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 .
Status of the Claims
Claims 1-20 are all the claims pending in the application.
Claims 1, 4, 14, and 20 are amended.
Claims 1-20 are rejected.
The following is a Final Office Action in response to amendments and remarks filed Nov. 24, 2025.
Response to Arguments
Regarding the 103 rejections, the rejections are withdrawn because the cited references do not teach all the newly amendments limitations.
In response to arguments in reference to any depending claims that have not been individually addressed or amended, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the other dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
Regarding the 112(b) rejections, the rejections are withdrawn in light of the amendments to the claims. Examiner notes, the claims now require the candidates changes include a range of parameter values and a number of refueling stations.
Regarding the 101 rejections, the rejections are maintained for the following reasons. Applicant asserts the human mind is not capable of modifying parameters of airport baseline data, as claimed, similarly to Example 39. Examiner respectfully does not find this assertion persuasive because the human mind is capable of modifying parameters of airport baseline data (e.g., assessing different types and blends of biofuel, different layouts of the airport, different equipment types, etc.).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention. Applying Step 1 to the claims it is determined that: claims 1-13 are directed to a process; and claims 14-20 are directed to a machine. Therefore, we proceed to Step 2.
Independent Claims
Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter that amount to a judicial exception to patentability.
The independent claims recite an abstract idea. Specifically, independent claim 1 recites an abstract idea in the limitations (emphasized):
…obtaining airport baseline data associated with an airport, wherein the airport baseline data is descriptive of operational characteristics of the airport, infrastructure of the airport, or both;
modifying two or more first parameters of the airport baseline data to generate first candidate modification data indicative of a modification to fuel source availability at the airport, wherein the first candidate modification data comprises two or more sets of candidate changes to two or more different parameters of the two or more first parameters, wherein each set of the two or more sets of candidate changes corresponds to a respective range of parameter values for a respective parameter of the two or more different parameters, wherein a first set of candidate changes includes a first number of refueling stations of a first fuel type, wherein a second set of candidate changes includes a second number of refueling stations of the first fuel type and a third number of refueling stations of a second fuel type at a second location, and wherein the first number is distinct from the second number;
instantiating multiple copies of a trained machine learning model based on a number of the two or more sets of candidate changes;
providing first model input data based on the first candidate modification data as input to the multiple copies of the trained machine learning model to generate first forecast data indicating a predicted result of the modification to the fuel source availability, wherein providing the first model input data as the input to the multiple copies of the trained machine learning model comprises providing each set of the two or more sets of candidate changes to a respective trained machine learning model of the multiple copies of the trained machine learning model, and wherein the first forecast data includes a predicted value of an environmental impact metric associated with operations at the airport if the modification occurs;
comparing the first forecast data, including the predicted value of the environmental impact metric to one or more target values;
and generating a notification in response to the first forecast data failing to satisfy the one or more target values.
These limitations recite an abstract idea because these limitations encompass a mental process. These limitations encompass a mental process because these limitations encompass observation, evaluation, judgment, and opinion. That is, these limitations encompass observation (obtaining airport baseline data), evaluation and judgment (modifying fuels source availability to generate a forecast), and opinion (comparing the forecast to target values and generating a notification) essentially because these limitations encompass modeling an airport and forecasting effects of changing aspects of the refueling process used at the airport. Claims that encompass a mental process recite an abstract idea. Claims 1, 14 and 20 recite an abstract idea.
Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional elements that integrate the abstract idea into a practical application.
The additional elements of the independent claims do not integrate the abstract idea into a practical application. Claim 1 recites the additional elements (emphasized):
…obtaining airport baseline data associated with an airport, wherein the airport baseline data is descriptive of operational characteristics of the airport, infrastructure of the airport, or both;
modifying two or more first parameters of the airport baseline data to generate first candidate modification data indicative of a modification to fuel source availability at the airport, wherein the first candidate modification data comprises two or more sets of candidate changes to two or more different parameters of the two or more first parameters, wherein each set of the two or more sets of candidate changes corresponds to a respective range of parameter values for a respective parameter of the two or more different parameters, wherein a first set of candidate changes includes a first number of refueling stations of a first fuel type, wherein a second set of candidate changes includes a second number of refueling stations of the first fuel type and a third number of refueling stations of a second fuel type at a second location, and wherein the first number is distinct from the second number;
instantiating multiple copies of a trained machine learning model based on a number of the two or more sets of candidate changes;
providing first model input data based on the first candidate modification data as input to the multiple copies of the trained machine learning model to generate first forecast data indicating a predicted result of the modification to the fuel source availability, wherein providing the first model input data as the input to the multiple copies of the trained machine learning model comprises providing each set of the two or more sets of candidate changes to a respective trained machine learning model of the multiple copies of the trained machine learning model, and wherein the first forecast data includes a predicted value of an environmental impact metric associated with operations at the airport if the modification occurs;
comparing the first forecast data, including the predicted value of the environmental impact metric to one or more target values;
and generating a notification in response to the first forecast data failing to satisfy the one or more target values.
