DETAILED ACTION
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
Applicant's arguments filed 06/23/2025 have been fully considered but they are not persuasive. Note that in view of applicant’s amendments, the previously indicated claim objections due to informalities have been withdrawn.
With respect to applicant’s arguments concerning the applicability of the prior art reference CN 114118537 A (Wan) to the amended independent claims 1, 10 and 19 (and notwithstanding that there is no specific/particular region of travel claimed in terms of there being a clear distinction between the at least one region of travel per Wan and the “one or more” regions of travel claimed), note that Wan is not being relied upon in rejecting the amended independent claims, and as such, applicant’s arguments concerning the applicability of Wan to the amended independent claims are rendered moot.
Furthermore, while the examiner agrees with applicant in that the prior art reference US 20110046818 A1 (Herkes) does not explicitly or expressly discuss the building and training of a machine learning model based on the data correlations per Herkes [e.g., the one or more correlations between the predicted greenhouse gas emissions data and the one or more regions of travel], the building of a machine learning model using a machine learning algorithm trained on known and/or established data correlations for future use (or to make future predictions) is a routine and/or conventional practice in the general field(s) of endeavor concerning computer systems [e.g., including computer systems mountable on aircraft as per Herkes], such that one of ordinary skill in the art routinely builds and trains machine learning models using known and/or established data correlations to accordingly improve the accuracy and/or efficiency of future predictions in a particular application, without exercising inventive skill [e.g., the application and/or utilization of generic machine learning on known and/or established data correlations for the same (or a substantially similar) purpose does not involve the exercise of inventive skill, and one of ordinary skill in the art would readily expect to yield an improvement in the accuracy and/or efficiency of future predictions via the application and/or utilization of generic machine learning on the known and/or established data correlations per Herkes] (implicit in view of well-known and/or basic engineering logic/principles pertaining to the application and/or utilization of generic machine learning models).
The above discussion(s) is/are similarly applicable to applicant’s remarks concerning the respective dependent claims [e.g., the remarks that the dependent claims are allowable based on Herkes not explicitly or expressly discussing the building and training of a machine learning model based on the data correlations per Herkes as required by the amended independent claims], further noting that Herkes or Herkes in view of Wan still fairly renders the subject matter of the respective dependent claims obvious [e.g., as discussed above, the building of a machine learning model using a machine learning algorithm trained on known and/or established data correlations for future use (or to make future predictions) is a routine and/or conventional practice in the general field(s) of endeavor concerning computer systems, and the additional limitations of the respective dependent claims 2-4, 6-7, 11-13 and 15-16 (e.g., the application of mathematical models and/or calculations to various flight data and/or data associated with the flight) are fairly rendered obvious when further considering the analogous prior art teachings per Wan]. See detailed rejection below.
Claim Rejections - 35 USC § 103
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.
Claims 1, 5, 8-10, 14 and 17-20 are rejected under 35 U.S.C. 103 as being obvious over US 20110046818 A1 (Herkes).
Regarding claims 1, 10 and 19, Herkes (Figures 1-7) teaches a method (100) [and commensurate system (200) and computer-readable storage medium (204)] for monitoring and predicting greenhouse emissions for a flight of an aircraft (see Fig. 1-2 in conjunction with abstract and paragraph [0001]), the method comprising:
accessing, using one or more processors in communication with a non-transitory computer-readable medium having executable instructions stored thereon [e.g., using a computer or computing platform], navigation and location data relating to the flight of the aircraft, including navigation and location data of the flight through one or more regions of travel (see Fig. 1-7 in conjunction with paragraphs [0025], [0027], [0031], [0049]) [e.g., “In block 104, a predetermined set of airplane flight data or key flight data to support predicting or forecasting noise and emissions levels of the airplane may be received or acquired by the noise and emissions monitor or monitoring system. Examples of the predetermined set of airplane flight data may include geographic location”]; [e.g., “The EFB may be a general purpose computer or computing platform intended to reduce, or replace, paper-based reference material formerly contained in a pilot's carry-on flight bag. Such materials may have included aircraft operating manual, aircrew operating manual, navigation charts including maps for air and ground operations and other materials for flight operations. The EFB includes electronic moving maps for ground and air operations. An example of an EFB is illustrated in FIGS. 3-7 including an example of an electronic moving map in Fig. 7”]; [e.g., “A global position system (GPS) 224, inertial measuring unit (IMU) or other device may provide geographic location information to the FMC 222 and EFB 220 for use in predicting and monitoring the noise and emissions generated by the airplane 205”];
