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
This is a non-final rejection. Claims 1-20 are pending.
Information Disclosure Statement (IDS)
The information disclosure statement(s) filed on 07/20/2023 comply with the provisions 37 CFR 1.97, 1.98, and MPEP 609 and is considered by the Examiner.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/23/2025 has been entered.
Status of Claims
Applicant’s amendment date 10/23/2025. Amending claims 1 and 12.
Response to Amendment
The previously pending rejection under 35 USC 101, will be maintained. The 101 is updated in light of the amendments.
With regard to the rejection under 35 USC 103- Applicant’s arguments, see pages 17-21, filed 10/23/2025, with respect to the art rejection have been fully considered and are persuasive, the rejection under 35 USC 102/103 has been withdrawn. No art rejection has been put forth in the rejection for the reason found in the “Allowable Subject Matter” section found below.
Response to Arguments
Applicant’s argument received 10/23/2025 have been fully considered, but they are not persuasive.
Response to Arguments under 35 USC 101:
Applicant argues (Page 10 of the remarks): abstract idea
Claims 1-20 stand rejected under 35 U.S.C. § 101 as allegedly being directed towards an abstract idea without significantly more, and in particular towards certain methods of organizing human activity, including commercial or legal interactions.
Applicant respectfully disagrees and traverses for at least the following reasons. According to the MPEP: "A claim reciting a judicial exception is not directed to the judicial exception … integrates the exception into a practical application. One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technologv or technical field." (MPEP § 2106.04(d)(l)) (emphasis added) According to the MPEP, determining whether a claim demonstrates an improvement to a technology or a technical field includes an evaluation of whether "the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art." (MPEP § 2106.04(d)(l)) (emphasis added).
Examiner respectfully disagrees:
Independent Claims 1, similar steps likewise reflect in claim 12, the claims, when “taken as a whole,” are directed to the abstract idea and substantially recite the limitations: A method, comprising:
receiving, by a processor, a first dataset with time-independent characteristics associated with a plurality of infrastructure assets of an infrastructural system;
wherein the first dataset with time-independent characteristics comprises:
a plurality of length features representative of a plurality of measures of length associated with the plurality of infrastructure assets, and
a plurality of track-layout-related features representing a plurality of track-layout-related characteristics of the plurality of infrastructure assets;
receiving, by the processor, a second dataset with time-dependent characteristics associated with the plurality of infrastructure assets;
segmenting, by the processor, the infrastructural system to group feature-based segments of a plurality of asset components into the plurality of infrastructure assets based at least in part on an optimization function applied to at least one of the time independent characteristics or the time-dependent characteristics;
wherein the optimization function applied to the at least one of the time-independent characteristics or the time-dependent characteristics is configured to minimize information loss based on weighted variances of features for more accurate predictions by a degradation machine learning model;
generating, by the processor, a plurality of data records comprising a data record for each infrastructure asset of the plurality of infrastructure assets wherein each data record from the plurality of data records comprises:
i) a subset of the first dataset comprising time-independent characteristics associated with the plurality of asset components, and
ii) a subset of the second dataset comprising time-dependent characteristics associated with plurality of asset components;
generating, by the processor, a set of features associated with the infrastructural system utilizing the plurality of data records;
inputting, by the processor, the set of features into a degradation machine learning model;
receiving, by the processor, an output from the degradation machine learning model indicative of a prediction of a condition of an infrastructure asset component of the plurality of asset components within a predetermined time; and
effectuating, by the processor, based at least in part on the condition predicted for the infrastructure asset component within the predetermined time, one remediation action comprising at least one modification to a speed of at least one train approaching the infrastructure asset component.
The Applicant's Specification titled " SYSTEMS FOR INFRASTRUCTURE DEGRADATION MODELLING AND METHODS OF USE THEREOF" emphasizes the business need for data analysis, "In summary, the present disclosure relates to methods and systems for rendering a representation of a location, the condition predicted from the infrastructure asset component within the predetermined time, and at least one recommended asset management decision" (Spec. [4]).
