DETAILED ACTION
This office action is in response to the amendment filed on 02/04/2026. This action is made Final.
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
The Examiner acknowledges that the current application is a continuation (CON) of the parent application 16897699, which has an effective filing date of 06/10/2019.
Response to Amendment
The amendment filed on 02/04/2026 has been entered. Claim 21 - 40 remain pending in the application. The previous double patenting rejection has been withdrawn in view of Applicant’s amendment
Response to Arguments
Applicant’s arguments with respect to the 103 rejection of claims 21, 28, and 25 have been considered but are moot in view of new ground of rejection necessitated by Applicant’s amendment.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 21 – 40 are rejected under 35 U.S.C. 103 as being unpatentable over Jordan et al. (Patent No. US 11157867 B1; hereinafter Jordan) in view of Beaurepaire et al. (Publication No. US 20210012669 A1; hereinafter Beaurepaire) in further view of Eisenmann et al. (Patent No. US 10872304 B1; hereinafter Eisenmann) and in further view of Groden et al. (Publication No. US 20190033861 A1; hereinafter Groden).
Regarding to claim 1, Jordan teaches A computer-implemented method for aircraft routing comprising:
accessing static feature data and dynamic feature data for a geographic region, the dynamic feature data being captured via one or more sensors associated with the geographic region; ( [Col. 19, line 49 – 65], “embodiments, a model may be generated for a region based on intrinsic or extrinsic factors or attributes associated with flight operations within the region, including but not limited to sounds radiated from aerial vehicles during such flight operations. As is shown in FIG. 4D, a model 455-3 includes exposure scores for portions or sectors that, like the model 455-2 of FIG. 4C, correspond to defined areas of land within the region 400, including one or more regions having dwellings or other structures thereon, and are not regularly sized or shaped. The exposure scores of FIG. 4D, however, are calculated for the parcels or properties based on the estimated noise exposure at such parcels or properties during the mission of the aerial vehicle 410. For example, the exposure scores at such parcels or properties may be calculated based on a number of decibels or other measure of intensity (e.g., sound pressure levels) of sounds radiated by the aerial vehicle 410 during the mission or, alternatively or additionally, one or more frequencies (e.g., frequency spectra) of such sounds.”; [Col. 22, line 17 – 34], “Accordingly, as is shown in FIG. 6B, where the aerial vehicle 610 is operating, an intensity level of the noise at various ground-based locations may be determined based on a power level of a source of noise radiated from the aerial vehicle 610 or based on an intensity of the noise determined at a fixed distance from the source, e.g., by one or more onboard or ground-based acoustic sensors. The power level of the source of noise (or an intensity of the noise determined at a given distance from the source of the noise) may then be used to calculate ground-level exposures to noise within a region 600 at a variety of locations 670-1, 670-2, 670-3, 670- 4, 670-5, 670-6 at various distances r.sub.1, r.sub.2, r.sub.3, r.sub.4, r.sub.5, r.sub.6 from positions of the aerial vehicle 610 while operating in flight in the region 600, according to the inverse square law. The intensity levels calculated according to the inverse square law may then be transmitted to one or more servers or other computer-based systems and used to select or modify one or more routes or paths.”
Wherein the “intrinsic factor or attribute” corresponds to the “static feature” and the “extrinsic factors or attributes” corresponds to the “dynamic feature”.)
computing, based on the static feature data and the dynamic feature data, a predicted background noise loudness in the geographic region using a model; ([Col. 13, line 22 – 38], “the servers 282 may be configured to generate models in the form of two dimensional or three-dimensional representations of flight exposure at a plurality of locations over time, or to modify such representations over time, or in response to variations in operating characteristics or environmental conditions. Furthermore, in some embodiments, the servers 282 may be configured to calculate amounts of energy to be expended by aerial vehicles (including but not limited to the aerial vehicle 210) during the performance of one or more missions along a route or path, or to calculate changes in such amounts of energy that may be expended or predicted to expended where one or more variations to a path or route are executed. The servers 282 may be further configured to generate two-dimensional or three-dimensional representations of noise exposure or energy in the form of maps or other cartographic representations, for any purpose.”; [Col. 19, line 49 – 65], “embodiments, a model may be generated for a region based on intrinsic or extrinsic factors or attributes associated with flight operations within the region, including but not limited to sounds radiated from aerial vehicles during such flight operations. As is shown in FIG. 4D, a model 455-3 includes exposure scores for portions or sectors that, like the model 455-2 of FIG. 4C, correspond to defined areas of land within the region 400, including one or more regions having dwellings or other structures thereon, and are not regularly sized or shaped. The exposure scores of FIG. 4D, however, are calculated for the parcels or properties based on the estimated noise exposure at such parcels or properties during the mission of the aerial vehicle 410. For example, the exposure scores at such parcels or properties may be calculated based on a number of decibels or other measure of intensity (e.g., sound pressure levels) of sounds radiated by the aerial vehicle 410 during the mission or, alternatively or additionally, one or more frequencies (e.g., frequency spectra) of such sounds.”)
