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 .
Detailed Action
Claims 1-21 are currently pending.
Response to Amendment
This action is in response to the Amendment filled on 01/30/2026. The amendment has been entered. Claims 1,10 and 11 have been amended, claims 1-21 are pending, with claims 1 and 10 being independent in the instant application.
Response to Arguments
Applicant's Arguments/Remarks filed on 01/30/2026 on page 9 regarding Claim objection regarding claim 11 has been fully considered and is found persuasive in view of the amended claim.
Applicant's Arguments/Remarks on page 9-14 regarding 35 U.S.C. 103 rejections have been fully considered and are found persuasive in view of the amended claims and presented Arguments/Remarks by the Applicant. However, a new ground of rejections is necessitated by Applicant's claim amendments. Therefore, the previous rejections regarding 35 U.S.C.103 are being amended in this current office action. (See analysis below Claim Rejections-35 U.S.C. 103).
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The entire reference is considered to provide disclosure relating to the claimed invention. The claims & only the claims form the metes & bounds of the invention. Office personnel are to give the claims their broadest reasonable interpretation in light of the supporting disclosure. Unclaimed limitations appearing in the specification are not read into the claim. Prior art was referenced using terminology familiar to one of ordinary skill in the art. Such an approach is broad in concept and can be either explicit or implicit in meaning. Examiner's Notes are provided with the cited references to assist the applicant to better understand how the examiner interprets the applied prior art. Such comments are entirely consistent with the intent & spirit of compact prosecution.
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 (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.
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 1,2,4-6,10,11,14-16, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (Pub. No. US2017/0185707A1), and in view of Ganti et al. (Pub. No. US20170364043A1).
Regarding Claim 1, Rao teaches a system comprising: a processor and a memory configured to store non-transitory instructions that, when executed by the processor, (Rao disclosed in page 1 para [0011]: “An embodiment of the present invention is directed to a computer system for providing a simulation of a physical real-world System. Such a computer system comprises a processor and memory with computer code instructions stored thereon. The processor and the memory, with the computer code instructions, are configured to cause the system to generate a system of equations …”).
wherein Rao teaches each of the plurality of physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions, (Rao disclosed in page 5 para [0049]: “FIG. 1 is a flowchart of a method 110 for providing a simulation of a physical real-world system … In an embodiment, the system of equations is a discrete representation of the real-world system. This discrete representation of the real-world physical system represents the continuum as a finite set of points in space. In such an example, the real-world physical system may be governed by partial differential equations and a discrete version of these equations can govern each of these points in space. … Embodiments of the method 110 may simulate a variety of systems. … the system of equations may be a system of partial differential equations that indicates properties of the physical system. For example, the system of equations may contain data/properties reflecting the mass, stiffness, size, etc., of the real-world system.”).
However, Rao doesn’t explicitly teach the limitations “receiving a selection of a selected component from a plurality of modeled components in a system, wherein each of the plurality of modeled components is a physics computation modeled component of a corresponding physical component of a corresponding physical system; receiving a setup for a virtual experiment for the selected component using a corresponding one of the physics computation modeled components for the selected component, wherein the virtual experiment executes a simulation of the physics computation modeled component and records results produced by the physics computation modeled component during the simulation; defining a plurality of input parameters to the physics computation model of the selected component for the virtual experiment; selecting a varied input parameter for the virtual experiment from the plurality of input parameters; identifying an output parameter of the physics computation model of the selected component to be modeled by a calibrated model of the selected component; executing the virtual experiment for the defined input parameters and over a predefined range of values for the varied input parameter; recording result data from the virtual experiment; producing the calibrated model of the selected component based upon the result data from the virtual experiment relating output values of the identified output parameter over the predefined range of input values for the varied input parameter to the varied input parameter values; and conducting a simulation of the system in which the calibrated model of the selected component interacts with the physics computation modeled components that were not selected, a computer-based simulation based on derived mathematical expressions modeling the behavior of a corresponding one of the modeled components based on underlying physical properties of the corresponding one of the plurality of the modeled components, and wherein the calibrated model comprises a multi-dimensional interface boundary to receive one or more input value and produce one or more output value based on a modeled behavior, without having to perform iterative computations in real time during the simulation of the system, and wherein the calibrated model comprises a functional representation of the result data, wherein the virtual experiment comprises a process for execution of the setup for the virtual experiment using the physics computation modeled component of the selected component, wherein executing the virtual experiment further comprises executing a series of passes each corresponding to one of a plurality of input values of the varied input parameter over the predefined value range, and wherein the result data comprises an output value of the output parameter for each pass of the series of passes.”
Ganti teaches perform the steps of: receiving a selection of a selected component from a plurality of modeled components in a system, wherein each of the plurality of modeled components is a physics computation modeled component of a corresponding physical component of a corresponding physical system; (Ganti disclosed in page 6 para [0063-0064]: “According to other embodiments, as discussed in more detail with FIG. 4, the optimizer 64 of the plant controller 22 may include or operate in conjunction with a filter, such as a Kalman filter, to assist in tuning, adjusting and calibrating the digital models so that the models accurately simulate the operation of the power plant 12. … As part of the control system, the filter also may be used to adjust or calibrate the models in real time or in near real time, such as every few minutes or hour or as specified.” The optimized setpoints generated by the plant controller 22 represents a recommended mode of operation and, for example, may include fuel and air settings for the gas turbine system, the temperature and water mass flow for the inlet conditioning system, the level of duct firing within the steam turbine system 50. According to certain embodiments, these suggested operating setpoints may be provided to the operator 39 via an interface device ... Knowing the optimized setpoints, the operator then may input the set points into the plant controller 22 and/or the component controller 31, which then generates control information for achieving the recommended mode of operation.”
Ganti teaches receiving a setup for a virtual experiment for the selected component using a corresponding one of the physics computation modeled components for the selected component, (Ganti disclosed in page 7 para [0068]: “According to certain embodiments, the plant controller 22 issues recommendations to the operator 39 regarding desired operating setpoints for the gas turbine system 30, inlet conditioning system 51, and steam turbine system 50. The plant controller 22 may receive and store data on the operation of the components and subsystems of the power plant 12. … The computer system may be embodied in a single physical or virtual computing device or distributed over local or remote computing devices. The digital models 60, 61, 62, 63 may be embodied as a set of algorithms, e. g. transfer functions, that relate operating parameters of each of the systems. The models may include a physics-based aero-thermodynamic computer model, a regression-fit model, or other suitable computer-implemented model. According to preferred embodiments, the models 60, 61, 62, 63 may be regularly, automatically and in real-time or near real-time tuned, adjusted or calibrated or tuned pursuant to ongoing comparisons between predicted operation and the measured parameters of actual operation.” The disclosure above “desired operating setpoints for the gas turbine system” corresponds to claim element “selected component”).
wherein Ganti teaches the virtual experiment executes a simulation of the physics computation modeled component and records results produced by the physics computation modeled component during the simulation; (Ganti disclosed in page 10 para [0084]: “The data from economic model 63 may be used by the optimizer 64 to evaluate each of the operational states of the power plant pursuant to operator defined performance objectives. The optimizer 64 may identify which of the operational states of the power plant 12 is optimal … As described, the digital models may be used to simulate the operation of the plant components 49 of the power plant 12, such as modeling thermodynamic operation of the gas turbine system, the inlet conditioning system, or the steam turbine system. The models may include algorithms, such as mathematical equations and look-up tables, which may be stored locally and updated periodically or acquired remotely via data resources 26, that simulate the response of plant components 49 to specific input conditions.”
