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 .
Status of Claims
Claims 1, 6-13, 15-21, 23-25, 28 and 29 are pending in this application.
Claims 1, 8, 9, 16, 17 and 21 are currently amended claims.
Claims 28-29 are newly presented.
Claims 26-27 are newly cancelled.
Examiner's Note
Examiner has cited particular paragraphs / columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to Applicants’ definition which is not specifically set forth in the claims.
Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on August 21, 2026 has been entered.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 6, 8-13, 15-21, 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Gibbons et al. (US 20210383708 A1) in view of Matsumoto (US 20230281475 A1) in view of Nathan et al. (US 20210020049 A1) (the combination of which is referred to as “combination Gibbons” hereinafter)
Regarding claim 1, Gibbons teaches an information processing device comprising:
a processor; and a non-transitory memory storing a program, wherein: the program, when executed by the processor, causes the processor to perform operations including: (Gibbons: ¶ 012; instructions may be executed by an electronic processor) (i) acquiring (Gibbons: ¶ 010; a measurement of a weather system or event may comprise a wind velocity or other characteristic of the event and be measured by a sensor on the other aircraft and automatically provided to aircraft expected to encounter the weather system or event.) (Gibbons: ¶¶ 135-137; in each of a plurality of aircraft to: Acquire current data, information, etc. regarding weather conditions along a flightpath and within a certain range around the flightpath (as suggested by module 406); this may include measurements from on-board sensors) (Gibbons: ¶ 138; application at the ground-based server to process the received weather-related data, information, etc. (if needed) and execute logic to determine which aircraft/pilots to communicate with regarding a specific weather system or event) (ii) acquiring, through a wireless network, other aircraft information regarding another aircraft different from the target aircraft, the other aircraft information comprising atmospheric information; (Gibbons: ¶ 115; collect weather-related data and information from a plurality of aircraft that have encountered or flown nearby weather systems and events while flying along their flight trajectory)(Gibbons: ¶ 077; information, etc. obtained from the other aircraft may be used to generate the overlay(s), where the overlay(s) may indicate one or more of: [0078] A general shape of a weather system or event in relation to the aircraft's current trajectory (where the trajectory is indicated by waypoints, altitudes, airspeed, etc.)) (iii) acquiring weather information regarding an atmospheric condition at different altitudes in an airspace regarding a flight path of the target aircraft, (Gibbons: ¶ 117; server receives data, information, etc. from a plurality of aircraft [which is] provided to and used by aircraft that are expected to encounter or be impacted by one of the weather systems)(Gibbons: ¶ 118-119; identify a location of a weather system or event for which it has received data, information . . . this information may be relevant to pilots in front of and behind a weather system) . . . (iv) acquiring one or more candidate fight paths based on the condition information, the other aircraft information and the weather information; (Gibbons: ¶¶ 119-120; determine one or more aircraft that may be expected to encounter or be impacted by a weather system or event for which data, information, etc., has been received (as suggested by step or stage 438); this may include accessing a database or other source of information regarding the planned flight trajectories of a plurality of aircraft expected to be airborne during a specified period and/or within a specific region) (Gibbons: ¶ 009; overlay or overlays may include indications of weather systems, weather events, observations or measurements provided by other aircraft, and in some embodiments, suggested route changes to avoid or minimize the impact of a weather system or event.) (Gibbons: ¶ 131; whether the reported or predicted weather system or event is present along the flightpath.) (Gibbons: ¶ 115; collect weather-related data and information from a plurality of aircraft that have encountered or flown nearby weather systems and events while flying along their flight trajectory) (Gibbons: ¶ 119; this information may be relevant to pilots in front of and behind a weather system or event due to considerations of turbulence, headwinds, tailwinds, etc;). . . (vii) transmitting, through the wireless network, the recommended flight path information (Gibbons: ¶ 019; if the crew member accepts the received data and information, then to generate one or more displays showing the trajectory of the selected aircraft and the weather system or event in relation to the trajectory.) to an electronic flight bay (EFB) of the target aircraft (Gibbons: ¶ 055; Electronic Flight Bag [operates] TAP [which] is a subset of the overall system and is where the route change recommendations are generated. A pilot interfaces with TAP through a user interface. TAP and the user interface can be co-resident)such that a pilot of the target aircraft determines whether to change the flight path (Gibbons: ¶ 101; a set of user interface elements to permit a user to change information regarding an aircraft's heading or altitude and in response to be able to examine the current weather system information and weather events based on those changes)
