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. The amendment filed on 04/02/2026 has been received and considered. Claims 1-10 and 12-21 are presented for examination.
Claim Objections
2. Claims 1, 14, and 21 are objected to because of the following informalities:
As per Claim 1 and 14, they recite the limitation “faster than the cluster-executable version of the digital system” which is unclear what the limitation “digital system” refers. Each of these claims introduces "a digital system model" but does not introduce "a digital system". The Examiner reads "the digital system" as "the digital system model" for compact prosecution.
As per Claim 21, it recites the limitation “configured to: determining…., performing…” which would be better as “configured to: determine…., perform…”
Appropriate correction is required.
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.
3. Claims 1-10 and 12-21 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), further in view of Fasano (US 20210248289 A1), and further in view of Tallman (US 2020/0042659 A1).
As per Claim 1, 14 and 20, Betts teaches A system comprising: a memory; and a processor in communication with the memory, wherein the processor is configured to: (Betts, [0006] "a system includes a computing device configured to receive sensor data from an apparatus. The computing device is also configured to execute a surrogate model for the system"; 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, ”), the system comprising:
wherein the high-fidelity version of the digital system model is generated based on operational data that includes sensor data or in-service data from the 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.”: the physics-based system model and surrogate are built from the sensor data received from the monitored apparatus, i.e., from operational data);
train a surrogate model using at least the simulated data to approximate the digital system model of the vehicle or machine (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.”; [0042] "the weights are determined based on one or more machine learning techniques": the surrogate models are trained, including by machine learning, to approximate the modeled system); and
generate, using the trained surrogate model, estimated values for conditions or parameters of the vehicle or machine based on operational data, wherein the operational data includes sensor data or in-service data from the vehicle or machine ([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.”: the determined system state is the claimed estimated values for conditions or parameters of the vehicle or machine). In particular, Betts teaches a surrogate-model digital-twin system in which a computing device receives sensor data from a monitored apparatus, builds and trains surrogate models from a physics-based system model using machine-learning techniques, and executes the trained surrogate model on the received sensor data to estimate the present and future states of the apparatus.
However, Betts fails to teach explicitly
receive a high-fidelity version of a digital system model of a vehicle or machine,
generate a cluster-executable version of the digital system model based on the high-fidelity version of the digital system model, wherein the cluster-executable version of the digital system model is configured to be executed in parallel on clusters of computer hardware such that the cluster-executable version of the digital system model runs simulations faster than the high-fidelity version of the digital system model;
execute the cluster-executable version of the digital system model based on a plurality of simulated conditions in parallel on clusters of computer hardware to generate simulated data;
wherein the surrogate model estimates the values for the conditions or the parameters of the vehicle or machine faster than the cluster-executable version of the digital system;
execute the digital system model of the vehicle or machine to generate simulation data based on the operational data and the estimated values for the conditions or the parameters of the vehicle or machine generated by the surrogate model; and
synchronize or update a digital twin of the vehicle or machine based on the simulation data, wherein the digital twin represents a state or condition of the system vehicle or machine.
Klenner teaches execute the digital system model of the vehicle or machine to generate simulation data based on the operational data and the estimated values for the conditions or the parameters of the vehicle or machine generated by the surrogate model (Klenner, [0006] "execute a physics-driven model when the calculated region of competence for the data-driven model falls outside of a threshold region of competence"; 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.”: the underlying physics-driven model, i.e., the digital system model, is executed on the operational data and the data-driven surrogate’s estimates to generate simulation data where the surrogate alone is not competent). In particular, Klenner teaches a hybrid modeling framework in which a fast data-driven model and the underlying physics-driven model are executed together, the physics-driven model being run on the operational data and on the data-driven model’s estimates to produce simulation results that refine and extend the data-driven model’s coverage, and notes that "both the data-driven model 110 and the physics-driven model 112 may be surrogate models" ([0059]).
Betts and Klenner are analogous art because they are both related to digital-twin modeling and simulation-based state estimation of physical systems.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Klenner into Betts’s invention to provide more coverage of the response surface and more confidence that the output of the hybrid model is accurate (Klenner: [0029]).
However, Betts as modified by Klenner fails to teach explicitly receive a high-fidelity version of a digital system model of a vehicle or machine … , generate a cluster-executable version of the digital system model based on the high-fidelity version of the digital system model, wherein the cluster-executable version of the digital system model is configured to be executed in parallel on clusters of computer hardware such that the cluster-executable version of the digital system model runs simulations faster than the high-fidelity version of the digital system model;
execute the cluster-executable version of the digital system model based on a plurality of simulated conditions in parallel on clusters of computer hardware to generate simulated data;
wherein the surrogate model estimates the values for the conditions or the parameters of the vehicle or machine faster than the cluster-executable version of the digital system; and
synchronize or update a digital twin of the vehicle or machine based on the simulation data, wherein the digital twin represents a state or condition of the system vehicle or machine.
