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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to a judicial exception without significantly more.
Claim 1.
STEP 1: Yes. The claim is directed to a “method” which is a process.
STEP 2A PRONE ONE:
The claim recites multiple mathematical abstractions.
compressing the test data and the model data to generate compressed test data and compressed model data;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, there is a set of mathematical calculations to perform compression on test and model data.
fusing the compressed test data with the compressed model data to generate fused data;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, there is a set of mathematical calculations to perform fusing.
performing parallel Bayesian inference simulations using the fused data to identify a tuning parameter value for the model;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, there is a set of mathematical calculations to perform Bayesian inferences using Bayes theorem.
using the tuning parameter value in the model to obtain a tuned model;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, a set of mathematical relationships using the tuning parameter to represent the tuned model.
predicting a performance parameter for the engine using the tuned model; and
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, a set of mathematical calculations to predict a performance parameter for the model.
STEP 2A PRONG TWO: The claim does not integrate the exception into a practical application.
STEP 2B: The claim does not recite an inventive concept or significantly more than the exception.
receiving, from a testing device, test data for an operational parameter of an engine;
MPEP 2106.05(g) – This is pre solution data gathering activity.
receiving model data for the operational parameter from simulations performed via a model of the engine;
MPEP 2106.05(g) – This is pre solution data gathering activity.
causing a modification to the physical design or operation of the engine based on the performance parameter.
MPEP 2106.05(f) – This is mere instructions to “apply it” by causing some modification to a design and with no specifics means to do so.
Conclusion: Claim 1 is directed to multiple mathematical abstractions, not integrated into a practical application and lacks an inventive concept. Therefore, it is ineligible under 35 U.S.C 101.
Regarding Claims 2-5, 7, and 10:
These claims merely add pre solution data gathering MPEP 2106.05(g) or they narrow the input data type and link the field of use. MPEP 2106.05(h). Claims 7 and 10 narrow the gathered data (data on a plurality of operational parameters of the engine; a sensor in the testing device that determines the observed values), and claims 2-5 narrow the field of application of the modification without integrating the exception by an adjustment to a design parameter of the engine (claim 2), or to a control setting of the engine (claim 4), by causing a device to form the component (claim 3), and causing an engine control system to adjust operation (claim 5) amount to insignificant post solution activity. This does not integrate the judicial exception into a practical application. The claims do not resolve the issues from the claims they depend upon.
Regarding Claims 6 and 8-9:
These claims merely narrow the abstract idea by specifying parameters and operations of the calibration mathematics like comparing the performance parameter against a defined performance standard (claim 6), generating calibrated predicted values and a discrepancy model that provides a difference between the calibrated predicted values and observed values (claim 8), and generating a probability distribution for the tuning parameter providing a probability that a particular value is a suitable candidate (claim 9). This does not integrate the judicial exception into a practical application. The claims do not resolve the issues from the claims they depend upon.
Claim 11.
STEP 1: Yes. The claim is directed to a “system” which is a process.
STEP 2A PRONE ONE:
The claim recites multiple mathematical abstractions.
compress the test data and the model data to generate compressed test data and compressed model data;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, there is a set of mathematical calculations to perform compression on test and model data.
fuse the compressed test data with the compressed model data to generate fused data;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, there is a set of mathematical calculations to perform fusing.
perform parallel Bayesian inference simulations using the fused data to identify a tuning parameter value for the model;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, there is a set of mathematical calculations to perform Bayesian inferences using Bayes theorem.
use the tuning parameter value in the model to obtain a tuned model;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, a set of mathematical relationships using the tuning parameter to represent the tuned model.
predict a performance parameter for the engine using the tuned model; and
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, a set of mathematical calculations to predict a performance parameter for the model.
STEP 2A PRONG TWO: The claim does not integrate the exception into a practical application.
STEP 2B: The claim does not recite an inventive concept or significantly more than the exception.
at least one processor; and
a memory device, the memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations, the at least one processor configured to:
MPEP 2106.05(f) – These are mere instructions to apply the exception on generic computer components.
receive, via a testing device in communication with the at least one processor, test data for an operational parameter of an engine;
MPEP 2106.05(g) – This is pre solution data gathering activity.
receive model data for the operational parameter from simulations performed via a model of the engine;
MPEP 2106.05(g) – This is pre solution data gathering activity.
cause a modification to the physical design or operation of the engine based on the performance parameter.
MPEP 2106.05(f) – This is mere instructions to “apply it” by causing some modification to a design and with no specifics means to do so.
Conclusion: Claim 11 is directed to multiple mathematical abstractions, not integrated into a practical application and lacks an inventive concept. Therefore, it is ineligible under 35 U.S.C 101.
Regarding Claims 12 and 15-16:
These claims merely add pre solution data gathering MPEP 2106.05(g) or they narrow the input data type and link the field of use. MPEP 2106.05(h). The test data and the model data being transient time series data of the operational parameter of the engine (claim 15), a testing device communication with the at least one processor by which the data is acquired (claim 16), and (claim 12) narrows the field of application of the modification without integrating the practical exception by the at least one of the enumerated list. This does not integrate the judicial exception into a practical application. The claims do not resolve the issues from the claims they depend upon.
Regarding Claims 13 and 14:
These claims merely narrow the abstract idea by specifying operations of the calibration mathematics using the tuned model to generate calibrated predicted values and generating a discrepancy model that provides a difference between the calibrated predicted values and the observed values (claim 13), and generating a probability distribution for the tuning parameter providing a probability that a particular value is a suitable candidate (claim 14). Thus, the claims remain as mathematical concepts. This does not integrate the judicial exception into a practical application. The claims do not resolve the issues from the claims they depend upon.
Claim 17.
STEP 1: Yes. The claim is directed to a “method” which is a process.
