DETAILED ACTION
Introduction
This Final Office Action is in response to amendments and remarks filed on January 21, 2026, for the application with serial number 18/649,593.
Claims 1, 2, 4, 6-9, 11, 13-16, 18, and 20 are amended.
Claims 1-20 are pending.
Interview
The Examiner acknowledges the interview conducted on January 16, 2026, in which proposed amendments were discussed.
Response to Remarks/Amendments
35 USC §101 Rejections
The Applicant traverses the rejection of the claims as being directed to an ineligible abstract idea, contending that the claims are subject matter eligible because the newly amended language of the claims recites generating a flight plan: “to cause steering of the second aircraft based on the flight plan.” See Remarks p. 11. According to the Applicant, this step does not involve human activity. In response, the Examiner submits that steering an aircraft is a human activity. Moreover, the present claims are directed to determining a flight plan based on a risk map. The steps could be implemented mentally or on paper by a human being, but a general purpose computer is recited for implementation. Therefore, the claims attempt to manage human behavior.
The Applicant further submits that the step, discussed above, of causing steering of an aircraft provides a practical solution. See Remarks pp. 12-13. In response, the Examiner submits that the step of causing steering of an aircraft is, at best, insignificant extrasolution activity. The step is a well-known step that is tangentially related to the inventive concept, and the step is also a logical and necessary step for implementing the inventive concept of determining a flight plan. See MPEP §2106.05(g). However, the step could also be considered mere instructions to apply the judicial exception. See MPEP §2106.05(f). The Examiner notes that remote or autonomous piloting is not explicitly recited in the claims.
The Applicant also raises the issue of preemption. See Remarks p. 13. While preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility See MPEP §2106.04[I]. The test for eligibility outlined in MPEP §2106 has been properly followed in the analysis, below, to arrive at a conclusion of ineligibility. The present claims are directed to ineligible subject matter because the claims are directed to an abstract idea without significantly more.
The Applicant further submits that the claims are subject matter because the claims contain unconventional elements. See Remarks p. 15. In response, the Examiner points out that an abstract idea without significantly more is just that – an abstract idea. Additional elements outside the scope of the abstract idea of generating a flight plan based on a risk map have been considered, but they have been found to amount to generic computer hardware. The generic computer hardware does not provide a practical application or significantly more than the recited abstract idea.
The rejection for lack of subject matter eligibility is updated and maintained.
35 USC §103 Rejections
Amendments to the claims changed the scope of the claims, necessitating further search and consideration of the prior art. Independent claims 1, 8, and 15 now stand rejected as being obvious over Gobin in view of Schleede and Love. The Applicant traverses the rejection, contending that the combination of Gobin and Schleede do not teach the horizontal and vertical distances between an aircraft and an encounter event. See Remarks pp. 17-18. In response, the Examiner submits that the combination of Gobin and Schleede would allow the skilled artisan to arrive at the claimed invention. While Schleede is generally directed to objects in space, Gobin teaches aircraft. Therefore, the skilled artisan would be motivated to combine the references to substitute the aircraft in Gobin for the object in Schleede. Gobin teaches a flight plan or path that is adjusted based on the determined risk. The broadest reasonable interpretation of Gobin includes planning for any number of aircraft.
The rejection of the dependent claims stands or falls with the rejection of the independent claims.
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.
The Manual of Patent Examining Procedure (MPEP) provides detailed rules for determining subject matter eligibility for claims in §2106. Those rules provide a basis for the analysis and finding of ineligibility that follows.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to non-statutory subject matter because the claimed invention recites a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Although claims(s) 1-20 are all directed to one of the four statutory categories of invention, the claims are directed to generating a flight plan based on a risk map (as evidenced by exemplary independent claim 1; “generate a risk map based on the risk metric;” and “generate, based on the risk map, a flight plan”), an abstract idea. Certain methods of organizing human activity are ineligible abstract ideas, including managing personal behavior or relationships or interactions between people. See MPEP §2106.04(a). The limitations of exemplary claim 1 include: “determine a horizontal distance and a vertical distance between a grid location;” “determine a first overlap probability between [horizontal probability distributions];” “determine a second overlap probability between [vertical probability distributions];” “determine . . . a risk metric corresponding to the grid location;” “generate a risk map based on the risk metric;” and “generate, based on the risk map, a flight plan.” The steps are all steps for managing personal behavior related to the abstract idea of generating a risk map that, when considered alone and in combination, are part of the abstract idea of generating a risk map. The dependent claims further recite steps for managing personal behavior that are part of the abstract idea of generating a risk map. These claim elements, when considered alone and in combination, are considered to be abstract ideas because they are directed to a method of organizing human activity which includes mapping risks based on probability of an encounter event to determine an optimal flight plan.
