Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Response to Arguments
Applicant’s arguments with respect to claim(s) 10/22/2025 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-8, 11-14, and 16-22 are rejected under 35 U.S.C. 103 as being unpatentable over Dixit (U.S. Patent No. 10,814,883) in view of Kipersztok, et al., hereinafter Kipersztok ‘184 (U.S. Patent Application Pub. No. 2002/0138184), and further in view of Wiedemann, et al., hereinafter Wiedemann (U.S. Patent Application Pub. No. 2022/0081129) and Dong (U.S. Patent Application Pub. No. 2019/0049953).
Regarding Claim 1, Dixit teaches: An apparatus for diagnosing faults of a system (Dixit, Col. 4 Line 37-Col. 5 Line 50 – a “IVHM (Integrated Vehicle Health Management) system” which includes a diagnostic model and methods to “to isolate and identify the locations and types of faults and false alarms that exist”) comprising:
a memory (Dixit, Col. 23 Lines 13-44 – “memory” i.e. ROM, RAM, nonvolatile memory device, etc.); and
a processor in communication with the memory (Dixit, Col. 23 Lines 13-44– a microprocessor which uses “stored program instructions and data” stored in the memory), wherein the processor is configured to:
receive operational data associated with the system, wherein the operational data includes a plurality of sensor measurements for each sensor of a plurality of sensors of the system, and wherein the sensor measurements are indicative of conditions or states of the system (Dixit, Col. 4 Line 48-Col. 5 Line 19, Col. 5 Lines 51-67, and Col. 11 Line 53-Col. 14 Line 49 – “observed behavior” obtained from real-time sensor data collected from a group of sensors; where a plurality of values are collected by the sensors in a fixed window of time and analyzed to determine whether a component is within a “normal range of operation” or if the component is experiencing an “anomaly/failure”);
compare the plurality of sensor measurements from each sensor to a respective threshold value (Dixit, Col. 22 Lines 1-43 – where “the difference between the sensor values and the corresponding normal detection parameters is calculated and stored” and for a moving window, determine trends and compare the trends to “historical trend data to determine if off normal conditions exist” utilizing “a stored predetermined standard deviation threshold value”), wherein the system includes one or more components of an aircraft (Dixit, Col. 4 Line 48-Col. 5 Line 19, Col. 5 Lines 51-67, and Col. 11 Line 53-Col. 14 Line 49 – “observed behavior” obtained from real-time sensor data collected from a group of sensors associated with “various subsystems, line replaceable units (LRUs), and components of entire system”),
determine, based on the comparisons, a condition of the system having a degraded state and one or more conditions of the system having a normal state (Dixit, Col. 5 Lines 33-50, Col. 14 Lines 24-49, and Col. 22 Line 21-Col. 23 Line 12 – determination of whether “off normal conditions” exist and set “anomaly/degradation flags” if so, where the flag data is sent to data fusion and assigned a “conditional code” indicating “a normal range of operation”, “a component anomaly/failure”, etc.; where in a system, faults can be “isolated” to specific components, such that only that component is experiencing a failure while other components are operating normally);
select at least one of the one or more conditions of the system having a normal state (Dixit, Col. 24 Lines 35-56 and Col 29. Lines 39-56 – a “LRU/System Selection & Data Transport Module” which identifies and separates data associated with a component to be analyzed, or selected component, following identification of data associated with the component indicating an abnormality; for example a brine pump previously operating normally is selected and monitored following detection of onset degradation);
input the condition having degraded state where sensor data streams are inputted and mapped such that groups of sensor data are “correlated, i.e. where the sensor data for each sensor within one group has a mutual relationship in which a component anomaly or failure sensed by data from one sensor in the group should also be reflected by an anomaly or failure as indicated by data from other sensors in the group” in a model; where the state of a component in the model is updated according to sensor data and other data received as inputs), wherein the data structure includes a plurality of the nodes representing components of the system (Dixit, Col. 7 Lines 8-20 – “A parametric and BIT MBR model may include components and sensors that are related by their functions”, where in an embodiment “a model of a vehicle system or subsystem may be represented as nodes in a graph”), and wherein each of the nodes includes a plurality of states (Dixit, Col. 7 Line 54-Col. 8 Line 2 and Col. 14 Lines 24-49 – where each node in a graph has a “state” which “defines the various states of the component” and “available and in use parameters” which may indicate “the component is both available and in use” or “failure and/or due to other reasons such as loss of power, etc.”),
isolate, using the diagnostic model, a failed or degraded component of the system (Dixit, Col. 5 Lines 33-50 – “determine whether the system is operating normally, if any system anomalies exist, and if so, to isolate and identify the locations and types of faults”); and
“determining whether maintenance at the component level is required for the aircraft/system while providing aircraft/system equipment degradation/failure situational awareness”),
Dixit teaches input the condition having degraded state into a diagnostic model, wherein the diagnostic model represents a data structure defining causal relationships between nodes, but Dixit does not specifically teach input the condition having degraded state and the at least one condition having a normal state into a diagnostic model. Furthermore, while Dixit teaches one or more maintenance actions pertaining to the failed or degraded component, Dixit does not teach automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component, nor does Dixit teach wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft; and, wherein one or more nodes of the plurality of the nodes are associated with one or more conditional probabilities defined using conditional probability tables, wherein the conditional probability tables define, for a parent node and a child node, a probability of occurrence for a respective transition from each state of the parent node to each state of the child node, and wherein the probability of occurrence is determined based at least in part on estimates derived from component reliability data.
