DETAILED ACTION
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 .
Response to Amendment
Claims 1, 5, 8, 12, 15, and 19 are amended. Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed 4/30/2026 have been fully considered.
Regarding the rejections of claims 5, 12, and 19 under 112(b), and as noted by Applicant on page 12 of the response, claims 5, 12, and 19 are amended to clarify that the claims require both “automatically electronically transmitting, across a communications network, an alert for display on an electronic display” and “automatically dispatching or scheduling a repair technician to repair the progressive defect location,” to be carried out. Claim 1, for instance, recites “initiate one or more actions”, which could be met by carrying out a single action, while claim 5 specifies that the “one or more actions” include both the steps of “transmitting … an alert” and “dispatching or scheduling a repair technician”. These are not options from which a single “one or more action” is chosen, but two actions which must both be performed to meet the claim. The previous grounds of rejection are overcome and are withdrawn.
Regarding the rejections of claims 1-20 under 101, Examiner respectfully disagrees with Applicant’s arguments for the following reasons.
On page 14 of the response, Applicant contends that the claims do not recite an abstract idea. In support, Applicant contends that the Office Action improperly asserts that most of the claim language falls within the abstract idea exception and has thus untethered the claimed subject matter from the claim language that all but ensures a flawed conclusion that the claims recite an abstract idea. Essentially, Applicant’s argument appears to be that the grounds for finding that the combination of elements fall within the abstract idea exception are over-generalized, failing to account for the “memory units” and “computer processors” of a specialized computing system configured to access and process railroad track data, noted by Applicant on page 15.
Examiner submits that the claimed computing structures including memory and processors are recited at a high level of generality and do not appear to be configured in any but a substantially conventional manner for implementing functions that individually and/or in combination fall within the abstract idea exception.
Applicant further contends on page 15 that the processing steps are not merely mental or mathematical processes and that “[a] human mind cannot practically identify, by clustering a plurality of measurements that exceed the predetermined value, a particular track location on the railroad track as a progressive defect location or determine, using a remaining useful life model and the track geometry data for the particular type of surface condition at the progressive defect location, a future time when the particular type of surface condition at the progressive defect location will exceed a predetermined limit.”
Examiner submits that a broadest reasonable interpretation in view of Applicant’s specification of “clustering” entails substantially any form of grouping/gathering of two or more data items, such that it clearly practicably possible for identifying (e.g., estimating) a defect location based on “clustering” measurement data (e.g., displayed measurements associated with generalized or specific location data) to be performed via mental processes (evaluation and judgment). Furthermore, it is also clearly possible to determine (e.g., estimate) a future time (time point or generalized time frame estimation) when a surface condition at the determined/estimated defect location will exceed a predetermined limit (e.g., abnormal or dangerous) based on RUL and track geometry data to be performed via mental processes (evaluation of RUL and track geometry data and judgment).
On page 16 of the response, Applicant contends that the combined claim elements integrate any alleged abstract idea into a practical application. In support, Applicant cites the steps “determine, using a remaining useful life model and the track geometry data for the particular type of surface condition at the progressive defect location, a future time when the particular type of surface condition at the progressive defect location will exceed a predetermined limit; and automatically initiate one or more actions based on the determined future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit,” (in combination with the computer processing features) as enabling the system to avoid the need for personnel to manually monitor railroad track defects such that safety and efficiency in track monitoring is increased as explained in Applicant’s specification.
Examiner acknowledges that computer-implementation of the steps likely improves efficiency but notes that such efficiency gain is realized in the ordinary manner of computer-implemented processes (processing and communication speeds and connectivity) rather than being obtained by a particularized type of computer configuration. Therefore, the utility of the processing in terms of technical field of track monitoring/maintenance is confined to the processing steps themselves, which individually and in combination, fall within the abstract idea exception, such that there appears to be no integration of the abstract idea into a practical application in terms of improvement to a technical field.