obtaining fuel consumption data of the flight of the aircraft through the one or more regions of travel (see Fig. 1-2 in conjunction with paragraphs [0024], [0044]) [e.g., “Examples of the predetermined set of engine performance data may include flight data, fan speed, engine pressure ratio, fuel flow, operating and exhaust gas temperatures, and any other data or parameters that may be useful in predicting noise and emission levels”]; [e.g., “The emissions model 206 may determine nitrogen oxide (NO.sub.X) levels, sulfur oxide (SO.sub.X), particulate matter (PM.sub.XX), carbon dioxide CO.sub.2 levels or other pollutants and emissions as a function of engine parameters such as fuel flow, airspeed, fan speed, engine pressure ratio, operating and exhaust gas temperatures, and other parameters”]; [e.g., fuel flow is a measurement of the amount of fuel consumed per unit of time (fuel flow is calculated by dividing the total mass of fuel consumed by the time it took to consume it)];
converting the fuel consumption data into predicted greenhouse gas emissions data of the flight of the aircraft through the one or more regions of travel (see Fig. 1-2 in conjunction with paragraphs [0044], [0046]) [e.g., “The emissions model 206 may determine or model emissions based on engine parameters or engine data. The emissions model 206 may determine nitrogen oxide (NO.sub.X) levels, sulfur oxide (SO.sub.X), particulate matter (PM.sub.XX), carbon dioxide CO.sub.2 levels or other pollutants and emissions as a function of engine parameters such as fuel flow, airspeed, fan speed, engine pressure ratio, operating and exhaust gas temperatures, and other parameters. The emissions model 206 may receive emissions data and other supporting measurements 210”]; [e.g., reference at least blocks 212, 206, and 202 per Fig. 2];
comparing the predicted greenhouse gas emissions data of the aircraft with the navigation and location data to determine one or more correlations between the predicted greenhouse gas emissions data and the one or more regions of travel (see Fig. 1-2, 7 in conjunction with paragraphs [0033], [0037], [0039]-[0040], [0046], [0048]-[0050], [0053]) [e.g., “the emissions and noise models may be based on measured data that has been correlated with key parameters of the engine and airplane operation. Emissions data acquired from engine test cell data and flight test data may be correlated with relevant engine operating parameters and the relevant airplane operating parameters to project current local air quality pollutants and CO.sub.2 emissions”]; [e.g., “any preset limits may be applied to the predicted emissions levels and the actual measured emissions levels. Each of the predicted emissions levels and/or actual measured emissions levels may be compared to at least one preset emissions limit”]; [e.g., reference block 116 per Fig. 1]; and
outputting the one or more correlations to a display (226, 300, 400, 500, 600, 700) for monitoring the greenhouse gas emissions data of the aircraft (see Fig. 1-7 in conjunction with paragraphs [0037], [0039]-[0040], [0046], [0048]-[0050], [0058]-[0062], [0064]-[0065]) [e.g., “A display 226 and control devices or means 228 may be associated with the EFB 220 or a separate display and controls may be associated with the noise and control monitor module 202 for presenting the noise and emissions levels”]; [e.g., “The display 700 may also selectively present multiple metrics of noise and emissions data from multiple sources (predicted and measured) at multiple locations. The noise and emissions data may be color-coded for rapid identification of problem locations (for example: red for over limit, orange for within tolerance of limit, green for under limit). Selecting a monitoring location with use of the touch screen or pointer may expand a location tag 710, similar to that illustrated in FIG. 7, to show more detail such as noise and emissions predictions, measurements, limits, location name or other arbitrary data”]; [e.g., “Noise and emissions levels in areas below a preset highest level or below the cautionary range just below the highest preset level may be illustrated by green shading on those areas of the map. Similar to that previously described a noise or emissions area intensity field may be estimated from a sufficiently large number of discrete noise or emission prediction or monitoring sites. Iso-contours of noise or emissions, commonly referred to as "footprints or contours" may be generated from the intensity field information and presented on the map. Discrete ground based noise and emissions monitoring stations and their respective noise or emission prediction may also be presented”].