However, Kipersztok ‘184 teaches input the condition having degraded state and the at least one condition having a normal state into a diagnostic model (Kipersztok ‘184, Para. 0033-0043 – where the “diagnostic model” receives “input relating to various observed symptoms”, such as “a failed LRU”, where “every component node has at least two states, (i.e., normal and failed)”); and wherein one or more nodes of the plurality of the nodes are associated with one or more conditional probabilities defined using conditional probability tables, wherein the conditional probability tables define, for a parent node and a child node, a probability of occurrence for a respective transition from each state of the parent node to each state of the child node, and wherein the probability of occurrence is determined based at least in part on estimates derived from component reliability data (Kipersztok ‘184, Table 1 and Para. 0043-0046 – where a probability “that is assigned to the failed state is obtained from the reliability and maintainability data and/or experiential data” and wherein “nodes, such as the intermediate nodes, contain conditional probability tables” which are “derived from experiential information, with distributions specified over the states of the child nodes conditioned over the states of the parent nodes” by a Bayesian network; for example, Table 1 shows “given that the combustor/atomizer has a degraded spray pattern, the probability that an APU-explosive bang will be heard at 0.4”, which illustrates “that the probability assigned to the different states of the APU-explosive bang node, i.e., the bang being heard or no bang, is dependent upon the state of the parent node”, in this case, the “spray pattern”).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of Dixit to include input the condition having degraded state and the at least one condition having a normal state into a diagnostic model; and wherein one or more nodes of the plurality of the nodes are associated with one or more conditional probabilities defined using conditional probability tables, wherein the conditional probability tables define, for a parent node and a child node, a probability of occurrence for a respective transition from each state of the parent node to each state of the child node, and wherein the probability of occurrence is determined based at least in part on estimates derived from component reliability data, as taught by Kipersztok ‘184, in order to improve system reliability, failure and condition analysis, and diagnostics using conditional probabilities as defined in a Bayesian network.
Dixit in view of Kipersztok ‘184 does not teach automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component, and wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft.
However, Wiedemann teaches automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component (Wiedemann, Para. 0033 and 0082-0083 – where a “maintenance system” may “automatically schedule work scope activities” in addition to scheduling “service activities for the next service visit” based on a probability percentage indicating how close an engine or component is to exceed a predetermined threshold damage sum, such that the engine or component is degraded).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the apparatus including the above limitations of Dixit in view of Kipersztok ‘184 to include automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component, as taught by Wiedemann, in order to automatically schedule a maintenance appointment to prevent a lack of component maintenance in a case where an operator forgot to schedule an appointment and to prevent failure of the components.
Dixit in view of Kipersztok ‘184 in view of Wiedemann does not teach wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft.
However, Dong teaches wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft (Dong, Para. 0066-0070 – extracting “key intelligences for the flight data stream” from each sensor of the aircraft, including a “root mean square of sensor data” representative “of characteristics of the outputs over the time period”; where the extracted “key intelligence”, including a “root mean square of sensor data”, is compared with “thresholds”, based on “data of the reference flight data”, or prior flight leg, defining “a predefined scale of seriousness of abnormality or deviation from the reference flight data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the apparatus including the above limitations of Dixit in view of Kipersztok ‘184 and Wiedemann to include wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft, as taught by Dong, in order to utilize a known method of calculating deviation for sensor data to account for signal noise and sensor errors.
In regards to Claim 2, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 1, and Dixit further teaches wherein the processor (Dixit, Col. 23 Lines 13-44 – a microprocessor) is further configured to:
receive maintenance information relating to the system (Dixit, Para. Col. 4 Line 48-Col. 5 Line 19, Col. 10 Lines 4-22, and Col. 24 Line 57-Col. 25 Line 50 – “Observed behavior” collected from sensor data and “maintenance records” which include “analyzed diagnostics results” and “previously stored inflight real time assessments by the prognostics engine”);
determine a maintenance condition based on the maintenance information (Dixit, Col. 4 Line 48-Col. 5 Line 50 and Col. 24 Line 57-Col. 25 Line 50 – where based on “observed behavior”, determine “anomalous behavior (discrepancies/residues)” and assess if any system anomalies exist; where “maintenance records” are utilized in the creation of the model for assessment and contains “the original BIT and sensor parametric recorded data from which degradation is determined”);
input the maintenance condition into the diagnostic model (Dixit, Col. 24 Line 57-Col. 26 Line 8 – where maintenance history is utilized in a model which contains “diagnostics and prognostics definition of and knowledge of the component in terms of the respective component attributes, functions, behaviors, and semantics”); and
isolate, using the diagnostic model, the degraded component of the system (Dixit, Col. 5 Lines 33-50 and Col. 26 Lines 9-34 – “determine whether the system is operating normally, if any system anomalies exist, and if so, to isolate and identify the locations and types of faults”; where damage estimation is performed utilizing the created model).