Fasano teaches synchronize or update a digital twin of the vehicle or machine based on the simulation data, wherein the digital twin represents a state or condition of the system vehicle or machine (Fasano, [0010], [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] “the digital twin 4 of the twinned physical system 3, i.e. the digital virtual replicas are constantly updated”: the digital twin representation of the asset is synchronized and updated from simulation-generated future-state data and from measured parameters). In particular, Fasano teaches a digital platform whose digital twin representation of a real-world asset is continuously synchronized: the twin is updated from measured status parameters and is propagated into simulated future states generated with cumulative damage models.
Betts, Klenner, and Fasano are analogous art because they are all related to digital-twin modeling and simulation-based state estimation of physical systems.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Fasano into Betts as modified by Klenner’s invention to provide digital virtual replicas that are constantly updated and analyzed by measuring data from their real counterparts so that what will happen in each case and the associated risk can be predicted and actions automatically proposed (Fasano: [0034]).
However, Betts as modified by Klenner and Fasano fails to teach explicitly receive a high-fidelity version of a digital system model of a vehicle or machine;
generate a cluster-executable version of the digital system model based on the high-fidelity version of the digital system model, wherein the cluster-executable version of the digital system model is configured to be executed in parallel on clusters of computer hardware such that the cluster-executable version of the digital system model runs simulations faster than the high-fidelity version of the digital system model;
execute the cluster-executable version of the digital system model based on a plurality of simulated conditions in parallel on clusters of computer hardware to generate simulated data;
wherein the surrogate model estimates the values for the conditions or the parameters of the vehicle or machine faster than the cluster-executable version of the digital system;
Tallman teaches receive a high-fidelity version of a digital system model of a vehicle or machine (0016] "a simulation model that results when machine learning 156 algorithms are applied to training data that was generated by a traditional “physics-based model” 154 simulation"; [0017] "While the physics-based model 154 can take a substantially long period of time to execute (e.g., days, weeks, or months depending on the complexity of the problem being solved), the surrogate model may execute nearly instantaneously (e.g., seconds or minutes) regardless of the complexity of the underlying physics-based model 154 that it reproduces": the full-fidelity physics-based model is the claimed high-fidelity version);
generate a cluster-executable version of the digital system model based on the high-fidelity version of the digital system model, wherein the cluster-executable version of the digital system model is configured to be executed in parallel on clusters of computer hardware such that the cluster-executable version of the digital system model runs simulations faster than the high-fidelity version of the digital system model ([0037] "The resultant response surfaces from both DOEs may be stored in a locally-available database (where they can then be accessed for machine learning and surrogate model training)"; [0050] "The system may then automatically arrange for DOE jobs to be run via that cloud-based computing resource": deploying its design-of-experiments simulations on high-performance or cloud computing systems, i.e., clusters of computer hardware, is the claimed cluster-executable version executed in parallel and running simulations faster than a single-machine full-fidelity run);
execute the cluster-executable version of the digital system model based on a plurality of simulated conditions in parallel on clusters of computer hardware to generate simulated data ([0037] "The resultant response surfaces from both DOEs may be stored in a locally-available database (where they can then be accessed for machine learning and surrogate model training)": the resulting response surfaces are the claimed simulated data); and
wherein the surrogate model estimates the values for the conditions or the parameters of the vehicle or machine faster than the cluster-executable version of the digital system ([0017] "While the physics-based model 154 can take a substantially long period of time to execute (e.g., days, weeks, or months depending on the complexity of the problem being solved), the surrogate model may execute nearly instantaneously (e.g., seconds or minutes) regardless of the complexity of the underlying physics-based model 154 that it reproduces": the surrogate model trained on that data executes nearly instantaneously, faster than the cluster-executed model). In particular, Tallman teaches a three-tier modeling architecture in which the full-fidelity physics-based model is the accuracy reference, the model’s design-of-experiments simulations are executed expediently on high-performance or cloud computing resources to generate response-surface training data, and a surrogate model trained on that data executes nearly instantaneously.
Betts, Klenner, Fasano, and Tallman are analogous art because they are all related to digital-twin modeling and simulation-based state estimation of physical systems.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of cited references. Thus, one of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to incorporate Tallman into Betts as modified by Klenner and Fasano’s invention to provide expedience that opens new opportunities for high-fidelity engineering simulation applications in areas where time limitations would not have previously allowed (Tallman: [0017]).
As per Claim 2 and 15, Betts fails to teach explicitly wherein the synchronized or updated digital twin corresponds to a current state or condition of the system of the vehicle or machine.
Fasano teaches wherein the synchronized or updated digital twin corresponds to a current state or condition of the system of the vehicle or machine (Fasano, [0010], [0024], [0034] "constantly updated and analyzed by measuring data from their real counterparts"; [0026]: the twin is maintained in correspondence with the asset’s current state).
As per Claim 3 and 16, Betts teaches wherein the digital system model includes a physics-based model of the vehicle or machine (Betts, [0041] "identify one or more principal physical or causal relationships between the input and output variables determined to be most correlated"; [0116]: the system model is generated from principal physical relationships, i.e., a physics-based model).
As per Claim 4 and 17, Betts teaches wherein the estimated values of the conditions or parameters of the vehicle or machine are generated by the surrogate model to minimize an error between an output of the digital system model and real-world data (Betts, [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 surrogate’s fitting parameters are optimized to minimize the error between model output and real-world data).
