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 on 01/09/2026. The applicant submitted an argument/amendment on 12/02/2025.
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 descent phase parameters” must be shown or the features 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.
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, 11-19, 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 -22 contain the phrase "efficient decent phase parameters" and isn't defined but has a possible definition in claims 21 and 22. The features do not show how the descent 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.
Claims 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 Gomez US 2010/0318244.
As per claim 1, A system for operating an aircraft during a descent phase of flight, the system comprising:
a control unit configured to receive data regarding one or both of the descent phase of a current flight or the descent 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 descent phase parameters for the aircraft based on the data, and wherein the aircraft is operated during the descent phase of one or both of the current flight or one or more future flights according to the efficient descent phase parameters; (Gomez paragraph 0011 teaches, "As the optimum aerodynamic flight path angle is likely to vary for any particular aircraft type, and may even vary for different models within that type, the aircraft navigation system is preferably arranged to calculate the aerodynamic flight path angle with reference to the type of the aircraft. Further parameters are also likely to be used when calculating the aerodynamic flight path angle. Optionally, the aircraft navigation system is arranged to calculate the aerodynamic flight path angle with reference to any of the weight of the aircraft (preferably a value corresponding to the weight at the top of descent), the expected wind and wind gradient and the expected atmospheric conditions." And paragraph 0022 teaches, "the present disclosure resides in an aircraft management system for use in managing aircraft flying continuous descent approaches into an airport, wherein the system is arranged: to determine aircraft types expected to fly into the airport; to determine, for each aircraft type, an optimum coefficient of lift that provide maximum predictability in the time to fly the continuous descent approach; and to calculate a common ground speed to be flown by the aircraft at the top of descent of their continuous descent approaches, wherein the common ground speed is calculated using the optimum coefficients of lift determined for the aircraft types.") and
wherein the control unit is further configured to automatically operate controls of the aircraft during the descent phase according to the efficient descent phase parameters. (Gomez paragraph 0017 teaches, "the aircraft navigation system may be part of an autopilot or may provide information to an autopilot such that the autopilot flies the continuous descent approach flight plan. In this sense, "guide" may mean provide the necessary instructions to the autopilot or it may mean the actual flying of the aircraft.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
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 (Gomez paragraph 0051 teaches, "These functions or tables may be available for use by the aircraft's computers,")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho docs not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
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 (Gomez paragraph 0051 teaches, "These functions or tables may be available for use by the aircraft's computers, ") Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.)
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
As per claim 6, The system of claim 1, wherein the control unit is configured to determine the efficient descent phase parameters for a future flight of the aircraft based on the data received from one or more previous flights of the aircraft. (Gomez paragraph 0022 teaches, "the present disclosure resides in an aircraft management system for use in managing aircraft flying continuous descent approaches into an airport, wherein the system is arranged: to determine aircraft types expected to fly into the airport; to determine, for each aircraft type, an optimum coefficient of lift that provides maximum predictability in the time to fly the continuous descent approach; and to calculate a common ground speed to be flown by the aircraft at the top of descent of their continuous descent approaches, wherein the common ground speed is calculated using the optimum coefficients of lift determined for the aircraft types." Where the FMS is able to store previous descents.)
