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 .
Drawings
The drawings were received on December 13th 2023. These drawings are accepted.
Specification
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware of, in the specification.
Status of Claims
This Final rejection is in response to the applicant’s filing on January 2nd 2026;
Claims 2-3 and 14 are canceled
Claims 1, 4-13, 15-23 are pending and examined below.
Response to Arguments
Applicant’s amendments with respect to the rejection of claims under 35 USC § 101 have been fully considered and overcome the claim rejection. Therefore, the rejection of claims under 35 USC § 101 has been withdrawn.
Applicant’s amendments with respect to the rejection of claims under 35 USC § 103 have been fully considered but are moot. While the Examiner notes that the applicant is arguing the claim limitations recite " … and further based on a current operating state of the AV including position, trajectory, and speed as determined from sensors located on-board the AV and retrieving forecast data for the cell corresponding to the cell-specific forecast time from one or more remote computing devices over a wireless communications link to the AV… and further based on a current operating state of the AV including position, trajectory, and speed as determined from sensors located on-board the AV… from one or more remote computing devices over a wireless communications link to the AV…“. Therefore, the rejection has been withdrawn; However, upon further consideration a new ground(s) of rejection is made for Claims 1, 13 and 20 over Blomberg (Patent No. US20170263133A1) in view of Davalos (Patent No. US20230131160A1) and Lewis (Paten No. US20220327942A1).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-13, 15-23 are rejected under 35 U.S.C. 103 as being unpatentable over Blomberg (Patent No. US20170263133A1) in view of Davalos (Patent No. US20230131160A1) and Lewis (Paten No. US20220327942A1).
Regarding claim 1 Blomberg teaches a method performed by a computing system of one or more computing devices; (See Blomberg paragraph 0033; “FIG. 1 illustrates an example air traffic control and flight plan management system 200, in accordance with an embodiment of the invention…”; also See Blomberg Figure 1 and 2);
determining an anticipated flight path for the AV in three dimensions through an operating environment between a current location of the AV and a target destination location based on flight plan data; (See Blomberg paragraph 0038; “The system 200 further comprises a locking module 460 configured to exclusively lock 4D cells on behalf of a drone 50 (FIG. 1). Specifically, the system 200 may receive, as input, a flight plan request for a drone 50 either from a zone controller 60 (FIG. 1) or the drone 50. The flight plan request may include a filing of a flight plan for the drone 50 (“the initially filed flight plan”). The initially filed flight plan comprises an identity of the drone 50 (e.g., corresponding unique identifier), earliest requested flight time and/or latest desired arrival time for the drone 50, departure location for the drone 50, and one or more arrival locations for the drone 50. The locations included in the initially filed flight plan may be specified as three-dimensional (3D) coordinates. The locking module 460 constructs a modified flight plan for the drone 50 based on the initially filed flight plan. The modified flight plan is an approved and executable flight plan for the drone 50. The modified flight plan comprises the identity of the drone 50 and a planned flight path for the drone 50. The planned flight path comprises a sequence of 4D cells, such as an approved departure cell and a sequence of approved arrival cells. In one example implementation, the 4D cells represent 4D locations represented by two horizontal intervals (e.g., longitude and latitude), one vertical interval (e.g., elevation), and one time interval. An air traffic control zone or a 4D cell is on a flight path if any part of the air traffic control zone or the 4D cell is within a pre-specified distance of any point on the flight path.”);