On pages 17-18 of the response, Applicant contends that Example 46 of the 2019 Patent Eligibility Guidelines supports the position that Applicant’s claims integrate the judicial exception into a practical application. In particular, Applicant cites the treatment of claim 3 and in particular the significant effect of element “automatically operating the sorting gate, by the processor sending a control signal to the sorting gate to route the animal into a holding pen when the analysis results from step (iii) for the animal indicate that the animal is exhibiting an aberrant behavioral pattern, and by the processor sending a control signal to the sorting gate to permit the animal to freely pass through the sorting gate when the analysis results for the animal indicate that the animal is not exhibiting an aberrant behavioral pattern,” in finding the abstract idea integrated into a practical application.
Examiner submits that Example Claim 3 is found to integrate the judicial exception into a practical application largely due to the particularized relation between the additional elements and the abstract idea (analyzing step). Specifically, the “actions” performed in claim 3 include operating a particular structural element (sorting gate) in a particularized manner (processor control signal sent to gate to route the animal into a holding pen) that is specifically controlled (option 1 or 2) in accordance with a particularized functional output from the analyzing step.
In sharp contrast, the “actions” recited in Applicant’s claims are recited at a high level of generality that effectively encompasses any form of readily foreseeable responses (e.g., sending alerts and/or scheduling or dispatching a repair technician) to the data processing steps falling within the abstract idea exception such that no improvement to the technical field is evident by the combined elements of the claims.
Examiner notes Applicant’s objection on pages 18-19 to the framework for interpretive implementation 101 that is implemented by the Office.
Regarding the rejections of independent claims 1, 8, and 15 under 102, Applicant contends on pages 19 that Hampapur fails to teach “identify, by clustering the plurality of measurements that exceed the predetermined value, a particular track location on the railroad track as a progressive defect location.” In support, Applicant asserts on pages 19-20 and 21-22 that while Hampapur may disclose aggregating defects occurring in a single segment into a single defect, it does not disclose “identify, by clustering the plurality of measurements that exceed the predetermined value.”
Examiner respectfully disagrees. Regarding claims 1, 8, and 15, Hampapur teaches clustering/aggregating a plurality of measurements ([0054] track logically divided spatially (into smaller segments) for defect modeling that per [0057] entails modeling the deterioration of Class II defects (measurements exceeding a predetermined value/criterion that defines Class II classification) in which the clustering/aggregating is performed with respect to location in a manner such that a particular track location is effectively identified ([0045], [0054], [0056], [0058], FIG. 11 block 1106, [0107]). Regarding “measurements that exceed the predetermined value,” the defects being processed by modeling described in [0057] are Class I and Class II defects for which it is facially evident that thresholding (e.g., application of an eligibility criterion) is necessary for belonging to the class (e.g., Class I as part of the location-dependent measurements and/or Class II as part of corresponding location-dependent defects corresponding to the Class I defects).
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 in each of these claims is directed to the abstract idea judicial exception without significantly more.
Claim 1, substantially representative also of independent claims 8 and 15, recites:
“[a] system comprising:
one or more memory units; and
one or more computer processors communicatively coupled to the one or more memory units and configured to:
access track geometry data for a railroad track, the track geometry data comprising historical measurements for a plurality of types of surface conditions of the railroad track over a period of time;
determine, by analyzing the track geometry data for a particular type of surface condition, a plurality of measurements that exceed a predetermined value;
identify, by clustering the plurality of measurements that exceed the predetermined value, a particular track location on the railroad track as a progressive defect location; and
determine, using a remaining useful life model and the track geometry data for the particular type of surface condition at the progressive defect location, a future time when the particular type of surface condition at the progressive defect location will exceed a predetermined limit; and
automatically initiate one or more actions based on the determined future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above and the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a system, claim 8 recites a method, and claim 15 recites an article of manufacture and therefore each falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portions of claim 1 fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category (including an observation, evaluation, judgment, opinion) and the mathematical concepts category (mathematical relationships, mathematical formulas or equations, mathematical calculations). MPEP § 2106.04(a)(2).
The recited functions:
“access track geometry data for a railroad track, the track geometry data comprising historical measurements for a plurality of types of surface conditions of the railroad track over a period of time;
determine, by analyzing the track geometry data for a particular type of surface condition, a plurality of measurements that exceed a predetermined value
identify, by clustering the plurality of measurements that exceed the predetermined value, a particular track location on the railroad track as a progressive defect location; and
determine, using” “the track geometry data for the particular type of surface condition at the progressive defect location, a future time when the particular type of surface condition at the progressive defect location will exceed a predetermined limit,” and initiating actions “based on the determined future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit,”
may be performed as mental processes.