computing a route based on the predicted background noise loudness in the geographic region; ([Col. 19, line 17 – 35], “The exposure scores of FIG. 4D, however, are calculated for the parcels or properties based on the estimated noise exposure at such parcels or properties during the mission of the aerial vehicle 410. For example, the exposure scores at such parcels or properties may be calculated based on a number of decibels or other measure of intensity (e.g., sound pressure levels) of sounds radiated by the aerial vehicle 410 during the mission or, alternatively or additionally, one or more frequencies (e.g., frequency spectra) of such sounds. Once the model 455-3 has been generated, one or more routes or paths for aerial vehicles may be selected based at least in part on the model 455-3.”; [Col. 22, line 60 – 65], “Information or data regarding flight exposure within a neighborhood or other region may be modeled and used to select an optimal route, or a modification to one or more paths of an optimal route, based on the flight exposure throughout the neighborhood or other region. Referring to FIGS. 7A through 7E, views of aspects of systems for selecting flight routes based on historical exposure in accordance with embodiments of the present disclosure are shown.”)
routing the aircraft through the geographic region based on the route and the skylane. ([Col. 23, line 55 – 67], “87) Once generated, the model 755 may be used to select an optimal route for performing one or more missions (e.g., deliveries) in the neighborhood 700. As is shown in FIG. 7D, an order is received for a delivery to a home 770-1 in the neighborhood 700. As is shown in FIG. 7E, an aerial vehicle 710-5 may be programmed to complete the delivery to the home 770-1 along a path P.sub.1, and to depart from the home 770-1 along a path P.sub.2. The paths P.sub.1, P.sub.2 may be selected based on values represented in the model 755 at a time when the order is received or, alternatively, estimated values represented in the model 755 at a time when the delivery is to be performed. For example, as is shown in FIGS. 7C and 7E, the paths P.sub.1, P.sub.2 call for travel over areas that have been subjected to low levels of exposure to noise, e.g., including areas where the aerial vehicles 710-1, 710-2, 710-3, 710-4 did not travel. In some embodiments, the values of the model 755 may be accumulated over time to determine a total level of noise exposure in the neighborhood 700.”)
Jordan teaches computing the background noise loudness associated with the geographic region along the aircraft’s projected travel path as described above, but does not explicitly disclose the predicted background noise loudness indicative of a noise level in the geographic region.
However, Beaurepaire teaches the predicted background noise loudness indicative of a noise level in the geographic region. ([Par. 0004], “the apparatus to retrieve environmental noise map data for a geographic area, wherein the environmental noise map data indicates existing noise levels measured in the geographic area. The apparatus is also caused to determine a vehicle noise characteristic of the aerial vehicle. The apparatus is further caused to generate a route for the aerial vehicle over the geographic area based on the relative noise impact of the aerial vehicle while operating over the geographic area, wherein the relative noise impact is computed based on the vehicle noise characteristic relative to the existing noise levels of the environmental noise map data for portions of the geographic area under the route of the aerial vehicle.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify Jordan to incorporate the teaching of Beaurepaire. The modification would have been obvious because generating a noise impact based on background noise loudness indicative of the existing noise level of the geographic region enables a more accurate assessment of the total noise impact as the aerial vehicle travels through the region, thereby facilitating the generation of a more effective route through the region.