The disclosure above “the digital models may be used to simulate the operation of the plant components 49 of the power plant 12, such as modeling thermodynamic operation of the gas turbine system, the inlet conditioning system, or the steam turbine system; the models may include algorithms, look-up tables, which may be stored locally and updated periodically” correspond to claim limitation “the virtual experiment executes a simulation of the physics computation modeled component and records results produced by the physics computation modeled component during the simulation”).
Ganti teaches defining a plurality of input parameters to the physics computation model of the selected component for the virtual experiment; selecting a varied input parameter for the virtual experiment from the plurality of input parameters; (Ganti disclosed in page 11 para [0086]: “as illustrated in FIG. 4, the neural network 71 may interact with and provide communications between each of the digital models of the several plant components 49 of the power plant 12 of FIG. 3. The interaction may include collecting output data from the models and generating input data used by the models to generate further output data. … The logic elements may each embody an algorithm that accepts data inputs to generate one or more data outputs. ... Other logic elements may multiply values of the inputs or apply other mathematical relationships to the input data. The data inputs to each of the logic elements of the neural network 71 may be assigned a weight, such as multiplier between one and zero. The weights may be modified during a learning mode which adjusts the neural network to better model the performance of the power plant … Adjusting the weights of the data inputs to the logic units in the neural network is one example of the way in which the neural network may be dynamically modified during operation of the combined cycle power plant. Other examples include modifying weights of data inputs to algorithms (which are an example of a logic unit) in each of thermodynamic digital models for the steam turbine system, inlet conditioning system, and gas turbine.”).
Ganti teaches identifying an output parameter of the physics computation model of the selected component to be modeled by a calibrated model of the selected component; (Ganti disclosed in page 15 para [0103]: “according to the present invention, a minimum variable operating cost may be achieved for a thermal generating unit or power plant that balances variable performance characteristics and cost parameters (i.e., fuel cost, ambient conditions, market conditions, etc.) with life-cycle cost (i.e., variable operation and its effect on maintenance schedules, part replacement, etc.) ... For example, in power plants that include a gas turbine, firing temperature may be varied to provide a desired load level more economically based on operating profile, ambient conditions, market conditions, forecasts, power plant performance, and/or other factors. … Further, a power plant control system that includes a feedback loop updated with substantially real-time data from sensors that are regularly tested and confirmed as operating correctly will allow further plant optimization That is, according to certain embodiments of the present invention, by introducing a real-time feedback loop between the power plant control system and dispatch authority, target load and unit commitment may be based on highly accurate offer curves that are constructed based on real-time engine performance parameters.” In page 50 para [0271]: “One important goal of performance anomaly detection may be to detect when this excessive degradation may be occurring. … With the automated real-time detection, a proactive solution such as an Offline Water Wash and instrumentation calibration, hardware replacement, or earlier maintenance may be provided to the customer in order to enable the site to restore the lost performance, while minimizing the total non-recoverable degradation that can occur. Based on the performance analytics results, the multiple performance degradation analytics may be developed to automate the detection of severe performance degradation. Three-layer alarming logics may be incorporated in the system, including orange (serious), red (significant), and yellow (medium) alarms. An alarm may be triggered in terms of the percentage reduction of corrected power output and corrected heat rate relative to the corresponding values after latest offline water wash, or baseline performance after unit was commissioned.”).
Ganti teaches executing the virtual experiment for the defined input parameters and over a predefined range of values for the varied input parameter; (Ganti disclosed in page 11 para [0086]: “as illustrated in FIG. 4, the neural network 71 may interact with and provide communications between each of the digital models of the several plant components 49 of the power plant 12 of FIG. 3. The interaction may include collecting output data from the models and generating input data used by the models to generate further output data. … The logic elements may each embody an algorithm that accepts data inputs to generate one or more data outputs. ... Other logic elements may multiply values of the inputs or apply other mathematical relationships to the input data. The data inputs to each of the logic elements of the neural network 71 may be assigned a weight, such as multiplier between one and zero. The weights may be modified during a learning mode which adjusts the neural network to better model the performance of the power plant … Adjusting the weights of the data inputs to the logic units in the neural network is one example of the way in which the neural network may be dynamically modified during operation of the combined cycle power plant. Other examples include modifying weights of data inputs to algorithms (which are an example of a logic unit) in each of thermodynamic digital models for the steam turbine system, inlet conditioning system, and gas turbine.”
The disclosure above “Adjusting the weights of the data inputs to the logic units in the neural network is one example of the way in which the neural network may be dynamically modified during operation of the combined cycle power plant; other examples include modifying weights of data inputs to algorithms (which are an example of a logic unit) in each of thermodynamic digital models for the steam turbine system, inlet conditioning system, and gas turbine” correspond to claim limitation “executing the virtual experiment for the defined input parameters and over a predefined range of values for the varied input parameter; recording result data from the virtual experiment”).
Ganti teaches recording result data from the virtual experiment; (Ganti disclosed in page 43 para [0240]: “According to the embodiments represented in FIG. 30, an additional aspect of the present invention is discussed that relates to a multiple step procedure for evaluating the functioning of plant sensors by analyzing the data that the sensors record ... As will be described, the present method may include checking and evaluating data in real-time as it is collected as well as perform evaluations after communicating and cataloguing the data measurements at a remote or off-site storage system, such as a central or cloud-hosted data repository. The evaluation of the sensors and that data collected by them may be configured to repeat in set time increments so to create a time based and evolving view of sensor performance. Further, as will be described, the present method may include real-time data evaluations for sensor malfunction or failures, such as, shift, drift, senility, noise, spikes, etc., as well as evaluations that are done less frequently and that are focused on data accumulated over a longer period of plant operation.”).
Ganti teaches producing the calibrated model of the selected component based upon the result data from the virtual experiment relating output values of the identified output parameter over the predefined range of input values for the varied input parameter to the varied input parameter values; (Ganti disclosed in page 11 para [0086-0087]: “as illustrated in FIG. 4, the neural network 71 may interact with and provide communications between each of the digital models of the several plant components 49 of the power plant 12 of FIG. 3. The interaction may include collecting output data from the models and generating input data used by the models to generate further output data. … The logic elements may each embody an algorithm that accepts data inputs to generate one or more data outputs. ... Other logic elements may multiply values of the inputs or apply other mathematical relationships to the input data. The data inputs to each of the logic elements of the neural network 71 may be assigned a weight, such as multiplier between one and zero. The weights may be modified during a learning mode which adjusts the neural network to better model the performance of the power plant … Adjusting the weights of the data inputs to the logic units in the neural network is one example of the way in which the neural network may be dynamically modified during operation of the combined cycle power plant. Other examples include modifying weights of data inputs to algorithms (which are an example of a logic unit) in each of thermodynamic digital models for the steam turbine system, inlet conditioning system, and gas turbine. The plant controller 22 may be modified in other ways, such as, adjustments made to the logic units and algorithms, based on the data provided by the optimizer and/or filter. The plant controller 22 may generate an output of recommended or optimized setpoints 74 for the combined cycle power plant 12, … It will be appreciated that optimized setpoints 74 also may be then used by the neural network 71 and models 60, 61, 62, 63 so that the ongoing plant simulation may predict operating data that may later be compared to actual operating data so that the plant model may continually be refined.”).
and Ganti teaches conducting a simulation of the system in which the calibrated model of the selected component interacts with the physics computation modeled components that were not selected, (Regarding this claim limitation Applicant didn’t give enough information related to the claim element “conducting a simulation … modeled components that were not selected”. Under BRI and for purposes of applying prior art and to facilitate compact prosecution, Examiner would construe this claim element, in light of specification page 8 (last para), where Applicant stated: “In scenarios when a very specialized component is required and the calibrated model may not have been generated for such an item, a consumer may resort to using the archived virtual experimental setup in-situ and run a subset of input parameter variations and produce a limited range calibrated model to then integrate into the system physics simulations.”
