DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
2. Applicant’s arguments filed 2/13/2026 regarding the rejection of claims 11-13, 15, and 21 under 35 USC 101 as directed to an abstract idea without significantly more have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
Applicant’s arguments file 2/13/2026 regarding the rejection of claim 1 under 35 USC 102 as being anticipated by Westervelt (US 20180096608 A1) and Rodriguez Bravo (US 11164465B2) have been fully considered, but are moot due to amended claim 1.
3. With regards to amended independent claim 1, the applicant argues that Westervelt does not take into account an aircraft specific variable related to “health” or “degradation” of the aircraft, and applicant indicates that Rodrigues Bravo does not cure deficiencies of Westervelt. The applicant also indicates that Rodriguez Bravo does not contemplate a target parameter value for the aircraft based at least in part on parameter values extracted from the downlink ACARS message and a tail specific offset determined using a machine-learned model based at least in part on past flight data of the aircraft, the tail specific offset indicating a deviation of an actual aircraft performance of the aircraft from a baseline aircraft performance for the aircraft.
However, examiner argues that claim 1 was rejected under 35 USC 103 as being unpatentable over Westervelt in view of Rodriguez Bravo, not through Rodriguez Bravo alone. In addition, examiner indicates that amended independent claim 1 with regards to tail specific offset representing health and degradation of an aircraft requires further search and consideration due to new amended limitations of the claim.
Hence, the examiner indicates that the applicant’s arguments are moot.
4. With regards to amended independent claim 1, the examiner indicates that upon further research and consideration claim 1 is rejected under 35 USC 103 as being unpatentable over Westervelt in view of Gibbons, Il et al. (US 20210383706A1).
Gibbons teaches newly added limitation of the tail specific offset representing the health or degradation of the aircraft.
Gibbons teaches a tail specific performance models used as predictive tool where rate of variations in performance along with age of an aircraft, maintenance history, and historical flight data indicates degradations, i.e. tail offset representing health and degradation of an aircraft (see [0100]). Note also in [0102], the aircraft specific information, which is the tail specific information, is used to schedule maintenance and repair through tracking of aging of the aircraft overtime, i.e. tracking of degradation of the aircraft overtime through aircraft specific information.
Further, Gibbons also teaches machine learning model that is trained to predict fuel mileage performance or other characteristics based on gathering information that includes routes flown, miles flown, weight, operating altitude, i.e. baseline aircraft performance model, which is used to find tail specific offset, based at least in part on past flight data (see [0066]-[0067]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify downlink and uplink message of aircraft parameters for optimization flight plan control of Westervelt by incorporating teaching of Gibbons such that machine learning model is used to predict fuel mileage and other characteristics based on past and other parameter information such as routes flown, miles flown, operating altitude, combined with tail specific performance model variance rate along with age and historical flight data of an aircraft, i.e. tail specific offset representing health or degradation, so that aircraft and machine learning model will consider tail specific offset regarding health or degradation to be used to update and optimize flight plan.
The motivation to combine machine learning and tail specific performance variance rate to uplink and downlink message of an aircraft parameter for optimizing flight plan is that, as indicated by Gibbons, this would allow for decrease fuel consumption, reduce flight time, improve flight attribute desired by an operator of an aircraft, and schedule maintenance and repair more effectively so as to improve on time performance and durability (see [0008] and [0102]).
5. Applicant argues that amended independent claim 11 and 16 are allowable for having been amended similarly to amended independent claim 1. However, examiner indicates, as stated above, that applicant’s arguments on amended independent claim 1 are moot and therefore, the applicant’s argument regarding amended independent claim 11 and 16 are moot.
In addition, examiner also indicates that claim 1 is rejected under 35 USC 103 as being unpatentable over Westervelt in view of Gibbons. Therefore, claims 11 and 16 are also rejected under similar recitation and including Hale et al. (US 10121384B2) to address additional claim of generating, during the flight, an advisory that presents the target parameter value to a flight crew of the aircraft as mentioned in previous Office Action regarding claim 16.
6. Applicant argues that claims 2-4 and 5-8 are allowable as they depend on amended independent claim 1. However, examiner indicates, as stated above, that applicant’s arguments on amended independent claim 1 are moot and are fully rejected under 35 USC 103 as being unpatentable over Westervelt in view of Gibbons. Therefore, 2-4 and 5-8 are rejected as they depend on amended independent claim 1.
7. Applicant argues that claims 12 and 15-21 are allowable as they depend on amended independent claims 11 and 16. However, examiner indicates, as stated above, that applicant’s arguments on amended independent claims 11 and 16 are moot and are fully rejected under 35 USC 103 as being unpatentable over Westervelt in view of Gibbons in further view of Hale. Therefore, claims 12 and 15-21 are rejected as they depend on amended independent claims 11 and 16.
