DETAILED ACTION
Status of the Application
Claims 1-20 have been examined in this application. This communication is the first action on the merits. The Information Disclosure Statement (IDS) filed on December 27, 2024 has been reviewed and is acknowledged.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims (claim 1, and similarly claims 2-20) recite “accessing … data indicative of a plurality of vertiports that are online with a first flight network, each of the vertiports comprising infrastructure available for take-off and landing of VTOL aircraft that provide transportation for the first flight network; accessing … real-time data indicative of a constraint affecting the operation of the VTOL aircraft of the first flight network; based on the real-time data, performing … one or more simulations that simulate the routing and transportation of the VTOL aircraft using at least a portion of the plurality of vertiports; based on the one or more simulations, computing … a simulation output, the simulation output being indicative of a deficit in vertiport capacity for the first flight network; based on the simulation output, computing … a data exchange request for a second flight network, the data exchange request comprising a request for data associated with an additional vertiport of the second flight network; transmitting … the data exchange request to … the second flight network; and … associated with the additional vertiport to bring the additional vertiport online with the first flight network.” Claims 1-20, in view of the claim limitations, recite the abstract idea of accessing data regarding vertiports that are in a flight network, accessing real-time data regarding a constraint affecting the operations of VTOL aircraft, simulating the routing and transportation of the VTOL, computing a simulation output indicating a deficit in vertiport capacity, computing a request for second flight network with additional vertiports, transmitting the request to the second flight network, and associating the additional vertiport to include it within the flight network.
As a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited accessing data regarding vertiports that are in a flight network, accessing real-time data regarding a constraint affecting the operations of VTOL aircraft, simulating the routing and transportation of the VTOL, computing a simulation output indicating a deficit in vertiport capacity, computing a request for second flight network with additional vertiports, transmitting the request to the second flight network, and associating the additional vertiport to include it within the flight network could all be reasonably interpreted as a human observing information regarding vertiports and constraints, a human mentally performing an evaluation of the observed information to simulate the routing and transportation of the VTOL, compute a simulation output indicating a deficit in vertiport capacity, and compute a request for second flight network with additional vertiports, and a human outputting the resulting analysis manually and/or with a pen and paper to transmit the request and associate the additional vertiport within the flight network c; therefore, the claims recite a mental process. Further, all of the limitations recite a process for planning the flight service operations of a transportation service provider, which manages the commercial activity and sales and marketing activity of a transportation service provider business, and thus, the claims recite a certain method of organizing human activity. Accordingly, the claims recite a mental process and a certain method of organizing human activity, and thus, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “[a] computer-implemented method for … telecommunications,” “by a first flight network computing system,” “by the first flight network computing system using an API,” “to a second flight network computing system,” and “communicatively coupling, over a network, the first flight network computing system to a computing system” in claim 1, and similarly claim 2-20, “[a] computing system comprising: one or more processors; and one or more computer storage media storing instructions that are executable by the one or more processors to perform operations, the operations comprising” in claim 11, and “[o]ne or more tangible and non-transitory computer storage media comprising instructions that are executable by one or more processors to perform operations, the operations comprising” in claim 18, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-10, 12-17, 19, & 20 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s specification at [0079], [0092]-[0093] (describing the embodiments include a machine in the form of a computer device (e.g., a computer) and within which instructions (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine to perform any one or more of the methodologies discussed herein may be executed, and a hardware module performing operations described herein may include software encompassed within a general-purpose processor or other programmable processor). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, electronic record keeping, storing and retrieving information in memory, and presenting offers, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2-10, 12-17, 19, & 20 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Goel, et al. (US 20180308366 A1), hereinafter Goel, in view of DiCosola (US 20200349852 A1), hereinafter DiCosola.