Ganti disclosed in page 27 para [0158]: “According to certain alternative embodiments, the present method includes the step of disqualifying any of the proposed parameter sets that produce simulated operation violating any one of the defined operability constraints. Operability constraints, for example, may include emission thresholds, maximum operating temperatures, maximum mechanical stress levels, etc., as well as legal or environmental regulations, contractual terms, safety regulations, and/ or machine or component operability thresholds and limitations.” In page 35 para [0199]: “According to certain preferred embodiments, the step of communicating the result of the comparison may include indicating an emission rate of the power plant derived by averaging a cumulative emission level for the power plant over a portion of a current regulatory emission period relative to an emission rate derived by averaging a cumulative emission limit over the current regulatory emission period. This may be done to determine how the power plant stands when compared to the average emissions rate allowable without incurring a violation. The method may determine the emissions still available to the power plant during the current regulatory period, and whether or not there is sufficient levels available to accommodate either of the proposed operating modes or, rather, if the emissions impact impermissibly increases the probability of a future regulatory violation.”
The disclosure above “emission threshold or emission rate of the power plant” as an operability constraint, which has been considered during simulation to see if any violation is incurred. However, this parameter (emission rate) had not selected or incorporated earlier when the simulation of the system was conducted, since the “emission rate” has been derived from the output result during simulation. Further, it has been discussed in page 11-12 para [0089] that emission is one of the constraints associated with operating the power plant, considered as “disturbance variables” which cannot be manipulated or controlled).
Ganti teaches a computer-based simulation based on derived mathematical expressions modeling the behavior of a corresponding one of the modeled components based on underlying physical properties of the corresponding one of the plurality of the modeled components, (Ganti disclosed in page 22 para [0136]: “The method 320 may begin at step 325, in which the controller may model, by a first or primary model, one or more current performance parameters of a turbine according to the current operation. In order to generate this first model, the controller may receive as inputs to the model one or more operating parameters indicating the current operation of the turbine. As described above, these operating parameters may be sensed or measured … The current operating parameters may include any parameter that is indicative of current turbine operation, as described above. … The controller may include, for example, a generated model of the gas turbine. The model may be an arrangement of one or more mathematical representations of the operating parameters. Each of these representations may rely on input values to generate an estimated value of a modeled operating parameter. The mathematical representations may generate a surrogate operating parameter value that may be used …”. In page 24 para [0145]: “As part of the present method, the sensors 511 may take measurements of operating parameters during an initial, current, or first period of operation (hereinafter, “first operating period”), and those measurements may be used to tune a mathematical model of the power plant, … The measured operating parameters may themselves be used to evaluate plant performance or be used in calculations to derive performance indicators that relate specific aspects of the power plant's operation and performance.”).
and wherein Ganti teaches the calibrated model comprises a multi-dimensional interface boundary to receive one or more input value and produce one or more output value based on a modeled behavior, without having to perform iterative computations in real time during the simulation of the system, (Ganti disclosed in page 22 para [0135]: “With reference to FIG. 14, an operator specified operating mode or scenario 313 is provided as one or more inputs via the interface 307 to the second or predictive model 306, which then models or predicts future turbine behavior under a variety of conditions. For example, an operator may supply commands to the interface 307 to generate a scenario in which the power plant 302 operates at a different operating point (e.g., different loads, configuration, efficiency, etc.). As an illustrative example, a set of operating conditions may be supplied via the operator specified scenario 313 that represent conditions that are expected for the following day (or other future timeframe), such as ambient conditions or demand requirements. These conditions then may be used by the second model 306 to generate expected or predicted turbine operating characteristics 314 for the power plant 302 during that time frame. Upon running the second model 306 under the operator specified scenario, the predicted operating characteristics 314 represent turbine behavior such as, but not limited to, base load output capability, peak output capability, minimum turndown points, emissions levels, heat rate, and the like.” This disclosure corresponds to claim limitation “the calibrated model comprises a multi-dimensional interface boundary to receive one or more input value and produce one or more output value based on a modeled behavior”.
In page 10 para [0080]: “the power plant model may have one or more of the following characteristics: ... 4) adaptive (the model may be updated at the beginning of each optimization to reflect the current operating conditions); and 5) derived from empirical data (since each power plant is unique, the model may be derived from empirical data obtained from the power generating unit). Given the foregoing requirements, a neural network based approach is a preferred technology for implementing the necessary plant models. … Neural networks can also be used to represent systems with multiple inputs and outputs. In addition, neural networks can be updated using either feedback biasing or on-line adaptive learning. ... Many of the neural network model architectures require a large amount of data to successfully train the dynamic neural network. Given a robust power plant model, it is possible to compute the effects of changes in the manipulated variables on the controlled variables.” This disclosure corresponds to claim limitation “performing calibration without having to perform iterative computations in real time (by implementing Neural networks) during the simulation of the system”).
and wherein Ganti teaches the calibrated model comprises a functional representation of the result data, (Ganti disclosed in page 17 para [0112-0113]: “Known techniques may be employed, such as by enhancement module 114 (FIG. 8), to solve an optimization problem for operation of the power plant 12. … In addition, as seen in the example objective function, the optimization problem may be solved over a prediction horizon, providing an array of values for at least one operating parameter of the power plant 12. While enhancement or augmentation may be performed over a relatively short prediction horizon, such as 24 hours or even on the order of minutes, enhancement module 114 (FIG. 8) may employ a longer prediction horizon, … In embodiments, initial setpoints determined, such as by controls model 111 (FIG. 8), 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. In addition, iteration may be used with determining an initial setpoint, … and enhancing or augmenting (at steps 172 -175 of FIG. 9) to refine results and/or better enhance or augment control setpoints of the power plant 12.” This disclosure corresponds to claim limitation “the calibrated model comprises a functional representation of the result data”).