identifying, based on the anticipated flight path, a set of cells within a three- dimensional cell array forming a spatial representation of the operating environment; (See Blomberg paragraph 0031, 0036 and 0038; “…the system configured to provide a distributed service that partitions and locks available air-space into four-dimensional (4D) cells. Each 4D cell is specified by intervals of four different dimensions. In one embodiment, the four different dimensions comprise three spatial dimensions (e.g., longitude, latitude and elevation) and one temporal dimension (e.g., time)… The system 200 further comprises a partition module 450 configured to partition available air-space into fine grained 4D cells. In one embodiment, each 4D cell is specified by intervals of three spatial dimensions, such as longitude, latitude and elevation, and one temporal dimension, such as time. Minimum dimensions for 4D cells must satisfy the condition that each 4D cell is large enough to allow a drone to maintain its location within the 4D cell and to move vertically (i.e., change its elevation) from one 4D cell to another 4D cell immediately above or below the current 4D cell, without changing the horizontal intervals (e.g., latitude and longitude) defining the current 4D cell… The system 200 further comprises a locking module 460 configured to exclusively lock 4D cells on behalf of a drone 50 (FIG. 1). Specifically, the system 200 may receive, as input, a flight plan request for a drone 50 either from a zone controller 60 (FIG. 1) or the drone 50. The flight plan request may include a filing of a flight plan for the drone 50 (“the initially filed flight plan”). The initially filed flight plan comprises an identity of the drone 50 (e.g., corresponding unique identifier), earliest requested flight time and/or latest desired arrival time for the drone 50, departure location for the drone 50, and one or more arrival locations for the drone 50. The locations included in the initially filed flight plan may be specified as three-dimensional (3D) coordinates. The locking module 460 constructs a modified flight plan for the drone 50 based on the initially filed flight plan. The modified flight plan is an approved and executable flight plan for the drone 50. The modified flight plan comprises the identity of the drone 50 and a planned flight path for the drone 50. The planned flight path comprises a sequence of 4D cells, such as an approved departure cell and a sequence of approved arrival cells. In one example implementation, the 4D cells represent 4D locations represented by two horizontal intervals (e.g., longitude and latitude), one vertical interval (e.g., elevation), and one time interval. An air traffic control zone or a 4D cell is on a flight path if any part of the air traffic control zone or the 4D cell is within a pre-specified distance of any point on the flight path.”); and for each cell of the set of cells: determining a cell-specific forecast time for the cell based on the time- based location profile of the AV; (See Blomberg paragraph 0078-0080; “The framework 550 is configured to receive and maintain zone-wide weather forecast information 575 for an air traffic control zone. The zone-wide weather forecast information 575 includes wind velocity and intensity (e.g., maximum amplitude and direction of gusts) for the air traffic control zone. The framework 550 further maintains a weather model 580 for the air traffic control zone. The weather model 580 is based on observations on the weather conditions of the air traffic control zone (e.g., the zone-wide weather forecast information 575), and is used to predict how wind conditions may change with elevation, horizontally in each direction, and daily/seasonally with time.
The framework 550 further comprises an interpolate and extrapolate unit 594 configured to interpolate in space and extrapolate in time weather conditions at any 4D cell within an air traffic control zone that is on a flight path for a drone 50. Specifically, when a flight plan calls for a drone 50 with a corresponding drone profile 560 to use the air traffic control zone, the interpolate and extrapolate unit 594 determines, based on the drone profile 560, a sequence of time-stamped GPS coordinates representing a flight path of the drone 50 within the zone, by inferring weather conditions at each 4D cell on the flight path. For example, the interpolate and extrapolate unit 594 is configured to extrapolate in time wind conditions at any 4D cell on the flight path based on the weather model 580. As another example, the interpolate and extrapolate unit 594 is configured to interpolate in space wind conditions at any 4D cell on the flight path based on a latitude or longitude line through the 4D cell and between two nearby 4D cells within the zone based on independent observations of wind conditions at the two/nearby 4D cells, or based on one independent observation and one prediction from the weather model 580, or based on two predictions from the weather model 580. The ability to interpolate in space and extrapolate in time weather conditions at any 4D cell on a flight path for a drone 50 removes the need to predict weather conditions for all 4D cells within the air traffic control zone (i.e., weather conditions for unused 4D cells that are not on the flight path may be ignored).