In regards to Claim 3, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 2, and Dixit further teaches wherein the operational data includes historical operational data of the system, wherein the maintenance information includes a failure message or event, and wherein the one or more maintenance action identify the failed or degraded component of the system (Dixit, Col. 24 Lines 15-34 – “maintenance records” having “pertinent records [which] contain the original BIT and sensor parametric recorded data from which degradation is determined” and “analysis of why the component alarm was issued and what remediation steps were taken to fix the alarm/problem”).
In regards to Claim 4, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 1, and Dixit in view of Kipersztok ‘184 further teaches wherein the diagnostic model includes a Bayesian network (Kipersztok ‘184, Para. 0037 – “the diagnostic model can be constructed” utilizing “bayesian networks”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus including the above limitations of Dixit in view of Kipersztok ‘184, Wiedemann, and Dong to further include wherein the diagnostic model includes a Bayesian network, as taught by Kipersztok ‘184, in order to utilize a model which provides an efficient way to represent and compute probabilities between parent and child nodes.
In regards to Claim 5, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 1, and Dixit in view of Kipersztok ‘184 further teaches wherein the data structure of the diagnostic model comprises a directed acyclic graph representing the causal relationships between at least some of the nodes (Kipersztok ‘184, Para. 0040 – “Based upon the failure state of a component, the intermediate nodes may interconnect the node(s) representing one or more components with the node(s) representing one or more of the observed symptoms in an acyclic manner.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus including the above limitations of Dixit in view of Kipersztok ‘184, Wiedemann, and Dong to further include wherein the data structure of the diagnosis model comprises a directed acyclic graph representing the causal relationships between at least some of the nodes, as taught by Kipersztok ‘184, in order to model the relationships between component nodes to show dependency and data flow.
In regards to Claim 6, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 1, and Dixit further teaches wherein the plurality of states include a first state and a second state, wherein the first state corresponds to a normal state and the second state corresponds to a failed or degraded state (Dixit, Col. 5 Lines 33-50, Col. 14 Lines 24-49, and Col. 22 Line 21-Col. 23 Line 12 – determination of whether “off normal conditions” exist and set “anomaly/degradation flags” if so, where the flag data is sent to data fusion and assigned a “conditional code” indicating “a normal range of operation”, “a component anomaly/failure”, etc.).
In regards to Claim 7, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 1, and Dixit in view of Kipersztok ‘184, Wiedemann, and Dong further teaches wherein the probability of occurrence is further based on fault information (Dixit, Col. 7 Line 54-Col. 8 Line 2 – “a failure probability (failure modes)” from either a component supplier or “from historical performance data”; where the probability can be recalculated based on “degradation events, i.e. the failure probability increases with degradation events”; Kipersztok ‘184, Para. 0043-0045 – where the probabilities listed within a “conditional probability table” are “derived from experiential information” or “reliability and maintainability data” and “specified over the states of the child nodes conditioned over the states of the parent nodes”).
In regards to Claim 8, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 1, and Dixit further teaches wherein the data structure of the diagnostic model is created using information of the system, and wherein the information indicates hierarchical cause and effect relationships of failures between components of the system (Dixit, Col. 5 Lines 33-50 and Col. 13 Line 37-Col. 14 Line 49– where a “diagnostic Model Based Reasoner (MBR) Engine” receives sensor data streams for data fusion, including information of “sensor data for each sensor within one group has a mutual relationship in which a component anomaly or failure sensed by data from one sensor in the group should also be reflected by an anomaly or failure as indicated by data from other sensors in the group”; for example, if “bearings are sufficiently worn”, a pump will fail).
In regards to Claim 11, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 1, and Dixit in view of Kipersztok ‘184, Wiedemann, and Dong further teaches wherein the processor is further configured to define the probability of occurrence for each state of a node of the diagnostic model (Dixit, Col. 7 Line 21-Col. 8 Line 2 – “a failure probability (failure modes)” for each “component node” obtained from either a component supplier or “from historical performance data”, where the probability can be recalculated based on “degradation events, i.e. the failure probability increases with degradation events”; Kipersztok ‘184, Para. 0043-0045 – where the probabilities listed within a “conditional probability table” are “derived from experiential information” or “reliability and maintainability data” and “specified over the states of the child nodes conditioned over the states of the parent nodes”).
In regards to Claim 12, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 11, and Dixit further teaches wherein the probability of occurrence is generated using historical knowledge, machine learning techniques, failure modes effects and criticality analysis (FMECA) information, probability of failure (POF) information, or a combination thereof (Dixit, Col. 7 Line 21-Col. 8 Line 2 and Col. 33 Line 66-Col. 34 Line 16 – “a failure probability (failure modes)” for each “component node” obtained from either a component supplier or “from historical performance data”, where the probability can be recalculated based on “degradation events, i.e. the failure probability increases with degradation events”; where the models and degradation events are stored and used for “learning/training” to “enhance model refinement”).
In regards to Claim 13, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 1, and Dixit further teaches wherein the processor is further configured to determine whether the plurality of sensor measurements from each sensor is equal to or exceeds a predetermined limit (Dixit, Col. 8 Lines 3-22 and Col. 22 Lines 1-43 – where “the difference between the sensor values and the corresponding normal detection parameters is calculated and stored” and for a moving window, determine trends and compare the trends to “historical trend data to determine if off normal conditions exist” utilizing “a stored predetermined standard deviation threshold value”; where the threshold has defined “fixed upper and lower bounds”).