Jordan further teaches to determine whether the mission is feasible based on the predicted background noise of the region and the availability of the aircraft (see Col. 17, line 59 – 67 and Col. 18, line 1 – 40) but does not explicitly disclose selecting a particular aircraft, from among a plurality of aircraft, for the route and the skylane based on the predicted background noise loudness and one or more capabilities of the particular aircraft;
However, Eisenmann teaches selecting a particular aircraft, from among a plurality of aircraft, for the route and the skylane based on the predicted background noise loudness and one or more capabilities of the particular aircraft; ([Col. 2, line 15 – 21], “The route or a portion thereof may include one or more pathways, destinations, waypoints, altitudes, or one or more other aspects of a route. In addition, one or more route characteristics of the route may be determined. The route characteristics may include information related to distance, altitude, duration, desired efficiency, desired noise profile, desired flight profile, or other characteristics associated with the intended route.”; [Col. 2, line 29 – 37], “Using at least the route, route characteristics, environment, and/or environment characteristics as inputs, one or more simulations of one or more models may be performed to determine a particular configuration of an aerial vehicle for completing the delivery of the item to the delivery destination. Other inputs to the simulation(s) of the model(s) may include vehicle characteristics, available components, manufacturable components, optimization parameters, and/or other characteristics or parameters as described herein.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the combination of Jordan and Beaurepaire to incorporate the teaching of Eisenmann. The modification would have been obvious because, by selecting a particular aircraft for the route based on route characteristics such as predicted noise levels and the capabilities of the aircraft, the system ensures that the mission remains feasible while satisfying operational requirements and route constraints. Such selection would predictably allow the aircraft to complete the mission efficiently and achieve the desired optimal performance outcome.
Jordan teaches to determine an optimal route for the aircraft based on background noise exposure in the region, in which the route includes one or more constraint such as course, speed, altitude or any other factors as described in Col. 17, line 65 – 68, but the combination of Jordan and Eisenmann does not explicitly discloses computing a skylane based on the route, the skylane defining a volume around the route in which aircraft are to stay within to maintain an acceptable noise level while within the geographic region;
However, Groden teaches computing a skylane based on the route, the skylane defining a volume around the route in which aircraft are to stay within to maintain an acceptable noise level while within the geographic region; ([Par. 0152], “Generation of the safety corridor is preferably implemented in real time or near real time, such that boundaries of the corridor and other guidance logic aspects are continuously/dynamically updated as the vehicle (e.g., aircraft) moves in an environment. Generation of the safety corridor can be performed during pre-flight phases of operation, during flight phases of operation, and/or during post-flight phases of operation (e.g., in relation to evaluation of actual flight data for optimization of safety corridor generating algorithms). Furthermore, dynamic determination of the safety corridor can be implemented for global path optimization (e.g., for complete definition of a safety corridor) and/or local path optimization (e.g., for definition of an optimized sub-corridor within a full safety corridor).”; [Par. 0155], “Each factor derived from the data stream(s) can have an associated weight or prioritization. For example, factors associated with weather conditions or terrain conditions can be weighted more heavily than factors associated with vehicle noise in relation to proximity to noise-sensitive populations. Furthermore, for each factor derived from the data stream(s), a threshold can be set to increase or decrease safety margins associated with the corridor generated.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claim invention to modify the combination of Jordan, Beaurepaire and Eisenmann to incorporate the teaching of Groden. The modification would have been obvious because, by generating a safety corridor along the aircraft’s flight path, the system ensures that the aircraft remains within an allowable airspace volume that accounts for vehicle noise relative to nearby noise-sensitive populations. Such a configuration would predictably reduce community noise impact and maintain compliance with operational and environmental constraints.
Regarding to claim 22, the combination of Jordan, Beaurepaire, Eisenmann, and Groden teaches the method of claim 21.