8. Applicant argues that claims 22 and 23 are allowable as they depend on amended independent claims 1 and 16 respectively. However, examiner indicates, as stated above, that claim 1 is fully rejected under 35 USC 103 as being unpatentable over Westervelt in view of Gibbons and claim 16 is fully rejected under 35 USC 103 as being unpatentable over Westervelt in view of Gibbons and Hale. Therefore, claims 22 and 23 are rejected as they depend on amended independent claims 1 and 16.
Claim Objections
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.
9. Claim 1, 11, and 16 recites the limitation "the health". There is insufficient antecedent basis for this limitation in the claim. “The health” should be changed to “a health”.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
10. Claims 1, 3, and 5-8 are rejected under 35 USC 103 as being unpatentable over Westervelt et al. (US 20180096608 A1) in view of Gibbons, Il et al. (US 20210383706A1).
Regarding claim 1, (AS PREVIOUSLY STATED) Westervelt teaches a method, comprising:
receiving a downlink aircraft communications, addressing and reporting system (ACARS) message from an aircraft in flight (see [0030] where there is a communication link between an aircraft and a ground-based computational asset referred to as downlink; see also [0036] and Fig. 3 where there is a gathering information of flight parameters from airborne system via a downlink. This is an ACARS system);
determining a target parameter value for the aircraft based at least in part on parameter values extracted from the downlink ACARS message and a tail specific offset, the tail specific offset indicating a deviation of an actual aircraft performance of the aircraft from a baseline aircraft performance for the aircraft (see [0038]-[0041] and Fig. 3 where an off-aircraft system receive downlink from aircraft and other systems that includes tail specific airplane characteristics such as thrust and drag, air traffic information, and weather information, as well as a tail specific offset and set the tail specific offset as a target when optimization is performed; these are used to perform a control optimization by predicting a trajectory and develop an optimized flight plan, i.e. creating a baseline path and adjusting aircraft accordingly depending on how much aircraft has deviated. Note also that the optimization is dynamic where optimization happens over a period of time with gathering more information that is updated as it changes, which would indicate continuous offset determination and adjustment of the aircraft; see also [0023]-[0025] where ground-based system receives flight data from airborne system, which details parameters of a prescribed flight plan that is based on actual historical performance and based on history of previous flights conducted by a particular aircraft, i.e. baseline performance according to previous flights.); and
transmitting an uplink ACARS message containing the target parameter value to the aircraft in flight (see [0040] where an optimized flight plan mentioned above is communicated, i.e. uplinked, to an aircraft.); and
automatically controlling, by a computing device of the aircraft, the aircraft according to the target parameter value provided in the uplink ACARS message to the aircraft (see [0040] and Fig. 3 where framework, which involves ground-based system and airborne system on an aircraft, perform a control optimization to uplink an optimized flight plan to the aircraft and guide the aircraft according to the optimal control; see also in claim 16 where aircraft executes flight in accordance with the optimized flight plan. Note also in [00169] that there is a control unit, i.e. a computing device, that includes an auto pilot/throttle system, that operates the aircraft.).
Westervelt also teaches: tail specific offset determined (see [0023]-[0025] where ground-based system receives flight data from airborne system, which details parameters of a prescribed flight pan that is based on actual historical performance and based on history of previous flights conducted by a particular aircraft; see also [0036] where framework support calculation of control histories that minimizes operating cost of the particular aircraft.).
Westervelt does not teach using a machine-learned model based at least in part on the past flight data of the aircraft.
Westervelt also does not teach: the tail specific offset representing the health or degradation of the aircraft.
However, Gibbons teaches a tail specific performance models used as predictive tool where rate of variations in performance along with age of an aircraft, maintenance history, and historical flight data indicates degradations, i.e. tail offset representing health and degradation of an aircraft (see [0100]). Note also in [0102], the aircraft specific information, which is the tail specific information, is used to schedule maintenance and repair through tracking of aging of the aircraft overtime, i.e. tracking of degradation of the aircraft overtime through aircraft specific information.
Further, Gibbons also teaches machine learning model that is trained to predict fuel mileage performance or other characteristics based on gathering information that includes routes flown, miles flown, weight, operating altitude, i.e. baseline aircraft performance model, which is used to find tail specific offset, based at least in part on past flight data (see [0066]-[0067]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify downlink and uplink message of aircraft parameters for optimization flight plan control of Westervelt by incorporating teaching of Gibbons such that machine learning model is used to predict fuel mileage and other characteristics based on past and other parameter information such as routes flown, miles flown, operating altitude, combined with tail specific performance model variance rate along with age and historical flight data of an aircraft, i.e. tail specific offset representing health or degradation, so that aircraft and machine learning model will consider tail specific offset regarding health or degradation to be used to update and optimize flight plan.
