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 .
This action is in response to the amendments filed on 06/19/2026, in which claims 1-20 are pending and addressed below.
Response to Amendment
Applicant has amended the claims to overcome the 35 U.S.C. 112(b) rejections. Accordingly, the 35 U.S.C. 112(b) rejections have been withdrawn.
Applicant has amended the claims to overcome the 35 U.S.C. 101 rejections of claims 1-20 for being directed to an abstract idea without significantly more. Accordingly, the 35 U.S.C. 101 rejections of claims 1-20 for being directed to an abstract idea have been withdrawn.
Response to Arguments
Applicant's arguments filed 06/19/2026 have been fully considered but they are not persuasive.
With respect to the 35 U.S.C. 103 rejections:
Applicant argues on page 16 of the remarks that Lu in view of Ledbetter fail to teach the amended limitations of the independent claims. Applicant argues on page 17 of the remarks that Anfriani fails to teach a simulation performed to generate variables that includes “one or more of a list of flights to check, a number of passed inspections, a number of alerts in a current alert group, and previous alerts” as recited in the amended independent claims. Applicant argues on pages 17-18 of the remarks that the dependent claims are patentable for at least the reasons argued with respect to the independent claims.
In response to applicant’s argument that Anfriani fails to teach the “generating one or more variables…” limitation of the independent claims, the examiner respectfully disagrees. Anfriani teaches generating variables to simulate intermediate degradation and advanced degradation of aircraft components (Anfriani [0103]-[0113]). Anfriani also teaches iteratively simulating maintenance actions on components by using failure models and a safety threshold, and re-evaluating the degradation risk at each iteration (Anfriani [0072]). Accordingly, the inter-inspection intervals can be increasingly relaxed by evaluating the degradation risk rates at each iteration (Anfriani [0072]). Anfriani further teaches that the simulation continues until an optimum solution is found that satisfies the safety threshold (Anfriani [0137]-[0138]). Therefore, Anfriani teaches at least “simulating a plurality of iterations for the individual combination, each iteration comprising: generating one or more variables in real-time, the one or more variables comprising…a number of passed inspections” because Anfriani teaches determining whether a component passes inspection by comparing a degradation to a safety threshold. Furthermore, Anfriani teaches that the simulation is iteratively performed while a safety threshold is satisfied (i.e., a passed inspection), and the simulation continues to examine the failure models until an optimum solution is found that fulfills the safety requirements.
Therefore, Anfriani teaches a simulation performed to generate variables that includes “one or more of a list of flights to check, a number of passed inspections, a number of alerts in a current alert group, and previous alerts” as recited in the amended independent claims. Additionally, the dependent claims are not patentable for at least the reasons discussed above and outlined in the 35 U.S.C. 103 rejections below.
Applicant’s arguments have been fully considered and have been found not persuasive.
Applicant’s arguments with respect to Lu in view of Ledbetter teaching the amended limitations of the independent claims have been considered but are moot because the new ground of rejection does not rely on Lu or Ledbetter for any teaching or matter specifically challenged in the argument.
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 8-14 are rejected under 35 U.S.C. 101 because the claims are directed to computer software per se.
Regarding claims 8-14, the preamble of the claims recite a “computer program product…". In light of the instant specification [0087] and [0089], the program appears to comprise software elements. None of the comprising elements of the claimed system appear to be physical components.
Therefore the “program” of claims 8-14 is computer software per se and is not a “process, machine, manufacture, or composition of matter" as defined in 35 U.S.C. 101. See MPEP 2106.03.
Claim Interpretation
Regarding claims 4-5, the limitations “when no previous alert exists for the aircraft component, generating an alert group and the new alert for the condition,” “when a current runtime is within an inspection interval of a previous alert, awaiting an inspection result in the inspection data for the previous alert,” “when an inspection result of the inspection data indicates that the aircraft component should be repaired or replaced, determining to not generate the new alert,” “when the current runtime is at least a group gap setting after a time of the previous alert and a time of the inspection result, and a count of alert groups is less than a maximum group count, generating an alert group and the new alert for the condition,” and “when a count of alerts in a current alert group is less than a group size, and the current runtime is at least a suppress windows setting that is based on the count of alerts, generating the new alert for the condition” are conditional limitations. The broadest reasonable interpretation of these limitations do not require “generating an alert group and the new alert for the condition,” “awaiting an inspection result in the inspection data for the previous alert,” “determining to not generate the new alert,” and “generating the new alert for the condition” to be performed because it is not required for the preceding clauses to occur.