STEP 2A PRONE ONE:
The claim recites multiple mathematical abstractions.
compressing the test data and the model data to generate compressed test data and compressed model data;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, there is a set of mathematical calculations to perform compression on test and model data.
fusing the compressed test data with the compressed model data to generate fused data;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, there is a set of mathematical calculations to perform fusing.
generating a first distribution for a first tuning parameter of the model by performing parallel Bayesian inference simulations using the fused data, the first distribution providing a probability that a particular value is a suitable candidate for the first tuning parameter;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, a set of mathematical calculations to generate a first distribution by Bayesian inference simulations.
generating a second distribution for a second tuning parameter of the model by performing parallel Bayesian inference simulations using the fused data, the second distribution providing a probability that a particular value is a suitable candidate for the second tuning parameter;
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, a set of mathematical calculations to generate a second distribution by Bayesian inference simulations.
generating a tuned model by using the particular value for the first tuning parameter and the particular value for the second tuning parameter in the model; and
This is a mathematical abstraction that can be a calculation, relationship, or equation/formula. In this case, a set of mathematical relationships to tune the model via the parameters.
STEP 2A PRONG TWO: The claim does not integrate the exception into a practical application.
STEP 2B: The claim does not recite an inventive concept or significantly more than the exception.
receiving, from a testing device, test data for an operational parameter of an engine, the test data including observed values for the operational parameter from the operational tests;
MPEP 2106.05(g) – This is pre solution data gathering activity.
receiving model data for the operational parameter from simulations performed via a model of the engine;
MPEP 2106.05(g) – This is pre solution data gathering activity.
causing a modification to the physical design or operation of the engine based on the performance parameter.
MPEP 2106.05(f) – This is mere instructions to “apply it” by causing some modification to a design and with no specifics means to do so.
Conclusion: Claim 17 is directed to multiple mathematical abstractions, not integrated into a practical application and lacks an inventive concept. Therefore, it is ineligible under 35 U.S.C 101.
Regarding Claims 18 and 19:
These claims merely narrow the abstract idea by specifying operations of the calibration mathematics by selecting a value for the first tuning parameter based on the first distribution and a value for the second tuning parameter based on the second distribution (claim 18), and generating pluralities of possible values and combinations of values, determining a likelihood that each combination aligns the model data with the test data, and selecting the combination with the highest likelihood (claim 19). Thus, the claims remain as mathematical concepts. This does not integrate the judicial exception into a practical application. The claims do not resolve the issues from the claims they depend upon.
Regarding Claim 20:
These claims merely narrow the input data type and links the field of use, MPEP 2106.05(g) and (h). The test data includes data on plurality of operational parameters of the engine and the model data including predicted values for the plurality of operational parameters. To the extent the claim recites selecting the particular combination of values to align the plurality of operational parameters, it merely narrows the abstract idea. Thus, the claims remain as mathematical concepts. This does not integrate the judicial exception into a practical application. The claims do not resolve the issues from the claims they depend upon.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4-5, 7-14, and 16-20 are rejected under 35 U.S.C 103 as being unpatentable over WEI et al. “Bayesian Calibration of Performance Degradation in a Gas Turbine-Driven Compressor Unit for Prognosis Health Management” (2022) [herein “WEI”], MERTLER et al. EP2924585A1 (2015) [herein “MERTLER”], HIGDON et al. “Computer Model Calibration Using High Dimensional Output” (2008) [herein “HIGDON”], WAGNER et al. “Bayesian calibration and sensitivity analysis of heat transfer models for fire insulation panels” (2020) [herein “WAGNER”], and PAL et al. “Data-driven model-based calibration for optimizing electrically boosted diesel engine performance” (2022) [herein “PAL”].
Regarding Claim 1, WEI teaches
A method comprising:
receiving, from a testing device, test data for an operational parameter of an engine;
“Equipment sensors collect data at a time frequency (one point per minute) that is much higher than necessary or practical for our model to handle.”. (Pg. 051014-5 Section 3.3.1).
“Among them, only sensor data are valid for the calibration process since we need the field data to correct the model bias.”. (Pg. 051014-4 Section 3.2).
“Table 2 reports data from 4000h of an NGC’s operation in the long-distance pipeline transportation of China.”. (Pg. 051014-6 Section 4).
“The calibration and prediction processes are tested by a computer with a 2.6GHz i7 processor and 16.0GB RAM.”. (Pg. 051014-9 Section 5.3).
This shows the instrumented sensor suite on the gas turbine compressor unit serving as the testing device, with the collected filed data by discharge temperature, outlet pressure, rotating speeds (tables 2-3) being the received test data for operational parameters of the engine.
receiving model data for the operational parameter from simulations performed via a model of the engine;
“Our performance degradation model by Python has three types of inputs: control variables (e.g., rotating speed and shaft power), environmental variables (e.g., atmospheric pressure and temperature), and health indicators (i.e., degree of equipment performance degradation). Its outputs are thermodynamic variables, such as pressure, temperature, flow rate, efficiency, and power.”. (Pg. 051014-4 Section 3.2).
“In addition, simulation input data D1 is collected through an optimal Latin hypercube design [36] ... Then, the corresponding outputs ym are obtained from the performance degradation model.”. (Pg. 051014-5 Section 3.3.1).
This shows the thermodynamic performance model of the gas turbine and compressor (model of engine), whose simulation runs produce the received model data for the same operational parameters that the sensors measure.
using the tuning parameter value in the model to obtain a tuned model;
“The prerequisite for calibrating health indicators is to add them as model inputs as shown in Fig. 2.”. (Section 3.2 Pg. 051014-3).
“Moreover, we add two health indicators hc g and hc G to correct the degradation effect on gc and Gc…”. (Section 3.2.2 Pg. 0511014-3).
This shows the identified health indicator values inserted into the performance model as corrective inputs, converting the nominal engine model into the degradation model corrected that is a tuned model.
predicting a performance parameter for the engine using the tuned model; and
“As shown in Fig. 9, the predicted mean of two responses has a close distance to the true values, meaning that the GP surrogate model has a high degree of accuracy in predicting the performance parameters.”. (Section 5.3 Pg. 051014-8).