The motivation to combine machine learning and tail specific performance variance rate to uplink and downlink message of an aircraft parameter for optimizing flight plan is that, as indicated by Gibbons, this would allow for decrease fuel consumption, reduce flight time, improve flight attribute desired by an operator of an aircraft, and schedule maintenance and repair more effectively so as to improve on time performance and durability (see [0008] and [0102]).
Regarding claim 3, modified Westervelt in view of Gibbons teaches the method of claim 1,
wherein the downlink ACARS message is generated and downlinked from the aircraft to a ground station automatically (see Westervelt [0041], [0047] and Fig. 3 where optimization of flight plan is dynamic which optimization happens over a period of time with gathering more information that is updated as it changes, i.e. continuous downlink from aircraft. Note also that in [0047], there is a continuous or periodic updates to develop an accurate model for performance; see further [0030] where ground-based system is used for greater computational capacity and functionality.).
Regarding claim 5, modified Westervelt in view of Gibbons teaches the method of claim 1,
wherein the target parameter value is a target speed for the aircraft (see Westervelt [0044] and claim 9 where a listing of optimization flight plan commands includes speed commands.).
Regarding claim 6, modified Westervelt in view of Gibbons teaches the method of claim 1,
wherein the target parameter value is a target altitude for the aircraft (see Westervelt [0044] where a listing of optimization flight plan commands includes altitude commands.).
Regarding claim 7, modified Westervelt in view of Gibbons teaches the method of claim 1,
wherein the target parameter value is one of a plurality of target parameter values for the aircraft, and wherein the plurality of target parameter values include a target speed and a target altitude for the aircraft (see Westervelt [0044] and claim 9 where a listing of optimization flight plan commands includes speed commands and altitude commands.).
Regarding claim 8, modified Westervelt in view of Gibbons teaches the method of claim 1,
wherein the receiving, the determining, and the providing are performed at a ground station (see Westervelt [0030] where ground-based system is used for greater computational capacity and functionality; see also in Fig. 3 and [0038]-[0040] where one or more systems other than airborne system, i.e. ground-based systems mentioned previously, gathers information from both aircraft and other external systems, determines a control optimization flight plan, and uplink the plan to the aircraft.).
11. Claims 2, 4, 11-13, and 15-21 are rejected under pre-35 U.S.C. 103 as being unpatentable over Westervelt in view of Gibbons in further view of Hale et al. (US 10121384 B2).
Regarding claim 2, modified Westervelt in view of Gibbons teaches the method of claim 1,
Modified Westervelt in view of Gibbons does not teach presenting an advisory suggesting the target parameter value to a flight crew of the aircraft.
However, Hale teaches an updated flight plan/route message uplinked to an aircraft where it is reviewed and accepted by a flight crew (see [col 16 lns 18-48]). Note also that the message uplinked to the aircraft also includes various flight information parameters such as current position, speed altitude, and weather information (see [col 16 ln 49 thru col 17 ln 3].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify optimization flight plan control through machine learning of past flight data and tail specific performance with historical data and aging of an aircraft that represents health or degradation of Westervelt in view of Gibbons by incorporating teachings of Hale such that once trajectory predictor, updating waypoints to a flight plan, with a message constructor can send updated list of commands and flight plan/route to a flight crew to execute and follow the flight plan/route.
The motivation to send a message to a flight crew is that, as indicated by Hale, this would allow improvement of safety through having an advisory service and accepting in real-time depending on various constraints such as crew cost, crew rest, flight schedule, connecting passenger, fuel loading, and time profiles. Also, an approved user can view and select the most efficient route based on the said constraints and other flight information and even configured accordingly (see [col 17 lns 49-67 and col 18 lns 1-14] and [col 23 lns 62-67 and col 24 lns 1-10]).
Regarding claim 4, modified Westervelt in view of Gibbons teaches the method of claim 1,
wherein the parameter values include (see Westervelt Fig. 3, [0016], [0034], [0038] and [0042] where weather information, including temperature, and tail specific airplane characteristic, which includes thrust, drag and other parameters, such as altitude, are updated for optimized path planning.).
Modified Westervelt in view of Gibbons does not teach a parameter value for a gross weight of the aircraft.
However, Hale teaches an efficiency and operational flight object system that accesses aircraft current state data that includes weight of aircraft, altitude, speed, center of gravity, weather, etc. (see [col 11 lns 42-63]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify optimization flight plan control through machine learning of past flight data and tail specific performance with historical data and aging of an aircraft that represents health or degradation of Westervelt in view of Gibbons by incorporating teachings of Hale such that once trajectory predictor, using weight of aircraft, altitude, speed, center of gravity, weather, etc., and updating waypoints to a flight plan, with a message constructor can send updated list of commands and flight plan/route to a flight crew to execute and follow the flight plan/route.