See Ex parte Schulhauser, 2013-007847 (PTAB 2016) (precedential) where the board held that when method steps are to be carried out only upon the occurrence of a condition precedent, the broadest reasonable interpretation holds that those steps are not required to be performed. (id. at
*7). See, e.g., Reactive Surfaces v. Toyota Motor Corp., IPR2016-01914 (PTAB 2018) (“[t]he use of ‘when’
instead of ‘if’ does not change whether the method step is conditional”) (citing Ex parte Kaundinya, No.
2016-000917, 2017 WL 5510012, at *5-6 (PTAB Nov. 14, 2017) ("when" may indicate a conditional
method step); Ex parte Zhou, No. 2016-004913, 2017 WL 5171533, at *2 (PTAB Nov. 1, 2017) (same); Ex
parte Lee, No. 2014-009364, 2017 WL 1101681, at *2 (PTAB Mar. 16, 2017) (same)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 7-11, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al., U.S. Patent Application Publication No. 2019/0092495 A1 (hereinafter Lu), in view of Ledbetter et al., U.S. Patent Application Publication No. 2021/0406742 A1 (hereinafter Ledbetter), further in view of Anfriani et al., U.S. Patent Application Publication No. 2015/0186568 A1 (hereinafter Anfriani), and further in view of Mojtahedzadeh et al., U.S. Patent Application Publication No. 2020/0210968 A1 (hereinafter Mojtahedzadeh).
Regarding claim 1, Lu teaches a method (Lu Fig. 7, Fig. 8) comprising:
receiving flight sensor data and component fault data corresponding to a plurality of flights (see at least Lu [0031]: “The database 105 may be coupled to the at least one aircraft system 101 in any suitable manner (e.g., through a wired or wireless connection) so as to receive the operational data 110 from the sensors 100S.”; [0041]: “The machine learning model 121 may be trained to recognize faults in the operational data 110 for a respective one or more maintenance message (s) 116 based on training of the machine learning model 121 with at least training data 130TR included in a collection of existing data 130…The database 105 may receive the collection of existing data 130 in any suitable manner, such as through the user interface 199 and/or through connection with the other aircraft 900A-900n”);
applying a predictive model to individual flights of the plurality of flights to generate a plurality of fault probabilities for at least one aircraft component (see at least Lu [0059]: “The occurrence of the future maintenance message 116F may be predicted for the aircraft component 100C within the predetermined analysis time period 125 using the at least one classification plot 600 (as described above), where each classification plot 600 identifies how the classified data 119 within a single flight 4-5 (FIG. 5) is classified in the second classification.”; [0040]: “Referring again to FIG. 1, the aircraft controller 120 is also configured, such as with e.g., the classification module 120M, to classify the classified data 119, of the at least one classified multidimensional operation data matrix 118 with a machine learning model 121, in a second classification, to predict an occurrence of a future maintenance message 116F for the aircraft component 100C within a predetermined analysis time period 125. As noted above, in the second classification, the classification obscuring data 119R has been removed so that the second classification is performed on the preprocessed at least one classified multidimensional operation data matrix 118CM having only the flight numbers corresponding to the predetermined tail number 117 and predetermined maintenance message 116UI.”);
wherein selecting the plurality of settings comprises: determining an optimal combination of the one or more detection window settings (see at least Lu [0057]: “The classified data 119 of the at least one classified multidimensional operation data matrix 118 is classified, with the aircraft maintenance controller 120, with the machine learning model 121, in a second classification, (FIG. 7, Block 720) to predict an occurrence of a future maintenance message 116F for the aircraft component 100C within the predetermined analysis time period 125. The number of false positive failure indications in the second classification is reduced by the aircraft maintenance controller 120. For example, reducing the number of false positive failure indications includes grouping flights 4-5 (FIG. 5) into the positive blocks 150 and the negative blocks (FIG. 8, Block 800). As described above, the positive blocks 150 are indicative of the occurrence of the future maintenance message 116F within the predetermined analysis time period 125, and the negative blocks 151 are indicative that the future maintenance message 116F will not occur within the predetermined analysis time period 125. Flights 4-5 (FIG. 5) occurring within a predetermined blocking time period (as described above) preceding a maintenance message 116 occurrence are grouped into a positive block 150. Flights 4-5 (FIG. 5) occurring after the maintenance message 116 occurrence and flights 4-5 (FIG. 5) occurring more than the predetermined blocking time period preceding the maintenance message 116 occurrence are grouped into respective negative blocks 151.”),
which comprises, for individual combinations of a plurality of combinations: for individual records of the flight sensor data and the component fault data, assigning arrival times to the individual records according to at least a first probability distribution (see at least Lu [0037]: “controller 120 is configured to classify the operational data 110, in the first classification, by comparing a flight number 1-5 in the at least one multidimensional maintenance message matrix 115 for which a maintenance message 116 exists with flight numbers 1-5 in the at least one multidimensional operation data matrix 111 to determine matching flight numbers 1-5 for a predetermined maintenance message 116UI and whether the matching flight numbers 1-5 are within a predetermined time period (e.g., within about 24 hours or more or less than about 24 hours) from each other. Matching the maintenance messages 116 to flights within the predetermined time period may ensure matching the maintenance message 116 with a single flight having flight number 1 rather than two or more flights having flight number 1 where the two or more flights occur on different, days (e.g., flight numbers may be reused from day to day).”; [0038]: “For example, Referring to FIGS. 3, 4, and 5, if a prediction of maintenance message “JKL” is to be made by the aircraft component failure prediction apparatus 100 the aircraft maintenance controller 120 is configured to classify the flights 1-5 in the at least one classified multidimensional operation data matrix 118 with a maintenance message correspondence classification 410 that indicates whether the maintenance message for the flight 1-5 matches/corresponds with the maintenance message “56789” being analyzed.”);
