DETAILED ACTION
This is a non-final Office Action in response to communications received on 07/18/2024 and 08/20/2026. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority or Provisional
Priority to 07/20/2023 is recognized.
Drawings
The drawings filed on 07/18/2024 are acknowledged.
Response to Arguments
Applicant’s arguments, filed 08/20/2026, to claims 1-11 have been fully considered. Claims 4, and 10 have been canceled. No new claims have been added.
With respect to the rejections of independent claims 1, 9, and dependent claims, under applied prior art references of record in the office action dated 07/06/2026, have been fully considered.
Applicant’s arguments have been found persuasive. Accordingly, the rejection has been modified. Please see the new grounds of rejection set forth below.
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.
Claims 1-3, 5-6, and 9 are rejected under 35 U.S.C. 103 over LeFebvre (US 2010/0174428) in view of Li (WO 2020/062449), and further in view of Walczyk (US 2024/0361733), and Lederman (US 10,351,150).
Regarding claim 1, LeFebvre in view of Li and further in view of Walczyk, and Lederman discloses the limitations of claim 1 as follows:
A system for diagnosing operational safety of railcars, the system comprising: LeFebvre, Abstract, Paras. [0001]-[0006] a vibration diagnosis module (110) configured to acquire a vibration signal by measuring vibrations generated from a running car (11) through one or more vibration sensors (101) installed in each car (11) of a train (10) and generate vibration data by analyzing the vibration signal; LeFebvre, Paras. [0021]-[0024], [0033]-[0036], [0053]-[0054], Figs. 3-4, teaches instrumented pads with sensors analyzing time series data, stating: “…the instrumented pads 4 contain several sensors for measurement of dynamic and static vertical loads and shear or lateral force…..”, and “…the microprocessors 19 at the instrumented pads 16 take a series of measurements and treat them as a time series. …… can identify, for example, periodicities and cross correlations…..”.
a car computer (100) installed in each car (11) and configured to receive the vibration data generated by the vibration diagnosis module (110) of the corresponding car (11) and then generate running data by adding running information including a travel location and a travel speed of the car (11) to the vibration data; LeFebvre, Paras. [0021]-[0024], [0033]-[0036], Figs. 1-2, teaches a data control unit adding vehicle speed and location, stating: “Mounted on the railcar body, …… is a data control unit 23 ….”, “It passes on information from the communications device 24. For example, the communications device 24 may include a means of measurement such as a global positioning system to provide information about vehicle speed…”.
a train control and monitoring system (TCMS) (12) configured to generate integrated running data by aggregating the running data received from the car computers (100) of the cars (11) constituting the train (10) and then transmit the integrated running data to a central control system (1); Li , Paras. [0001], [0011]-[0012], [0038]-[0039], [0049]-[0050], teaches a train-level aggregation tier, stating: “This invention belongs to the field of electric locomotive equipment, specifically a VCU (Vehicle Control Unit) multiple-unit cabinet for TCMS (Train Control and Management System) network control”, and teaches a “GW (Gateway)” acting as a bridge where the vehicle bus MVB module is used to connect to the internal vehicle bus, and the train bus WTB module is used to connect to the train bus.
LeFebvre, Para. [0095], Figs. 1-4, teaches transmitting the aggregated data off the train to a remote receiver, stating: “As a conduit, the data control unit 23 passes messages on to the communications device 24 for onward transmission to the locomotive or to other remote receivers…”.
a server (2) configured to build big data by storing the integrated running data transmitted to the central control system (1); LeFebvre, Paras. [0094]-[0095], Figs. 1-4, teaches a central repository, stating: “…this regular pattern can be superseded by commands sent to the data control unit from the central data repository or by command of the owners or operators”.