The motivation to send a message to a flight crew is that, as indicated by Hale, this would allow improvement of safety through having an advisory service and accepting in real-time depending on various constraints such as crew cost, crew rest, flight schedule, connecting passenger, fuel loading, and time profiles. Also, an approved user can view and select the most efficient route based on the said constraints and other flight information and even configured accordingly (see [col 17 lns 49-67 and col 18 lns 1-14] and [col 23 lns 62-67 and col 24 lns 1-10]).
Regarding claim 11, Westervelt teaches a ground station (see [0020] where ground-based computer system is used and is linked to an aircraft), comprising:
one or more processors (see [0020] where ground-based computer system has a processor that executes instructions); and
one or more memory devices that store a program executable by the one or more processors to perform an operation (see [0020] where the ground-based computer system has a processor that executes program instructions and also multiple ground-based systems act together as a data storage; see also [0052] where it refers to data storage device to hold data.), the operation comprising:
receiving a downlink aircraft communications from a datalink communication system, addressing and reporting system (ACARS) message from an aircraft in flight (see [0030] where there is a communication link between an aircraft and a ground-based computational asset referred to as downlink; see also [0036] and Fig. 3 where there is a gathering information of flight parameters from airborne system, i.e. datalink communication, via a downlink.);
determining a target parameter value for the aircraft based at least in part on parameter values extracted from the downlink ACARS message and a tail specific offset, the tail specific offset indicating a deviation of an actual aircraft performance of the aircraft from a baseline aircraft performance for the aircraft (see [0038]-[0041] and Fig. 3 where an off-aircraft system receive downlink from aircraft and other systems that includes tail specific airplane characteristics such as thrust and drag, air traffic information, and weather information. These are used to perform a control optimization by predicting a trajectory and develop an optimized flight plan, i.e. creating a baseline path and adjusting aircraft accordingly depending on how much aircraft has deviated. Note also that the optimization is dynamic where optimization happens over a period of time with gathering more information that is updated as it changes, which would indicate continuous offset determination and adjustment of the aircraft; see also [0023]-[0025] where ground-based system receives flight data from airborne system, which details parameters of a prescribed flight pan that is based on actual historical performance and based on history of previous flights conducted by a particular aircraft, i.e. baseline performance according to previous flights.); and
transmitting, with a ground transceiver of the ground station to a transceiver of the datalink communication system in the aircraft, an uplink ACARS message containing the target parameter value to the aircraft in flight in response to determining the target parameter (see [0040] where an optimized flight plan mentioned above is communicated, i.e. uplinked in response to determining target parameter, to an aircraft from ground-based system.).
Westervelt also teaches: tail specific offset determined (see [0023]-[0025] where ground-based system receives flight data from airborne system, which details parameters of a prescribed flight pan that is based on actual historical performance and based on history of previous flights conducted by a particular aircraft; see also [0036] where framework support calculation of control histories that minimizes operating cost of the particular aircraft.).
Westervelt does not teach using a machine-learned model based at least in part on the past flight data of the aircraft.
Westervelt also does not teach:
the tail specific offset representing the health or degradation of the aircraft; and
generation, during the flight, an advisory that presents the target parameter value to a flight crew of the aircraft.
However, Gibbons teaches a tail specific performance models used as predictive tool where rate of variations in performance along with age of an aircraft, maintenance history, and historical flight data indicates degradations, i.e. tail offset representing health and degradation of an aircraft (see [0100]). Note also in [0102], the aircraft specific information, which is the tail specific information, is used to schedule maintenance and repair through tracking of aging of the aircraft overtime, i.e. tracking of degradation of the aircraft overtime through aircraft specific information.
Further, Gibbons also teaches machine learning model that is trained to predict fuel mileage performance or other characteristics based on gathering information that includes routes flown, miles flown, weight, operating altitude, i.e. baseline aircraft performance model, which is used to find tail specific offset, based at least in part on past flight data (see [0066]-[0067]).
Still further, Hale teaches an updated flight plan/route message uplinked to an aircraft where it is reviewed and accepted by a flight crew (see [col 16 lns 18-48]). Note also that the message uplinked to the aircraft also includes various flight information parameters such as current position, speed, altitude, and weather information (see [col 16 ln 49 thru col 17 ln 3].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify downlink and uplink message of aircraft parameters for optimization flight plan control of Westervelt by incorporating teaching of Gibbons and Hale such that machine learning model is used to predict fuel mileage and other characteristics based on past and other parameter information such as routes flown, miles flown, operating altitude, combined with tail specific performance model variance rate along with age and historical flight data of an aircraft, i.e. tail specific offset representing health or degradation, so that aircraft and machine learning model will consider tail specific offset regarding health or degradation to be used to update and optimize flight plan. Then, afterwards, updated flight plan message, which includes parameter values of speed, altitude, and position, is uplinked to an aircraft so that it is reviewed and accepted by a flight crew.
