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 office action is in response to a request for continued examination to an amendment/argument submitted on 12/02/2025. The applicant amends claims 1, 11, and 20.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the efficient cruise parameters must be shown or the feature canceled from the claims. No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 – 9, 11 – 18, and 20 – 22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 - 43 of U.S. Patent No. 10,665,114. Although the claims at issue are not identical, they are not patentably distinct from each other because the claimed invention is the same inventive concept as the patent. The application is rejected pursuant to the standards or MPEP 804, where the application is more generic to the species or the subspecies of the patent. In this case, the claims are of the application are broadly claimed to determine the efficient cruise operation of an aircraft. The patent ‘114 is more specific in the claims of the features used to calculate the same operation. In addition, the application does no identify any specific unique structure that is required to perform the function.
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– 9, 11– 18, and 20-22 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. Claims 1, 6 – 9, 11, 15 – 18, and 20 contain the phrase "efficient cruise phase parameters" and isn't defined but has a possible definition in claims 21 and 22. The features do not show how the cruise phase is implemented based upon the possible limitations. The applicant alleges the phrase is an adjective and not known but the dependent claims 21 and 22 are being used as nouns. In addition, the term “efficient” is a term of approximation under 2173.05(b)(III) and undefined. Therefore, the claim feature is indefinite.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 – 9, 11 – 18, and 20 - 22 are rejected under 35 U.S.C. 103 as being unpatentable over Carvalho US 2020/0309810 in view of Schelfaut US 2018/0258785.
As per claim 1, A system for operating an aircraft during a cruise phase of flight, the system comprising:
a control unit configured to receive data regarding one or both of the cruise phase of a current flight or the cruise phase of one or more previous flights of the aircraft from one or more sensors of the aircraft, (Carvalho paragraph 0038 discloses, "Real Flight Test Data (12). Signals recorded along flights during aircraft development phase (12) may also be used as exemplars for training the neural network." And paragraph 0044 discloses, "Include the neural network as part of the Flight Controls Software and embed it into Flight Control Computers.")
wherein the control unit is further configured to determine efficient cruise phase parameters for the aircraft based on the data, (Schelfaut paragraph 0044 teaches, "By tracking the actual and unique way an aircraft is flown for a particular flight mission and adjusting the clearance targets accordingly, blade tip clearances can be better optimized, leading to improved engine performance and efficiency." And paragraph 0065 teaches, "As further shown in FIG. 3, the computing device 211 of the engine controller 210 includes various control model(s) 260 for modeling and controlling various control systems of the engine 100 and aircraft 200. In particular, control model(s) 260 includes an Active Clearance Control (ACC) model 262 and a High Efficiency Cruise (HEC) model 264, among other potential control models." and paragraph 0116 teaches, "The memory device(s) 213 of one or more of the computing device(s) 211 of the engine controllers 210 or other computing devices communicatively coupled to the engine controller 210 may store the past operating parameters 322. For example, the past operating parameters 322 can be stored in the flight data library 250 such that the data can be obtained and input into the model trainer 314 to train, test, or validate the machine-learned model 290.") and
wherein the aircraft is operated during the cruise phase of one or both of the current flight or one or more future flights according to the efficient cruise phase parameters; (Schelfaut paragraph 0044, 0065, and 0116 cited earlier) and
wherein the control unit is further configured to automatically operate controls of the aircraft during the cruise phase according to the efficient cruise phase parameters. (Shelfaut paragraph 0119 teaches, "autopilot input, may cause the aircraft 220 to fly a particular route or to deviate therefrom.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of an aircraft performance.