In regards to Claim 14, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the apparatus of Claim 1, and Dixit in view of Kipersztok ‘184 further teaches wherein the diagnostic model is constructed based on schematics of the system (Kipersztok ‘184, Para. 0018 and 0048 – the “diagnostic system” includes a “database can include schematic images of the suspect component that can be displayed during replacement or repair of the suspect component”), failure modes effects and criticality analysis (FMECA) information, probability of failure (POF), or a combination thereof (Dixit, Col. 7 Line 21-Col. 8 Line 2 and Col. 33 Line 66-Col. 34 Line 16 – “a failure probability (failure modes)” for each “component node” obtained from either a component supplier or “from historical performance data”, where the probability can be recalculated based on “degradation events, i.e. the failure probability increases with degradation events”; where the models and degradation events are stored and used for “learning/training” to “enhance model refinement”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system including the above limitations of Dixit in view of Kipersztok ‘184, Wiedemann, and Dong to further include wherein the diagnostic model is constructed based on schematics of the system, as taught by Kipersztok ‘184, in order to utilize existing schematic and textual descriptions of components when constructing a model to improve model construction efficiency and accuracy.
Regarding Claim 16, Dixit teaches: A method for diagnosing faults of a system comprising: (Dixit, Col. 4 Line 37-Col. 5 Line 50 – a “IVHM (Integrated Vehicle Health Management) system” which includes a diagnostic model and methods to “to isolate and identify the locations and types of faults and false alarms that exist”) comprising:
receiving, by one or more processors (Dixit, Col. 23 Lines 13-44– a microprocessor), operational data associated with the system, wherein the operational data includes a plurality of sensor measurements for each sensor of a plurality of sensors of the system, and wherein the sensor measurements are indicative of conditions or states of the system (Dixit, Col. 4 Line 48-Col. 5 Line 19, Col. 5 Lines 51-67, and Col. 11 Line 53-Col. 14 Line 49 – “observed behavior” obtained from real-time sensor data collected from a group of sensors; where a plurality of values are collected by the sensors in a fixed window of time and analyzed to determine whether a component is within a “normal range of operation” or if the component is experiencing an “anomaly/failure”);
comparing, by one or more processors, the plurality of sensor measurements from each sensor to a respective threshold value (Dixit, Col. 22 Lines 1-43 – where “the difference between the sensor values and the corresponding normal detection parameters is calculated and stored” and for a moving window, determine trends and compare the trends to “historical trend data to determine if off normal conditions exist” utilizing “a stored predetermined standard deviation threshold value”), wherein the system includes one or more components of an aircraft (Dixit, Col. 4 Line 48-Col. 5 Line 19, Col. 5 Lines 51-67, and Col. 11 Line 53-Col. 14 Line 49 – “observed behavior” obtained from real-time sensor data collected from a group of sensors associated with “various subsystems, line replaceable units (LRUs), and components of entire system”),
determining, based on the comparisons, a condition of the system having a degraded state and one or more conditions of the system having a normal state (Dixit, Col. 5 Lines 33-50, Col. 14 Lines 24-49, and Col. 22 Line 21-Col. 23 Line 12– determination of whether “off normal conditions” exist and set “anomaly/degradation flags” if so, where the flag data is sent to data fusion and assigned a “conditional code” indicating “a normal range of operation”, “a component anomaly/failure”, etc.; where in a system, faults can be “isolated” to specific components, such that only that component is experiencing a failure while other components are operating normally);
selecting, by the one or more processors, at least one of the one or more conditions of the system having a normal state (Dixit, Col. 24 Lines 35-56 and Col 29. Lines 39-56 – a “LRU/System Selection & Data Transport Module” which identifies and separates data associated with a component to be analyzed, or selected component, following identification of data associated with the component indicating an abnormality; for example a brine pump previously operating normally is selected and monitored following detection of onset degradation);
inputting, by the one or more processors, the condition having degraded state represents a data structure defining causal relationships between nodes (Dixit, Col. 13 Lines 37-61 and Col. 27 Lines 1-17 – where sensor data streams are inputted and mapped such that groups of sensor data are “correlated, i.e. where the sensor data for each sensor within one group has a mutual relationship in which a component anomaly or failure sensed by data from one sensor in the group should also be reflected by an anomaly or failure as indicated by data from other sensors in the group” in a model; where the state of a component in the model is updated according to sensor data and other data received as inputs), wherein the data structure includes a plurality of the nodes representing components of the system (Dixit, Col. 7 Lines 8-20 – “A parametric and BIT MBR model may include components and sensors that are related by their functions”, where in an embodiment “a model of a vehicle system or subsystem may be represented as nodes in a graph”), wherein each of the nodes includes a plurality of states (Dixit, Col. 7 Line 54-Col. 8 Line 2 and Col. 14 Lines 24-49 – where each node in a graph has a “state” which “defines the various states of the component” and “available and in use parameters” which may indicate “the component is both available and in use” or “failure and/or due to other reasons such as loss of power, etc.”),
isolating, using the diagnostic model, a failed or degraded component of the system (Dixit, Col. 5 Lines 33-50 – “determine whether the system is operating normally, if any system anomalies exist, and if so, to isolate and identify the locations and types of faults”); and
using the one or more processors, automatically perform one or more maintenance actions pertaining to for the failed or degraded component (Dixit, Col. 24 Lines 15-34 – “determining whether maintenance at the component level is required for the aircraft/system while providing aircraft/system equipment degradation/failure situational awareness”), the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component.
