 under 35 U.S.C § 102/103, the arguments have been fully considered but are deemed moot, in view of new grounds of rejections necessitated by Applicant’s amendments.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 10-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Asati (US 20160261115 A1, prior art of record, hereinafter as “Asati”) in view of CHARBONNEL (US 20220155772 A1, hereinafter as “CHARBONNEL”).
Regarding claim 1, Asati teaches:
A method for operating a power generating plant having one or more power generating assets (FIG. 27 and [0191]: operating power plant 861 having multiple power generating assets 860), the method comprising:
receiving, via a plurality of independent applications of a supervisory controller (controller 855 in FIG. 27 and [0191]), a plurality of operational parameters relating to the one or more power generating assets in the power generating plant ([0194]: as Applicant discloses in paragraph [0049] of the specification: “As used herein, the terms "application" and “computer application" generally encompass their ordinary meanings, to include, for example, software programs designed to carry out one or more specific tasks”. Therefore, Asati teaches in [0194] to receive multiple specific operational parameters via multiple independent applications of the controller 855);
generating, via the plurality of independent applications of the supervisory controller, a plurality of marginal effect maps based on the plurality of operational parameters, each of the plurality of the marginal effect maps comprising a model of information relating operational setpoint selections of the one or more power generating assets to an expected value (FIG. 27 and [0202]: controller 855 generates multiple power generation models/(marginal effect maps) which relate operational settings to predicted value) or an effect on a component and failure mode;
receiving, via a central optimizer module (“optimizer” of controller 855 in FIG. 27) of the supervisory controller, the plurality of marginal effect maps from the plurality of independent applications ([0202]);
determining, via the central optimizer module of the supervisory controller, one or more operational setpoints for the one or more power generating assets in the power generating plant based on the plurality of marginal effect maps to optimize an economic value of operating the one or more power generating assets ([0202]: the optimizer of the controller 855 determines a “preferred scenario”/(operational settings) based on the models to optimize the cost); and
communicating, via the central optimizer module of the supervisory controller, the one or more operational setpoints to the one or more power generating assets in the power generating plant ([0193]).
Asati teaches specifically (underlines are added by Examiner for emphasis):
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[0191] Also related to centralize control and optimization of multiple power units, FIGS. 26 and 27 illustrates a power system 850 in which a block controller 855 is used to control a plurality of power blocks 860. The power blocks 860, as indicated, may define a fleet 861 of the generating assets (“assets”). As will be appreciated, these embodiments provide another exemplary application of the optimization and control methods described in more detail above, though include broadening the optimization perspective to a fleet level. In so doing, the present invention may further offer ways of reducing certain inefficiencies that still impact modern power generating systems, particularly those having a large number of remote and varied thermal generating units. Each of the assets may represent any of the thermal generating units discussed herein, such as, for example, gas and steam turbines, as well as related subcomponents, like HRSGs, inlet conditioners, duct burners, etc. The assets may be operable pursuant to multiple generating configurations according to how the subcomponents are engaged. The power generation from the multiple power blocks 860 may be centrally controlled by a block controller 855. With respect to the system in FIG. 27, which will be discussed in more detail below, the block controller 855 may control the system pursuant to optimization processes that take into consideration asset and power block health, as well as generation schedules, maintenance schedules, as well as other factors that might be particular to one of the assets or power blocks 860, including location dependent variables. In addition, learning from operational data collected from similarly configured assets and power blocks, but not part of the fleet, may be utilized so to further refine control strategies.
[0193] As indicated, the control system, as represented by the block controller 855, may interact with the asset controllers. The block controller 855 also may communicate with the grid 862, as well as with a central dispatch or other governing authority that is associated with its management. In this manner, for example, supply and demand information may be exchanged between the fleet 861 and a central authority. According to an exemplary embodiment, supply information, such as dispatch bids, may be based on the block controller's optimization of the fleet 861. The present invention may further include optimization processes that occur between bid periods, which may be used periodically to optimize the way in which the fleet 861 is configured so to satisfy an already established load level. Specifically, such inter-bid optimization may be used to address dynamic and unanticipated operating variables. Appropriate control actions for the assets of the power blocks 860 may be communicated by the block controller 855 to the control systems within each of the power blocks 860 or, more directly, to the assets. According to preferred embodiments, implementation of control solutions of the block controller 855 may include enabling it to override asset controllers when certain predefined conditions are met. Factors affecting such override may include variable generating cost for each of the power blocks/assets, remaining useful part-life of hot gas path components, changing levels of demand, changing ambient conditions, as well as others.
