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 Amendment/Arguments
The 01.16.2026 Amendments are entered. Claims 1, 15, and 19 are amended. No claims have been canceled and no new claims have been added. Claims 1-20 remain pending.
The §103 Rejections
Applicant’s 01.16.2026 arguments with respect to these rejections have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over PG Pub No. US 20230038012 A1 to Erozlu, Murat et al. (hereinafter “Erozlu”) in view of PG Pub No. US 20190241192 A1 to Matthews, Gregory et al. (hereinafter “Matthews”), further in view of PG Pub No. US 20230382570 A1 to Foland, Steven et al. (hereinafter “Foland”).
Regarding claim 1, Erozlu teaches a computer-implemented method comprising:
accessing data associated with a first flight plan of a first flight (Erozlu [0052]: “The partial range may be determined using the route details retrieved in 406.”; [0023]: “Electric vehicle 101 may be . . . an aircraft . . ..” Route details taken as data at least associated with a first flight plan);
based on the data associated with the first flight plan, computing a first expected power demand on an energy storage system of an aircraft for the first flight (Erozlu [0039]: “That is, processing circuitry 102 may determine how far electric vehicle 101 will have traveled when the SOC of electric battery 110 reaches 20% of the original SOC.”; [0052]: “The partial range may be determined using the route details retrieved in 406. In some embodiments, the processing circuitry 102 determines the partial range by determining the current energy of electric battery 110 and applying a scaling factor as explained further in 406.” Predicting a partial range to where battery SOC reaches a predetermined level (representing usage of battery power) taken as computing a first expected power demand..);
accessing data indicative of an initial battery state of the energy storage system of the aircraft (Erozlu [Table 1, 0039]: Starting SOC taken as initial battery state data.);
computing, using a battery model (Erozlu [0039]: “Processing circuitry 102 may estimate how far along the initial route electric vehicle 101 can travel (e.g., the estimated range), based on the route information and the current SOC of electric battery 110. . . . As shown below in Table 1, based on a current SOC (“Starting SOC”) of 60% . . .” Erozlu inherently teaches a battery model by teaching determining the range based on the starting battery SOC.), a first capability output based the first expected power demand on the energy storage system of the aircraft and the initial battery state of the energy storage system of the aircraft, wherein the first capability output is indicative of a range or an available time of flight of the aircraft for the first flight (Erozlu [Table 1]: Partial range from the starting SOC to the first charging station taken as the first capability output. The Examiner notes that usage of the term “or” requires consideration of only one of the two options.).
Erozlu does not appear to expressly teach the battery model is configured to compute capability outputs by comparing previous capability output predictions for one or more previous flights with actual capability outputs of the one or more previous flights.
However, Matthews teaches the battery model (Matthews FIG. 1: Prediction system 4.) is configured to compute capability outputs by comparing previous capability output predictions for one or more previous flights with actual capability outputs of the one or more previous flights (Matthews FIG. 3, [0065]-[0070]: Steps 12-16 depict updating a range prediction algorithm, taken as the battery model, by comparing the estimated range for a plurality of past trips to the actual range of those past trips to derive an error which is minimized to improve battery model accuracy.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that estimates a partial range attainable by a battery-powered vehicle using a battery model taught by Erozlu with the battery model that optimizes itself by considering estimation errors on past trips taught by Matthews. Doing so would have made the model “more accurate at predicting the energy requirement for future journeys” as taught in [0070] of Matthews.
APOSITA would have understood that the above combination of Erozlu and Matthews further teaches the steps of determining an ability of the aircraft to perform the first flight based on the first capability output (Erozlu [0041]: “In response to determining that electric vehicle 101 will need to stop and recharge at a charging station, processing circuitry 102 may search for charging stations in the vicinity of a location along the initial route that corresponds to the partial range.” See also Table 1. Selecting a charging station from among those within range taken as determining an ability to reach one of those charging stations as the first charging station, where the flight to the first charging station is taken as the first flight.);
generating one or more real-time adjustments to an itinerary for the aircraft based on the ability of the aircraft to perform the first flight (Erozlu [0040]: “If processing circuitry 102 determines that electric vehicle 101 is expected to reach destination 208 using 80% or less of the current SOC, navigation interface 300 may display the initial route to destination 208 without any added charging stops. If, however, processing circuitry 102 determines that electric vehicle 101 is not expected to reach destination 208 using 80% or less of the current SOC, processing circuitry 102 identifies a charging station to add as a waypoint to the initial route (e.g., by adjusting the initial route to stop at the charging station). ” Adding a charging station to the initial route taken as a real-time adjustment to an itinerary. The adjustments are real time because they are the product of the current location and SOC of the battery along with the route that was entered, see for example [0038]-[0039] of Erozlu.).