Lu fails to expressly discloses selecting, based on a factor indicating a tolerance of false alerts, a plurality of settings comprising a minimum count of flights, a threshold probability, one or more detection window settings, and one or more alert group settings and detecting a condition that at least the minimum count of flights within a detection window have a respective fault probability greater than a threshold. However, Ledbetter teaches
selecting, based on a factor indicating a tolerance of false alerts, a plurality of settings comprising a minimum count of flights, a threshold probability, one or more detection window settings, and one or more alert group settings (see at least Ledbetter [0050]: “The data server 305 can use trend significance to detect changes in the condition indicator well before a static indicator threshold is crossed. While a static indicator threshold may be lowered to detect potential problems in a condition indicator earlier, this may lead to false alarms for noisier signals. Thus, trend detection using various principles of trend significance tends to provide earlier detection, with fewer false alarms, than static indicator thresholds. Examples will be described relative to FIGS. 4-7, 8A-C, 9A-B, 10 and 11A-B.”; [0084]: “In some cases, both the resultant data and the criteria related to the relative trend significance can be specified in terms of a particular percentile or percentage of the historical window 412 according to the cumulative distribution 424 (e.g., 95, percent, 98 percent, etc.). In an example, in various cases, the criteria related to the relative trend significance can be satisfied in response to: (1) at least one value in the model 420 being greater than the particular percentage; (2) at least one sample in the trend window 410 being greater than the particular percentage; (3) at least a minimum number of samples in the trend window 410 being greater than the particular percentage; (4) a last or most recent sample in the trend window 410 being greater than the particular percentage; (5) a last or most recent value in the model 420 being greater than the particular percentage; (6) an aggregate value for the individual samples in the trend window 410 being greater than the particular percentage, where the aggregate value can be, for example, a mean, median, mode, maximum, minimum or the like; and/or (7) an aggregate value for the model 420 being greater than the particular percentage, where the aggregate value can be, for example, a mean, median, mode, maximum, minimum or the like.”),
detecting, based on the one or more detection window settings, a condition that at least the minimum count of flights within a detection window have a respective fault probability, of the plurality of fault probabilities, that is greater than the threshold probability (see at least Ledbetter [0087]: “For example, the data server 305 can determine, based on the cumulative distribution 424 for the historical window 412, a probability that the indicator threshold 406 will be crossed. In general, a high value for this probability may make it less notable that the criteria related to absolute trend significance would be satisfied by the resultant data from the trend window 410.”; [0078]: “For example, the criteria related to the absolute trend significance can be satisfied in response to at least a specified minimum portion of the plurality of data points crossing the indicator threshold. The specified minimum portion could be expressed, for example, as a percentage (e.g., ten percent), as a raw number (e.g., at least one), or in other ways.”; [0026] Ledbetter discloses trends include operating elements having wear, damage, etc. and require inspection, repair, or replacement);
and determining, based on the one or more alert group settings, whether to generate a new alert for the condition (see at least Ledbetter [0049]: “The alert system 317 may generate a warning or problem message such as automated remote message and/or an in-cockpit warning when the trend data exceeds a higher threshold, indicating that further use of the gear should be avoided. Thus, the alert system 317 may take different alert actions based on the comparison of the trend data to different thresholds.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the method disclosed by Lu with Ledbetter with reasonable expectation of success. Ledbetter is directed towards the related field of vehicle trend detection and monitoring. Therefore, one of ordinary skill in the art would be motivated to modify Lu with Ledbetter to determine whether machine components are safely operating (see at least Ledbetter [0002]-[0004]: “The systems for engines, transmissions, drive system, rotors, and the like, are critical to the safe operation of the rotorcraft in flight. The elements of system such as mechanical systems, electrical systems, hydraulic systems, and the like, are each subject to unique wear factors and monitoring, inspection or maintenance requirements…In one general aspect, in an embodiment, a method includes acquiring a current condition indicator of a condition indicator set associated with an operating condition of a machine, the condition indicator set indicating sensor readings associated with an operating element of the machine under the operating condition.”).
