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 .
Introduction
The following is a final Office action in response to Applicant’s submission filed on 12/19/2025. 1-20 are pending and claims 1, 17, 20 are independent. Claims 1, 14, 15, 17, 20 have been amended from the original claim set dated 8/9/2024. No claims have been added or cancelled.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP23383052, filed on 10/13/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/11/2026 appears to be in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the Examiner.
Response to Amendments
Applicant’s amendments are acknowledged and necessitated any new grounds of rejection in this Office Action.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea), specifically an abstract idea, without significantly more. With respect to claims 1-20, following the guidance contained within MPEP 2106, the inquiry for patent eligibility follows two steps: Step 1: Does the claimed invention fall within one of the four statutory categories of invention? Step 2A (Prong 1): Is the claim “directed to” an abstract idea? Step 2A (Prong 2): Is the claim integrated into a practical application? Step 2B: Does the claim recite additional elements that amount to “significantly more” than the abstract idea?
In accordance with these steps, the Examiner finds the following:
Step 1: Claim 1 and its dependent claims (claims 2-16) are directed to a statutory category, namely a system/machine. Claim 17 and its dependent claims (claims 18-19) are directed to a statutory category, namely a method. Claim 20 is directed to a statutory category, namely an article of manufacture.
Step 2A (Prong 1): Claims 1, 17, and 20, which are substantially similar claims to one another, are directed to the abstract idea of “Mental processes”, or more particularly, “concepts performed in the human mind (including an observation, evaluation, judgment, opinion).” In this application that refers to using a computer system to manage and analyze airplane traffic on the ground at an airport. To clarify this further, the Applicant’s disclosed invention is a conceptual system meant to perform the same function that an air traffic controller performs at an airport. The abstract elements of claims 1, 17, and 20, recite in part “Obtain historical data…Generate model…Obtain current usage…Determine congestion level…”. Dependent claims 2-16, 18, 19 add to the abstract idea the following limitations which recite in part “Model corresponds to a KDE model…Data includes…Historical metrics include…Current metrics include…Process data…Determine count…Generate usage metrics…Generate congestion index…Determine congestion level…Generate multiple models…Base models on weathers…Periodically update models…Generate alert…Update predictions…Reallocate resources…Adjust flight plan…Generate congestion warning…Generate congestion alert…”. All of these additional limitations, however, only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 17, and 20.
Step 2A (Prong 2): Independent claims 1, 17, and 20, which are substantially similar claims to one another, do not contain additional elements, either considered individually or in combination, that effectively integrate the exception into a practical application of the exception. These claims do include the limitation that recites in part “Device…Databases…Processors…Non-transitory computer readable medium…” which limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Additionally, dependent claims 2-16, 18, 19 do not include any additional elements to conduct a further Step 2A (Prong 2) analysis.
Step 2B: Independent claims 1, 17, and 20, which are substantially similar claims to one another, include additional elements, when considered both individually and as an ordered combination, which are insufficient to amount to significantly more than the judicial exception. The additional elements of these claims recite in part “Device…Databases…Processors…Non-transitory computer readable medium…”. These items are not significantly more because these are merely the software and/or hardware components used to implement the abstract idea (manage and analyze airplane traffic on the ground at an airport) on a general purpose computer (See MPEP 2106.05(f)). This is exemplified in the Applicant’s specification in [0029] – “As an example, programming of a general purpose processor with instructions that, when executed by the processor, cause the processor to perform a particular operation”.
Additionally, dependent claims 2-16, 18, 19 do not include any additional elements to conduct a further 2B analysis.
Accordingly, whether taken individually or as an ordered combination claims 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to a judicial exception, an abstract idea, without significantly more.
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.
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.