To the extent Gibbon is silent about or does not Gibbons teach: the weather information being acquired by performing machine learning based on forecast information and the atmospheric information acquired by the another aircraft . . . wherein: the processor executes the machine learning using a neural network trained on historical flight data and atmospheric measurements to predict turbulence and wind conditions at the different altitudes, the operations further comprise acquiring, from an information server, past weather information corresponding to each area and altitude . . . and the weather information is acquired by performing the machine learning based on the forecast information, the atmospheric information acquired by the another aircraft and the past weather information acquired from the information server; Matsumoto does teach:
the weather information being acquired by performing machine learning (Matsumoto: ¶ 039; convolutional neural network) based on forecast information and the atmospheric information acquired by the another aircraft; (Matsumoto: ¶ 069; In the learning process by the turbulence prediction apparatus 2 of Embodiment 1, past meteorological data and observation data that includes the presence or absence of a past turbulence actually observed by the aircraft are used) . . . wherein: the processor executes the machine learning using a neural network (Matsumoto: ¶ 039; convolutional neural network) trained on historical flight data (Matsumoto: ¶ 069; In the learning process by the turbulence prediction apparatus 2 of Embodiment 1, past meteorological data and observation data that includes the presence or absence of a past turbulence actually observed by the aircraft are used. The past observation data made by the actual observation by the aircraft include data regarding a zone where a turbulence occurred in the past.) and atmospheric measurements (Matsumoto: ¶ 074; creation of the turbulence prediction pattern data N as teacher data in the learning process of the turbulence prediction process . . . meteorological parameters α, β, γ, and δ have been already known as meteorological parameters that are effective in predicting a turbulence, . . . Then, past meteorological data K about the evaluation meteorological parameter in an arbitrary wide area is obtained (S101). The term “past” means a discrete time point P.sub.0 that serves as a past reference prior to the current time) to predict turbulence and wind conditions at the different altitudes, (Matsumoto: ¶ 006; method is used to predict the occurrence of turbulence) the operations further comprise acquiring, from an information server, (Matsumoto: Fig. 2A; [2A, 2B]) past weather information corresponding to each area and altitude (Matsumoto: ¶ 069-072; past meteorological data [include] weather predict data that includes arbitrary meteorological parameters provided by an organization such as the Meteorological Agency . . . α = Absolute Value of Vertical Wind Shear [0071] (2) Meteorological Parameter β = Vertical Temperature Gradient [0072] (3) Meteorological Parameter γ = Vertical Air Density Gradient). . . and the weather information is acquired by performing the machine learning based on the forecast information, the atmospheric information acquired by the another aircraft and the past weather information acquired from the information server. (Matsumoto: ¶ 052; configuration can be adopted in which the transmitting/receiving apparatus 4 that is capable of conducting communication with the aircraft 5 is connected to the cloud 7, so that it is possible to provide the output of the turbulence prediction system 1 to the aircraft 5.)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Matsumoto with the teachings of Gibbon because doing so would result in the predicable benefit of reduced ”computational load” (Matsumoto: ¶ 006).
To the extent Gibbon is silent about or does not Gibbons teach: . . . (v) calculating a score for each of the one or more candidate paths based on an objective function relating to at least one of fuel consumption, operational cost, safety index or required time . . . in the airspace regarding the flight path of the target aircraft; Nathan does teach:
(v) calculating a score for each of the one or more candidate paths based on an objective function relating to at least one of fuel consumption, operational cost, safety index or required time; (Nathan: ¶ 073; process of selecting the optimal alternate flight path may be performed by a cost-optimization process based on one or a plurality of factors, such as the (estimated) fuel expenditure and flight time required by each of the plurality of alternate flight paths. In certain embodiments, the cost-optimization process may consider either fuel expenditure or flight time as the main consideration) (vi) selecting a recommended flight path information for the target aircraft based on the score, (Nathan: ¶ 035; constructing a plurality of alternate flight paths around the zone of avoidance based on the safety envelope (operation 304); determining an optimal flight path from among the plurality of alternate flight paths) the recommended flight path information comprising altitude information; (Nathan: ¶ 074; Fuel expenditure and flight time may be estimated based on any or combination of suitable factors, such as the characteristics of the aircraft (e.g., weight), the altitude of the alternate flight path) . . . in the airspace regarding the flight path of the target aircraft, (Nathan: ¶ 034-035; method includes: obtaining information of a zone of avoidance located on a currently planned flight path of the aircraft (operation 301); determining a safe distance from the zone of avoidance (operation 302); constructing a safety envelope outside of the zone of avoidance based on the determined safe distance (operation 303); constructing a plurality of alternate flight paths around the zone of avoidance based on the safety envelope (operation 304); determining an optimal flight path from among the plurality of alternate flight paths)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Nathan with the teachings of Gibbon because doing so would result in the predicable benefit of selecting the “economically optimal [flight path] among the candidate alternate flight paths. . ..” Nathan: ¶ 022).
