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 .
Response to Arguments
Applicant’s amendments to claim 16 are acknowledged. The objections to the claims are hereby withdrawn.
Applicant’s arguments with respect to the rejections of claims 1 and 11-13 under 35 U.S.C. § 103 have been fully considered but are not persuasive. As discussed in further detail below, Ivanov does teach the limitation of planning the drone’s path in real time based on a wind change cost indicating a change in the wind received at a position in the path. Ivanov teaches updating a wind model 145 in real time based on real time wind condition data (see at least [0068]), and recalculating wind factor values in real time based on the updated wind model 145 (see at least [0073]). The wind factor values include wind factor volatility values indicative of a change in wind over time (see at least [0074]), and the wind factor values are applied as an incentive or penalty value (i.e., a cost) during real-time path planning (see at least [0070], [0083]-[0084], [0087], and [0091]). Therefore, Ivanov does teach planning the drone’s path in real time based on a wind change cost indicating a change in the wind received at a position of the path.
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.
Claims 1, 3-9, and 11-17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20200233439 A1, filed 01/22/2019 and published 07/23/2020, hereinafter “Ivanov”, in view of US 20220308598 A1, with an earliest priority date of 04/30/2020, hereinafter “Nakazawa”, further in view of US 20160328979 A1, filed 07/15/2015, hereinafter “Postrel”.
Regarding claim 1, Ivanov teaches An information processing apparatus. See at least [0096] and figure 11, device 122.
comprising: circuitry configured to: estimate one of a wind or a turbulence distribution at an altitude based on global wind information and a distribution of a plurality of obstacles on a ground surface. See at least [0100] and figure 11, model generation unit that generates wind model 145. Additionally, see at least [0062] and figure 9B, wherein wind model 145 comprises estimations of a distribution of wind and its altitude. Additionally, see at least [0071]-[0074], wherein the wind model 145 is used to estimate wind magnitude, wind heading, and turbulence by applying the wind model 145 to the location 140. See at least [0032]-[0033], wherein wind condition data is obtained from a database or purchased from external weather services. Additionally, see at least [0062]-[0065] and figure 9B, wherein wind model 145 is based on weather data received from a third-party source, and applied to a 3D map 143 to estimate the wind and turbulence values. See at least [0057]-[0061] and figure 9A, wherein 3D map 143 represents a distribution of objects on a group surface.
plan, in real time, a path along which a flight vehicle flies. See at least [0096]-[0098] and figure 11, wherein device 122 performs path planning for the flight vehicle. See at least [0084], wherein a flight vehicle requests a route at a predetermined desired altitude, and the route is planned on a basis of the estimated wind factor values. Additionally, see at least [0068], [0073], and [0089]-[0091], wherein the wind model 145 is updated in real-time, and the drone’s route is stopped or modified based on the real-time wind model 145.
wherein the path is planned based on a wind change cost on the path, the distribution of the plurality of obstacles, and the estimated one of the wind or the turbulence distribution. See at least [0069]-[0072] and figure 10, wherein the wind factor values used in path planning are derived from the estimated wind magnitude, heading, and turbulence values from wind model 145. Additionally, see at least [0062]-[0065] and figure 9B, wherein wind model 145 is based on weather data received from a third-party source, and applied to a 3D map 143 to estimate the wind and turbulence values. See at least [0069]-[0070] and figure 10, wherein the route is planned based on an incentive or penalty cost at positions along the route, the cost being determined based on the wind factor value. See at least [0074], wherein the wind factor value includes a volatility value corresponding to how much the wind changes over time, or temporally. See at least [0083], wherein the flight vehicle’s path is planned based on volatility values.
and the wind change cost is based on a change in wind received at a position on the path. See at least [0074]-[0075], [0080], and figure 8, wherein the wind factor volatility value represents a change in wind speed for different wind sections, or positions in the wind model.
and generate a command based on the planned path. See at least [0093], wherein the device 122 controls the flight vehicle through the planned route by generating flight commands.
and control, based on the generated command, a driving operation of at least one motor to generate lift for the flight vehicle, wherein the at least one motor, based on the controlled driving operation, rotates a rotary wind of the flight vehicle to generate the light, the flight vehicle flies along the planned path based on the generated lift. See at least [0036], [0040], and [0049], wherein movement of the flight vehicle is controlled based on commands sent to motors of the flight vehicle, which controls propellers of the vehicle to create lift.