wherein Ganti teaches the virtual experiment comprises a process for execution of the setup for the virtual experiment using the physics computation modeled component of the selected component, (Ganti disclosed in page 7 para [0068]: “According to certain embodiments, the plant controller 22 issues recommendations to the operator 39 regarding desired operating setpoints for the gas turbine system 30, inlet conditioning system 51, and steam turbine system 50. The plant controller 22 may receive and store data on the operation of the components and subsystems of the power plant 12. … The computer system may be embodied in a single physical or virtual computing device or distributed over local or remote computing devices. The digital models 60, 61, 62, 63 may be embodied as a set of algorithms, e. g. transfer functions, that relate operating parameters of each of the systems. The models may include a physics-based aero-thermodynamic computer model, a regression-fit model, or other suitable computer-implemented model. According to preferred embodiments, the models 60, 61, 62, 63 may be regularly, automatically and in real-time or near real-time tuned, adjusted or calibrated or tuned pursuant to ongoing comparisons between predicted operation and the measured parameters of actual operation.” The disclosure above “desired operating setpoints for the gas turbine system” corresponds to claim element “selected component”).
wherein Ganti teaches executing the virtual experiment further comprises executing a series of passes each corresponding to one of a plurality of input values of the varied input parameter over the predefined value range, (Ganti disclosed in page 45-46 para [0247]: “Pursuant to an exemplary embodiment, the range check (represented by step 1057 of FIG. 30) may include a lookback period that is relatively short in length, for example, approximately 5 minutes. This variation of a sensor health check includes determining whether the data readings fall within an expected predefined range. Sensor readings for the lookback period may be gathered. Then, at a next step, the procedure may initiate a loop by which each data point is then tested. Specifically, each of the data points is tested to determine if the data point is greater than a predefined maximum or less than a predefined minimum. As will be appreciated, the predefined maximum and minimum may be a range that is defined by an operator and/or be defined relative to historical readings based on past operation, and thereby configured to represent a ceiling and a floor by which nonconforming or deviant data points are discerned. According to preferred embodiments, the maximum and minimum thresholds may be configured as values having a low probability of occurring during a given mode of operation. If the data point is found to be in excess of the predefined maximum or less than the predefined minimum, the sensor responsible for the data point may be flagged.”).
and wherein Ganti teaches the result data comprises an output value of the output parameter for each pass of the series of passes. (Ganti disclosed in page 31 para [0177]: “Once the present method has cycled through the iterations given the intervals and the different rows of the permutation matrix, the results of the optimization may be communicated to the plant operator at step 611. These results may include an optimized case for each of the rows of the permutation matrix for each of the time intervals. According to one example, the output describes an optimized operation that is defined by a cost function of fuel consumption for the power plant for each of the permutations for each of the intervals. Specifically, the output may include the minimum fuel required (as optimized using the tuned power plant model pursuant to methods already described) for each of the possible plant configurations (as represented by the rows of the permutation matrix) for each interval, while also satisfying operability constraints, performance objectives, and anticipated ambient conditions. According to another embodiment, the output includes an optimization that minimizes a generating output level (i.e., megawatts) for the possible plant configurations for each of the intervals in the same way.”).
Rao and Ganti are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao and Ganti, to modify physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions in Rao’s teaching, to include performing simulation of a system with selected component modeled by calibrated model and deriving mathematical relation/expression corresponding to modeled component in Ganti’s teaching. The suggestion/motivation for doing so would have been obvious by Ganti because “The present application thus describes system including a power plant having thermal generating units that may operate according to multiple possible operating modes. The process may include: receiving the selected operating period; selecting the possible operating modes as competing operating modes for the power plant during the selected operating period according to a selection criteria; simulating the operation of the power plant during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode; and communicating at least one recommendation that relates to the optimized operating mode.” (Ganti disclosed in page 2 para [0008]).
Regarding claim 2, Rao and Ganti teach the system of claim 1, wherein executing the stored non-transitory instructions by the processor further performs the steps of: Rao teaches generating a response surface corresponding to the interface boundary; and identifying a plurality of points on the response surface as output points. (Rao disclosed in page 6 para [0055]: “In an embodiment, the aforementioned error field is used to determine the experimental constant used in simulating the real-world system at step 112. In such an embodiment, the constant is determined as a function of the error field, a residual of the second system of equations, and an estimate of the minimum eigenvalue of the second system of equations.” Further, in para [0060]: “The error, which can be expressed by equation (4) is a list in computer memory that has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented by equation (1) is being solved. … To illustrate, consider an example where the real-world physical system is described by a finite set of points (computational grid points). Each point is governed by the discrete version of the partial differential equation. In such an example, a solution at each point is determined, e.g. the velocity at each point, and for a given iteration there is a guess for each point, and thus, there is an error for each point, which is represented by the array e.”
The disclosure above “the error has a value for every computational grid point in the mesh that represents the solution domain where the physical system represented and the real-world physical system is described by a finite set of points (computational grid points)” teaches the claim limitation “generating a response surface corresponding to the interface boundary”. Further, the disclosure above “Each point is governed by the discrete version of the partial differential equation, a solution at each point is determined, e.g. the velocity at each point, and for a given iteration there is a guess for each point, and thus, there is an error for each point” corresponds to claim limitation “identifying a plurality of points on the response surface as output points”).
However, Rao does not explicitly teach the limitation “identifying the multi-dimensional interface boundary for the calibrated model of the selected component;”
Ganti teaches identifying the multi-dimensional interface boundary for the calibrated model of the selected component; (Ganti disclosed in page 22 para [0135]: “With reference to FIG. 14, an operator specified operating mode or scenario 313 is provided as one or more inputs via the interface 307 to the second or predictive model 306, which then models or predicts future turbine behavior under a variety of conditions. For example, an operator may supply commands to the interface 307 to generate a scenario in which the power plant 302 operates at a different operating point (e.g., different loads, configuration, efficiency, etc.). As an illustrative example, a set of operating conditions may be supplied via the operator specified scenario 313 that represent conditions that are expected for the following day (or other future timeframe), such as ambient conditions or demand requirements. These conditions then may be used by the second model 306 to generate expected or predicted turbine operating characteristics 314 for the power plant 302 during that time frame. Upon running the second model 306 under the operator specified scenario, the predicted operating characteristics 314 represent turbine behavior such as, but not limited to, base load output capability, peak output capability, minimum turndown points, emissions levels, heat rate, and the like.” This disclosure corresponds to claim limitation “identifying the multi-dimensional interface boundary for the calibrated model of the selected component”).
Rao and Ganti are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao and Ganti, to modify physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions in Rao’s teaching, to include performing simulation of a system with selected component modeled by calibrated model and deriving mathematical relation/expression corresponding to modeled component in Ganti’s teaching. The suggestion/motivation for doing so would have been obvious by Ganti because “The present application thus describes system including a power plant having thermal generating units that may operate according to multiple possible operating modes. The process may include: receiving the selected operating period; selecting the possible operating modes as competing operating modes for the power plant during the selected operating period according to a selection criteria; simulating the operation of the power plant during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode; and communicating at least one recommendation that relates to the optimized operating mode.” (Ganti disclosed in page 2 para [0008]).
