DETAILED ACTION
Notice of Pre-AIA or AIA Status
Claims 2, 4-10 and 12-16 are currently presented for Examination.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/21/2025 has been entered.
Response to Amendment
4. The amendment filed on 07/21/2025 has been entered and considered by the examiner. By the
amendment, claims 2, 4 and 12 are amended and claims 3, 11 and 17 are cancelled. Following Applicants amendments, the previous claim objection to claim 2 is withdrawn. And the 112 written description rejection of the rejection is withdrawn to replace the term “car body” with “train body”. However, the 101 rejection of the claims is still maintained and an explanation is provided below. See office action for detail.
Response to Arguments
Applicant 101 arguments step 2A Prone one
Specifically, the claimed invention relates to a train crash energy management (CEM) optimization method that integrates machine learning with finite element simulations and empirical material testing to improve passive safety in railway industry. This is not a mathematical abstraction, nor a mental process. Rather, it addresses a defined engineering challenge-namely, how to accurately and efficiently predict and control crash energy distribution in an eight-marshalling high-speed train. The claim recites the use of physical testing devices-including a mechanical testing and simulation (MTS) universal tester, a high-speed material tester, and a separated Hopkinson bar device-to measure dynamic mechanical properties of structural materials under varying strain rates. These instruments produce real-world data that is used to define strain rate-sensitive material constitutive models in the finite element modelling. The resulting model simulates the interaction of train subsystems, including wheel-rail contact and energy absorbers, under crash conditions. These testing steps cannot be performed mentally or with pen and paper and do not fall under the definition of "mathematical concepts" or "mental processes" under the USPTO's 2019 PEG. Moreover, the machine learning component is not claimed in the abstract. It is trained on simulation results generated from this physically validated finite element model and used to perform multi-objective optimization of crash energy distribution. The model outputs inform how to configure crash energy absorption structures to minimize deformation of the lead train car and distribute impact forces more evenly-thereby achieving quantifiable improvements in crashworthiness and safety. This integration of real-world data acquisition, engineering simulation, and predictive optimization represents a specific and concrete technical solution. It is not a generic "apply it" directive. As MPEP § 2106.05(c) recognizes, the use of specific machines integral to the claimed process-including the Hopkinson bar device-strongly supports the conclusion that the claim integrates any judicial exception into a practical application. The Examiner contends that the claim lacks detail or structure, citing a generalized statement that it aims to "improve crashworthiness and passive safety." However, the claim includes structural modeling of rail vehicle systems, detailed simulation methods, and specific instrumentation to generate dynamic response data. These limitations go beyond aspirational language and provide a concrete pathway to implement the crash energy optimization method.
The Examiner's suggestion that the claim does not include "technical specifics" or "concrete and likely patentable" elements is plainly contradicted by the recited testing steps, simulation models, and optimization structure. Additionally, the Examiner requests additional "details of the fruits" of the optimization. These are in fact embedded within the claimed steps and described in the specification. The trained machine learning model influences how energy absorption structures are arranged and evaluated during crash conditions. The outcome is a CEM configuration that distributes impact energy in a more controlled and effective manner, directly improving passive safety of rail vehicles. Taken together, the recited method includes specific machine-based measurements, structured simulation steps, and a predictive optimization layer-all working together to produce a technological improvement in the crash performance of rail vehicles. The claim is not an abstract idea, nor a mental or mathematical process, but a practical application of engineering principles using concrete devices and computational tools in a novel arrangement. Accordingly, in view of the Federal Circuit's guidance in Contour IP Holding LLC v. GoPro, Inc, the claimed advance should be recognized as a patent-eligible improvement to a technical field through specific and non-conventional means. Applicant respectfully requests that the Section 101 rejection be withdrawn.
Examiner response
Examiner respectfully disagrees. The act of “establishing a finite element model” in instant claim is a mathematical or computational method-is often considered abstract under patent law since the FEM involves mathematical algorithms and thus generic claims like modeling a structure using FEM lacks a tangible, practical application. Also, the complex mathematical calculations are generally considered as abstract ideas unless they are integrated into a practical application that solves a real-world problem. Also, the pending claims falls under mental process since it solves equations manually. For example, applying velocity to the wheel. According to the MPEP 2106.04(a)(2)(I)(C)-A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. Thus, the features identified by the Examiner are considered mathematical concepts. For example, establishing a finite element model” in instant claim is a mathematical or computational method-is often considered abstract idea and falls under mathematical concepts since the FEM involves mathematical algorithms.