The framework 550 further maintains a collection of 4D cell weather profiles 570. Each 4D cell weather profile 570 corresponds to a 4D cell within an air traffic control zone, and maintains information relating to wind conditions at the 4D cell, such as estimated prevailing (net) wind direction and speed, and estimated gust intensity direction and frequency. As described above, the wind conditions at a 4D cell may be interpolated in space and extrapolated in time based on the weather model 580 and/or independent observations of wind conditions at nearby 4D cells.”); retrieving forecast data for the cell corresponding to the cell-specific forecast time; (See Blomberg paragraph 0079; “The framework 550 further comprises an interpolate and extrapolate unit 594 configured to interpolate in space and extrapolate in time weather conditions at any 4D cell within an air traffic control zone that is on a flight path for a drone 50. Specifically, when a flight plan calls for a drone 50 with a corresponding drone profile 560 to use the air traffic control zone, the interpolate and extrapolate unit 594 determines, based on the drone profile 560, a sequence of time-stamped GPS coordinates representing a flight path of the drone 50 within the zone, by inferring weather conditions at each 4D cell on the flight path. For example, the interpolate and extrapolate unit 594 is configured to extrapolate in time wind conditions at any 4D cell on the flight path based on the weather model 580. As another example, the interpolate and extrapolate unit 594 is configured to interpolate in space wind conditions at any 4D cell on the flight path based on a latitude or longitude line through the 4D cell and between two nearby 4D cells within the zone based on independent observations of wind conditions at the two/nearby 4D cells, or based on one independent observation and one prediction from the weather model 580, or based on two predictions from the weather model 580. The ability to interpolate in space and extrapolate in time weather conditions at any 4D cell on a flight path for a drone 50 removes the need to predict weather conditions for all 4D cells within the air traffic control zone (i.e., weather conditions for unused 4D cells that are not on the flight path may be ignored).”).
Blomberg does not explicitly teach but Davalos teaches, wherein the anticipated flight path includes an altitude profile of the AV over a geographic region through the operating environment and a time-based location profile of the AV along the anticipated flight path; (See Davalos paragraph 0040-0041; “In some examples, these machine learning models may use large amounts of data for training. In some examples, the prediction model may be trained using emulated onboard radar based on publicly available Multi-Radar/Multi-Sensor (MRMS) data provided by NOAA/National Severe Storms Laboratory. In other examples, the prediction model may also be trained using historical 3D reflectivity data downloaded from many aircraft flights, e.g., where the flight path passed near convective weather. Once trained, for the deployed onboard application in the aircraft, the model will be configured to output short-term weather predictions using the sensor data available on the aircraft.
Some differences of the system of this disclosure, when compared to some ground-based forecasting systems, may include that the ground-based systems developed a model to forecast precipitation rates observed on the ground rather than full 3D radar reflectivities, weather hazards, or hail size forecast estimates. Also, some ground-based systems output a forecast for a longer 8-hour duration based on longer prior observation history of ground-based weather radar returns, for example, 90-minutes. In contrast, weather forecast unit 106 may forecast less than thirty minutes ahead, based on less than about twenty minutes of stored 3D volumetric weather radar observations. In addition, ground based systems analyze larger geographical areas to generate a larger-scale model for weather systems across the country. However, the modeling applied by weather forecast unit 106 may focuses only on what is in range for the volume of airspace that may affect the moving aircraft. Lastly, weather forecast unit 106 is forecasting on a moving vehicle for a constantly shifting radar range while the ground-based systems use a static range.”);
and retrieving forecast data for the cell corresponding to the cell-specific forecast time from one or more remote computing devices over a wireless communications link to the AV; (See Davalos paragraph 0016-0017; “he onboard and in-flight nature present a unique challenge for weather forecasting when limited to using onboard radar sensors because of the limited range of the radar sensor and the fast velocity of some commercial and military aircraft. Applying the forecast modeling techniques to the 3D volumetric radar data generated by the radar system of this disclosure, may provide a display of the future state of detected weather as the weather cells move, dissipate, and/or increase. The onboard weather forecast may provide more accurate and useful information to the flight crew, and therefore improve on current onboard technology of extrapolating direction and speed of an existing weather cell.