Dixit teaches inputting the condition having degraded state into a diagnostic model, wherein the diagnostic model represents a data structure defining causal relationships between nodes, but Dixit does not specifically teach inputting the condition having degraded state and the at least one condition having a normal state into a diagnostic model. While Dixit teaches one or more maintenance actions pertaining to the failed or degraded component, Dixit does not teach automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component¸ nor does Dixit teach wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft; and wherein one or more nodes of the plurality of the nodes are associated with one or more conditional probabilities defined using conditional probability tables, wherein the conditional probability tables define, for a parent node and a child node, a probability of occurrence for a respective transition from each state of the parent node to each state of the child node, and wherein the probability of occurrence is determined based at least in part on estimates derived from component reliability data.
However, Kipersztok ‘184 teaches inputting the condition having degraded state and the at least one condition having a normal state into a diagnostic model (Kipersztok ‘184, Para. 0033-0043 – where the “diagnostic model” receives “input relating to various observed symptoms”, such as “a failed LRU”, where “every component node has at least two states, (i.e., normal and failed)”); and wherein one or more nodes of the plurality of the nodes are associated with one or more conditional probabilities defined using conditional probability tables, wherein the conditional probability tables define, for a parent node and a child node, a probability of occurrence for a respective transition from each state of the parent node to each state of the child node, and wherein the probability of occurrence is determined based at least in part on estimates derived from component reliability data (Kipersztok ‘184, Table 1 and Para. 0043-0046 – where a probability “that is assigned to the failed state is obtained from the reliability and maintainability data and/or experiential data” and wherein “nodes, such as the intermediate nodes, contain conditional probability tables” which are “derived from experiential information, with distributions specified over the states of the child nodes conditioned over the states of the parent nodes” by a Bayesian network; for example, Table 1 shows “given that the combustor/atomizer has a degraded spray pattern, the probability that an APU-explosive bang will be heard at 0.4”, which illustrates “that the probability assigned to the different states of the APU-explosive bang node, i.e., the bang being heard or no bang, is dependent upon the state of the parent node”, in this case, the “spray pattern”).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Dixit to include inputting the condition having degraded state and the at least one condition having a normal state into a diagnostic model; and wherein one or more nodes of the plurality of the nodes are associated with one or more conditional probabilities defined using conditional probability tables, wherein the conditional probability tables define, for a parent node and a child node, a probability of occurrence for a respective transition from each state of the parent node to each state of the child node, and wherein the probability of occurrence is determined based at least in part on estimates derived from component reliability data, as taught by Kipersztok ‘184, in order to improve system reliability, failure and condition analysis, and diagnostics using conditional probabilities as defined in a Bayesian network.
Dixit in view of Kipersztok ‘184 does not teach automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component, and wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft.
However, Wiedemann teaches automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component (Wiedemann, Para. 0033, 0082-0083, and 0089 – where a “maintenance system”, including one or more processors, may “automatically schedule work scope activities” in addition to scheduling “service activities for the next service visit” based on a probability percentage indicating how close an engine or component is to exceed a predetermined threshold damage sum, such that the engine or component is degraded).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the method including the above limitations of Dixit in view of Kipersztok ‘184 to include automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component, as taught by Wiedemann, in order to automatically schedule a maintenance appointment to prevent a lack of component maintenance in a case where an operator forgot to schedule an appointment and to prevent failure of the components.
Dixit in view of Kipersztok ‘184 in view of Wiedemann does not teach wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft.
However, Dong teaches wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft (Dong, Para. 0066-0070 – extracting “key intelligences for the flight data stream” from each sensor of the aircraft, including a “root mean square of sensor data” representative “of characteristics of the outputs over the time period”; where the extracted “key intelligence”, including a “root mean square of sensor data”, is compared with “thresholds”, based on “data of the reference flight data”, or prior flight leg, defining “a predefined scale of seriousness of abnormality or deviation from the reference flight data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the method including the above limitations of Dixit in view of Kipersztok ‘184 and Wiedemann to include wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft, as taught by Dong, in order to utilize a known method of calculating deviation for sensor data to account for signal noise and sensor errors.