Accessing track geometry data for a railroad track in which the track geometry data comprises historical measurements for multiple types of surface conditions of the railroad track over a period of time may be performed via mental processes (e.g., observation of the track geometry data such as may be provided by a computer user interface). Analyzing the track geometry data for a particular type of surface condition to determine measurements that exceed a predetermined value may be performed via mental processes (e.g., evaluation and judgement). Identifying, by clustering the plurality of measurements that exceed the predetermined value, a particular track location on the railroad track as a progressive defect location may be performed via mental processes (e.g., evaluation, possibly aided by pen-and-paper of measurement data that may include location data and judgement to ascertain similarities/correlations for clustering that itself conveys location of the defect). Using the track geometry data for the particular type of surface condition at the progressive defect location to determine a future time when the particular type of surface condition at the progressive defect location will exceed a predetermined limit may also be performed via mental processes (e.g., evaluation of track geometry data and judgement in predicting a time when the defect will exceed a threshold). Initiating actions “based on the determined future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit” may also be performed via mental processes (e.g., decision judgment in identifying/selecting an action to perform and/or when to implement the action in accordance with the determined time at which the condition will exceed the limit).
The recited functions “identify, by clustering the plurality of measurements that exceed the predetermined value, a particular track location on the railroad track as a progressive defect location,” and “determine, using” “the track geometry data for the particular type of surface condition at the progressive defect location, a future time when the particular type of surface condition at the progressive defect location will exceed a predetermined limit,” are each determined by the Examiner as falling within the mathematical relationships sub-category of mathematical concepts (MPEP 2106.04(a)(2)). As disclosed in Applicant’s specification (e.g., [0051]), clustering measurements that exceed the predetermined value to identify a particular track location may entail a clustering algorithm such as dbscan, which is fundamentally characterized by mathematical relations/calculations and therefore constitutes mathematical relationships. As disclosed in Applicant’s specification (e.g., [0052]-[0055]), using track geometry data for a surface condition to determine a future time when the particular type of surface condition at the progressive defect location will exceed a predetermined limit may be implemented by linear regression and related mathematical processing, which are fundamentally characterized by mathematical relations/calculations and therefore constitutes mathematical relationships.
Step 2A, Prong Two of the analysis entails determining whether the claim includes additional elements that integrate the recited judicial exception into a practical application. “A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception” (MPEP § 2106.04(d)).
MPEP § 2106.04(d) sets forth considerations to be applied in Step 2A, Prong Two for determining whether or not a claim integrates a judicial exception into a practical application. Based on the individual and collective limitations of claim 1 and applying a broadest reasonable interpretation, the most applicable of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements,” including “one or more memory units” and “one or more computer processors communicatively coupled to the one or more memory units” for implementing the processing steps and “using a remaining useful life model” for determining a future time when the particular type of surface condition at the progressive defect location will exceed a predetermined limit, and “automatically initiate one or more actions” based on the determined future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit, in any combination appear to integrate the abstract idea in a manner that technologically improves any aspect of a device or system that may be used to implement the highlighted steps or a device for implementing the highlighted steps such as a signal processing device or a generic computer. For example, using memory unit and computer processors to perform the processing steps represents high-level and routine, conventional data processing components for implementing the underlying functions and therefore constitutes insignificant extra solution activity that fails to integrate the judicial exception into a practical application. Similarly, using a remaining useful life model represents application of program instructions (modeling) to implement the function falling within the judicial exception and therefore also constitutes extra solution activity that fails to integrate the judicial exception into a practical application. Automatically initiating an action based on the determined time when the condition will exceed the limit represents generalized activity having no particularized functional relation to the steps falling within the judicial exception and therefore constitute insignificant post-solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements are configured and implemented in a general rather than a particularized manner of implementing monitoring of track condition.