Lu in view of Ledbetter fail to expressly disclose simulating a plurality of iterations for the individual combination, each iteration comprising: generating inspection data according to at least a second probability distribution. However, Anfriani teaches
simulating a plurality of iterations for the individual combination, each iteration comprising: generating one or more variables in real-time, the one or more variables comprising one or more of a list of flights to check, a number of passed inspections, a number of alerts in a current alert group, and previous alerts (see at least Anfriani [0072]: “In accordance with the invention, the processing means 7 are configured to iteratively simulate maintenance actions on the set of components by using the set of failure models and the maintenance strategy to build a global model of relaxed maintenance operations which takes the opportunistic inspections into account while fulfilling the predetermined safety threshold of each component…Thus, starting from an initial condition, the simulation can be recursively defined by re-evaluating at each iteration the advanced degradation risk rates generating step by step increasingly relaxed inter-inspection intervals.”; [0137]-[0138]: “In block B43, the processing means 7 compare the risk rate associated with each component according to the current maintenance model B42 with the predetermined safety threshold of the component according to the failure models from block B2. As long as the safety criterion of block B43 is fulfilled, the processing means 7 continue simulating by looping on block B41 until the current maintenance model converges on a last current maintenance model which maximizes the inter-inspection intervals III of the components while fulfilling the predetermined safety threshold of each component…It is an optimum solution in terms of minimizing maintenance costs while fulfilling the safety requirements of the flights.”; Anfriani teaches at least a number of passed inspections because Anfriani teaches re-evaluating the degradation based on a safety threshold and failure models until an optimum is found);
generating simulated inspection data according to at least a second probability distribution (see at least Anfriani [0072]: “In accordance with the invention, the processing means 7 are configured to iteratively simulate maintenance actions on the set of components by using the set of failure models and the maintenance strategy to build a global model of relaxed maintenance operations which takes the opportunistic inspections into account while fulfilling the predetermined safety threshold of each component. The conditions visited by the set of components can be simulated by taking the maintenance strategy into account, according to a Petri network type Monte Carlo method. Thus, starting from an initial condition, the simulation can be recursively defined by re-evaluating at each iteration the advanced degradation risk rates generating step by step increasingly relaxed inter-inspection intervals.”; [0016]: “continuing the simulation until the current maintenance model converges on a last current maintenance model which maximizes the inter-inspection intervals of the components while fulfilling the predetermined safety threshold of each component, said last current maintenance model being said global model of relaxed maintenance of operations.”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the method disclosed by Lu in view of Ledbetter with Anfriani with reasonable expectation of success. Anfriani is directed towards the related field of optimizing forecasting of maintenance for an aircraft engine. Therefore, one of ordinary skill in the art would be motivated to modify Lu in view of Ledbetter with Anfriani to optimize maintenance based on cost and safety requirements (see at least Anfriani [0005]-[0006]: “Thus, in reality, far more inspections are made than is demanded by regulation. This generates an increase in the maintenance cost and down time periods. Furthermore, this can cause a lack of accuracy in forecasting maintenance operations and inventory management. The object of the present invention is consequently to accurately and optimally plan maintenance operations on an engine while fulfilling all the safety requirements and constraints.”).