The above combination of Erozlu and Matthews does not appear to expressly teach making a real-time itinerary adjustment for the aircraft by modifying at least one of a take-off or landing maneuver.
However, Foland teaches making a real-time itinerary adjustment for the aircraft by modifying at least one of a take-off or landing maneuver (Foland [0036]: “Still referring to FIG. 1, apparatus 100 may generate a power saving flight plan as a function of resource remaining datum 140.” Resource remaining datum is taught in [0032] to be calculated based on current battery charge; APOSITA would have understood that the datum measurement, and thus, the power saving flight plan (taken as the itinerary adjustment) as being computed in real time. [Id.]: “Apparatus 100 may be configured to automatically engage in a power saving plan. As a non-limiting example, apparatus 100 may determine electric aircraft 104 is at 30% fuel remaining. . . . Power saving flight plan may include changing a vertical rotor-based landing to conventional fixed-wing landing, which requires less energy.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that generates an itinerary based on estimated battery consumption and updates the itinerary based on the capability of the aircraft to complete it taught by the above combination of Erozlu and Matthews with the system that implements a real-time flight plan adjustment that alters the type of landing maneuver performed based on the capability of the aircraft to continue flying taught by Foland. Doing so would have improved the range of the vehicle by allowing it to select a less energy-intensive type of landing, freeing up available power to be used for flight.
One of ordinary skill in the art would have recognized that this combination further teaches transmitting, over a network, instructions associated with implementing the one or more real-time adjustments to the itinerary for the aircraft (Erozlu [0031]: “In some embodiments, communications circuitry and/or user device 138 may be in communication with one or more servers 140 (e.g., over a communications network such as, for example, the Internet), which may be configured to provide information related to electric charging stations, information that can be used to determine driving range (e.g., elevation maps), charging locations, weather information, and/or mapping or GPS information to electric vehicle 101 and/or user device 138, and provide an updated display based on user inputs.”; [Fig. 2, 0034]: “ . . . navigation interface 200 may generate and display a route to the selected destination, as described in further detail below.” Adding charging stations to the route taken as instructions associated with implementing the itinerary. See also FIG. 1 and [0022] of Foland, all operations performed by that system also include use of a network.).
Regarding claim 2, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 1, wherein it is determined that the aircraft is able to perform the first flight, and wherein generating the one or more real-time adjustments itinerary for the aircraft comprises adding the first flight to the itinerary for the aircraft (Erozlu [0040]: “If, however, processing circuitry 102 determines that electric vehicle 101 is not expected to reach destination 208 using 80% or less of the current SOC, processing circuitry 102 identifies a charging station to add as a waypoint to the initial route (e.g., by adjusting the initial route to stop at the charging station).” Adjusting the route to accommodate travel to the charging station taken as adding the first flight to the itinerary, in response to determining that the aircraft is able to go to a charging station rather than drive straight to the destination.).
Regarding claim 3, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 1, wherein it is determined that aircraft is not able to perform the first flight, and wherein generating the one or more real-time adjustments to the itinerary for the aircraft comprises omitting the first flight from the itinerary for the aircraft (Erozlu [0040]: “If processing circuitry 102 determines that electric vehicle 101 is expected to reach destination 208 using 80% or less of the current SOC, navigation interface 300 may display the initial route to destination 208 without any added charging stops.” Displaying a route without a first route portion to a first charging station taken as omission of the first flight from the itinerary.).
Regarding claim 4, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 1, further comprising:
accessing data associated with a second flight plan of a second flight (Erozlu [0042]: “To do this, processing circuitry 102 may determine a partial range of electric vehicle 101 based on a predetermined recharge SOC of electric battery 110.” Understood based on par. 0052 that determining a partial range (discussed in claim 1 above) requires accessing route information, taken as data at least associated with the second flight plan.);
computing a second expected power demand on the energy storage system of the aircraft based on the data associated with the second flight plan (Erozlu [0056]: “At 418, processing circuitry 102 may determine a partial recharged range of the electric vehicle 101 corresponding to a predetermined percentage of a state of recharge of electric battery 110.” Partial recharged range understood as calculated similarly to the recharge range, requiring a second power demand from the second SOC depletion. The route traveled after recharge taken as the second flight plan.);
accessing data indicative of a predicted future battery state of the aircraft (Erozlu [0042]: “To do this, processing circuitry 102 may determine a partial range of electric vehicle 101 based on a predetermined recharge SOC of electric battery 110.” Predetermined recharge SOC taken as predicted future battery state. See also Tables 1 and 2, which show predicted recharge SOC for stops at charging stations.); and
computing, using the battery model (Erozlu [0042]: The above combination of Erozlu and Matthews inherently teaches a battery model by teaching range estimation based on battery SOC, see the discussion in claim 1 above.), a second capability output based on the second expected power demand on the energy storage system of the aircraft and the predicted future battery state of the aircraft, wherein the second capability output is indicative of a future range or a future available time of flight of the aircraft for the second flight (Erozlu [0042]: “As shown below in Table 1, based on the predetermined recharge SOC of 80%, processing circuitry 102 may determine that the partial recharged range of electric vehicle 101 (e.g., based on the predetermined percentage of the predetermined recharge SOC) is 176 miles from the location of first charging station 311.”).