This shows the calibrated model producing forward predictions of engine performance (outlet temperature T2, outlet pressure P2 and efficiency degradation over operating time) which are the predicted performance parameters.
WEI does not explicitly teach but MERTLER teaches
compressing the test data and the model data to generate compressed test data and compressed model data;
“The data generation unit 2 may be a real test setup 2 'for, for example, one or be several real vehicle crash test. Alternatively, the data generation unit 2 may be a simulation environment 2 "which, for example, generates a plurality of crash test simulation results as observations Σ B.”. (Pg. 7).
This shows singular value decomposition and reduced basis compression to observations regardless of their origin, the data generation unit being a real test setup producing simulation results, such that compression of the test data and compression of simulation data are each disclosed by the reference.
It would have been obvious before the effective filing date of the claimed invention to incorporate MERTLER’s teaching of singular value decomposition with WEI’s method of Bayesian calibration of an engine performance model using test data and model data. The reason for doing so would have been to reduce the size of the large data sets handled during the calibration. As expressed by MERTLER, the compressed dataset “… only covers a few MB to a few GB and can therefore be obtained from the storage unit over an Internet connection in a few seconds to minutes.”. (Pg. 7).
WEI and MERTLER do not explicitly teach but HIGDON teaches
fusing the compressed test data with the compressed model data to generate fused data;
“Here a vector of experimental observations y(x) taken at input condition x is modeled as y(x) = η(x,θ)+δ(x)+e, where η(x,θ) is the simulated output at the true parameter setting θ, δ(x) accounts for discrepancy between the simulator and physical reality, and e models observation error.”. (Pg. 11).
This shows the compressed experimental weights and compressed simulation weights joined into one stacked data vector governed by one statistical model linking the observations to the simulator output, the resulting combined vector being the fused data on which all subsequent inference operates.
It would have been obvious before the effective filing date of the claimed invention to incorporate HIGDON’s teaching of Bayesian calibration over basis reduced test and simulation data with WEI-MERTLER’s combination’s method of calibrating an engine model using compressed data. The reason for doing so would have been to perform the calibration directly on compressed representations. As expressed by HIGDON “… we make use of basis representations (e.g. principal components) to reduce the dimensionality of the problem and speed up the computations required for exploring the posterior distribution.”. (Abstract).
WEI, MERTLER, and HIGDON do not explicitly teach but WAGNER teaches
performing parallel Bayesian inference simulations using the fused data to identify a tuning parameter value for the model;
“The AIES algorithm relies on a set of parallel chains where proposal samples are obtained by moving in the direction of a randomly chosen conjugate sample from a different chain.”. (Pg. 15).
“Run the AIES defined in Algorithm 1 where the likelihood function uses the surrogate model MPC(XM) instead of the original model M(XM) to obtain a sample from the posterior distribution π(x|y)”. (Pg. 20).
“The process of finding model parameters so that the model evaluation using this parameter vector agrees with some observations is called calibration.”. (Pg. 12).
This shows the Bayesian inference carried out as a set of concurrently advancing Markov chains. That each chain a parallel inference simulation by sampling the posterior distribution of the model parameters from a likelihood that joins the measured data with the model output, the sample posterior identifying the parameter values that calibrate i.e. tune the model.
It would have been obvious before the effective filing date of the claimed invention to incorporate WAGNER’s teaching of Bayesian inference in parallel with WEI-MERTLER-HIGDON’s combination’s method of calibrating an engine model by sampling the posterior distribution. The reason for doing so would have been to substitute of a known sample that is easier to tune and works better when the model parameters are correlated. As expressed by WAGNER “This algorithm requires only a single tuning parameter and its performance is invariant to affine transformations of the target distribution. This property makes it particularly useful for real-world applications, where strong correlations between individual parameters often hinder conventional MCMC algorithms.”. (Pg. 14-15).
WEI, MERTLER, HIGDON, and WAGNER do not explicitly teach but PAL teaches
causing a modification to the physical design or operation of the engine based on the performance parameter.
“… the MATLAB algorithm overwrites the commanded VGT and EGR positions through INCA.”. (Pg. 1523).
“With this interface, control parameters, such as fuel injection quantity, injection timing, VGT position, EGR valve position, can be adjusted in real-time.”. (Pg. 1521-1522).
“… the learning algorithm is able to search for points in the region with better BSFCeff and NOx values than these initial ones.”. (Pg. 1525).
This shows altering operation of the engine based on a performance parameter.
It would have been obvious before the effective filing date of the claimed invention to incorporate PAL’s teaching of commanding of identified optimal settings into the engine control module of a running engine with WEI-MERTLER-HIGDON-WAGNER’s combination’s method of calibrating an engine model and predicting engine performance. The reason for doing so would have been to put the calibration results to use on the engine itself and obtain improved performance. As expressed by PAL “The calibration results demonstrate improved engine performance in terms of both eBSFC and NOx emissions using the eBoost, compared with the baseline results…”. (Pg. 1528).
Regarding Claim 2, MERTLER, HIGDON, WAGNER, and PAL do not explicitly teach but WEI teaches
The method of claim 1, wherein the modification is an adjustment to a design parameter of the engine.
“For example, early in 2004, Alstom initiated a gas turbine (GT) design modification program for the GT11N2 based on operation experience and validated design approaches [1]. They redesigned all four turbine stages to optimize aerodynamic performance and minimize cooling air consumption.”. (Section 1 Pg. 051014-1).
“Moreover, the insight that our approach provides can be leveraged to aid the design processes by improving service assessment criteria, aerothermodynamics and mechanical design, and material research.”. (Section 7 Pg. 051014-11).
This shows performance data feeding redesign of engine hardware, turbine state geometry and cooling air provisioning being design parameters of the engine, with the reference expressly directing its calibrated degradation predictions to that same design improvement use.