Regarding claim 1, Goel discloses a computer-implemented method for flight network telecommunications, the method comprising ([0111], fig. 9):
accessing, by a first flight network computing system, data indicative of a plurality of vertiports that are online with a first flight network, each of the vertiports comprising infrastructure available for take-off and landing of VTOL aircraft that provide transportation for the first flight network ([0031], the demand estimation subsystem 210 initially predicts demand based on usage data for one or more existing ground-based transportation services in the geographic region, when planning continues after the transport network becomes operational ( e.g., where a first set of hubs are built and an expansion including adding additional hubs is planned), the model for predicting demand may be updated over time based on usage data that includes flights between hubs in the transport network, and the demand estimation subsystem 210 predicts demand for transport services in a geographic region to provide as input to the other subsystems to plan the transport network, as described in further below detail in fig. 3, [0105], in a method for planning an aviation transport network, the method begins with estimating demand, which is a set of hypothetical transport requests, as described previously, estimated from historical requests, [0115], at 930, the parameter selection module 610 supplements current demand with a an estimate of future demand, as described above);
accessing, by the first flight network computing system, real-time data indicative of a constraint affecting the operation of the VTOL aircraft of the first flight network ([0063]-[0064], in situations where the routing is being performed for an operational transport network (e.g., in real time or substantially in real time), the parameter selection module 610 may determine the parameters from data available from the VTOLs 120 and hub management systems 130, wherein the parameters include the number of available VTOL aircraft 120, en route speed, number of seats, maximum flying range, battery consumption rate, battery recharging rate, [0111], [0113], in a method 900 for determining routing for a fleet of VTOLs 120 within a transport network, the method begins with the parameter selection module 610 retrieving 910 current VTOL and routing data, the VTOL data about each of the VTOLs 120 may include: a current location, whether the VTOL is on the ground or air, a current battery level, a maximum battery level);
based on the real-time data, performing, by the first flight network computing system, one or more simulations that simulate the routing and transportation of the VTOL aircraft using at least a portion of the plurality of vertiports ([0116], at 940, the route optimization subsystem 240 the routing data based on the demand data, the flow modelling module 620 determines the optimum routing based on the demand data as well as the VTOL and routing data as described in fig. 6, wherein the input into the optimization subsystem 240 may include the retrieved 910 VTOL data , the demand data, weather data, and the like, [0101], the results of each simulation performed by the flow modelling module 620 are stored in routing data store 640, and a user may perform multiple simulations using different parameters and then compare the results at a later time, [0113], at 910 routing data may be retrieved from routing data store 640);
based on the one or more simulations, computing, by the first flight network computing system, a simulation output, the simulation output being indicative of a deficit in vertiport capacity for the first flight network ([0116], based on the updated routing data, the route optimization subsystem 240 may send routing instructions to some or all of the VTOLs 120 including instructions to direct a VTOL 120 to fly to a particular hub and pick up specified riders, [0066]-[0067], [0073], [0087], calculating an optimum routing for the fleet of VTOLs 120 for each discretized segment of time using a network flow model includes the parameters representing supply and demand of VTOL aircraft at hub i and time t, the capacity of hub i, and the cost of repositioning a VTOL aircraft by arc i, and [0064]-[0066], [0089], [0092], [0094], [0095], the network flow module 620 may solve the model to meet a specified objective including maximize VTOL utilization and minimize total cost traveling cost, repositioning cost, the optimized routing includes minimizing the VTOL aircraft repositioning costs (either to other hubs or within a hub, such as from a landing pad to a storage area)).
While Goel discloses all of the above, including based on the simulation output, computing, by the first flight network computing system … (([0116], based on the updated routing data, the route optimization subsystem 240 may send routing instructions to some or all of the VTOLs 120 including instructions to direct a VTOL 120 to fly to a particular hub and pick up specified riders, [0066]-[0067], [0073], [0087], calculating an optimum routing for the fleet of VTOLs 120 for each discretized segment of time using a network flow model includes the parameters representing supply and demand of VTOL aircraft at hub i and time t, the capacity of hub i, and the cost of repositioning a VTOL aircraft by arc i), Goel does not expressly disclose the remaining elements of the following limitation, which however, are taught by further teachings in DiCosola.