Ivanov remains silent as to the specifics of estimating the wind/turbulence distribution at a specific altitude, generating thrust, and a change in the generated lift and the generated thrust is based on a change in a number of rotations per unit time of the rotary wing.
Nakazawa teaches a specific altitude. See at least [0055], wherein the wind forecast map is divided into a plurality of parts at different altitudes. See at least [0085]-[0086], wherein a predetermined altitude range is stipulated.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Ivanov with Nakazawa’s technique of a predetermined altitude. It would have been obvious to modify because doing so enables flight vehicles to flexibly navigate their environment while resisting the influence of wind, as recognized by Nakazawa (see at least [0004]-[0005] and [0022]).
Postrel teaches generating thrust, and a change in the generated lift and the generated thrust is based on a change in a number of rotations per unit time of the rotary wing. See at least [0049], wherein the aircraft controls lift and thrust based on altering the rotation rate of one or more rotor discs.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Ivanov with Postrel’s teaching of an aerial vehicle controlling lift and torque based on altering the rotation rate of one or more rotor discs. It would have been obvious to modify because this type of vehicle control is generally known in the art, as recognized by Postrel (see at least [0049]). Additionally, it would have been obvious to modify because doing so enables drone fleets to navigate in view of weather parameters and natural or manmade barrier locations, reducing the risk of collisions, as recognized by Postrel (see at least [0002]-[0008]).
Regarding claim 3, Ivanov, Nakazawa, and Postrel in combination teach all of the limitations of claim 1 as discussed above, and Ivanov remains silent on wherein the circuitry is further configured to estimate the one of wind or the turbulence distribution at the specific altitude based on trained machine learning model.
Nakazawa wherein the circuitry is further configured to estimate the one of wind or the turbulence distribution at the specific altitude based on trained machine learning model. See at least [0056]-[0057], wherein control model M comprises a machine learning model using reinforcement learning.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Ivanov with Nakazawa’s technique of a trained machine learning model. It would have been obvious to modify because doing so enables flight vehicles to flexibly navigate their environment while resisting the influence of wind, as recognized by Nakazawa (see at least [0004]-[0005] and [0022]).
Regarding claim 5, Ivanov, Nakazawa, and Postrel in combination teach all of the limitations of claim 1 as discussed above, and Ivanov additionally teaches wherein the change in the wind includes a spatial change component of the wind and a temporal change component of the wind. See at least [0069]-[0070] and figure 10, wherein the route is planned based on an incentive or penalty cost at positions along the route, the cost being determined based on the wind factor value. See at least [0071], wherein the wind factor value includes wind pattern data. See at least [0065], wherein the wind pattern data indicates changes (increase or decrease in wind speed) at spatial locations, caused by building or structure geometry distributed over the space 140. See at least [0072] and [0074], wherein the wind factor value includes a value corresponding to how much the wind changes over time, or temporally.
Regarding claim 6, Ivanov, Nakazawa, and Postrel in combination teach all of the limitations of claim 1 as discussed above, and Ivanov additionally teaches wherein the circuitry is further configured to plan the path based on a distribution of the plurality of obstacles on the ground surface. See at least [0069]-[0070], [0075], and [0083], wherein the 3D map data 143 representing the distribution of obstacles on a ground surface is applied to the wind model during path planning, and the path planning prioritizes minimizing the likelihood of collision.
Ivanov remains silent on an obstacle cost.
Nakazawa teaches an obstacle cost. See at least [0040] and[0091]-[0095], wherein a positive or negative score r is identified based on the distance between the flight vehicle and obstacles.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Ivanov with Nakazawa’s technique of an obstacle cost. It would have been obvious to modify because doing so enables flight vehicles to flexibly navigate their environment while resisting the influence of wind, as recognized by Nakazawa (see at least [0004]-[0005] and [0022]).
Regarding claim 7, Ivanov, Nakazawa, and Postrel in combination teach all of the limitations of claim 6 as discussed above, and Ivanov additionally teaches wherein the circuitry is further configured to: superimpose the wind change cost on the obstacles to obtain an integrated cost; and plan the path based on the obtained integrated cost. See at least [0069]-[0070], wherein the wind factor value is obtained via the wind model 145. See at least [0066] and figure 9B, wherein the wind model is composed of superimposing a wind pattern layer over the 3D map layer.