As per claim 2, The system of claim 1, further comprising the one or more sensors. (Carvalho paragraphs 0057 - 0067 discloses a lot various sensor inputs for the neural network or model to use for performing the computations)
As per claim 3, The system of claim 2, wherein the one or more sensors comprise one or more flight recorders. (Carvalho paragraph 0038 discloses, "Real Flight Test Data (12). Signals recorded along flights during aircraft development phase (12) may also be used as exemplars for training the neural network." And paragraph 0044 discloses, "Include the neural network as part of the Flight Controls Software and embed it into Flight Control Computers.") and (Schelfaut paragraph 0063 teaches, "The memory device(s) 213 can further store data 215 that can be accessed by the one or more processor(s) 112. For example, the data 215 can include past flight data stored in a flight data library 250.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of a aircraft performance
As per claim 4, The system of claim 3, wherein the one or more sensors further comprise one or more of one or more speed sensors, one or more altitude sensors, one or more position sensors, one or more ambient sensors, or one or more weight sensors. (Carvalho paragraphs 0057 - 0067 discloses a lot various sensor inputs for the neural network or model to use for performing the computations)
As per claim 5, The system of claim 1, wherein the control unit is onboard the aircraft. (Carvalho paragraph 0010 discloses, "FIG. 2 provides more details about the example non- limiting operation of the neural network onboard a flight computer.) and (Schelfaut paragraph 0121 teaches, "Some or all of the method (400) can be implemented by one or engine controllers 210 described herein. Some or all of the method (400) can be performed onboard the aircraft 200 and while the aircraft 200 is in operation, such as when an aircraft 200 is in flight.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of a aircraft performance.
As per claim 6, The system of claim 1, wherein the control unit is configured to determine the efficient cruise phase parameters for a future flight of the aircraft based on the data received from one or more previous flights of the aircraft. (Schelfaut paragraph 0071 teaches, "FMS 220 provides flight planning and navigation capability and may be communicatively coupled with other avionics and aircraft systems as well, such as e.g., a global positioning system (GPS), VHF omnidirectional range/distance measuring equipment (VOR/DMB), Inertial Reference/Navigation Systems (IRS/INS), flight controls, etc." The function of flight planning is to plan for a future flight)
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of a aircraft performance.
As per claim 7, The system of claim 1, wherein the control unit is configured to determine the efficient cruise phase parameters by generating one or more neural network models for the aircraft based on the data. (Carvalho paragraph 0035 discloses, "The neural networks of example non-limiting embodiments herein are trained (13) on desktop or other computers using data obtained by aircraft model simulations (11) and real flight results (12).")
As per claim 8, The system of claim 1, wherein the control unit is further configured to show the efficient cruise phase parameters on a monitor within a flight deck of the aircraft. (Carvalho paragraph 0053 discloses, "The filtered estimated air data may be used by some onboard computer such as the Flight Control Computer (25) or may be used to display a synthetic air data (such as airspeed) to pilots (26). Some logics (10) that may consume the neural network's output (unfiltered or filtered, as previously described) are Flight Control System Logic (25) (for example Control Laws or Common Design Error Monitors) and Flight Crew Indications (26) (like an indication of estimated airspeed to the pilots through avionics displays).")
As per claim 9, The system of claim 1, wherein the control unit is configured to determine the efficient cruise phase parameters by determining an optimum cost index from a plurality of cost indices. (Carvalho paragraph 0080 discloses, "One possible solution is using the weight or center of gravity informed by the Flight Management System (FMS). If these values already contain information about the fuel consumption, i.e., if weight value decreases with time in a magnitude that represents fuel consumption and if center of gravity moves accordingly, they may be good alternatives. However, in cases where the fuel consumption is not properly informed through these signals, another possibility is to hold the last trustworthy calculated values and then, from this point, integrate the fuel consumption, or alternatively using the volume of fuel in the fuel tank.")
10. (Cancelled)
As per claim 11, A method for operating an aircraft during a cruise phase of flight, the method comprising: (Carvalho paragraph 0041 discloses, 'After the neural network is trained by the method shown in FIG. 1, the neural network gains, bias and topology are frozen and, as a consequence, ready to be embedded in the aircraft (14). The frozen relationship is stored in a non-transitory memory device in any onboard computer (27) which is capable of performing the same kind of estimations in air, in real time.")
receiving, by a control unit, data regarding one or both of the cruise phase of a current flight or the cruise phase of one or more previous flights of the aircraft from one or more sensors of the aircraft; (Carvalho paragraph 0038 discloses, "Real Flight Test Data (12). Signals recorded along flights during aircraft development phase (12) may also be used as exemplars for training the neural network." And paragraph 0044 discloses, "Include the neural network as part of the Flight Controls Software and embed it into Flight Control Computers.")