Regarding claim 6, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Nathan teaches:
wherein the recommended flight path information is selected to minimize fuel consumption of the target aircraft or(Nathan: ¶ 073; process of selecting the optimal alternate flight path may be performed by a cost-optimization process based on one or a plurality of factors, such as the (estimated) fuel expenditure and flight time required by each of the plurality of alternate flight paths. In certain embodiments, the cost-optimization process may consider either fuel expenditure or flight time as the main consideration)
Regarding claim 8, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Nathan further teaches:
wherein: the operations further comprise acquiring(Nathan: ¶ 002; Aircraft traveling on a flight path may encounter hazards or airspace restrictions, . . . no-fly zones . . . If a zone of avoidance is ahead on the aircraft's current flight path, then the aircraft may need to find an alternate path around the zone.) and the score is calculated using the allowed airspace information. (Nathan: ¶ 035; constructing a plurality of alternate flight paths around the zone of avoidance based on the safety envelope (operation 304); determining an optimal flight path from among the plurality of alternate flight paths) ((Nathan: ¶ 074; Fuel expenditure and flight time may be estimated)
Regarding claim 9, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Gibbons does not explicitly teach:
the other aircraft information comprise information regarding positions of one or more other aircrafts after a specified time has elapsed, the operations comprises acquiring information regarding the positions of the other aircraft after a specified time has elapsed based on information regarding the positions of the one or more other aircrafts; however, Nathan does teach:
A system in which information regarding the positions of the other aircraft wherein the aircraft information comprises information regarding positions of one or more other aircrafts is continuously acquired and used to update flight routing. (Nathan: ¶ 038; reflectivity data may be adjusted to account for movement of the aircraft 210. The computer system 211 may store the reflectivity data obtained in a memory of the computer system 211. The memory may be continuously updated with data from the radar so that the data is up-to-date whenever computation of an alternate flight path around a zone of avoidance is performed.)
Therefore, before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill that Nathan teaches or suggests the other aircraft information comprises information regarding positions of one or more other aircrafts after a specified time has elapsed, the operations comprises acquiring information regarding the positions of the other aircraft after a specified time has elapsed based on information regarding the positions of the one or more other aircrafts because a person of ordinary skill in the art would recognize that radar data is provided regularly at specific time intervals based on the frequency of radar pulse transmission.
And Nathan teaches: and the score is calculated using the information regarding the positions(Nathan: ¶ 073; process of selecting the optimal alternate flight path may be performed by a cost-optimization process based on one or a plurality of factors, such as the (estimated) fuel expenditure and flight time required by each of the plurality of alternate flight paths. In certain embodiments, the cost-optimization process may consider either fuel expenditure or flight time as the main consideration)
Regarding claim 10, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Nathan teaches:
wherein (Nathan: ¶ 042; safe distance may be determined based on (e.g., a mathematical function of) one or a combination of factors, which may include characteristics of the airplane and characteristics of the zone of avoidance. Characteristics of the airplane may include the size, weight, and payload of the airplane.)