Claim reciting “the mechanical testing and simulation (MTS) universal tester, a high-speed material tester, and a separated Hopkinson bar device-to measure dynamic mechanical properties of structural materials under varying strain rates” is a mere data gathering step and falls under the insignificant extra solution activity as discussed in MPEP 2106.05(g). And the “establishing the dynamic constitutive relationship related to the strain rate of the material of the train body, and introducing the dynamic constitutive relationship into the finite element model for the train body” is an abstract idea since it introduces the derived constitutive relationship (a set of mathematical relationship describing the material's behavior under different strain rates and analyze the nature of introducing this relationship into a finite element model that involves applying the mathematical relationship to a FEM model without sufficiently demonstrating a specific, inventive, and tangible technical application and improvement beyond the abstract mathematical concepts. Finite element models are used to analyze the behavior of materials and structures under various conditions.
In view of machine leaning arguments in view of dependent claim they either falls under the mental process or mathematical concepts or the combination of mental process and mathematical concepts of abstract ideas. For example, claim limitation, “establishing a machine learning database for train crash energy absorption”; (With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., see claim 4) are “within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas.) “Constructing a machine learning prediction model for the train crash energy absorption”; (With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., see claim 6) are “within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas.) and “performing multi-objective optimization on CEM of a train based on the machine learning to achieve designs that minimize the degree of deformation of the head train and ensure even distribution of crash energy; achieving the train CEM more accurately and quickly for eight-marshalling train”;(With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., see claim 9) are “within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas.) and
Unlike the claims in Contour where the technological improvement was identified to be "the real time viewing capabilities of a POV camera's recordings on a remote device, the instant claim does not include additional elements that provide practical application nor more that judicial exception. Broad statement like “utilizing train CEM to improve crashworthiness and passive safety performance of a rail vehicle” without technical specifics are likely unpatentable. According to the MPEP 2106.05(f) -The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". The instant claim does not include additional elements that provide technological improvement to improving crashworthiness and operational passive safety of a rail vehicle. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Applicant arguments step 2A Prong 2
Claim 2 is directed to a train crash energy management (CEM) optimization method based on machine learning, which applies finite element modeling, empirical data acquisition using physical testing devices (e.g., MTS universal tester and Hopkinson bar device), and computational simulation to optimize crash energy absorption in multi-marshalling trains. The method addresses a specific technological problem-namely, how to improve the passive safety performance of high-speed rail vehicles during crashes-and provides a meaningful solution that is practical and technological in nature. The method recited in claim 2 integrates alleged mathematical tools (such as a finite element model or machine learning predictions) into a real-world application that improves the design of physical systems-in this case, the mechanical configuration of trains subject to crash scenarios. The process is not an abstraction but rather a method involving:
" Empirical data collection using specialized physical equipment;
" Constructing finite element models that simulate physical interactions (e.g., wheel-rail rolling contact, energy absorption structure deformation);
" Integrating strain rate-dependent constitutive models derived from mechanical testing into those simulations;
" Applying machine learning to optimize physical performance parameters based on this complex, real-world modeling.
The MPEP is clear that "all additional elements-whether conventional or not-must be given weight" when determining whether a judicial exception is integrated into a practical application. Here, the additional elements-including the MTS device, the physical train model, the layered finite element construction, and the crashworthiness optimization-all serve to root the claimed method firmly within the domain of technological implementation. Accordingly, under the USPTO's own eligibility framework, claim 2 is not directed to a judicial exception alone, but rather integrates any such exception into a practical application that provides a concrete and technological solution to a real-world engineering challenge. Applicant therefore respectfully requests that the § 101 rejection be withdrawn.