Claims 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20is rejected under 35 U.S.C. 103 as being unpatentable over Ince et al. (US 12159544 B2) in view of Boozarjomehri et al. (US 20190147748 A1)
Regarding claims 1, 17, 20, Ince discloses a device (Ince ABS - A departure sequencing system) comprising: one or more processors configured to: obtain, from one or more sources of global data associated with aircraft traffic, historical data corresponding to historical usage metrics for a particular airport; process the historical usage metrics to generate a distribution model associated with usage of the particular airport (Ince COL 1 ROW 59 - in another embodiment, a method for departure sequencing comprises: creating a graph network representation of an airport; associating business rules to state transitions in the graph network; repeatedly executing, by a processor for departure sequencing, the graph network representation to obtain suggested gate pushback times for a plurality of flights; and calibrating, by the processor, a parameter of the graph network representation utilizing historical flight information for the airport); obtain, from the one or more sources of global data, data corresponding to current usage metrics for the particular airport (Ince COL 11 ROW 36 - Airport state analyzer 145 monitors airport configuration, airspace restrictions, take-offs, landings, ramp and taxi movements, and the like. Airport state analyzer 145 may monitor on a continuous basis; alternatively, airport state analyzer 145 may monitor on a discrete basis (for example, every 5 seconds, every 10 seconds, every 20 seconds, and/or the like)); and determine, based on the distribution model and the current usage metrics for the particular airport, an airport congestion level for the particular airport (Ince COL 12 ROW 24 - In various embodiments, taxi-out predictor 146 simulates the current state of an airport multiple times, for example 1000 times. For each simulation cycle, taxi-out predictor 146 calculates each flight's simulated taxi time, measures ground congestion (i.e., aircraft density based on area), and records taxi conflicts and gate conflicts which are resolved by a business rules engine. Taxi-out predictor 146 may provide expectations to a ramp controller, for example estimates of each flight's taxi path, taxi time and variance, take-off sequence and runway queue and status, and congestion and conflicts based on a timeline).
Ince lacks a plurality of distribution models associated with usage of the particular airport, wherein a first distribution model of the plurality of distribution models is associated with a first time and a first weather condition, and wherein a second distribution model of the plurality of distribution models is associated with the first time and a second weather condition; and determine an airport congestion level for the particular airport, the particular distribution model associated with a particular time and a particular weather condition.
Boozarjomehri, from the same field of endeavor, teaches a plurality of distribution models associated with usage of the particular airport(Boozarjomehri ¶21 - The flight prediction module 113 includes a machine learning model 114 that is trained/configured, in any suitable manner, to determine the number of predicted flight departures 122 from the predetermined airport 103 within the future predetermined time period 199 based on the weather data 131 for the current point in time and the flight information 135. In one aspect, the machine learning model 114 is a linear regression model 114A, a bootstrap regression model 114B or a Markov model 114C), wherein a first distribution model of the plurality of distribution models is associated with a first time and a first weather condition, and wherein a second distribution model of the plurality of distribution models is associated with the first time and a second weather condition; and determine an airport congestion level for the particular airport, the particular distribution model associated with a particular time and a particular weather condition (Boozarjomehri ¶17 - the predictor input module 105 is configured to, by itself or under the command of the controller 110, obtain from the multiple airport information system 130 weather data 131 for a current point in time and flight information 135 for a predetermined airport 103. As used herein the “current point in time” refers to substantially at the instance the weather data is obtained from the multiple airport information system 130 and for which instance an airport congestion determination is made).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the airport gate management methodology/system of Ince by including the airport congestion techniques of Boozarjomehri because Boozarjomehri discloses “determining a congestion of an airport to facilitate air navigation planning through a modification of a flight plan of an aircraft (Boozarjomehri ¶1)”. Additionally, Ince further details “analysis methods and tools suitable for use in airline and airport ground control and air traffic control (Ince COL 1 ROW 24)” so it would be obvious to consider including the additional airport congestion techniques that Boozarjomehri discloses because it would improve the system of Ince by enabling its analysis to consider congestion and plan accordingly.
Regarding claims 3, 18, Ince in view of Boozarjomehri discloses the one or more sources of global data include an airport mapping database, an aircraft tracking data source, and a weather data source (Ince COL 14 ROW 39 - various embodiments, departure sequencing system 115 and/or components thereof (for example, taxi-out predictor 146) may utilize information about an airport from any suitable source, for example airport architectural drawings, public records, survey information, web-based mapping utilities, and/or the like).