These limitations recite an abstract idea because instantiating multiple copies of a trained machine learning model and providing input data to the copies is recited too broadly and too generally to be more than mere instructions to apply the exception, see MPEP 2106.05(f). Claim 1 is directed to an abstract idea.
Claims 14 and 20 recite similar additional elements as claim 1 and further recite "one or more processors" and a "non-transitory computer-readable storage device storing instructions", respectively. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, claims 14 and 20 are directed to an abstract idea for similar reasons as claim 1.
Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept).
The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claims 1, 14 and 20 are not patent eligible.
Dependent Claims
The dependent claims are rejected under 35 USC 101 as directed to an abstract idea for the following reasons.
Claims 2, 3, 5, 15 and 16 are directed to the same abstract idea as the independent claims because claims 2, 3, 5, 15 and 16 further specify what data is being collected but these types of data are still only a part of observing the airport to create a model of it.
Claim 4 recites the same abstract idea as the independent claims because analyzing fuel blends and recharging stations, as claimed, are only further specifying what kinds of changes are being considered which is still only a part of modeling the airport.
Claim 6 essentially recites repeating the steps of the independent claims but for a second parameter. Accordingly, claim 6 is rejected under 35 USC 101 as directed to an abstract idea for essentially the same reasons as the independent claims.
Claim 7 recites the same abstract idea as the independent claims because generating a list of viable modifications, as claimed, is a part of modeling an airport and forecasting effects of changing fuel availability of the airport.
Claims 8-11 and 18 recite the same abstract idea as the independent claims because analyzing and forecasting an environmental impact, as claimed, is a part of modeling an airport and forecasting effects of changing fuel availability of the airport.
Claim 12 recites the same abstract idea as the independent claims because the target values being a sustainable fuel utilization value is a part of modeling an airport and forecasting effects of changing fuel availability of the airport.
Claims 13 and 19 recite the same abstract idea as the independent claims because modifying fuel sources, as claimed, is a part of modeling an airport and forecasting effects of changing fuel availability of the airport.
Claim 17 recites the additional elements of obtaining the data from a database. These additional elements do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer function of receiving data (e.g., receiving data over a network), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Monshi, Samira, et al. "An Analysis of Alternative Jet Fuel Supply for Manassas Regional Airport." 2013, herein referred to as “Monshi”, in view of Russo US Pub. No. 2024/0094016, herein referred to as “Russo”; further in view of Rudow et al, US Pub. No. 2011/0184784, herein referred to as “Rudow”; further in view of Trainelli, Lorenzo, et al. "Optimal sizing and operation of airport infrastructures in support of electric-powered aviation." Aerospace 8.2 (2021): 40, herein referred to as “Trainelli”.
Regarding claim 1, Monshi teaches:
obtaining airport baseline data associated with an airport, wherein the airport baseline data is descriptive of operational characteristics of the airport, infrastructure of the airport, or both (obtained yearly fuel flowage and information on fuel capacity and construction of the airport, pgs. 12-13, Tbl. 1, and Fig. 1; see also pgs. 22-23 discussing jet fuel demand);
modifying two or more first parameters of the airport baseline data to generate first candidate modification data indicative of a modification to fuel source availability at the airport (analyzes three options for using biofuel and continuing to use conventional jet fuel, pgs. 6-7 and Summary of the Analysis Results; see also e.g., pgs. 27-28, 38-41 discussing economic analyses);
wherein the first candidate modification data comprises two or more sets of candidate changes to two or more different parameters of the two or more first parameters (analyzes three options for using biofuel, drop-in delivery, on-site blending, and on-site production, pgs. 6-7 and Summary of the Analysis Results)
and wherein each set of the two or more sets of candidate changes corresponds to a respective range of parameter values for a respective parameter of the two or more different parameters (use of biofuel ranges from 0%, i.e., continuing to use conventional jet fuel, to some biofuel, i.e., more than 0%, pgs. 6-7 and Summary of the Analysis Results; see also pgs. 10-11 discussing ranges of approved biofuel and the highest being 50%);
wherein a first set of candidate changes includes a first number of refueling stations of a first fuel type (fuel distributor, pgs. 13-14 and the three options for using biofuel and continuing to use conventional jet fuel, pgs. 6-7)
generate first forecast data indicating a predicted result of modification of the one or more first parameters, wherein the first forecast data includes a predicted value associated with operations at the airport if the modification occurs (evaluates the financial viability of each option by comparing each option’s cost over a 20 year period, pgs. 6-7 and Summary of the Analysis Results; see also pg. 43 showing results);
comparing the first forecast data to one or more target values (compares each option’s cost over a 20 year period to the “Do-Nothing” option, pgs. 6-7, 43, Summary of the Analysis Results);
and generating a notification in response to the first forecast data failing to satisfy the one or more target values (report shows all three options are too expensive, pgs. 6-7).