Ivanov remains silent on the obstacle cost.
Nakazawa teaches the obstacle cost. See at least [0040] and [0091]-[0095], wherein a positive or negative score r is identified based on the distance between the flight vehicle and obstacles.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Ivanov with Nakazawa’s technique of an obstacle cost. It would have been obvious to modify because doing so enables flight vehicles to flexibly navigate their environment while resisting the influence of wind, as recognized by Nakazawa (see at least [0004]-[0005] and [0022]).
Regarding claim 8, Ivanov, Nakazawa, and Postrel in combination teach all of the limitations of claim 1 as discussed above, and Ivanov additionally teaches wherein wind change cost on the path is equal to or less than a specific threshold. See at least [0084], wherein the route planned is based on requested or predetermined threshold values. Additionally, see at least [0077], wherein the threshold value indicates a minimum acceptable wind factor value, or cost. For example, a wind factor threshold cost of -2 means that the planned route avoids any areas with wind conditions with a cost worse than -2.
Regarding claim 9, Ivanov, Nakazawa, and Postrel in combination teach all of the limitations of claim 1 as discussed above, and Ivanov additionally teaches wherein the wind change cost is based on the change in the wind, excluding a temporal change component of the wind. See at least [0072], wherein estimated wind turbulence may optionally be included in the wind model. Per [0023] of Applicant’s specification, the temporal change component is turbulence.
Regarding claim 11, Ivanov teaches An information processing method. See at least figures 14-15.
comprising: estimating, by circuitry, one of a wind or a turbulence distribution at an altitude based on global wind information and a distribution of a plurality of obstacles on a ground surface. See at least [0100] and figure 11, model generation unit that generates wind model 145. Additionally, see at least [0062] and figure 9B, wherein wind model 145 comprises estimations of a distribution of wind and its altitude. Additionally, see at least [0071]-[0074], wherein the wind model 145 is used to estimate wind magnitude, wind heading, and turbulence by applying the wind model 145 to the location 140. See at least [0032]-[0033], wherein wind condition data is obtained from a database or purchased from external weather services. Additionally, see at least [0062]-[0065] and figure 9B, wherein wind model 145 is based on weather data received from a third-party source, and applied to a 3D map 143 to estimate the wind and turbulence values. See at least [0057]-[0061] and figure 9A, wherein 3D map 143 represents a distribution of objects on a group surface.
planning, in real time by the circuitry, a path along which a flight vehicle flies. See at least [0096]-[0098] and figure 11, wherein device 122 performs path planning for the flight vehicle. See at least [0084], wherein a flight vehicle requests a route at a predetermined desired altitude, and the route is planned on a basis of the estimated wind factor values. Additionally, see at least [0068], [0073], and [0089]-[0091], wherein the wind model 145 is updated in real-time, and the drone’s route is stopped or modified based on the real-time wind model 145.
wherein the path is planned based on a wind change cost on the path, the distribution of the plurality of obstacles, and the estimated one of the wind or the turbulence. See at least [0069]-[0072] and figure 10, wherein the wind factor values used in path planning are derived from the estimated wind magnitude, heading, and turbulence values from wind model 145. Additionally, see at least [0062]-[0065] and figure 9B, wherein wind model 145 is based on weather data received from a third-party source, and applied to a 3D map 143 to estimate the wind and turbulence values. See at least [0069]-[0070] and figure 10, wherein the route is planned based on an incentive or penalty cost at positions along the route, the cost being determined based on the wind factor value. See at least [0074], wherein the wind factor value includes a volatility value corresponding to how much the wind changes over time, or temporally. See at least [0083], wherein the flight vehicle’s path is planned based on volatility values.
and the wind change cost is based on a change in wind received at a position on the path. See at least [0074]-[0075], [0080], and figure 8, wherein the wind factor volatility value represents a change in wind speed for different wind sections, or positions in the wind model.
generating, by the circuitry, a command based on the planned path. See at least [0093], wherein the device 122 controls the flight vehicle through the planned route by generating flight commands.
and controlling, based on the generated command, driving of at least one motor to generate lift for the flight vehicle, wherein the at least one motor, based on the controlled driving, rotates a rotary wing of the flight vehicle to generate the lift. the flight vehicle flies along the planned path based on the generated lift. See at least [0036], [0040], and [0049], wherein movement of the flight vehicle is controlled based on commands sent to motors of the flight vehicle, which controls propellers of the vehicle to create lift.