In addition, onboard forecast modeling based on radar reflectivity data that is a few seconds old may provide significant advantages compared to up-linking ground-based or satellite-based forecasts. The weather depicted by the computationally intensive ground-based forecasts may be ten or more minutes delayed compared to the actual weather situation in the volume of airspace through which the aircraft is traveling. For some commercial and military aircraft that travel approximately 450 to 500 knots, a fifteen minute delay means a travel distance of over hundred nautical miles. Also, up linking the data may consume significant communication bandwidth and may be degraded by weather, distance, and other factors. In this manner, the onboard forecast modeling and display of system of this disclosure may be more desirable than other techniques because, for example, the flight crew may have timely and accurate information to change the flight path of their aircraft away from hazardous weather, thereby promoting fuel efficiency, hazard avoidance, and shorter flights for on-time performance.”); and outputting the forecast data retrieved for the cell each of one or more cells of the set of cells via a graphical user interface presented on a display device on-board the AV; (See Davalos paragraph 0037 and 0057; “…Onboard weather radar system 102 may receive reflected radar signals over time. The reflected radar signals received at a first time and a second time may represent two consecutive measurements by the weather radar device. In some examples, the system may receive reflected radar signals at additional times (e.g., third, fourth, fifth, etc.). In some examples, onboard weather radar system 102 may scan the field of regard and store data to build the 3D reflectivity characteristics of the selected volume of airspace at times that are a minutes apart, or seconds apart. Processing circuitry, e.g., processors 122, may execute instructions to determine magnitudes of reflectivity based on the reflected radar signals received at the first time and the second time, and additional times. Processors 122 may be configured to predict the presence of ice crystals in a volume of airspace at a present time using the reflectivity magnitudes for the first time and the second time and their difference. In some examples, temporal variation of reflectivity magnitude estimated from more than two consecutive samples may be used. For example, the processing circuitry may determine the temporal variance based on the average temporal variance between consecutive measurements for three or more measurements to predict the presence of ice crystals at the present time. Enhanced weather radar processing system 104, with weather forecast unit 106, may improve on the present time prediction of ice crystals, by providing a forecast of future ice crystals, weather cell changes, lightning and so on in the airspace volume of interest for a duration subsequent to the present time. In some examples, the displayed forecasted weather information may include future locations of high altitude ice crystals (HAIC) and high ice water content (HIWC)…processors 122 may output an electronic signal comprising forecasted weather changes to the volume of airspace over a predetermined future duration subsequent to the current time, wherein the forecasted weather changes comprise future 3D radar reflectivity characteristics for the predetermined future duration. (406). The electronic signal may be received by a variety of equipment, including weather radar graphical display device 108, server 118, multi-function display 138…”).
Both Blomberg and Davalos are in the same field of forecast for an anticipated flight path of an AV. It would have been obvious for one ordinary skilled in the art before the effective filing date of
present invention to modify Blomberg a computing system of one or more computing devices performing a forecast update process for an aeronautical vehicle (AV) during flight with Davalos an altitude profile of the AV over a geographic region through the operating environment and forecast output. No new functionality would arise from the combination and the combination would improve usability of Blomberg by adding an altitude profile of the AV over a geographic region through the operating environment and forecast output and will provide better flight data. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Blomberg does not teach but Lewis teaches, and further based on a current operating state of the AV including position, trajectory, and speed as determined from sensors located on-board the AV; (See Lewis paragraph 0040; “The navigation system 125 may determine the distance of the vehicle 105 relative to a given waypoint W, and the time until the vehicle 105 reaches the waypoint W based on monitoring of progress of the vehicle 105 through the flight plan 200, such as by extracting position data (e.g., GPS data, heading data, track data, etc.) from the sensor data 50, and comparing the position data to points of the planned flight path 205. For instance, the navigation system 125 may determine the position data indicates the vehicle 105 is a distance d away (or a time t away) from a given waypoint W on the planned flight path 205.”);
the method comprising; periodically performing a forecast update process for an aeronautical vehicle (AV) at a forecast update time interval during flight of the AV along an actual flight path, that wherein the forecast update process periodically performed at the forecast update time interval includes; (See Lewis paragraph 0029; “…The FMS 130 may continuously perform calculations along the planned flight path 205, as the vehicle 105 proceeds along or near to the planned flight path 205. In performing these calculations, the FMS 130 may account for a required time of arrival (RTA) of the vehicle 105 to a destination, restricted airspace, weather or atmospheric conditions, air traffic from other aircraft, limitations to ensure passenger comfort, etc. And, with respect to some of the data accounted for by the FMS 130, the FMS 130 may continually (e.g., periodically) update the planned flight path 205 based on this data, such as data relating to weather or atmospheric conditions. The FMS 130 may also update the planned flight path 205 based on changes in data, e.g., changes in the weather or atmospheric conditions, during a flight. In addition, the FMS 130 may use performance data of the vehicle, described in more detail below, to determine locations, times, and amounts for transitions, such as acceleration or deceleration, along the planned flight path 205. And, the FMS 130, as part of the control system 120, may control the vehicle 105 as it proceeds along the planned flight path 205.”).