Regarding claim 4, Ince in view of Boozarjomehri discloses the historical usage metrics include one or more of: taxi-in data, taxi-out data, departures data, arrivals data, approaches data, ground movement data, weather data, or aircraft holding data (Ince COL 19 ROW 12 - Taxi-out predictor 146 determines taxi-out times for a given airport state for all departing flights (i.e., at gate or already pushed back). Taxi-out predictor 146 may utilize a stochastic approach and determine estimated times via simulation; historical information may also be utilized).
Regarding claim 5, Ince in view of Boozarjomehri discloses the data corresponding to the current usage metrics includes one or more of: taxi-in data, taxi-out data, departures data, arrivals data, approaches data, ground movement data, weather data, or aircraft holding data (Ince COL 19 ROW 12 - Taxi-out predictor 146 determines taxi-out times for a given airport state for all departing flights (i.e., at gate or already pushed back). Taxi-out predictor 146 may utilize a stochastic approach and determine estimated times via simulation; historical information may also be utilized).
Regarding claim 6, Ince in view of Boozarjomehri discloses the one or more processors are configured to process data from an airport mapping database and data from an aircraft tracking data source of the one or more sources of global data to determine, for a particular time interval at the particular airport, a count of aircraft that have landed, a count of aircraft that have taken off, a count of taxi operations for arriving aircraft, a count of taxi operations for departing aircraft, and a count of aircraft moving on ground (Ince COL 11 ROW 36 - Airport state analyzer 145 monitors airport configuration, airspace restrictions, take-offs, landings, ramp and taxi movements, and the like. Airport state analyzer 145 may monitor on a continuous basis; alternatively, airport state analyzer 145 may monitor on a discrete basis (for example, every 5 seconds, every 10 seconds, every 20 seconds, and/or the like)).
Regarding claim 7, Ince in view of Boozarjomehri discloses the one or more processors are further configured to determine, based on the data from the aircraft tracking data source, a count of aircraft on approach that have not yet landed during the particular time interval at the particular airport (Ince COL 12 ROW 35 - In various embodiments, components of departure sequencing system 115 (for example, taxi-out predictor 146) is initialized with a snapshot of an airport state, which may include location of aircraft on the airport surface, location of aircraft on final approach, estimated arrivals and departures within a simulation time horizon).
Regarding claim 8, Ince in view of Boozarjomehri discloses a device (Ince ABS - A departure sequencing system) comprising: one or more processors configured to: obtain, from one or more sources of global data associated with aircraft traffic, historical data corresponding to historical usage metrics for a particular airport; process the historical usage metrics to generate a distribution model associated with usage of the particular airport (Ince COL 1 ROW 59 - in another embodiment, a method for departure sequencing comprises: creating a graph network representation of an airport; associating business rules to state transitions in the graph network; repeatedly executing, by a processor for departure sequencing, the graph network representation to obtain suggested gate pushback times for a plurality of flights; and calibrating, by the processor, a parameter of the graph network representation utilizing historical flight information for the airport).
Boozarjomehri further teaches the one or more processors are further configured to determine, based on the data from the aircraft tracking data source, a count of aircraft following a holding pattern during the particular time interval at the particular airport (Boozarjomehri ¶25 - The arrival congestion index 121A may be tracked over time to establish a relationship between the arrival congestion index 121A and holding pattern delays {i.e. aircraft in holding pattern} at the predetermined airport 103 to assist with air navigation planning 102P at the predetermined airport 103).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the airport gate management methodology/system of Ince by including the airport congestion techniques of Boozarjomehri because Boozarjomehri discloses “determining a congestion of an airport to facilitate air navigation planning through a modification of a flight plan of an aircraft (Boozarjomehri ¶1)”. Additionally, Ince further details “analysis methods and tools suitable for use in airline and airport ground control and air traffic control (Ince COL 1 ROW 24)” so it would be obvious to consider including the additional airport congestion techniques that Boozarjomehri discloses because it would improve the system of Ince by enabling its analysis to consider congestion and plan accordingly.