Regarding Claim 4, WEI, MERTLER, HIGDON, and WAGNER do not explicitly teach but PAL teaches
The method of claim 1, wherein the modification is an adjustment to a control setting of the engine.
“With this interface, control parameters, such as fuel injection quantity, injection timing, VGT position, EGR valve position, can be adjusted in real-time.”. (Pg. 1521-1522).
This shows the modification taking the specific form of changed engine control settings, namely injection quantity and timing, turbocharger vane position, and EFR valve position, adjusted on the running engine.
Regarding Claim 5, WEI, MERTLER, HIGDON, and WAGNER do not explicitly teach but PAL teaches
The method of claim 4, further comprising determining an updated engine control setting for the engine based on the performance parameter; and causing an engine control system to adjust operation of the engine based on the updated engine control setting.
“… the learning algorithm is able to search for points in the region with better BSFCeff and NOx values than these initial ones.”. (Pg. 1525).
“The diesel engine is controlled by a production Ford ECM (engine control module) with a calibration host computer running ETAS INCA software through ETAS ES600.1 interface box.”. (Pg. 1525).
“… the MATLAB algorithm overwrites the commanded VGT and EGR positions through INCA.”. (Pg. 1523).
This shows the two recited steps in sequence, first the optimizer determines updated control settings from the measure performance objectives, and those settings are then pushed through the production engine control module which is the engine control system, and which adjusts the running engine accordingly.
Regarding Claim 7, MERTLER, HIGDON, and WAGNER, and PAL do not explicitly teach but WEI teaches
The method of claim 1, wherein the test data includes data on a plurality of operational parameters of the engine, wherein the model data includes predicted values for the plurality of operational parameters, the method including:
performing parallel Bayesian inference simulations using the fused data to identify the tuning parameter value that aligns the model data and the test data.
“For i ¼ 1; ;q, where q denotes the number of response variables. ym i , ye i , di, and ei represent the ith response of the corresponding computer model, experiment, model discrepancy, and observation error.”. (Pg. 051014-2 Section 2).
“Among them, only sensor data are valid for the calibration process since we need the field data to correct the model bias.”. (Pg. 051014-2 Section 3.2).
“In this approach, the model updating formulation is given as ye i x ð Þ¼ym i x;h ð Þþdi xð Þþei (1) where x ¼½x1; ; xd T indicates the controllable parameters while the calibration parameters h ¼½h1; ; hr T are the unknown but constant inputs that introduce parametric uncertainty.”. (Pg. 051014-2 Section 2 and Eq. 1).
“Compressor outlet temperatureT2 and outlet pressureP2 are selected as the outputs.”. (Pg. 051014-7 Section 5).
This shows the calibration operating on multiple operational parameters at once, temperature and pressure each being measured in field data and predicted by the simulation, with the model updating formulation trying the two together so that the inferred calibration parameter values are those under which the model output, corrected for bias, reproduces the sensor measurements, thereby aligning the model data with the test data i.e. the parallel Bayesian inference.
Regarding Claim 8, MERTLER, HIGDON, and WAGNER, and PAL do not explicitly teach but WEI teaches
The method of claim 1, wherein the method further comprises:
using the tuned model to generate calibrated predicted values for the operational parameter; and
generating a discrepancy model that provides a difference between the calibrated predicted values and observed values for the operational parameter.
“… we can obtain the posterior distribution of the calibration parameters and the calibrated posterior predictive distribution for unobserved responses.”. (Pg. 051014-3 Section 2).
“Model discrepancy means the difference between simulation data and experimental data, caused by underlying missing physics, numerical approximations, and any inaccuracies of the computer model. After repeating the same process to establish the surrogate model of di ðÞ, the posterior of calibration parameters is …”. (Pg. 051014-3 Section 2).
This shows both recited elements as named components of WEI’s framework by the calibrated posterior predictive distribution supplies the calibrated predicated values, and the explicitly constructed discrepancy (bias) surrogate model of the difference between the simulation and experiment plotted per response in FIGS. 5 and 10, is the discrepancy model.
Regarding Claim 9, MERTLER, HIGDON, and WAGNER, and PAL do not explicitly teach but WEI teaches
The method of claim 1, wherein identifying a tuning parameter for the model includes generating a probability distribution for the tuning parameter, the probability distribution providing a probability that a particular value is a suitable candidate for the tuning parameter.
“We marginalize the joint likelihood with respect to the GP models’ hyperparameters to obtain the posterior distribution of hg presented in Fig. 4. Its posterior is normally distributed with a mean of 0.019. In other words, the most likely degree of performance degradation in the NGCis1.9%.”. (Pg. 051014-6 Section 4.1).
This shows identification of the tuning parameter producing a full posterior probability density (Fig. 4) whose height at each candidate values express how probable that values are, the references own reading of the mode as the “most likely” value being exactly the recited suitability probability.
Regarding Claim 10, MERTLER, HIGDON, and WAGNER, and PAL do not explicitly teach but WEI teaches
The method of claim 1, wherein the testing device includes a sensor that determines observed values for the operational parameter of the engine.
“Equipment sensors collect data at a time frequency (one point per minute) that is much higher than necessary or practical for our model to handle.”. (Pg. 051014-5 Section 3.3.1).
“Among them, only sensor data are valid for the calibration process since we need the field data to correct the model bias.”. (Pg. 051014-3 Section 3.2).
This shows the testing device built around equipment sensors whose per minute readings are the observed values of the engine’s operational parameters used in the calibration.
Claim 11 recites substantially the same limitations as claim 1 except the claim is directed to a “system”. Therefore, the claim is rejected for the same rationale as addressed above.
Claim 12 recites substantially the same limitations as claim 2-6 except the claim is directed to a “system”. Therefore, the claim is rejected for the same rationale as addressed above.
Claim 13 recites substantially the same limitations as claim 8 except the claim is directed to a “system”. Therefore, the claim is rejected for the same rationale as addressed above.