Regarding claim 5, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 4, further comprising:
based on the second capability output, updating the itinerary for the aircraft to include the second flight (Erozlu [0042]: “ . . . processing circuitry 101 may search for charging stations 176 miles from first charging station 311. As shown, processing circuitry 102 may identify and select second charging station 319, which is 155 miles from first charging station 311.” Selecting a second charging station taken as updating the itinerary for the aircraft to include the second flight.).
Regarding claim 6, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 4, further comprising:
based on the second capability output, computing one or more charging parameters for charging the energy storage system of the aircraft between the first flight and the second flight (Erozlu [0066]: “At 508, processing circuitry 102 may determine a recharge SOC to reach the next charging station with intermediate arrival SOC threshold. The recharge SOC may be determined based on the expected energy consumption for the route portion” The Examiner understands the recharge SOC in Erozlu being computed for each added charging station; thus, the recharge SOC for the first charging station between the first and second portions of the route, chosen because it is within partial range (based on capability output), is taken as the charging parameter.).
Regarding claim 7, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 6, wherein the charging parameters are indicative of at least one of: a target level of charge, a target temperature, or charging infrastructure (Erozlu [0066]: “ At 508, processing circuitry 102 may determine a recharge SOC to reach the next charging station with intermediate arrival SOC threshold.” Recharge SOC taken as target level of charge.).
Regarding claim 8, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 7, further comprising:
determining whether to include the second flight in the itinerary for the aircraft based on the charging parameters (Erozlu [0042]: “Processing circuitry 102 may then repeat the process described above to determine if destination 208 is within range of first charging station 311. To do this, processing circuitry 102 may determine a partial range of electric vehicle 101 based on a predetermined recharge SOC of electric battery 110.” Predetermined recharge SOC of the battery taken as one of the one or more charging parameters.).
Regarding claim 9, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 4, wherein the predicted future battery state of the aircraft is computed by the battery model based on the first expected power demand on the energy storage system of the aircraft and the initial battery state of the energy storage system of the aircraft (Erozlu [0041]: “In response to determining that electric vehicle 101 will need to stop and recharge at a charging station, processing circuitry 102 may search for charging stations in the vicinity of a location along the initial route that corresponds to the partial range.” Because the system does not require generating Table 1 if no stops at a charging station are required, the predetermined recharge SOC of the battery is accessed at least based on whether the range of the aircraft, calculated from an initial battery state and a first energy consumption as detailed in claim 1 above, requires stopping at a charging station. ).
Regarding claim 10, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 1, wherein the data associated with the first flight plan of the first flight is indicative of one or more aircraft maneuvers for performing the first flight (Erozlu [0035]: “As explained in further detail below, the estimated range may be refined based on retrieved route information (e.g., speed limits, elevation change, traffic, weather, etc.) once a destination is selected and a route to the destination is determined.” Elevation change and speed limit taken as aircraft maneuvers. See also Fig. 3, the route contains turns and places to stop to charge, also taken as aircraft maneuvers.).
Regarding claim 11, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 1, wherein the data associated with the first flight plan of the first flight is indicative of at least one of: (i) a route (Erozlu [0035]: “As explained in further detail below, the estimated range may be refined based on retrieved route information (e.g., speed limits, elevation change, traffic, weather, etc.) once a destination is selected and a route to the destination is determined.” The Examiner notes that usage of the phrase “at least one of” requires consideration of only one of the listed options.), (ii) an altitude (Erozlu [0035]: See above citation.), (iv) an environmental condition (Erozlu [0035]: See above citation.), (v) a noise constraint, or (vi) a speed (Erozlu [0035]: See above citation.).
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Erozlu as applied to claim 1 above, in view of PG Pub No. US 20190241192 A1 to Matthews, Gregory et al. (hereinafter “Matthews”) and PG Pub No. US 20230382570 A1 to Foland, Steven et al. (hereinafter “Foland”), further in view of PG Pub. No. US 20200070801 A1 to Staats, Andrew et al. (hereinafter “Staats”).
Regarding claim 12, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 1 in the rejection thereof, incorporated herein by reference.
This combination does not appear to expressly teach accessing data indicative of a current battery state of the aircraft while the aircraft is performing the first flight;
computing, using the battery model, an updated capability output based on the current battery state of the aircraft and the battery model; and
determining, based on the updated capability, one more second real-time adjustments to the itinerary of the aircraft; and
adjusting the itinerary of the aircraft based on the one or more second real-time adjustments.