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
As per claim 7, The system of claim 1, wherein the control unit is configured to determine the efficient descent phase parameters by generating one or more descent phase 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 descent 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 descent 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 descent phase of flight, the method comprising:
receiving, by a control unit, data regarding one or both of the descent phase of a current flight or the descent 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 descent phase parameters for the aircraft based on the data, wherein the aircraft is operated during the descent phase of one or both of the current flight or one or more future flights according to the efficient descent phase parameters; (Gomez paragraph 0011 teaches, "As the optimum aerodynamic flight path angle is likely to vary for any particular aircraft type, and may even vary for different models within that type, the aircraft navigation system is preferably arranged to calculate the aerodynamic flight path angle with reference to the type of the aircraft. Further parameters are also likely to be used when calculating the aerodynamic flight path angle. Optionally, the aircraft navigation system is arranged to calculate the aerodynamic flight path angle with reference to any of the weight of the aircraft (preferably a value corresponding to the weight at the top of descent), the expected wind and wind gradient and the expected atmospheric conditions." And paragraph 0022 teaches, "the present disclosure resides in an aircraft management system for use in managing aircraft flying continuous descent approaches into an airport, wherein the system is arranged: to determine aircraft types expected to fly into the airport; to determine, for each aircraft type, an optimum coefficient of lift that provides maximum predictability in the time to fly the continuous descent approach; and to calculate a common ground speed to be flown by the aircraft at the top of descent of their continuous descent approaches, wherein the common ground speed is calculated using the optimum coefficients of lift determined for the aircraft types.") and
automatically operating, by the control unit, controls of the aircraft during the descent phase according to the efficient descent phase parameters. (Gomez paragraph 0017 teaches, "the aircraft navigation system may be part of an autopilot or may provide information to an autopilot such that the autopilot flies the continuous descent approach flight plan. In this sense, "guide" may mean provide the necessary instructions to the autopilot or it may mean the actual flying of the aircraft.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho docs not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
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 (Gomez paragraph 0051 teaches, "These functions or tables may be available for use by the aircraft's computers,")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
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 (Gomez paragraph 0051 teaches, "These functions or tables may be available for use by the aircraft's computers,")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
As per claim 15, The method of claim 11, wherein said determining comprises determining the efficient descent phase parameters for a future flight of the aircraft based on the data received from one or more previous flights of the aircraft. (Gomez paragraph 0022 teaches, "the present disclosure resides in an aircraft management system for use in managing aircraft flying continuous descent approaches into an airport, wherein the system is arranged: to determine aircraft types expected to fly into the airport; to determine, for each aircraft type, an optimum coefficient of lift that provides maximum predictability in the time to fly the continuous descent approach; and to calculate a common ground speed to be flown by the aircraft at the top of descent of their continuous descent approaches, wherein the common ground speed is calculated using the optimum coefficients of lift determined for the aircraft types." Where the FMS is able to store previous descents.)
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
As per claim 16, The method of claim 11, wherein said determining comprises determining the efficient descent phase parameters by generating one or more descent phase 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 descent 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 descent 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 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.")
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:
receiving data regarding one or both of the descent phase of a current flight or the descent 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 descent phase parameters for the aircraft based on the data, wherein the aircraft is operated during a descent phase of one or both of the current flight or one or more future flights according to the efficient descent phase parameters; (Gomez paragraph 0011 teaches, "As the optimum aerodynamic flight path angle is likely to vary for any particular aircraft type, and may even vary for different models within that type, the aircraft navigation system is preferably arranged to calculate the aerodynamic flight path angle with reference to the type of the aircraft. Further parameters are also likely to be used when calculating the aerodynamic flight path angle. Optionally, the aircraft navigation system is arranged to calculate the aerodynamic flight path angle with reference to any of the weight of the aircraft (preferably a value corresponding to the weight at the top of descent), the expected wind and wind gradient and the expected atmospheric conditions." And paragraph 0022 teaches, "the present disclosure resides in an aircraft management system for use in managing aircraft flying continuous descent approaches into an airport, wherein the system is arranged: to determine aircraft types expected to fly into the airport; to determine, for each aircraft type, an optimum coefficient of lift that provides maximum predictability in the time to fly the continuous descent approach; and to calculate a common ground speed to be flown by the aircraft at the top of descent of their continuous descent approaches, wherein the common ground speed is calculated using the optimum coefficients of lift determined for the aircraft types.") and
automatically operating, by a control unit, controls of the aircraft during the descent phase according to the efficient descent phase parameters. (Gomez paragraph 0017 teaches, "the aircraft navigation system may be part of an autopilot or may provide information to an autopilot such that the autopilot flies the continuous descent approach flight plan. In this sense, "guide" may mean provide the necessary instructions to the autopilot or it may mean the actual flying of the aircraft.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
As per claim 21, The system of claim 1, wherein the descent phase parameters include at least one of airspeed, rate of descent, or time of descent. (Gomez paragraph 0044 teaches, "The control laws considered are constant airspeed, constant rate of descent, constant geometric flight path angle and constant aerodynamic flight path angle.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
As per claim 22, The method of claim 11, wherein the descent phase parameters include at least one of airspeed, rate of descent, or time of descent. (Gomez paragraph 0044 teaches, "The control laws considered are constant airspeed, constant rate of descent, constant geometric flight path angle and constant aerodynamic flight path angle.")
Carvalho discloses a neural network trained to estimate aircraft air data. Carvalho does not disclose an operation to ensure efficient aircraft descent. Gomez teaches operations to ensure efficient aircraft descent. Therefore, at the time of filing it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Gomez et.al. into the invention of Carvalho. Such incorporation is motivated by the need to ensure descent of an aircraft.
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 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.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TYLER D PAIGE/Primary Examiner, Art Unit 3664
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