Both Blomberg and Lewis are in the same field of forecast for an anticipated flight path of an AV. It would have been obvious for one ordinary skilled in the art before the effective filing date of
present invention to modify Blomberg a computing system of one or more computing devices performing a forecast update process for an aeronautical vehicle (AV) during flight with Lewes determining an alternative flight path for the AV. No new functionality would arise from the combination and the combination would improve usability of Blomberg by adding a determining an alternative flight path for the AV. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 4 Blomberg in view of Davalos and Lewis teaches the method of claim 1, Blomberg further teaches, wherein the forecast update time interval is user-defined as a setting stored at the computing system; (See Blomberg paragraph 0038; “The system 200 further comprises a locking module 460 configured to exclusively lock 4D cells on behalf of a drone 50 (FIG. 1). Specifically, the system 200 may receive, as input, a flight plan request for a drone 50 either from a zone controller 60 (FIG. 1) or the drone 50. The flight plan request may include a filing of a flight plan for the drone 50 (“the initially filed flight plan”). The initially filed flight plan comprises an identity of the drone 50 (e.g., corresponding unique identifier), earliest requested flight time and/or latest desired arrival time for the drone 50, departure location for the drone 50, and one or more arrival locations for the drone 50. The locations included in the initially filed flight plan may be specified as three-dimensional (3D) coordinates. The locking module 460 constructs a modified flight plan for the drone 50 based on the initially filed flight plan. The modified flight plan is an approved and executable flight plan for the drone 50. The modified flight plan comprises the identity of the drone 50 and a planned flight path for the drone 50. The planned flight path comprises a sequence of 4D cells, such as an approved departure cell and a sequence of approved arrival cells. In one example implementation, the 4D cells represent 4D locations represented by two horizontal intervals (e.g., longitude and latitude), one vertical interval (e.g., elevation), and one time interval. An air traffic control zone or a 4D cell is on a flight path if any part of the air traffic control zone or the 4D cell is within a pre-specified distance of any point on the flight path.”).
Regarding claim 5 Blomberg in view of Davalos and Lewis teaches los teaches the method of claim 1, Blomberg does not explicitly teach but Davalos teaches, wherein a computing device of the computing system that periodically performs the forecast update process resides on-board the AV; (See Davalos paragraph 0033 and 0034; “…FIG. 1, aircraft system 100 also includes server 118, which has its own communication circuitry, e.g., communications unit 119, which may enable or configure server 118 to communicate with an external data service. MFD 138 may be connected to data interface 128 of enhanced weather radar processing system 104 via onboard server 118. A flight crew member of the aircraft may also operate an electronic flight bag (EFB) 145 executing on, e.g., a tablet computer 143, or a tablet computer 143 executing another display application other than an EFB. MFD 138, tablet 143, and/or weather radar graphical display device 108 may receive (e.g., through a datalink connection via server 118, or through an in-flight WiFi connection of tablet computer 143) the signal including two-dimensional weather map from enhanced weather radar processing system 104. The user, e.g., flight crew member may configure any of the displays above to display forecasted weather, present time weather, and manipulate the display controls to enhance, filter or otherwise emphasize various aspects of the received signal on the display.
Onboard weather radar system 102 may measure reflectivity of a radar signal as an electrical quantity related to the percentage of power, normalized for range, returned from the weather being illuminated with a radar transmission emitted by onboard weather radar system 102. Reflectivity is generally related to rate of rainfall or rate of precipitation, though this relation may be complicated or distorted by effects such as attenuation of radar sensitivity to a more distant weather structure by a closer, intervening body of precipitation, or bright band due partially melted hydrometeors when snow falls through a freezing/melting altitude.”).