Ivanov remains silent as to the specifics of estimating the wind/turbulence distribution at a specific altitude, generating thrust, and a change in the generated lift and the generated thrust is based on a change in a number of rotations per unit time of the rotary wing.
Nakazawa teaches a specific altitude. See at least [0055], wherein the wind forecast map is divided into a plurality of parts at different altitudes. See at least [0085]-[0086], wherein a predetermined altitude range is stipulated.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Ivanov with Nakazawa’s technique of a predetermined altitude. It would have been obvious to modify because doing so enables flight vehicles to flexibly navigate their environment while resisting the influence of wind, as recognized by Nakazawa (see at least [0004]-[0005] and [0022]).
Postrel teaches generating thrust, and a change in the generated lift and the generated thrust is based on a change in a number of rotations per unit time of the rotary wing. See at least [0049], wherein the aircraft controls lift and thrust based on altering the rotation rate of one or more rotor discs.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Ivanov with Postrel’s teaching of an aerial vehicle controlling lift and torque based on altering the rotation rate of one or more rotor discs. It would have been obvious to modify because this type of vehicle control is generally known in the art, as recognized by Postrel (see at least [0049]). Additionally, it would have been obvious to modify because doing so enables drone fleets to navigate in view of weather parameters and natural or manmade barrier locations, reducing the risk of collisions, as recognized by Postrel (see at least [0002]-[0008]).
Regarding claim 12, Ivanov teaches A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by a processor, cause the processor to execute operations. See at least [0144].
the operations comprising: estimating one of a wind or a turbulence distribution at an altitude based on global wind information and a distribution of a plurality of obstacles on a ground surface. See at least [0100] and figure 11, model generation unit that generates wind model 145. Additionally, see at least [0062] and figure 9B, wherein wind model 145 comprises estimations of a distribution of wind and its altitude. Additionally, see at least [0071]-[0074], wherein the wind model 145 is used to estimate wind magnitude, wind heading, and turbulence by applying the wind model 145 to the location 140. See at least [0032]-[0033], wherein wind condition data is obtained from a database or purchased from external weather services. Additionally, see at least [0062]-[0065] and figure 9B, wherein wind model 145 is based on weather data received from a third-party source, and applied to a 3D map 143 to estimate the wind and turbulence values. See at least [0057]-[0061] and figure 9A, wherein 3D map 143 represents a distribution of objects on a group surface.
planning, in real time, a path along which a flight vehicle flies. See at least [0096]-[0098] and figure 11, wherein device 122 performs path planning for the flight vehicle. See at least [0084], wherein a flight vehicle requests a route at a predetermined desired altitude, and the route is planned on a basis of the estimated wind factor values. Additionally, see at least [0068], [0073], and [0089]-[0091], wherein the wind model 145 is updated in real-time, and the drone’s route is stopped or modified based on the real-time wind model 145.
wherein the path is planned based on a wind change cost on the path, the distribution of the plurality of obstacles, and the estimated one of the wind or the turbulence. See at least [0069]-[0072] and figure 10, wherein the wind factor values used in path planning are derived from the estimated wind magnitude, heading, and turbulence values from wind model 145. Additionally, see at least [0062]-[0065] and figure 9B, wherein wind model 145 is based on weather data received from a third-party source, and applied to a 3D map 143 to estimate the wind and turbulence values. See at least [0069]-[0070] and figure 10, wherein the route is planned based on an incentive or penalty cost at positions along the route, the cost being determined based on the wind factor value. See at least [0074], wherein the wind factor value includes a volatility value corresponding to how much the wind changes over time, or temporally. See at least [0083], wherein the flight vehicle’s path is planned based on volatility values.
and the wind change cost is based on a change in wind received at a position on the path. See at least [0074]-[0075], [0080], and figure 8, wherein the wind factor volatility value represents a change in wind speed for different wind sections, or positions in the wind model.
generating a command based on the planned path. See at least [0093], wherein the device 122 controls the flight vehicle through the planned route by generating flight commands.
and controlling, based on the generated command, driving of at least one motor to generate lift for the flight vehicle, wherein the at least one motor, based on the controlled driving, rotates a rotary wind of the flight vehicle to generate the light, the flight vehicle flies along the planned path based on the generated lift. See at least [0036], [0040], and [0049], wherein movement of the flight vehicle is controlled based on commands sent to motors of the flight vehicle, which controls propellers of the vehicle to create lift.