and a big data analysis module (200) configured to estimate an abnormal part of a car or a track by analyzing the integrated running data through pretrained artificial intelligence (Al) and transmit the estimated abnormal part to the central control system (1) such that the Al is retrained using data which is acquired by comparing an estimation result with an actual measurement result of an abnormality check, Walczyk, Paras. [0033]-[0034], Fig. 12, teaches machine learning models identifying abnormalities verified by analyst feedback, stating: “…incorporate machine learning (ML) models that can automatically analyze and identify possible abnormalities in the vibration data sets”, and “…any vibration data sets tagged by the ML model as abnormal can be provided to an analyst for further review…. can provide feedback regarding whether building equipment associated with abnormal data sets is actually in a fault state”.
wherein the entire track on which the train (10) runs is divided into a certain number of sections, and the vibration data measured from the car (11) is generated section by section such that the big data analysis module (200) estimates an abnormal part of a specific car (11) or the tracks Lederman, Col. 14, ll. 29-45, Col. 21, ll. 15-26, Figs. 1 and 17, teaches extracting data for specific spatial sections of track and isolating localized track anomalies from system-wide anomalies, illustrated in Fig. 1, “Change Detection -Look for statistical anomalies at a given section of track -Reject anomalies occurring over the whole system”, and stating: “Data from one pass of the train traversing a 50 m section of track is shown at the top of FIG. 17. The vibration signal is plotted as a function of the train's position according to GPS”, and “…For our analysis, we consider a 1 km section of track; thus the observed data vector of the extracted energy feature, ….has a length of 1500”.
wherein the vibration diagnosis module (110) determines whether the generated vibration data falls within a preset vibration value tolerance range for a corresponding track section and transmits a determination result to the car computer (100) along with the vibration data, Lederman, Col. 7, ll. 52-67, Col. 8, ll. 1-16, Col. 9, ll. 1-5, Figs. 1 and 17, teaches extracting data for a particular track location and evaluating it against a specific threshold to detect changes, stating: “The signal-energy feature from the vibration signal was computed, then the data was extracted for a particular track location across all N passes”, “All three change-detection methods listed above effectively detected a change in the mean-value of the feature at a particular location of track compared to that value for a window … of previous passes”, “A change has occurred when g exceeds a threshold, h, i.e. (n, i)>h. In other words, beyond some threshold, the data is so much more likely to have come from a new distribution than from the historical distribution that we say a change in the data has occurred”.
and wherein the preset vibration value tolerance range is continuously updated by feeding back, to the vibration diagnosis module (110), an Al retraining result based on big data analysis by the big data analysis module (200). Walczyk, Paras. [0012], [0033]-[0034], Fig. 12, teaches pushing configuration updates to the edge model, stating: “…customizing logic used in the steps of ingesting, performing, and/or determining by modifying programming of the edge device based on instructions received from a computing system over the communications network”.
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the invention to modify the monitoring system of LeFebvre to incorporate the standard TCMS network topology (incorporating the MVB/WTB gateway) as taught by Li, the edge-based machine learning anomaly detection and configuration update feedback loop of Walczyk, and further incorporating the section-by-section spatial track analysis and localized threshold as taught by Lederman, in order to consolidate multi-car data at a single train-level gateway before transmitting it to the remote server to prevent wireless bandwidth congestion, and further reduce communication latency and isolate localized track defect and minimize false positive.
Regarding claim 2, LeFebvre-Li, Walczyk and Lederman disclose the limitations of claim 1. LeFebvre-Li, in view of Walczyk and Lederman discloses:
The system of claim 1, wherein the vibration diagnosis module (110) generates the vibration data using an analysis method based on any one of a root mean square (RMS), a vibration level, a ride comfort index, and a ride comfort level which are representative values of a dynamic state of the car (11). Lederman, Col. 7, ll. 35-44, 52-61, teaches extracting data for particular track locations and calculating the signal-energy of the vibration signal (i.e., RMS), stating: “ The signal-energy feature from the vibration signal was computed, then the data was extracted for a particular track location across all N passes”, “In industry, the most common parameter for measuring track geometry is the standard deviation of the track profile, and signal-energy of the vibration signal is related to this parameter, as both involve squaring their respective data points”.
The same motivation to combine utilized in claim 1 is equally applicable in the instant claim.