In regards to Claim 17, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the method of Claim 16, and Dixit further teaches further comprising:
receiving, by the one or more processors, maintenance information relating to the system (Dixit, Para. Col. 4 Line 48-Col. 5 Line 19, Col. 10 Lines 4-22, and Col. 24 Line 57-Col. 25 Line 50 – “Observed behavior” collected from sensor data and “maintenance records” which include “analyzed diagnostics results” and “previously stored inflight real time assessments by the prognostics engine”);
determining, by the one or more processors, a maintenance condition based on the maintenance information (Dixit, Col. 4 Line 48-Col. 5 Line 50 and Col. 24 Line 57-Col. 25 Line 50 – where based on “observed behavior”, determine “anomalous behavior (discrepancies/residues)” and assess if any system anomalies exist; where “maintenance records” are utilized in the creation of the model for assessment and contains “the original BIT and sensor parametric recorded data from which degradation is determined”);
inputting, by the one or more processors, the maintenance condition into the diagnostic model (Dixit, Col. 24 Line 57-Col. 26 Line 8 – where maintenance history is utilized in a model which contains “diagnostics and prognostics definition of and knowledge of the component in terms of the respective component attributes, functions, behaviors, and semantics”); and
isolating, using the diagnostic model, the failed or degraded component of the system (Dixit, Col. 5 Lines 33-50 and Col. 26 Lines 9-34 – “determine whether the system is operating normally, if any system anomalies exist, and if so, to isolate and identify the locations and types of faults”; where damage estimation is performed utilizing the created model).
In regards to Claim 18, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the method of Claim 17, and Dixit in view of Kipersztok ‘184, Wiedemann, and Dong further teaches wherein the maintenance information includes a failure event or message, wherein the one or more maintenance action identify the failed or degraded components of the system for replacement or repair (Dixit, Col. 24 Lines 15-34 – “maintenance records” having “pertinent records [which] contain the original BIT and sensor parametric recorded data from which degradation is determined” and “analysis of why the component alarm was issued and what remediation steps were taken to fix the alarm/problem”), wherein the plurality of states include a first state and a second state, wherein the first state corresponds to a normal state and the second state corresponds to a failed state, and wherein the probability of occurrence is further based on fault information (Dixit, Col. 5 Lines 33-50, Col. 14 Lines 24-49, and Col. 22 Line 21-Col. 23 Line 12 – determination of whether “off normal conditions” exist and set “anomaly/degradation flags” if so, where the flag data is sent to data fusion and assigned a “conditional code” indicating “a normal range of operation”, “a component anomaly/failure”, etc.; Kipersztok ‘184, Para. 0043-0045 – where the probabilities listed within a “conditional probability table” are “derived from experiential information” or “reliability and maintainability data” and “specified over the states of the child nodes conditioned over the states of the parent nodes”).
In regards to Claim 19, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the method of Claim 17, and Dixit in view of Kipersztok ‘184 further teaches wherein the diagnostic model includes a Bayesian network (Kipersztok ‘184, Para. 0037 – “the diagnostic model can be constructed” utilizing “bayesian networks”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method including the above limitations of Dixit in view of Kipersztok ‘184, Wiedemann, and Dong to further include wherein the diagnostic model includes a Bayesian network, as taught by Kipersztok ‘184, in order to utilize a model which provides an efficient way to represent and compute probabilities between parent and child nodes.
Regarding Claim 20, Dixit teaches: A non-transitory computer-readable medium having stored thereon instruction code (Dixit, Col. 23 Lines 13-44 – “memory” i.e. ROM, RAM, nonvolatile memory device, etc. which contains “contains stored program instructions”), wherein the instruction code is executable by a processor of a computer to perform operations (Dixit, Col. 23 Lines 13-44 – a microprocessor which executes “stored program instructions and data” stored in the memory) comprising:
receiving operational data associated with a system, wherein the operational data includes a plurality of sensor measurements for each sensor of a plurality of sensors of the system, and wherein the sensor measurements are indicative of conditions or states of the system (Dixit, Col. 4 Line 48-Col. 5 Line 19, Col. 5 Lines 51-67, and Col. 11 Line 53-Col. 14 Line 49 – “observed behavior” obtained from real-time sensor data collected from a group of sensors; where a plurality of values are collected by the sensors in a fixed window of time and analyzed to determine whether a component is within a “normal range of operation” or if the component is experiencing an “anomaly/failure”);
comparing the plurality of sensor measurements from each sensor to a respective threshold value (Dixit, Col. 22 Lines 1-43 – where “the difference between the sensor values and the corresponding normal detection parameters is calculated and stored” and for a moving window, determine trends and compare the trends to “historical trend data to determine if off normal conditions exist” utilizing “a stored predetermined standard deviation threshold value”), wherein the system includes one or more components of an aircraft (Dixit, Col. 4 Line 48-Col. 5 Line 19, Col. 5 Lines 51-67, and Col. 11 Line 53-Col. 14 Line 49 – “observed behavior” obtained from real-time sensor data collected from a group of sensors associated with “various subsystems, line replaceable units (LRUs), and components of entire system”), ;
determining a condition of the system having a degraded state and one or more conditions of the system having a normal state (Dixit, Col. 5 Lines 33-50, Col. 14 Lines 24-49, and Col. 22 Line 21-Col. 23 Line 12 – determination of whether “off normal conditions” exist and set “anomaly/degradation flags” if so, where the flag data is sent to data fusion and assigned a “conditional code” indicating “a normal range of operation”, “a component anomaly/failure”, etc.; where in a system, faults can be “isolated” to specific components, such that only that component is experiencing a failure while other components are operating normally);
selecting at least one of the one or more conditions of the system having a normal state (Dixit, Col. 24 Lines 35-56 and Col 29. Lines 39-56 – a “LRU/System Selection & Data Transport Module” which identifies and separates data associated with a component to be analyzed, or selected component, following identification of data associated with the component indicating an abnormality; for example a brine pump previously operating normally is selected and monitored following detection of onset degradation);