To the extent that Herkes provides for obtaining a fuel flow, and any other engine data or engine parameters that may be useful in predicting emission levels (see paragraphs [0024], [0044]), rather than explicitly providing for the claimed fuel consumption data [e.g., for the sake of argument that Herkes does not provide for obtaining fuel consumption data as claimed], it would have been obvious to one of ordinary skill in the art that the fuel flow per Herkes directly relates to fuel consumption [e.g., fuel flow is a measurement of the amount of fuel consumed per unit of time (fuel flow is calculated by dividing the total mass of fuel consumed by the time it took to consume it)], and/or that a fuel consumption would constitute engine data and/or an engine parameter that is reasonably suggested and/or encompassed by the recitation “and any other data or parameters that may be useful in predicting noise and emission levels” per Herkes [e.g., one of ordinary skill in the art would readily consider a fuel consumption as being useful in predicting emission levels, such that one of ordinary skill readily understands that the amount of emissions a vehicle emits is directly related to the amount of fuel the vehicle consumes]; [e.g., generally speaking, as fuel consumption increases, emissions increase].
Furthermore, Herkes fails to expressly teach outputting the predicted greenhouse gas emissions data to a guidance system of the aircraft to automatically maneuver the aircraft through the one or more regions of travel to reduce greenhouse gasses emitted by the aircraft during the flight.
However, Herkes teaches and/or suggests outputting (or presenting) the predicted greenhouse gas emissions data on a display to a pilot such that the pilot can accordingly take action [e.g., manual action] to substantially minimize an actual emissions level emitted by the aircraft during the flight (see Fig. 1 in conjunction with paragraphs [0037], [0058]) [e.g., “the predicted emissions level may be presented on a display to the pilot of the airplane to permit operation of the airplane to substantially minimize an actual noise and an actual emissions level at each of the plurality of ground based noise and emissions monitoring stations based on the flight profile and other engine and airplane parameters entered by the pilot to predict the noise and emissions levels”]; [e.g., “The pilot may then select the best or optimum flight path, engine settings, airplane configuration settings, and any other parameter settings to substantially minimize the actual noise and emissions levels at the different ground based monitoring stations”]; [e.g., “Any predicted levels or actual measured levels exceeding any preset limited may be identified or highlighted for easy recognition and awareness by the pilot for any corrective or mitigating actions that may be taken to reduce noise and emissions levels”].
In consideration that the only notable distinction between the invention(s) per Herkes and the claimed invention(s) is with respect to an obvious matter of automating the otherwise manual input(s)/action(s) by the pilot to accomplish the same result(s) of reducing emissions [e.g., having a guidance system programmed to utilize predicted emissions data to autonomously maneuver the aircraft to reduce emissions as opposed to having the pilot utilize predicted emissions data to manually maneuver the aircraft to reduce emissions], it would have been obvious to one of ordinary skill in the art and/or merely involve routine skill in the art to additionally (or alternatively) output the predicted greenhouse gas emissions data per Herkes to a guidance system of the aircraft to automatically maneuver (or facilitate automatic maneuvering of) the aircraft through the one or more regions of travel to reduce greenhouse gasses emitted by the aircraft during the flight, so as to improve one or more of efficiency, accuracy, and speed via which the aircraft is maneuvered to reduce emissions (implicit in view of basic engineering logic/principles concerning the provision(s) of automating otherwise manual processes that rely on human effort and skills) (see MPEP 2144; III; Automating a Manual Activity) [e.g., “The court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art”]; [e.g., the claimed invention(s) is/are nonetheless utilizing the same (or substantially similar) prediction emissions data to select the best or optimal flight path, engine settings, etc. to accomplish the same result(s) of reducing and/or minimizing actual emissions, and the aforementioned distinction pertaining to the implementation of automation to replace a manual activity is not sufficient to distinguish over the prior art].
Lastly, Herkes fails to explicitly or expressly teach building a machine learning model using a machine learning algorithm trained with the one or more correlations between the predicted greenhouse gas emissions data and the one or more regions of travel, such that the machine learning model is useable to predict future greenhouse gas emissions data for a future flight of the aircraft through the one or more regions of travel.
However, the building of a machine learning model using a machine learning algorithm trained on known and/or established data correlations [e.g., the one or more correlations between the predicted greenhouse gas emissions data and the one or more regions of travel per Herkes] for future use (or to make future predictions) is a routine and/or conventional practice in the general field(s) of endeavor concerning computer systems [e.g., including computer systems mountable on aircraft as per Herkes], such that one of ordinary skill in the art routinely builds and trains machine learning models using known and/or established data correlations to accordingly improve the accuracy and/or efficiency of future predictions in a particular application, without exercising inventive skill [e.g., the application and/or utilization of generic machine learning on known and/or established data correlations for the same (or a substantially similar) purpose does not involve the exercise of inventive skill, and one of ordinary skill in the art would readily expect to yield an improvement in the accuracy and/or efficiency of future predictions via the application and/or utilization of generic machine learning on the known and/or established data correlations per Herkes] (implicit in view of well-known and/or basic engineering logic/principles pertaining to the application and/or utilization of generic machine learning models).