[0194] The block controller 855, as illustrated, may be communicatively linked to the several power blocks 860 of the fleet 861 as well as directly to the assets, and thereby may receive many data inputs upon which the control solutions described herein are based. The optimization procedures may consider one or more of the following inputs: health and performance degradation; power generation schedules; grid frequency; maintenance and inspection schedules; fuel availability; fuel costs; fuel usage patterns and predictions; past issues and equipment failures; true performance capabilities; lifing models; startup and shutdown features; measurement operating parameter data, past and present; weather data; cost data; etc. As discussed in more detail in relation to other embodiments, inputs may include detailed present and historical data regarding measured operating parameters for each of the generating assets of the fleet 861. All such inputs, past and present, may be stored pursuant to conventional methods in, for example, a central database, and thereby made available upon query from the block controller 855 as might be necessary according to any of the procedural steps described herein.
[0202] The block controller 855, as indicated, further may include modules directed toward power generation models (which may include asset models, block models, fleet models, as well as degradation or loss models), an optimizer, and a cost function. The asset, power block, and/or fleet models may be created, tuned and/or reconciled and maintained according to the methods already described herein. These models may be used to simulate or otherwise predict the operation of the fleet, or a selected portion thereof, over the selected operating period such that the optimizer module is able to determine a preferred scenario according to a defined cost function. More specifically, the results from the simulations may be used to calculate a cost result for each, which may include a summation across the power blocks and/or fleet assets of revenue, operating costs, degradation, expended useful part-life, and other costs mentioned herein. The revenue, as will be appreciated, may be determined via a projected output level multiplied by a market unit price. The calculation of the costs, as stated, may include degradation models or algorithms that correlate an economic result to the manner in which the assets operate within the simulations. Performance data from the simulation results may be used to determine fleet-wide operating costs, degradation, and other losses as already described. As will be appreciated, certain cost considerations, such as fixed aspects of operating costs, may not be appreciable different between the competing fleet operating modes and, thus, be excluded from such calculations. Additionally, the simulations described herein may be configured so to include the entire fleet of assets or a portion thereof, and may be focused on limited aspects of asset operation that, as provided herein, have been found particularly relevant at predicting cost results.
Asati teaches all the limitations except at least one of the plurality of independent applications uses one or more machine learning algorithms to train one or more models relating one or more experienced conditions to damage and creates a trained model relating conditions to selections of operational curves to at least one of predicted damage or risk of failure.
However, CHARBONNEL teaches in an analogous art:
one or more machine learning algorithms to train one or more models relating one or more experienced conditions to damage and creates a trained model relating conditions to selections of operation to at least one of predicted damage or risk of failure ([0006]: “a method includes receiving historical usage data associated with a plurality of engines, of an engine type, that are associated with a plurality of respective machines; training, based on the historical usage data, an engine monitoring model to identify a usage profile that indicates potential failure of the engine type, wherein the usage profile identifies an operational range of an operating parameter for the engine type according to an operating profile associated with operating one or more of the plurality of engines”).
Since trained model comprises trained parameters corresponding to selections of different operational curves, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asati based on the teaching of CHARBONNEL, to make the method wherein at least one of the plurality of independent applications uses one or more machine learning algorithms to train one or more models relating one or more experienced conditions to damage and creates a trained model relating conditions to selections of operational curves to at least one of predicted damage or risk of failure. One of ordinary skill in the art would have been motivated to do this modification in order to prevent reduce of “a useful life” of assets, as CHARBONNEL suggests in [0002].
Regarding claim 2, Asati-CHARBONNEL teach(es) all the limitations of its base claim from which the claim depends on.