Examiner response
Examiner respectfully disagrees. Clam 2 do not recite the additional elements that provide a practical application. Claim does not include additional elements that lead to a tangible and specific technical improvement in the design, manufacturing, or performance of the train body that goes beyond just the abstract idea. Claim only recites the steps that only improves the abstract idea itself. Broad statement such as improving crashworthiness and passive safety utilizing train CEM are merely apply it with the judicial exception since it does not provide technical specifics. A train crash energy management (CEM) optimization method based on machine learning does not include additional elements that provide practical application nor amount to more than judicial exception. The claim does not include specific method or structure that is concrete and likely patentable.
Claim reciting “the mechanical testing and simulation (MTS) universal tester, a high-speed material tester, and a separated Hopkinson bar device-to measure dynamic mechanical properties of structural materials under varying strain rates” is a mere data gathering step i.e. the use of the measuring tool as recited in the claim is part of the insignificant extra-solution activity of data gathering as discussed in MPEP 2106.05(g). And the “establishing the dynamic constitutive relationship related to the strain rate of the material of the train body, and introducing the dynamic constitutive relationship into the finite element model for the train body” is an abstract idea since it introduces the derived constitutive relationship (a set of mathematical relationship describing the material's behavior under different strain rates and analyze the nature of introducing this relationship into a finite element model that involves applying the mathematical relationship to a FEM model without sufficiently demonstrating a specific, inventive, and tangible technical application and improvement beyond the abstract mathematical concepts. Finite element models are used to analyze the behavior of materials and structures under various conditions. The abstract idea cannot provide the improvement, MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. Also see MPEP § 2106.04(1): “a new abstract idea is still an abstract idea”. The improvement can be provided by one or more additional elements….”. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Applicant arguments step 2B
Applicant respectfully submits that claim 2 does include such additional elements, and that the rejection under 35 U.S.C. § 101 is therefore improper. The claim as a whole recites a specific, non-conventional and non-generic arrangement of known components that collectively form a technological improvement in the field of train passive safety. This position is supported by the Federal Circuit's decision in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341 (Fed. Cir. 2016), where the Court held that even if individual claim elements were known, a specific non-generic and non-conventional combination of those elements could amount to "significantly more" than a judicial exception. In that case, the Court found the inventive concept in how the components were arranged and applied to solve a particular technical problem-in BASCOM, internet content filtering in a distributed architecture. Likewise, in Classen Immunotherapies Inc. v. Biogen IDEC, 659 F.3d 1057 (Fed. Cir. 2011), the Court upheld patent eligibility for claims that applied data analysis techniques in a specific and meaningful medical context-namely, optimizing immunization schedules. The key was not whether a data analysis technique was involved, but whether that analysis was tied to a specific technological or therapeutic application. Here, the claim recites the use of specialized mechanical testing equipment (including an MTS universal tester, high-speed material tester, and Hopkinson bar device) to empirically determine dynamic material properties across a range of strain rates. These physical steps are essential for constructing a realistic finite element model of an eight-marshalling train system. The claim then recites the integration of that model into a machine learning prediction platform for multi-objective optimization of crash energy management. These additional steps-such as empirical testing, real-world physical simulation, meshing techniques, and coupling of multiple mechanical subsystems-are not conventional or routine in the abstract, nor are they mere post- solution activity. Rather, they are integral to the performance of the method and represent a non- generic implementation that improves crashworthiness of rail vehicles-a technical domain requiring significant engineering specificity. Thus, under Step 2B, even if a judicial exception were involved, the combination of simulation, physical modeling, empirical material testing, and machine learning optimization constitutes "significantly more" than the judicial exception itself. The claim applies abstract principles in a technologically grounded, non-conventional framework that materially improves a tangible, physical system: train crash safety. For the foregoing reasons, Applicant respectfully submits that the § 101 rejection is improper and should be withdrawn.