As the bolded claim limitations above demonstrate, independent claims 1, and 12 are recites the abstract idea of rendering a representation of a location, the condition predicted from the infrastructure asset component within the predetermined time, and at least one recommended asset management decision. which is considered certain methods of organizing human activity because the bolded claim limitations pertain to (i) commercial or legal interactions. See MPEP §2106.04(a)(2)(II).
Applicant's claims as recited above provide a business solution of generating an output flight schedule using the model to obtain an extrema using a set of objectives. Applicant's claimed invention pertains to commercial/legal interactions because the limitations recite rendering a representation of a location, the condition predicted from the infrastructure asset component within the predetermined time, and at least one recommended asset management decision. which pertain to "agreements in the form of contracts; legal obligation; behaviors; business relations" expressly categorized under commercial/legal interactions. See MPEP §2106.04(a)(2)(II).
Also, Applicant's claims as recited above provide a business solution of segmenting the infrastructural system to group segments of a plurality of asset components based on different characteristics data (see figure . Applicant's claimed invention pertains to Mental Processes. which pertain to "concepts performed in the human mind (including an observation, evaluation, judgment, opinion)" expressly categorized under Mental Processes. See MPEP §2106.04(a)(2)(II).
Applicant argues (Pages 11-16 of the remarks): prong two and 2B
That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"). "(MPEP § 2106.04(d)(l)) (emphasis added).
Further, the Application as filed identifies a particular solution to the information loss problem by creating a feature-based segmentation that segments the infrastructure network according to features in each segment, rather than length, thus preserving information applicable to each segment that results in improved modelling, and as a result, improved remediation actions to prevent derailments … As a result, the associated data records are structured in such a way as to enhance and improve the performance, accuracy and reliability of a model applied to the data.
Examiner respectfully disagrees:
First, examiner point out that there is no automatically controlling step of a train speed, the claim recite that one remediation action comprising at least on modification to a speed of at least train (see applicant specification [368])
Second, In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional element, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use exception, such that it is more than a drafting effort designed to monopolize the exception.
The claims recites the additional limitation of a system, database, a processor, a degradation machine learning model are recited in a high level of generality and recited as performing generic computer functions routinely used in computer applications. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp. 134 S. Ct, at 2360,110 USPQ2d at 1984 (see MPEP 2106.05(f).
The use of generic computer component to “generate a recommended action” does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (step 2A-prong two: NO).
The Alice framework, we turn to step 2B (Part 2 of Mayo) to determine if the claim is sufficient to ensure that the claim amounts to “significantly more” than the abstract idea itself. These additional elements recite conventional computer components and conventional functions of:
Claims 1, and 12 does not include my limitations amounting to significantly more than the abstract idea, along. Claims 1, and 12 includes various elements that are not directed to the abstract idea. These elements include a system, database, a processor, a degradation machine learning model.
Examiner asserts that the additional elements in the claims are a generic computing element performing generic computing functions.
Further, with data mining (i.e., searching over a network), receiving, processing, storing data, and parsing (i.e. extract, transform data) the courts have recognized the following computer function as well-understood, routing, and conventional functions when they are claimed in merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (i.e. “receiving, processing, transmitting, storing data”, etc.) are well-understood, routine, etc. (MPEP 2106.059d)).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices.
Claim Objections
Claims 1 and 12 objected to because of the following informalities:
Claims 1 and 12 recite “inputting, by the processor, the set of features into a degradation machine learning model;”, it should be “inputting, by the processor, the set of features into the degradation machine learning model;”
Appropriate correction is required.
Claim Rejections 35 USC §101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without a practical application or significantly more than the abstract idea.