Claim 14 recites substantially the same limitations as claim 9 except the claim is directed to a “system”. Therefore, the claim is rejected for the same rationale as addressed above.
Regarding Claim 16, WEI, MERTLER, HIGDON, and WAGNER do not explicitly teach but PAL teaches
The system of claim 11, further comprising a testing device in communication with the at least one processor, wherein the test data is acquired by the testing device.
“For brake torque measurement, a Himmelstein wireless digital torque meter is installed, and the torque measurement is directly fed to the MATLAB script running on the INCA host computer. For the NOx measurement, a separate NOx sensor is installed in the exhaust pipe and connected to the Ford ECM, and the measurement is broadcasted through CAN and directly accessed by ETAS INCA software.”. (Pg. 1522).
This shows the installed instrumentation serving as a testing device that is part of the system and in communication with the processor running the calibration, with the test data being acquired by that instrumentation.
Regarding Claim 17, WEI teaches
A method comprising:
receiving, from a testing device, test data for an operational parameter of an engine, the test data including observed values for the operational parameter from the operational tests;
“Equipment sensors collect data at a time frequency (one point per minute) that is much higher than necessary or practical for our model to handle.”. (Pg. 051014-5 Section 3.3.1).
“Among them, only sensor data are valid for the calibration process since we need the field data to correct the model bias.”. (Pg. 051014-4 Section 3.2).
“Table 2 reports data from 4000h of an NGC’s operation in the long-distance pipeline transportation of China.”. (Pg. 051014-6 Section 4).
This shows the instrumented sensor suite on the turbine unit serving as the testing device, with the sensor readings collected during 4000 hours of operational running being the observed values for the operational parameters received as test data.
receiving model data for the operational parameter from simulations performed via a model of the engine;
“Our performance degradation model by Python has three types of inputs: control variables (e.g., rotating speed and shaft power), environmental variables (e.g., atmospheric pressure and temperature), and health indicators (i.e., degree of equipment performance degradation). Its outputs are thermodynamic variables, such as pressure, temperature, flow rate, efficiency, and power.”. (Pg. 051014-4 Section 3.2).
“In addition, simulation input data D1 is collected through an optimal Latin hypercube design [36] ... Then, the corresponding outputs ym are obtained from the performance degradation model.”. (Pg. 051014-5 Section 3.3.1).
This shows the thermodynamic performance model of the gas turbine and compressor (model of engine), whose simulation runs produce the received model data for the same operational parameters that the sensors measure.
WEI does not explicitly teach but MERTLER teaches
compressing the test data and the model data to generate compressed test data and compressed model data;
“The data generation unit 2 may be a real test setup 2 'for, for example, one or be several real vehicle crash test. Alternatively, the data generation unit 2 may be a simulation environment 2 "which, for example, generates a plurality of crash test simulation results as observations Σ B.”. (Pg. 7).
This shows singular value decomposition and reduced basis compression to observations regardless of their origin, the data generation unit being a real test setup producing simulation results, such that compression of the test data and compression of simulation data are each disclosed by the reference.
It would have been obvious before the effective filing date of the claimed invention to incorporate MERTLER’s teaching of singular value decomposition with WEI’s method of Bayesian calibration of an engine performance model using test data and model data. The reason for doing so would have been to reduce the size of the large data sets handled during the calibration. As expressed by MERTLER, the compressed dataset “… only covers a few MB to a few GB and can therefore be obtained from the storage unit over an Internet connection in a few seconds to minutes.”. (Pg. 7).
WEI and MERTLER do not explicitly teach but HIGDON teaches
fusing the compressed test data with the compressed model data to generate fused data;
“Here a vector of experimental observations y(x) taken at input condition x is modeled as y(x) = η(x,θ)+δ(x)+e, where η(x,θ) is the simulated output at the true parameter setting θ, δ(x) accounts for discrepancy between the simulator and physical reality, and e models observation error.”. (Pg. 11).
This shows the compressed experimental weights and compressed simulation weights joined into one stacked data vector governed by one statistical model linking the observations to the simulator output, the resulting combined vector being the fused data on which all subsequent inference operates.
It would have been obvious before the effective filing date of the claimed invention to incorporate HIGDON’s teaching of Bayesian calibration over basis reduced test and simulation data with WEI-MERTLER’s combination’s method of calibrating an engine model using compressed data. The reason for doing so would have been to perform the calibration directly on compressed representations. As expressed by HIGDON “… we make use of basis representations (e.g. principal components) to reduce the dimensionality of the problem and speed up the computations required for exploring the posterior distribution.”. (Abstract).
WEI, MERTLER, and HIGDON do not explicitly teach but WAGNER teaches
generating a first distribution for a first tuning parameter of the model by performing parallel Bayesian inference simulations using the fused data, the first distribution providing a probability that a particular value is a suitable candidate for the first tuning parameter;
“The AIES algorithm relies on a set of parallel chains where proposal samples are obtained by moving in the direction of a randomly chosen conjugate sample from a different chain.”. (Pg. 15).
“Posterior characteristics (e.g. quantities of interest, expected values, marginal distributions etc.) can then be estimated using this sample.”. (Pg. 14).
“Univariate and bivariate marginals from the posterior distribution of the model parameters π(xM|y) and discrepancy parameters π(xε|y) calibrated using the data from Product A (E1). The vertical line (dot) indicates the MAP parameter xMAP defined in Eq. (40)”. (Pg. 24 FIG. 6).
“… this parameter set is located at the maximum value of the posterior distribution (maximum a posteriori, MAP). It can be found by solving the optimization problem xMAP=argmax x π(x|y). (40)”. (Pg. 25).
This shows the parallel chain Bayesian inference producing an individual univariate distribution for the first of the plural model parameters, the density of the marginal over candidate values being the probability that any particular values is a suitable candidate, with the maximum a point being the most suitable value identified.