Examiner response
Examiner respectfully disagrees. Unlike the claim in BASCOM Global Internet v. AT&T Mobility LLC, that describe a specific, technical solution to a problem in internet content filtering, offering a concrete and inventive application of that abstract idea, rather than simply stating the idea along with generic computer components. Similarly with the claims In Classen Immunotherapies, Inc. v. Biogen Idec, the claim step of immunizing a patient based on a determined schedule is a specific application beyond an abstract idea. The instant claim does not include additional elements that is more than the judicial exception. Simply "establishing a dynamic constitutive relationship" (which is inherently mathematical) and "introducing it into a finite element model" (a computational process using mathematical models) is seen as abstract ideas if not tied to a specific and concrete technical application that provides a tangible improvement. The use of “mechanical testing and simulation (MTS) universal tester, a high-speed material tester, and a separated Hopkinson bar device-to measure dynamic mechanical properties of structural materials under varying strain rates” is a mere data gathering step i.e. the use of the measuring tool as recited in the claim is part of the insignificant extra-solution activity of data gathering as discussed in MPEP 2106.05(g). Neither the specification nor the claim introduces the novel or non-obvious improvements to the testing methodologies themselves: Are there specific, unique ways the MTS, high-speed tester, or Hopkinson bar are being used or modified to obtain more accurate, efficient, or reliable data for this specific type of material or application? The application should emphasize how the invention provides a technical solution to a technical problem in the railway industry. For example, how does it address the challenges of designing for high-speed impact events, reducing damage during derailments, or optimizing passenger safety under various operational stresses?
While machine learning and prediction platforms are valuable tools, merely integrating a model into one seen as implementing an abstract idea (the model itself) using generic computer components. While the domain of application (crash energy management) is clearly technical, simply applying an abstract idea (like multi-objective optimization) to a technical field isn't necessarily enough to establish patentability. The invention needs to demonstrate a specific, technical implementation that enhances the crash energy management system.
Claim Objections
Claims 13-16 objected to because of the following informalities: Claims 13 is dependent on claim 11, however claim 11 is cancelled. The applicant can amend the claim, removing the improper dependency. Appropriate correction is required.
Examiner’s Note: For the purposes of examination, claim 13 will be interpreted as dependent on claim 2.
Claim Rejections - 35 USC §101
5. 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.
6. Claims 2, 4-10 and 12-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
(Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 2, 4-10 and 12-16 are directed to process or method, which falls into the one of the statutory categories.
(Step 2A) (Prong 1) Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? (Judicially recognized exceptions)?
Claim 2 recites
establishing a finite element model, wherein the finite element model is for an eight-marshalling train crash and considers a train energy absorption subsystem and a wheel-rail rolling contact behavior; wherein establishing a finite element model of the eight-marshalling train including energy absorption structures, a body and bogies of the train, based on a characteristic of geometric structures of the train, performing mid-plane extraction on a physical model of the train and using a four-node shell element for discretization, connecting a mid-plane model to each component of the physical model in a same manner, simulating a device on the train by using a mass element, and connecting the device on the train to the train body through a three-node beam element; based on a characteristic of a geometric structure of the bogie, discretizing a framework of the bogie, a traction device, an axle box, and a related structure by using the four-node shell element; simulating an air spring and a spring of the axle box by using a discrete beam material model, and connecting a traction base and a sleeper beam of the train body by using a rigid body and a deformable body; constructing a finite element model for wheel-rail rolling contact based on a type of a wheel tread and a rail structure, discretizing a steel rail and a wheelset by using an eight-node solid element, simulating materials of a wheel and the steel rail by using an elastic-plastic material model considering a strain rate effect, setting automatic surface-to-surface contact between wheel-rails, and locally refining a mesh of a wheel-rail contact region; based on mechanical performance of a coupler buffer device, simulating the coupler buffer device by using a discrete beam element, matching the coupler buffer device with a material model, and applying a stroke failure to the discrete beam element, wherein when a stroke of the coupler buffer device exceeds a rated stroke, the discrete beam element automatically fails; and applying a same translational velocity to both the wheelset and the train body, and applying a corresponding rotational velocity to the wheel, to obtain the finite element model, wherein the finite element model is for the eight-marshalling train crash and considers a crash energy absorption structure of the train body and a wheel-rail rolling contact behavior. (With the broadest reasonable interpretation, the cited features include the combination of mental process and mathematical concepts since it contains steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., finite element) are “within the realm of abstract ideas. So, it falls under the combination of mental process and mathematical concepts of abstract ideas.)