determining, by the control unit, efficient cruise phase parameters for the aircraft based on the data, (Schelfaut paragraph 0044 teaches, "By tracking the actual and unique way an aircraft is flown fora particular flight mission and adjusting the clearance targets accordingly, blade tip clearances can be better optimized, leading to improved engine performance and efficiency.” And paragraph 0065 teaches, "As further shown in FIG. 3, the computing device 211 of the engine controller 210 includes various control model(s) 260 for modeling and controlling various control systems of the engine 100 and aircraft 200. In particular, control model(s) 260 includes an Active Clearance Control (ACC) model 262 and a High Efficiency Cruise (HEC) model 264, among other potential control models." and paragraph 0116 teaches, "The memory device(s) 213 of one or more of the computing device(s) 211 of the engine controllers 210 or other computing devices communicatively coupled to the engine controller 210 may store the past operating parameters 322. For example, the past operating parameters 322 can be stored in the flight data library 250 such that the data can be obtained and input into the model trainer 314 to train, test, or validate the machine-learned model 290.")
wherein the aircraft is operated during the cruise phase of one or both of the current flight or one or more future flights according to the efficient cruise phase parameters; (Schelfaut paragraph 0044, 0065, and 0116 cited earlier) and
automatically operating, by the control unit, controls of the aircraft during the cruise phase according to the efficient descent phase parameters. (Shelfaut paragraph 0119 teaches, "autopilot input, may cause the aircraft 220 to fly a particular route or to deviate therefrom.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of a aircraft performance.
As per claim 12, The method of claim 11, wherein the one or more sensors comprise one or more flight recorders. (Carvalho paragraph 0038 discloses, "Real Flight Test Data (12). Signals recorded along flights during aircraft development phase (12) may also be used as exemplars for training the neural network." And paragraph 0044 discloses, "Include the neural network as part of the Flight Controls Software and embed it into Flight Control Computers.") and (Schelfaut paragraph 0063 teaches, "The memory device(s) 213 can further store data 215 that can be accessed by the one or more processor(s) 112. For example, the data 215 can include past flight data stored in a flight data library 250.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of a aircraft performance.
As per claim 13, The method of claim 12, wherein the one or more sensors further comprise one or more of one or more speed sensors, one or more altitude sensors, one or more position sensors, one or more ambient sensors, or one or more weight sensors. (Carvalho paragraphs 0057 - 0067 discloses a lot various sensor inputs for the neural network or model to use for performing the computations)
As per claim 14, The method of claim 11, further comprising disposing the control unit onboard the aircraft. (Carvalho paragraph 0010 discloses, "FIG. 2 provides more details about the example non- limiting operation of the neural network onboard a flight computer.) and (Schelfaut paragraph 0121 teaches, "Some or all of the method (400) can be implemented by one or engine controllers 210 described herein. Some or all of the method (400) can be performed onboard the aircraft 200 and while the aircraft 200 is in operation, such as when an aircraft 200 is in flight.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. There fore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of a aircraft performance.
As per claim 15, The method of claim 11, wherein said determining comprises determining the efficient cruise phase parameters for a future flight of the aircraft based on the data received from one or more previous flights of the aircraft. (Schelfaut paragraph 0071 teaches, "FMS 220 provides flight planning and navigation capability and may be communicatively coupled with other avionics and aircraft systems as well, such as e.g., a global positioning system (GPS), VHF omnidirectional range/distance measurin g equipment (VOR/DME), Inertial Reference/Navigation Systems (IRS/INS), flight controls, etc." The function of flight planning is to plan for a future flight)
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. There fore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of a aircraft performance.
As per claim 16, The method of claim 11, wherein said determining comprises determining the efficient cruise phase parameters by generating one or more neural network models for the aircraft based on the data. (Carvalho paragraph 0035 discloses, "The neural networks of example non-limiting embodiments herein are trained (13) on desktop or other computers using data obtained by aircraft model simulations (11) and real flight results (12).")