However, Staats teaches accessing data indicative of a current battery state of the aircraft while the aircraft is performing the first flight ([Staats 0120]: “The energy management system 826 can determine the amounts of electric energy that are available, will be available, and/or are estimated to be available at various locations and/or times along the trip.” Available electric energy can be the energy in an onboard storage device (taught by Staats to be a battery), see for example Staats par. 0095 0122. Energy that is available taken as current battery state, estimated multiple times. Staats inherently teaches a battery model by teaching that the system can determine the amount of available energy, requiring the use of some kind of model.);
computing, using the battery model, an updated capability output based on the current battery state of the aircraft and the battery model ([Staats 0120]: “The energy management system 826 compares the available electric energy to the estimated trip load over one or more of the segments of the trip and/or over the entire trip in order to ensure that the vehicle has sufficient electric energy to reach one or more designated locations.” The comparison is taken as an updated capability output.); and
determining, based on the updated capability, one or more second real-time adjustments to the itinerary of the aircraft ([Staats 0123]: “Based on the comparison of the estimated trip load and the stored electric energy onboard the vehicle, the energy management system 826 may generate and/or change a trip plan.”); and
adjusting the itinerary of the aircraft based on the one or more second real-time adjustments ([Staats 0123]: “Based on the comparison of the estimated trip load and the stored electric energy onboard the vehicle, the energy management system 826 may generate and/or change a trip plan.” The adjustments are understood as real-time because they are based in part on current location and battery SOC, see [0038]-[0039] of Erozlu.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that determines the capability of an aircraft based on a battery level and battery model and generates an itinerary taught by the above combination of Erozlu, Matthews, and Foland with the system that determines the updated capability of an electric vehicle based on amount of energy stored in the battery and changes a trip plan accordingly at multiple times during the trip taught by Staats. Doing so would have improved the adaptability of the system by allowing it to repeatedly change the trip plan to suit changes in amounts of energy stored and required. Doing so would have also improved the accuracy of the system by allowing it to regularly recalculate, accommodating for unforeseen conditions.
Regarding claim 13, the above combination of Erozlu, Matthews, Foland, and Staats teaches the computer-implemented method of claim 12 in the rejection thereof, incorporated herein by reference.
Staats further teaches wherein adjusting the itinerary of the aircraft comprises at least one of: (i) adjusting a payload of the aircraft for a subsequent flight, (ii) adjusting one or more charging parameters for charging the aircraft after the first flight ([Staats 0139]: “With respect to the second and third trip segments 912B-C, this additional electric energy may be supplied from the off-board energy sources. The third trip plan can direct the energy storage device 802 to receive the additional electric energy from wayside stations 606, 608, 702, 708 . . ..” Wayside stations would have been taken by one of ordinary skill in the art as the charging stations of Erozlu in the above combination.), or (iii) removing a subsequent flight from the itinerary of the aircraft.
Claims 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Erozlu as applied to claim 1 above, in view of PG Pub. No. US 20190325757 A1 to Goel, Nikhil et al. (hereinafter “Goel”), and PG Pub No. US 20190241192 A1 to Matthews, Gregory et al. (hereinafter “Matthews”), further in view of PG Pub No. US 20230382570 A1 to Foland, Steven et al. (hereinafter “Foland”).
Regarding claim 14, the above combination of Erozlu, Matthews, and Foland teaches the computer-implemented method of claim 1 in the rejection thereof, incorporated herein by reference.
This combination does not appear to expressly teach that the first flight is associated with a multi-modal transportation service, the multi-modal transportation service comprises a first transportation leg comprising a first ground transportation service from an origin, an intermediate transportation leg comprising the first flight, and a second ground transportation service to a destination.
However, Goel teaches the first flight is associated with a multi-modal transportation service, the multi-modal transportation service comprises a first transportation leg comprising a first ground transportation service from an origin, an intermediate transportation leg comprising the first flight, and a second ground transportation service to a destination ( [Goel 0060]: “In one embodiment, for a given subset of candidate nodes, the node selection module 520 determines how to service each request. . . . a set of three legs where the middle leg is serviced by a VTOL aircraft 120. The first and third legs are ground-based, and can be walking legs or serviced by ground-based transportation.” See also Par. 0059, each request has an origin and a destination.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system for generating an itinerary for a flight from a starting point to a distance aircraft taught by the above combination of Erozlu, Matthews, and Foland with the system that provides an aircraft-based second leg from a start to an end node to service a trip request taught by Goel. Doing so would have integrated the trip planner for the aircraft with a trip planner that includes other modes of transportation, improving user mobility by allowing them to travel to places unreachable by aircraft.