Regarding claim 11, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Nathan teaches:
wherein at least the operations (iii)-(vii) are repeatedly performed each time specified acquisition conditions are met (Nathan: ¶ 045; location, altitude, speed, and direction of each vehicle 10 can be continuously monitored and reported back to the servers to manage this data in real-time in an automated fashion)
Regarding claim 12, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Gibbons further teaches:
wherein the recommended flight path information is output on the EFB (Gibbons: ¶ 055; Electronic Flight Bag [operates] TAP [which] is a subset of the overall system and is where the route change recommendations are generated. A pilot interfaces with TAP through a user interface. TAP and the user interface can be co-resident)each time specified output conditions are met during the flight of the target aircraft. (Gibbons: ¶¶ 124-125; generate one or more overlays or displays that present the weather-related data, information, etc. to the pilot or crew member (as suggested by step or stage 444), where the overlay(s) or displays may indicate one or more of: a general shape of a weather system or event in relation to the aircraft's current trajectory)
Regarding claim 13, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Gibbons further teaches:
wherein the recommended flight path information is output on the EFB in a specified notification mode. (Gibbons: ¶ 009; overlay or overlays may include indications of weather systems, weather events, observations or measurements provided by other aircraft, and in some embodiments, suggested route changes to avoid or minimize the impact of a weather system or event.)
Regarding claim 15, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 51. Gibbons further teaches:
wherein the recommended flight path information further includes information regarding heading or speed. (Gibbons: ¶¶ 129-130; generate and provide a recommended change to a trajectory or flight plan . . . by using the received data, information, etc. as an input to an existing or separate flight planning system (as suggested by step or stage 446).) (Gibbons: ¶ 053; flight plan consists of a sequence of waypoints, which are fixed location latitude/longitude points that typically have a three to five letter name. A flight plan will include specifics of anticipated wind strength, altitude, and airspeed.)
Regarding claim 16, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 15. Gibbons teaches:
the operations further comprise acquiring result information regarding an operation result of the target aircraft based on the recommended flight path information the recommended flight path information is updated using the result information; (Gibbons: ¶ 129-130; updated weather-related data, information, etc. may be used to generate and provide a recommended change to a trajectory or flight plan; [0130] this may be an optional capability and if desired, may be implemented by using the received data, information, etc. as an input to an existing or separate flight planning system (as suggested by step or stage 446).)
Regarding claim 17, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 15. Gibbons further teaches:
operations further comprise transmitting flight setting information used for the flight by the target aircraft in instrument flight mode based on the recommended flight path information (Gibbons: ¶¶ 129-130; generate and provide a recommended change to a trajectory or flight plan . . . by using the received data, information, etc. as an input to an existing or separate flight planning system (as suggested by step or stage 446).)
Regarding claim 18, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Nathan further teaches:
wherein the recommended flight path information is further transmitted to a terminal device used by a ground user (Nathan: ¶ 034; alternate flight path is computed, the ground-based computer system may transmit the computed alternate flight path to the air traffic controller 123 for clearance (approval), before transmitting the computed alternate flight path to the aircraft 2)
Regarding claim 19, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Gibbons further teaches:
wherein the recommended flight path information (Gibbons: ¶¶ 129-130; generate and provide a recommended change to a trajectory or flight plan . . . by using the received data, information, etc. as an input to an existing or separate flight planning system (as suggested by step or stage 446).) comprises at least one of an ascent profile and a descent profile that the target aircraft is recommended to pass through. (Gibbons: ¶ 053; flight plan consists of a sequence of waypoints, which are fixed location latitude/longitude points that typically have a three to five letter name. A flight plan will include specifics of anticipated wind strength, altitude, and airspeed)
Regarding claim 20, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 19. Gibbons further teaches:
wherein the altitude information in the recommended flight path information (Gibbons: ¶¶ 129-130; generate and provide a recommended change to a trajectory or flight plan . . . by using the received data, information, etc. as an input to an existing or separate flight planning system (as suggested by step or stage 446).) comprises altitudes of two or more points in the ascent profile or descent profile. (Gibbons: ¶ 053; flight plan consists of a sequence of waypoints, which are fixed location latitude/longitude points that typically have a three to five letter name. A flight plan will include specifics of anticipated wind strength, altitude, and airspeed)