Ivanov remains silent as to the specifics of estimating the wind/turbulence distribution at a specific altitude, generating thrust, and a change in the generated lift and the generated thrust is based on a change in a number of rotations per unit time of the rotary wing.
Nakazawa teaches a specific altitude. See at least [0055], wherein the wind forecast map is divided into a plurality of parts at different altitudes. See at least [0085]-[0086], wherein a predetermined altitude range is stipulated.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Ivanov with Nakazawa’s technique of a predetermined altitude. It would have been obvious to modify because doing so enables flight vehicles to flexibly navigate their environment while resisting the influence of wind, as recognized by Nakazawa (see at least [0004]-[0005] and [0022]).
Postrel teaches generating thrust, and a change in the generated lift and the generated thrust is based on a change in a number of rotations per unit time of the rotary wing. See at least [0049], wherein the aircraft controls lift and thrust based on altering the rotation rate of one or more rotor discs.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Ivanov with Postrel’s teaching of an aerial vehicle controlling lift and torque based on altering the rotation rate of one or more rotor discs. It would have been obvious to modify because this type of vehicle control is generally known in the art, as recognized by Postrel (see at least [0049]). Additionally, it would have been obvious to modify because doing so enables drone fleets to navigate in view of weather parameters and natural or manmade barrier locations, reducing the risk of collisions, as recognized by Postrel (see at least [0002]-[0008]).
Regarding claim 13, Ivanov teaches A mobile device. See at least [0029] and figures 1-2, device 122 which is integrated in drones 124.
comprising: a flying object configured to fly, wherein the flying object includes a rotary wing, a motor. See at least [0035]-[0036], [0049], [0093], and figure 2, wherein the drone 124 comprises a propulsion system including a motor that flies the drone based on the generated commands.
circuitry configured to: estimate one of a wind or a turbulence distribution at an altitude based on global wind information and a distribution of a plurality of obstacles on a ground surface. See at least [0100] and figure 11, model generation unit that generates wind model 145. Additionally, see at least [0062] and figure 9B, wherein wind model 145 comprises estimations of a distribution of wind and its altitude. Additionally, see at least [0071]-[0074], wherein the wind model 145 is used to estimate wind magnitude, wind heading, and turbulence by applying the wind model 145 to the location 140. See at least [0032]-[0033], wherein wind condition data is obtained from a database or purchased from external weather services. Additionally, see at least [0062]-[0065] and figure 9B, wherein wind model 145 is based on weather data received from a third-party source, and applied to a 3D map 143 to estimate the wind and turbulence values. See at least [0057]-[0061] and figure
plan, in real time, a path along which a flight vehicle flies. See at least [0096]-[0098] and figure 11, wherein device 122 performs path planning for the flight vehicle. See at least [0084], wherein a flight vehicle requests a route at a predetermined desired altitude, and the route is planned on a basis of the estimated wind factor values. Additionally, see at least [0068], [0073], and [0089]-[0091], wherein the wind model 145 is updated in real-time, and the drone’s route is stopped or modified based on the real-time wind model 145.
wherein the path is planned based on a wind change cost on the path, the distribution of the plurality of obstacles, and the estimated one of the wind or the turbulence. See at least [0069]-[0072] and figure 10, wherein the wind factor values used in path planning are derived from the estimated wind magnitude, heading, and turbulence values from wind model 145. Additionally, see at least [0062]-[0065] and figure 9B, wherein wind model 145 is based on weather data received from a third-party source, and applied to a 3D map 143 to estimate the wind and turbulence values. See at least [0069]-[0070] and figure 10, wherein the route is planned based on an incentive or penalty cost at positions along the route, the cost being determined based on the wind factor value. See at least [0074], wherein the wind factor value includes a volatility value corresponding to how much the wind changes over time, or temporally. See at least [0083], wherein the flight vehicle’s path is planned based on volatility values.