The motivation to combine machine learning and tail specific performance variance rate to uplink and downlink message of an aircraft parameter for optimizing flight plan is that, as indicated by Gibbons, this would allow for decrease fuel consumption, reduce flight time, improve flight attribute desired by an operator of an aircraft, and schedule maintenance and repair more effectively so as to improve on time performance and durability (see [0008] and [0102]).
The motivation to send a message to a flight crew is that, as indicated by Hale, this would allow improvement of safety through having an advisory service and accepting in real-time depending on various constraints such as crew cost, crew rest, flight schedule, connecting passenger, fuel loading, and time profiles. Also, an approved user can view and select the most efficient route based on the said constraints and other flight information and even configured accordingly (see [col 17 lns 49-67 and col 18 lns 1-14] and [col 23 lns 62-67 and col 24 lns 1-10]).
Regarding claim 12, modified Westervelt in view of Gibbons and Hale teaches the ground station of claim 11,
wherein the parameter values include (see Westervelt Fig. 3, [0016], [0034], [0038] and [0042] where weather information, including temperature, and tail specific airplane characteristic, which includes thrust, drag and other parameters, such as altitude, are updated for optimized path planning.).
Modified Westervelt in view of Gibbons does not teach a parameter value for a gross weight of the aircraft.
However, Hale teaches an efficiency and operational flight object system that accesses aircraft current state data that includes weight of aircraft, altitude, speed, center of gravity, weather, etc. (see [col 11 lns 42-63]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify optimization flight plan control through machine learning of past flight data and tail specific performance with historical data and aging of an aircraft that represents health or degradation of Westervelt in view of Gibbons by incorporating teachings of Hale such that once trajectory predictor, using weight of aircraft, altitude, speed, center of gravity, weather, etc., and updating waypoints to a flight plan, with a message constructor can send updated list of commands and flight plan/route to a flight crew to execute and follow the flight plan/route.
The motivation to send a message to a flight crew is that, as indicated by Hale, this would allow improvement of safety through having an advisory service and accepting in real-time depending on various constraints such as crew cost, crew rest, flight schedule, connecting passenger, fuel loading, and time profiles. Also, an approved user can view and select the most efficient route based on the said constraints and other flight information and even configured accordingly (see [col 17 lns 49-67 and col 18 lns 1-14] and [col 23 lns 62-67 and col 24 lns 1-10]).
Regarding dependent claims 13, modified Westervelt in view of Gibbons and Hale teaches the ground station of claim 11, wherein the target parameter value is one of plurality of target parameter values include a target speed and a target altitude for the aircraft (see Westervelt [0044] and claim 9 where a listing of optimization flight plan commands includes speed commands and altitude commands.).
Regarding claim 15, modified Westervelt in view of Gibbons and Hale teaches the ground station of claim 11,
Modified Westervelt in view of Gibbons does not teach wherein the operation further comprises generating the uplink ACARS message, the uplink ACARS message has an advisory that contains the target parameter value.
However, Hale teaches an updated flight plan/route message uplinked to an aircraft where it is reviewed and accepted by a flight crew (see [col 16 lns 18-48]). Note also that the message uplinked to the aircraft also includes various flight information parameters such as current position, speed altitude, and weather information (see [col 16 ln 49 thru col 17 ln 3].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify optimization flight plan control through machine learning of past flight data and tail specific performance with historical data and aging of an aircraft that represents health or degradation of Westervelt in view of Gibbons by incorporating teachings of Hale such that once trajectory predictor, updating waypoints to a flight plan, with a message constructor can send updated list of commands and flight plan/route to a flight crew to execute and follow the flight plan/route.
The motivation to send a message to a flight crew is that, as indicated by Hale, this would allow improvement of safety through having an advisory service and accepting in real-time depending on various constraints such as crew cost, crew rest, flight schedule, connecting passenger, fuel loading, and time profiles. Also, an approved user can view and select the most efficient route based on the said constraints and other flight information and even configured accordingly (see [col 17 lns 49-67 and col 18 lns 1-14] and [col 23 lns 62-67 and col 24 lns 1-10]).