However Monshi does not teach but Russo does teach:
instantiating a trained machine learning model (trains machine learning model to determine carbon emissions, e.g. ¶[0041] and Fig. 1),
providing first model input data based on the first candidate modification data as input to the trained machine learning model to (trains and uses machine learning model to determine carbon emissions, e.g. ¶[0041] and Fig. 1).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the machine learning based analysis of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the analysis of Monshi would likely be improved by employing machine learning techniques (e.g., when there is sufficient data to benefit from the use of machine learning) and accordingly would have modified Monshi to use machine learning, e.g., as taught by Russo.
However the combination of Monshi and Russo does not explicitly teach
instantiating multiple copies of a trained machine learning model based on a number of the two or more sets of candidate changes;
wherein providing the first model input data as the input to the multiple copies of the trained machine learning model comprises providing each set of the two or more sets of candidate changes to a respective trained machine learning model of the multiple copies of the trained machine learning model.
Nevertheless, it would have been obvious, before the effective filing date of the claimed invention, to instantiate multiple copies of the machine learning model and provide the candidate changes to respective copies, as claimed, because it is proper to take into account not only specific teachings of a reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom, see MPEP 2144.01. That is, Russo teaches training and using a machine learning model to determine carbon emissions, e.g. ¶[0041] and Fig. 1, and that any step in the disclosure may be performed in parallel including simultaneously performing a step two or more times using two or more parallel threads suitable for division of tasks, ¶[0016]. Based on those disclosures, one skilled in the art would infer that Russo contemplates training and using multiple machine model in parallel to analyze the various scenarios, i.e., similarly to the parallel use of machine learning models in the present Application, see ¶[0035] of the Specification as filed.
However the combination of Monshi and Russo does not teach but Rudow does teach:
wherein the first forecast data includes a predicted value of an environmental impact metric associated with operations (tracks project carbon emission values, ¶¶[0047], [0175]; see also e.g., ¶¶[0075], [0154] discussing estimating carbon output; and e.g., ¶¶[0011], [0041] discussing modifications to reduce carbon emissions)
comparing the first forecast data, including the predicted value of the environmental impact metric, to one or more target values (compares project carbon emission values with target values, ¶¶[0047], [0177])
generating a notification in response to the first forecast data failing to satisfy the one or more target values (determines project carbon emissions exceed target and provides report, ¶¶[0179], [0181] and provides alert, ¶[0163]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the machine learning based analysis of alternative jet fuel of Monshi and Russo with the carbon footprint tracking of Rudow because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Monshi would likely not only be interested in the costs associated with bio-based alternative jet fuel options but would also be interested in potential carbon emissions reductions associated with bio-based alternative jet fuel options and accordingly would have modified the combination Monshi and Russo to also analyze carbon emissions, e.g., as taught by Rudow.
However the combination of Monshi, Russo, and Rudow does not teach but Trainelli does teach:
wherein a second set of candidate changes includes a second number of refueling stations of the first fuel type and a third number of refueling stations of a second fuel type at a second location, and wherein the first number is distinct from the second number (analyzes two types of recharging processes, Battery Plug-in Chargers (BPCs) and Battery Swapping Stations (BSSs), pg. 2 of 29, and determines optimal values of the number of needed charging devices and the number of necessary batteries, pgs. 3-4 of 29; see also Eqn. 6, pg. 5 discussing units of BPCs; Eqn. 7, pgs. 5-6 discussing determining number of batteries).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the machine learning based analysis of alternative jet fuel with the carbon footprint tracking of Monshi, Russo and Rudow with the analysis of electric-powered aviation of Trainelli because Trainelli explicitly suggests such analysis will be needed to fulfill long-term reduction emission goals, pg. 1; see also MPEP 2143.I.G.