Regarding claim 15, Erozlu teaches computer-implemented method comprising:
accessing data associated with a flight plan of a flight currently being performed by an aircraft ([Erozlu 0052]: “The partial range may be determined using the route details retrieved in 406.”; [0023]: “Electric vehicle 101 may be . . . an aircraft . . ..” Route details taken as data at least associated with a first flight plan. See also 0039, the current battery state is used to estimate the partial range.).
Erozlu does not appear to expressly teach wherein the flight is associated with a multi-modal transportation service.
However, Goel teaches wherein the flight is associated with a multi-modal transportation service ([Goel 0026]: “ . . . the transport services coordination system 115 treats a journey involving a VTOL aircraft 120 as having three legs: (1) from the rider's initial location to a first node; (2) from the first node to a second node in a VTOL; and (3) from the second node to the rider's destination.” The route from the first node to the second node in a VTOL taken as the initial (entire) route taught by Erozlu.; [Goel 0060]: “In one embodiment, for a given subset of candidate nodes, the node selection module 520 determines how to service each request. A request will either be serviced by . . . a set of three legs where the middle leg is serviced by a VTOL aircraft 120. The first and third legs are ground-based, and can be walking legs or serviced by ground-based transportation.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system for generating an itinerary for an aircraft to travel a route taught by Erozlu with the system that provides an aircraft-based second travel leg comprising an origin and destination to service a trip request taught by Goel. Doing so would have granted the user greater mobility by allowing them to travel to places unreachable by aircraft.
This combination further teaches computing an expected power demand on an energy storage system of the aircraft based on the data indicative of the flight plan (Erozlu [0039]: “That is, processing circuitry 102 may determine how far electric vehicle 101 will have traveled when the SOC of electric battery 110 reaches 20% of the original SOC.”; [Erozlu 0052]: “The partial range may be determined using the route details retrieved in 406. In some embodiments, the processing circuitry 102 determines the partial range by determining the current energy of electric battery 110 and applying a scaling factor as explained further in 406.” Using a scaling factor based on route information to predict a partial range to where battery SOC reaches a predetermined level (representing usage of battery power) taken as computing a first expected power demand..);
accessing data indicative of a current battery state of the energy storage system of the aircraft ([Erozlu Table 1, 0039]: Starting SOC taken as current battery state data.);
computing, using a battery model ([Erozlu 0039]: “Processing circuitry 102 may estimate how far along the initial route electric vehicle 101 can travel (e.g., the estimated range), based on the route information and the current SOC of electric battery 110. . . . As shown below in Table 1, based on a current SOC (“Starting SOC”) of 60% . . .” Erozlu inherently teaches a battery model by teaching determining the range based on the starting battery SOC.), a predicted future battery state of the aircraft based on the expected power demand on the energy storage system of the aircraft and the current battery state of the energy storage system of the aircraft ([Erozlu 0039]: “ . . . processing circuitry 102 may determine how far electric vehicle 101 will have traveled when the SOC of electric battery 110 reaches 20% of the original SOC.” 20% number taken as the predicted future battery state, inherently determined based on power demand (from initial to final SOC) and current battery state (original SOC).).
This combination does not appear to expressly teach the battery model is configured to compute capability outputs by comparing previous capability output predictions for one or more previous flights with actual capability outputs of the one or more previous flights.
However, Matthews teaches the battery model (Matthews FIG. 1: Prediction system 4.) is configured to compute capability outputs by comparing previous capability output predictions for one or more previous flights with actual capability outputs of the one or more previous flights (Matthews FIG. 3, [0065]-[0070]: Steps 12-16 depict updating a range prediction algorithm, taken as the battery model, by comparing the estimated range for a plurality of past trips to the actual range of those past trips to derive an error which is minimized to improve battery model accuracy.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that estimates a partial range attainable by a battery-powered vehicle using a battery model taught by the above combination of Erozlu and Goel with the battery model that optimizes itself by considering estimation errors on past trips taught by Matthews. Doing so would have made the model “more accurate at predicting the energy requirement for future journeys” as taught in [0070] of Matthews.
APOSITA would have recognized that the above combination of Erozlu, Goel, and Matthews further teaches the steps of accessing data associated with the multi-modal transportation service ([Goel 0026]: “ . . . from the first node to a second node in a VTOL . . . ” APOSITA would have understood that the first node of Goel represents the starting point of the route of Erozlu and the second node of Goel represents the destination point of Erozlu in the above combination. Therefore, APOSITA would have understood that the route of Erozlu is at least associated with the multi-modal transportation service in the above combination.);
determining one or more real-time actions associated with the multi-modal transportation service based on the predicted future battery state of the aircraft and the data associated with the multi-modal transportation service (Erozlu [0041]: “In response to determining that electric vehicle 101 will need to stop and recharge at a charging station, processing circuitry 102 may search for charging stations in the vicinity of a location along the initial route that corresponds to the partial range.” See also Table 1. Selecting a charging station from among those within range taken as determining an action. APOSITA would have understood that in the above combination any actions associated with the second VTOL leg of Goel taken by the route planning system of Erozlu are at least associated with the multi-modal transportation service. The adjustments are real time because they are the product of the current location and SOC of the battery along with the route that was entered, see for example [0038]-[0039] of Erozlu.).