Lu in view of Ledbetter and Anfriani fail to expressly disclose generating alerts based on the inspection data and determining the factor based on the generated alerts. However, Mojtahedzadeh teaches
and generating alerts based on the simulated inspection data and the generated one or more variables; and determining the factor based on the generated alerts (see at least Mojtahedzadeh [0055]: “A data acquisition and preprocessing stage includes using the data acquisition and the pre-processing component 432 to acquire and/or process the SBAS data and alerts 902 with a process 932a, the fault isolation information 904 with a process 932b, and the historical scheduled maintenance data 906 and the historical unscheduled maintenance data 908 with a process 932c. These results are fed into the data mapping component 424. A task and alert selection process 910 selects various maintenance tasks as candidates for removal from physical inspection and/or escalation. Each task is then fed into the analysis component 428. The analysis component 428 then makes a decision in decision operation 912 whether to keep the original interval at 914, remove the task from physical inspection at 916 (thereby making it a virtual inspection), or escalate the interval at 918.”; [0058]: “Operation 1102 is a portion of the task and alert selection process 910, in which candidate SBAS alerts are identified. In some implementations, operation 1102 includes adjusting an alert threshold (e.g., the threshold 506 of FIG. 5) in order to find an optimum alert threshold. If a sensor alert provides too many false positives that are relied upon, the resulting unscheduled inspections will increase maintenance costs. In some example, the sensor itself is adjusted to change the alert conditions to a lower false positive rate. This is used with the fault isolation information 904 which, in some implementations, is sourced from fault isolation manuals provided by component vendors.”; Mojtahedzadeh [0045], [0055] teaches inspections can be simulated virtually; Mojtahedzadeh teaches at least one or more variables including previous alerts because Mojtahedzadeh teaches determining if previous sensor alerts provide a high false positive rate).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the method disclosed by Lu in view of Ledbetter and Anfriani with Mojtahedzadeh with reasonable expectation of success. Mojtahedzadeh is directed towards the related field of optimizing maintenance plans based on sensor data. Therefore, one of ordinary skill in the art would be motivated to modify Lu in view of Ledbetter and Anfriani with Mojtahedzadeh to optimize maintenance based on cost and risk (see at least Mojtahedzadeh [0002]: “However, maintenance plan schedule adjustments that are blind to actual operational conditions and available historical data, introduces the likelihood of performing maintenance more often than is necessary (thereby increasing cost), or less often than is optimal to maintain operational risks below a target acceptable maintenance plan that leverage historical maintenance data and sensor data.”).
Regarding claim 2, Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh teach all elements of the method according to claim 1 as explained above. Lu further teaches
determining, using inspection data, a count of false alerts for the aircraft component; and selecting the factor using the count of false alerts (see at least Lu [0057]: “The classified data 119 of the at least one classified multidimensional operation data matrix 118 is classified, with the aircraft maintenance controller 120, with the machine learning model 121, in a second classification, (FIG. 7, Block 720) to predict an occurrence of a future maintenance message 116F for the aircraft component 100C within the predetermined analysis time period 125. The number of false positive failure indications in the second classification is reduced by the aircraft maintenance controller 120. For example, reducing the number of false positive failure indications includes grouping flights 4-5 (FIG. 5) into the positive blocks 150 and the negative blocks (FIG. 8, Block 800).”; [0041]: “The machine learning model 121 may be trained to recognize faults in the operational data 110 for a respective one or more maintenance message (s) 116 based on training of the machine learning model 121 with at least training data 130TR included in a collection of existing data 130…In one aspect, there may be a machine learning model 121 for predicting the occurrence of each maintenance message 116 (e.g., there may be a number of different maintenance messages as illustrated in FIGS. 2-5 where a machine learning model exists for predicting the occurrence of each different maintenance message).”).
Regarding claim 3, Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh teach all elements of the method according to claim 1 as explained above. Mojtahedzadeh teaches the method further comprising:
receiving inspection data for the aircraft component, wherein the plurality of settings further comprises one or more inspection settings (see at least Mojtahedzadeh [0044]: “FIG. 6 illustrates a cumulative distribution function (CDF) curve 602 for a cumulative probability of detecting a condition requiring a repair or other maintenance action. In one implementation, the CDF curve 602 is obtained by applying statistical analysis methods to historical inspection data, such as a maintenance record dataset for scheduled maintenance findings, and logbook dataset for unscheduled maintenance. In another implementation, the CDF curve 602 is generated using statistical analysis scheduled maintenance optimization engine (SASMO), which is described in U.S. Pat. No. 8,117,007. The CFD curve 602 shows the probability of a non-routine finding, and some implementations of the CDF curve 602 are obtained by analyzing the historical maintenance data of an apparatus and its components…An inspection prior to the interval T 606 is an early inspection and is less likely to result in the identification of a condition requiring a repair or other maintenance action. An inspection after the interval T 606 is a late (escalated) inspection and, under some situations, is more likely to result in the identification of a condition requiring a repair or other maintenance action.”),
and wherein determining whether to generate the new alert for the condition is based on the inspection data (see at least Mojtahedzadeh [0055]: “A data acquisition and preprocessing stage includes using the data acquisition and the pre-processing component 432 to acquire and/or process the SBAS data and alerts 902 with a process 932a, the fault isolation information 904 with a process 932b, and the historical scheduled maintenance data 906 and the historical unscheduled maintenance data 908 with a process 932c. These results are fed into the data mapping component 424. A task and alert selection process 910 selects various maintenance tasks as candidates for removal from physical inspection and/or escalation. Each task is then fed into the analysis component 428. The analysis component 428 then makes a decision in decision operation 912 whether to keep the original interval at 914, remove the task from physical inspection at 916 (thereby making it a virtual inspection), or escalate the interval at 918.”; [0058]: “Operation 1102 is a portion of the task and alert selection process 910, in which candidate SBAS alerts are identified. In some implementations, operation 1102 includes adjusting an alert threshold (e.g., the threshold 506 of FIG. 5) in order to find an optimum alert threshold. If a sensor alert provides too many false positives that are relied upon, the resulting unscheduled inspections will increase maintenance costs. In some example, the sensor itself is adjusted to change the alert conditions to a lower false positive rate. This is used with the fault isolation information 904 which, in some implementations, is sourced from fault isolation manuals provided by component vendors.”).