Both Blomberg and Davalos are in the same field of forecast for an anticipated flight path of an AV. It would have been obvious for one ordinary skilled in the art before the effective filing date of
present invention to modify Blomberg a computing system of one or more computing devices performing a forecast update process for an aeronautical vehicle (AV) during flight with Davalos computing system on-board the AV. No new functionality would arise from the combination and the combination would improve usability of Blomberg by adding a computing system on-board the AV to view the flight data. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6 Blomberg in view of Davalos and Lewis teaches teaches the method of claim 5, Blomberg further teaches, wherein for each cell of the set of cells, retrieving the forecast data for the cell corresponding to the cell-specific forecast time includes: sending one or more requests from the computing device on-board the AV to a remote computing device over the wireless communications link, and receiving one or more responses that include the forecast data for the cell from the remote computing device at the computing device on-board the AV over the wireless communications link; (See Blomberg paragraph 0038; “The system 200 further comprises a locking module 460 configured to exclusively lock 4D cells on behalf of a drone 50 (FIG. 1). Specifically, the system 200 may receive, as input, a flight plan request for a drone 50 either from a zone controller 60 (FIG. 1) or the drone 50. The flight plan request may include a filing of a flight plan for the drone 50 (“the initially filed flight plan”). The initially filed flight plan comprises an identity of the drone 50 (e.g., corresponding unique identifier), earliest requested flight time and/or latest desired arrival time for the drone 50, departure location for the drone 50, and one or more arrival locations for the drone 50. The locations included in the initially filed flight plan may be specified as three-dimensional (3D) coordinates. The locking module 460 constructs a modified flight plan for the drone 50 based on the initially filed flight plan. The modified flight plan is an approved and executable flight plan for the drone 50. The modified flight plan comprises the identity of the drone 50 and a planned flight path for the drone 50. The planned flight path comprises a sequence of 4D cells, such as an approved departure cell and a sequence of approved arrival cells. In one example implementation, the 4D cells represent 4D locations represented by two horizontal intervals (e.g., longitude and latitude), one vertical interval (e.g., elevation), and one time interval. An air traffic control zone or a 4D cell is on a flight path if any part of the air traffic control zone or the 4D cell is within a pre-specified distance of any point on the flight path.”).
Regarding claim 7 Blomberg in view of Davalos and Lewis teaches teaches the method of claim 1, Blomberg further teaches, wherein the cell-specific forecast time is determined for each cell based on an anticipated time of arrival of the AV at the cell; (See Blomberg paragraph 0041; “If the locking module 460 fails to obtain/place an exclusive lock on behalf of a drone 50 on a 4D cell included in a modified flight plan for the drone 50 (i.e., the 4D cell is already locked on behalf of another drone 50), the locking module 460 reroutes the modified flight plan around the 4D cell to a random adjacent/neighboring 4D cell that is to the left of, right of, above, below, or later in time than the 4D cell, from the point of view of the modified flight plan toward the 4D cell. An available set of randomly chosen adjacent/neighboring 4D cells may include “later” cells with the same 3D coordinates, where each “later” cell indicates that the drone 50 is to remain in the same 3D cell (or wait before takeoff) for one time unit, the time unit being the time duration (i.e., time interval) of the 4D cell.”).
Regarding claim 8 Blomberg in view of Davalos and Lewis teaches teaches the method of claim 1, Blomberg further teaches, wherein the cell-specific forecast time is determined for each cell based on an anticipated time of arrival of the AV at a location within a threshold proximity to the cell; (See Blomberg paragraph 0053, 0060 and 0078; “FIG. 4 illustrates a flowchart of an example process 500 that a zone controller 60 implements for locking successive 4D cells included in a modified flight plan for a drone 50, in accordance with an embodiment of the present invention. In process block 501, receive a flight plan request (FPR)… In process block 512, determine if the reroute count for the FPR exceeds a pre-determined threshold and fork. If the reroute count for the FPR exceeds a pre-determined threshold and fork, proceed to process block 504. If the reroute count for the FPR does not exceed a pre-determined threshold and fork, proceed to process block 513…The framework 550 is configured to receive and maintain zone-wide weather forecast information 575 for an air traffic control zone. The zone-wide weather forecast information 575 includes wind velocity and intensity (e.g., maximum amplitude and direction of gusts) for the air traffic control zone. The framework 550 further maintains a weather model 580 for the air traffic control zone. The weather model 580 is based on observations on the weather conditions of the air traffic control zone (e.g., the zone-wide weather forecast information 575), and is used to predict how wind conditions may change with elevation, horizontally in each direction, and daily/seasonally with time.”).