Under step 2A of the subject matter eligibility analysis, a claim that recites a judicial exception must be evaluated to determine whether the claim provides a practical application of the judicial exception. Additional elements of the independent claims amount to generic computer hardware that does not provide a practical application (an apparatus with interface circuitry, machine-readable instructions, and a processor in independent claim 1; a non-transitory machine-readable medium in independent claim 8; no hardware is recited in independent claim 15). See MPEP §2106.04(d)[I]. The claims do not recite an improvement to another technology or technical field, nor do they recite an improvement to the functioning of the computer itself. See MPEP §2106.05(a). The claims require no more than a generic computer (an apparatus with interface circuitry, machine-readable instructions, and a processor in independent claim 1; a non-transitory machine-readable medium in independent claim 8; no hardware is recited in independent claim 15) to implement the abstract idea, which does not amount to significantly more than an abstract idea. See MPEP §2106.05(f). Because the claims only recite use of a generic computer, they do not apply the judicial exception with a particular machine. See MPEP §2106.05(b). For these reasons, the claims do not provide a practical application of the abstract idea, nor do they amount to significantly more than an abstract idea under step 2B of the subject matter eligibility analysis. Using a generic computer to implement an abstract idea does not provide an inventive concept. Therefore, the claims recite ineligible subject matter under 35 USC §101.
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.
Claim(s) 1, 3, 5, 7, 8, 10, 12, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230222927 A1 to Gobin et al. (hereinafter ‘GOBIN’) in view of US 20220135026 A1 to Schleede et al. (hereinafter ‘SCHLEEDE’) and US 20060089760 A1 to Love et al. (hereinafter ‘LOVE’).
Claim 1 (Currently Amended)
GOBIN discloses an apparatus comprising: interface circuitry (see ¶[0036] and Figs. 16 and 17; a client device having a user interface); machine-readable instructions (see abstract; the system includes program instructions); and at least one processor (see ¶[0004]; at least one hardware processor) circuit to be programmed by the machine-readable instructions to: determine a horizontal distance and a vertical distance between a grid location in an airspace and a point of a historical flight trajectory of a first aircraft (see ¶[0005] and [0038]; flight path waypoints include latitude, longitude, and altitude. The section of the nominal flight path can include a length of a distance traveled across a population cell defined in the population data. See also ¶[0141]; the interface can also have built in functions 512 to import select parts of the data from previous runs to enable reuse or modification. In the example shown in FIG. 16, the “Import Aircraft” button allows for the import of all of the data in the “Aircraft Information” section from a database.), the historical flight trajectory associated with an (see abstract and ¶[0003]; in the event of equipment failure, other failure, or termination of flight of the aircraft, it is helpful to have knowledge of the potential ground risk for a given flight. Calculate a potential crash area for a section of the nominal flight path based on a failure mode).
GOBIN does not specifically disclose, but SCHLEEDE discloses, the historical flight trajectory associated with an encounter event with respect to the grid location (see abstract; a collision probability between a vehicle and an object. See also ¶[0086]; GPS coordinates),
determine a first overlap probability between (a) a first horizontal probability distribution corresponding to the grid location and (b) a second horizontal probability distribution corresponding to the point, the second horizontal probability distribution corresponding to the horizontal distance (see ¶[0013]-[0015]; a geometric model can be utilized to determine intersections and/or collision probabilities associated with the vehicle and the object. The predicted locations of the vehicle and the object at the intersection can be determined based on trajectories of the vehicle and the object. The planning system can determine an overlap between the geometric representation of the vehicle and the geometric representation of the object, as well as probability distribution(s) associated with the object. Include a probability distribution in a first direction over an x-axis);
determine a second overlap probability between (c) a first vertical probability distribution corresponding to the grid location and (d) a second vertical probability distribution corresponding to the point, the second vertical probability distribution corresponding to the vertical distance (see again ¶[0013]-[0015]; a geometric model can be utilized to determine intersections and/or collision probabilities associated with the vehicle and the object. The predicted locations of the vehicle and the object at the intersection can be determined based on trajectories of the vehicle and the object. The planning system can determine an overlap between the geometric representation of the vehicle and the geometric representation of the object, as well as probability distribution(s) associated with the object. Include a probability distribution in a second direction over a y-axis);
determine, based on the first overlap probability and the second overlap probability, a risk metric corresponding to the grid location (see ¶[0012] and [0019]; determine the risk of collision based on sensor data. Probability distributions allow for calculations that represent risk associated with trajectories).