Jordan further teaches further comprising: based on the predicted background noise loudness, computing one or more operating constraints associated with the route, the one or more operating constraints indicative of at least one of: (i) a take-off maneuver or (ii) a landing maneuver associated with the route. ([Col. 23, line 55 – 67 and col. 24, line 7 – 20], “(87) Once generated, the model 755 may be used to select an optimal route for performing one or more missions (e.g., deliveries) in the neighborhood 700. As is shown in FIG. 7D, an order is received for a delivery to a home 770-1 in the neighborhood 700. As is shown in FIG. 7E, an aerial vehicle 710-5 may be programmed to complete the delivery to the home 770-1 along a path P.sub.1, and to depart from the home 770-1 along a path P.sub.2. The paths P.sub.1, P.sub.2 may be selected based on values represented in the model 755 at a time when the order is received or, alternatively, estimated values represented in the model 755 at a time when the delivery is to be performed. For example, as is shown in FIGS. 7C and 7E, the paths P.sub.1, P.sub.2 call for travel over areas that have been subjected to low levels of exposure to noise, e.g., including areas where the aerial vehicles 710-1, 710-2, 710-3, 710-4 did not travel. In some embodiments, the values of the model 755 may be accumulated over time to determine a total level of noise exposure in the neighborhood 700. (88) As is discussed above, an optimal route for the performance of a mission by an aerial vehicle in a region (e.g., a neighborhood) may be modified based on information or data regarding exposure to flight operations (e.g., to noises emitted by aerial vehicles during such operations) at locations within a region. For example, where an optimal route is generated according to a traditional shortest path or shortest route algorithm, one or more paths of the optimal route may be modified based on information or data that is available regarding sounds radiated by aerial vehicles or any other sources within the region. Such information or data may be received and considered prior to the aerial vehicle embarking upon the mission or, alternatively, after the aerial vehicle has departed to perform the mission.”; [Col. 27, line 63 – 67], “a path or a route may be modified by changing any aspect of an aerial vehicle's operations. For example, based on levels of noise exposure, a course, a speed, an altitude or any operating characteristic of an aerial vehicle may be selected or modified. The systems and methods of the present disclosure are not limited to merely selecting or changing course, speed or altitude, or selecting or changing operating characteristics.”
The mapping is understood as the optimal route of the aircraft can be modified based on the noise exposure in the operating area of the aircraft. The modification includes changes in course, speed, or altitude. This inherently includes the take-off and landing of the aircraft following the route.)
Regarding to claim 23, the combination of Jordan, Beaurepaire, Eisenmann, and Groden teaches the method of claim 21.
Jordan further teaches computing, based on the predicted background noise loudness, a frequency in a number of flights to be assigned to at least one of: (i) the route or (ii) the skylane. ([Col. 23, line 32 – 50], “Based on the exposure to noise experienced by the homes 770-n, a model 755 of the neighborhood 700 may be generated. As is shown in FIG. 7C, the model 755 includes values representing exposure to noise in various areas of the neighborhood 700. For example, the model 755 indicates high levels of exposure to noise near areas where the routes traveled by the aerial vehicles 710-3, 710-4 intersect, as well as areas around or near the homes 770-1, 770-2, or where the routes traveled by the aerial vehicles 710-1, 710-2 intersect. The model 755 also indicates low levels of exposure to noise near areas where the aerial vehicles 710-1, 710-2, 710-3, 710-4 did not travel. In some embodiments, the values of the model 755 may be accumulated over time to determine a total level of noise exposure throughout the neighborhood 700.”)
Regarding to claim 24, the combination of Jordan, Beaurepaire, Eisenmann, and Groden teaches the method of claim 21.
Groden further teaches wherein computing the skylane further comprising: computing, based on the predicted background noise, an altitude associated with the skylane. ([Par. 0055 – 0056], “Each factor derived from the data stream(s) can have an associated weight or prioritization. For example, factors associated with weather conditions or terrain conditions can be weighted more heavily than factors associated with vehicle noise in relation to proximity to noise-sensitive populations. Furthermore, for each factor derived from the data stream(s), a threshold can be set to increase or decrease safety margins associated with the corridor generated. [0156] The corridor can be a three dimensional corridor in space, or can alternatively be a two-dimensional or one-dimensional corridor. The corridor can be referenced to the vehicle, and in specific examples, can be centered about vehicle, not centered about vehicle, or otherwise displaced from vehicle in any other suitable manner.” Wherein this implies the altitude variation as part of dynamically shaping the 3D corridor. The altitude is one of the 3D corridor’s spatial dimensions.
Regarding to claim 25, the combination of Jordan, Beaurepaire, Eisenmann, and Gorden teaches the method of claim 21.
