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 responsive to communication filed on 06/05/2025.
Claims 1-10, 12-21 are pending. Claim 11 have been cancelled. Claims 1-3, 8-10, 12-13, 17, and 20-21 have been amended. Entry of this amendment is accepted and made of record.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: in independent claims 20 and 21:
a control input interface for receiving aircraft control inputs in claim 20
a control output interface for receiving aircraft control outputs in claim 20.
a control input interface to receive aircraft control inputs and store the inputs in the memory in claim 21;
a model processing engine to process the aircraft control inputs using a trained model of expected behavior for aircraft control software under test, to determine expected control outputs and to store the outputs in the memory, in claim 21
a control output interface to receive actual aircraft control outputs and store the outputs in the memory, in claim 21.
a difference engine to compare the expected control outputs to the actual control outputs to identify any differences between them, in claim 21.
an output generator to generate a signal indicative of a difference identified by the difference engine, in claim 21.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Examiner has identified the corresponding structure of a control input interface,
a model processing engine, a trained model of expected behavior, a control output interface, a difference engine, an output generator as processor 120 in Figure 1, (see para. ¶0053-0054, 0081).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10, and 12-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 12, 20 and 21, the claim recites “process the plurality of input values representing an operating of at least part of the aircraft system to generate a first set of output values” and “process the first and second sets of output values to identify any difference between operation of the at least part of the aircraft system and the operation modelled by the trained classifier” and “generate a signal indicative of the identified difference, wherein the signal is applied to modify the at least part of the aircraft system to reduce the identified difference” (emphasis added) renders the claim indefinite as the metes and bounds of the claim are unclear. It is not clear from the claimed limitations what is required by the scope of the claim as the limitations highlighted in bold, appears to be intended use of the claimed limitation. It is unclear from the claim limitation highlighted in bold are required by the claim language and whether it does requires the actual control of the aircraft control parts. Clarification and correction is required.
Regarding claims 1, 12, 20 and 21, the recitation “modification modifies the at least part of the aircraft system to more closely match a desired operation of the at least part of the aircraft system” renders the claim indefinite as the metes and bounds of the claim are unclear. It is unclear from the claimed limitation what is required by the scope of the claim as the limitation highlighted in bold is not defined by the scope of the claimed as to ascertain a level of degree as to what is the scope the claimed “more closely match a desired operation…” is trying to cover and which criteria is used as to determine what operation will be considered to “more closely match a desired operation”. Clarification and correction is required.
Dependent claims 2-10, and 13-19 are rejected under 35 USC 112(b) for the reasons discussed above with respect to their respective independent claims, 1 and 12 from which they depend.
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) 1-4, 6-10, 12-14, and 16-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanov et al. US2020/0180788 (hereinafter Hanov) in view of Nutaro et al. US 2016/0086396A1 (hereinafter Nutaro) in further view of Bosworth et al. US 2018/0364707 (hereinafter Bosworth).
Regarding claims 1 and 12, Hanov discloses a test apparatus, and method for testing at least part of an aircraft system (see abstract, ¶ 0001-0002, 0006), the apparatus comprising at least one memory and at least one processor, the test apparatus comprising program code stored in the at least one memory and being configured to control the test apparatus (see abstract, ¶0006) to:
obtain first data comprising a plurality of input values representing an operating state of an aircraft (see abstract, ¶0006, 0030, 0040, 0070, wherein feature data corresponding to each grouping of sensors and/or parameters are disclosed);
process the plurality of input values using a trained classifier configured to model an operation of at least part of an aircraft system to generate a first set of output values (see abstract, ¶0006, 0030, 0043, 0072-0073, 0076, 0077, 0084 wherein values for additional operational metrics/output values, which are determined based on comparing values for the predetermined operational metrics obtained from feature data to values of predetermined operational metrics that correspond to at least one other flight of the aircraft, and wherein a machine learning model is trained using at least the values for the additional operational metrics is disclosed).
obtain second data comprising a second set of output values generated by operating the at least part of an aircraft system based on the plurality of input values (see abstract, ¶0006, 0030, 0071, wherein values for predetermined operational metrics that correspond to the plurality of groupings of sensors and/or parameters are determined from the feature data, and wherein the values for the predetermined operational metrics are indicative of responses of the aircraft to forces experienced by each respective portion of the aircraft that corresponds to a selected grouping of sensors and/or parameters during a given flight of the aircraft is disclosed);
process the first and second sets of output values to identify any difference between operation of the at least part of an aircraft system (see abstract, ¶0006, 0030, 0040, 0042-0043, 0072-0073, 0076-0077 wherein the feature values, predetermined operational metrics and additional operational metrics are processed and where a comparison/difference between predetermined operational metrics and values of additional operational metrics are compared to predetermined operational metrics); and
generate a signal indicative of the identified difference (see abstract, ¶0006, 0030, 0040, 0044-0046, wherein an output from the machine learning model indicative of whether maintenance should be performed is disclosed).
Hanov further discloses that the at least part of an aircraft system includes at least one of: an avionics system (see ¶0003, 0038, 0047-0048, 0050-0052, i.e. a forward fuselage 202, center fuselage 204, wings 206, ailerons/flaps 208, aft fuselage 210, vertical stabilizers 212, and horizontal stabilizers 214), a flight control system (see ¶0003, 0038, 0047-0048, 0050-0052, i.e. a forward fuselage 202, center fuselage 204, wings 206, ailerons/flaps 208, aft fuselage 210, vertical stabilizers 212, and horizontal stabilizers 214), a braking control system, an instrumentation and recording system , a landing gear control system and a fuel system.