inputting the condition having degraded state awhere sensor data streams are inputted and mapped such that groups of sensor data are “correlated, i.e. where the sensor data for each sensor within one group has a mutual relationship in which a component anomaly or failure sensed by data from one sensor in the group should also be reflected by an anomaly or failure as indicated by data from other sensors in the group” in a model; where the state of a component in the model is updated according to sensor data and other data received as inputs), wherein the data structure includes a plurality of the nodes representing components of the system (Dixit, Col. 7 Lines 8-20 – “A parametric and BIT MBR model may include components and sensors that are related by their functions”, where in an embodiment “a model of a vehicle system or subsystem may be represented as nodes in a graph”), and wherein each of the nodes includes a plurality of states (Dixit, Col. 7 Line 54-Col. 8 Line 2 and Col. 14 Lines 24-49 – where each node in a graph has a “state” which “defines the various states of the component” and “available and in use parameters” which may indicate “the component is both available and in use” or “failure and/or due to other reasons such as loss of power, etc.”),
isolating, using the diagnostic model, a failed or degraded component of the system (Dixit, Col. 5 Lines 33-50 – “determine whether the system is operating normally, if any system anomalies exist, and if so, to isolate and identify the locations and types of faults”); and
automatically perform one or more maintenance actions pertaining to the failed or degraded component (Dixit, Col. 24 Lines 15-34 – “determining whether maintenance at the component level is required for the aircraft/system while providing aircraft/system equipment degradation/failure situational awareness”), the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component.
Dixit teaches inputting the condition having degraded state into a diagnostic model, wherein the diagnostic model represents a data structure defining causal relationships between nodes, but Dixit does not specifically teach inputting the condition having degraded state and the at least one condition having a normal state into a diagnostic model. While Dixit teaches one or more maintenance actions pertaining to the failed or degraded component, Dixit does not teach automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component, nor does Dixit teach wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft; and wherein one or more nodes of the plurality of the nodes are associated with one or more conditional probabilities defined using conditional probability tables, wherein the conditional probability tables define, for a parent node and a child node, a probability of occurrence for a respective transition from each state of the parent node to each state of the child node, and wherein the probability of occurrence is determined based at least in part on estimates derived from component reliability data.
However, Kipersztok ‘184 teaches inputting the condition having degraded state and the at least one condition having a normal state into a diagnostic model (Kipersztok ‘184, Para. 0033-0043 – where the “diagnostic model” receives “input relating to various observed symptoms”, such as “a failed LRU”, where “every component node has at least two states, (i.e., normal and failed)”); and wherein one or more nodes of the plurality of the nodes are associated with one or more conditional probabilities defined using conditional probability tables, wherein the conditional probability tables define, for a parent node and a child node, a probability of occurrence for a respective transition from each state of the parent node to each state of the child node, and wherein the probability of occurrence is determined based at least in part on estimates derived from component reliability data (Kipersztok ‘184, Table 1 and Para. 0043-0046 – where a probability “that is assigned to the failed state is obtained from the reliability and maintainability data and/or experiential data” and wherein “nodes, such as the intermediate nodes, contain conditional probability tables” which are “derived from experiential information, with distributions specified over the states of the child nodes conditioned over the states of the parent nodes” by a Bayesian network; for example, Table 1 shows “given that the combustor/atomizer has a degraded spray pattern, the probability that an APU-explosive bang will be heard at 0.4”, which illustrates “that the probability assigned to the different states of the APU-explosive bang node, i.e., the bang being heard or no bang, is dependent upon the state of the parent node”, in this case, the “spray pattern”).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the non-transitory computer readable medium of Dixit to include inputting the condition having degraded state and the at least one condition having a normal state into a diagnostic model; and wherein one or more nodes of the plurality of the nodes are associated with one or more conditional probabilities defined using conditional probability tables, wherein the conditional probability tables define, for a parent node and a child node, a probability of occurrence for a respective transition from each state of the parent node to each state of the child node, and wherein the probability of occurrence is determined based at least in part on estimates derived from component reliability data, as taught by Kipersztok ‘184, in order to improve system reliability, failure and condition analysis, and diagnostics using conditional probabilities as defined in a Bayesian network.
Dixit in view of Kipersztok ‘184 does not teach automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component, and wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft.
However, Wiedemann teaches automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component (Wiedemann, Para. 0033 and 0082-0083 – where a “maintenance system” may “automatically schedule work scope activities” in addition to scheduling “service activities for the next service visit” based on a probability percentage indicating how close an engine or component is to exceed a predetermined threshold damage sum, such that the engine or component is degraded).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the non-transitory computer readable medium including the above limitations of Dixit in view of Kipersztok ‘184 to include automatically perform one or more maintenance actions pertaining to the failed or degraded component, the one or more maintenance actions including scheduling a maintenance appointment for the failed or degraded component, as taught by Wiedemann, in order to automatically schedule a maintenance appointment to prevent a lack of component maintenance in a case where an operator forgot to schedule an appointment and to prevent failure of the components.