Regarding claim 9, Ince in view of Boozarjomehri discloses one or more of the historical usage metrics or the current usage metrics are generated using a sliding window (Ince COL 11 ROW 51 - Moreover, different sets of parameters may be utilized for different time periods of the simulation. For example, a first set of parameters may be utilized for the first 15 minutes of the simulation, and a second set of parameters may be utilized for the second 15 minutes of the simulation).
Regarding claim 10, Ince in view of Boozarjomehri discloses determine the airport congestion level based on the current arrival congestion index, the current departure congestion index, and the distribution model (Ince COL 12 ROW 24 - In various embodiments, taxi-out predictor 146 simulates the current state of an airport multiple times, for example 1000 times. For each simulation cycle, taxi-out predictor 146 calculates each flight's simulated taxi time, measures ground congestion (i.e., aircraft density based on area), and records taxi conflicts and gate conflicts which are resolved by a business rules engine. Taxi-out predictor 146 may provide expectations to a ramp controller, for example estimates of each flight's taxi path, taxi time and variance, take-off sequence and runway queue and status, and congestion and conflicts based on a timeline).
Boozarjomehri further teaches process the current usage metrics to generate a current arrival congestion index and a current departure congestion index ((Boozarjomehri Fig. 2-3 - FIG. 2 is an exemplary flow diagram for determining a departure congestion index for flight departures leaving from a predetermined airport in accordance with aspects of the present disclosure; FIG. 3 is an exemplary flow diagram for determining an arrival congestion index for flight arrivals arriving at a predetermined airport in accordance with aspects of the present disclosure).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the airport gate management methodology/system of Ince by including the airport congestion techniques of Boozarjomehri because Boozarjomehri discloses “determining a congestion of an airport to facilitate air navigation planning through a modification of a flight plan of an aircraft (Boozarjomehri ¶1)”. Additionally, Ince further details “analysis methods and tools suitable for use in airline and airport ground control and air traffic control (Ince COL 1 ROW 24)” so it would be obvious to consider including the additional airport congestion techniques that Boozarjomehri discloses because it would improve the system of Ince by enabling its analysis to consider congestion and plan accordingly.
Regarding claim 11, Ince in view of Boozarjomehri discloses a device (Ince ABS - A departure sequencing system) comprising: one or more processors configured to: obtain, from one or more sources of global data associated with aircraft traffic, historical data corresponding to historical usage metrics for a particular airport; process the historical usage metrics to generate a distribution model associated with usage of the particular airport (Ince COL 1 ROW 59 - in another embodiment, a method for departure sequencing comprises: creating a graph network representation of an airport; associating business rules to state transitions in the graph network; repeatedly executing, by a processor for departure sequencing, the graph network representation to obtain suggested gate pushback times for a plurality of flights; and calibrating, by the processor, a parameter of the graph network representation utilizing historical flight information for the airport).
Boozarjomehri further teaches the one or more processors are configured to generate, for each of multiple airports, multiple distribution models based on time of day (Boozarjomehri ¶17 - The predictor input module 105 is configured to, by itself or under the command of the controller 110, obtain from the multiple airport information system 130 weather data 131 for a current point in time and flight information 135 for a predetermined airport 103. As used herein the “current point in time” refers to substantially at the instance the weather data is obtained from the multiple airport information system 130 and for which instance an airport congestion determination is made).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the airport gate management methodology/system of Ince by including the airport congestion techniques of Boozarjomehri because Boozarjomehri discloses “determining a congestion of an airport to facilitate air navigation planning through a modification of a flight plan of an aircraft (Boozarjomehri ¶1)”. Additionally, Ince further details “analysis methods and tools suitable for use in airline and airport ground control and air traffic control (Ince COL 1 ROW 24)” so it would be obvious to consider including the additional airport congestion techniques that Boozarjomehri discloses because it would improve the system of Ince by enabling its analysis to consider congestion and plan accordingly.