Although Hanov disclose a machine learning model which can produce outputs that are more likely accurate and that are tailored to unique operational qualities of the aircraft,( ¶0044), wherein the trained machine learning model can be executed to evaluate performance of operationally distinct portions of aircraft during testing, , wherein the learning model may include a performance rating for each portion of the aircraft for which an input was provided to the machine learning model and the performance rating may indicate how closely the inputs to the machine learning model match expected or desired values for the inputs, [i.e. predetermined operational metrics, and additional operational metrics], therefore a difference between operation of the at least part of an aircraft and the operation modeled by the machine learning model is being made (¶0045, 0084).
However Hanov do not specifically teach that process the first and second sets of output values to identify any difference between operation of the at least part of an aircraft system and the operation modelled by the trained classifier (emphasis added); and generate a signal indicative of the identified difference, wherein the signal is applied to modify the at least part of the aircraft system to reduce the identified difference.
Nutaro discloses an aircraft diagnostic and prognostic evaluation system which includes a fuel gauge system and fuel levels recorded as operating parameters, the computer may determine actual fuel use estimated with the models and if a difference between the actual fuel use and predicted fuel use exceeds a predetermined value or percentage the first aircraft is flagged and taxi for inclusion in a report, and wherein the system may account for differences in weight, speed, commanded speed or other factors (see Fig. 3A steps “318” and “324”; para. 0060 0092-0093). Nutaro further discloses the computer determine faults based on the data and would determine maintenance directives when a fault is determined based on data from the aircraft database and wherein a classifier is used to classify the information used in the determination of whether a fault exists in order to determine the maintenance directives (see para. 0043, 0046).
Given the teachings of Naturo discussed above, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to configure the system of Hanov to process the first and second sets of output values to identify any difference between operation of the at least part of an aircraft system and the operation modelled by the trained classifier and generate a signal indicative of the identified difference for the benefit of providing an enhanced system that would allow for maintenance of an aircraft to be scheduled without immediately taking an aircraft out of service and to enable the system to predict component and system wear, to detect faults and precursors to faults, predict impending failure, to predict when maintenance of those components and system may be needed and/or to evaluate fuel and other efficiencies of aircraft with ETS systems (see para. 0002, 0004)
However, Hanov and Nature do not specifically disclose wherein the signal is applied to modify the at least part of the aircraft system to reduce the identified difference, wherein the at least part of an aircraft system includes at least one of: an avionics system, a flight control system, a braking control system, an instrumentation and recording system, a landing gear control system or a fuel system, and wherein the modification modifies the at least part of the aircraft system to more closely match a desired operation of the at least part of the aircraft system.
Bosworth discloses a system and method monitoring an aircraft physical state and notifies of any deviations in expected state based on predictive models and an automation system perceiving state of aircraft and communicating deviations from expected aircraft state (see abstract). Bosworth further discloses wherein the signal is applied to modify the at least part of the aircraft system to reduce the identified difference, wherein the at least part of an aircraft system includes at least one of: an avionics system, a flight control system, a braking control system, an instrumentation and recording system, a landing gear control system or a fuel system, and wherein the modification modifies the at least part of the aircraft system to more closely match a desired operation of the at least part of the aircraft system (see para. 0074 wherein an anomaly detection application employs machine learning techniques to monitor aircraft state, cluster and classify sensor output in order to detect presence of non-normal situations, compare sensed states against set of threshold defined by operational documentation for aircraft, and may compared the sensed states against additional information and generate alerts in response to meeting predetermined or dynamically determine thresholds; para. 0075, wherein in case of contingency operation application predetermined procedures and actions specified by contingency application in order to maintain safe operation of aircraft is executed; para. 0122-0123, wherein auto landing procedure is performed and minor modifications to cockpit controls may be warrants to improve interaction between actuation system and various flight controllers to enable the actuation system to manipulate a landing gear lever in response to an auto-land trigger; para. 0069-0071, 0147-0148, 0150-0153, 0155, 0158-0160 wherein aircraft performance is determined according to current conditions and any deviation from expected performance i.e. climb rate, airspeed indicator faults, lower than expected values and wherein an automatic landing procedure is executed).
Therefore, it would have been obvious to one of ordinary skilled in the art at the time the invention was effectively filed, given the teachings of Bosworth, to modify the system of Hanov as modified by Nutaro such that a signal is applied to modify the at least part of the aircraft system to reduce the identified difference, wherein the at least part of an aircraft system includes at least one of: an avionics system, a flight control system, a braking control system, an instrumentation and recording system, a landing gear control system or a fuel system, and wherein the modification modifies the at least part of the aircraft system to more closely match a desired operation of the at least part of the aircraft system for the benefit of providing a means for correcting discrepancy between desired and actual values and to ensure proper operation of the aircraft and to enhance the safe operation of the aircraft (see para. 0004, 0069, 0152, 0183).