establishing the dynamic constitutive relationship related to the strain rate of the material of the train body, and introducing the dynamic constitutive relationship into the finite element model for the train body. (With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment) or by mathematical relationships that falls within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas.
establishing a machine learning database for train crash energy absorption; (With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., see claim 4) are “within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas.)
constructing a machine learning prediction model for the train crash energy absorption; (With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., see claim 6) are “within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas.) and
performing multi-objective optimization on CEM of a train based on the machine learning to achieve designs that minimize the degree of deformation of the head train and ensure even distribution of crash energy; achieving the train CEM more accurately and quickly for eight-marshalling train;(With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., see claim 9) are “within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas.) and
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. The additional elements of "improving crashworthiness and operational safety of a rail vehicle by utilizing the train CEM obtained" is merely recites the "apply it" with the judicial exception as discussed in MPEP 2106.05(f) and it recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". The additional elements of “using a mechanical testing and simulation (MTS) universal tester, a high-speed material tester, and a separated Hopkinson bar device-to study dynamic mechanical properties of structural material of the train body within a wide strain rate range” is a mere data gathering step i.e. the use of the measuring tool as recited in the claim is part of the insignificant extra-solution activity of data gathering as discussed in MPEP 2106.05(g). The use of machine learning can be considered as additional elements and it is merely reciting the words "apply it" (or an equivalent) with the judicial exception, as discussed in MPEP § 2106.05(f). Thus, a train crash energy management (CEM) optimization method based on machine learning just link the use of judicial exception to a particular field of use as discussed in MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In accordance with Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. The additional elements of "improving crashworthiness and operational safety of a rail vehicle by utilizing the train CEM obtained" is merely recites the "apply it" with the judicial exception as discussed in MPEP 2106.05(f) and it recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". The additional elements of “using a mechanical testing and simulation (MTS) universal tester, a high-speed material tester, and a separated Hopkinson bar device-to study dynamic mechanical properties of structural material of the train body within a wide strain rate range” is a mere data gathering step i.e. the use of the measuring tool as recited in the claim is part of the insignificant extra-solution activity of data gathering i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); ii. Testing a system for a response, the response being used to determine system malfunction, In re Meyers, 688 F.2d 789, 794; 215 USPQ 193, 196-97 (CCPA 1982); as discussed in MPEP 2106.05(g). The use of machine learning can be considered as additional elements and it is merely reciting the words "apply it" (or an equivalent) with the judicial exception, as discussed in MPEP § 2106.05(f). Thus, a train crash energy management (CEM) optimization method based on machine learning just link the use of judicial exception to a particular field of use. Thus, the claim 1 is not patent eligible.
Claim 4 further recites establishing a machine learning database for train crash energy absorption further comprises : based on a concept of the CEM, making energy absorption of crash interfaces of an intermediate carriage as evenly distributed as possible while ensuring that a crash interface of a train nose absorbs more energy; selecting a platform force of each energy absorption interface of a high-speed train as a characteristic parameter, selecting absorbed energy Ea of the crash interface of the train nose and a standard deviation between absorbed energy of the crash interfaces of the intermediate carriage as labels; conducting crash simulation analysis by using the finite element model for the eight-marshalling train crash, and generating a platform force sampling point for each energy absorption interface of the train by using an optimal Latin hypercube experimental design method; and performing batch calculation on train crashes of an energy absorption system under different plastic platform forces by using LS-DYNA explicit dynamics software, obtaining post-processed data based on simulation results, performing feature extraction, and establishing a train crash energy absorption database. With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., standard deviation, batch calculation) are “within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas. As described in MPEP 2106.05(f), limitations (using a LS-DYNA explicit dynamics software) that amount to merely reciting the words "apply it" (or an equivalent) with the judicial exception The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 5 and 12 further recites wherein a data size of the train crash energy absorption database is determined by drawing a learning curve. With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), are “within the realm of abstract ideas. So, it falls under the mental process of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 6 and 13 further recites constructing a machine learning prediction model for the train crash