Regarding claim 3, LeFebvre-Li, Walczyk and Lederman disclose the limitations of claim 1. LeFebvre-Li, in view of Walczyk and Lederman discloses:
The system of claim 1, wherein the vibration sensors (111) of the vibration diagnosis module (110) are installed on a main part of the car (11) including a wheelset or a bogie. LeFebvre, Paras. [0017]-[0018], [0033]-[0036], Fig. 4, teaches installing the sensors on the instrumented pads located on the bearing adapters of the railcar truck/bogie, which transmit forces from the wheelset, stating: “Turning now to FIGS. 1, 2 and 4, trucks 1, shown diagrammatically, each carry two axles 2 each with two wheels 3. The axle bearings 2a and bearing adapters 4, best shown in FIG. 4, are configured so that each bearing transmits the load which it carries and heat that it may generate through pads 16 and to the truck”, “FIG. 4 illustrates a portion of a railcar truck 1 showing the relationship of an instrumented pad 16 relative to other truck parts”, “In an exemplary system, the instrumented pads 4 contain several sensors for measurement of dynamic and static vertical loads and shear or lateral forces imposed by the railcar truck … Conversely, these are the forces which the axles, themselves attached rigidly to the wheels, and subject to track irregularities, are applying to the truck”.
The same motivation to combine utilized in claim 1 is equally applicable in the instant claim.
Regarding claim 5, LeFebvre-Li, in view of Walczyk and Lederman disclose the limitations of claim 1. LeFebvre-Li, in view of Walczyk and Lederman disclose:
The system of claim 1, wherein the preset vibration value tolerance range is set depending on types including a vehicle type of the train (10) or cars (11), Lederman, Col. 10, ll. 33-43, Col. 12, ll. 53-56, teaches modeling the expected vibration threshold based on the specific physical parameters (mass, damping) of the specific vehicle type, stating: “The vertical motion of the oscillator is governed by spring stiffness, k, damping, c, vehicle mass, m, and the roughness experienced by the train r′(t)”, “…The train car is a CAF light-rail vehicle that has an empty mass of 40 metric tons and a length of 27 m”.
a format of the train (10) or the cars (11), or the number of organized cars (11), LeFebvre, Para. [0054], teaches that vibration signature depend on the specific vehicle format, stating: “To various extents, depending on vehicle design, these irregularities in behavior will be apparent in the changing load and distribution of load in the instrumented pad, ….”.
and the integrated running data transmitted from the TCMS (12) to the central control system (1) includes type-specific data including the vehicle type of the train (10) or the cars (11), the format of the train (10) or the cars (11), or the number of organized cars (11). LeFebvre, Abstract, Para. [0011], [0054], teaches transmitting identifying parameters of the specific car along with the performance data to the central source, stating: “The system cyclically activates polling each pad on a car and communicates signals of critical departures and car identity to a remote source”, and “… the exact location on the train of the item or items in question can be unequivocally determined, and that the location of the train, or indeed of the freight car can be reported,…”.
As discussed in the rejection of the claim 1, Li teaches the TCMS gateway aggregating this local car data for train-level network transmission.
the same motivation to combine utilized in claim 1 is equally applicable in the instant claim.
Regarding claim 6, LeFebvre-Li, Walczyk and Lederman disclose the limitations of claim 1. LeFebvre-Li in view of Walczyk and Lederman discloses:
The system of claim 1, wherein the big data analysis module (200) builds an abnormality diagnosis map of the track and the cars (11) by diagnosing a specific track section as a dangerous section when abnormal vibration data is measured from a plurality of trains running on the track section, Lederman, Col. 17, ll. 29-41, Col. 22, ll. 6-12, teaches building an estimate of the track state by fusing vibration data from a plurality of trains running on the same track section to ensure statistical accuracy, stating: “Ideally, data from as many sensors and as many trains as possible is incorporated, …. To accomplish this, a data processing pipeline that incorporates state estimation elements from a Kalman filter is proposed”, “Data fusion is discussed at both the train level, i.e. combining data from sensors, and at the network level, i.e. combining data from trains. However in both cases, the most important component of fusion is combining data between passes, because individual passes can have abnormally low values or high position offsets, both of which are handled by the proposed fusion approach”.