Regarding claims 2 and 13, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above for independent claims 1 and 12 respectively. Hanov further discloses wherein the test apparatus is configured to: obtain training data representing a desired operation of the at least part of an aircraft system for at least one operating state (abstract, ¶0006, 0007, 0028-0030, wherein a selection from a plurality of sensors and parameters associated with an aircraft type based on expected in-flight forces to be experienced by a plurality of operationally distinct portions of the aircraft type, a plurality of groupings of sensors and/or parameters that correspond to the plurality of portions of the aircraft type is disclosed, wherein predetermined operational metrics that correspond to the plurality of groupings of sensors and/or parameters indicative of responses of the aircraft to forces experienced by each respective portion of the aircraft that corresponds to a selected grouping of sensors and/or parameters during a given flight of the aircraft and values of additional operational metrics indicative of responses of the aircraft to forces experienced by each respective portion of the aircraft that corresponds to a selected grouping of sensors and/or parameters during multiple flights of the aircraft are disclosed, and wherein the predetermined operational metrics may include specific data points determined to correspond to a level of performance of one or more portions of the aircraft during a particular flight and can be compared to values from previous flights to determine additional operational metrics), the training data comprising at least one further plurality of input values and at least one respective set of output values (abstract, ¶0006-0007, 0028-0030, wherein the predetermined operational metrics may include specific data points determined to correspond to a level of performance of one or more portions of the aircraft during a particular flight and can be compared to values from previous flights to determine additional operational metrics); and train the classifier using the training data (abstract, ¶0006-0007, 0028-0030, wherein the predetermined operational metrics may include specific data points determined to correspond to a level of performance of one or more portions of the aircraft during a particular flight and can be compared to values from previous flights to determine additional operational metrics and wherein the machine learning model can be trained using additional operational metrics and produce outputs that are more likely accurate and that are tailored to unique operational qualities of the aircraft).
Regarding claim 3, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed with respect to dependent claim 2. Hanov further discloses wherein the test apparatus is in communication with at least computing device (see ¶0005, 0033, 0034, 0036, wherein a computing system is disclosed, ¶0006, computing device 102, having a processor and a memory storing instructions executable by the processor), and the test apparatus is configured to receive data indicative of a decision of a user of the at least computing device with respect to an operation of the at least part of an aircraft system (¶0014, 0034-0035, 0039, 0056, 0058-0059, wherein a user may select manually by way of user interface groupings of sensors and/or parameters i.e. aft fuselage portion), and wherein the test apparatus is configured to generate training data based on the received data (see ¶0029, 0032, 0065, 0078, wherein the machine learning model is trained based on these groupings which may allow the model to provide targeted maintenance outputs).
However, Hanov do not expressly or explicitly discloses the computing device to be a mobile computing device.
Bosworth further discloses an aircraft monitoring system comprising a mobile computing device (e.g. laptop) (see para. 0003, wherein a flight plan is directed on a laptop; para. 0078, wherein a human/machine interface (HMI) system provides control and communication interface and is configurable to operate as a flight plan manager that enables the automation system and may employ for example a tablet computer, a laptop computer, a smart phone, head mounted display or combination thereof).
Therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was effectively filed to provide the system of Hanov as modified by Nutaro and Bosworth with a mobile computing device for the benefit of providing a decentralized computer system that would allow portability of equipment that would allow for handling numerous interaction requests remotely, therefore the system would be enhanced.
Regarding claim 4, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to dependent claim 2. Hanov further discloses that the test apparatus is configured to generate at least some of the training data based on the identified difference (see ¶0006, 0030, 0042, 0045 wherein predetermined operational metrics are compared to values from previous flights to determine additional operational metrics, these values can be used to train a machine learning model).
Regarding claim 6, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to independent claim 1 and further discloses wherein the plurality of input values represent at least one of: an operational state of at least one component associated with the aircraft system (see ¶0001, 0047-0048, wherein forces experienced by each respective operationally distinct portion and functions performed by each portion of the aircraft i.e. Roll, pitch, and yaw data of aircraft 200, in conjunction with aileron and elevator positions, and possibly other forces and/or parameters, may indicate how aft fuselage 210 responds to the forces experienced during flight); and an output from at least one sensor configured to sense a respective environmental condition (see ¶0048, wherein an accelerometer and gyroscopic sensor [e.g., an inertial measurement unit (IMU)], left and right aileron positions, elevator positions, and related control signals from an aircraft controller might be selected as a grouping of sensors and parameters that correspond to the aft fuselage 210 portion of aircraft 200. More or fewer sensors and/or parameters might be selected).
Regarding claim 7, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to dependent claim 6, and further discloses, wherein an operational state of at least one component associated with the aircraft system includes a control input (see ¶044, wherein an output based on receiving the test data from the machine learning model is received as an input at a computer device 102), the control input including at least one of: a value representing a control input (see ¶0044, where a binary indication that a portion of aircraft requires or does not require maintenance is disclosed); a value representing a rate of change of the control input; and a value representing a difference between the control input and a further control input (see ¶00045, wherein a performance rating for each portion of the aircraft for which an input was provided to the machine learning model, the performance rating may indicate how closely the inputs to the machine learning model match expected or desired values for the inputs, wherein the value representing the difference resides on the performance rating).
Examiner Note: the recitations of a value representing a rate of change of the control input, recited in claim 7 have been presented in the alternative and is not given patentable weight.
Regarding claim 8, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to independent claim 1. Hanov further discloses that the at least part of an aircraft system is a computer program for controlling at least part of an aircraft (see ¶0048, 0051 wherein aileron positions can be compared to corresponding signals of aircraft and roll data from an IMU; ¶0006, ¶0037, wherein a computer-readable program instructions stored in a memory is disclosed; ¶0063, where a program code is disclosed).
Regarding claim 9, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to independent claim 1. Hanov further discloses that the at least part of an aircraft system comprises a combination of aircraft equipment and a computer program for operating an aircraft (see Fig. 1, ¶0037, 0063, 0048, 0051, 0057, wherein a computer program/instruction/code is disclosed and wherein time stamp for changing aileron positions can be compared to corresponding control signals of aircraft and roll data from an IMU of aircraft to determine how aft fuselage is responding to forces experienced during flight of aircraft, therefore, the operation of at least aileron positions is being operated by a computer program).