Asati further teaches:
the plurality of operational parameters comprises at least one of environmental data, geographical data, forecasted data, seasonal data, historical data ([0194]: “nputs may include detailed present and historical data regarding measured operating parameters for each of the generating assets of the fleet 861”), loading data, power data, one or more grid parameters, one or more sensor measurements, or one or more electrical conditions.
Regarding claim 3, Asati-CHARBONNEL teach(es) all the limitations of its base claim from which the claim depends on.
CHARBONNEL further teaches:
at least one of a blade bending load, an odometer-based control application, an odometer-based maintenance application, a fatigue life application, a gearbox component life application, a pitch bearing life application, a wake steering for loads application, a wake steering application for power production, a component failure risk assessment application, a thermal trip risk application, a vibration trip risk application, a power demand prediction application, a learning-based optimization application ([0006]: “a method includes receiving historical usage data associated with a plurality of engines, of an engine type, that are associated with a plurality of respective machines; training, based on the historical usage data, an engine monitoring model to identify a usage profile that indicates potential failure of the engine type, wherein the usage profile identifies an operational range of an operating parameter for the engine type according to an operating profile associated with operating one or more of the plurality of engines”. This teaches to optimize machine operation based on machine learning), or a power production application
it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Asati-CHARBONNEL based on the teaching of CHARBONNEL, to make the method wherein the plurality of independent applications further comprises at least one of a blade bending load, an odometer-based control application, an odometer-based maintenance application, a fatigue life application, a gearbox component life application, a pitch bearing life application, a wake steering for loads application, a wake steering application for power production, a component failure risk assessment application, a thermal trip risk application, a vibration trip risk application, a power demand prediction application, a learning-based optimization application, or a power production application. One of ordinary skill in the art would have been motivated to do this modification in order to prevent reduce of “a useful life” of assets, as CHARBONNEL suggests in [0002].
Regarding claim 4, Asati-CHARBONNEL teach(es) all the limitations of its base claim from which the claim depends on.
Asati further teaches:
generating, via the plurality of independent applications of the supervisory controller, the plurality of marginal effect maps based on the plurality of operational parameters using machine learning (FIG. 4 and [0059-0060]: “an artificial neural network configuration 71 (“neural network 71”), ….. neural networks can be updated using either feedback biasing or on-line adaptive learning”).
Regarding claim 5, Asati-CHARBONNEL teach(es) all the limitations of its base claim from which the claim depends on.
Asati further teaches:
the central optimizer module of the supervisory controller utilizes a cost model ([0202]: “he block controller 855, as indicated, further may include modules directed toward power generation models (which may include asset models, block models, fleet models, as well as degradation or loss models), an optimizer, and a cost function”).
Regarding claim 6, Asati-CHARBONNEL teach(es) all the limitations of its base claim from which the claim depends on.
Asati further teaches:
the cost model comprises a cost table relating a plurality of costs to the information set forth in the plurality of the marginal effect maps, the plurality of costs comprising at least one of electricity price, maintenance costs ([0196]: “Data inputs may include the types already discussed herein, including those related to computer modeling, maintenance, optimization, and model-free adaptive learning processes. For example, according to the present embodiment, computer models, transfer functions, or algorithms may be developed and maintained so that the operation (or particular aspects of the operation) of the assets and/or, collectively, the power blocks or the fleet, may be simulated under a variety of scenarios. Results from the simulations may include values for certain performance indicators, which represent predictions as to aspects of the operation and performance of the assets, power block, or fleet performance over the selected operating period. The performance indicators may be selected because of a known or developed correlation to one or more cost results, and thus may be used to compare the economic aspects of each simulation. A “cost result”, as used herein, may include any economic ramification, positive or negative, associated with the operation of the fleet 861 over the selected operating period. Cost results, thus, may include any revenue earned from the generation of power over the period, as well as any operating and maintenance costs incurred by the fleet. These operating and maintenance costs may include resulting degradation to the assets of the fleet given the scenarios and the simulated operation resulting from each”), repair costs, service agreement terms, discount rates, upcharge rates, or combinations thereof.