Under the 35 U.S.C. §101 subject matter eligibility two-part analysis, Step 1 addresses whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. See MPEP §2106.03. If the claim does fall within one of the statutory categories, it must then be determined in Step 2A [prong 1] whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea). See MPEP §2106.04. If the claim is directed toward a judicial exception, it must then be determined in Step 2A [prong 2] whether the judicial exception is integrated into a practical application. See MPEP §2106.04(d). Finally, if the judicial exception is not integrated into a practical application, it must additionally be determined in Step 2B whether the claim recites "significantly more" than the abstract idea. See MPEP §2106.05.
Examiner note: The Office's 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG) is currently found in the Ninth Edition, Revision 10.2019 (revised June 2020) of the Manual of Patent Examination Procedure (MPEP), specifically incorporated in MPEP §2106.03 through MPEP §2106.07(c).
Regarding Step 1
Claims 1-11 are directed toward a method (process) and claims 12-20 are directed to a system (machine). Thus, all claims fall within one of the four statutory categories as required by Step 1.
Regarding Step 2A [prong 1]
Claims 1-20 are directed toward the judicial exception of an abstract idea. Independent claim 12 recites essentially the same abstract features as claim 1, thus are abstract for the same reasons as claim 1.
Regarding independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1. A method, comprising:
receiving, by a processor, a first dataset with time-independent characteristics associated with a plurality of infrastructure assets of an infrastructural system;
wherein the first dataset with time-independent characteristics comprises:
a plurality of length features representative of a plurality of measures of length associated with the plurality of infrastructure assets, and
a plurality of track-layout-related features representing a plurality of track-layout-related characteristics of the plurality of infrastructure assets;
receiving, by the processor, a second dataset with time-dependent characteristics associated with the plurality of infrastructure assets;
segmenting, by the processor, the infrastructural system to group feature-based segments of a plurality of asset components into the plurality of infrastructure assets based at least in part on an optimization function applied to at least one of the time independent characteristics or the time-dependent characteristics;
wherein the optimization function applied to the at least one of the time-independent characteristics or the time-dependent characteristics is configured to minimize information loss based on weighted variances of features for more accurate predictions by a degradation machine learning model;
generating, by the processor, a plurality of data records comprising a data record for each infrastructure asset of the plurality of infrastructure assets wherein each data record from the plurality of data records comprises:
i) a subset of the first dataset comprising time-independent characteristics associated with the plurality of asset components, and
ii) a subset of the second dataset comprising time-dependent characteristics associated with plurality of asset components;
generating, by the processor, a set of features associated with the infrastructural system utilizing the plurality of data records;
inputting, by the processor, the set of features into a degradation machine learning model;
receiving, by the processor, an output from the degradation machine learning model indicative of a prediction of a condition of an infrastructure asset component of the plurality of asset components within a predetermined time; and
effectuating, by the processor, based at least in part on the condition predicted for the infrastructure asset component within the predetermined time, one remediation action comprising at least one modification to a speed of at least one train approaching the infrastructure asset component.
The Applicant's Specification titled " SYSTEMS FOR INFRASTRUCTURE DEGRADATION MODELLING AND METHODS OF USE THEREOF" emphasizes the business need for data analysis, "In summary, the present disclosure relates to methods and systems for rendering a representation of a location, the condition predicted from the infrastructure asset component within the predetermined time, and at least one recommended asset management decision" (Spec. [4]).
As the bolded claim limitations above demonstrate, independent claims 1, and 12 are recites the abstract idea of rendering a representation of a location, the condition predicted from the infrastructure asset component within the predetermined time, and at least one recommended asset management decision. which is considered certain methods of organizing human activity because the bolded claim limitations pertain to (i) commercial or legal interactions. See MPEP §2106.04(a)(2)(II).
Applicant's claims as recited above provide a business solution of generating an output flight schedule using the model to obtain an extrema using a set of objectives. Applicant's claimed invention pertains to commercial/legal interactions because the limitations recite rendering a representation of a location, the condition predicted from the infrastructure asset component within the predetermined time, and at least one recommended asset management decision. which pertain to "agreements in the form of contracts; legal obligation; behaviors; business relations" expressly categorized under commercial/legal interactions. See MPEP §2106.04(a)(2)(II).