Regarding claim 10, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to independent claim 1. Hanov further discloses that the at least part of an aircraft system comprises aircraft equipment, the aircraft equipment comprising at least one sensor for generating at least one value of the second set of output values (abstract, ¶0002, 0005, 0034, 0048, 0053-0054, wherein a sensor i.e. accelerometer, gyroscopic sensor are disclosed, Fig. 1).
Regarding claim 14, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above for independent claim 12. Hanov further discloses wherein the training data is generated from a further aircraft system during operation of the further aircraft system (see ¶0005-0007, 0066, 0077, wherein another aircraft of the aircraft type is disclosed and wherein data from another aircraft is used for conducting training).
Regarding claim 16, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above for claim 12. Hanov further discloses the method comprises generating further training data based on the identified difference, (abstract, ¶0006-0007, 0028-0030, wherein the predetermined operational metrics may include specific data points determined to correspond to a level of performance of one or more portions of the aircraft during a particular flight and can be compared to values from previous flights to determine additional operational metrics) and training the classifier using the further training data (abstract, ¶0006-0007, 0028-0030, wherein the predetermined operational metrics may include specific data points determined to correspond to a level of performance of one or more portions of the aircraft during a particular flight and can be compared to values from previous flights to determine additional operational metrics and wherein the machine learning model can be trained using additional operational metrics and produce outputs that are more likely accurate and that are tailored to unique operational qualities of the aircraft).
Regarding claim 17, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to claim 12. Hanov further discloses modifying the at least part of an aircraft system based on the identified difference (¶0003, 0006, 0030, 0060, 0062, wherein based on the difference it is determined that a specific portion of the aircraft requires maintenance/modifying on one or more of the plurality of portions of the aircraft based on the difference).
Regarding claim 18, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to claim 12. Hanov further discloses an aircraft (see Fig. 2) comprising one or more aircraft systems (see Fig. 1), at least one aircraft system having been tested according to the method of claim12 (see abstract, ¶0006, 0030, 0040, 0060, 0062, 0072-0073, 0076, 0077, 0084, wherein an aircraft is being tested and maintenance need is determined, wherein a system for testing an aircraft is disclosed).
Regarding claim 19, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to independent claim 1. Hanov further discloses a test aircraft comprising one or more aircraft systems and the test apparatus according to claim 1 for testing at least part of the one or more aircraft systems (see abstract ¶0006, 0030, 0040, 0060, 0062, 0072-0073, 0077, wherein a test system is disclosed for an aircraft or another aircraft).
Regarding claim 20, Hanov discloses a test system for testing aircraft control software (see Fig. 1), the system comprising:
a memory, a processor, (¶0006, 0034-0035 wherein a computing device comprising a processor, and a memory is disclosed), a control input interface for receiving aircraft control inputs (¶0035, wherein a user interface is disclosed and ¶0044, wherein outputs from machine learning are received as inputs by computing device 102; see Fig. 1, abstract, ¶0034-0039, wherein user interface 104 for receiving input from a human user and transmit that input to a processor is disclosed, wherein feature data is stored in memory 108; alternatively computing device 102 may select groupings of sensors and/or parameters stored in memory 108 and wherein computing device 102 may receive an indication of an aircraft type by the way of user interface 104 and execute instructions stored on memory 108 to determine which sensors and/or parameters are associated with each portion of an aircraft in a predetermined list of portions of the aircraft type) and a control output interface for receiving aircraft control outputs (see abstract, Fig. 1, ¶0037, wherein the control output interface resides on processor 106 which may receive inputs from one or more of aircrafts 110, 112, 114 or other aircraft in the plurality of aircraft and process the inputs to generate feature data that is stored in memory 108; alternatively see ¶0044, wherein outputs are received by computing device 102; and ¶0006, 0030, 0071, wherein values for predetermined operational metrics that correspond to the plurality of groupings of sensors and/or parameters are determined from the feature data, and wherein the values for the predetermined operational metrics are indicative of responses of the aircraft to forces experienced by each respective portion of the aircraft that corresponds to a selected grouping of sensors and/or parameters during a given flight of the aircraft is disclosed), the test system being configured to:
receive aircraft control inputs via the control input interface (see abstract, ¶0006, 0030, 0040, 0070, wherein feature data corresponding to each grouping of sensors and/or parameters are disclosed; ¶0014, 0034-0035, 0039, 0056, 0058-0059, wherein a user may select manually by way of user interface 104 groupings of sensors and/or parameters i.e. aft fuselage portion);
process the aircraft control inputs using a trained model of expected behavior for the aircraft control software under test to determine expected control outputs (see abstract, (¶0006, 0030, 0043, 0072-0073, 0076, 0077, 0084 wherein values for additional operational metrics/output values, which are determined based on comparing values for the predetermined operational metrics obtained from feature data to values of predetermined operational metrics that correspond to at least one other flight of the aircraft, and wherein a machine learning model is trained using at least the values for the additional operational metrics is disclosed);
receive actual control outputs from the aircraft control software via the control output interface (see abstract, ¶0006, 0030, 0071, wherein values for predetermined operational metrics that correspond to the plurality of groupings of sensors and/or parameters are determined from the feature data, and wherein the values for the predetermined operational metrics are indicative of responses of the aircraft to forces experienced by each respective portion of the aircraft that corresponds to a selected grouping of sensors and/or parameters during a given flight of the aircraft is disclosed);
compare the expected control outputs to the actual control outputs to identify any differences between them (see abstract, ¶0006, 0030, 0040, 0042-0043, 0072-0073, 0076-0077 wherein the feature values, predetermined operational metrics and additional operational metrics are processed and where a comparison/difference between predetermined operational metrics and values of additional operational metrics are compared to predetermined operational metrics, ¶0030, wherein the machine learning model can produce outputs that are more likely accurate and that are tailored to unique operational qualities of the aircraft, ¶0044, wherein the trained machine learning model can be executed to evaluate performance of operationally distinct portions of aircraft during testing, ¶0045, 0084, wherein the learning model may include a performance rating for each portion of the aircraft for which an input was provided to the machine learning model and the performance rating may indicate how closely the inputs to the machine learning model match expected or desired values for the inputs, [i.e. predetermined operational metrics, and additional operational metrics], therefore a difference between operation of the at least part of an aircraft and the operation modeled by the machine learning model is being made); and
generate a signal indicative of a difference between the expected control outputs and the actual control outputs (see abstract, ¶0006, 0030, 0040, 0044-0046, wherein an output from the machine learning model indicative of whether maintenance should be performed is disclosed).