Regarding claim 4, Rao and Ganti teach the system of claim 2, however, Rao does not explicitly teach the limitation “the recorded result data comprises output values for each of the plurality of output points for each pass of the series of passes”.
wherein Ganti teaches the recorded result data comprises output values for each of the plurality of output points for each pass of the series of passes. (Ganti disclosed in page 31 para [0177]: “Once the present method has cycled through the iterations given the intervals and the different rows of the permutation matrix, the results of the optimization may be communicated to the plant operator at step 611. These results may include an optimized case for each of the rows of the permutation matrix for each of the time intervals. According to one example, the output describes an optimized operation that is defined by a cost function of fuel consumption for the power plant for each of the permutations for each of the intervals. Specifically, the output may include the minimum fuel required (as optimized using the tuned power plant model pursuant to methods already described) for each of the possible plant configurations (as represented by the rows of the permutation matrix) for each interval, while also satisfying operability constraints, performance objectives, and anticipated ambient conditions. According to another embodiment, the output includes an optimization that minimizes a generating output level (i.e., megawatts) for the possible plant configurations for each of the intervals in the same way.”).
Rao and Ganti are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao and Ganti, to modify physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions in Rao’s teaching, to include performing simulation of a system with selected component modeled by calibrated model and deriving mathematical relation/expression corresponding to modeled component in Ganti’s teaching. The suggestion/motivation for doing so would have been obvious by Ganti because “The present application thus describes system including a power plant having thermal generating units that may operate according to multiple possible operating modes. The process may include: receiving the selected operating period; selecting the possible operating modes as competing operating modes for the power plant during the selected operating period according to a selection criteria; simulating the operation of the power plant during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode; and communicating at least one recommendation that relates to the optimized operating mode.” (Ganti disclosed in page 2 para [0008]).
Regarding claim 5, Rao and Ganti teach the system of claim 1, however, Rao does not explicitly teach the limitation “the virtual experiment further comprises the test setup for the physics computation model of the selected component”.
wherein Ganti teaches the virtual experiment further comprises the test setup for the physics computation model of the selected component. (Ganti disclosed in page 7 para [0068]: “According to certain embodiments, the plant controller 22 issues recommendations to the operator 39 regarding desired operating setpoints for the gas turbine system 30, inlet conditioning system 51, and steam turbine system 50. The plant controller 22 may receive and store data on the operation of the components and subsystems of the power plant 12. … The computer system may be embodied in a single physical or virtual computing device or distributed over local or remote computing devices. The digital models 60, 61, 62, 63 may be embodied as a set of algorithms, e. g. transfer functions, that relate operating parameters of each of the systems. The models may include a physics-based aero-thermodynamic computer model, a regression-fit model, or other suitable computer-implemented model. According to preferred embodiments, the models 60, 61, 62, 63 may be regularly, automatically and in real-time or near real-time tuned, adjusted or calibrated or tuned pursuant to ongoing comparisons between predicted operation and the measured parameters of actual operation.” The disclosure above “desired operating setpoints for the gas turbine system” corresponds to claim element “selected component”).
Rao and Ganti are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao and Ganti, to modify physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions in Rao’s teaching, to include performing simulation of a system with selected component modeled by calibrated model and deriving mathematical relation/expression corresponding to modeled component in Ganti’s teaching. The suggestion/motivation for doing so would have been obvious by Ganti because “The present application thus describes system including a power plant having thermal generating units that may operate according to multiple possible operating modes. The process may include: receiving the selected operating period; selecting the possible operating modes as competing operating modes for the power plant during the selected operating period according to a selection criteria; simulating the operation of the power plant during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode; and communicating at least one recommendation that relates to the optimized operating mode.” (Ganti disclosed in page 2 para [0008]).
Regarding claim 6, Rao and Ganti teach the system of claim 1, however, Rao does not explicitly teach the limitation “the calibrated model comprises a functional representation of the response of the component to varying input parameters as per the virtual experiment”.
wherein Ganti teaches the calibrated model comprises a functional representation of the response of the component to varying input parameters as per the virtual experiment. (Ganti disclosed in page 11 para [0086-0087]: “as illustrated in FIG. 4, the neural network 71 may interact with and provide communications between each of the digital models of the several plant components 49 of the power plant 12 of FIG. 3. The interaction may include collecting output data from the models and generating input data used by the models to generate further output data. … The logic elements may each embody an algorithm that accepts data inputs to generate one or more data outputs. ... Other logic elements may multiply values of the inputs or apply other mathematical relationships to the input data. The data inputs to each of the logic elements of the neural network 71 may be assigned a weight, such as multiplier between one and zero. The weights may be modified during a learning mode which adjusts the neural network to better model the performance of the power plant … Adjusting the weights of the data inputs to the logic units in the neural network is one example of the way in which the neural network may be dynamically modified during operation of the combined cycle power plant. Other examples include modifying weights of data inputs to algorithms (which are an example of a logic unit) in each of thermodynamic digital models for the steam turbine system, inlet conditioning system, and gas turbine. The plant controller 22 may be modified in other ways, such as, adjustments made to the logic units and algorithms, based on the data provided by the optimizer and/or filter. The plant controller 22 may generate an output of recommended or optimized setpoints 74 for the combined cycle power plant 12, …”. Further, in page 18 para [0121]: “As used herein, real-time refers to outcomes occurring at a substantially short period after a change in the inputs affect the outcome, for example, computational calculations … The time period is a design parameter of the real-time system that may be selected based on the importance of the outcome and/or the capability of the system implementing processing of the inputs to generate the outcome.”).
Rao and Ganti are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao and Ganti, to modify physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions in Rao’s teaching, to include performing simulation of a system with selected component modeled by calibrated model and deriving mathematical relation/expression corresponding to modeled component in Ganti’s teaching. The suggestion/motivation for doing so would have been obvious by Ganti because “The present application thus describes system including a power plant having thermal generating units that may operate according to multiple possible operating modes. The process may include: receiving the selected operating period; selecting the possible operating modes as competing operating modes for the power plant during the selected operating period according to a selection criteria; simulating the operation of the power plant during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode; and communicating at least one recommendation that relates to the optimized operating mode.” (Ganti disclosed in page 2 para [0008]).
Regarding claim 10, the same ground of rejection is made as discussed in claim 1 for substantially similar rationale, therefore claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Rao and Ganti as discussed above for substantially similar rationale. In addition, claim 10 recites following limitations:
Rao teaches a computer-implemented method, (Rao disclosed in page 1 para [0008]: “An embodiment of the present invention provides a computer implemented method of providing a simulation of a physical real-world system. Such an embodiment begins by generating a system of equations in computer memory where the system of equations includes a discrete representation of the real-world system being simulated.”).
Regarding claim 14, Rao and Ganti teach the method of claim 10, however, Rao does not explicitly teach the limitation “the step of replacing the physics computation model of the selected component with the calibrated model”.
further Ganti teaches the step of replacing the physics computation model of the selected component with the calibrated model. (Ganti disclosed in page 18 para [0120]: “The optimizer module 218 may be selectable between an online (automatic) and an offline (manual) mode. In the online mode, the optimizer 218 automatically computes current plant economic parameters such as cost of electricity generated, incremental cost at each level of generation, cost of process steam, and plant operating profit on a predetermined periodicity, for example, in real-time or once every five minutes. An offline mode may be used to simulate steady-state performance, analyze "what-if" scenarios, analyze budget and upgrade options, and predict current power generation capability, target heat rate, correction of current plant operation to guarantee conditions, impact of operational constraints and maintenance actions, and fuel consumption. … The optimizer 218 may be tuned to match the degradation of each component individually, and may produce an advisory output 220 and/or may produce a closed feedback loop control output 222. Advisory output 220 recommends to operators where to set controllable parameters of the power plant so to optimize each plant component to facilitate maximizing profitability. In the exemplary embodiment, advisory output 220 is a computer display screen communicatively coupled to a computer executing optimizer module 218.”).