Also, Applicant's claims as recited above provide a business solution of segmenting the infrastructural system to group segments of a plurality of asset components based on different characteristics data (see figure . Applicant's claimed invention pertains to Mental Processes. which pertain to "concepts performed in the human mind (including an observation, evaluation, judgment, opinion)" expressly categorized under Mental Processes. See MPEP §2106.04(a)(2)(II).
Dependent claims 2-11 and 13-20 further reiterate the same abstract ideas with further embellishments, such as
claim 2 (Similarly Claim 13) wherein the infrastructural system comprises a rail system;
wherein the plurality of infrastructure assets comprise a plurality of rail segments; and
wherein the plurality of asset components comprise a plurality of adjacent rail subsegments.
claim 3 (Similarly Claim 14) segmenting, by the processor, the plurality of infrastructure assets into a plurality of segments of infrastructure assets based on length; and
generating, by the processor, the plurality of data records representing the plurality of segments of infrastructure assets.
claim 4 (Similarly Claim 15) segmenting, by the processor, the plurality of infrastructure assets into a plurality of segments of infrastructure assets based on asset features; and
generating, by the processor, the plurality of data records representing the plurality of segments of infrastructure assets.
claim 5 (Similarly Claim 16) wherein the asset features comprise at least one of traffic data, vehicle speed data, vehicle operational data, asset weight data, asset age data, asset design data, asset material data, asset condition data, asset defect data, asset failure data, inspection data, maintenance data, repair data, replacement data, rehabilitation data, asset usage data, asset geometry data or a combination thereof.
claim 6 (Similarly Claim 17) further comprising determining, by the processor, the plurality of segments of infrastructure assets according to a minimal internal variance of the asset features of the plurality of infrastructure assets in each segment of the plurality of segments of infrastructure assets.
claim 7 (Similarly Claim 18) wherein features of the set of features comprise at least one of: i) usage data, traffic data, speed data and operational data, ii) environmental impact data, iii) asset characteristics data, design and geometric data, and condition data, iv) inspection results data, v) inspection data, maintenance data, repair data, replacement data, rehabilitation data, or iv) any combination thereof.
claim 8 (Similarly Claim 19) generating, by the processor, features associated with the infrastructural system utilizing the plurality of data records; and
inputting, by the processor, the features into a feature selection machine learning algorithm to select the set of features.
claim 9 (Similarly Claim 20) inputting, by the processor, the set of features into the degradation machine learning model to produce event probabilities;
encoding, by the processor, outcome events of the set of features into a plurality of outcome labels;
mapping, by the processor, the event probabilities to the plurality of outcome labels; and
decoding, by the processor, the event probabilities based on the mapping to produce the prediction of the condition.
claim 10 further comprising encoding, by the processor, the outcome events of the set of features into at least one soft tiling of the plurality of outcome labels;
wherein the plurality of outcome labels comprises a plurality of time-based tiles of outcome labels.
claim 11 wherein the degradation machine learning model comprises at least one neural network.
which are nonetheless directed towards fundamentally the same abstract ideas as indicated for independent claims 1, and 12.
Regarding Step 2A [prong 2]
Claims 1-20 fail to integrate the abstract idea into a practical application. Independent claims 1, and 12 include the following additional elements which do not amount to a practical application:
Claim 1. A processor, a degradation machine learning model and a graphical user interface
Claim 12. A system, database, a processor, a degradation machine learning model and a graphical user interface.