Regarding claim 9 Blomberg in view of Davalos and Lewis teaches teaches the method of claim 1, Blomberg further teaches, wherein the set of cells identified include cells located along the anticipated flight path; (See Blomberg paragraph 0079; “The framework 550 further comprises an interpolate and extrapolate unit 594 configured to interpolate in space and extrapolate in time weather conditions at any 4D cell within an air traffic control zone that is on a flight path for a drone 50. Specifically, when a flight plan calls for a drone 50 with a corresponding drone profile 560 to use the air traffic control zone, the interpolate and extrapolate unit 594 determines, based on the drone profile 560, a sequence of time-stamped GPS coordinates representing a flight path of the drone 50 within the zone, by inferring weather conditions at each 4D cell on the flight path…”).
Regarding claim 10 Blomberg in view of Davalos and Lewis teaches teaches the method of claim 9, Blomberg further teaches, wherein the set of cells identified further include cells located within a threshold proximity to the anticipated flight path; and wherein the set of cells does not include cells located beyond the threshold proximity to the anticipated flight path; (See Blomberg paragraph 0032 and 0058; “Embodiments of the invention avoid congestion by locking 4D cells in air traffic control zones to ensure that different executable flight plans may not share 4D cells. In one embodiment, the air traffic control and flight plan management system is configured to receive a request for an executable flight plan for a drone, lock 4D cells in air traffic control zones exclusively for the flight plan, and return/provide the flight plan. The executable flight plan may be modified each time the drone moves from one air traffic control zone into another, thereby ensuring that the flight plan comprises best estimates for times of takeoff, landing, air traffic control zone arrival and/or air traffic control zone departure. The air traffic control and flight plan management system assumes collision avoidance is already implemented; the system provides sufficient congestion reduction, such that the situations in which avoiding collisions becomes an issue will rarely arise… In process block 508, reroute the FPR from the locked entry cell or a random neighboring 4D cell (“neighboring cell”). In process block 509, increment counters for the zone and the FPR, wherein each counter maintains a reroute count. In process block 510, determine if the reroute count for the zone exceeds a pre-determined threshold. If the reroute count for the zone exceeds a pre-determined threshold, proceed to process block 511. If the reroute count for the zone does not exceed a pre-determined threshold, proceed to process block 512.”).
Regarding claim 11 Blomberg in view of Davalos and Lewis teaches the method of claim 1, Blomberg does not explicitly teach but Lewis teaches, further comprising: determining an alternative flight path for the AV in three dimensions through the operating environment between the current location and the target destination location or an alternative target destination location; identifying, based on the alternative flight path, a set of supplemental cells within a three-dimensional cell array forming a spatial representation of the operating environment; and for each cell of the set of supplemental cells: determining a cell-specific forecast time for the cell, retrieving forecast data for the cell corresponding to the cell-specific forecast time, and outputting the forecast data retrieved for the cell; (See Lewis paragraph 0048; “With reference to FIG. 8, in another aspect of the disclosure, the navigation system 125 may perform the additional steps shown in the method 800. In particular, the navigation system 125 may determine in step 805 whether the generated adjustment is greater than a respective adjustment threshold. For example, the navigation system 125 may compare the generated adjustment to an altitude to a stored altitude adjustment threshold value. If the generated adjustment is greater than the adjustment threshold (yes in step 805), in step 810, the navigation system 125 may generate a request for authorization for a change in altitude, based on the adjusted altitude, to an operator of the vehicle 105. The request may be displayed on a display 1100 shown in FIG. 11, as part of the display system 135. The display 1100 may be configured to receive a selection from an operator, such as “YES,” indicating authorization of the change, or “NO,” declining authorization of the change. The selection may then be transmitted back to the navigation system 125 for additional processing, such as generation of alternative adjustments to the flight plan. For example, if the operator declines authorization of a change in altitude or heading, the navigation system 125 may repeat the steps 805 and 810, to propose a change of speed, in order to avoid generating a sonic boom that exceeds permissible threshold boom values. If, however, the generated adjustment is not greater than the adjustment threshold (no in step 805), in step 815, the navigation system 125 may proceed with using the regenerated portion of the flight plan, without requesting authorization from the operator. The steps of the method 800 may be performed after step 715, in which the portion of the flight plan 200 is regenerated. By virtue of the method 800 described above, the operator can be made aware of and can approve relatively large changes in altitude, speed, or heading, eliminating any confusion or surprise on the part of the operator when such adjustments to the flight plan are generated.”).