Rao and Ganti are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao and Ganti, to modify physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions in Rao’s teaching, to include performing simulation of a system with selected component modeled by calibrated model and deriving mathematical relation/expression corresponding to modeled component in Ganti’s teaching. The suggestion/motivation for doing so would have been obvious by Ganti because “The present application thus describes system including a power plant having thermal generating units that may operate according to multiple possible operating modes. The process may include: receiving the selected operating period; selecting the possible operating modes as competing operating modes for the power plant during the selected operating period according to a selection criteria; simulating the operation of the power plant during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode; and communicating at least one recommendation that relates to the optimized operating mode.” (Ganti disclosed in page 2 para [0008]).
Regarding claims 11,15 and 16, Rao and Ganti teach the method of claim 10, are incorporating the rejections of claims 2, 5 and 6 respectively, because claims 11, 15 and 16 have substantially similar claim language as claims 2, 5 and 6, therefore claims 11, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Rao and Ganti as discussed above for substantially similar rationale.
Regarding claim 20, Rao and Ganti teach the system of claim 1, however, Rao does not explicitly teach the limitation “the memory is configured to store additional non-transitory instructions that, when executed by the processor, perform the step of enabling the selection of the selected component from a plurality of modeled components in the system”.
wherein Ganti teaches the memory is configured to store additional non-transitory instructions that, when executed by the processor, perform the step of enabling the selection of the selected component from a plurality of modeled components in the system. (Ganti disclosed in page 6 para [0062-0063]: “The plant controller 22 may then use results from the simulations so to determine optimized operating modes. Such optimized operating modes may be described by parameter sets that include a plurality of operating parameters and/or setpoints for actuators and/ or other operating conditions. As used herein, the optimized operating mode is one that, at minimum, is preferable over at least one alternative operating mode pursuant to defined criteria or performance indicators, which may be selected by an operator to evaluate plant operation. … 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. The economic model 63 may provide the data used by the plant controller 22 to judge which of the proposed setpoints (i.e., those chosen setpoints for which operation is modeled for determining optimized setpoints) represents minimal production costs or maximum profitability. According to other embodiments, as discussed in more detail with FIG. 4, the optimizer 64 of the plant controller 22 may include or operate in conjunction with a filter, such as a Kalman filter, to assist in tuning, adjusting and calibrating the digital models so that the models accurately simulate the operation of the power plant 12. As discussed below, the model may be a dynamic one that includes a learning mode in which it is tuned or reconciled via comparisons made between actual operation (i.e., values for measured operating parameters that reflect the actual operation of the power plant 12) and predicted operation (i.e., values for the same operating parameters that the model predicted)”).
Rao and Ganti are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao and Ganti, to modify physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions in Rao’s teaching, to include performing simulation of a system with selected component modeled by calibrated model and deriving mathematical relation/expression corresponding to modeled component in Ganti’s teaching. The suggestion/motivation for doing so would have been obvious by Ganti because “The present application thus describes system including a power plant having thermal generating units that may operate according to multiple possible operating modes. The process may include: receiving the selected operating period; selecting the possible operating modes as competing operating modes for the power plant during the selected operating period according to a selection criteria; simulating the operation of the power plant during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode; and communicating at least one recommendation that relates to the optimized operating mode.” (Ganti disclosed in page 2 para [0008]).
Regarding claim 21, Rao and Ganti teach the system of claim 1, wherein Rao teaches the setup for the virtual experiment is provided by a first human user, and the plurality of input parameters are defined by a second human user who is different than the first human user. (Rao disclosed in page 4 para [0041]: “For transient solvers, convergence comes into play when sub-iterations are being done between every time increment. The most widely used technique when performing these sub-iterations is to set a pre-determined number of sub-iterations. This pre-determined number requires calibration by trial and error as well as user judgment. … Thus, the user must be conservative and limit the size of the time step or specify a larger than required number of iterations.” This disclosure teaches the limitation “the setup for the virtual experiment is provided by a first human user”.
In page 5 para [0050]: “The tolerance provided at step 112a indicates how accurate of a solution the user desires. … Thus, the tolerance provided at step 112a indicates how far the solution for a given iteration needs to be from the final solution for the current iterating to stop. … the user may specify the tolerance in response to a prompt by a computing device implementing the method 110. In the such an embodiment, the user may provide the tolerance through use of any variety of input devices of the computing device.” This disclosure teaches the limitation “the plurality of input parameters are defined by a second human user who is different than the first human user”. In first scenario above, a pre-determined number of sub-iterations requires calibration by trial and error as well as user judgment, to the setup for the virtual experiment. Further, in 2nd scenario/disclosure above user can specify or provide any variety of input (e.g., user can define/specify a tolerance level or stopping criteria for a current iteration)).
Claims 3,7-9,12,13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rao and Ganti and further in view of a Journal “A stopping criterion for the iterative solution of partial differential equations” by Kaustubh Rao (hereinafter Rao_NPL, journal available online 2017).
Regarding claim 3, Rao and Ganti teach the system of claim 2, wherein Rao teaches producing the calibrated model further comprises the steps of: providing a modeling function of the continuous representation. (Rao disclosed in page 6-7 para [0063]: “To summarize the method 220, a given problem is run for a very large number of iterations to safely conclude the solution is converged. Thus, the final solution field u from equation (1) is obtained and stored. The same problem is then re-run and the error, given by equation (4) is computed at every iteration. Where u* is the final solution from step 222 and u is the solution for each iteration. A norm of the error field, such as the volume weighted norm, is computed and this volume weighted norm of the error filed can be used to determine the constant used in embodiments. For example, in an embodiment equation (8) is used to determine the experimental constant. This process can further, be repeated for various physical problems with various types of meshes and a constant can be determined that roughly bounds the error within 1 order of magnitude.”
The disclosure of ‘mesh’ is a continuous representation resulted from the calibrated model, since the given problem is run for a very large number of iterations to safely conclude the solution is converged and same problem is then re-run and the error, given by equation (4) is computed at every iteration. It has been disclosed in page 1 para [0004]: “The advent of CAD and CAE systems allows for a wide range of representation possibilities for objects. One such representation is a finite element analysis model. … A finite element model is a system of points called nodes which are interconnected to make a grid, referred to as a mesh.” Therefore, it is understood the finite element model is a system of points called nodes which are interconnected to make a grid or mesh is converted to a continuous representation of a physical object).