Although Hanov disclose a machine learning model which can produce outputs that are more likely accurate and that are tailored to unique operational qualities of the aircraft,( ¶0044), wherein the trained machine learning model can be executed to evaluate performance of operationally distinct portions of aircraft during testing, wherein the learning model may include a performance rating for each portion of the aircraft for which an input was provided to the machine learning model and the performance rating may indicate how closely the inputs to the machine learning model match expected or desired values for the inputs, [i.e. predetermined operational metrics, and additional operational metrics], therefore a difference between operation of the at least part of an aircraft and the operation modeled by the machine learning model is being made (¶0045, 0084).
Even if Hanov were not found to clearly disclose or suggest to compare the expected control outputs determined using a trained model to the actual control outputs from the aircraft control software to identify any differences between them and generate a signal indicative of the identified difference, wherein he signal is applied to modify the at least part of the aircraft system to reduce the identified difference.
Nutaro discloses an aircraft diagnostic and prognostic evaluation system which includes a fuel gauge system and fuel levels recorded as operating parameters, the computer may determine actual fuel use estimated with the models and if a difference between the actual fuel use and predicted fuel use exceeds a predetermined value or percentage the first aircraft is flagged and taxi for inclusion in a report, and wherein the system may account for differences in weight, speed, commanded speed or other factors (see Fig. 3A steps “318” and “324”; para. 0060 0092-0093). Nutaro further discloses the computer determine faults based on the data and would determine maintenance directives when a fault is determined based on data from the aircraft database and wherein a classifier is used to classify the information used in the determination of whether a fault exists in order to determine the maintenance directives (see para. 0043, 0046).
Given the teachings of Nutaro discussed above, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to configure the system of Hanov to process the aircraft control inputs and compare the expected control outputs to the actual control outputs to identify any differences between them and generate a signal indicative of the identified difference for the benefit of providing an enhanced system that would allow for maintenance of an aircraft to be scheduled without immediately taking an aircraft out of service and to enable the system to predict component and system wear, to detect faults and precursors to faults, predict impending failure, to predict when maintenance of those components and system may be needed and/or to evaluate fuel and other efficiencies of aircraft with ETS systems (see para. 0002, 0004)
Hanov further discloses that the at least part of an aircraft system includes at least one of: an avionics system (see ¶0003, 0038, 0047-0048, 0050-0052, i.e. a forward fuselage 202, center fuselage 204, wings 206, ailerons/flaps 208, aft fuselage 210, vertical stabilizers 212, and horizontal stabilizers 214), a flight control system (see ¶0003, 0038, 0047-0048, 0050-0052, i.e. a forward fuselage 202, center fuselage 204, wings 206, ailerons/flaps 208, aft fuselage 210, vertical stabilizers 212, and horizontal stabilizers 214), a braking control system, an instrumentation and recording system , a landing gear control system and a fuel system.
However, Hanov do not expressly or explicitly discloses wherein the signal is applied to modify the at least part of the aircraft control software to reduce the identified difference, wherein the at least part of an aircraft control software included in at least one of: an avionics system, a flight control system, a braking control system, an instrumentation and recording system, a landing gear control system or a fuel system, and wherein the modification modifies the aircraft control software to generate actual control outputs that more closely match the expected behavior of the aircraft control software.
Bosworth discloses a system and method monitoring an aircraft physical state and notifies of any deviations in expected state based on predictive models and an automation system perceiving state of aircraft and communicating deviations from expected aircraft state (see abstract). Bosworth further discloses wherein the signal is applied to modify the at least part of the aircraft control software to reduce the identified difference, wherein the at least part of an aircraft control software included in at least one of: an avionics system, a flight control system, a braking control system, an instrumentation and recording system, a landing gear control system or a fuel system, and wherein the modification modifies the aircraft control software to generate actual control outputs that more closely match the expected behavior of the aircraft control software (see para. 0074 wherein an anomaly detection application employs machine learning techniques to monitor aircraft state, cluster and classify sensor output in order to detect presence of non-normal situations, compare sensed states against set of threshold defined by operational documentation for aircraft, and may compared the sensed states against additional information and generate alerts in response to meeting predetermined or dynamically determine thresholds; para. 0075, wherein in case of contingency operation application predetermined procedures and actions specified by contingency application in order to maintain safe operation of aircraft is executed; para. 0122-0123, wherein auto landing procedure is performed and minor modifications to cockpit controls may be warrants to improve interaction between actuation system and various flight controllers to enable the actuation system to manipulate a landing gear lever in response to an auto-land trigger; para. 0069-0071, 0147-0148, 0150-0153, 0155, 0158-0160 wherein aircraft performance is determined according to current conditions and any deviation from expected performance i.e. climb rate, airspeed indicator faults, lower than expected values and wherein an automatic landing procedure is executed).