Jordan further teaches wherein the static feature data is indicative of at least one of: (i) a distance from one or more roads or (ii) a distance from one or more airports. ([Col. 26, line 43 – 57], “a neighborhood 900 includes a plurality of homes 970-1, 970-2, 970-3, 970-4, 970-5, 970-6, 970-7, 970-8, 970-9, 970-10, 970-11, 970-12, 970-13, 970-14. An optimal route R.sub.1 is generated for the performance of a mission by an aerial vehicle at a location 975-14 associated with one of the homes 970-14 in the neighborhood 900. The location 975-14 may be a yard, a driveway, a sidewalk, a road or street, a roof, a deck, a patio, or any other surface associated with the home 970-14.”; [claim 3, “wherein each of the levels of exposure is calculated based at least in part on a power of sound radiated by the first aerial vehicle and a distance between the first aerial vehicle and one of the first plurality of locations.”)
Regarding to claim 26, the combination of Jordan, Beaurepaire, Eisenmann, and Gorden teaches the method of claim 21.
Jordan further teaches wherein the dynamic feature data is indicative of at least one of: (i) weather, (ii) sound levels, or (iii) traffic associated with the geographic region. ([Col. 23, line 32 – 53], “86) Based on the exposure to noise experienced by the homes 770-n, a model 755 of the neighborhood 700 may be generated. As is shown in FIG. 7C, the model 755 includes values representing exposure to noise in various areas of the neighborhood 700. For example, the model 755 indicates high levels of exposure to noise near areas where the routes traveled by the aerial vehicles 710-3, 710-4 intersect, as well as areas around or near the homes 770-1, 770-2, or where the routes traveled by the aerial vehicles 710-1, 710-2 intersect. The model 755 also indicates low levels of exposure to noise near areas where the aerial vehicles 710-1, 710-2, 710-3, 710-4 did not travel.”)
Regarding to claim 27, the combination of Jordan, Beaurepaire, Eisenmann, and Gorden teaches the method of claim 21.
Jordan further teaches further comprising: accessing map data, the map data comprising at least one of: (i) a position of other aircraft or (ii) a position of one or more landing zones within the geographic region; ([Col. 26, line 43 – 67], “a neighborhood 900 includes a plurality of homes 970-1, 970-2, 970-3, 970-4, 970-5, 970-6, 970-7, 970-8, 970-9, 970-10, 970-11, 970-12, 970-13, 970-14. An optimal route R.sub.1 is generated for the performance of a mission by an aerial vehicle at a location 975-14 associated with one of the homes 970-14 in the neighborhood 900. The location 975-14 may be a yard, a driveway, a sidewalk, a road or street, a roof, a deck, a patio, or any other surface associated with the home 970-14. The optimal route R.sub.1 may be determined in any manner, e.g., according to a shortest path or shortest route algorithm, or in any other manner. Alternatively, the optimal route R.sub.1 may be calculated as a straight line between an origin (not shown) and the location 975-14, or as an arc of a circle (e.g., a great circle) that includes the origin and the location 975-14.” Wherein the location of homes corresponds to the position of the landing zones.) and
routing the aircraft through the geographic region also based on the map data. ([Col. 26, line 13 – 17], “At box 880, the optimal route is updated to include the selected modification. At box 890, an aerial vehicle is caused to travel from the origin of the item to the location specified by the customer via the updated optimal route, and the process ends.”)
Claim 28 recite the computing system with substantially similar scope as claim 21, thus being rejected for the same basis as claim 21 above.
Jordan further teaches one or more processors; and one or more non-transitory, computer-readable media storing instructions that are executable by the one or more processors to cause the computing system to perform operations. ([Col. 15, line 45 – 65], “The data and/or computer-executable instructions, programs, firmware, software and the like (also referred to herein as “computer-executable” components) described herein may be stored on a computer-readable medium that is within or accessible by computers or computer components such as the processor 212, the servers 282 and/or the processors 284, or any other computers or control systems utilized by the aerial vehicle 210 or the data processing system 280 (e.g., by one or more other aerial vehicles), and having sequences of instructions which, when executed by a processor (e.g., a CPU or GPU), cause the processor to perform all or a portion of the functions, services and/or methods described herein.”)
Claims 29 - 34 recite the computing system with substantially similar scope as claims 22 – 27 respectively, thus being rejected for the same basis as claims 22 – 27 respectively above.
Claims 35 – 40 recites the non-transitory computer-readable medium with substantially similar scope as claims 21 – 26 respectively, thus being rejected for the same basis as claims 21 – 26 respectively above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN V NGUYEN whose telephone number is (571)272-7320. The examiner can normally be reached Monday -Friday 11am - 7pm EST.
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/STEVEN VU NGUYEN/Examiner, Art Unit 3668
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