Regarding claims 5 and 14, Herkes teaches the invention as claimed and as discussed above. Herkes (Figures 1-7) further teaches (at least implicitly) wherein obtaining the fuel consumption data of the flight of the aircraft includes obtaining the fuel consumption data directly from the aircraft (see Fig. 1-2 in conjunction with paragraphs [0024], [0044]) [e.g., reference block 102 per Fig. 1 and/or block 212 per Fig. 2]. Also refer to discussion per claims 1, 10 and 19.
Regarding claims 8, 17 and 20, Herkes teaches the invention as claimed and as discussed above. Herkes (Figures 1-7) further teaches wherein the prediction(s) is/are output to the display for use by a user [e.g., a pilot] to make decisions about altering the current or future flight of the aircraft, wherein the emissions predictions may be based on math models, algorithms, and/or similar mathematical representations, and wherein the emissions predictions may be based on and/or correlated with relevant engine operating parameters and/or relevant airplane operating parameters (see Fig. 1-7 in conjunction with abstract and paragraphs [0031]-[0035], [0037], [0039]-[0040], [0046], [0048]-[0050], [0058]-[0062], [0064]-[0065]) [e.g., “The method may also include presenting at least one of the predicted noise level and the predicted emissions level on a display to a pilot of the airplane to permit operation of the airplane to substantially minimize at least one of an actual noise level and an actual emissions level at each of the plurality of ground based noise and emissions monitoring stations”]; [e.g., “The method 100 or system may predict different noise levels and emissions levels at the different ground based monitoring stations based on different parameters as described above being entered by the pilot and the prediction results may present to the pilot as described herein to assist the pilot in selecting the best or optimum flight path, engine settings, airplane configuration, and any other parameter settings”]; [e.g., “A display 226 and control devices or means 228 may be associated with the EFB 220 or a separate display and controls may be associated with the noise and control monitor module 202 for presenting the noise and emissions levels”]; [e.g., “The noise and emissions monitor or system predictions may be based on math models, algorithms or similar mathematical representations which are semi-empirical in nature for computational speed and accuracy. Both the emissions and noise models may be based on measured data that has been correlated with key parameters of the engine and airplane operation”].
Herkes fails to explicitly or expressly teach wherein the aforementioned output(s) is/are provided during a future flight of the aircraft, since Herkes fails to explicitly or expressly teach building a machine learning model using a machine learning algorithm trained with the one or more correlations between the predicted greenhouse gas emissions data and the one or more regions of travel, such that the machine learning model is useable to predict future greenhouse gas emissions data for a future flight of the aircraft through the one or more regions of travel.
However, the building of a machine learning model using a machine learning algorithm trained on known and/or established data correlations [e.g., the one or more correlations between the predicted greenhouse gas emissions data and the one or more regions of travel per Herkes] for future use (or to make future predictions) is a routine and/or conventional practice in the general field(s) of endeavor concerning computer systems [e.g., including computer systems mountable on aircraft as per Herkes], such that one of ordinary skill in the art routinely builds and trains machine learning models using known and/or established data correlations to accordingly improve the accuracy and/or efficiency of future predictions in a particular application, without exercising inventive skill [e.g., the application and/or utilization of generic machine learning on known and/or established data correlations for the same (or a substantially similar) purpose does not involve the exercise of inventive skill, and one of ordinary skill in the art would readily expect to yield an improvement in the accuracy and/or efficiency of future predictions via the application and/or utilization of generic machine learning on the known and/or established data correlations per Herkes] (implicit in view of well-known and/or basic engineering logic/principles pertaining to the application and/or utilization of generic machine learning models). Also refer to discussion per claims 1, 10 and 19.
Regarding claims 9 and 18, Herkes teaches the invention as claimed and as discussed above. Herkes (Figures 1-7) further teaches (at least implicitly) wherein the one or more correlations characterize quantities of the greenhouse gasses emitted by the aircraft over a given area of the one or more regions of travel (see Fig. 1-2, 7 in conjunction with paragraphs [0031], [0044]) [e.g., “Emissions may be calculated as total quantities released, commonly referred to as an emissions inventory, or estimated at a location after dispersion”]; [e.g., “The emissions model 206 may determine or model emissions based on engine parameters or engine data. The emissions model 206 may determine nitrogen oxide (NO.sub.X) levels, sulfur oxide (SO.sub.X), particulate matter (PM.sub.XX), carbon dioxide CO.sub.2 levels or other pollutants and emissions as a function of engine parameters such as fuel flow, airspeed, fan speed, engine pressure ratio, operating and exhaust gas temperatures, and other parameters”]. Also refer to discussion per claims 1, 10 and 19.