Regarding claim 4, Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh teach all elements of the method according to claim 3 as explained above. Ledbetter further teaches wherein determining whether to generate the new alert for the condition comprises:
when no previous alert exists for the aircraft component, generating an alert group and the new alert for the condition (see at least Ledbetter [0064]: “If a determination is made at the decision block 513 that a trend is detected, an alerting process is executed at the block 515. In some cases, as will be described in greater detail relative to FIG. 5B, alerts based on unreliable models or data can be suppressed at the block 515. In other cases, an alert signal is provided at block 515…In some embodiments, an in-vehicle alert and a remote alert may be used in combination.”; Ledbetter Fig. 5A shows no previous alert exists when a trend is not detected);
when a current runtime is within an inspection interval of a previous alert, awaiting an inspection result in the inspection data for the previous alert (This limitation is conditional on a current runtime within an inspection interval of a previous alert. Since this is not required to occur, the prior art is not required to teach this limitation.);
when the current runtime is at least a group gap setting after a time of the previous alert and a time of the inspection result, and a count of alert groups is less than a maximum group count, generating an alert group and the new alert for the condition (This limitation is conditional on the current runtime is at least a group gap setting after a time of the previous alert and a time of the inspection result. Since this is not required to occur, the prior art is not required to teach this limitation.);
and when a count of alerts in a current alert group is less than a group size, and the current runtime is at least a suppress windows setting that is based on the count of alerts, generating the new alert for the condition (This limitation is conditional on a count of alerts in a current alert group is less than a group size and the current runtime is at least a suppress windows setting that is based on the count of alerts. Since this is not required to occur, the prior art is not required to teach this limitation.).
Regarding claim 7, Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh teach all elements of the method according to claim 1 as explained above. Mojtahedzadeh further teaches wherein the at least a second probability distribution comprises one or more of the following:
a probability distribution representing whether an inspection will be scheduled; a probability distribution representing a time that the inspection will be scheduled; a probability distribution representing whether the component will pass the inspection; and a probability distribution representing a communication delay from the alerts (see at least Mojtahedzadeh [0044]: “FIG. 6 illustrates a cumulative distribution function (CDF) curve 602 for a cumulative probability of detecting a condition requiring a repair or other maintenance action.”; under broadest reasonable interpretation Mojtahedzadeh teaches at least a probability distribution representing whether the component will pass the inspection because a condition that requires repair does not pass inspection).
Regarding claim 8, this claim recites a medium that performs the method of claim 1. The combination of Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh also teaches a medium that performs the method of claim 1 as outlined in the rejection to claim 1 above. Specifically, Lu teaches a computer-readable storage medium (Lu [0038]) and processors (Lu [0158]) that performs the method of claim 1. Therefore, claim 8 is rejected for the same rationale as claim 1.
Regarding claim 9, this claim recites a medium that performs the method of claim 2 as explained above. Therefore, claim 9 is rejected for the same rationale as claim 2.
Regarding claim 10, this claim recites a medium that performs the method of claim 3 as explained above. Therefore, claim 10 is rejected for the same rationale as claim 3.