The combination of GOBIN and SCHLEEDE does not specifically disclose, but LOVE discloses, the risk metric representative of a number of aircraft expected to fly in a geographic region of the grid location (see ¶[0005]-[0006]; predict sector counts as the number of aircraft in a sector to determine a problematic block of airspace. Predict sector counts for 15 minute intervals).
GOBIN further discloses generate a risk map based on the risk metric (see abstract; display calculated risk values plotted on a map of the geographical area); and
generate, based on the risk map, a flight plan for a second aircraft to cause steering of the second aircraft based on the flight plan (see ¶[0003] and [0044]; the flight path can be planned or adjusted based on potential ground risk. Avoid higher risk areas).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. SCHLEEDE discloses collision avoidance planning that includes determining risk of objects colliding based on a probability distribution. It would have been obvious to include the risk of object colliding as taught by SCHLEEDE in the system executing the method of GOBIN with the motivation to assess risk of vehicles and objects in motion.
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. LOVE discloses aircraft flight-path modeling that includes predicted sector counts for a block of airspace. It would have been obvious to include the sector counts as taught by LOVE in the system executing the method of GOBIN with the motivation to model flight paths.
Claim 3 (Original)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the apparatus as set forth in claim 1.
GOBIN does not specifically disclose, but LOVE discloses, wherein the first and second horizontal probability distributions correspond to Normal-Double Exponential distributions, and the first and second vertical probability distributions correspond to Laplacian distributions (see abstract and ¶[0013]; aircraft flight parameters represented as random variables with statistical distribution, such as normal, Laplacian, or logistic. Examiner Note: LaPlacian distribution is understood to be double exponential).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. LOVE discloses aircraft flight-path modeling that includes probability distributions that are normal and LaPlacian. It would have been obvious to include the probability distributions as taught by LOVE in the system executing the method of GOBIN with the motivation to model flight paths.
Claim 5 (Original)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the apparatus as set forth in claim 1.
GOBIN does not specifically disclose, but SCHLEEDE discloses, wherein the risk metric includes at least one of a first probability of an encounter event occurring when a movement occurs in the airspace, a second probability of a mid-air collision (MAC) occurring when the movement occurs in the airspace (see ¶[0012] and [0019]; determine the risk of collision based on sensor data. Probability distributions allow for calculations that represent risk associated with trajectories), a first encounter rate per hour, a second encounter rate per flight hour, a MAC rate per hour, or a MAC rate per flight hour.
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. SCHLEEDE discloses collision avoidance planning that includes determining risk of objects colliding based on a probability distribution. It would have been obvious to include the risk of object colliding as taught by SCHLEEDE in the system executing the method of GOBIN with the motivation to assess risk of vehicles and objects in motion.
Claim 8 (Currently Amended)
GOBIN discloses at least one non-transitory machine-readable medium comprising machine-readable instructions (see ¶[0004]; computer software on a computer-accessible medium) to cause at least one processor circuit (see ¶[0034[]; computer software on a computer-accessible medium.) to at least: determine a horizontal distance and a vertical distance between a grid location in an airspace and a point of a historical flight trajectory of a first aircraft (see ¶[0005] and [0038]; flight path waypoints include latitude, longitude, and altitude. The section of the nominal flight path can include a length of a distance traveled across a population cell defined in the population data. See also ¶[0141]; the interface can also have built in functions 512 to import select parts of the data from previous runs to enable reuse or modification. In the example shown in FIG. 16, the “Import Aircraft” button allows for the import of all of the data in the “Aircraft Information” section from a database.), the historical flight trajectory associated with an (see abstract and ¶[0003]; in the event of equipment failure, other failure, or termination of flight of the aircraft, it is helpful to have knowledge of the potential ground risk for a given flight. Calculate a potential crash area for a section of the nominal flight path based on a failure mode).