This combination does not appear to expressly teach wherein the one or more real-time actions comprises at least one of a modified take-off or modified landing maneuver.
However, Foland teaches wherein the one or more real-time actions comprises at least one of a modified take-off or modified landing maneuver (Foland [0036]: “Still referring to FIG. 1, apparatus 100 may generate a power saving flight plan as a function of resource remaining datum 140.” Resource remaining datum is taught in [0032] to be calculated based on current battery charge; APOSITA would have understood that the datum measurement, and thus, the power saving flight plan (taken as the itinerary adjustment) as being computed in real time. [Id.]: “Apparatus 100 may be configured to automatically engage in a power saving plan. As a non-limiting example, apparatus 100 may determine electric aircraft 104 is at 30% fuel remaining. . . . Power saving flight plan may include changing a vertical rotor-based landing to conventional fixed-wing landing, which requires less energy.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that generates an itinerary based on estimated battery consumption and updates the itinerary based on the capability of the aircraft to complete it taught by the above combination of Erozlu and Matthews with the system that implements a real-time flight plan adjustment that alters the type of landing maneuver performed based on the capability of the aircraft to continue flying taught by Foland. Doing so would have improved the range of the vehicle by allowing it to select a less energy-intensive type of landing, freeing up available power to be used for flight.
One of ordinary skill in the art would have recognized that the combination made above further teaches transmitting, over a network, instructions indicative of the one or more real-time actions associated with the multi-modal transportation service (Erozlu [0031]: “In some embodiments, communications circuitry and/or user device 138 may be in communication with one or more servers 140 (e.g., over a communications network such as, for example, the Internet), which may be configured to provide information related to electric charging stations, information that can be used to determine driving range (e.g., elevation maps), charging locations, weather information, and/or mapping or GPS information to electric vehicle 101 and/or user device 138, and provide an updated display based on user inputs.”; [Fig. 2, 0034]: “ . . . navigation interface 200 may generate and display a route to the selected destination, as described in further detail below.” Route with charging stations taken as instructions associated with implementing the itinerary. APOSITA would have understood that in the above combination any actions associated with the second VTOL leg of Goel taken by the route planning system of Erozlu are at least associated with the multi-modal transportation service.).
Regarding claim 16, the above combination of Erozlu, Goel, Matthews, and Foland teaches the computer-implemented method of claim 15 in the rejection thereof, incorporated herein by reference.
This combination further teaches wherein the data associated with the multi-modal transportation service comprises at least one of: an itinerary of the aircraft ([Goel 0026]: “ . . . from the first node to a second node in a VTOL . . . ” APOSITA would have understood that the first node of Goel represents the starting point of the route of Erozlu and the second node of Goel represents the destination point of Erozlu in the above combination. Therefore, APOSITA would have understood that the route of Erozlu, taken here as the itinerary of the aircraft, is at least associated with the multi-modal transportation service. Further, the Examiner notes that usage of the term “at least one of” requires consideration of only one of the presented options.), data associated with one or more other aircraft associated with the multi-modal transportation service, or data associated with one or more users of the multi-modal transportation service.
Regarding claim 17, the above combination of Erozlu, Matthews, Goel, and Foland teaches the computer-implemented method of claim 15 in the rejection thereof, incorporated herein by reference.
This combination further teaches wherein the one or more real-time actions associated with the multi-modal transportation service comprises at least one of: (i) an adjustment to the flight plan ([Goel 0026]: “ . . . from the first node to a second node in a VTOL . . . ” As previously discussed in for example the rejection of claim 17, APOSITA would have understood the above combination accesses data at least associated with the multi-modal transportation service. Further, the Examiner notes that usage of the term “at least one of” requires consideration of only one of the presented options.; [Erozlu 0040]: “If, however, processing circuitry 102 determines that electric vehicle 101 is not expected to reach destination 208 using 80% or less of the current SOC, processing circuitry 102 identifies a charging station to add as a waypoint to the initial route (e.g., by adjusting the initial route to stop at the charging station).” Adding a charging station taken as adjusting the flight plan.), (ii) an adjustment to an itinerary for the aircraft ([Goel 0026]; [Erozlu 0040]: Adding a charging station taken as adjusting the flight itinerary. The Examiner notes that without further illustration as to the difference between a flight plan and an itinerary, APOSITA would find that the combination of Erozlu and Goel reads on both options (i) and (ii).), (iii) an adjustment to an itinerary of another vehicle, (iv) an adjustment to an itinerary of a user ([Goel 0026]; [Erozlu 0040]: Adding a charging station taken as adjusting the user’s itinerary. The Examiner notes that without further illustration as to the difference between a flight plan and a user’s itinerary, APOSITA would find that the combination of Erozlu and Goel reads on both options (i) and (iii).), or (v) an adjustment to a ground transportation service.
