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
1. Claims 1-20 are presented for examination.
Claim Objections
2. Claim 8 is objected to because of the following informalities:
As per claim 8, it recites the phrase “claim 1” in line 1 which would be better as “claim 1,”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
3. Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “substantially faster” in claim 11 is a relative term which renders the claim indefinite. The term “substantially” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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 nonobviousness.
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.
4. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Betts et al. (US 20200401748 A1), in view of Klenner et al. (US 20180357343 A1), and further in view of Fasano (US 20210248289 A1).
As per Claim 1, 14 and 20, Betts et al. teaches a system/method/non-transitory computer-readable medium having stored thereon instruction code (Abstract) comprising: a memory; and a processor in communication with the memory (Fig. 2 [0021]-[0029] “Surrogate model computing device 102 includes one or more processors 201, working memory 202, one or more input/output devices 203, instruction memory 207, a transceiver 204, one or more communication ports 207, and a display 206, ”), wherein the processor is configured to:
receive a digital system model of a system of a vehicle or machine ([0035] “SM may be generated to predict the remaining useful life of a component in an engine. In this case, the SM may predict present machine states and future machine states of the engine.”, [0041] “surrogate model computing device 102 may identify one or more principal physical or causal relationships between the input and output variables determined to be most correlated, and/or those variables that have been mathematically combined into one variable, as described above in Step 1, to generate a physics model.”);
…
train a surrogate model using at least the simulated data to approximate the digital system model of the system ([0034]-[0035] “The SMs can be trained, improved, and validated to optimize predictive capabilities.”, “The output of the system O can be identified by, for example, experimental data, field data, IoT data, and/or simulation results.”);
generate, using the trained surrogate model, estimated values for conditions or parameters of the systems based on operational data, wherein the operational data includes sensor data or in-service data from the system ([0006], [0126] “The computing device is further configured to determine a system state for the system based on the received sensor data and execution of the surrogate model, and provide for display the system state.”);
….
Betts et al. fails to tech explicitly execute the digital system model based on simulated conditions to generate simulated data;
execute the digital system model of the system to generate simulation data based on the operational data and the estimated values or parameters generated by the surrogate model; and
synchronize or update a digital twin of the system based on the simulation data, wherein the digital twin represents a state or condition of the system.
Klenner et al. teaches execute the digital system model based on simulated conditions to generate simulated data (Fig. 2, element S216, [0029] “run simulations via the physics-driven model ”, [0036] “the installed product 102 may be, in various embodiments, a complex mechanical entity such as the production line of a factory, a gas-fired electrical generating plant, a jet engine on an aircraft amongst a fleet (e.g., two or more aircrafts or other assets), a wind farm (e.g., two or more wind turbines), a locomotive, an oil reservoir with multiple wells etc. The installed product 102 may include a considerable (or even very large) number of physical elements or components 104, which for example may include turbine blades, fasteners, rotors, bearings, support members, housings, etc.”, [0058]-[0067] “physic driven simulations”, “simulation physics-driven models 112 to generate an output 504 that makes predictions about the particular optimization.”);
execute the digital system model of the system to generate simulation data based on the operational data and the estimated values or parameters generated by the surrogate model (Fig. 2 element S222, [0006]-[0007] “execute a physics-driven model when the calculated region of competence for the data-driven model falls outside of a threshold region of competence; and calibrate the physics-driven model as a function of the discrepancy between physics-driven model and actual field data ”, [0058]-[0067] “the data sample 302 does not fall within the region of competence 416 for the data-driven model 110, the data sample 302 may then be entered as input, alone or with one or more additional samples 602 (FIG. 6) to a physics-driven model 112 in S216 for execution”, “the hybrid model 114 may be the physics-driven model 112 that has been calibrated with data samples 302, additional samples 602, and field data 606.”, “the physics-driven model 112 may be surrogate models”, “a hybrid model 114 may be executed. In one or more embodiments, the hybrid model 114 may be the physics-driven model 112 that has been calibrated with data samples 302, additional samples 602, and field data 606”).
Betts et al. and Klenner et al. are analogous art because they are both related to a simulation method using digital twin.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate Klenner et al. into Betts et al.’s invention for purpose of simulation system to generate a simulation output with a high level of accuracy based on particular inputs (Klenner et al.: [0058])).
Further Betts et al. as modified by Klenner et al. fails to teach explicitly synchronize or update a digital twin of the system based on the simulation data, wherein the digital twin represents a state or condition of the system.