Regarding claim 2, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 1 and Monshi further teaches:
wherein the first model input data also includes fixed airport data, wherein the fixed airport data is descriptive of geophysical characteristics of the airport, regulations governing the airport, procedures associated with the airport, weather characteristics of the airport, or a combination thereof (considered national and state environmental impact requirements, pgs. 35-38).
Regarding claim 3, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 2 and Monshi further teaches:
wherein the geophysical characteristics include a location of the airport, an altitude of the airport, geography of the airport, geography of an area surrounding the airport, or a combination thereof (considered creek surrounding airport, pgs. 37-38).
Regarding claim 4, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 2 and Monshi further teaches:
wherein the first fuel type includes a fuel blend (analyzes three options for using biofuel, drop-in delivery, on-site blending, and on-site production, pgs. 6-7 and Summary of the Analysis Results)
However Monshi does not teach but Trainelli does teach:
are wherein the second fuel type includes electricity, and wherein refueling stations corresponding to the second fuel type include recharging connectors (analyzes two types of recharging processes, Battery Plug-in Chargers (BPCs) and Battery Swapping Stations (BSSs), pg. 2 of 29).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the machine learning based analysis of alternative jet fuel with the carbon footprint tracking of Monshi, Russo and Rudow with the analysis of electric-powered aviation of Trainelli because Trainelli explicitly suggests such analysis will be needed to fulfill long-term reduction emission goals, pg. 1; see also MPEP 2143.I.G.
Regarding claim 5, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 1 and Monshi further teaches:
wherein the operational characteristics of the airport include one or more of operational configuration data, traffic condition data, or both (data includes operational and traffic data, e.g. pgs. 6, 22-23).
Regarding claim 6, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 1 and Monshi further teaches:
modifying one or more second parameters of the airport baseline data to generate second candidate modification data indicative of a second modification to the fuel source availability at the airport, wherein the second candidate modification data is different from the first candidate modification data (analyzes three options for using biofuel and continuing to use conventional jet fuel, pgs. 6-7 and Summary of the Analysis Results; see also e.g., pgs. 27-28, 38-41 discussing economic analyses);
providing second model input data based on the second candidate modification data as input to generate second forecast data indicating a predicted result of the second modification of the fuel source availability (evaluates the financial viability of each option by comparing each option’s cost over a 20 year period, pgs. 6-7 and Summary of the Analysis Results; see also pg. 43 showing results);
and comparing the second forecast data to one or more target values (compares each option’s cost over a 20 year period to the “Do-Nothing” option, pgs. 6-7, 43, Summary of the Analysis Results).
However Monshi does not teach but Russo does teach:
iteratively (steps are performed iteratively and/or recursively, ¶[0016])
the trained machine learning model to (trains and uses machine learning model to determine carbon emissions, e.g. ¶[0041] and Fig. 1).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the machine learning based analysis of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the analysis of Monshi would likely be improved by employing machine learning techniques (e.g., when there is sufficient data to benefit from the use of machine learning) and accordingly would have modified Monshi to use machine learning, e.g., as taught by Russo.
However the combination of Monshi, Russo and Rudow does not explicitly teach:
wherein the second forecast data indicates a second predicted value of the environmental impact metric if the second modification occurs
and comparing the second forecast data, including the second predicted value of the environmental impact metric, to the one or more target values.
Nevertheless, it would have been obvious before the effective filing date of the claimed invention to generate second forecast data and compare it to the target values because duplication of parts is obvious unless a new and unexpected result is produced, see MPEP 2144.04.VI.B. That is, Rudow teaches comparing carbon emission forecasts to targets, e.g., ¶¶[0047], [0177]. Examiner finds no reason repeating this process for various types of fuel would produce new or unexpected results.
Regarding claim 7, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 6 and Monshi further teaches:
generating a list of viable modifications, wherein each viable modification of the list of viable modifications corresponds to candidate modification data associated with forecast data that satisfies the one or more target values (identifies various scenarios where the three options would be financially viable, pgs. 51).