GOBIN does not specifically disclose, but SCHLEEDE discloses, the historical flight trajectory associated with an encounter event with respect to the grid location (see abstract; a collision probability between a vehicle and an object. See also ¶[0086]; GPS coordinates),
determine a first overlap probability between (a) a first horizontal probability distribution corresponding to the grid location and (b) a second horizontal probability distribution corresponding to the point, the second horizontal probability distribution corresponding to the horizontal distance (see ¶[0013]-[0015]; a geometric model can be utilized to determine intersections and/or collision probabilities associated with the vehicle and the object. The predicted locations of the vehicle and the object at the intersection can be determined based on trajectories of the vehicle and the object. The planning system can determine an overlap between the geometric representation of the vehicle and the geometric representation of the object, as well as probability distribution(s) associated with the object. Include a probability distribution in a first direction over an x-axis);
determine a second overlap probability between (c) a first vertical probability distribution corresponding to the grid location and (d) a second vertical probability distribution corresponding to the point, the second vertical probability distribution corresponding to the vertical distance (see again ¶[0013]-[0015]; a geometric model can be utilized to determine intersections and/or collision probabilities associated with the vehicle and the object. The predicted locations of the vehicle and the object at the intersection can be determined based on trajectories of the vehicle and the object. The planning system can determine an overlap between the geometric representation of the vehicle and the geometric representation of the object, as well as probability distribution(s) associated with the object. Include a probability distribution in a second direction over a y-axis);
determine, based on the first overlap probability and the second overlap probability, a risk metric corresponding to the grid location (see ¶[0012] and [0019]; determine the risk of collision based on sensor data. Probability distributions allow for calculations that represent risk associated with trajectories).
The combination of GOBIN and SCHLEEDE does not specifically disclose, but LOVE discloses, the risk metric representative of a number of aircraft expected to fly in a geographic region of the grid location (see ¶[0005]-[0006]; predict sector counts as the number of aircraft in a sector to determine a problematic block of airspace. Predict sector counts for 15 minute intervals).
GOBIN further discloses generate a risk map based on the risk metric (see abstract; display calculated risk values plotted on a map of the geographical area); and
generate, based on the risk map, a flight plan for a second aircraft to cause steering of the second aircraft based on the flight plan (see ¶[0003] and [0044]; the flight path can be planned or adjusted based on potential ground risk. Avoid higher risk areas).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. SCHLEEDE discloses collision avoidance planning that includes determining risk of objects colliding based on a probability distribution. It would have been obvious to include the risk of object colliding as taught by SCHLEEDE in the system executing the method of GOBIN with the motivation to assess risk of vehicles and objects in motion.
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. LOVE discloses aircraft flight-path modeling that includes predicted sector counts for a block of airspace. It would have been obvious to include the sector counts as taught by LOVE in the system executing the method of GOBIN with the motivation to model flight paths.
Claim 10 (Original)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the at least one non-transitory machine-readable medium as set forth in claim 8.
GOBIN does not specifically disclose, but LOVE discloses, wherein the machine-readable instructions are to cause one or more of the at least one processor circuit to: select the first and second horizontal probability distributions based on Normal-Double Exponential distributions; and select the first and second vertical probability distributions based on Laplacian distributions (see abstract and ¶[0013]; aircraft flight parameters represented as random variables with statistical distribution, such as normal, Laplacian, or logistic. Examiner Note: LaPlacian distribution is understood to be double exponential).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. LOVE discloses aircraft flight-path modeling that includes probability distributions that are normal and LaPlacian. It would have been obvious to include the probability distributions as taught by LOVE in the system executing the method of GOBIN with the motivation to model flight paths.
Claim 12 (Original)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the at least one non-transitory machine-readable medium as set forth in claim 8.