As per Claim 5, Betts fails to teach explicitly wherein the conditions or parameters of the vehicle or machine are unable to be measured or detected directly by a sensor of the vehicle or machine.
Klenner teaches wherein the conditions or parameters of the vehicle or machine are unable to be measured or detected directly by a sensor of the vehicle or machine (Klenner, [0016] "the hybrid model may identify unknown/unmeasured physics-based parameters based on a production (data) profile"; [0072] "Identification of these unmeasured inputs and their trends allows the hybrid model to identify optimization opportunities": unknown and unmeasured parameters that no sensor measures directly are estimated).
As per Claim 6, Betts fails to teach explicitly wherein the processor is further configured to use computation techniques to synchronize or update the digital twin of the vehicle or machine.
Klenner teaches wherein the processor is further configured to use computation techniques to synchronize or update the digital twin of the vehicle or machine (Klenner, [0030] "may estimate an optimal operating condition, remaining useful life, operating performance such as heart rate or other metric, of a twinned physical system using sensors, communications, modeling, history and computation": the twinned physical system’s state estimate is maintained using computation).
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 teaches wherein the processor is further configured to train the surrogate model based on machine learning hyper-parameter values and the simulation data (Betts, [0033]-[0035], [0042]-[0043]"the weights are determined based on one or more machine learning techniques": Examiner’s Note – the recited "machine learning hyper-parameter values" correspond to the machine-learning weights and coefficients Betts determines and applies in training the surrogate model on the simulation data).
As per Claim 9, Betts fails to teach explicitly wherein the surrogate model comprises a machine learning model, a physics-based model, a neural network, or a combination thereof.
Klenner teaches wherein the surrogate model comprises a machine learning model, a physics-based model, a neural network, or a combination thereof (Klenner, [0059] "These may be created using a neural network, support vector machines": the surrogate models may be neural-network or other machine-learning models).
As per Claim 10, Betts teaches wherein the digital system model includes a model of an environmental control system, and wherein the vehicle or machine includes an environmental control system (Betts, [0033]-[0035], [0046], [0084] "an SM may be generated to evaluate thermomechanical fatigue (TMF) in an exhaust manifold system, such as the exhaust manifold of an engine": Examiner’s Note – the claimed "model of an environmental control system" corresponds to Betts’ model of a thermally-governed vehicle subsystem, as Betts’ subsystem-level modeling applies in the same manner to an environmental control system of the vehicle, which is likewise a thermally-governed vehicle subsystem).
As per Claim 12 and 19, Betts teaches further comprising a display device configured to display the synchronized or updated digital twin of the vehicle or machine (Betts, [0006] "provide for display the system state"; [0021]: the computing device includes a display 206 on which the determined system state is displayed).
As per Claim 13, Betts fails to teach explicitly wherein the vehicle is an aircraft, wherein the digital twin represents a subsystem or component of the aircraft, and wherein the operational data includes operating historical data of the subsystem or component of the aircraft.
Klenner teaches wherein the vehicle is an aircraft, wherein the digital twin represents a subsystem or component of the aircraft, and wherein the operational data includes operating historical data of the subsystem or component of the aircraft (Klenner, [0035]-[0036] "a jet engine on an aircraft amongst a fleet"; [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": the twinned installed product is a jet engine of an aircraft, a subsystem of the aircraft, modeled from its collected operating historical data).
As per Claim 21, Betts teaches determining a fault or a degraded condition or state of the vehicle or machine based on analyzing the digital twin (Betts, [0035] "the SM may predict present machine states and future machine states of the engine": the surrogate-model digital twin is analyzed to predict degraded machine states such as the remaining useful life of an engine component).
However, Betts fails to teach explicitly in response to determining the fault or the degraded condition or state of the vehicle or machine, performing a responsive action related to addressing the fault or the degraded condition or state of the vehicle or machine.
Fasano teaches in response to determining the fault or the degraded condition or state of the vehicle or machine, performing a responsive action related to addressing the fault or the degraded condition or state of the vehicle or machine (Fasano, [0036] "a forecasted measure of an occurrence probability of one or more predefined risk events impacting the real world asset or object 3 can e.g. be generated by propagating the parameters of the digital twin representation 4"; [0014] "the digital platform triggers the transmission of a digital recommendation to a user interface generated by the expert system of the digital platform": upon forecasting a risk event from the twin, the platform performs a responsive action by triggering a recommendation addressing it).
Response to Arguments
4. Applicant's arguments filed on 03/02/2026 have been fully considered but they are not persuasive.
Examiner respectfully withdraws Claim Rejections - 35 USC § 112 in view of the amendment and/or applicant’s arguments.
Applicant’s arguments with respect to claims 1, 14 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument - in view of Tallman (US 2020/0042659 A1). In particular, Tallman teaches a three-tier modeling architecture in which the full-fidelity physics-based model is the accuracy reference, the model’s design-of-experiments simulations are executed expediently on high-performance or cloud computing resources to generate response-surface training data, and a surrogate model trained on that data executes nearly instantaneously.
Conclusion
5. 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.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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
Prosecution Insights