However Monshi does not teach but Russo does teach:
after a plurality of iterations (steps are performed iteratively and/or recursively, ¶[0016])
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the machine learning based analysis of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the analysis of Monshi would likely be improved by employing machine learning techniques (e.g., when there is sufficient data to benefit from the use of machine learning) and accordingly would have modified Monshi to use machine learning, e.g., as taught by Russo.
Regarding claim 8, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 1 and Russo further teaches:
wherein the operational characteristics of the airport described by the airport baseline data include an estimated or measured value of an environmental impact metric associated with operations at the airport (training data includes carbon emissions data, ¶[0041]; see also e.g., ¶[0025] discussing receiving transportation data).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the machine learning based analysis of emissions of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized stakeholders involved in an analysis of alternative jet fuels would likely also be interested in the environmental impacts of changing fuel types and accordingly would have modified Monshi to also analyze the carbon emissions of changing fuel types.
Regarding claim 9, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 8 and Russo further teaches:
wherein the environmental impact metric includes emissions of one or more chemicals of interest (machine learning model produces qualitative or quantitative measurement of carbon emissions produced by a transport, ¶[0038]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the machine learning based analysis of emissions of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized stakeholders involved in an analysis of alternative jet fuels would likely also be interested in the environmental impacts of changing fuel types and accordingly would have modified Monshi to also analyze the carbon emissions of changing fuel types.
Regarding claim 10, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 8 and Russo further teaches:
wherein the environmental impact metric includes carbon dioxide emissions, carbon dioxide equivalent emissions, or both (machine learning model produces qualitative or quantitative measurement of carbon emissions produced by a transport, ¶[0038]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the machine learning based analysis of emissions of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized stakeholders involved in an analysis of alternative jet fuels would likely also be interested in the environmental impacts of changing fuel types and accordingly would have modified Monshi to also analyze the carbon emissions of changing fuel types.
Regarding claim 11, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 8 and Russo further teaches:
wherein the one or more target values include a target value of the environmental impact metric (compares emissions metric to threshold, ¶[0067]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the machine learning based analysis of emissions of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized stakeholders involved in an analysis of alternative jet fuels would likely also be interested in the environmental impacts of changing fuel types and accordingly would have modified Monshi to also analyze the carbon emissions of changing fuel types.
Regarding claim 12, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 1 and Monshi further teaches:
wherein the one or more target values include a sustainable fuel utilization value (analyzes three options for using biofuel and continuing to use conventional jet fuel, pgs. 6-7 and Summary of the Analysis Results).
Regarding claim 13, the combination of Monshi, Russo, Rudow and Trainelli teaches all the limitations of claim 12 and Monshi further teaches:
wherein the modification to the fuel source availability at the airport includes making one or more sustainable fuels available at the airport, changing a sustainable fuel capacity at the airport, making one or more non-petroleum fuels available at the airport, changing a non-petroleum fuel capacity at the airport, or a combination thereof (analyzes three options for using biofuel and continuing to use conventional jet fuel, pgs. 6-7 and Summary of the Analysis Results).
Regarding claim 14, Monshi teaches:
obtain airport baseline data associated with an airport, wherein the airport baseline data is descriptive of operational characteristics of the airport, infrastructure of the airport, or both (obtained yearly fuel flowage and information on fuel capacity and construction of the airport, pgs. 12-13, Tbl. 1, and Fig. 1; see also pgs. 22-23 discussing jet fuel demand);
modify two or more first parameters of the airport baseline data to generate first candidate modification data indicative of a modification to fuel source availability at the airport (analyzes three options for using biofuel and continuing to use conventional jet fuel, pgs. 6-7 and Summary of the Analysis Results; see also e.g., pgs. 27-28, 38-41 discussing economic analyses);
wherein the first candidate modification data comprises two or more sets of candidate changes to two or more different parameters of the two or more first parameters (analyzes three options for using biofuel, drop-in delivery, on-site blending, and on-site production, pgs. 6-7 and Summary of the Analysis Results)
wherein each set of the two or more sets of candidate changes corresponds to a respective range of parameter values for a respective parameter of the two or more different parameters (use of biofuel ranges from 0%, i.e., continuing to use conventional jet fuel, to some biofuel, i.e., more than 0%, pgs. 6-7 and Summary of the Analysis Results; see also pgs. 10-11 discussing ranges of approved biofuel and the highest being 50%);
generate first forecast data indicating a predicted result of modification of the one or more first parameters, wherein the first forecast data includes a predicted value associated with operations at the airport if the modification occurs (evaluates the financial viability of each option by comparing each option’s cost over a 20 year period, pgs. 6-7 and Summary of the Analysis Results; see also pg. 43 showing results);
compare the first forecast data to one or more target values (compares each option’s cost over a 20 year period to the “Do-Nothing” option, pgs. 6-7, 43, Summary of the Analysis Results);
and generate a notification in response to the first forecast data failing to satisfy the one or more target values (report shows all three options are too expensive, pgs. 6-7).