GOBIN does not specifically disclose, but SCHLEEDE discloses, wherein the machine-readable instructions are to cause one or more of the at least one processor circuit to determine the risk metric by determining at least one of a first probability of an encounter event occurring when a movement occurs in the airspace , a second probability of a mid-air collision (MAC) occurring when the movement occurs in the airspace (see ¶[0012] and [0019]; determine the risk of collision based on sensor data. Probability distributions allow for calculations that represent risk associated with trajectories), a first encounter rate per hour, a second encounter rate per flight hour, a MAC rate per hour, or a MAC rate per flight hour.
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. SCHLEEDE discloses collision avoidance planning that includes determining risk of objects colliding based on a probability distribution. It would have been obvious to include the risk of object colliding as taught by SCHLEEDE in the system executing the method of GOBIN with the motivation to assess risk of vehicles and objects in motion.
Claim 15 (Currently Amended)
GOBIN discloses a method comprising: determining a horizontal distance and a vertical distance between a grid location in an airspace and a point of a historical flight trajectory of a first aircraft (see ¶[0005] and [0038]; flight path waypoints include latitude, longitude, and altitude. The section of the nominal flight path can include a length of a distance traveled across a population cell defined in the population data. See also ¶[0141]; the interface can also have built in functions 512 to import select parts of the data from previous runs to enable reuse or modification. In the example shown in FIG. 16, the “Import Aircraft” button allows for the import of all of the data in the “Aircraft Information” section from a database.), the historical flight trajectory associated with an (see abstract and ¶[0003]; in the event of equipment failure, other failure, or termination of flight of the aircraft, it is helpful to have knowledge of the potential ground risk for a given flight. Calculate a potential crash area for a section of the nominal flight path based on a failure mode).
GOBIN does not specifically disclose, but SCHLEEDE discloses, the historical flight trajectory associated with an encounter event with respect to the grid location (see abstract; a collision probability between a vehicle and an object. See also ¶[0086]; GPS coordinates),
determining a first overlap probability between (a) a first horizontal probability distribution corresponding to the grid location and (b) a second horizontal probability distribution corresponding to the point, the second horizontal probability distribution corresponding to the horizontal distance (see ¶[0013]-[0015]; a geometric model can be utilized to determine intersections and/or collision probabilities associated with the vehicle and the object. The predicted locations of the vehicle and the object at the intersection can be determined based on trajectories of the vehicle and the object. The planning system can determine an overlap between the geometric representation of the vehicle and the geometric representation of the object, as well as probability distribution(s) associated with the object. Include a probability distribution in a first direction over an x-axis);
determining a second overlap probability between (c) a first vertical probability distribution corresponding to the grid location and (d) a second vertical probability distribution corresponding to the point, the second vertical probability distribution corresponding to the vertical distance (see again ¶[0013]-[0015]; a geometric model can be utilized to determine intersections and/or collision probabilities associated with the vehicle and the object. The predicted locations of the vehicle and the object at the intersection can be determined based on trajectories of the vehicle and the object. The planning system can determine an overlap between the geometric representation of the vehicle and the geometric representation of the object, as well as probability distribution(s) associated with the object. Include a probability distribution in a second direction over a y-axis);
determining, based on the first overlap probability and the second overlap probability, a risk metric corresponding to the grid location (see ¶[0012] and [0019]; determine the risk of collision based on sensor data. Probability distributions allow for calculations that represent risk associated with trajectories)
The combination of GOBIN and SCHLEEDE does not specifically disclose, but LOVE discloses, the risk metric representative of a number of aircraft expected to fly in a geographic region of the grid location (see ¶[0005]-[0006]; predict sector counts as the number of aircraft in a sector to determine a problematic block of airspace. Predict sector counts for 15 minute intervals).
GOBIN further discloses generating a risk map based on the risk metric (see abstract; display calculated risk values plotted on a map of the geographical area); and
generating, based on the risk map, a flight plan for a second aircraft to cause steering of the second aircraft based on the flight plan (see ¶[0003] and [0044]; the flight path can be planned or adjusted based on potential ground risk. Avoid higher risk areas).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. SCHLEEDE discloses collision avoidance planning that includes determining risk of objects colliding based on a probability distribution. It would have been obvious to include the risk of object colliding as taught by SCHLEEDE in the system executing the method of GOBIN with the motivation to assess risk of vehicles and objects in motion.