Lederman, Col. 10, ll. 29-41, further teaches collecting operational validation data from “Data from hundreds of complete passes through the 30 km rail network” to evaluate track conditions.
or diagnosing a specific car (11) with an abnormality when abnormal vibration data is measured from the car (11) running on all track sections, and estimates an abnormal part of the track or the car (11). Lederman, Fig. 1, teaches differentiating localized track anomalies from vehicle anomalies by rejecting abnormal data that appears continuously across all sections, stating: “Change Detection -Look for statistical anomalies at a given section of track -Reject anomalies occurring over the whole system”.
The same motivation to combine utilized in claim 1 is equally applicable in the instant claim.
Regarding claim 9, LeFebvre-Li, in view of Walczyk and Lederman discloses the limitations of claim 9 as follows:
A method of diagnosing operational safety of railcars, the method comprising: LeFebvre, Abstract, Paras. [0001]-[0006] a vibration data generation operation (S 10) in which a vibration diagnosis module (110) receiving a measurement signal from one or more vibration sensors (111) installed in each car (11) of a train (10) acquires a vibration signal by measuring vibrations generated from the running car (11) and generates vibration data by analyzing the vibration signal; LeFebvre, Paras. [0021]-[0024], [0033]-[0036], [0053]-[0054], Figs. 3-4, teaches instrumented pads with sensors analyzing time series data, stating: “…the instrumented pads 4 contain several sensors for measurement of dynamic and static vertical loads and shear or lateral force…..”, and “…the microprocessors 19 at the instrumented pads 16 take a series of measurements and treat them as a time series. …… can identify, for example, periodicities and cross correlations…..”.
a running data generation operation (S20) in which a car computer (100) installed in the car (11) receives the vibration data generated by the vibration diagnosis module (110) of the car (11) and generates running data by adding running information including a travel location and a travel speed of the car (11) to the vibration data; LeFebvre, Paras. [0021]-[0024], [0033]-[0036], Figs. 1-2, teaches a railcar mounted data control unit that collects information from the local sensor pads and integrates it with data from a GPS to append vehicle speed and location parameters.
an integrated running data generation operation (S30) in which a train control and monitoring system (TCMS) generates integrated running data by aggregating the running data received from the car computers (100) of the cars (11) constituting the train (10) and then transmits the integrated running data to a central control system; Li, Paras. [0001], [0011]-[0012], [0049]-[0050], teaches a train-level aggregation tier bridging individual vehicle data to a train network, explicitly describing a VCU cabinet for “TCMS (train control and management system) network control” that utilizes a “gateway (GW)” to bridge the internal vehicle bus (MVB) to the train bus (WTB).
LeFebvre, Paras. [0021]-[0024], [0033]-[0038], [0095], Figs. 1-4, teaches transmitting this consolidated information off the train utilizing a communication device to a remote data facility.
an abnormality estimation operation (S40) of storing the integrated running data transmitted to the central control system (1) in a server, and analyzing the transmitted integrated running data through pretrained artificial intelligence (Al) included in a big data analysis module (200), LeFebvre, Paras. [0094]-[0095], Figs. 1-4, teaches storing the data at a central data repository.