and the wind change cost is based on a change in wind received at a position on the path. See at least [0074]-[0075], [0080], and figure 8, wherein the wind factor volatility value represents a change in wind speed for different wind sections, or positions in the wind model.
generate a command to control the flying object based on the planned path. See at least [0093], wherein the device 122 controls the flight vehicle through the planned route by generating flight commands.
control, based on the generated command, a driving operation of the motor, wherein the motor is configured to, based on the controlled driving operation, rotate the rotary wing to generate lift for the flying object, the flying object flies along the planned path based on the generated lift. See at least [0036], [0040], and [0049], wherein movement of the flight vehicle is controlled based on commands sent to motors of the flight vehicle, which controls propellers of the vehicle to create lift.
Ivanov remains silent as to the specifics of estimating the wind/turbulence distribution at a specific altitude, generating thrust, and a change in the generated lift and the generated thrust is based on a change in a number of rotations per unit time of the rotary wing.
Nakazawa teaches a specific altitude. See at least [0055], wherein the wind forecast map is divided into a plurality of parts at different altitudes. See at least [0085]-[0086], wherein a predetermined altitude range is stipulated.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to modify Ivanov with Nakazawa’s technique of a predetermined altitude. It would have been obvious to modify because doing so enables flight vehicles to flexibly navigate their environment while resisting the influence of wind, as recognized by Nakazawa (see at least [0004]-[0005] and [0022]).
Postrel teaches generating thrust, and a change in the generated lift and the generated thrust is based on a change in a number of rotations per unit time of the rotary wing. See at least [0049], wherein the aircraft controls lift and thrust based on altering the rotation rate of one or more rotor discs.
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to further modify Ivanov with Postrel’s teaching of an aerial vehicle controlling lift and torque based on altering the rotation rate of one or more rotor discs. It would have been obvious to modify because this type of vehicle control is generally known in the art, as recognized by Postrel (see at least [0049]). Additionally, it would have been obvious to modify because doing so enables drone fleets to navigate in view of weather parameters and natural or manmade barrier locations, reducing the risk of collisions, as recognized by Postrel (see at least [0002]-[0008]).
Regarding claim 14, Ivanov, Nakazawa, and Postrel in combination teach all of the limitations of claim 1 as discussed above, and Ivanov additionally teaches wherein the distribution of the plurality of obstacles includes at least one of information generated based on sensor information or information acquired from map information, and the sensor information is information from a sensor. See at least [0057]-[0061], wherein the 3D map data 143 is generated from acquired map information or LIDAR sensors on an aerial vehicle.
Regarding claim 15, Ivanov, Nakazawa, and Postrel in combination teach all of the limitations of claim 1 as discussed above, and Ivanov additionally teaches wherein the estimation of the wind or the turbulence distribution is based on a change in at least one of a wind speed or a wind direction, and the change in the at least one of the wind speed or the wind direction is based on the distribution of the plurality of obstacles on the ground surface. See at least [0072]-[0073], wherein the wind and turbulence distribution is estimated based on changes in wind behavior and wind velocity, respectively, due to changes in the 3D map data.
Regarding claim 16, Ivanov, Nakazawa, and Postrel in combination teach all of the limitations of claim 14 as discussed above, and Ivanov additionally teaches wherein the circuitry is further configured to perform object recognition to generate the distribution of obstacles, and the object recognition is performed based on the sensor information. See at least [0041], [0057]-[0061], and [0138], wherein the 3D map data 143 is generated from sensor data acquired from LIDAR sensors on the aerial vehicle. The point cloud generated by the sensor is analyzed to identify buildings and objects in the model.
Regarding claim 17, Ivanov, Nakazawa, and Postrel in combination teach all of the limitations of claim 1 as discussed above, and Ivanov additionally teaches wherein the circuitry is further configured to dynamically update the planned path in the real time to include a flight path, and the flight path is from a current position of the flight vehicle to a destination of the flight vehicle. See at least [0088]-[0090], wherein the flight vehicle’s path, from a starting location to a destination location, is updated in real-time based on real-time wind condition data. The drone reports its progress along the path and refines, or updates, the calculations.
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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/S.M.J./ Examiner, Art Unit 3667
/FARIS S ALMATRAHI/ Supervisory Patent Examiner, Art Unit 3667
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