As per claim 17, The method of claim 11, further comprising showing, by the control unit, the efficient cruise phase parameters on a monitor within a flight deck of the aircraft. (Carvalho paragraph 0035 discloses, "The neural networks of example non-limiting embodiments herein are trained (13) on desktop or other computers using data obtained by aircraft model simulations (11) and real flight results (12).")
As per claim 18, The method of claim 11, wherein said determining comprises determining the efficient cruise phase parameters by determining an optimum cost index from a plurality of cost indices. (Carvalho paragraph 0080 discloses, "One possible solution is using the weight or center of gravity informed by the Flight Management System (FMS). If these values already contain information about the fuel consumption, 1.c., if weight value decreases with time in a magnitude that represents fuel consumption and if center of gravity moves accordingly, they may bc good alternatives. However, in cases where the fuel consumption is not properly informed through these signals, another possibility is to hold the last trustworthy calculated values and then, from this point, integrate the fuel consumption, or alternatively using the volume of fuel in the fuel tank.")
19. (Cancelled)
As per claim 20, A non-transitory computer-readable storage medium comprising executable instructions that, in response to execution, cause one or more control units comprising a processor, to perform operations comprising: (Carvalho paragraph 0041 discloses, "After the neural network is trained by the method shown in FIG. 1, the neural network gains, bias and topology are frozen and, as a consequence, ready to be embedded in the aircraft (14). The frozen relationship is stored in a non-transitory memory device in any onboard computer (27) which is capable of performing the same kind of estimations in air, in real time.")
receiving data regarding one or both of the cruise phase of a current flight or the cruise phase of one or more previous flights of an aircraft from one or more sensors of the aircraft; (Carvalho paragraph 0038 discloses, "Real Flight Test Data (12). Signals recorded along flights during aircraft development phase (12) may also be used as exemplars for training the neural network." And paragraph 0044 discloses, "Include the neural network as part of the Flight Controls Software and embed it into Flight Control Computers.")
determining efficient cruise phase parameters for the aircraft based on the data, (Schelfaut paragraph 0044 teaches, "By tracking the actual and unique way an aircraft is flown for a particular flight mission and adjusting the clearance targets accordingly, blade tip clearances can be better optimized, leading to improved engine performance and efficiency." And paragraph 0065 teaches, "As further shown in FIG. 3, the computing device 211 of the engine controller 210 includes various control model(s) 260 for modeling and controlling various control systems of the engine 100 and aircraft 200. In particular, control model(s) 260 includes an Active Clearance Control (ACC) model 262 and a High Efficiency Cruise (HEC) model 264, among other potential control models." and paragraph 0116 teaches, "The memory device(s) 213 of one or more of the computing device(s) 211 of the engine controllers 210 or other computing devices communicatively coupled to the engine controller 210 may store the past operating parameters 322. For example, the past operating parameters 322 can be stored in the flight data library 250 such that the data can be obtained and input into the model trainer 314 to train, test, or validate the machine-learned model 290.")
wherein the aircraft is operated during a cruise phase of one or both of the current flight or one or more future flights according to the efficient cruise phase parameters; (Schelfaut paragraph 0044, 0065, and 0116 cited earlier) and
automatically operating, with a control unit, controls of the aircraft during the cruise phase according to the efficient descent phase parameters. (Shelfaut paragraph 0119 teaches, "autopilot input, may cause the aircraft 220 to fly a particular route or to deviate therefrom.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of a aircraft performance.
As per claim 21, The system of claim 1, wherein the cruise phase parameters include airspeed from a beginning of a cruise phase to an end of the cruise phase. (Shelfaut paragraph 0074 teaches, "The engine/aircraft performance database contains information stored on a memory device that allows the FMS 220 to compute optimal fuel burn, airspeed, altitude, and other performance-based indicators such that the flight plan can be adjusted in favor of a more efficient flight path." and paragraph 0084 teaches, "It will also be appreciated that the machine- learned model 290 can use certain mathematical methods alone or in combination with one or more machine or statistical learning models to adjust the clearance targets, such as by using flight profile averages, minimum time at phase, maximum time at phase, etc.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of a aircraft performance.