Walczyk, Paras. [0033]-[0034], teaches the artificial intelligence analysis using machine learning models that automatically evaluate incoming vibration datasets to classify the equipment’s operational data as normal or abnormal.
wherein the analyzing of the integrated running data comprises dividing an entire track on which the train (10) runs into a certain number of sections, separately generating vibration data measured from the car (11) section by section, diagnosing a specific track section as a dangerous section when abnormal vibration data is measured from a plurality of trains (10) running on the track section, or diagnosing a specific car (11) with an abnormality when abnormal vibration data is measured from the car (11) running on all track sections, and providing a result of estimating an abnormality in the track or the car (11) to the central control system; Lederman, Col. 14, ll. 29-45, Col. 21, ll. 15-26, Figs. 1 and 17, teaches a diagnostic method that plots vibration signals against GPS coordinates to isolate specific track blocks, such as 50 meter or 1 kilometer segments. And further teaches detecting localized track changes by confirming anomalies across multiple train passes at a specific location, and rejecting anomalies that occur continuously across the entire route, as a vehicle fault instead of a track defect.
an abnormality handling operation (S50) in which the central control system (1) takes measures against an abnormal situation when an abnormality in the track or the car (11) is estimated; Walczyk, Paras. [0033]-[0034], teaches a system routing data flagged as abnormal by the machine learning model to a portal for analysis review.
LeFebvre, Paras. [0006]-[0010], Figs. 1-4, teaches using the diagnostic output to issue timely warnings to operators so that necessary repairs can be executed.
and a retraining operation (S60) of comparing the estimation result with an actual measurement result of an abnormality check to retrain the AI, Walczyk, Paras. [0033]-[0034], teaches verifying the machine learning estimations by having a trained reviewer to review the flagged datasets and provide feedback.
wherein the vibration data generation operation (S10) comprises determining, by the vibration diagnosis module (110), whether the measured vibration data falls within a preset vibration value tolerance range for a corresponding track section and transmitting a determination result to the car computer (100) along with the vibration data, Lederman, Col. 7, ll. 52-67, Col. 8, ll. 1-16, Col. 9, ll. 1-5, Figs. 1 and 17, teaches evaluating the data against a localized range, extracting signal data for a specific track location, comparing against a threshold to determine if an structural change has occurred relevant to the historical data of the specific segment.
and the retraining operation (S60) comprises feeding an Al retraining result based on big data analysis by the big data analysis module (200) back to the vibration diagnosis module (110) to continuously update the preset vibration value tolerance range. Walczyk, Paras. [0011]-[0012], Fig. 12, teaches a feedback loop by illustrating configuration updates being pushed from the central edge device back to the local edge device, and modifies the machine learning model based on the remote network instruction.
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the invention to modify the monitoring method of LeFebvre to incorporate the standard TCMS network topology (incorporating the MVB/WTB gateway) as taught by Li, the edge-based machine learning anomaly detection and configuration update feedback loop of Walczyk, and further incorporating the section-by-section track analysis and localized threshold as taught by Lederman, in order to consolidate multi-car data at a single train-level gateway before transmitting it to the remote server, and preventing wireless bandwidth congestion, reduce communication latency and isolate localized track defect and minimize false positive.
Claims 7-8, and 11 are rejected under 35 U.S.C. 103 over LeFebvre (US 2010/0174428) in view of Li (WO 2020/062449), and further in view of Walczyk (US 2024/0361733), and Lederman (US 10,351,150), and Deo (US 2019/0147371).
Regarding claim 7, LeFebvre-Li-Walczyk and Lederman disclose the limitations of claim 1. Deo further discloses:
The system of claim 1, wherein the big data analysis module (200) retrains the AI in order of an operation of selecting integrated running data stored in the server (2), Deo, Paras. [0017]-[0024], teaches training data from stored database, stating: “…the validation platform may store the model and the data in the data structure”, “For example, the validation platform may utilize a temporal data selection technique to identify the training data”. an operation of preprocessing the selected data, Deo, Paras. [0017]-[0024], teaches preprocessing the selected data prior to training, stating: “For example, the validation platform may utilize one or more pre-processing techniques to pre-process the data, such as data cleansing techniques, data reduction techniques, data transformation techniques, and/or the like”. an operation of converting the preprocessed data, Deo, Paras. [0017]-[0024], teaches converting the data, stating: “the data transformation techniques may include techniques that convert the data from one format or structure into another format or structure”, a data mining operation, Deo, Paras. [0017]-[0024], teaches mining the data for underlying information, stating: “… the validation platform may pre-process the data by determining correlations, general trends, outliers, and/or the like associated with the data, and by performing an analysis of the data based on histograms, scatter plots, box plots, and/or the like….”, and a pattern analysis operation. Deo, Paras. [0034], teaches performing pattern analysis to execute the model, stating: “For example, the validation platform may perform an artificial neural network processing technique ….. to perform pattern recognition with regard to patterns”.