Regarding a transformation or reduction of a particular article to a different state or thing, claim 1 does not include any such transformation or reduction. Instead, claim 1 as a whole entails receiving input information (railroad track geometry data), applying standard processing techniques (instructions (modeling) executed via processor) to the information to determine track condition information with the additional elements, individually and in combination, failing to provide a meaningful integration of the abstract idea (determining and clustering measurement data and using the measurement data to predict exceeding of a limit) in an application that transforms an article to a different state. Instead, the additional elements represent extra-solution activity that does not integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 1 does not include additional elements that integrate the recited abstract idea into a practical application.
The Examiner notes that even if “access track geometry data for a railroad track, the track geometry data comprising historical measurements for a plurality of types of surface conditions of the railroad track over a period of time” is interpreted more narrowly to fall outside the mental processes exception, this element represents high-level data collection having no particularized functional relation to the steps falling within the judicial exception and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Therefore, claim 1 is directed to a judicial exception and requires further analysis under Step 2B.
Regarding Step 2B, and as explained in the Step 2A Prong Two analysis, the additional elements, individually and in combination, constitute insignificant extra solution activity and therefore fail to result in the claim as a whole amounting to significantly more than the judicial exception as well as failing to integrate the judicial exception into a practical application. Furthermore, the additional elements appear to be generic and well understood as evidenced by the disclosures of Hampapur (US 2014/0200828 A1) and Boucher (US 2022/0355839 A1), each of which teach substantially similar data collection and processing components for implementing railroad track monitoring.
As explained in the grounds for rejecting claim 1 under 102, Hampapur teaches “one or more memory units” and “one or more computer processors communicatively coupled to the one or more memory units” for implementing the processing steps, “using a remaining useful life model” for determining a future time when the particular type of surface condition at the progressive defect location will exceed a predetermined limit, and “automatically initiate one or more actions” based on the determined future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit, as does Boucher ([0472]-[0473] computer implemented system for predicting railroad components implemented by processor with memory; [0099]-[0101] predictive modeling for determining remaining useful life; [0049] method implemented in support of maintenance activities).
Therefore, the additional elements are insufficient to amount to significantly more than the judicial exception.
Claim 1 is therefore not patent eligible under 101.
Independent claims 8 and 15 recites substantially the same combination of elements falling within the judicial exception as claim 1 and include no significant additional elements that either integrate the judicial exception into a practical application or result in the claim as a whole amounting to significantly more than the judicial exception.
Claims 8 and 15 are therefore also not eligible under 101.
Claims 2-7, 9-14, and 16-20 depending from claims 1, 8, and 15, respectively, provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of the respective independent claims (Step 2A, Prong One). None of dependent claims 2-7, 9-14, and 16-20 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for substantially similar reasons as discussed with regards to the independent claims.
For example, claim 2, substantially representative also of claims 9 and 16, further characterizes the types of data processes by the steps falling within the judicial exception and therefore also fall within the same judicial exception.
Claim 3, substantially representative also of claims 10 and 17, recites “wherein the track geometry data is captured by a plurality of sensors” which represents high-level data collection in terms of the source of the collected data having no particularized functional relation to the steps falling within the judicial exception and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception. Claim 3 further recites the plurality of sensors are disposed with respect to “a geometry car while the geometry car travels over the railroad track.” Examiner notes that a geometry car collecting data as it travels over a track does not itself convey a sensor-based data collection having a particularized functional relation to the processing elements falling within the judicial exception (the collecting sensor data as a vehicle travels over a rail effectuates data collection at different locations that may be implemented by any of a number of alternative data collection techniques) and therefore constitutes extra solution activity. Furthermore, use of a traveling geometry car represents conventional means that is well known for collecting position-dependent rail data as disclosed by Hampapur (US 2014/0200828 A1) (paragraph [0045]) and Boucher (2022/0355839 A1) (paragraph [0017]).