Claims 2-4, 6-7, 11-13 and 15-16 are rejected under 35 U.S.C. 103 as being obvious over US 20110046818 A1 (Herkes) in view of CN 114118537 A (Wan).
Regarding claims 2-4, 6-7, 11-13 and 15-16, Herkes teaches the invention as claimed and as discussed above. Herkes fails to expressly teach wherein obtaining the fuel consumption data of the flight of the aircraft includes calculating the fuel consumption of the flight using mathematical models based on the location and navigation data, aircraft type and configuration, and weather data (or flight parameter data such as flight altitude and climatic conditions surrounding the aircraft), and wherein obtaining the fuel consumption data of the flight of the aircraft includes obtaining flight parameter data, and such that converting the fuel consumption data includes applying a mathematical model to at least one of the fuel consumption data and flight parameter data [e.g., per Herkes, the fuel consumption data is obtained directly via engine performance data and/or parameters]; [e.g., reference block 102 per Fig. 1 and/or block 212 per Fig. 2].
Note that Herkes does explicitly provide that the emissions monitor or system predictions may be based on math models, algorithms, or similar mathematical representations which are semi-empirical in nature for computational speed and accuracy (see paragraph [0033]) [e.g., “The noise and emissions monitor or system predictions may be based on math models, algorithms or similar mathematical representations which are semi-empirical in nature for computational speed and accuracy”]; [e.g., while not explicitly stated, Herkes at least suggests the possibility of (and compatibility for) using and/or applying mathematical models accordingly (or as claimed)].
However, Wan teaches an analogous prediction method for airspace flight emissions (see paragraph [0001]), and wherein the provision(s) and/or alternative(s) of calculating and/or converting the corresponding fuel consumption data via mathematical models is/are well-known in the same general field(s) of endeavor concerning methods/systems for predicting aircraft emissions, utilizing fuel data to predict aircraft emissions, etc. (see paragraphs [0007]-[0015], [0025], [0027], [0029], [0032]) [e.g., noting that paragraph [0011] provides for wherein the flight information used to calculate the fuel consumption rate includes a position/trajectory and/or flight plan data and meteorological/weather data]; [e.g., further noting that paragraph [0032] provides for wherein the calculation of the fuel consumption rate accounts for flight parameter data such as flight altitude and climatic conditions surrounding the aircraft, aircraft type, etc.].
As such, it would have been obvious to one of ordinary skill in the art and/or merely involve routine skill in the art to additionally and/or alternatively obtain the fuel flow/fuel consumption data per Herkes via the use and/or application of mathematical models as claimed, as suggested by Wan, so as to improve (or further improve) the accuracy and/or effectiveness of the predictions of emissions and/or improve prediction performance (see paragraphs [0057], [0133]) [e.g., via applying the teachings per Wan, the predictions per Herkes can be based on even more relevant data pertaining to the aircraft and/or flight (data directly obtained and obtained via the application of mathematical models), thereby resulting in more informed predictions]; [e.g., again noting that paragraph [0033] per Herkes similarly suggests wherein the emissions monitor or system predictions may be based on math models, algorithms, or similar mathematical representations which are semi-empirical in nature for computational speed and accuracy].
Additionally note that the aforementioned modification (or alternative) constitutes the application and/or combination of well-known analogous prior art elements/techniques in such a way as to yield highly predictable results [e.g., in consideration that Herkes and Wan are both relevant to at least the same general field(s) of endeavor concerning methods/systems for predicting aircraft emissions, utilizing fuel data to predict aircraft emissions, etc., there would be no unexpected result(s)/effect(s) yielded via accordingly applying the teachings per Wan to the method/system per Herkes, and similarly, one of ordinary skill can readily select from various well-known configurations based on certain factors concerning the particular application (cost(s) associated with utilizing mathematical models/additional computational resources, prediction accuracy and/or computational speed requirements, etc.), without exercising inventive skill]. Also refer to discussion per claims 1, 10 and 19.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTHONY D TAYLOR JR whose telephone number is (469)295-9192. The examiner can normally be reached Mon-Fri 9a-5p (central time).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Logan Kraft can be reached at 571-270-5065. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ANTHONY DONALD TAYLOR JR./Examiner, Art Unit 3747
/KURT PHILIP LIETHEN/Primary Examiner, Art Unit 3747