Therefore, it would have been obvious to one of ordinary skilled in the art at the time the invention was effectively filed, given the teachings of Bosworth, to modify the system of Hanov as modified by Nutaro such that a signal is applied to modify the aircraft control software to generate actual control outputs that more closely match the expected behavior of the aircraft control software, wherein the aircraft control software is included in at least one of: an avionics system, a fight control system, a braking control system, an instrumentation and recording system, a landing gear control system or a fuel system for the benefit of providing a means for correcting discrepancy between desired and actual values and to ensure proper operation of the aircraft and to enhance the safe operation of the aircraft (see para. 0004, 0069, 0152, 0183).
Regarding claim 21, Hanov discloses a test system for testing aircraft control software, the system comprising:
a memory (see fig. 1, ¶34-0036, wherein memory 108 is disclosed);
a control input interface to receive aircraft control inputs and store the inputs in the memory (see Fig. 1, abstract, ¶0034-0039, wherein user interface 104 for receiving input from a human user and transmit that input to a processor is disclosed, wherein feature data is stored in memory 108; alternatively computing device 102 may select groupings of sensors and/or parameters stored in memory 108 and wherein computing device 102 may receive an indication of an aircraft type by the way of user interface 104 and execute instructions stored on memory 108 to determine which sensors and/or parameters are associated with each portion of an aircraft in a predetermined list of portions of the aircraft type);
a model processing engine to process the aircraft control inputs using a trained model of expected behavior for aircraft control software under test, to determine expected control outputs and to store the outputs in the memory (see abstract, ¶0006, 0030, 0043, 0072-0073, 0076, 0077, 0084 wherein values for additional operational metrics/output values, which are determined based on comparing values for the predetermined operational metrics obtained from feature data to values of predetermined operational metrics that correspond to at least one other flight of the aircraft, and wherein a machine learning model is trained using at least the values for the additional operational metrics is disclosed);
a control output interface to receive actual aircraft control outputs and store the outputs in the memory (see abstract, Fig. 1, ¶0037, wherein processor 106 may receive inputs from one or more of aircrafts 110, 112, 114 or other aircraft in the plurality of aircraft and process the inputs to generate feature data that is stored in memory 108; and ¶0006, 0030, 0071, wherein values for predetermined operational metrics that correspond to the plurality of groupings of sensors and/or parameters are determined from the feature data, and wherein the values for the predetermined operational metrics are indicative of responses of the aircraft to forces experienced by each respective portion of the aircraft that corresponds to a selected grouping of sensors and/or parameters during a given flight of the aircraft is disclosed);
a difference engine to compare the expected control outputs to the actual control outputs to identify any differences between them (see abstract, ¶0006, 0030, 0040, 0042-0043, 0072-0073, 0076-0077 wherein the feature values, predetermined operational metrics and additional operational metrics are processed and where a comparison/difference between predetermined operational metrics and values of additional operational metrics are compared to predetermined operational metrics, ¶0030, wherein the machine learning model can produce outputs that are more likely accurate and that are tailored to unique operational qualities of the aircraft, ¶0044, wherein the trained machine learning model can be executed to evaluate performance of operationally distinct portions of aircraft during testing, ¶0045, 0084, wherein the learning model may include a performance rating for each portion of the aircraft for which an input was provided to the machine learning model and the performance rating may indicate how closely the inputs to the machine learning model match expected or desired values for the inputs, [i.e. predetermined operational metrics, and additional operational metrics], therefore a difference between operation of the at least part of an aircraft and the operation modeled by the machine learning model is being made); and
an output generator to generate a signal indicative of a difference identified by the difference engine (see abstract, ¶0006, 0030, 0040, 0044-0046, wherein an output from the machine learning model indicative of whether maintenance should be performed is disclosed).
Even if Hanov were not found to clearly disclose or suggest to compare the expected control outputs determined using a trained model to the actual control outputs from the aircraft control software to identify any differences between them; generate a signal indicative of a difference between expected control outputs and the actual control outputs, wherein the signal is applied to modify the aircraft control software to reduce the difference.
Nutaro discloses an aircraft diagnostic and prognostic evaluation system which includes a fuel gauge system and fuel levels recorded as operating parameters, the computer may determine actual fuel use estimated with the models and if a difference between the actual fuel use and predicted fuel use exceeds a predetermined value or percentage the first aircraft is flagged and taxi for inclusion in a report, and wherein the system may account for differences in weight, speed, commanded speed or other factors (see Fig. 3A steps “318” and “324”; para. 0060 0092-0093). Nutaro further discloses the computer determine faults based on the data and would determine maintenance directives when a fault is determined based on data from the aircraft database and wherein a classifier is used to classify the information used in the determination of whether a fault exists in order to determine the maintenance directives (see para. 0043, 0046).
Given the teachings of Nutaro discussed above, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to configure the system of Hanov to process the aircraft control inputs and compare the expected control outputs to the actual control outputs to identify any differences between them and generate a signal indicative of the identified difference for the benefit of providing an enhanced system that would allow for maintenance of an aircraft to be scheduled without immediately taking an aircraft out of service and to enable the system to predict component and system wear, to detect faults and precursors to faults, predict impending failure, to predict when maintenance of those components and system may be needed and/or to evaluate fuel and other efficiencies of aircraft with ETS systems (see para. 0002, 0004).