DiCosola teaches computing, by the first flight network computing system using an API ([0526]-[0527], Unmanned System Service Network (USSN) (or Drone System Service Network (DSSN)) consists of three types of nodes: Controllers, rooftop clients (R-Clients), and Extended Clients (E-Clients), controller node sends commands and configuration information to the client nodes and receives data and service requests from them, controller node hosts a web application featuring a RESTful API, through which r-clients and e-clients may request reservations, data and other services, [0534], an E-Client is any remote device or application that requests or uses the services of the rooftop airport, e.g., E-Clients include API Apps), a data exchange request for a second flight network, the data exchange request comprising a request for data associated with an additional vertiport of the second flight network ([0646], fig. 54, the customer requests service from the drone request system (DRS) 5403 which asks the drone flight planner (DFP) 5401 to plan a flight, and the DFP 5401 sends the origin and destination to the drone system slate (DSS) 5405 which responds with the status information of candidate nodes for the mission);
transmitting, by the first flight network computing system, the data exchange request to a second flight network computing system of the second flight network ([0646], fig. 54, the DSS 5405 responds with the status information of candidate nodes for the mission, DFP 5401 invites nodes to be part of a mission via the DAA 5411 which sends an authentication message to the nodes which may accept or reject the invitation which may be returned to the DFP 5401); and
communicatively coupling, over a network, the first flight network computing system to a computing system that is associated with the additional vertiport to bring the additional vertiport online with the first flight network ([0646], fig. 54, DFP 5401 invites nodes to be part of a mission via the DAA 5411 which sends an authentication message to the nodes which may accept or reject the invitation which may be returned to the DFP 5401, DFP 5401 may then transmit a confirmation to the DAA 5411 which passes the confirmation to the nodes, DFP 5401 logs the flight in the DMDB 5409 and the nodes send status changes to the DAA 5411 which forwards the status changes to the DSS 5405).
Goel and DiCosola are analogous fields of invention because both address the problem of optimizing the vertiport locations. At the time the invention was effectively filed, it would have been obvious to one of ordinary skill in the art to include in the system of Goel the ability to compute a data exchange request for a second flight network, transmit the data exchange request, and communicatively couple the first flight network computing system to a computing system that is associated with the additional vertiport to bring the additional vertiport online, as taught by DiCosola, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the combination would produce the predictable results of computing a data exchange request for a second flight network, transmitting the data exchange request, and communicatively coupling the first flight network computing system to a computing system that is associated with the additional vertiport to bring the additional vertiport online, as claimed. Further, it would have been obvious to one of ordinary skill in the art to have modified Goel with the aforementioned teachings of DiCosola in order to produce the added benefit of improving a network of landing locations for VTOL vehicles by providing flexibility, extensibility, and scalability to the drone airport system. [0646].
Regarding claim 2, the combined teachings of Goel and DiCosola teach computer-implemented method of claim 1 (as above). Further, Goel discloses further comprising: routing, by the first flight network computing system, a VTOL aircraft based on the additional vertiport ([0113], the routing data about the routes assigned to each VTOL 120 is retrieved, [0116], based on the updated routing data, the route optimization subsystem 240 may send routing instructions to some or all of the VTOLs 120 including instructions to direct a VTOL 120 to fly to a particular hub and pick up specified riders, [0064]-[0066], [0089], [0095], the optimized routing includes minimizing the VTOL aircraft repositioning costs (either to other hubs or within a hub, such as from a landing pad to a storage area), [0031], where planning continues after the transport network becomes operational (e.g., where a first set of hubs are built and an expansion including adding additional hubs is planned), the model for predicting demand may be updated over time based on usage data that includes flights between hubs in the transport network).
Regarding claim 3, the combined teachings of Goel and DiCosola teach computer-implemented method of claim 1 (as above). Further, Goel discloses wherein performing the one or more simulations comprises:
performing, by the first flight network computing system, a first simulation that simulates the routing and transportation of the VTOL aircraft using the plurality of vertiports ([0111]-[0112], [0116], in fig. 9 illustrates a single iteration of the method 900 for determining routing for a fleet of VTOLs 120 within a transport network, at 940, the route optimization subsystem 240 updates the routing data based on the demand data, the flow modelling module 620 determines the optimum routing based on the demand data as well as the VTOL and routing data as described in fig. 6, wherein the input into the optimization subsystem 240 may include the retrieved 910 VTOL data , the demand data, weather data, and the like, [0101], the results of each simulation performed by the flow modelling module 620 are stored in routing data store 640, and a user may perform multiple simulations using different parameters and then compare the results at a later time);