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the invention to configure the machine learning retraining module of the combined reference to execute specific multi-step data preparation as taught by Deo, in order to ensure raw data is properly formatted, and reduces errors before being ingested by the AI, and increasing the overall accuracy of the machine learning model.
Regarding claim 8, LeFebvre-Li-Walczyk-Lederman and further in view of Deo disclose the limitations of claim 7. Deo discloses:
The system of claim 7, wherein the pattern analysis operation is performed using any one selected from among linear regression, an artificial neural network, K-nearest neighbor, and unsupervised learning. Deo, Paras. [0013]-[0024], [0030]-[0034], teaches performing the pattern analysis using an artificial neural network by describing the application of neural network processing techniques, such as multi-layer feed-forward to execute pattern recognition on the validation platform. And further teaches performing the operation using unsupervised learning by describing the training of the model using an unsupervised training procedure (i.e., any one selected from among ….).
The same motivation to combine utilized in claim 7 is equally applicable in the instant claim.
Regarding claim 11, LeFebvre-Li-Walczyk and Lederman disclose the limitations of claim 9. Deo disclose:
The method of claim 9, wherein the retraining operation (S60) comprises retraining, by the big data analysis module (200), the AI in order of an operation of selecting integrated running data stored in the server (2) (S61), Deo, Paras. [0013]-[0024], [0030]-[0034], teaches selecting training data from stored database, stating that the validation platform stores the data in a data structure and utilizes temporal data selection, to identify the specific data for training. an operation of preprocessing the selected data (S62), Deo, Paras. [0013]-[0024], [0030]-[0034], teaches preprocessing the selected data prior to training by applying techniques such as cleansing and data reduction. an operation of converting the preprocessed data (S63), Deo, Paras. [0013]-[0024], [0030]-[0034], teaches converting data from one format to another format or structure. a data mining operation (S64), Deo, Paras. [0013]-[0024], [0030]-[0034], teaches mining the data for underlying information by determining correlations, general trends, and outliers, and by performing statistical analysis. and a pattern analysis operation (S65), Deo, Paras. [0013]-[0024], [0030]-[0034], teaches performing pattern analysis to execute the model training by applying artificial neural network processing techniques specifically configured to perform pattern recognition. and the pattern analysis operation (S65) is performed using any one selected from among linear regression, an artificial neural network, K-nearest neighbor, and unsupervised learning. Deo, Paras. [0013]-[0024], [0030]-[0034], teaches performing the pattern analysis using an artificial neural network, and performing operations using unsupervised learning procedure (i.e., any one selected from among …….).
It would have been obvious to a person having ordinary skill in the art before the effective filling date of the invention to configure the machine learning retraining operation of the combined reference to execute specific multi-step data preparation and unsupervised/neural network algorithm as taught by Deo, in order to ensure raw data is properly formatted, and reduces errors before being ingested by the AI, and increasing the accuracy of the machine learning model.
References Considered But Not Relied Upon
Lang (US 11,447,166) teaches a railway track condition monitoring system including a plurality of sensors installed on a rail of the track and spaced by a predetermined distance from each other.
Yuan (US 2021/0182296) teaches machine condition monitoring to determine state estimation and anomaly localization jointly. Systems configured using at least a synthetic training dataset including sensor output data that incorporates synthetic a random amount of noise to each one of multiple sensor devices that probe an industrial machine
Conclusion
Accordingly, claims 1-3, 5-9, and 11 are rejected.
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/P.B./Examiner, Art Unit 3615
/S. Joseph Morano/Supervisory Patent Examiner, Art Unit 3615