Claim 4, substantially representative also of claims 11 and 18, and similarly to claims 3, 10, and 17, further characterizes the source of the collected data (sensors of an aerial vehicle collecting position-dependent rail data similarly to a geo car and also a known vehicular means for collecting rail data per Lacaze (US 2021/0041877 A1) (paragraphs ([0029]-[0032])) in terms having no particularized relation to the underlying processing steps and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 5, substantially representative also of claims 12 and 19, recites actions performed in response to the steps falling within the judicial exception such as electronically transmitting an alert across a network and/or dispatching or scheduling a repair technician, which represent generalized computer processing (outputting some form of message) having no particularized functional relation to the steps falling within the judicial exception and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 6, substantially representative also of claims 13 and 20, recites that determining the future time when the surface condition at the defect location will exceed the limit comprises “calculating a correlation coefficient; and fitting a regression line,” each of which falls within the mathematical relations sub-category of the mathematical concepts judicial exception because each of calculating a correlation coefficient and fitting a regression line are fundamentally characterized by mathematical relations/calculations.
Claim 7, substantially representative also of claim 14, further recites “wherein the calculated correlation coefficient is compared to a correlation threshold prior to determining the future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit,” which falls within the mental processes judicial exception because it may be performed via mental processes (e.g., determination and evaluation of potential limit(s) and judgement in selecting a limit for implementation).
Dependent claims 2-7, 9-14, and 16-20 therefore also constitute ineligible subject matter under 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 6, 8-10, 13, 15-17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hampapur (US 2014/0200828 A1).
As to claims 1, 8, and 15, Hampapur teaches “[a] system ([0005] describing the system; FIG. 2 system 200, [0043]) comprising:
one or more memory units ([0005] system includes computer processing system (inherently requires memory); [0113] computer readable medium for implementing embodiments includes memory); and
one or more computer processors communicatively coupled to the one or more memory units ([0004]-[0005] system includes computer processing system (inherently requires a processor for implementing processing of instructions/data stored in memory); [0113] computer readable medium for implementing embodiments includes memory storing program instructions for processing; Abstract describing processors for implementing the method) and configured to:,” “[a] method by a computing system (method implemented by system 200 in FIG. 2),” and “[o]ne of more computer-readable non-transitory storage media ([0113] computer readable medium for implementing embodiments includes memory) embodying instructions, that when executed by a processor, cause the processor to perform operations ([0004]-[0005] system includes computer processing system; [0113] computer readable medium for implementing embodiments includes memory storing program instructions for processing; Abstract describing processors for implementing the method)” to
“access track geometry data for a railroad track (FIG. 1 host system 202 configured to receive data via network 210 from data sources 204; [0045] data sources 204 include sensors/detectors for measuring geometry data of railroad tracks; [0047] data from data sources 240 transmitted to (received by) host system 202; [0053] application 212 accesses data from storage device 214), the track geometry data comprising historical measurements (Examiner notes the sensed measurement data received form data sources 204 falls within a broadest reasonable interpretation of “historical” because the measured data requires transfer and is not described in terms of real-time streaming; [0051] measurement data received from data sources 204 is stored as historical data in storage device 214) for a plurality of types of surface conditions of the railroad track ([0045] and [0048] different types of conditions sensed by different types of sensors; [0023]-[0040] describing various types of surface conditions tracked as part of geo-defect determinations) over a period of time [0053] application 212 accesses data from storage device 214 reflecting measurements of a defined period);
determine, by analyzing the track geometry data for a particular type of surface condition ([0048] and [0055] geo-defects (surface defects such as described in [0023]-[0040]) identified based on measurements), a plurality of measurements that exceed a predetermined value (Abstract describing method as including identifying geo-defect by processing (Examiner notes identifying defects from measurements inherently entails a defect threshold determination); ([0023] defects may be initially classified as Class I and may progress to being classified as Class II); ;