Fasano teaches synchronize or update a digital twin of the system based on the simulation data, wherein the digital twin represents a state or condition of the system ([0010] “the digital twin representation are dynamically monitored and adapted based on the transmitted parameters, and wherein the digital twin representation comprises data structures representing states of each of the plurality of subsystems of the real-world asset or object holding the parameter values as a time series of a time period, in that, by means of the digital platform, data structures representing future states of each of the plurality of subsystems of the real-world asset or object are generatable as value time series over a future time period based on an application of simulations using cumulative damage models,”, [0024] “The invention provides the technical structure for making assessments and/or predictions regarding the operation or status of a real world physical system 3, such as industrial plants,… such as an aircraft engine or a mill plant,”, [0026], [0029] “the digital twin representation 4 representing future states 441 of each of the plurality of subsystems 41 of the real-world asset or object 3 are generated as value time series over a future time period based on an application of simulations using cumulative damage models, the cumulative damage models generating the effect of the operational and/or environmental asset or object parameters on the twinned real-world asset or object 3 of the future time period.” [0034] “he digital twin 4 of the twinned physical system 3, i.e. the digital virtual replicas are constantly updated”).
Betts et al., Klenner et al. and Fasano are analogous art because they are all related to a simulation method using digital twin. The motivated to incorporate Fasano is to enhance the ability to make or trigger proactive, measuring data-driven decisions, increasing efficiency and avoiding potential issues by reducing the risk measure ([0007]).
As per Claim 2 and 15, Betts et al. fails to teach explicitly wherein the synchronized or updated digital twin corresponds to a current state or condition of the system of the vehicle.
Fasano teaches wherein the synchronized or updated digital twin corresponds to a current state or condition of the system of the vehicle (0010] “the digital twin representation are dynamically monitored and adapted based on the transmitted parameters, and wherein the digital twin representation comprises data structures representing states of each of the plurality of subsystems of the real-world asset or object holding the parameter values as a time series of a time period, in that, by means of the digital platform, data structures representing future states of each of the plurality of subsystems of the real-world asset or object are generatable as value time series over a future time period based on an application of simulations using cumulative damage models,”, [0024] “The invention provides the technical structure for making assessments and/or predictions regarding the operation or status of a real world physical system 3, such as industrial plants,… such as an aircraft engine or a mill plant,”).
As per Claim 3 and 16, Betts et al. teaches wherein the digital system model includes a physics-based model of the system ([0041] “surrogate model computing device 102 may identify one or more principal physical or causal relationships between the input and output variables determined to be most correlated, and/or those variables that have been mathematically combined into one variable, as described above in Step 1, to generate a physics model.”, [0116] “The computing device is further configured to determine an estimated error for each of the first feature function and the at least second feature function. The computing device is also configured to determine a final feature function based on the determined estimated errors. The computing device is further configured to generate a surrogate model for the system based on the mathematical representation of the system and the determined feature function.”).
As per Claim 4 and 17, Betts et al. teaches wherein the estimated values of the conditions or parameters of the system are generated by the surrogate model to minimize an error between an output of the digital system model and real-world data ([0046] “fitting parameters optimized to reduce the mean square error between SM prediction of thermomechanical fatigue life and actual thermomechanical fatigue of the part.”, [0116] “The computing device is further configured to determine an estimated error for each of the first feature function and the at least second feature function. The computing device is also configured to determine a final feature function based on the determined estimated errors. The computing device is further configured to generate a surrogate model for the system based on the mathematical representation of the system and the determined feature function.”).
As per Claim 5, Betts et al. fails to teach explicitly wherein the conditions or parameters of the system are unable to be measured or detected directly by a sensor of the system
Klenner et al. teaches wherein the conditions or parameters of the system are unable to be measured or detected directly by a sensor of the system ([0016], [0072] “the hybrid model may identify unknown/unmeasured physics-based parameters based on a production (data) profile, allowing for the data to be mapped to, infer, or identify physics-based trends”).
As per Claim 6, Betts et al. fails to teach explicitly wherein the processor is further configured to use computation techniques to synchronize or update the digital twin of the system.
Klenner et al. teaches wherein the processor is further configured to use computation techniques to synchronize or update the digital twin of the system ([0030] “The digital twin may be a computer model that virtually represents the state of an installed product. The digital twin may include a code object with parameters and dimensions of its physical twin's parameters and dimensions that provide measured values, and keeps the values of those parameters and dimensions current by receiving and updating values via outputs from sensors embedded in the physical twin. The digital twin may have respective virtual components that correspond to essentially all physical and operational components of the installed product and combinations of products or assets that comprise an operation.”).
As per Claim 7, Betts et al. fails to teach explicitly wherein the digital system model is implemented in a Hadoop/Spark computational environment.