Regarding claim 16, (AS PREVIOUSLY STATED) Westervelt teaches a computing system for an aircraft (see fig. 1 and [0017]-[0019] where it shows a system for guidance and navigation of an aircraft; see also Fig. 4 and [0050]-[0054] where it indicates an apparatus that communicates with external devices and the apparatus includes a processor and a memory.), comprising:
one or more processors (see Fig. 4 and [0050]-[0054] where it indicates an apparatus that communicates with external devices and the apparatus includes a processor and a memory; see also Fig. 1 and [0016]-[0019] where flight management system is part of an airborne system includes guidance module, control module, and navigation module. The flight management system is capable of calculation, i.e. a computer with a processor to perform calculation.); and
one or more memory devices that store a program executable by the one or more processors to perform an operation, the operation comprising (see [0052]-[0054] and Fig. 4 where apparatus has a data storage device and optimization engine, aircraft modeler, and application and data within the apparatus to perform one or more processes mentioned for operation of aircraft.):
instructing, during a flight of the aircraft, a transmission of a downlink aircraft communications, addressing and reporting system (ACARS) message to a ground station (see Fig. 4 and [0052]-[0054] as mentioned above. Also, note that in paragraph [0053] the apparatus performs one or more processes mentioned before that includes aspects of Figs 2 and 3; see further [0030] where there is a communication link between an aircraft and a ground-based computational asset referred to as downlink; see also [0036] and Fig. 3 where there is a gathering information of flight parameters from airborne system via a downlink.);
receiving, during the flight, an uplink ACARS message transmitted by the ground station, the uplink ACARS message contains a target parameter value based on parameter values extracted from the downlink ACARS message and a tail specific offset, the tail specific offset indicating a deviation of an actual aircraft performance of the aircraft from a baseline aircraft performance for the aircraft (see Fig. 4 and [0052]-[0054] as mentioned above; see further [0038]-[0041] and Fig. 3 where an off-aircraft system receive downlink from aircraft and other systems that includes tail specific airplane characteristics such as thrust and drag, air traffic information, and weather information. These are used to perform a control optimization by predicting a trajectory and develop an optimized flight plan, i.e. creating a baseline path and adjusting aircraft accordingly depending on how much aircraft has deviated. Note also that the optimization is dynamic where optimization happens over a period of time with gathering more information that is updated as it changes, which would indicate continuous offset determination and adjustment of the aircraft; see also [0023]-[0025] where ground-based system receives flight data from airborne system, which details parameters of a prescribed flight pan that is based on actual historical performance and based on history of previous flights conducted by a particular aircraft, i.e. baseline performance according to previous flights.).
Westervelt also teaches: tail specific offset determined (see [0023]-[0025] where ground-based system receives flight data from airborne system, which details parameters of a prescribed flight pan that is based on actual historical performance and based on history of previous flights conducted by a particular aircraft; see also [0036] where framework support calculation of control histories that minimizes operating cost of the particular aircraft.).
Westervelt does not teach using a machine-learned model based at least in part on the past flight data of the aircraft.
Westervelt also does not teach:
the tail specific offset representing the health or degradation of the aircraft; and
generation, during the flight, an advisory that presents the target parameter value to a flight crew of the aircraft.
However, Gibbons teaches a tail specific performance models used as predictive tool where rate of variations in performance along with age of an aircraft, maintenance history, and historical flight data indicates degradations, i.e. tail offset representing health and degradation of an aircraft (see [0100]). Note also in [0102], the aircraft specific information, which is the tail specific information, is used to schedule maintenance and repair through tracking of aging of the aircraft overtime, i.e. tracking of degradation of the aircraft overtime through aircraft specific information.
Further, Gibbons also teaches machine learning model that is trained to predict fuel mileage performance or other characteristics based on gathering information that includes routes flown, miles flown, weight, operating altitude, i.e. baseline aircraft performance model, which is used to find tail specific offset, based at least in part on past flight data (see [0066]-[0067]).
Still further, Hale teaches an updated flight plan/route message uplinked to an aircraft where it is reviewed and accepted by a flight crew (see [col 16 lns 18-48]). Note also that the message uplinked to the aircraft also includes various flight information parameters such as current position, speed, altitude, and weather information (see [col 16 ln 49 thru col 17 ln 3].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify downlink and uplink message of aircraft parameters for optimization flight plan control of Westervelt by incorporating teaching of Gibbons and Hale such that machine learning model is used to predict fuel mileage and other characteristics based on past and other parameter information such as routes flown, miles flown, operating altitude, combined with tail specific performance model variance rate along with age and historical flight data of an aircraft, i.e. tail specific offset representing health or degradation, so that aircraft and machine learning model will consider tail specific offset regarding health or degradation to be used to update and optimize flight plan. Then, afterwards, updated flight plan message, which includes parameter values of speed, altitude, and position, is uplinked to an aircraft so that it is reviewed and accepted by a flight crew.
The motivation to combine machine learning and tail specific performance variance rate to uplink and downlink message of an aircraft parameter for optimizing flight plan is that, as indicated by Gibbons, this would allow for decrease fuel consumption, reduce flight time, improve flight attribute desired by an operator of an aircraft, and schedule maintenance and repair more effectively so as to improve on time performance and durability (see [0008] and [0102]).
The motivation to send a message to a flight crew is that, as indicated by Hale, this would allow improvement of safety through having an advisory service and accepting in real-time depending on various constraints such as crew cost, crew rest, flight schedule, connecting passenger, fuel loading, and time profiles. Also, an approved user can view and select the most efficient route based on the said constraints and other flight information and even configured accordingly (see [col 17 lns 49-67 and col 18 lns 1-14] and [col 23 lns 62-67 and col 24 lns 1-10]).