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. LOVE discloses aircraft flight-path modeling that includes predicted sector counts for a block of airspace. It would have been obvious to include the sector counts as taught by LOVE in the system executing the method of GOBIN with the motivation to model flight paths.
Claim 17 (Original)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the method as set forth in claim 15.
GOBIN does not specifically disclose, but LOVE discloses, further including: selecting the first and second horizontal probability distributions based on Normal-Double Exponential distributions; and selecting the first and second vertical probability distributions based on Laplacian distributions (see abstract and ¶[0013]; aircraft flight parameters represented as random variables with statistical distribution, such as normal, Laplacian, or logistic. Examiner Note: LaPlacian distribution is understood to be double exponential).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. LOVE discloses aircraft flight-path modeling that includes probability distributions that are normal and LaPlacian. It would have been obvious to include the probability distributions as taught by LOVE in the system executing the method of GOBIN with the motivation to model flight paths.
Claim 19 (Original)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the method as set forth in claim 15.
GOBIN does not specifically disclose, but SCHLEEDE discloses, further including determining the risk metric by determining at least one of a first probability of an encounter event occurring when a movement occurs in the airspace, a second probability of a mid-air collision (MAC) occurring when the movement occurs in the airspace (see ¶[0012] and [0019]; determine the risk of collision based on sensor data. Probability distributions allow for calculations that represent risk associated with trajectories), a first encounter rate per hour, a second encounter rate per flight hour, a MAC rate per hour, or a MAC rate per flight hour.
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. SCHLEEDE discloses collision avoidance planning that includes determining risk of objects colliding based on a probability distribution. It would have been obvious to include the risk of object colliding as taught by SCHLEEDE in the system executing the method of GOBIN with the motivation to assess risk of vehicles and objects in motion.
Claim(s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230222927 A1 to Gobin et al. (hereinafter ‘GOBIN’) in view of US 20220135026 A1 to SCHLEEDE et al. and US 20060089760 A1 to LOVE et al. as applied to claim 1 above, and further in view of US 20220051569 A1 to Larson et al. (hereinafter ‘LARSON’).
Claim 2 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the apparatus as set forth in claim 1.
The combination of GOBIN, SCHLEEDE, and LOVE does not specifically disclose, but LARSON discloses, wherein one or more of the at least one processor circuit is to: determine, based on the risk map, a mission risk metric associated with the flight plan; compare the mission risk metric to a risk threshold; and adjust the flight plan based on the comparison (see ¶[0124]; a user may redesign one or more aspects of the mission to mitigate risk. If the expected casualty value is above a threshold, the planned trajectory of a flight may be redesigned to avoid a population center. See also abstract; trajectory information associated with a planned flight).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. LARSON discloses flight risk analysis that includes redesigning a flight plan when risk is too high. It would have been obvious for one of ordinary skill in the art to take a mitigating action as taught by LARSON in the system executing the method of GOBIN with the motivation to reduce risk in populated areas.
Claim 9 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the at least one non-transitory machine-readable medium as set forth in claim 8.
The combination of GOBIN and SCHLEEDE does not specifically disclose, but LARSON discloses, wherein the machine-readable instructions are to cause one or more of the at least one processor circuit to: determine, based on the risk map, a mission risk metric associated with the flight plan; compare the mission risk metric to a risk threshold; and adjust the flight plan based on the comparison (see ¶[0124]; a user may redesign one or more aspects of the mission to mitigate risk. If the expected casualty value is above a threshold, the planned trajectory of a flight may be redesigned to avoid a population center. See also abstract; trajectory information associated with a planned flight).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. LARSON discloses flight risk analysis that includes redesigning a flight plan when risk is too high. It would have been obvious for one of ordinary skill in the art to take a mitigating action as taught by LARSON in the system executing the method of GOBIN with the motivation to reduce risk in populated areas.
Claim 16 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the method as set forth in claim 15.