identify, by clustering the plurality of measurements that exceed the predetermined value ([0054] track logically divided spatially (into smaller segments) for defect modeling that per [0057] entails deterioration of Class II defects (measurement exceeding a predetermined value) into Class I defects (i.e., Class I defects are clustered with respect to spatial location in particular segment); [0056] geo-defects are identified in association with (clustered with respect to) spatial dimensions as part of detecting geo-defect amplitude changes; [0058] aggregating defects by type for a segment location; FIG. 11 block 1106, [0107] aggregation (clustering) of defects at a single segment into a single defect), a particular track location on the railroad track as a progressive defect location ([0045] data sources 104 capture location in association with measurements; [0054] track logically divided spatially (into smaller segments) for defect modeling; [0056] geo-defects are associated with spatial dimensions as part of detecting geo-defect amplitude changes; [0058] aggregating defects by type for a segment location; FIG. 11 block 1106, [0107] aggregation of defects at a single segment into a single defect for the segment); and
determine, using a remaining useful life model ([0057] modelling predicts deterioration of Class II to Class I and per [0023] Class I represents a failure condition (violation of track safety standard), and per [0063]-[0064] modeling entails determine a future timing of such deterioration, such that the modelling is effectively a remaining useful life model) and the track geometry data for the particular type of surface condition at the progressive defect location ([0063] defect amplitudes (measurement data) [0058] for defect aggregation location), a future time when the particular type of surface condition at the progressive defect location will exceed a predetermined limit ([0057] defect modeling includes predicting deterioration of Class II defect becoming a Class I defect in the future; [0063]-[0064] modeling prediction includes probability that a Class II defect deteriorates to Class I defect at a future time); and
automatically initiate one or more actions (FIG. 3 block 312 determine repair decision for each of the geo-defects,[0079]-[0080] repair implemented as soon as possible if Class II found to deteriorate to Class I) based on the determined future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit (FIG. 3 block 308 calculate track deterioration condition that per [0063] entails determining future time at which Class II defect deteriorates to Class I).
As to claims 2, 9, and 16 Hampapur further teaches “wherein the plurality of types of surface conditions of rails of the railroad track comprises:
a rise or depression in a left rail of the railroad track ([0026] geo-defect may be DIP indicting a depression or hump in a track (appears to apply to left and/or right track/rail));
a rise or depression in a right rail of the railroad track ([0026] geo-defect may be DIP indicting a depression or hump in a track (appears to apply to left and/or right track/rail); and
an amount of difference in elevation between top surfaces of the left and right rails of the railroad track ([0040] geo-defect may be XLEVEL which represent difference in elevation between the top surfaces of the rails).”
As to claims 3, 10, and 17 Hampapur teaches “wherein the track geometry data is captured by a plurality of sensors of a geometry car while the geometry car travels over the railroad track ([0045] sources 104 may comprise rail car “assets” that travel along the tracks and are used to collect measurement data using detectors, sensors, and other instrumentation).”
As to claims 6, 13, and 20 Hampapur teaches “wherein determining the future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit ([0057] defect modeling includes predicting deterioration of Class II defect becoming a Class I defect in the future; [0063]-[0064] modeling prediction includes probability that a Class II defect deteriorates to Class I defect at a future time) comprises:
calculating a correlation coefficient ([0060]-[0062] track deterioration modeling includes application of coefficients α (therefore coefficients have been determined/calculated), that as indicated in the equation in [0060] and FIG. 7 (appears to be the “Table 400” referred to in [0062]) are covariate coefficients that correlate defect amplitude and external factors); and
fitting a regression line ([0059]-[0060] and [0062] regression line (deterioration rate equation in [0060] defines a line representing the relation of the external factors to the deterioration and hence is a regression function) is fitted per the coefficients α).”
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 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hampapur in view of Lacaze (US 2021/0041877 A1).
As to claims 4, 11, and 18 Hampapur does not appear to teach “wherein the track geometry data is captured by a plurality of sensors of an aerial vehicle.”
Lacaze discloses a system for implementing drone based inspection of railroad crossings that is configured to detect surface deformation proximate to railroad tracks includes deformations of the rails themselves (Abstract describing drone-based system for detecting ground conditions including rail deformations) and in which the surface data is captured by a plurality of sensors of an aerial vehicle ([0029]-[0032] aerial vehicle equipped with multiple cameras for surveying rail deformations).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Lacaze’s teaching of using multi-sensor aerial measurement for determining track surface conditions to the system/method taught by Hampapur, which teaches inspection vehicles that utilize multiple sensors on a track car to capture track geometry data, such that in combination the system is configured to capture the track geometry data by a plurality of sensors of an aerial vehicle in addition and/or as an alternative to the ground-based detection disclosed by Hampapur.