Regarding claim 18, the above combination of Erozlu, Goel, Matthews, Foland teaches the computer-implemented method of claim 15 in the rejection thereof, incorporated herein by reference.
This combination further teaches determining one or more charging parameters for the aircraft based on the predicted future battery state of the aircraft ([Erozlu 0041]: “In response to determining that electric vehicle 101 will need to stop and recharge at a charging station, processing circuitry 102 may search for charging stations in the vicinity of a location along the initial route that corresponds to the partial range.” Determining that the vehicle will need to be recharged taken as determining a charging parameter).; and
determining the one or more real-time actions associated with the multi-modal transportation service based on the one or more charging parameters ([Erozlu 0041]: Searching for and selecting a charging station understood as the action taken based on the charging parameters. APOSITA would have understood that in the above combination, the destination is determined to be the second node of the second leg taught in Goel, and therefore is at least associated with the multi-modal transportation service.).
Regarding claim 19, Erozlu teaches one or more non-transitory, computer-readable media storing instructions that are executable by one or more processors to cause the one or more processors to perform operations (Erozlu [0024]: “Memory 106 may comprise hardware elements for non-transitory storage of commands or instructions, that, when executed by processor 104, cause processor 104 to operate electric vehicle 101 in accordance with embodiments described above and below.”), the operations comprising:
accessing data associated with a flight plan of a flight (Erozlu [0052]: “The partial range may be determined using the route details retrieved in 406.”; [0023]: “Electric vehicle 101 may be . . . an aircraft . . ..” Route details taken as data at least associated with a first flight plan);
based on the data associated with the flight plan, computing a power profile of an aircraft for the flight (Erozlu [0039]: “That is, processing circuitry 102 may determine how far electric vehicle 101 will have traveled when the SOC of electric battery 110 reaches 20% of the original SOC.”; [0052]: “The partial range may be determined using the route details retrieved in 406. In some embodiments, the processing circuitry 102 determines the partial range by determining the current energy of electric battery 110 and applying a scaling factor as explained further in 406.” Scaling factor based on the route details. The partial range taken as the power profile as discussed in the claims above, because it considers how much battery SOC is consumed as the aircraft travels the route. This change in SOC taken as the power profile, since it represents usage of battery power.);
computing, using a battery model (Erozlu [0039]: “That is, processing circuitry 102 may determine how far electric vehicle 101 will have traveled when the SOC of electric battery 110 reaches 20% of the original SOC. . . . As shown below in Table 1, based on a current SOC (“Starting SOC”) of 60% . . .” Erozlu inherently teaches a battery model by teaching determining the range based on the starting battery SOC.), a capability output based on the power profile and data indicative of an initial battery state of the aircraft (Erozlu [Table 1]: Partial range from the starting SOC to the first charging station taken as the first capability output. The Examiner notes that usage of the term “or” requires consideration of only one of the two options.).
Erozlu does not appear to expressly teach the battery model is configured to compute capability outputs by comparing previous capability output predictions for one or more previous flights with actual capability outputs of the one or more previous flights.
However, Matthews teaches the battery model (Matthews FIG. 1: Prediction system 4.) is configured to compute capability outputs by comparing previous capability output predictions for one or more previous flights with actual capability outputs of the one or more previous flights (Matthews FIG. 3, [0065]-[0070]: Steps 12-16 depict updating a range prediction algorithm, taken as the battery model, by comparing the estimated range for a plurality of past trips to the actual range of those past trips to derive an error which is minimized to improve battery model accuracy.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that estimates a partial range attainable by a battery-powered vehicle using a battery model taught by Erozlu with the battery model that optimizes itself by considering estimation errors on past trips taught by Matthews. Doing so would have made the model “more accurate at predicting the energy requirement for future journeys” as taught in [0070] of Matthews.
APOSITA would have understood that the above combination of Erozlu and Matthews further teaches generating one or more real-time adjustments to an itinerary for the aircraft based on the capability output (Erozlu [0040]: “If processing circuitry 102 determines that electric vehicle 101 is expected to reach destination 208 using 80% or less of the current SOC, navigation interface 300 may display the initial route to destination 208 without any added charging stops. If, however, processing circuitry 102 determines that electric vehicle 101 is not expected to reach destination 208 using 80% or less of the current SOC, processing circuitry 102 identifies a charging station to add as a waypoint to the initial route (e.g., by adjusting the initial route to stop at the charging station). ” Adding a charging station to the initial route taken as a real-time adjustment to an itinerary. The adjustments are real time because they are the product of the current location and SOC of the battery along with the route that was entered, see for example [0038]-[0039] of Erozlu.).