Regarding claim 7, Asati-CHARBONNEL teach(es) all the limitations of its base claim from which the claim depends on.
Asati further teaches:
the component and the failure mode further comprise at least one of a risk of failure, damage, power production, or compliance ([0202]: ” The block controller 855, as indicated, further may include modules directed toward power generation models (which may include asset models, block models, fleet models, as well as degradation or loss models”. This teaches the power generation models/(marginal effect maps) relate operational settings to a component and failure mode comprising damage) .
Regarding claim 10, Asati-CHARBONNEL teach(es) all the limitations of its base claim from which the claim depends on.
Asati further teaches:
the power generating asset comprises at least one of a wind turbine, a solar power generating asset, a hydroelectric asset, and a hybrid power generating asset ([0041]: “It will be appreciated that thermal power plants may include generating units such as gas turbines, coal-fired steam turbines, and/or combined-cycle plants. In addition, power system 10 may include other types of power plants (not shown), such as solar power installations, hydroelectric, geothermal, nuclear, and/or any other suitable power sources now known or discovered hereafter”).
Claim 11 recites a system conducting operational steps of the method of claim 1 with patentably the same limitations. Therefore, claim 11 is rejected for the same reason recited in the rejection of claim 1.
Claims 12, 14, 15, 16, 17 and 20 recite a system conducting operational steps of the method of claims 2, 4, 5, 6, 7 and 10 respectively with patentably the same limitations. Therefore, claims 12, 13, 14, 15, 16, 17 and 20 are rejected for the same reason recited in the rejection of claims 2, 3, 4, 5, 6, 7 and 10, respectively.
Claims 8-9 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Asati in view of CHARBONNEL, and in further view of Giertz (US 20190178229 A1, prior art of record, hereinafter as “Giertz”).
Regarding claim 8, Asati-CHARBONNEL teach(es) all the limitations of its base claim from which the claim depends on, but do not teach communicating the one or more operational setpoints to the one or more power generating assets in the power generating plant continuously for predefined time intervals.
However, Giertz teaches in an analogous art:
communicating the one or more operational setpoints to the one or more power generating assets in the power generating plant continuously for predefined time intervals ([0005]: “A cluster controller transmits the current setpoints specified by the network operator, if necessary after adjustment in the cluster controller, as individual current setpoints for a plurality of power generators, for example windfarms, of a subnetwork of the supply network. The subnetwork of the supply network is also referred to as a cluster. These current setpoints are therefore received by wind power installations or windfarms, for example, at minute intervals”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Asati-CHARBONNEL based on the teaching of Giertz, to make the method to further comprise communicating the one or more operational setpoints to the one or more power generating assets in the power generating plant continuously for predefined time intervals. One of ordinary skill in the art would have been motivated to do this modification since it can help “regulate the power”, as Giertz suggests in [0005].
Regarding claim 9, Asati-CHARBONNEL-Giertz teach(es) all the limitations of its base claim from which the claim depends on.
Giertz further teaches:
the predefined time intervals range from about one (1) minute to about ten (10) minutes ([0005]: “A cluster controller transmits the current setpoints specified by the network operator, if necessary after adjustment in the cluster controller, as individual current setpoints for a plurality of power generators, for example windfarms, of a subnetwork of the supply network. The subnetwork of the supply network is also referred to as a cluster. These current setpoints are therefore received by wind power installations or windfarms, for example, at minute intervals”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified Asati-CHARBONNEL based on the teaching of Giertz, to make the method wherein the predefined time intervals range from about one (1) minute to about ten (10) minutes. One of ordinary skill in the art would have been motivated to do this modification since it can help “regulate the power”, as Giertz suggests in [0005].
Claims 18 and 19 recite a system conducting operational steps of the method of claims 8 and 9 respectively with patentably the same limitations. Therefore, claims 18 and 19 are rejected for the same reason recited in the rejection of claims 8 and 9, respectively.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES CAI whose telephone number is (571)272-7192. The examiner can normally be reached on M-F 8-5 EST.
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/CHARLES CAI/
Primary Patent Examiner, Art Unit 2115
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