Regarding claim 21, Gibbons teaches an information processing method comprising:
(i) acquiring, by a processor, (Gibbons: ¶ 012; instructions may be executed by an electronic processor)condition information regarding the condition of a target aircraft; (Gibbons: ¶ 010; a measurement of a weather system or event may comprise a wind velocity or other characteristic of the event and be measured by a sensor on the other aircraft and automatically provided to aircraft expected to encounter the weather system or event.) (Gibbons: ¶¶ 135-137; in each of a plurality of aircraft to: Acquire current data, information, etc. regarding weather conditions along a flightpath and within a certain range around the flightpath (as suggested by module 406); this may include measurements from on-board sensors) (Gibbons: ¶ 138; application at the ground-based server to process the received weather-related data, information, etc. (if needed) and execute logic to determine which aircraft/pilots to communicate with regarding a specific weather system or event) (ii) acquiring, by the processor, through a wireless network, other aircraft information regarding another aircraft different from the target aircraft, the other aircraft information comprising atmospheric information; (Gibbons: ¶ 115; collect weather-related data and information from a plurality of aircraft that have encountered or flown nearby weather systems and events while flying along their flight trajectory)(Gibbons: ¶ 077; information, etc. obtained from the other aircraft may be used to generate the overlay(s), where the overlay(s) may indicate one or more of: [0078] A general shape of a weather system or event in relation to the aircraft's current trajectory (where the trajectory is indicated by waypoints, altitudes, airspeed, etc.)) (iii) acquiring, by the processor, weather information regarding an atmospheric condition at different altitudes in an airspace regarding a flight path of the target aircraft, (Gibbons: ¶ 117; server receives data, information, etc. from a plurality of aircraft [which is] provided to and used by aircraft that are expected to encounter or be impacted by one of the weather systems)(Gibbons: ¶ 118-119; identify a location of a weather system or event for which it has received data, information . . . this information may be relevant to pilots in front of and behind a weather system) . . . (iv) acquiring, by the processor, one or more candidate fight paths based on the condition information, the other aircraft information and the weather information; (Gibbons: ¶¶ 119-120; determine one or more aircraft that may be expected to encounter or be impacted by a weather system or event for which data, information, etc., has been received (as suggested by step or stage 438); this may include accessing a database or other source of information regarding the planned flight trajectories of a plurality of aircraft expected to be airborne during a specified period and/or within a specific region) (Gibbons: ¶ 009; overlay or overlays may include indications of weather systems, weather events, observations or measurements provided by other aircraft, and in some embodiments, suggested route changes to avoid or minimize the impact of a weather system or event.) (Gibbons: ¶ 131; whether the reported or predicted weather system or event is present along the flightpath.) (Gibbons: ¶ 115; collect weather-related data and information from a plurality of aircraft that have encountered or flown nearby weather systems and events while flying along their flight trajectory) (Gibbons: ¶ 119; this information may be relevant to pilots in front of and behind a weather system or event due to considerations of turbulence, headwinds, tailwinds, etc;). . . (vii) transmitting, by the processor, through the wireless network, the recommended flight path information(Gibbons: ¶ 019; if the crew member accepts the received data and information, then to generate one or more displays showing the trajectory of the selected aircraft and the weather system or event in relation to the trajectory.) to an electronic flight bay (EFB) of the target aircraft (Gibbons: ¶ 055; Electronic Flight Bag [operates] TAP [which] is a subset of the overall system and is where the route change recommendations are generated. A pilot interfaces with TAP through a user interface. TAP and the user interface can be co-resident)such that a pilot of the target aircraft determines whether to change the flight path (Gibbons: ¶ 101; a set of user interface elements to permit a user to change information regarding an aircraft's heading or altitude and in response to be able to examine the current weather system information and weather events based on those changes)
To the extent Gibbon is silent about or does not Gibbons teach: the weather information being acquired by performing machine learning based on forecast information and the atmospheric information acquired by the another aircraft . . . wherein: the machine learning is performed using a neural network trained on historical flight data and atmospheric measurements to predict turbulence and wind conditions at the different altitudes, the method further comprises acquiring, from an information server, past weather information corresponding to each area and altitude . . . and the weather information is acquired by performing the machine learning based on the forecast information, the atmospheric information acquired by the another aircraft and the past weather information acquired from the information server; Matsumoto does teach:
the weather information being acquired by performing machine learning (Matsumoto: ¶ 039; convolutional neural network) based on forecast information and the atmospheric information acquired by the another aircraft; (Matsumoto: ¶ 069; In the learning process by the turbulence prediction apparatus 2 of Embodiment 1, past meteorological data and observation data that includes the presence or absence of a past turbulence actually observed by the aircraft are used) . . . wherein: the machine learning is performed using a neural network (Matsumoto: ¶ 039; convolutional neural network) trained on historical flight data (Matsumoto: ¶ 069; In the learning process by the turbulence prediction apparatus 2 of Embodiment 1, past meteorological data and observation data that includes the presence or absence of a past turbulence actually observed by the aircraft are used. The past observation data made by the actual observation by the aircraft include data regarding a zone where a turbulence occurred in the past.) and atmospheric measurements (Matsumoto: ¶ 074; creation of the turbulence prediction pattern data N as teacher data in the learning process of the turbulence prediction process . . . meteorological parameters α, β, γ, and δ have been already known as meteorological parameters that are effective in predicting a turbulence, . . . Then, past meteorological data K about the evaluation meteorological parameter in an arbitrary wide area is obtained (S101). The term “past” means a discrete time point P.sub.0 that serves as a past reference prior to the current time) to predict turbulence and wind conditions at the different altitudes, (Matsumoto: ¶ 006; method is used to predict the occurrence of turbulence) (Matsumoto: ¶ 079; the absolute values of vertical wind shears as the meteorological parameter α are discretized for divided sections Lij (i = 1 to h, j = 1 to h) into which the meteorological data is equally divided, and the discretized numeric values are allocated to the divided sections Lij. ) the method further comprises acquiring, from an information server, (Matsumoto: Fig. 2A; [2A, 2B]) past weather information corresponding to each area and altitude (Matsumoto: ¶ 069-072; past meteorological data [include] weather predict data that includes arbitrary meteorological parameters provided by an organization such as the Meteorological Agency . . . α = Absolute Value of Vertical Wind Shear [0071] (2) Meteorological Parameter β = Vertical Temperature Gradient [0072] (3) Meteorological Parameter γ = Vertical Air Density Gradient) (Matsumoto: ¶ 079; the absolute values of vertical wind shears). . . and the weather information is acquired by performing the machine learning based on the forecast information, the atmospheric information acquired by the another aircraft and the past weather information acquired from the information server (Matsumoto: ¶ 052; configuration can be adopted in which the transmitting/receiving apparatus 4 that is capable of conducting communication with the aircraft 5 is connected to the cloud 7, so that it is possible to provide the output of the turbulence prediction system 1 to the aircraft 5.)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Matsumoto with the teachings of Gibbon because doing so would result in the predicable benefit of reduced ”computational load” (Matsumoto: ¶ 006).
To the extent Gibbon is silent about or does not Gibbons teach: (v) calculating a score for each of the one or more candidate paths based on an objective function relating to at least one of fuel consumption, operational cost, safety index or required time . . . in the airspace regarding the flight path of the target aircraft,; Nathan does teach:
(v) calculating a score for each of the one or more candidate paths based on an objective function relating to at least one of fuel consumption, operational cost, safety index or required time; (Nathan: ¶ 073; process of selecting the optimal alternate flight path may be performed by a cost-optimization process based on one or a plurality of factors, such as the (estimated) fuel expenditure and flight time required by each of the plurality of alternate flight paths. In certain embodiments, the cost-optimization process may consider either fuel expenditure or flight time as the main consideration) (vi) selecting a recommended flight path information for the target aircraft based on the score, (Nathan: ¶ 035; constructing a plurality of alternate flight paths around the zone of avoidance based on the safety envelope (operation 304); determining an optimal flight path from among the plurality of alternate flight paths) the recommended flight path information comprising altitude information; (Nathan: ¶ 074; Fuel expenditure and flight time may be estimated based on any or combination of suitable factors, such as the characteristics of the aircraft (e.g., weight), the altitude of the alternate flight path) . . . in the airspace regarding the flight path of the target aircraft, (Nathan: ¶ 034-035; method includes: obtaining information of a zone of avoidance located on a currently planned flight path of the aircraft (operation 301); determining a safe distance from the zone of avoidance (operation 302); constructing a safety envelope outside of the zone of avoidance based on the determined safe distance (operation 303); constructing a plurality of alternate flight paths around the zone of avoidance based on the safety envelope (operation 304); determining an optimal flight path from among the plurality of alternate flight paths)
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Nathan with the teachings of Gibbon because doing so would result in the predicable benefit of selecting the “economically optimal [flight path] among the candidate alternate flight paths. . ..” Nathan: ¶ 022).