It would have been obvious before the effective filing date of the claimed invention to incorporate WAGNER’s teaching of parallel chain Bayesian inference producing a probability distribution for each parameter with WEI-MERTLER-HIGDON’s combination’s method of calibrating an engine model using compressed data. The reason for doing so would have been to obtain a full probability distribution for each tuning parameter instead of a single value, giving confidence information about the calibrated values. As expressed by WAGNER This distribution contains much more information about the calibrated properties than the single point estimate from the conventional approach. For example, it allows computing expected values, maximum a posteriori estimates, confidence intervals on the calibrated values and the full correlation structure.”. (Pg. 3).
generating a second distribution for a second tuning parameter of the model by performing parallel Bayesian inference simulations using the fused data, the second distribution providing a probability that a particular value is a suitable candidate for the second tuning parameter;
“By gathering information from such previous attempts, the thermal properties are parameterized by six parameters XM = (X1,...,X6). This parametrization is flexible enough to enable inference on XM and follows physical and empirical reasoning as described next.”. (Pg. 8).
“The AIES algorithm relies on a set of parallel chains where proposal samples are obtained by moving in the direction of a randomly chosen conjugate sample from a different chain.”. (Pg. 15).
“Posterior characteristics (e.g. quantities of interest, expected values, marginal distributions etc.) can then be estimated using this sample.”. (Pg. 14).
“Univariate and bivariate marginals from the posterior distribution of the model parameters π(xM|y) and discrepancy parameters π(xε|y) calibrated using the data from Product A (E1). The vertical line (dot) indicates the MAP parameter xMAP defined in Eq. (40)”. (Pg. 24 FIG. 6).
“… this parameter set is located at the maximum value of the posterior distribution (maximum a posteriori, MAP). It can be found by solving the optimization problem xMAP=argmax x π(x|y). (40)”. (Pg. 25).
“Table4: Summary of the prior distribution π(x)= 14 i=1=πi(xi) for the parameter vector X=(X1,...,X14”. (Pg. 25).
This shows the same parallel Bayesian inference producing a posterior distribution for each of the model parameters, the marginal for the second parameter X2 being the second distribution providing the probability that a particular value is a suitable candidate for the second tuning parameter.
generating a tuned model by using the particular value for the first tuning parameter and the particular value for the second tuning parameter in the model; and
“The calibration is finally validated by using the calibrated material properties to predict the temperature development in different experimental setups.”. (Abstract).
“… this parameter set is located at the maximum value of the posterior distribution (maximum a posteriori ,MAP). It can be found by solving the optimization problem xMAP=argmax x π(x|y). (40)”. (Pg. 25).
“The calibrated posterior parameters for Product A(E1) are summarized in Table5.”. (Pg. 25).
This shows the particular value of each tunning parameter, taken together as the calibrated parameter set, applied in the model, the model evaluated with those calibrated values being the tuned model used for the subsequent predictions.
WEI, MERTLER, HIGDON, and WAGNER do not explicitly teach but PAL teaches
causing a modification to the physical design or operation of the engine based on the performance parameter.
“… the MATLAB algorithm overwrites the commanded VGT and EGR positions through INCA.”. (Pg. 1523).
“With this interface, control parameters, such as fuel injection quantity, injection timing, VGT position, EGR valve position, can be adjusted in real-time.”. (Pg. 1521-1522).
“… the learning algorithm is able to search for points in the region with better BSFCeff and NOx values than these initial ones.”. (Pg. 1525).
This shows altering the operation of the engine based on the parameter.
It would have been obvious before the effective filing date of the claimed invention to incorporate PAL’s teaching of commanding of identified optimal settings into the engine control module of a running engine with WEI-MERTLER-HIGDON-WAGNER’s combination’s method of calibrating an engine model and predicting engine performance. The reason for doing so would have been to put the calibration results to use on the engine itself and obtain improved performance. As expressed by PAL “The calibration results demonstrate improved engine performance in terms of both eBSFC and NOx emissions using the eBoost, compared with the baseline results…”. (Pg. 1528).
Regarding Claim 18, WEI, MERTLER, HIGDON, and PAL but WAGNER teaches
The method of claim 17, further comprising:
selecting a value for the first tuning parameter based on the first distribution; and
selecting a value for the second tuning parameter based on the second distribution.
“This problem can be approximately solved by picking the parameter point from the available posterior sample that maximizes the unnormalized posterior distribution˜π(X|y)= L(x;y)π(x)∝π(x|y).”. (Pg. 25).
“The resulting maximum a posteriori estimator is also shown in Figure6.”. (Pg. 25).
“The vertical line (dot) indicates the MAP parameter xMAP defined in Eq. (40).”. (Pg. 24).
This shows a value selected for each tuning parameter on the basis of its distribution, the selected value being the one at which the distribution is maximal, marked on each parameter’s own marginal in Figure 6.
Regarding Claim 19, WEI, MERTLER, HIGDON, and PAL but WAGNER teaches
The method of claim 18, further comprising:
generating a plurality of possible values for the first tuning parameter based on the first distribution;
“MCMC … constructs Markov chains that are guaranteed to produce samples distributed according to the posterior distribution.”. (Pg. 14).
This shows the chains drawing many sampled values of the first parameter, the samples being distributed according to that parameter’s posterior distribution.
generating a plurality of possible values for the second tuning parameter based on the second distribution;
“Since the full posterior distribution of the parameters is inferred, the plots do not only show one line for prior and posterior, but 1,000 samples each.”. (Pg. 27).
This shows the same sampling yielding a plurality of drawn values for each further parameter, the 1,000 posterior draws providing the possible values for the second tuning parameter according to its distribution.
generating a plurality of combinations of values for the first tuning parameter and the second tuning parameter based on the plurality of possible values;
“Denoting by x = (xM,xε) a realization of X, this probability as a function of the parameters is the so-called likelihood function L(xM,xε;y) = N(y|M(xM),Σ(xε)), (6) which reads more explicitly: L(xM,xε;y) = 1 (2π)N/2 detΣ(xε) exp (M(xM)−y)Σ(xε)−1(M(xM)−y) . (7)”. (Pg. 13).