However Monshi does not teach but Russo does teach:
one or more processors configured to (processor, e.g. ¶[0017] and Fig. 1)
instantiate a trained machine learning model (trains machine learning model to determine carbon emissions, e.g. ¶[0041] and Fig. 1),
provide first model input data based on the first candidate modification data as input to the trained machine learning model to (trains and uses machine learning model to determine carbon emissions, e.g. ¶[0041] and Fig. 1).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the machine learning based analysis of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the analysis of Monshi would likely be improved by employing machine learning techniques (e.g., when there is sufficient data to benefit from the use of machine learning) and accordingly would have modified Monshi to use machine learning, e.g., as taught by Russo.
However the combination of Monshi and Russo does not explicitly teach
instantiate multiple copies of a trained machine learning model based on a number of the two or more sets of candidate changes;
wherein provide the first model input data as the input to the multiple copies of the trained machine learning model comprises providing each set of the two or more sets of candidate changes to a respective trained machine learning model of the multiple copies of the trained machine learning model.
Nevertheless, it would have been obvious, before the effective filing date of the claimed invention, to instantiate multiple copies of the machine learning model and provide the candidate changes to respective copies, as claimed, because it is proper to take into account not only specific teachings of a reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom, see MPEP 2144.01. That is, Monshi teaches training and using a machine learning model to determine carbon emissions, e.g. ¶[0041] and Fig. 1, and that any step in the disclosure may be performed in parallel including simultaneously performing a step two or more times using two or more parallel threads suitable for division of tasks, ¶[0016]. Based on those disclosures, one skilled in the art would infer that Monshi contemplates training and using multiple machine model in parallel to analyze the various scenarios, i.e., similarly to the parallel use of machine learning models in the present Application, see ¶[0035] of the Specification as filed.
However the combination of Monshi and Russo does not teach but Rudow does teach:
wherein the first forecast data includes a predicted value of an environmental impact metric associated with operations (tracks project carbon emission values, ¶¶[0047], [0175]; see also e.g., ¶¶[0075], [0154] discussing estimating carbon output; and e.g., ¶¶[0011], [0041] discussing modifications to reduce carbon emissions)
comparing the first forecast data, including the predicted value of the environmental impact metric, to one or more target values (compares project carbon emission values with target values, ¶¶[0047], [0177])
generating a notification in response to the first forecast data failing to satisfy the one or more target values (determines project carbon emissions exceed target and provides report, ¶¶[0179], [0181] and provides alert, ¶[0163]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the machine learning based analysis of alternative jet fuel of Monshi and Russo with the carbon footprint tracking of Rudow because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Monshi would likely not only be interested in the costs associated with bio-based alternative jet fuel options but would also be interested in potential carbon emissions reductions associated with bio-based alternative jet fuel options and accordingly would have modified the combination Monshi and Russo to also analyze carbon emissions, e.g., as taught by Rudow.
However the combination of Monshi, Russo, and Rudow does not teach but Trainelli does teach:
wherein a second set of candidate changes includes a second number of refueling stations of the first fuel type and a third number of refueling stations of a second fuel type at a second location, and wherein the first number is distinct from the second number (analyzes two types of recharging processes, Battery Plug-in Chargers (BPCs) and Battery Swapping Stations (BSSs), pg. 2 of 29, and determines optimal values of the number of needed charging devices and the number of necessary batteries, pgs. 3-4 of 29; see also Eqn. 6, pg. 5 discussing units of BPCs; Eqn. 7, pgs. 5-6 discussing determining number of batteries).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the machine learning based analysis of alternative jet fuel with the carbon footprint tracking of Monshi, Russo and Rudow with the analysis of electric-powered aviation of Trainelli because Trainelli explicitly suggests such analysis will be needed to fulfill long-term reduction emission goals, pg. 1; see also MPEP 2143.I.G.