Regarding claim 23, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Nathan further teaches:
wherein the operations are performed in real time during the flight of the target aircraft to dynamically optimize fuel consumption and operational cost. (Nathan: ¶ 045; location, altitude, speed, and direction of each vehicle 10 can be continuously monitored and reported back to the servers to manage this data in real-time in an automated fashion) (Nathan: ¶ 073; process of selecting the optimal alternate flight path may be performed by a cost-optimization process based on one or a plurality of factors, such as the (estimated) fuel expenditure)
Regarding claim 24, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 21. Nathan further teaches:
wherein at least the operations (iii)-(vii) are repeatedly performed each time specified acquisition conditions are met. (Nathan: ¶ 045; location, altitude, speed, and direction of each vehicle 10 can be continuously monitored and reported back to the servers to manage this data in real-time in an automated fashion)
Regarding claim 25, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 21. Nathan further teaches:
wherein the method performed in real time during the flight of the target aircraft to dynamically optimize fuel consumption and operational cost. (Nathan: ¶ 045; location, altitude, speed, and direction of each vehicle 10 can be continuously monitored and reported back to the servers to manage this data in real-time in an automated fashion) (Nathan: ¶ 073; process of selecting the optimal alternate flight path may be performed by a cost-optimization process based on one or a plurality of factors, such as the (estimated) fuel expenditure)
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over combination Gibbons as applied to claim 1 above, and further in view of Lebbos (US 20210192961 A1).
Regarding claim 7, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 1. Gibbons does not explicitly teach:
wherein the recommended flight path information is selected to minimize fuel consumption or costs associated with one or more specified related aircraft different from the target aircraft; however, Lebbos does teach:
wherein the recommended flight path information is selected to minimize fuel consumption or costs associated with one or more specified related aircraft different from the target aircraft (Lebbos: ¶ 056; In the fine filtering step 110 of the process 100, for each possible pair of flights 106, the process 100 analyzes whether a modification to one or both of the initial flight plans for the leader and follower aircraft to fly in formation over the common ground track section can generate net trip (i.e., entire flight) fuel burn savings for the pair (considered as a whole/in its entirety).) (Lebbos: ¶ 146; optimization of departure schedules could allow maximizing the fuel savings of a whole fleet
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Lebbos with the teachings of Gibbons because doing so would result in the predicable benefit of "allow[ing] net trip fuel savings " (Lebbos: ¶ 010).
Claims 28 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over combination Gibbons as applied to claim 1 above, and further in view of Goossen (US 20070030173 A1).
Regarding claim 28, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 21. Matsumoto teaches:
wherein the atmospheric information comprises wind speed, (Matsumoto : ¶ 056; wind speed) wind direction, (Matsumoto : ¶ 041; east-west wind speed, north-south wind speed, east-west wind shear, and north-south wind shear) static temperature, (Matsumoto : ¶ 056; air temperature). . . latitude, longitude, (Matsumoto : Fig. 2D; [showing grid that represents lines of latitude and longitude]) altitude, (Matsumoto: ¶ 079; the absolute values of vertical wind shears as the meteorological parameter α are discretized for divided sections Lij (i = 1 to h, j = 1 to h) into which the meteorological data is equally divided, and the discretized numeric values are allocated to the divided sections Lij. ) and time (Matsumoto : ¶ 030; example of determination-purposed meteorological data based on a plurality of continuous-discrete time meteorological data)
And Gibbons teaches: static pressure, (Gibbons: ¶ 055; barometric pressure)
To the extent Gibbons is silent or does not explicitly teach: total temperature,; Goossen teaches: total temperature, (Goossen: ¶ 006; This information is used to make weather maps and predictions. Weather and predictions appropriate to an area are uplinked to aircraft flying in that area.) (Goossen: ¶ 021; Air data system 20 can either provide raw data, such as static and impact pressure and total temperature)
Regarding claim 29, as detailed above, combination Gibbons teaches the invention as detailed with respect to claim 21. Matsumoto further teaches:
wherein the atmospheric information comprises wind speed, (Matsumoto : ¶ 056; wind speed) wind direction, (Matsumoto : ¶ 041; east-west wind speed, north-south wind speed, east-west wind shear, and north-south wind shear) static temperature, (Matsumoto : ¶ 056; air temperature). . . latitude, longitude, (Matsumoto : Fig. 2D; [showing grid that represents lines of latitude and longitude]) altitude, (Matsumoto: ¶ 079; the absolute values of vertical wind shears as the meteorological parameter α are discretized for divided sections Lij (i = 1 to h, j = 1 to h) into which the meteorological data is equally divided, and the discretized numeric values are allocated to the divided sections Lij. ) and time (Matsumoto : ¶ 030; example of determination-purposed meteorological data based on a plurality of continuous-discrete time meteorological data)
And Gibbons teaches: static pressure, (Gibbons: ¶ 055; barometric pressure)
To the extent Gibbons is silent or does not explicitly teach: total temperature,; Goossen teaches: total temperature, (Goossen: ¶ 006; This information is used to make weather maps and predictions. Weather and predictions appropriate to an area are uplinked to aircraft flying in that area.) (Goossen: ¶ 021; Air data system 20 can either provide raw data, such as static and impact pressure and total temperature)
Response to Arguments
Applicant's remarks filed August 21, 2026 have been fully considered.
Applicant’s argument and amendments with respect to the previous applied objection is persuasive and the objection is overcome.
Applicant’s arguments with respect to claims 1, 6-13, 15-2 1, 23-25, 28 and 29 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues:
[t]he Office Action asserted that Gibbons discloses acquiring atmospheric information from other aircraft, and Tran discloses the use of machine learning. However, the rejection does not establish that the claimed combination of inputs to the machine learning process is taught or suggested. Specifically, Gibbons appears to disclose acquiring weather-related data from other aircraft, but does not perform any machine learning, nor does it disclose using forecast information in any predictive model. Tran merely discloses machine learning techniques, but does not disclose or suggest using atmospheric information acquired from other aircraft, nor combining such information with forecast data as inputs. Accordingly, the cited references fail to teach or suggest a machine learning process that receives forecast information, receives atmospheric information acquired from other aircraft, and processes both together to generate weather information. The Office Action does not explain how the atmospheric information of Gibbons would be provided as an input to the machine learning framework of Tran, nor how forecast information would be incorporated into such processing. Thus, the rejection improperly treats machine learning as a generic component, while the claim instead defines a specific input structure and data fusion requirement, which is not disclosed or suggested by the applied references. requested. In addition, the cited references fail to disclose generating weather information at different altitudes using machine learning. This limitation requires not merely weather awareness, but a structured, altitude-resolved atmospheric representation produced via the claimed machine learning process. Specifically, the Examiner asserted during the aforementioned interview that paragraph [0142] of Tran discloses "generating weather information at different altitude using machine learning." (Applicant’s Arguments filed Aug. 8, 2026, pg. 10).
Newly applied prior art Matsumoto (US 20230281475 A1) teaches a weather forecasting method that specifically teaches using historical weather data inputs from a meteorological agency including vertical temperature gradients and vertical wind shear (Matsumoto: ¶ 069-072) and aircraft aloft inputs (Matsumoto: ¶ 069) into either a convolutional neural network (Matsumoto: ¶ 039) or recurrent neural network (RNN) to predict weather and turbulence (Matsumoto: ¶ 069). Matsumoto explicitly teaches using “teacher data” to train the machine learning algorithm. Matsumoto: ¶ 074. Matsumoto further teaches transmitting this prediction data to aircraft in travel. Matsumoto: ¶ 052. In combination with prior art Nathan’s teaching of a creating a “safe” 3d corridor around the perspective flight path of an aircraft a person of ordinary skill in the art would be taught "(iii) acquiring weather information regarding an atmospheric condition at different altitudes in an airspace regarding a flight path of the target aircraft, the weather information being acquired by performing machine learning based on forecast information and the atmospheric information acquired by the another aircraft" as recited by claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure Borgyos (US 20230008742 A1) which discloses a method which allows rerouting an aircraft during a flight of an aircraft system based on a potential conflict with another aircraft in which fuel consumption is considered.
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/C.P./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