Regarding claim 12, Ince in view of Boozarjomehri discloses a device (Ince ABS - A departure sequencing system) comprising: one or more processors configured to: obtain, from one or more sources of global data associated with aircraft traffic, historical data corresponding to historical usage metrics for a particular airport; process the historical usage metrics to generate a distribution model associated with usage of the particular airport (Ince COL 1 ROW 59 - in another embodiment, a method for departure sequencing comprises: creating a graph network representation of an airport; associating business rules to state transitions in the graph network; repeatedly executing, by a processor for departure sequencing, the graph network representation to obtain suggested gate pushback times for a plurality of flights; and calibrating, by the processor, a parameter of the graph network representation utilizing historical flight information for the airport).
Boozarjomehri further teaches the multiple distribution models are further based on weather condition (Boozarjomehri ¶17 - The predictor input module 105 is configured to, by itself or under the command of the controller 110, obtain from the multiple airport information system 130 weather data 131 for a current point in time and flight information 135 for a predetermined airport 103. As used herein the “current point in time” refers to substantially at the instance the weather data is obtained from the multiple airport information system 130 and for which instance an airport congestion determination is made).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the airport gate management methodology/system of Ince by including the airport congestion techniques of Boozarjomehri because Boozarjomehri discloses “determining a congestion of an airport to facilitate air navigation planning through a modification of a flight plan of an aircraft (Boozarjomehri ¶1)”. Additionally, Ince further details “analysis methods and tools suitable for use in airline and airport ground control and air traffic control (Ince COL 1 ROW 24)” so it would be obvious to consider including the additional airport congestion techniques that Boozarjomehri discloses because it would improve the system of Ince by enabling its analysis to consider congestion and plan accordingly.
Regarding claim 13, Ince in view of Boozarjomehri discloses the one or more processors are configured to periodically update the multiple distribution models based on updated historical data (Ince COL 4 ROW 16 - In various embodiments, a system 101 may include a user 105 interfacing with a departure sequencing system 115 by way of a client 110. Departure sequencing system 115 may be a partially or fully integrated system comprised of various subsystems, modules and databases. Client 110 comprises any hardware and/or software suitably configured to facilitate entering, accessing, requesting, retrieving, updating, analyzing, and/or modifying data. The data may include operational data (e.g., schedules, resources, routes, operational alerts, weather, etc.), airport data (for example, taxi queue information, runway information, arriving and/or departing flight information, and/or the like), cost data, forecasts, historical data, verification data, asset (e.g., airplane) data, regulatory data, authentication data, demographic data, transaction data, or any other suitable information discussed herein).
Regarding claim 15, Ince in view of Boozarjomehri discloses in response to the airport congestion level exceeding a threshold percentile of the distribution model (Ince COL 18 ROW 16 - In various embodiments, departure sequencing system 115 may be configured to adhere to various general guidelines, for example: maintenance of a departing runway minimum queue size; avoidance of a threshold for maximum departing runway queue size; ensuring the hold time of any aircraft does not exceed a threshold; reducing or eliminating gate conflicts for arriving aircraft, and/or the like) adjusting a plan in order to improve fuel efficiency of the aircraft (Ince COL 17 ROW 51 - Additionally, departure sequencing system 115 may provide an optimally valued suggested hold time (for example, 30 seconds, one minute, two minutes, three minutes, five minutes, ten minutes, and/or the like) before releasing a flight for pushback. In this manner, departure sequencing system 115 can reduce fuel costs (by avoiding premature pushback, and consequently reducing the amount of fuel burned while waiting in a taxi queue). Similarly, departure sequencing system 115 can reduce crew expenses (for example, pilot compensation expenses, which may begin accruing once the cabin door is closed and the aircraft parking brake is released)).