Regarding claim 17, the modified Westervelt in view of Gibbons and Hale teaches the computing system of claim 16,
wherein at least one processor of the one or more processors and at least one memory device of the one or more memory devices are embodied in a flight management computer, and wherein the at least one processor of the flight management computer receives the uplink ACARS message and causes the advisory to be presented to the flight crew (see Westervelt [0050]-[0052] as shown above where an apparatus has processors and memory devices that performs, but not limited to, operations mentioned in Figs 2 and 3, which includes updated optimized path specific controls being uplinked to a particular aircraft; see further Hale [col 16 lns 18-48] where an updated flight plan/route message uplinked to an aircraft where it is reviewed and accepted by a flight crew.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify an apparatus with processors and memory devices that performs optimization flight plan control through machine learning of past flight data and tail specific performance with historical data and aging of an aircraft that represents health or degradation of Westervelt in view of Gibbons by incorporating teachings of Hale such that ground-based system receives flight message downlinked from an aircraft and once trajectory predictor, updating waypoints to a flight plan, with a message constructor can send updated list of commands and flight plan/route to a flight crew to execute and follow the flight plan/route.
The motivation to send a message to a flight crew is that, as indicated by Hale, this would allow improvement of safety through having an advisory service and accepting in real-time depending on various constraints such as crew cost, crew rest, flight schedule, connecting passenger, fuel loading, and time profiles. Also, an approved user can view and select the most efficient route based on the said constraints and other flight information and even configured accordingly (see [col 17 lns 49-67 and col 18 lns 1-14] and [col 23 lns 62-67 and col 24 lns 1-10]).
Regarding claim 18, the modified Westervelt in view of Gibbons and Hale teaches the computing system of claim 16,
wherein the target parameter value is one of a plurality of target parameter values for the aircraft, and wherein the plurality of target parameter values include a target speed and a target altitude for the aircraft (see Westervelt [0044] and claim 9 where a listing of optimization flight plan commands includes speed commands and altitude commands.).
Regarding claim 19, the modified Westervelt in view of Gibbons and Hale teaches the computing system of claim 16,
wherein the parameter values include a parameter value for a gross weight of the aircraft, a parameter value for a static air temperature, and parameter value for an altitude of the aircraft in flight (see also Hale [col 11 lns 42-63] where an efficiency and operational flight object system that accesses aircraft current state data that includes weight of an aircraft, altitude, speed, center of gravity, weather, etc.; see further [col 8 lns 32-54] where environmental information pertains to information that includes temperature and humidity and pressure.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify optimization flight plan control through machine learning of past flight data and tail specific performance with historical data and aging of an aircraft that represents health or degradation of Westervelt in view of Gibbons by incorporating teachings of Hale such that once trajectory predictor, using weight of aircraft, altitude, speed, center of gravity, weather, etc., and updating waypoints to a flight plan, with a message constructor can send updated list of commands and flight plan/route to a flight crew to execute and follow the flight plan/route.
The motivation to send a message to a flight crew is that, as indicated by Hale, this would allow improvement of safety through having an advisory service and accepting in real-time depending on various constraints such as crew cost, crew rest, flight schedule, connecting passenger, fuel loading, and time profiles. Also, an approved user can view and select the most efficient route based on the said constraints and other flight information and even configured accordingly (see [col 17 lns 49-67 and col 18 lns 1-14] and [col 23 lns 62-67 and col 24 lns 1-10]).
Regarding claim 20, modified Westervelt in view of Gibbons and Hale teaches the computing system of claim 16, wherein the operation further comprises:
generating the downlink ACARS message, and wherein the generating and the causing, during the flight of the aircraft, transmission of the downlink ACARS message to the ground station occur automatically without flight crew intervention (see Hale Fig. 1 and [col 28 lns 21-28] where ground server automatically capture and compile current and predicted flight information, i.e. automatic communication between ground and aircraft without another intervention. The ground server is communicatively linked to an aircraft as shown in air/ground messaging service in Fig. 1 and in [col 8 lns 60-67 and col 9 lns 1-13]. Note also that message is transmitted through ACARS.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify optimization flight plan control through machine learning of past flight data and tail specific performance with historical data and aging of an aircraft that represents health or degradation of Westervelt in view of Gibbons by incorporating teachings of Hale such that ground-based system receives flight message, which is transmitted in any unique format by a user or in a standardized ground messaging format, downlinked from an aircraft via ACARS, Aeronautical Telecommunication Network, internet or other aircraft datalink technology, and trajectory predictor, updating waypoints to a flight plan, with a message constructor can send updated list of commands and flight plan/route to a flight crew to execute and follow the flight plan/route.