Regarding claim 11, Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh teach all elements of the medium according to claim 10 as explained above. Ledbetter further teaches wherein determining whether to generate the new alert for the condition comprises:
when no previous alert exists for the aircraft component, generating an alert group and the new alert for the condition (see at least Ledbetter [0064]: “If a determination is made at the decision block 513 that a trend is detected, an alerting process is executed at the block 515. In some cases, as will be described in greater detail relative to FIG. 5B, alerts based on unreliable models or data can be suppressed at the block 515. In other cases, an alert signal is provided at block 515…In some embodiments, an in-vehicle alert and a remote alert may be used in combination.”; Ledbetter Fig. 5A shows no previous alert exists when a trend is not detected);
and when a count of alerts in a current alert group is less than a group size, and the current runtime is at least a suppress windows setting that is based on the count of alerts, generating the new alert for the condition (see at least Ledbetter [0076]: “At block 602, the data server 305 creates the predictions 422. The block 602 can involve predicting a plurality of data points of a future window of a condition indicator set, such as future values of the condition indicator data 404, using the model 420. In various embodiments, the future window can be a window of the same size as the trend window 410. For example, if the trend window 410 were to include ten data points, the future window could include predictions for the next ten data points following the trend window 410.”; [0049]: “Upon detecting that a trend or condition data for a particular operating parameter has exceeded a particular threshold such as a static trend threshold or an adaptable trend threshold, the data server 305 may generate one or more alert signals, which may include generating a problem report indicating that a particular vehicle system, element or the like needs to be inspected, replaced, or otherwise addressed by a technician. The report may be generated in response to determining that the trend indicates a problem, or in response to query for the report.”; under broadest reasonable interpretation Ledbetter teaches a suppress windows setting because the analysis is limited to a specific window, as evidenced by Ledbetter [0052]-[0053] and instant application [0062]).
Mojtahedzadeh further teaches
when a current runtime is within an inspection interval of a previous alert, awaiting an inspection result in the inspection data for the previous alert (see at least Mojtahedzadeh [0045]: “In one embodiment, the inspection tasks whose findings are perfectly predicted by the sensor data 422 are performed virtually, whereas, if the sensor data 422 is only a partial predictor of maintenance task findings, both virtual and locational inspection of tasks are performed. Thus, the actual locational inspection interval is escalated according to the predictive value of the sensor data 422.”; [0050]: “As an illustrative example, a maintenance task reads: “General visual inspection of the engine oil filter element bypass condition,” with the interval, T, of 150 flight hours. The initial interval, T, of 150 hours is derived from the CDF curve 602 with an acceptable risk level, R, of 0.4. A sensor continuously measures the oil filter element bypass condition. If the oil filter element bypass passes a certain threshold, the sensor generates an alert from the sensor.”);
when the current runtime is at least a group gap setting after a time of the previous alert and a time of the inspection result, and a count of alert groups is less than a maximum group count, generating an alert group and the new alert for the condition (see at least Mojtahedzadeh [0044]: “The CDF curve 602 is an estimate using the historical maintenance data 420 from one or more operators, and represents the likelihood (probability) of detecting a condition requiring a repair or other maintenance action at some inspection time after a prior inspection had not identified such a condition.”; [0060]: “FIG. 12 is an illustration of a timeline 1200 for related events, a first event 1202 and a second event 1204, relevant in generating the SDOMP 430. The first event 1202 is a maintenance message, for example a sensor alert or other reported sensor data, followed by the second event 1204 after an interval 1206. The second event 1204 is a non-routine finding, identified as non-routine finding X.”)
Regarding claim 14, this claim recites a medium that performs the method of claim 7 as explained above. Therefore, claim 14 is rejected for the same rationale as claim 7.
Regarding claim 15, this claim recites a system that performs the method of claim 1. The combination of Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh also teaches a system that performs the method of claim 1 as outlined in the rejection to claim 1 above. Specifically, Lu teaches processors (Lu [0158]) and a memory (Lu [0029]) that performs the method of claim 1. Therefore, claim 15 is rejected for the same rationale as claim 1.
Regarding claim 16, this claim recites a system that performs the method of claim 2 as explained above. Therefore, claim 16 is rejected for the same rationale as claim 2.
Regarding claim 17, this claim recites a system that performs the method of claim 3 as explained above. Therefore, claim 17 is rejected for the same rationale as claim 3.
Regarding claim 18, this claim recites a system embodying the medium of claim 11 as explained above. Therefore, claim 18 is rejected for the same rationale as claim 11.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh, and further in view of Arantes et al., U.S. Patent Application Publication No. 2020/0075027 A1 (hereinafter Arantes).