As per claim 22, The method of claim 11, wherein the cruise phase parameters include airspeed from a beginning of a cruise phase to an end of the cruise phase. (Shelfaut paragraph 0074 teaches, "The engine/aircraft performance database contains information stored on a memory device that allows the FMS 220 to compute optimal fuel burn, airspeed, altitude, and other performance-based indicators such that the flight plan can be adjusted in favor of a more efficient flight path." and paragraph 0084 teaches, "It will also be appreciated that the machine- learned model 290 can use certain mathematical methods alone or in combination with one or more machine or statistical learning models to adjust the clearance targets, such as by using flight profile averages, minimum time at phase, maximum time at phase, etc.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose specific adjustment to aircraft engine components to improve performance. Schelfaut teaches of adjusting aircraft components to improve performance. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Schelfaut et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure accurate control and maintenance of a aircraft performance.
Response to Arguments
Applicant's arguments filed 12/02/2025 have been fully considered but they are not persuasive. With respect to the drawings, the applicant alleges the feature of “efficient cruise phase parameters” doesn’t need to be illustrated. Applicant states Fig. 3 shows, the feature and argues there is no legal basis for the requirement and 37 CFR 1.83 is satisfied. However, the threshold for 37 CFR. 1.83 as articulated in MPEP 608.02(d)(a) requires, “must show every feature of the invention specified in the claims.” Therefore, the drawings must depict what constitutes “efficient cruise phase parameters”. Applicant states the feature are depicted in Fig.3, however the figure states the feature it is not defined so that one of ordinary skill in the art would know the scope of the feature in the drawing. Therefore, one of ordinary skill in the art would not be able to look at the drawings and know what “efficient cruise phase parameters” to then program a control unit to manipulate to control the aircraft during cruise phase.
With respect to applicant’s arguments regarding the section 112 rejection for an indefinite feature. The MPEP section 2173.02 states the test for claim features clarity is defined in section 2173.02 (I) as, “During prosecution the Office construes claims by giving them their broadest reasonable interpretation consistent with the specification in an effort to establish a clear record of what the applicant intends to claim. Such claim construction during prosecution may effectively result in a lower threshold for ambiguity than a court's determination.” Based upon that requirement, examiner reviewed applicant’s cited paragraphs for defining the scope of the feature. In paragraph 0033 states, “Instead of relying on a generic determination for the cruise phase, the control unit 110 determines efficient cruise phase parameters (such as vertical speed, horizontal speed, time of cruise, altitude, and/or the like) based on actual data 108 output by the sensors 106 of the aircraft 102 during one or more actual flights of the aircraft 102.”. The features are not identified in a way so that one of ordinary skill in the art would know how the aircraft is controlled to execute a cruise phase where the variables are within “efficient cruise phase parameters”. In addition, MPEP 2173.05(b)(III) governs terms of relativity - approximation, the word “efficient” is a relative term with no definition of what constitutes “efficient”. Applicant cites a portion of the MPEP but fails to show how the example in the citation, which identifies a concrete dimension for which one skilled in the art may determine, is similar to the application where the variables in the applicant’s specification of, “vertical speed, horizontal speed, time of cruise, altitude and/or the like” are not defined.
The other specification paragraphs cited by the applicant refer to data collection. The paragraphs do not define the specific data collected to define the feature such 0034, 0036, 0038, 0040, 0045, 0047, 0049, 0057, 0059, 0065 – 0069. The paragraphs do not define the specific data collected to define the feature. Therefore, one of ordinary skill in the art would not know the scope of the feature and thus is indefinite.
With respect to the 101 rejection, it is withdrawn as applicant’s arguments are pursausive.
With respect toa applicant’s section 103 rejection. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Therefore, the section 103 rejection is maintained.
Applicant fails to argue the double patenting rejection; therefore, it is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYLER D PAIGE whose telephone number is (571)270-5425. The examiner can normally be reached M-F 7:00am - 6:00pm (mst).
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/TYLER D PAIGE/Primary Examiner, Art Unit 3664