The above combination of Erozlu and Matthews does not appear to expressly teach making a real-time itinerary adjustment for the aircraft by modifying at least one of a take-off or landing maneuver.
However, Foland teaches making a real-time itinerary adjustment for the aircraft by modifying at least one of a take-off or landing maneuver (Foland [0036]: “Still referring to FIG. 1, apparatus 100 may generate a power saving flight plan as a function of resource remaining datum 140.” Resource remaining datum is taught in [0032] to be calculated based on current battery charge; APOSITA would have understood that the datum measurement, and thus, the power saving flight plan (taken as the itinerary adjustment) as being computed in real time. [Id.]: “Apparatus 100 may be configured to automatically engage in a power saving plan. As a non-limiting example, apparatus 100 may determine electric aircraft 104 is at 30% fuel remaining. . . . Power saving flight plan may include changing a vertical rotor-based landing to conventional fixed-wing landing, which requires less energy.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system that generates an itinerary based on estimated battery consumption and updates the itinerary based on the capability of the aircraft to complete it taught by the above combination of Erozlu and Matthews with the system that implements a real-time flight plan adjustment that alters the type of landing maneuver performed based on the capability of the aircraft to continue flying taught by Foland. Doing so would have improved the range of the vehicle by allowing it to select a less energy-intensive type of landing, freeing up available power to be used for flight.
This combination does not appear to expressly teach wherein the itinerary is associated with a multi-modal transportation service.
However, Goel teaches wherein the itinerary is associated with a multi-modal transportation service ([Goel 0026]: “ . . . the transport services coordination system 115 treats a journey involving a VTOL aircraft 120 as having three legs: (1) from the rider's initial location to a first node; (2) from the first node to a second node in a VTOL; and (3) from the second node to the rider's destination.” The route from the first node to the second node in a VTOL taken as the initial (entire) route taught by Erozlu.; [Goel 0060]: “In one embodiment, for a given subset of candidate nodes, the node selection module 520 determines how to service each request. A request will either be serviced by . . . a set of three legs where the middle leg is serviced by a VTOL aircraft 120. The first and third legs are ground-based, and can be walking legs or serviced by ground-based transportation.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to have combined the system for generating an itinerary for an aircraft to travel a route taught by the combination of Erozlu, Matthews, and Foland with the system that provides an aircraft-based second travel leg comprising an origin and destination to service a trip request taught by Goel. Doing so would have granted the user greater mobility by allowing them to travel to places unreachable by aircraft.
One of ordinary skill in the art would have recognized that this combination further teaches transmitting, over a network, instructions associated with implementing the one or more real-time adjustments to the itinerary for the aircraft (Erozlu [0031]: “In some embodiments, communications circuitry and/or user device 138 may be in communication with one or more servers 140 (e.g., over a communications network such as, for example, the Internet), which may be configured to provide information related to electric charging stations, information that can be used to determine driving range (e.g., elevation maps), charging locations, weather information, and/or mapping or GPS information to electric vehicle 101 and/or user device 138, and provide an updated display based on user inputs.”; [Fig. 2, 0034]: “ . . . navigation interface 200 may generate and display a route to the selected destination, as described in further detail below.” Route with charging stations taken as instructions associated with implementing the itinerary.).
Regarding claim 20, the above combination of Erozlu, Matthews, Foland, and Goel teaches the one or more non-transitory, computer-readable media of claim 19, wherein the power profile is indicative of an expected power demand on an energy storage system of an aircraft for the flight (Erozlu [0039]: “That is, processing circuitry 102 may determine how far electric vehicle 101 will have traveled when the SOC of electric battery 110 reaches 20% of the original SOC.”; [0052]: “The partial range may be determined using the route details retrieved in 406. In some embodiments, the processing circuitry 102 determines the partial range by determining the current energy of electric battery 110 and applying a scaling factor as explained further in 406.” This change in SOC taken as the power profile, since it represents usage of battery power. Usage of battery power taken as a power demand, predicted based on the route details.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Buchmueller, Daniel et al.. US 20160257401 A1. LANDING OF UNMANNED AERIAL VEHICLES ON TRANSPORTATION VEHICLES FOR TRANSPORT.
Ma, Tao. US 20170197710 A1. PASSENGER TRANSPORT SYSTEMS BASED ON PILOTLESS VERTICAL TAKEOFF AND LANDING (VTOL) AIRCRAFT.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY RICHARD HINTON whose telephone number is (703)756-1051. The examiner can normally be reached Monday-Friday 7:30-4:30.
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/HENRY R HINTON/ Examiner, Art Unit 3665
/HUNTER B LONSBERRY/ Supervisory Patent Examiner, Art Unit 3665