Boozarjomehri further teaches adjusting a flight plan of an aircraft in flight, wherein adjusting the flight plan includes changing a speed of the aircraft in the flight plan.(Boozarjomehri ¶27 - The user interface 120 is configured to present to an operator of the airport congestion detection apparatus 100 the congestion index 121 (e.g., one or more of the departure congestion index 121D and the arrival congestion index 121A) so that one or more of a flight plan characteristic 140 or an aircraft loading characteristic 150 for the aircraft 500 (FIG. 5) is modified based on the congestion index 121. The flight plan characteristic 140 includes one or more of an aircraft cruise time period 140A, a length of an aircraft holding pattern 140B, an arrival airport 140C, a length of time an aircraft is held on the ground 140D prior to take off relative to a scheduled departure time, and any other suitable flight characteristics where a change in the flight plan characteristic 140 affects air traffic control planning for the predetermined airport 103).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the airport gate management methodology/system of Ince by including the airport congestion techniques of Boozarjomehri because Boozarjomehri discloses “determining a congestion of an airport to facilitate air navigation planning through a modification of a flight plan of an aircraft (Boozarjomehri ¶1)”. Additionally, Ince further details “analysis methods and tools suitable for use in airline and airport ground control and air traffic control (Ince COL 1 ROW 24)” so it would be obvious to consider including the additional airport congestion techniques that Boozarjomehri discloses because it would improve the system of Ince by enabling its analysis to consider congestion and plan accordingly.
Regarding claim 16, Ince in view of Boozarjomehri discloses the one or more processors are configured to: generate a congestion warning in response to the airport congestion level exceeding a first threshold percentile of the distribution model; and generate a congestion alert in response to the airport congestion level exceeding a second threshold percentile of the distribution model, wherein the second threshold percentile is larger than the first threshold percentile (Ince COL 18 ROW 16 - In various embodiments, departure sequencing system 115 may be configured to adhere to various general guidelines {i.e. alert}, for example: maintenance of a departing runway minimum queue size; avoidance of a threshold for maximum departing runway queue size; ensuring the hold time of any aircraft does not exceed a threshold; reducing or eliminating gate conflicts for arriving aircraft, and/or the like).
Regarding claim 19, Ince in view of Boozarjomehri discloses the one or more processors are further configured to, in response to the airport congestion level exceeding a threshold percentile of the distribution model, perform a mitigation action including one or more of: generate an alert; update one or more flight arrival or departure predictions at the particular airport based on congestion at the particular airport; or reallocate one or more resources at the particular airport (Ince COL 18 ROW 49 - It will be appreciated that, in various embodiments, the simulation time horizon in departure sequencing system 115 may be extended, for example to 3 hours, to forecast future periods of airport congestion and/or to work with FAA air traffic control or other third parties to take actions (for example, implementing ground stop or ground delay programs) to reduce, mitigate, and/or eliminate potential gridlock situations).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ince et al. (US 12159544 B2) in view of Boozarjomehri et al. (US 20190147748 A1) further in view of Zeng et al. (CN 110533220 A)
Regarding claim 2, Ince in view of Boozarjomehri discloses a device (Ince ABS - A departure sequencing system) comprising: one or more processors configured to: obtain, from one or more sources of global data associated with aircraft traffic, historical data corresponding to historical usage metrics for a particular airport; process the historical usage metrics to generate a distribution model associated with usage of the particular airport (Ince COL 1 ROW 59 - in another embodiment, a method for departure sequencing comprises: creating a graph network representation of an airport; associating business rules to state transitions in the graph network; repeatedly executing, by a processor for departure sequencing, the graph network representation to obtain suggested gate pushback times for a plurality of flights; and calibrating, by the processor, a parameter of the graph network representation utilizing historical flight information for the airport).
Ince in view of Boozarjomehri lacks the distribution model corresponds to a kernel density estimator (KDE) model.