Regarding claim 5, Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh teach all elements of the method according to claim 3 as explained above. Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh fail to expressly disclose not generating the new alert when an inspection result indicates the aircraft component should be replaced. However, Arantes teaches wherein determining whether to generate the new alert for the condition comprises:
when an inspection result of the inspection data indicates that the aircraft component should be repaired or replaced, determining to not generate the new alert (see at least Arantes [0044]: “In the system 100, the operator 110 can initiate a system interaction by providing a voice input 134, such as by making an observation, complaint or other comment about the equipment operation, and in response, may receive a voice output 156 providing instructions for course of action…An example of three instruction types are: “ignore”, “quick fix”, and “equipment repair”. For example, the “quick fix” may be performed by the operator 110 in the field, based on received instructions, while the “equipment repair” might be performed by the technician 112 at a maintenance site.”; [0092]: “As mentioned above, in some cases and for some types of equipment 114, the management program(s) 122 may send a control signal to the one or actuators 282, such as for shutting down the equipment 114 or otherwise controlling one or more functions of the equipment 114, such as in the event that repair is required and the severity level is high, or the like.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the method disclosed by Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh with Arantes with reasonable expectation of success. Arantes is directed towards the related field of management and execution of equipment maintenance. Therefore, one of ordinary skill in the art would be motivated to modify Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh with Arantes to ensure equipment remains operative, efficient, and cost-effective (see at least Arantes [0001]: “The objective of equipment maintenance is to keep the equipment in an operative, efficient, and cost-effective condition…However, the typical maintenance schedule may not take into account the current condition and the unique history of an individual piece of equipment. Furthermore, when an unexpected problem with the equipment is detected in the field, it may be difficult for an equipment operator to determine a proper course of action to take.”).
Regarding claim 12, this claim recites a medium performing the method of claim 5 as explained above. Therefore, claim 12 is rejected for the same rationale as claim 5.
Regarding claim 19, this claim recites a system that performs the method of claim 5 as explained above. Therefore, claim 19 is rejected for the same rationale as claim 5.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh, and further in view of Baldwin et al., U.S. Patent Application Publication No. 2024/0233560 A1 (hereinafter Baldwin).
Regarding claim 6, Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh teach all elements of the method according to claim 1 as explained above. Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh fail to expressly disclose a probability distribution representing a generalized data delay or out-of-sequence data arrivals. However, Baldwin teaches wherein the at least a first probability distribution comprises one or more of the following:
a probability distribution representing a generalized data delay; and a probability distribution representing out-of-sequence data arrivals (see at least Baldwin [0047]: “The ID fields may include one or more of an ADS-B target ID, ADS-B target address, timestamp. In some cases, the ADS-B target ID is a unique identifier and may be used as a key for downstream grouping and scoring operations. The timestamp may be used for windowing the ADS-B data by time 308.”; [0136]: “FIG. 8 illustrates a Top Threats view 800 in which the ADS-B targets may be sorted by threat level 802, which may be based on score, the most recent first, a combination of time and score, or some other threat metric. In some cases, the Top Threats view 800 is configurable and an expanded view of the particular scoring event may display a timeline of anomalies for that ADS-B target ID, and may further display field importances indicating in what way the flight was found to be anomalous.”; Baldwin teaches at least a probability distribution representing out-of-sequence data arrivals because data can be organized by time).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the method disclosed by Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh with the probability distribution taught by Baldwin with reasonable expectation of success. Baldwin is directed towards the related field of detecting anomalous aircraft flights from surveillance data. Therefore, one of ordinary skill in the art would be motivated to modify Lu in view of Ledbetter, Anfriani, and Mojtahedzadeh with Baldwin to accurately monitor and identify flight threats (see at least Baldwin [0026]: “Accurately monitoring thousands of flights in the air at any time for possible threats is a huge challenge for human operators. The goal of EDT is to automatically process flight data and assist operators by providing an easy to interpret, ML-based threat score for each active flight that address two essential questions: (1) is the flight behaving normally for an aircraft of that type, and (2) is the flight where it is supposed to be relative to a flight plan and historical flight patterns.”).
Regarding claim 13, this claim recites a medium that performs the method of claim 6 as explained above. Therefore, claim 13 is rejected for the same rationale as claim 6.
Regarding claim 20, this claim recites a system that performs the method of claim 6 as explained above. Therefore, claim 20 is rejected for the same rationale as claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Leitch et al., U.S. Patent Application Publication No. 2022/0026895 A1, directed towards a predictive maintenance model design system.
Lienhardt, U.S. Patent Application Publication No. 2010/0017241 A1, directed towards a maintenance optimization model.
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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/ELIZABETH J SLOWIK/Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
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