Regarding claim 15, the combination of Monshi, Russo and Rudow teaches all the limitations of claim 14 and Monshi further teaches:
wherein the first model input data also includes fixed airport data, wherein the fixed airport data is descriptive of geophysical characteristics of the airport, regulations governing the airport, procedures associated with the airport, weather characteristics of the airport, or a combination thereof (considered national and state environmental impact requirements, pgs. 35-38).
Regarding claim 16, the combination of Monshi, Russo and Rudow teaches all the limitations of claim 15 and Monshi further teaches:
wherein the geophysical characteristics include a location of the airport, an altitude of the airport, geography of the airport, geography of an area surrounding the airport, or a combination thereof (considered creek surrounding airport, pgs. 37-38).
Regarding claim 17, the combination of Monshi, Russo and Rudow teaches all the limitations of claim 15 and Monshi further teaches:
wherein the fixed airport data, the airport baseline data, or both, are obtained from an airport based on an airport identifier (obtains various data, e.g., data about Manassas Regional Airport (HEF), pg. 12).
However Monshi does not explicitly teach but Russo does teach:
are obtained from a database (obtains data from a database, ¶[0036]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the database of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized at least some of the data collected in Monshi would have been retrieved from a database and accordingly would have modified the teachings of Monshi to collect data from a database, e.g. as taught by Russo.
Regarding claim 18, the combination of Monshi, Russo and Rudow teaches all the limitations of claim 14 and Russo further teaches:
wherein the operational characteristics of the airport described by the airport baseline data include an estimated or measured value of the environmental impact metric associated with operations at the airport (training data includes carbon emissions data, ¶[0041]; see also e.g., ¶[0025] discussing receiving transportation data),
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the machine learning based analysis of emissions of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is one of ordinary skill would have recognized stakeholders involved in an analysis of alternative jet fuels would likely also be interested in the environmental impacts of changing fuel types and accordingly would have modified Monshi to also analyze the carbon emissions of changing fuel types.
Regarding claim 19, the combination of Monshi, Russo and Rudow teaches all the limitations of claim 14 and Monshi further teaches:
wherein the modification to the fuel source availability at the airport includes making one or more sustainable fuels available at the airport, changing a sustainable fuel capacity at the airport, making one or more non-petroleum fuels available at the airport, changing a non-petroleum fuel capacity at the airport, or a combination thereof (analyzes three options for using biofuel and continuing to use conventional jet fuel, pgs. 6-7 and Summary of the Analysis Results).
Regarding claim 20, Monshi teaches:
obtain airport baseline data associated with an airport, wherein the airport baseline data is descriptive of operational characteristics of the airport, infrastructure of the airport, or both (obtained yearly fuel flowage and information on fuel capacity and construction of the airport, pgs. 12-13, Tbl. 1, and Fig. 1; see also pgs. 22-23 discussing jet fuel demand);
modify two or more first parameters of the airport baseline data to generate first candidate modification data indicative of a modification to fuel source availability at the airport (analyzes three options for using biofuel and continuing to use conventional jet fuel, pgs. 6-7 and Summary of the Analysis Results; see also e.g., pgs. 27-28, 38-41 discussing economic analyses);
wherein the first candidate modification data comprises two or more sets of candidate changes to two or more different parameters of the two or more first parameters (analyzes three options for using biofuel, drop-in delivery, on-site blending, and on-site production, pgs. 6-7 and Summary of the Analysis Results)
and wherein each set of the two or more sets of candidate changes corresponds to a respective range of parameter values for a respective parameter of the two or more different parameters (use of biofuel ranges from 0%, i.e., continuing to use conventional jet fuel, to some biofuel, i.e., more than 0%, pgs. 6-7 and Summary of the Analysis Results; see also pgs. 10-11 discussing ranges of approved biofuel and the highest being 50%);
generate first forecast data indicating a predicted result of modification of the one or more first parameters, wherein the first forecast data includes a predicted value associated with operations at the airport if the modification occurs (evaluates the financial viability of each option by comparing each option’s cost over a 20 year period, pgs. 6-7 and Summary of the Analysis Results; see also pg. 43 showing results);
compare the first forecast data to one or more target values (compares each option’s cost over a 20 year period to the “Do-Nothing” option, pgs. 6-7, 43, Summary of the Analysis Results);
and generate a notification in response to the first forecast data failing to satisfy the one or more target values (report shows all three options are too expensive, pgs. 6-7).