The motivation to send a message to a flight crew is that, as indicated by Hale, this would allow improvement of safety through having an advisory service and accepting in real-time depending on various constraints such as crew cost, crew rest, flight schedule, connecting passenger, fuel loading, and time profiles. Also, an approved user can view and select the most efficient route based on the said constraints and other flight information and even configured accordingly (see [col 17 lns 49-67 and col 18 lns 1-14] and [col 23 lns 62-67 and col 24 lns 1-10]).
Regarding claim 21, modified Westervelt in view of Gibbons and Hale teaches the ground station of claim 11, decoding the downlink aircraft communications into a message format; and wherein determining the target parameter value for the aircraft based at least in part on the parameter values extracted from the downlink ACARS message is based on the downlink aircraft communications decoded (see Hale [col 8 ln 60 thru col 9 ln 13] where a ground-based system receiving flight message downlinked from an aircraft that includes environmental information and flight plan/route information via ACARS, and the message is in any unique format specified by a user or in a standardized ground messaging format.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify optimization flight plan control through machine learning of past flight data and tail specific performance with historical data and aging of an aircraft that represents health or degradation of Westervelt in view of Gibbons by incorporating teachings of Hale such that ground-based system receives flight message, which is in any unique format specified by a user or in standardized ground messaging format, downlinked from an aircraft and trajectory predictor, updating waypoints to a flight plan, with a message constructor can send updated list of commands and flight plan/route to a flight crew to execute and follow the flight plan/route.
The motivation to receive message downlinked from an aircraft and uplink a message to a flight crew is that, as indicated by Hale, this would allow improvement of safety through having an advisory service and accepting in real-time depending on various constraints such as crew cost, crew rest, flight schedule, connecting passenger, fuel loading, and time profiles. Also, an approved user can view and select the most efficient route based on the said constraints and other flight information and even configured accordingly (see [col 17 lns 49-67 and col 18 lns 1-14] and [col 23 lns 62-67 and col 24 lns 1-10]).
12. Claims 22 and 23 are rejected under pre-35 U.S.C. 103 as being unpatentable over Westervelt in view of Gibbons in further view of Cachia (“Using Flight Data to support fuel savings at Transavia”, https://www.aircraftit.com/articles/using-flight-data-to-support-fuel-savings-at-transavia/).
Regarding claim 22, modified Westervelt in view of Gibbons teaches the method of claim 1,
Modified Westervelt in view of Gibbons does not teach wherein the past flight data includes at least two hundred flights of the aircraft.
However, Cachia does teach machine learning performance models are generated by using past two hundred flights of each tail number that includes flight data and weather forecasts (see [page 6/20]-[page 7/20]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify optimization flight plan control with machine learning model to predict, compare, and update to a baseline performance of Westervelt in view of Gibbons by incorporating teachings of Cachia such that machine learning model will acquire past two hundred flight data.
The motivation to have a machine learning model to acquire past two hundred flight data, as indicated by Cachia, this would allow for predictions of fuel consumption of trajectories and choose an optimum trajectory that will save fuel usage (see [page 4/20] and [page7/20]).
Regarding claim 23, modified Westervelt in view of Gibbons and Hale teaches the computing system of claim 16,
Modified Westervelt in view of Gibbons and Hale does not teach wherein the past flight data includes at least two hundred flights from the aircraft.
However, Cachia does teach machine learning performance models are generated by using past two hundred flights of each tail number that includes flight data and weather forecasts (see [page 6/20]-[page 7/20]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify optimization flight plan control with machine learning model to predict, compare, and update to a baseline performance, and uplink message of an updated flight plan to a flew crew for review of Westervelt in view of Gibbons and Hale by incorporating teachings of Cachia such that machine learning model will acquire past two hundred flight data.
The motivation to have a machine learning model to acquire past two hundred flight data, as indicated by Cachia, this would allow for predictions of fuel consumption of trajectories and choose an optimum trajectory that will save fuel usage (see [page 4/20] and [page7/20]).
Conclusion
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a. Raman et al. (“Machine Learning Model for Aircraft Tail Performance & Degradation Predictions”, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9622357&tag=1), teaches tail specific performance analyzed over years and assess degradation, such as airframe deterioration.
b. Rodriguez Bravo et al. (US 11164465B2), teaches machine learning that gives suggestions on which archive save data are useful flight data such as past fuel efficient path and parameters relating to it.
c. Kim et al. (US 20190035178A1), a system that monitors airplane and communicates with ground station via ACARS. Uses data gathered to provide accurate flight profile and trip prediction.
d. Chan et al . (US 20130085661A1), an apparatus and method to dynamically monitor deviations of flights. There is communication of better and more accurate parameters to follow.
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/H.A./Examiner, Art Unit 3661
/MATTHIAS S WEISFELD/Examiner, Art Unit 3661