Klenner et al. teaches wherein the digital system model is implemented in a Hadoop/Spark computational environment ([0076] “The programs 712, 714 may be stored in a compressed, uncompiled and/or encrypted format. The programs 712, 714 may furthermore include other program elements, such as an operating system, a database management system, and/or device drivers used by the processor 710 to interface with peripheral devices.”).
As per Claim 8 and 18, Betts et al. teaches wherein the processor is further configured to train the surrogate model based on machine learning hyper-parameter values and the simulation data ([0034]-[0035] “SM may be generated to predict the remaining useful life of a component in an engine. In this case, the SM may predict present machine states and future machine states of the engine.”, [0042]-[0043] “The SMs can be trained, improved, and validated to optimize predictive capabilities.”, “The output of the system O can be identified by, for example, experimental data, field data, IoT data, and/or simulation results.”, “a machine learning algorithm may be trained with historical system input and output data (e.g., input or output data as represented by a variable of the function)”).
As per Claim 9, Betts et al. teaches wherein the surrogate model comprises a machine learning model, a physics-based model, a neural network, or a combination thereof ([0003], [0034] “Each SM may include an architecture that uses physics or mathematically informed approaches (simplified physics, finite element analysis, chemical processes, etc.) and data-driven statistical approaches (regression, multivariate statistics, Bayesian approaches, Uncertainty Quantification (UQ) methods, etc.) in a multi-stage structure. The SMs can be trained, improved, and validated to optimize predictive capabilities”, [0035] “SM may be generated to predict the remaining useful life of a component in an engine. In this case, the SM may predict present machine states and future machine states of the engine.”).
As per Claim 10, Betts et al. teaches wherein the digital system model includes a model of an environmental control system, and wherein the system includes an environmental control system ([0034] “SM may be generated to predict the remaining useful life of a component in an engine.”, [0035] “SM may be generated to predict the remaining useful life of a component in an engine. In this case, the SM may predict present machine states and future machine states of the engine.”, [0046] “engine exhaust manifold TMF evaluation”, [0084] “The SM can simulate the TMF process experienced by the exhaust manifold.”).
As per Claim 11, Betts et al. fails to teach explicitly wherein the processor executes the surrogate model substantially faster than the digital system model.
Klenner et al. teaches wherein the processor executes the surrogate model substantially faster than the digital system model ([0009] “the hybrid model may be executed to provide results in a much faster time (e.g., a fraction of a second) as compared to running a scenario in a numerical simulator (e.g., hours to days)”, [0059]-[0060], “the physics-driven model 112 may be surrogate models”, “the hybrid model 114 may be the physics-driven model 112 that has been calibrated with data samples 302, additional samples 602, and field data 606”).
As per Claim 12 an d19, Betts et al. teaches further comprising a display device configured to display the synchronized or updated digital twin of the system of the vehicle or machine (Fig. 2 element 206, [0006] “The computing device is further configured to determine a system state for the system based on the received sensor data and execution of the surrogate model, and provide for display the system state.”, [0035] “SM may be generated to predict the remaining useful life of a component in an engine. In this case, the SM may predict present machine states and future machine states of the engine.”, [0126] “the computing device may display the system state”).
As per Claim 13, Betts et al. fails to teach explicitly wherein the vehicle is an aircraft, wherein the system comprises a subsystem or component, and wherein the operational data includes operating historical data of the system of the vehicle.
Klenner et al. teaches wherein the vehicle is an aircraft, wherein the system comprises a subsystem or component, and wherein the operational data includes operating historical data of the system of the vehicle ([0036] “the installed product 102 may be, in various embodiments, a complex mechanical entity such as the production line of a factory, a gas-fired electrical generating plant, a jet engine on an aircraft amongst a fleet (e.g., two or more aircrafts or other assets), a wind farm (e.g., two or more wind turbines), a locomotive, an oil reservoir with multiple wells etc. The installed product 102 may include a considerable (or even very large) number of physical elements or components 104, which for example may include turbine blades, fasteners, rotors, bearings, support members, housings, etc. As used herein, the terms “physical element” and “component” may be used interchangeably. The installed product 102 may also include subsystems, such as sensing and localized control, in one or more embodiments.”, [0051] “the historical data may be used to train the model. The historical data may be collected from data sources, such as sensors associated with the industrial asset 102”).
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al. (US 20210191342 A1)
HERSHEY et al. (US 20170286572 A1)
Song et al. (US 20160247129 A1)
Rasheed et al (“Digital twin: Values, challenges and enablers from a modeling perspective.”)
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EUNHEE KIM whose telephone number is (571)272-2164. The examiner can normally be reached Monday-Friday 9am-5pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Pitaro can be reached at (571)272-4071. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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EUNHEE KIM
Primary Examiner
Art Unit 2188
/EUNHEE KIM/Primary Examiner, Art Unit 2188