However, Rao and Ganti do not explicitly teach the limitation “converting a discrete representation of the interface boundary to a continuous representation;”
Rao_NPL teaches converting a discrete representation of the interface boundary to a continuous representation; (Rao_NPL disclosed in page 268 section 2.2 (2nd para): “For a given PDE problem, different mesh types (triangles, quads, etc.) and different mesh resolutions, produce roughly the same norm value … A good definition for a norm of a field variable ||x|| should produce nearly the same value irrespective of the underlying discretization of that variable. The better norms to use for PDE variables, and therefore for this work, are integral norms, LVn, represented in this work with the superscript, V … In theory, computing this norm requires having a prescribed interpolation method available for the discrete unknowns (to be able to produce continuous functions that can be integrated) … This norm then becomes effectively a discrete volume weighted norm. … For a cell or element based unknown this would be the cell/element volume. For a node based unknown it would the dual-volume surrounding that node (which is usually the summation of some fraction of all the cell/element volumes touching that node).” The “cell/element volumes” is a discrete volume, corresponds to the claim element “response surface”, which has a discretized representation, is able to produce continuous functions that integrate prescribed interpolation method available for the discrete unknowns).
Rao, Ganti and Rao_NPL are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao, Ganti and Rao_NPL to modify the executing the virtual experiment using varied input parameters of Ganti, to include converting a discrete representation of the interface boundary to a continuous representation of Rao_NPL. The suggestion/motivation for doing so would have been obvious by Rao_NPL because “this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. This hybrid method uses the good extrapolation estimates to precompute and store the ratio of the error to the residual” (Rao_NPL disclosed in page 283 section 7).
Regarding claim 7, Rao and Ganti teach the system of claim 4, however, Rao doesn’t explicitly teach the limitation “compiling the recorded result data, wherein compiling the recorded result data from the virtual experiment into the calibrated model”.
further Ganti teaches compiling the recorded result data, wherein compiling the recorded result data from the virtual experiment into the calibrated model (Under BRI and for purposes of applying prior art and to facilitate compact prosecution, Examiner would construe the claim term “compiling” as assembling or collecting data. Ganti disclosed in page 36 para [0203-0204]: “FIG. 22 illustrates a high-level logic flow diagram or method for fleet level optimization according to certain aspects of the present invention. As shown, the fleet may include multiple generating units or assets 802, which may represent separate generating units across multiple power plants or the power plants themselves. … At step 803, performance data that is collected by the sensors at the various assets of the plants may be communicated electronically to a central data repository. Then, at step 804, the measured data may be reconciled or filtered so, … at step 805, may use the most currently collected data to tune the power plant models. This process may include tuning the models for each of the assets, i.e., each of the generating units and/or power plants, as well as more generalized models covering the operation of multiple power plants or aspects of fleet operation. The reconciliation process also may involve the collected data being compared between similar assets 802 so to resolve discrepancies and/or identify anomalies, particularly data collected from the same type of assets having similar configurations. … In this manner, the data collected may be comparatively cross-checked, verified and reconciled so to construct a single consistent set of data that may be used to calculate more accurate actual fleet performance.”
Rao and Ganti are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao and Ganti, to modify physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions in Rao’s teaching, to include performing simulation of a system with selected component modeled by calibrated model and deriving mathematical relation/expression corresponding to modeled component in Ganti’s teaching. The suggestion/motivation for doing so would have been obvious by Ganti because “The present application thus describes system including a power plant having thermal generating units that may operate according to multiple possible operating modes. The process may include: receiving the selected operating period; selecting the possible operating modes as competing operating modes for the power plant during the selected operating period according to a selection criteria; simulating the operation of the power plant during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode; and communicating at least one recommendation that relates to the optimized operating mode.” (Ganti disclosed in page 2 para [0008]).
However, Rao and Ganti do not explicitly teach the limitation “the step of storing the output value corresponding to a virtual experiment pass for each output point in an array.”
Rao_NPL teaches the step of storing the output value corresponding to a virtual experiment pass for each output point in an array. (Rao_NPL disclosed in page 283 section 7 (2nd para-last para): “PDE context was found to be just as important for matrix norms. The popular PDE discretization methods, such as FV and FE methods, produce a discretization in which the matrix (and also the residual) has a cell/element volume in it … PDE context (and mesh independence) was used again when showing how the Rayleigh Quotient can be used to estimate the smallest singular value (of the volume weighted Jacobian). ... Finally, this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. ... The only free parameter in the extrapolation error estimate is the number of prior data points to use for that extrapolation. We use this one free parameter to our advantage by computing multiple error estimates with different numbers of data points … We have used the fact the methods are fundamentally different to develop a hybrid method that reverts to the classic estimator … This hybrid method uses the good extrapolation estimates to precompute and store the ratio of the error to the residual, R, so that this constant is available if/when the reversion to classical (residual based) estimation is needed.”
The current work (in this prior art) related to error extrapolation from current iterative progress worked well and the presented algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. Further, the developed hybrid method uses the good extrapolation estimates to precompute and store the ratio of the error to the residual, which is similar concept to the claim element “step of storing the output value corresponding to a virtual experiment pass for each output point in an array” (since matrix along with the residual having a cell/element volume used in PDE discretization method, as discussed above)).
Rao, Ganti and Rao_NPL are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao, Ganti and Rao_NPL to modify the executing the virtual experiment using varied input parameters of Ganti, to include converting a discrete representation of the interface boundary to a continuous representation of Rao_NPL. The suggestion/motivation for doing so would have been obvious by Rao_NPL because “this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. This hybrid method uses the good extrapolation estimates to precompute and store the ratio of the error to the residual” (Rao_NPL disclosed in page 283 section 7).
Regarding claim 8, Rao and Ganti teach the system of claim 4, however, Rao doesn’t explicitly teach the limitation “compiling the recorded result data, wherein compiling the recorded result data from the virtual experiment into the calibrated model”.
further Ganti teaches compiling the recorded result data, wherein compiling the recorded result data from the virtual experiment into the calibrated model (Under BRI and for purposes of applying prior art and to facilitate compact prosecution, Examiner would construe the claim term “compiling” as assembling or collecting data. Ganti disclosed in page 36 para [0203-0204]: “FIG. 22 illustrates a high-level logic flow diagram or method for fleet level optimization according to certain aspects of the present invention. As shown, the fleet may include multiple generating units or assets 802, which may represent separate generating units across multiple power plants or the power plants themselves. … At step 803, performance data that is collected by the sensors at the various assets of the plants may be communicated electronically to a central data repository. Then, at step 804, the measured data may be reconciled or filtered so, … at step 805, may use the most currently collected data to tune the power plant models. This process may include tuning the models for each of the assets, i.e., each of the generating units and/or power plants, as well as more generalized models covering the operation of multiple power plants or aspects of fleet operation. The reconciliation process also may involve the collected data being compared between similar assets 802 so to resolve discrepancies and/or identify anomalies, particularly data collected from the same type of assets having similar configurations. … In this manner, the data collected may be comparatively cross-checked, verified and reconciled so to construct a single consistent set of data that may be used to calculate more accurate actual fleet performance.”