The combination of GOBIN and SCHLEEDE does not specifically disclose, but LARSON discloses, further including: determining, based on the risk map, a mission risk metric associated with the flight plan; comparing the mission risk metric to a risk threshold; and adjusting the flight plan based on the comparison (see ¶[0124]; a user may redesign one or more aspects of the mission to mitigate risk. If the expected casualty value is above a threshold, the planned trajectory of a flight may be redesigned to avoid a population center. See also abstract; trajectory information associated with a planned flight).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. LARSON discloses flight risk analysis that includes redesigning a flight plan when risk is too high. It would have been obvious for one of ordinary skill in the art to take a mitigating action as taught by LARSON in the system executing the method of GOBIN with the motivation to reduce risk in populated areas.
Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230222927 A1 to Gobin et al. (hereinafter ‘GOBIN’) in view of SCHLEEDE et al. and US 20060089760 A1 to LOVE et al. as applied to claim 1 above, and further in view of US 20210255641 A1 to Tang et al. (hereinafter ‘TANG’).
Claim 4 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the apparatus as set forth in claim 1.
The combination of GOBIN, SCHLEEDE, and LOVE does not specifically disclose, but TANG discloses, wherein one or more of the at least one processor circuit is to: calculate three-dimensional (3-D) distances between the grid location and a plurality of points along the historical flight trajectory; and select the point corresponding to a lesser one of the calculated 3-D distances (see claims 1 and 10; design a three-dimensional trajectory of an aerial vehicle where an objective function minimizes flight distance).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. TANG discloses designing a trajectory of a vehicle that includes minimizing the distance traveled of the vehicle in a three dimensional space. It would have been obvious to include the minimization of distance as taught by TANG in the system executing the method of GOBIN with the motivation to optimize distance traveled.
Claim 11 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the at least one non-transitory machine-readable medium as set forth in claim 8.
The combination of GOBIN, SCHLEEDE, and LOVE does not specifically disclose, but TANG discloses, wherein the machine-readable instructions are to cause one or more of the at least one processor circuit to: calculate three-dimensional (3-D) distances between the grid location and a plurality of points along the historical flight trajectory; and select the point corresponding to a lesser one of the calculated 3-D distances (see claims 1 and 10; design a three-dimensional trajectory of an aerial vehicle where an objective function minimizes flight distance).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. TANG discloses designing a trajectory of a vehicle that includes minimizing the distance traveled of the vehicle in a three dimensional space. It would have been obvious to include the minimization of distance as taught by TANG in the system executing the method of GOBIN with the motivation to optimize distance traveled.
Claim 18 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the method as set forth in claim 15.
The combination of GOBIN, SCHLEEDE, and LOVE does not specifically disclose, but TANG discloses, further including: calculating three-dimensional (3-D) distances between the grid location and a plurality of points along the historical flight trajectory; and selecting the point corresponding to a lesser one of the calculated 3-D distances (see claims 1 and 10; design a three-dimensional trajectory of an aerial vehicle where an objective function minimizes flight distance).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. TANG discloses designing a trajectory of a vehicle that includes minimizing the distance traveled of the vehicle in a three dimensional space. It would have been obvious to include the minimization of distance as taught by TANG in the system executing the method of GOBIN with the motivation to optimize distance traveled.
Claim(s) 6, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230222927 A1 to Gobin et al. (hereinafter ‘GOBIN’) in view of SCHLEEDE et al. and US 20060089760 A1 to LOVE et al. as applied to claim 1 above, and further in view of US 20220051565 A1 to Hui et al. (hereinafter ‘HUI’).
Claim 6 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the apparatus as set forth in claim 1.
The combination of GOBIN, SCHLEEDE, and LOVE does not explicitly disclose, but HUI discloses, wherein one or more of the at least one processor circuit is to: predict intent of a third aircraft based on a position of the third aircraft and the risk map, the intent corresponding to an expected flight trajectory of the third aircraft; and adjust the flight plan of the second aircraft based on the predicted intent (see ¶[0048] and Fig. 5; monitor vehicle and object trajectories, predict collision risks, and take mitigating collision avoiding actions considering the trajectory of all vehicles. Real-time collision avoidance may be provided for vehicles in the 3D domain).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. HUI discloses collision avoidance based on coordination of vehicle operations that includes taking mitigating actions when a collision is predicted. It would have been obvious to include the mitigating actions as taught by HUI in the system executing the method of GOBIN with the motivation to avoid collisions for risky flight paths.