Dixit in view of Kipersztok ‘184 in view of Wiedemann does not teach wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft.
However, Dong teaches wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft (Dong, Para. 0066-0070 – extracting “key intelligences for the flight data stream” from each sensor of the aircraft, including a “root mean square of sensor data” representative “of characteristics of the outputs over the time period”; where the extracted “key intelligence”, including a “root mean square of sensor data”, is compared with “thresholds”, based on “data of the reference flight data”, or prior flight leg, defining “a predefined scale of seriousness of abnormality or deviation from the reference flight data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the non-transitory computer readable medium including the above limitations of Dixit in view of Kipersztok ‘184 and Wiedemann to include wherein each respective threshold value is dynamically computed based at least in part on a root mean square of the sensor measurements over a predetermined number of prior flight legs of the aircraft, as taught by Dong, in order to utilize a known method of calculating deviation for sensor data to account for signal noise and sensor errors.
In regards to Claim 21, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the system of Claim 1, and Dixit in view of Kipersztok ‘184, Wiedemann, and Dong further teaches wherein at least one of the conditional probability tables comprises conditional probabilities having values other than zero or one, the values representing fractional likelihoods of state transitions between the parent node and the child node (Kipersztok ‘184, Table 1 and Para. 0043-0046 – wherein “nodes, such as the intermediate nodes, contain conditional probability tables” which are “derived from experiential information, with distributions specified over the states of the child nodes conditioned over the states of the parent nodes”; for example, Table 1 shows “given that the combustor/atomizer has a degraded spray pattern, the probability that an APU-explosive bang will be heard at 0.4”, which illustrates “that the probability assigned to the different states of the APU-explosive bang node, i.e., the bang being heard or no bang, is dependent upon the state of the parent node”, in this case, the “spray pattern”, where 0.4 is a value between zero and one).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus including the above limitations of Dixit in view of Kipersztok ‘184, Wiedemann, and Dong to further include wherein at least one of the conditional probability tables comprises conditional probabilities having values other than zero or one, the values representing fractional likelihoods of state transitions between the parent node and the child node, as taught by Kipersztok ‘184, in order to improve the accuracy of the failure analysis and diagnostics by utilizing values other than zero and one.
In regards to Claim 22, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the system of Claim 16, and Dixit in view of Kipersztok ‘184, Wiedemann, and Dong further teaches wherein at least one of the conditional probability tables comprises conditional probabilities having values other than zero or one, the values representing fractional likelihoods of state transitions between the parent node and the child node (Kipersztok ‘184, Table 1 and Para. 0043-0046 – wherein “nodes, such as the intermediate nodes, contain conditional probability tables” which are “derived from experiential information, with distributions specified over the states of the child nodes conditioned over the states of the parent nodes”; for example, Table 1 shows “given that the combustor/atomizer has a degraded spray pattern, the probability that an APU-explosive bang will be heard at 0.4”, which illustrates “that the probability assigned to the different states of the APU-explosive bang node, i.e., the bang being heard or no bang, is dependent upon the state of the parent node”, in this case, the “spray pattern”, where 0.4 is a value between zero and one).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method including the above limitations of Dixit in view of Kipersztok ‘184, Wiedemann, and Dong to further include wherein at least one of the conditional probability tables comprises conditional probabilities having values other than zero or one, the values representing fractional likelihoods of state transitions between the parent node and the child node, as taught by Kipersztok ‘184, in order to improve the accuracy of the failure analysis and diagnostics by utilizing values other than zero and one.
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Dixit in view of Kipersztok ‘184, Wiedemann, and Dong, and further in view of Gautam, et al., hereinafter Gautam (U.S. Patent Application Pub. No. 2021/0118242).
In regards to Claim 15, Dixit in view of Kipersztok ‘184, Wiedemann, and Dong teaches the system of Claim 1, but Dixit in view of Kipersztok ‘184, Wiedemann, and Dong does not teach wherein the system includes an air trim system of an aircraft.
Gautam teaches wherein the system includes an air trim system of an aircraft (Gautam, Para. 0001-0003 and 0017 – “a method of predicting a needed repair and/or maintenance activity for an aircraft system”, where the aircraft system contains a “trim air system” to “maintain a desirable cabin air temperature and cabin pressurization for the passengers in the aircraft cabin and the aircraft crew in the aircraft cabin and flight deck”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the system including the above limitations of Dixit in view of Kipersztok ‘184, Wiedemann, and Dong to include wherein the system includes an air trim system of an aircraft, as taught by Gautam, in order to utilize a diagnostic model to determine any possible faults in the air trim system to prevent loss of aircraft cabin and crew comfort.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Yuksel, et al. (U.S. Patent Application Pub. No. 2021/0245893) teaches a method for monitoring a condition of a VTOL-aircraft using an estimation algorithm to generate an estimation result that is representative of a condition of the VTOL-aircraft, preferably a health status of at least one actuator, where the estimation result is subject to uncertainty.
Discenzo (U.S. Patent Application Pub. No. 2010/0076714) teaches an adaptable self-powered sensor node and methods of operation providing real-time monitoring and management of node operation, where the sensor node provides variables which may be used in determining an equipment maintenance schedule or predicting equipment component failure.
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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/H.L./Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665