energy absorption further comprises: selecting ensemble learning regression algorithms, comparing, based on a same dataset, capabilities of different algorithms in predicting the absorbed energy Ea and the standard deviation σ between the absorbed energy of the crash interfaces of the intermediate carriage, and selecting an appropriate model to construct a final machine learning prediction model. With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., algorithms) are “within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 7 and 14 further recites wherein the train crash energy absorption database is split into a training set and a test set according to a ratio of 9:1, wherein the training set is configured to train a machine learning prediction model for crash energy absorption, in other words, is configured to perform hyperparameter tuning, and the test set is retained from participating in model training and configured to evaluate a finally trained machine learning prediction model. With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), are “within the realm of abstract ideas. So, it falls under the mental process of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 8 and 15 further recites wherein a hyperparameter of the machine learning prediction model is tuned by using a 10-fold cross-validation method, comprising: in a training process, dividing the training set into ten equal subsets; selecting each subset to validate the machine learning prediction model, and using the other nine subsets to construct the machine learning prediction model in each iteration; training a plurality of models through a plurality of repetitions to obtain a plurality of trained models, and obtaining an average score of the plurality of trained models on a corresponding validation subset; after finding a hyperparameter set with a highest score through grid search and cross-validation, performing training on the entire training set to construct a final prediction model; and validating prediction performance and accuracy of a final optimal hyperparameter model by using the test set, wherein the prediction accuracy of the final optimal hyperparameter model is evaluated by using three indicators: R2, mean absolute error (MAE), and root-mean-square error (RMSE). With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., RMSE, MAE) are “within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 9 and 16 further recites performing multi-objective optimization on CEM of a train based on the machine learning further comprises: based on the concept of the CEM, taking the coupler platform force of each energy absorption interface of the train as a design variable, and taking maximum absorbed energy of the crash interface of the train nose and a minimum standard deviation between the absorbed energy of the crash interfaces of the intermediate carriage as optimization goals, to minimize a degree of damage to the train nose and ensure even distribution of crash energy; predicting a nonlinear relationship between the design variable and the optimization goal and a constraint by using the machine learning prediction model for the train crash energy absorption, and using the nonlinear relationship obtained by the machine learning prediction model as a fitness function to establish a multi-objective optimization model for the CEM; and performing an optimization by using a non-dominated sorting genetic algorithm-II (NSGA-II) method and combining a machine learning agent model to obtain a Pareto solution set. With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), or by mathematical calculation (i.e., algorithm) are “within the realm of abstract ideas. So, it falls under the mental process or mathematical concepts of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 10 further recites based on a questionnaire survey result scored by an expert, comparing impacts of different weights of the two optimization goals on an optimization result, performing comparative analysis on the optimization result and a finite element simulation result to validate accuracy of a prediction result of the machine learning agent model, and comprehensively evaluating crashworthiness of an optimized train to validate effectiveness of an energy management method. With the broadest reasonable interpretation, the cited features contain steps people go through in their minds (or using pen and paper since it includes evaluation or judgment), are “within the realm of abstract ideas. So, it falls under the mental process of abstract ideas. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Conclusion
7. Claims 2, 4-10 and 12-16 is/are rejected.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Guan, Weiyuan, Yao Yu, and Guangjun Gao. "Crashworthiness performance and multiobjective optimization of a combined splitting circular tube energy absorber under eccentric impact for subway vehicles." International Journal of Impact Engineering 158 (2021): 104006.
Discussing the crashworthiness of the combined splitting tube energy absorber under eccentric impact for subway vehicles. First, the combined splitting circular tube energy absorber is designed, and the corresponding engineering prototype is processed. The crashworthiness of the structure is verified by the actual vehicle impact test. Second, an FE model of the combined splitting circular tube energy absorber is established and verified by impact experiments.
CN111353220A LIU et al.
Discussing the method for rapidly analyzing collision energy distribution of a train, which can effectively solve the technical problem of theoretical support of passive safety design of the train.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to PURSOTTAM GIRI whose telephone number is (469)295-9101. The examiner can normally be reached 7:30-5:30 PM, Monday to Friday.
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/PURSOTTAM GIRI/Examiner, Art Unit 2188
/MICHAEL EDWARD COCCHI/Primary Examiner, Art Unit 2188