Claim 13 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the at least one non-transitory machine-readable medium as set forth in claim 8.
The combination of GOBIN, SCHLEEDE, and LOVE does not explicitly disclose, but HUI discloses, wherein the machine-readable instructions are to cause one or more of the at least one processor circuit to: predict intent of a third aircraft based on a position of the third aircraft and the risk map, the intent corresponding to an expected flight trajectory of the third aircraft; and adjust the flight plan of the second aircraft based on the predicted intent (see ¶[0048] and Fig. 5; monitor vehicle and object trajectories, predict collision risks, and take mitigating collision avoiding actions considering the trajectory of all vehicles. Real-time collision avoidance may be provided for vehicles in the 3D domain).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. HUI discloses collision avoidance based on coordination of vehicle operations that includes taking mitigating actions when a collision is predicted. It would have been obvious to include the mitigating actions as taught by HUI in the system executing the method of GOBIN with the motivation to avoid collisions for risky flight paths.
Claim 20 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the method as set forth in claim 15.
The combination of GOBIN, SCHLEEDE, and LOVE does not explicitly disclose, but HUI discloses, further including: predicting intent of a third aircraft based on a position of the third aircraft and the risk map, the intent corresponding to an expected flight trajectory of the third aircraft; and adjusting the flight plan of the second aircraft based on the predicted intent (see ¶[0048] and Fig. 5; monitor vehicle and object trajectories, predict collision risks, and take mitigating collision avoiding actions considering the trajectory of all vehicles. Real-time collision avoidance may be provided for vehicles in the 3D domain).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. HUI discloses collision avoidance based on coordination of vehicle operations that includes taking mitigating actions when a collision is predicted. It would have been obvious to include the mitigating actions as taught by HUI in the system executing the method of GOBIN with the motivation to avoid collisions for risky flight paths.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20230222927 A1 to Gobin et al. (hereinafter ‘GOBIN’) in view of SCHLEEDE et al. and US 20060089760 A1 to LOVE et al. as applied to claim 1 above, and further in view US 20210405187 A1 to Rosner et al. (hereinafter ‘ROSNER’).
Claim 7 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the apparatus as set forth in claim 1.
The combination of GOBIN, SCHLEEDE, and LOVE does not specifically disclose, but ROSNER discloses, wherein one or more of the at least one processor circuit is to identify the historical flight trajectory based on surveillance data associated with the airspace, the surveillance data filtered based on at least one of an altitude level corresponding to the grid location or a time frame (see ¶[0050]-[0051] and [0056]; perform an altitude filter operation to compare altitude differences to a 100km screening difference threshold. See also ¶[0037]-[0038]; temporospatial descriptors).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. ROSNER discloses determining a possible risk of collision based on an altitude filter operation. It would have been obvious to include the altitude filter operation as taught by ROSNER in the system executing the method of GOBIN with the motivation to assess risk of a collision.
Claim 14 (Currently Amended)
The combination of GOBIN, SCHLEEDE, and LOVE discloses the at least one non-transitory machine-readable medium as set forth in claim 8.
The combination of GOBIN, SCHLEEDE, and LOVE does not specifically disclose, but ROSNER discloses, wherein the machine-readable instructions are to cause one or more of the at least one processor circuit to identify the historical flight trajectory based on surveillance data associated with the airspace, the surveillance data filtered based on at least one of an altitude level corresponding to the grid location or a time frame (see ¶[0050]-[0051] and [0056]; perform an altitude filter operation to compare altitude differences to a 100km screening difference threshold. See also ¶[0037]-[0038]; temporospatial descriptors).
GOBIN discloses a quantitative approach and departure risk assessment system that includes mapping risk based an assessment of flight paths over a variety of terrain and populated areas that is based on a probability distribution of the flight path (see abstract and ¶[0006]. ROSNER discloses determining a possible risk of collision based on an altitude filter operation. It would have been obvious to include the altitude filter operation as taught by ROSNER in the system executing the method of GOBIN with the motivation to assess risk of a collision.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/RICHARD N SCHEUNEMANN/Primary Examiner, Art Unit 3624
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