However Monshi does not teach but Russo does teach:
A non-transitory computer-readable storage device storing instructions that are executable by one or more processors to cause the one or more processors to (memory, processor and instructions, e.g. ¶¶[0017]-[0018], [0070] and Fig. 1)
instantiate a trained machine learning model (trains machine learning model to determine carbon emissions, e.g. ¶[0041] and Fig. 1),
provide first model input data based on the first candidate modification data as input to the trained machine learning model to (trains and uses machine learning model to determine carbon emissions, e.g. ¶[0041] and Fig. 1).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the analysis of alternative jet fuel of Monshi with the machine learning based analysis of Russo because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the analysis of Monshi would likely be improved by employing machine learning techniques (e.g., when there is sufficient data to benefit from the use of machine learning) and accordingly would have modified Monshi to use machine learning, e.g., as taught by Russo.
However the combination of Monshi and Russo does not explicitly teach
instantiate multiple copies of a trained machine learning model based on a number of the two or more sets of candidate changes;
wherein provide the first model input data as the input to the multiple copies of the trained machine learning model comprises providing each set of the two or more sets of candidate changes to a respective trained machine learning model of the multiple copies of the trained machine learning model.
Nevertheless, it would have been obvious, before the effective filing date of the claimed invention, to instantiate multiple copies of the machine learning model and provide the candidate changes to respective copies, as claimed, because it is proper to take into account not only specific teachings of a reference but also the inferences which one skilled in the art would reasonably be expected to draw therefrom, see MPEP 2144.01. That is, Monshi teaches training and using a machine learning model to determine carbon emissions, e.g. ¶[0041] and Fig. 1, and that any step in the disclosure may be performed in parallel including simultaneously performing a step two or more times using two or more parallel threads suitable for division of tasks, ¶[0016]. Based on those disclosures, one skilled in the art would infer that Monshi contemplates training and using multiple machine model in parallel to analyze the various scenarios, i.e., similarly to the parallel use of machine learning models in the present Application, see ¶[0035] of the Specification as filed.
However the combination of Monshi and Russo does not teach but Rudow does teach:
wherein the first forecast data includes a predicted value of an environmental impact metric associated with operations (tracks project carbon emission values, ¶¶[0047], [0175]; see also e.g., ¶¶[0075], [0154] discussing estimating carbon output; and e.g., ¶¶[0011], [0041] discussing modifications to reduce carbon emissions)
comparing the first forecast data, including the predicted value of the environmental impact metric, to one or more target values (compares project carbon emission values with target values, ¶¶[0047], [0177])
generating a notification in response to the first forecast data failing to satisfy the one or more target values (determines project carbon emissions exceed target and provides report, ¶¶[0179], [0181] and provides alert, ¶[0163]).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the machine learning based analysis of alternative jet fuel of Monshi and Russo with the carbon footprint tracking of Rudow because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Monshi would likely not only be interested in the costs associated with bio-based alternative jet fuel options but would also be interested in potential carbon emissions reductions associated with bio-based alternative jet fuel options and accordingly would have modified the combination Monshi and Russo to also analyze carbon emissions, e.g., as taught by Rudow.
However the combination of Monshi, Russo, and Rudow does not teach but Trainelli does teach:
wherein a second set of candidate changes includes a second number of refueling stations of the first fuel type and a third number of refueling stations of a second fuel type at a second location, and wherein the first number is distinct from the second number (analyzes two types of recharging processes, Battery Plug-in Chargers (BPCs) and Battery Swapping Stations (BSSs), pg. 2 of 29, and determines optimal values of the number of needed charging devices and the number of necessary batteries, pgs. 3-4 of 29; see also Eqn. 6, pg. 5 discussing units of BPCs; Eqn. 7, pgs. 5-6 discussing determining number of batteries).
Further, it would have been obvious before the effective filing date of the claimed invention, to combine the machine learning based analysis of alternative jet fuel with the carbon footprint tracking of Monshi, Russo and Rudow with the analysis of electric-powered aviation of Trainelli because Trainelli explicitly suggests such analysis will be needed to fulfill long-term reduction emission goals, pg. 1; see also MPEP 2143.I.G.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Hosseini, Meysam, and Seyyed A. MirHassani. "Refueling-station location problem under uncertainty." Transportation Research Part E: Logistics and Transportation Review 84 (2015): 101-116 teaches a similar refueling location concept
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 10-6.
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, Nathan Uber can be reached at (571) 270-3923. 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.
/BRENDAN S O'SHEA/Examiner, Art Unit 3626