The motivation would have been to provide adequate and/or enhanced track condition surveillance as suggested by Lacaze such as in [0005], which explains that ground-based vehicles may also be used for determining railroad track geometry.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hampapur in view of Nwichi-Holdsworth (US 2024/0035919 A).
As to claims 5, 12, and 19, Hampapur teaches that actions in response to track deterioration determinations may include “automatically” “scheduling a repair” “to repair the progressive defect location ([0050] and [0052] information acquired by the modeling used to adopt repair plans (via the system and therefore performed “automatically”); [0023] track quality used for scheduling maintenance including scheduling for Class I defects),” and further teaches that repairs may involve an actor/technician for implementing the repair ([0003] track master usually performs repairs).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Hampapur’s teaching of automatically planning/scheduling repair based on the deterioration modeling data that includes determining a temporally based deterioration of Class II defect to Class I in combination with Hampapur’s further teaching that an actor/technician implements the repairs, such that in combination the system, method, computer-readable storage media are configured to automatically schedule a repair technician to repair the progressive defect location as an action performed based on the determined future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit.
The motivation would have been to leverage the predictive modeling system processing capability to efficiently address potentially dangerous geometry defects in railroad tracks using by scheduling a technician as suggested by Hampapur.
Hampapur discloses automatic electronic communications across a network (FIG. 2 host system 202 configured to communicate with data sources 204 across network 210), but does not appear to teach that the actions comprise automatically electronically transmitting across a communications network, “an alert for display on an electronic device.”
Nwichi-Holdsworth discloses a system/method for monitoring rail line variations (Abstract) that includes transmitting an alert relating to condition monitoring for display on an electronic device ([0013]-[0014] and [0031] an alert generated if condition monitoring parameters predicted to exceed a threshold within a period; [0058] alert may be in furtherance of maintenance work; [0155] alert may be received (has been transmitted) and displayed).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Nwichi-Holdsworth’s teaching of transmitting an alert for display related to need for maintenance pertaining to condition data indicating a threshold will be exceeded within a period to the system taught by Hampapur in which communications relating to rail degradation are transmitted over a network, such that in combination the system is configured such that the actions include automatically electronically transmitting across a communications network, an alert for display on an electronic device, as well as automatically scheduling a repair technician to repair the defect location.
The motivation would have been to provide displayed notification to users that may play a remedial role in addressing the defect such that defect remediation is enhanced as suggested by Nwichi-Holdsworth and as would be facially evident to one of ordinary skill in the art.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hampapur in view of Uehara (US 2023/0351218 A1).
As to claims 7 and 14, Hampapur further teaches determining values for the coefficients prior to use of the model for predicting failure times (coefficients used for modeling have been determined), but does not appear to expressly teach that the determination of the coefficient(s) includes “wherein the calculated correlation coefficient is compared to a correlation threshold prior to determining the future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit.”
Uehara discloses a method/system for implementing regression modeling for monitoring/detecting equipment abnormalities (Abstract; FIG. 1 regression equation generation module 112 and target apparatus 2; [0043]) and is configured to generate a regression equation/model in part by comparing a correlation coefficient to a correlation threshold ([0094] regression coefficients (i.e., coefficients applied for regression correlation) are determined and then set to 0 if equal to or below a predetermined threshold value (inherently entails comparison)) prior to model usage (coefficient level determined for generating the equation for subsequent usage).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Uehara’s teaching of comparing a correlation coefficient with a predetermined threshold as part of generating the regression equation/model to be subsequently utilized to the method/system taught by Hampapur that implements regression modeling that includes coefficients that correlate independent and dependent variables for determining the future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit, such that in combination the system/method is configured such that the calculated correlation coefficient is compared to a correlation threshold prior to determining the future time when the particular type of surface condition at the progressive defect location will exceed the predetermined limit.
The motivation would have been to identify and preselect variables corresponding to the coefficients having sufficient statistical significance (e.g., cancelling application of variables corresponding to the coefficients set to 0) to ensure more accurate regression modeling as suggested by Uehara.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MATTHEW W. BACA/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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