Zeng, from the same field of endeavor, teaches the distribution model corresponds to a kernel density estimator (KDE) model (Zeng - In some embodiments, the probability density distribution using kernel density estimation to fit the airport departure delay).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the airport gate management methodology/system of Ince by including the airport management techniques of Zeng because Zeng discloses “reducing flight delay and improving reference point rate of airport flight (Zeng ABS)”. Additionally, Ince further details “analysis methods and tools suitable for use in airline and airport ground control and air traffic control (Ince COL 1 ROW 24)” so it would be obvious to consider including the additional airport management techniques that Zeng discloses because it would reduce airplane delays within the system of Ince.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Ince et al. (US 12159544 B2) in view of Boozarjomehri et al. (US 20190147748 A1) further in view of White et al. (US 20120245836 A1)
Regarding claim 14, Ince in view of Boozarjomehri discloses the one or more processors are further configured to, in response to the airport congestion level exceeding a threshold percentile of the distribution model, perform a mitigation action (Ince COL 18 ROW 49 - It will be appreciated that, in various embodiments, the simulation time horizon in departure sequencing system 115 may be extended, for example to 3 hours, to forecast future periods of airport congestion and/or to work with FAA air traffic control or other third parties to take actions (for example, implementing ground stop or ground delay programs) to reduce, mitigate, and/or eliminate potential gridlock situations).
Ince in view of including Boozarjomehri lacks reallocating one or more resources at the particular airport, wherein reallocating the one or more resources includes performing a tail swap.
White, from the same field of endeavor, teaches reallocating one or more resources at the particular airport, wherein reallocating the one or more resources includes performing a tail swap (White ¶122 - Furthermore, in step 950, the SMS module 100 may receive a swap request from an airline based on the delayed flight. The SMS module 100 may determine whether the swap request will be approved or denied. If approved, in step 952, the SMS module 100 may substitute the delayed flight with a further flight. If denied, in step 954, the SMS module 100 may reject the swap request. Furthermore, in step 956, the SMS module 100 may display either an approval message including substitution information or a denial message including denial description to the user via a user interface (e.g., a web-based dashboard)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the airport gate management methodology/system of Ince by including the airport surface management techniques of White because White discloses “These exemplary systems and methods described herein may reduce airline fuel costs and CO.sub.2 emissions, minimize taxi/tarmac delays, and improve the overall passenger experience (White ¶18)”. Additionally, Ince further details “analysis methods and tools suitable for use in airline and airport ground control and air traffic control (Ince COL 1 ROW 24)” so it would be obvious to consider including the additional airport surface management techniques that White discloses because it would reduce fuel costs within the system of Ince.
Response to Arguments
Applicant's arguments filed 12/19/2025 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above.
Regarding the arguments related to the 35 USC § 101 rejections, as addressed above according to the guidance for 35 USC § 101 rejections contained within MPEP 2106, the Examiner maintains that the claimed invention is an abstract idea, without significantly more, and not integrated into a practical application.
Applicant argues that the claimed invention is patent eligible because it does not fall into one of the enumerated buckets of abstract ideas. Examiner does not find this persuasive because the claimed invention is interpreted as an airport management tool that happens to be facilitated through the use of computers. Specifically, the items identified by applicant within their arguments as unable to be performed by a human mind are interpreted be examiner as additional elements which are the hardware/software used to implement the abstract idea on a general purpose computer. These additional elements do not change the nature of the abstract idea.
Examiner does not find Example 39 analogous to the current claims because within Example 39, the collected data is transformed by the system. Within Applicant’s claims, the data is merely collected and analyzed in order to provide usable information.
Regarding the 35 USC § 102 and 35 USC § 103 rejections on the original Office Action, Applicant amended the independent claims to further limit the claims with respect to using multiple models. In light of this amendment, Examiner agrees that the original reference did not teach this, however the amendment necessitated further search and consideration. As a result of this necessitated further search and consideration, the previously cited prior art was found that does teach these limitations and is now cited for these limitations (Boozarjomehri as discussed above). As such, Applicant’s arguments (with respect to the independent claims and their respective dependent claims) are unpersuasive.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST.
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, Jerry O'Connor can be reached at (571) 272-6787. 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.
/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624