Hanov further discloses that the at least part of an aircraft system includes at least one of: an avionics system (see ¶0003, 0038, 0047-0048, 0050-0052, i.e. a forward fuselage 202, center fuselage 204, wings 206, ailerons/flaps 208, aft fuselage 210, vertical stabilizers 212, and horizontal stabilizers 214), a flight control system (see ¶0003, 0038, 0047-0048, 0050-0052, i.e. a forward fuselage 202, center fuselage 204, wings 206, ailerons/flaps 208, aft fuselage 210, vertical stabilizers 212, and horizontal stabilizers 214), a braking control system, an instrumentation and recording system , a landing gear control system and a fuel system.
However, Hanov and Nutaro do not expressly or explicitly discloses wherein the signal is applied to modify the at least part of the aircraft control software to generate control outputs that more closely match the expected behavior for the aircraft control software to reduce the identified difference, wherein the at least part of an aircraft control software included in at least one of: an avionics system, a flight control system, a braking control system, an instrumentation and recording system, a landing gear control system or a fuel system, and wherein the modification modifies the aircraft control software to generate actual control outputs that more closely match the expected behavior of the aircraft control software.
Bosworth discloses a system and method monitoring an aircraft physical state and notifies of any deviations in expected state based on predictive models and an automation system perceiving state of aircraft and communicating deviations from expected aircraft state (see abstract). Bosworth further discloses wherein the signal is applied to modify the at least part of the aircraft control software to reduce the identified difference, wherein the at least part of an aircraft control software included in at least one of: an avionics system, a flight control system, a braking control system, an instrumentation and recording system, a landing gear control system or a fuel system, and wherein the modification modifies the aircraft control software to generate actual control outputs that more closely match the expected behavior of the aircraft control software (see para. 0074 wherein an anomaly detection application employs machine
learning techniques to monitor aircraft state, cluster and classify sensor output in order to detect presence of non-normal situations, compare sensed states against set of threshold defined by operational documentation for aircraft, and may compared the sensed states against additional information and generate alerts in response to meeting predetermined or dynamically determine thresholds; para. 0075, wherein in case of contingency operation application predetermined procedures and actions specified by contingency application in order to maintain safe operation of aircraft is executed; para. 0122-0123, wherein auto landing procedure is performed and minor modifications to cockpit controls may be warrants to improve interaction between actuation system and various flight controllers to enable the actuation system to manipulate a landing gear lever in response to an auto-land trigger; para. 0069-0071, 0147-0148, 0150-0153, 0155, 0158-0160 wherein aircraft performance is determined according to current conditions and any deviation from expected performance i.e. climb rate, airspeed indicator faults, lower than expected values and wherein an automatic landing procedure is executed).
Therefore, it would have been obvious to one of ordinary skilled in the art at the time the invention was effectively filed, given the teachings of Bosworth, to modify the system of Hanov as modified by Nutaro such that a signal indicative of a difference between the expected control outputs and the actual control outputs, wherein the signal is applied to modify the aircraft control software to reduce the difference, wherein the aircraft control software is included in at least in at least one of: an avionics system, flight control system, a braking control system, an instrumentation and recording system, a landing gear control system or a fuel system, and wherein the modification modifieds the aircraft control software to generate actual control outputs that more closely match the expected behavior of the aircraft control software for the benefit of providing a means for correcting discrepancy between desired and actual values and to ensure proper operation of the aircraft and to enhance the safe operation of the aircraft (see para. 0004, 0069, 0152, 0183).
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hanov et al. US2020/0180788 (hereinafter Hanov) in view of Nutaro et al. US 2016/0086396A1 (hereinafter Nutaro) in view of Bosworth et al. US 2018/0364707 (hereinafter Bosworth) in further view of Chang et al. US 20180314573 A1 (hereinafter Chang).
Regarding claim 5, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to dependent claim 4. Hanov further discloses wherein generating training data based on the identified difference comprises comparing the identified difference (see ¶0035,0038-0043,0060 wherein an user can select through an user interface, aircraft type and groupings of sensors and/or parameters [i.e. selectable options for predetermined operational metrics] corresponding to operationally distinct portions of the aircraft and once the selection is performed feature data is received at the computing device that would be used in determining operational metrics that would be compared in order to determine additional operational metrics needed to train the machine learning model).
However, Hanov, Nutaro and Bosworth do not expressly or explicitly discloses that generating the training data comprises comparing the identified difference with a user input indicative of a decision with respect to the identified difference.
Chang discloses a system for correcting input time-series data for analysis and predictions (see abstract), wherein generating the training data comprises comparing the identified difference with a user input indicative of a decision with respect to the identified difference (see para. 0110-0114; Fig. 10, steps 4 and 5; wherein classification models are validated, wherein validation or correction are received from the user via the user interface and wherein the training data is updated using the user feedback, and a separated training set is formed using the user feedback and classifiers are trained using the user feedback).
Therefore, it would have been obvious to one of ordinary skilled in the art at the time the invention was effectively filed given the teachings of Chang to configure the system of Hanov as modified by Nutaro and Bosworth such that generating training data based on the identified difference comprises comparing the identified difference with a user input indicative of a decision with respect to the identified difference for the benefit of providing a means for analyzing user input data and generating valid, accurate, and personalized insights and predictions in order to provide results to users and establishing confidence in the analytical results.
Regarding claim 15, the combination of Hanov, Nutaro and Bosworth discloses the materials discussed above with respect to claim 13. Hanov further discloses that the training data is generated based on an input from a user indicative of an identified difference (see ¶0035,0038-0043,0060 wherein an user can select through an user interface, aircraft type and groupings of sensors and/or parameters [i.e. selectable options for predetermined operational metrics] corresponding to operationally distinct portions of the aircraft and once the selection is performed feature data is received at the computing device that would be used in determining operational metrics that would be compared in order to determine additional operational metrics needed to train the machine learning model).