Rao and Ganti are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao and Ganti, to modify physics computation modeled components comprises a computer-based simulation based on derived mathematical expressions in Rao’s teaching, to include performing simulation of a system with selected component modeled by calibrated model and deriving mathematical relation/expression corresponding to modeled component in Ganti’s teaching. The suggestion/motivation for doing so would have been obvious by Ganti because “The present application thus describes system including a power plant having thermal generating units that may operate according to multiple possible operating modes. The process may include: receiving the selected operating period; selecting the possible operating modes as competing operating modes for the power plant during the selected operating period according to a selection criteria; simulating the operation of the power plant during the selected operating period for each of the competing operating modes and deriving simulation results therefrom; evaluating each of the simulation results pursuant to a cost function and, based thereupon, designating at least one of the competing operating modes as an optimized operating mode; and communicating at least one recommendation that relates to the optimized operating mode.” (Ganti disclosed in page 2 para [0008]).
However, Rao and Ganti do not explicitly teach the limitation “the step of fitting each output value corresponding to a virtual experiment pass for each output point to a polynomial curve”.
Rao_NPL teaches the step of fitting each output value corresponding to a virtual experiment pass for each output point to a polynomial curve. (According to the conventional meaning in the art Examiner would construe the claim element “polynomial curve” as nonlinear. Rao_NPL disclosed in page 270 section 3 (in 2nd para): “The presented test cases all involve solutions of the incompressible Navier–Stokes equations or the incompressible Reynolds Averaged Navier–Stokes (RANS) equations that include turbulence. We will show error estimates for both the velocity components and for the turbulence model quantities. … The iterative method used for these tests is a segregated solver in which each field variable is solved uncoupled from the others sequentially inside each non-linear iteration.” Further, in page 277-278 section 5.2: “The proposed smooth extrapolation approach therefore uses a least squares best-fit line through the data to extrapolate the slope and the intercept … the goal is to curve fit a line on a log–linear plot … where eb is the best fit for the slope α, and ea is the best fit line’s approximation for the most recent solution increment … Note that M+2 is the number of data points being used in the curve fit. So M=0 for the 2-increment extrapolation (of the previous section). And M=2 for a 4 data-point smoothed extrapolation. M is the number of interior (or extra) smoothing data points.”
The iterative method used in test cases above disclosure is solved using sequentially with each non-linear iteration, corresponds to the claim limitation “a virtual experiment pass for each output point” (error estimation for turbulence model). Further, the proposed smooth extrapolation approach uses a least squares best-fit line, to curve fit a line on a log–linear plot where ea is the best fit line’s approximation for the most recent solution increment and M+2 is the number of data points as output point being used in the curve fit and this curve would be considered as “polynomial curve”).
Rao, Ganti and Rao_NPL are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao, Ganti and Rao_NPL to modify the executing the virtual experiment using varied input parameters of Ganti, to include converting a discrete representation of the interface boundary to a continuous representation of Rao_NPL. The suggestion/motivation for doing so would have been obvious by Rao_NPL because “this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. This hybrid method uses the good extrapolation estimates to precompute and store the ratio of the error to the residual” (Rao_NPL disclosed in page 283 section 7).
Regarding claim 9, Rao and Ganti teach the system of claim 1, however, Rao and Ganti do not explicitly teach the limitations “the steps of: selecting a reduced subset of the calibrated model data corresponding to a reduction of at least one of the group consisting of an output parameter, an input parameter, and a predefined value range of the varied input parameter; removing data from the calibrated model that is not associated with the reduced subset of the calibrated model data”.
further Rao_NPL teaches the steps of: selecting a reduced subset of the calibrated model data corresponding to a reduction of at least one of the group consisting of an output parameter, an input parameter, and a predefined value range of the varied input parameter; (Rao_NPL disclosed in page 277-278 section 5.2: “There is ambiguity in what sort of average to use. There also remains a strong (and noisy) dependence on the most recent solution increment, … Much of this ambiguity can be removed by noting that the goal is to curve fit a line on a log–linear plot. The proposed smooth extrapolation approach therefore uses a least squares best-fit line through the data to extrapolate the slope and the intercept … the goal is to curve fit a line on a log–linear plot … where eb is the best fit for the slope α, and ea is the best fit line’s approximation for the most recent solution increment … Note that M+2 is the number of data points being used in the curve fit. So M=0 for the 2-increment extrapolation (of the previous section). And M=2 for a 4 data-point smoothed extrapolation. M is the number of interior (or extra) smoothing data points.” Here, the number of interior (or extra) smoothing data points corresponds to the claim element “an output parameter” which is reduced by fitting a curve line on a log–linear plot and the proposed smooth extrapolation approach uses a least squares best-fit line through the data to extrapolate).
and Rao_NPL teaches removing data from the calibrated model that is not associated with the reduced subset of the calibrated model data. (Rao_NPL disclosed in page 269 section 2.4: “Cell/element volumes creep into the analysis in one more place. They appear in the Jacobian itself. We believe there is one version of the Jacobian that is particularly useful, especially in the context of convergence estimates. Specifically, for the case of PDE problems, one very particular scaling of the Jacobian matrix has a minimum singular value that is essentially independent of the mesh size and the discretization type (triangle, quad, etc.) … The scaling ambiguity of a Jacobian is clear. Each equation in the original system f(¯x) =0 can be multiplied by a non-zero weight and the solution of the system, ¯x, will remain unchanged. But the row in the Jacobian corresponding to that equation will change (it will be multiplied by the weight). In this work, we are only interested in this simple act of weighting each equation … The multiplicative scaling ambiguity in the equations is critical for systems that come from discretized PDEs. … On a simple 2D Cartesian mesh a finite difference (FD) discretization of Laplace’s equation produces a ‘neighbor stencil’ for the Jacobian that is not the same as the stencil that a finite volume (FV) or finite element (FE) method produces.”).
Rao, Ganti and Rao_NPL are analogous art because they are related in simulating physical model to generate calibrated model. Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Rao, Ganti and Rao_NPL to modify the executing the virtual experiment using varied input parameters of Ganti, to include converting a discrete representation of the interface boundary to a continuous representation of Rao_NPL. The suggestion/motivation for doing so would have been obvious by Rao_NPL because “this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. this work demonstrated how well error extrapolation from current iterative progress can work if the extrapolation is appropriately smoothed. In particular, our algorithm performs a least-squares curve fit to an assumed local exponential solution convergence. This hybrid method uses the good extrapolation estimates to precompute and store the ratio of the error to the residual” (Rao_NPL disclosed in page 283 section 7).
Regarding claims 12,13 and 17-19, Rao and Ganti teach the method of claim 10, is incorporating the rejections of claims 3,4, and 7-9 respectively, because claims 12,13, and 17-19 have substantially similar claim language as claims 3,4, and 7-9, therefore claims 12,13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rao, Ganti and Rao_NPL as discussed above for substantially similar rationale.
Conclusion
7. 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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. A Journal “A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/ validation data in watershed modeling” by Haw Yen et al. developed a framework entitled the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT), utilizing Bayesian inferences, an input error model and modified goodness-of-fit statistics to incorporate uncertainty in parameter, model structure, input data, and calibration/validation data in watershed modeling. Accounting for the major sources of uncertainty associated with watershed modeling produces more realistic predictions, improves the quality of calibrated solutions, and consequently reduces predictive uncertainty. IPEAT is an innovative tool to investigate and explore the significance of uncertainty sources, which enhances watershed modeling by improved characterization and assessment of predictive uncertainty. IPEAT framework is a flexible tool to enhance watershed modeling with improved confidence even during validation period.
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/NUPUR DEBNATH/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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