The bolded limitations recited above in independent claims 1, and 12 pertain to additional elements which merely provide an abstract-idea-based-solution implemented with computer hardware and software components, including the additional elements of a system, database, a processor, a degradation machine learning model. which fail to integrate the abstract idea into a practical application because there are (1) no actual improvements to the functioning of a computer, (2) nor to any other technology or technical field, (3) nor do the claims apply the judicial exception with, or by use of, a particular machine, (4) nor do the claims provide a transformation or reduction of a particular article to a different state or thing, (5) nor provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment, in view of MPEP §2106.04(d)(1) and §2106.05 (a-c & e-h), (6) nor do the claims apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, in view of MPEP §2106.04(d)(2). The Specification provides a high level of generality regarding the additional elements claimed without sufficient detail or specific implementation structure so as to limit the abstract idea, for instance, (fig. 10). Nothing in the Specification describes the specific operations recited in claim 1 (Similarly claim 12) as particularly invoking any inventive programming, or requiring any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is somehow implemented using any specialized element other than all-purpose computer components to perform recited computer functions. The claimed invention is merely directed to utilizing computer technology as a tool for solving a business problem of data analytics. Nowhere in the Specification does the Applicant emphasize additional hardware and/or software elements which provide an actual improvement in computer functionality, or to a technology or technical field, other than using these elements as a computational tool to automate and perform the abstract idea. See MPEP §2106.05(a & e).
The additional elements of a “machine learning model”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea.
The relevant question under Step 2A [prong 2] is not whether the claimed invention itself is a practical application, instead, the question is whether the claimed invention includes additional elements beyond the judicial exception that integrate the judicial exception into a practical application by imposing a meaningful limit on the judicial exception. This is not the case with Applicant's claimed invention which merely pertains to steps for rendering a representation of a location, the condition predicted from the infrastructure asset component within the predetermined time, and at least one recommended asset management decision and the additional computer elements a tool to perform the abstract idea, and merely linking the use of the abstract idea to a particular technological environment. See MPEP §2106.04 and §21062106.05(f-h). Alternatively, the Office has long considered data gathering, analysis and data output to be insignificant extra-solution activity, and these additional elements do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.04 and §2106.05(g). Thus, the additional elements recited above fail to provide an actual improvement in computer functionality, or to a technology or technical field. See MPEP §2106.04(d)(1) and §2106§2106.05 (a & e).
Instead, the recited additional elements above, merely limit the invention to a technological environment in which the abstract concept identified above is implemented utilizing the computational tools provided by the additional elements to automate and perform the abstract idea, which is insufficient to provide a practical application since the additional elements do no more than generally link the use of the abstract idea to a particular technological environment. See MPEP §2106.04. Automating the recited claimed features as a combination of computer instructions implemented by computer hardware and/or software elements as recited above does not qualify an otherwise unpatentable abstract idea as patent eligible. Alternatively, the Office has long considered data gathering and data processing as well as data output recruitment information on a social network to be insignificant extra-solution activity, and these additional elements used to gather and output recruitment information on a social network are insignificant extra-solution limitations that do not impose any meaningful limits on practicing the abstract idea. See MPEP §2106.05(g). The current invention rendering a representation of a location, the condition predicted from the infrastructure asset component within the predetermined time, and at least one recommended asset management decision. When considered in combination, the claims do not amount to improvements of the functioning of a computer, or to any technology or technical field. Applicant's limitations as recited above do nothing more than supplement the abstract idea using additional hardware/software computer components as a tool to perform the abstract idea and generally link the use of the abstract idea to a technological environment, which is not sufficient to integrate the judicial exception into a practical application since they do not impose any meaningful limits.
Dependent claims 2-11, and 13-20 merely incorporate the additional elements recited above, along with further embellishments of the abstract idea of independent claims 1, and 12 respectively, for example claim 11 recite a neural network but these features only serve to further limit the abstract idea of independent claims 1, and 12, furthermore, merely using/applying in a computer environment such as merely using the computer as a tool to apply instructions of the abstract idea do nothing more than provide insignificant extra-solution activity since they amount to data gathering, analysis and outputting. Furthermore, they do not pertain to a technological problem being solved in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, and/or the limitations fail to achieve an actual improvement in computer functionality or improvement in specific technology other than using the computer as a tool to perform the abstract idea.