However, Hanov, Nutaro and Bosworth do not expressly or explicitly discloses that the training data is generated based on an input from a user indicative of a decision with respect to identified difference .
Chang discloses a system for correcting input time-series data for analysis and predictions (see abstract), wherein that the training data is generated based on an input from a user indicative of a decision with respect to identified difference (see para. 0110-0114; Fig. 10, steps 4 and 5; wherein classification models are validated, wherein validation or correction are received from the user via the user interface and wherein the training data is updated using the user feedback, and a separated training set is formed using the user feedback and classifiers are trained using the user feedback).
Therefore, it would have been obvious to one of ordinary skilled in the art at the time the invention was effectively filed given the teachings of Chang to configure the system of Hanov as modified by Nutaro and Bosworth such that the training data is generated based on an input from a user indicative of a decision with respect to identified difference for the benefit of providing a means for analyzing user input data and generating valid, accurate, and personalized insights and predictions in order to provide results to users and establishing confidence in the analytical results.
Response to Arguments
Applicant’s arguments with respect to rejections made in view of Dreamer prior art with respect to claim(s) 1, 12, 20 and 21 and with respect to rejections made in view of Harrigan prior art with respect to claim 3 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.
With respect to claims 1, 12, 20 and 21 applicant argues that Hanov Does not describe comparing outputs of a trained classifier with outputs of an aircraft system, that Hanov does not describe a classifier (machine learning model) that generates output values based on the same input values used to control an aircraft system or software and that Hanov describe the additional operating metrics as inputs to the machine learning model, and does not describe them as outputs of the machine learning model (see Section III on page 14 of the remarks filed on 08/18/2025).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “comparing outputs of a trained classifier with outputs of an aircraft system" “a classifier (machine learning model) that generates output values based on the same input values used to control an aircraft system or software”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
As claimed, claim 1 requires the “obtain first data, process the plurality of input values using a trained classifier to generate a first set of output values, obtain second data comprising a second set of output values generated by operating the at least a part of an aircraft system” and requires “process the first and second sets of output values to identify any difference between the operation of the at least part of the aircraft system and the operation modelled by the trained classifier”.
As required by the claimed language Hanov discloses process the plurality of input values using a trained classifier configured to model an operation of at least part of an aircraft system to generate a first set of output values (see abstract, ¶0006, 0030, 0043, 0072-0073, 0076, 0077, 0084). Hanov discloses that values for additional operational metrics/output values, which are determined based on comparing values for the predetermined operational metrics obtained from feature data to values of predetermined operational metrics that correspond to at least one other flight of the aircraft, and wherein a machine learning model is trained using at least the values for the additional operational metrics is disclosed.
Hanov further discloses process the first and second sets of output values to identify any difference between operation of the at least part of an aircraft system and the operation modelled by the trained classifier (see abstract, ¶0006, 0030, 0040, 0042-0043, 0072-0073, 0076-0077). In Hanov the feature values, predetermined operational metrics and additional operational metrics are processed and a comparison/difference between predetermined operational metrics and values of additional operational metrics are compared to predetermined operational metrics (see ¶0030, wherein the machine learning model can produce outputs that are more likely accurate and that are tailored to unique operational qualities of the aircraft, ¶0044, wherein the trained machine learning model can be executed to evaluate performance of operationally distinct portions of aircraft during testing, ¶0045, 0084, wherein the learning model may include a performance rating for each portion of the aircraft for which an input was provided to the machine learning model and the performance rating may indicate how closely the inputs to the machine learning model match expected or desired values for the inputs, [i.e. predetermined operational metrics, and additional operational metrics], therefore a difference between operation of the at least part of an aircraft and the operation modeled by the machine learning model is being made).
Applicant’s arguments, see section IV of page 15 through 16 of the remarks, filed 08/18/2025, with respect to the rejection(s) of claim(s) 1, 12, 20 and 21 under 35 USC 103 over Hanov in view of Damer have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Hanov et al. US2020/0180788 in view of Nutaro et al. US 2016/0086396A1 in further view of Bosworth et al. US 2018/0364707.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Harrigan et al. US2017/0331844A1 (hereinafter Harrigan).Harrigan discloses an aircraft monitoring system comprising a mobile computing device (e.g. laptop computer) (see para. 0027).
Dramer et al. US Patent 3,591,110. Dramer discloses wherein the signal is applied to modify the at least part of the aircraft system to reduce the identified difference, wherein the at least part of an aircraft system includes at least one of: an avionics system, a flight control system, a braking control system, an instrumentation and recording system, a landing gear control system or a fuel system, and wherein the modification modifies the at least part of the aircraft system to more closely match a desired operation of the at least part of the aircraft system (abstract, see col. 1, ll. 41-65; col. 2, ll. 52-68; claim 1), wherein at least a flight control system/throttle control is disclosed). In Dramer the electrical signal representing the difference (identified difference) between the desired and actual values of an aircraft parameter used by an automatic throttle control (flight control system) as the aircraft approaches touchdown is utilized and the selected desired descent value of the aircraft parameter is lowered to a near stall value slightly below the desired touchdown value of the parameter (modify at least part of the aircraft system to reduce the identified difference to more closely match a desired operation of the at least one part of the aircraft).
Nicholas et al. US 20040010354 A1 disclose a flight control system includes a downstream processing unit which determines the most limiting flight envelope limiting parameter, and determines the tactile cueing position for the flight control input apparatus based on the most limiting parameter. The predictive neural networks (6) predict flight envelope limiting parameters based on observed parameters and non-dimensional aircraft state parameters.
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/YARITZA H. PEREZ BERMUDEZ/
Examiner
Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857