This shows each sampled point being a realization of the full parameter vector, that is, one combination of a value for the first parameter with a value for the second, the posterior sample as a whole being the plurality of combinations.
determining a likelihood that each of the plurality of combinations aligns the model data with the test data; and
“Denoting by x = (xM,xε) a realization of X, this probability as a function of the parameters is the so-called likelihood function L(xM,xε;y) = N(y|M(xM),Σ(xε)), (6) which reads more explicitly: L(xM,xε;y) = 1 (2π)N/2 detΣ(xε) exp (M(xM)−y)Σ(xε)−1(M(xM)−y) . (7)”. (Pg. 13).
This shows a likelihood evaluated at each parameter combination, the likelihood function by its definition quantifying the probability of the measure data given the model output at that combination, which is the likelihood that the combination aligns the model data with the test data.
selecting a particular combination of values that results in the highest likelihood aligning the model data with the test data.
“This problem can be approximately solved by picking the parameter point from the available posterior sample that maximizes the unnormalized posterior distribution˜π(X|y)= L(x;y)π(x)∝π(x|y). The resulting maximum a posteriori estimator is also shown in Figure6.”. (Pg. 25).
This shows the selection of the single parameter combination at which the likelihood weighted posterior is highest, the picked maximum a posteriori point being the particular combination of values with the highest likelihood of aligning model with measurement.
Regarding Claim 20, MERTLER, HIGDON, WAGNER, and PAL but WEI teaches
The method of claim 19, the test data includes data on a plurality of operational parameters of the engine, wherein the model data includes predicted values for the plurality of operational parameters, the method including:
selecting the particular combination of values to align the plurality of operational parameters of the model data and the test data.
“For i ¼ 1; ;q, where q denotes the number of response variables. ym i , ye i , di, and ei represent the ith response of the corresponding computer model, experiment, model discrepancy, and observation error.”. (Pg. 051014-2 Section 2).
“Among them, only sensor data are valid for the calibration process since we need the field data to correct the model bias.”. (Pg. 051014-2 Section 3.2).
“In this approach, the model updating formulation is given as ye i x ð Þ¼ym i x;h ð Þþdi xð Þþei (1) where x ¼½x1; ; xd T indicates the controllable parameters while the calibration parameters h ¼½h1; ; hr T are the unknown but constant inputs that introduce parametric uncertainty.”. (Pg. 051014-2 Section 2 and Eq. 1).
“Compressor outlet temperatureT2 and outlet pressureP2 are selected as the outputs.”. (Pg. 051014-7 Section 5).
“The joint likelihood is marginalized to get the posterior distributions of calibration parameters after the surrogate modeling.”. (Pg. 051014-5 Section 3.3.3.).
This shows the calibration performed over a plurality of engine operational parameters at once, temperature and pressure each being measured and each being predicted by the model, with the calibration parameter values determined from the joint likelihood over all responses so that the selected combination brings the model into agreement with the test data across the plurality of parameters simultaneously.
Claim 3 is rejected under 35 U.S.C 103 as being unpatentable over WEI et al. “Bayesian Calibration of Performance Degradation in a Gas Turbine-Driven Compressor Unit for Prognosis Health Management” (2022) [herein “WEI”], MERTLER et al. EP2924585A1 (2015) [herein “MERTLER”], HIGDON et al. “Computer Model Calibration Using High Dimensional Output” (2008) [herein “HIGDON”], WAGNER et al. “Bayesian calibration and sensitivity analysis of heat transfer models for fire insulation panels” (2020) [herein “WAGNER”], PAL et al. “Data-driven model-based calibration for optimizing electrically boosted diesel engine performance” (2022) [herein “PAL”], and NARDI et al. US20140277669A1 (2014) [herein “NARDI”].
Regarding Claim 3, MERTLER, HIGDON, WAGNER, and PAL do not explicitly teach but WEI teaches
The method of claim 2, further comprising determining a design parameter for an engine component based on the performance parameter; and
“They redesigned all four turbine stages to optimize aerodynamic performance and minimize cooling air consumption.”. (Section 1 Pg. 051014-1).
“… the accurate assessments of performance degradation of previous generations may support its aerothermodynamics and mechanical design and aid material research.”. (Section 1 Pg. 051014-2).
This shows determination of components level design parameters (turbine stage aerodynamic and cooling design) from observed and predicted performance.
WEI, MERTLER, HIGDON, WAGNER, and PAL do not explicitly teach but NARDI teaches
causing a manufacturing device to form the engine component according to the design parameter.
“As shown, the specification is transferred to an AM machine 120 which performs the AM techniques according to the specification in order to create the end item. While not required in all aspects, the AM machine 120 can include processors which interpret the specification, and controls other elements which apply the materials using robots, printers, lasers or the like to add the materials as layers or coatings to produce the AM end item. The AM machine 120 can receive the specification manually, such as where an end user re-enters or uploads the specification into the AM machine 120, or digitally via a wired and/or wireless network. While shown as part of the system 100, it is understood that the AM machine 120 can be separate from the system 100 such as where the AM machine 120 is in a separate location from the elements of the system 100 which generates the specification.”. (0040).
“In block 206, the design may be optimized for multi-functional uses. For example, in the context of a component to be used as a jet engine turbine disk, it may be a first requirement to have a course microstructure at the edges of the disk to mitigate against so-called “creep performance.” …”. (0045).
“The design concept may be “parameterizable” into a vector of design variables x=(x1, x2, xn). The design optimizer 454 may seek to find the optimal x* according to an objective function f(x) such that all constraints are satisfied. A detailed evaluation (e.g., FEA) of the concept part may then be performed to identify the critical areas where a wider design space would be beneficial.”. (0062).
This shows the optimized component design, determined from performance objectives and constraints, transferred as a build specification to an additive manufacturing machine that forms the physical component according to that design, the reference expressly applying this to a jet engine turbine disk.