Both Blomberg and Lewis are in the same field of forecast for an anticipated flight path of an AV. It would have been obvious for one ordinary skilled in the art before the effective filing date of
present invention to modify Blomberg a computing system of one or more computing devices performing a forecast update process for an aeronautical vehicle (AV) during flight with Lewes determining an alternative flight path for the AV. No new functionality would arise from the combination and the combination would improve usability of Blomberg by adding a determining an alternative flight path for the AV. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 12 Blomberg in view of Davalos and Lewis teaches teaches the method of claim 1, Blomberg further teaches, wherein the forecast data includes weather forecast data; (See Blomberg paragraph 0078; “The framework 550 is configured to receive and maintain zone-wide weather forecast information 575 for an air traffic control zone. The zone-wide weather forecast information 575 includes wind velocity and intensity (e.g., maximum amplitude and direction of gusts) for the air traffic control zone. The framework 550 further maintains a weather model 580 for the air traffic control zone. The weather model 580 is based on observations on the weather conditions of the air traffic control zone (e.g., the zone-wide weather forecast information 575), and is used to predict how wind conditions may change with elevation, horizontally in each direction, and daily/seasonally with time.”).
With respect to dependent claim 13, please see the rejection above with respect to claim 1 which is commensurate in scope to claim 13, with claim 1 being drown to a method, and claim 13 being drawn to a corresponding system, except for limitation; logic machine; and a storage machine having instructions stored thereon executable by the logic machine; (See Blomberg paragraph 0166; “Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.”).
With respect to dependent claims 15-19, please see the rejection above with respect to claims 7-8 and 10-12, which is commensurate in scope to claims 15-19, with claims 7-8 and 10-12being drown to a method, and claims 15-19 being drawn to a system.
With respect to independent claim 20, please see the rejection above with respect to claim 13 which is commensurate in scope to claim 20, with claim 13 being drown to a system, and claim 20 being drawn to a machine.
Regarding claim 21 Blomberg in view of Davalos and Lewis teaches the method of claim 1, wherein the forecast data includes aeronautical forecast data of other AVs that are forecast to be present at the cell; (See Lewis paragraph 0029; “…The FMS 130 may continuously perform calculations along the planned flight path 205, as the vehicle 105 proceeds along or near to the planned flight path 205. In performing these calculations, the FMS 130 may account for a required time of arrival (RTA) of the vehicle 105 to a destination, restricted airspace, weather or atmospheric conditions, air traffic from other aircraft, limitations to ensure passenger comfort, etc. And, with respect to some of the data accounted for by the FMS 130, the FMS 130 may continually (e.g., periodically) update the planned flight path 205 based on this data, such as data relating to weather or atmospheric conditions. The FMS 130 may also update the planned flight path 205 based on changes in data, e.g., changes in the weather or atmospheric conditions, during a flight. In addition, the FMS 130 may use performance data of the vehicle, described in more detail below, to determine locations, times, and amounts for transitions, such as acceleration or deceleration, along the planned flight path 205. And, the FMS 130, as part of the control system 120, may control the vehicle 105 as it proceeds along the planned flight path 205.”).
Both Blomberg and Lewis are in the same field of forecast for an anticipated flight path of an AV. It would have been obvious for one ordinary skilled in the art before the effective filing date of
present invention to modify Blomberg a computing system of one or more computing devices performing a forecast update process for an aeronautical vehicle (AV) during flight with Lewes determining an alternative flight path for the AV. No new functionality would arise from the combination and the combination would improve usability of Blomberg by adding a determining an alternative flight path for the AV. Further, finding that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
With respect to dependent claims 22-23, please see the rejection above with respect to claim 21, which is commensurate in scope to claims 22-23, with claim 21 being drown to a method claim 22 being drawn to a system and claim 23 being drown to a machine.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/L.K./Examiner, Art Unit 3666
/SCOTT A BROWNE/Supervisory Patent Examiner, Art Unit 3666
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