Therefore, the additional elements recited in the claimed invention individually, and in combination fail to integrate the recited judicial exception into any practical application.
Regarding Step 2B
Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional element(s) as described above with respect to Step 2A Prong 2, the additional element of claims 1, and 12 include a system, database, a processor, a degradation machine learning model and a graphical user interface. Claim 11 a neural network . The displaying interface and storing data merely amount to a general purpose computer used to apply the abstract idea(s) (MPEP 2106.05(f)) and/or performs insignificant extra-solution activity, e.g. data retrieval and storage, as described above (MPEP 2106.05(g)) which are further merely well-understood, routine, and conventional activit(ies) as evidenced by MPEP 2106.06(05)(d)(II) (describing conventional activities that include transmitting and receiving data over a network, electronic recordkeeping, storing and retrieving information from memory, electronically scanning or extracting data from a physical document, and a web browser’s back and forward button functionality). Therefore, similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea directed to rendering a representation of a location, the condition predicted from the infrastructure asset component within the predetermined time, and at least one recommended asset management decision.
Claims 1-5, 7-15, and 17-23 is accordingly rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea(s)) without significantly more.
Allowable Subject Matter
Regarding the 35 USC 103 rejection, No art rejections has been put forth in the rejection.
The closest prior art of record are Boucher et al. US 2022/0355839: Monitoring, prediction and maintaining the condition of railroad elements with digital twins, Bhattacharjya et al. US 2014/0200827: Railway track geometry defect modeling for predicting deterioration, derailment risk, and optimal repair, Oldknow et al. US 2021/0150370: Optimizing rail track performance, Tepper, Mariano. "Do place cells dream of conditional probabilities? Learning Neural Nystr\" om representations." arXiv preprint arXiv:1906.01102 (2019). None of the prior art of record, taken individually or in combination, teach, inter alia, teaches the claimed invention as detailed in independent claims, “wherein the first dataset with time-independent characteristics comprises: a plurality of length features representative of a plurality of measures of length associated with the plurality of infrastructure assets, and a plurality of track-layout-related features representing a plurality of track-layout-related characteristics of the plurality of infrastructure assets; receiving, by the processor, a second dataset with time-dependent characteristics associated with the plurality of infrastructure assets; segmenting, by the processor, the infrastructural system to group feature-based segments of a plurality of asset components into the plurality of infrastructure assets based at least in part on an optimization function applied to at least one of the time independent characteristics or the time-dependent characteristics; wherein the optimization function applied to the at least one of the time-independent characteristics or the time-dependent characteristics is configured to minimize information loss based on weighted variances of features for more accurate predictions by a degradation machine learning model;”. The 35 USC 103 rejection of claims 1-20 in the instant application is not apply because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li Q, Zhou J, Jiang Z, Ma J. Train wheel degradation modeling and remaining useful life prediction based on mixed effect model considering dependent measurement errors. IEEE Access. 2019 Oct 31;7:159058-68.
Ouyang M. Review on modeling and simulation of interdependent critical infrastructure systems. Reliability engineering & System safety. 2014 Jan 1;121:43-60.
Elkhoury, Najwa, et al. "Degradation prediction of rail tracks: A review of the existing literature." The Open Transportation Journal 12.1 (2018).
Zhou Kang Artificial-intelligence-aided Prediction of Broken Rail-caused Derailment Risk Rutgers The State University of New Jersey, School of Graduate Studies ProQuest Dissertations & Theses, 2019. 27540335.
Bertoni Scarton et al. US 11,488,083: Risk failure prediction for line assets.
Makeev et al. US 2020/0374537: Methods and apparatuses for encoding a bytestream.
Baker, James, K WO2020/033645: Companion analysis network in deep learning.
Martin US 2019/0012627: Railroad engineering asset management systems and methods.
Currin et al. US 2015/0112647: Systems and methods for advanced sanitary sewer infrastructure management.
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/HAMZEH OBAID/Primary Examiner, Art Unit 3624