It would have been obvious before the effective filing date of the claimed invention to incorporate NARDI’s teaching of commanding of identified optimal settings into the engine control module of a running engine with WEI-MERTLER-HIGDON-WAGNER-PAL’s combination method of calibrating an engine model and predicting engine performance. The reason for doing so would have been to produce the physical component embodying the determined design. As expressed by NARDI, additive manufacturing “… is an emerging trend that may provide benefits in terms of, e.g., weight and cost.”. (0138).
Claim 6 is rejected under 35 U.S.C 103 as being unpatentable over WEI et al. “Bayesian Calibration of Performance Degradation in a Gas Turbine-Driven Compressor Unit for Prognosis Health Management” (2022) [herein “WEI”], MERTLER et al. EP2924585A1 (2015) [herein “MERTLER”], HIGDON et al. “Computer Model Calibration Using High Dimensional Output” (2008) [herein “HIGDON”], WAGNER et al. “Bayesian calibration and sensitivity analysis of heat transfer models for fire insulation panels” (2020) [herein “WAGNER”], PAL et al. “Data-driven model-based calibration for optimizing electrically boosted diesel engine performance” (2022) [herein “PAL”], and MAUERY et al. “A Guide for Aircraft Certification by Analysis” (2021) [herein “MAUERY”].
Regarding Claim 6, WEI, MERTLER, HIGDON, WAGNER, and PAL do not explicitly teach but MAUERY teaches
The method of claim 1, further comprising, determining compliance with an engine certification standard based on the performance parameter by comparing the performance parameter against a defined performance standard.
“In its most essential definition, CbA refers to flight modeling or engine modeling, where analysis (such as numerical or wind tunnel methods) and/or simulation methods are used to obtain results for certification compliance that have traditionally been acquired using physical testing, such as flight testing or ground-based testing for engines.”. (Pg. 9 Section II).
“Airworthiness standards for the transport airplane category are provided in Part 25 of Title 14 (Aeronautics and Space) of the US Code of Federal Regulations (CFR) [10], where 14 CFR 25, Subpart B addresses flight related regulations. Similarly, airworthiness standards for the airplane engine category are provided in Part 33; 14 CFR 33, Subpart F addresses regulations for testing of aircraft turbine-powered engines.”. (Pg. 10 Section 2).
“In this case, the flight modeling would be considered successful when the airplane speed, weight, and attitude measured in flight test matches the numerical result to within a certain accuracy.”. (Pg. 17 Section Analysis of Selected Airplane Maneuvers and Engine Tests).
This shows performance predicted by engine and flight modeling used to determine compliance with expressly name certification standards, the predicted performance being evaluated against the defined airworthiness performance requirements of 14 CFR Parts 25 and 33, which is the recited comparison of the performance parameter against a defined performance standard to determine certification compliance.
It would have been obvious before the effective filing date of the claimed invention to incorporate MAUERY’s teaching of determination of certification compliance from engine and flight model predictions with WEI-MERTLER-HIGDON-WAGNER-PAL’s combination method of calibrating an engine model and predicting engine performance. The reason for doing so would have been to reduce the physical testing needed for certification. As expressed by MAUERY, certification analysis “… has the potential to shorten product testing programs, thus reducing their associated costs, while maintaining equivalent levels of safety, ensuring security and confidence for the flying public.”. (Pg. 9).
Claim 15 is rejected under 35 U.S.C 103 as being unpatentable over WEI et al. “Bayesian Calibration of Performance Degradation in a Gas Turbine-Driven Compressor Unit for Prognosis Health Management” (2022) [herein “WEI”], MERTLER et al. EP2924585A1 (2015) [herein “MERTLER”], HIGDON et al. “Computer Model Calibration Using High Dimensional Output” (2008) [herein “HIGDON”], WAGNER et al. “Bayesian calibration and sensitivity analysis of heat transfer models for fire insulation panels” (2020) [herein “WAGNER”], PAL et al. “Data-driven model-based calibration for optimizing electrically boosted diesel engine performance” (2022) [herein “PAL”], and URANO et al. US7349795B2 (2007) [herein “URANO”].
Regarding Claim 15, WEI, MERTLER, HIGDON, WAGNER, and PAL do not explicitly teach but URANO teaches
The system of claim 11, wherein the test data and the model data are transient time-series data of the operational parameter of the engine.
“… the test is performed by using the actual engine in a transient state in which the rotation speed and torque of the engine vary in time sequence.”. (Abstract).
“The results of the transient characteristic measurements in an actual engine are illustrated in FIG. 3. In the present embodiment, the number of grams of NOx per hour (g/h) and the number of grams of fumes per second (g/s) are plotted along the respective Y-axis and time is plotted along the X-axis. In addition, VGT control values and EGR control values used in this state are plotted along the respective Y-axis and time is plotted along the X-axis.”. (0037).
“FIG. 4 illustrates the target values (dotted line) for the virtual observation values (solid line) for NOx and fumes, respectively.”. (0039).
This shows both recited data streams as transient time series of engine operational parameters by the measured emissions and speed traces recorded against time while the engine runs through transient maneuvers, and the model’s virtual observation values plotted on the same time base for comparing during the adaptation.
It would have been obvious before the effective filing date of the claimed invention to incorporate URANO’s teaching of use of transient time series test data and model data of engine operational parameters with WEI-MERTLER-HIGDON-WAGNER-PAL’s combination method of calibrating an engine model and predicting engine performance. The reason for doing so would have been to calibrate the model under time varying conditions in which the engine operates. As expressed by URANO, “… the test is performed by using the actual engine in a transient state in which the rotation speed and torque of the engine vary in time sequence.”. (Abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US20170286572A1 teaches systems and methods to facilitate assessments and/or predictions for a physical system in an automatic and accurate manner that include aircraft turbines.
US7472100B2 teaches real-time on board engine component performance tracking system for deriving high fidelity engine models.
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/N.E.M./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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