based on the first simulation, computing, by the first flight network computing system, an initial simulation output indicative of one or more flights between at least a subset of the plurality of vertiports ([0116], the flow modelling module 620 determines the optimum routing based on the demand data as well as the VTOL and routing data as described in fig. 6, based on the updated routing data, the route optimization subsystem 240 may send routing instructions to some or all of the VTOLs 120, wherein the instructions might direct a VTOL 120 to fly to a particular hub, charge its battery for a specified time, pick up specified riders, and perform other suitable activities to optimize the use of the VTOLs in the transport network, [0066]-[0067], [0073], [0087], calculating an optimum routing for the fleet of VTOLs 120 for each discretized segment of time using a network flow model includes the parameters representing supply and demand of VTOL aircraft at hub i and time t, the capacity of hub i, and the cost of repositioning a VTOL aircraft by arc i);
computing, by the first flight network computing system, simulation feedback data based on the real-time data and the initial simulation output ([0112], the method 900 is repeated periodically (e.g., every minute, every five minutes, etc.) to update the routing data for the fleet of VTOLs 120 based on the current conditions, e.g., an unexpected failure in another mode of transport (e.g., a subway system shutting down due to an accident) may result in a sudden surge in requests for transport services that may be serviced by VTOL 120, altering the optimal routing for the fleet, and thus iterating the method, [0039]-[0040], the demand estimation module 320 periodically (e.g., every minute, every five minutes, etc.) estimates demand for a future window of time (e.g., the next hour, the next four hours, the next day, etc.), the model update module 330 updates the model used to predict demand as new data becomes available, [0101], a user may perform multiple simulations using different parameters and then compare the results at a later time); and
based on the simulation feedback data, performing, by the first flight network computing system, a second simulation ([0112], the method 900 is repeated periodically (e.g., every minute, every five minutes, etc.) to update the routing data for the fleet of VTOLs 120 based on the current conditions, e.g., an unexpected failure in another mode of transport (e.g., a subway system shutting down due to an accident) may result in a sudden surge in requests for transport services that may be serviced by VTOL 120, altering the optimal routing for the fleet, and thus iterating the method, [0039]-[0040], the demand estimation module 320 periodically (e.g., every minute, every five minutes, etc.) estimates demand for a future window of time (e.g., the next hour, the next four hours, the next day, etc.), the model update module 330 updates the model used to predict demand as new data becomes available) that simulates the routing and transportation of the VTOL aircraft using the plurality of vertiports and a constraint affecting the operation of the VTOL aircraft of the first flight network, the second simulation being a refined version of the first simulation based on the simulation feedback data ([0111]-[0113], [0116], in fig. 9 illustrates a single iteration of the method 900 for determining routing for a fleet of VTOLs 120 within a transport network, at 940, the route optimization subsystem 240 updates the routing data based on the demand data, the flow modelling module 620 determines the optimum routing based on the demand data as well as the VTOL and routing data as described in fig. 6, wherein the input into the optimization subsystem 240 may include the retrieved 910 VTOL data , the demand data, weather data, and the like, based on the updated routing data, the route optimization subsystem 240 may send routing instructions to some or all of the VTOLs 120, wherein the instructions might direct a VTOL 120 to fly to a particular hub, charge its battery for a specified time, pick up specified riders, and perform other suitable activities to optimize the use of the VTOLs in the transport network, [0101], the results of each simulation performed by the flow modelling module 620 are stored in routing data store 640, and a user may perform multiple simulations using different parameters and then compare the results at a later time).
Regarding claim 4, the combined teachings of Goel and DiCosola teach computer-implemented method of claim 3 (as above). Further, Goel discloses further comprising: computing, by the first flight network computing system, the simulation output based on the second simulation ([0116], the flow modelling module 620 determines the optimum routing based on the demand data as well as the VTOL and routing data as described in fig. 6, the route optimization subsystem 240 may send routing instructions to some or all of the VTOLs 120, wherein the instructions might direct a VTOL 120 to fly to a particular hub, charge its battery for a specified time, pick up specified riders, and perform other suitable activities to optimize the use of the VTOLs in the transport network).
Regarding claim 5, the combined teachings of Goel and DiCosola teach computer-implemented method of claim 1 (as above). Further, Goel discloses wherein the constraint affecting the operation of the VTOL aircraft of the first flight network comprises at least one of: (i) take-off or landing pad availability; (ii) energy usage at a respective vertiport; (iii) energy capacity at a respective vertiport; (iv) a temporary flight restriction; (v) an air traffic configuration or reconfiguration; (vi) a weather constraint; (vii) an emergency request; (viii) a change associated with a skylane; or (ix) a noise budget ([0116], the input to the route optimization subsystem 240 may include the retrieved 910 VTOL data, the demand data, weather data, and the like, [0038], the demand prediction module 320 applies the model to predict demand for VTOL services by learning how current inputs (e.g., weather, special events, planned outages or limitations for other modes of transport, and the like) may be mapped to future demand, [0063]-[0064], the parameter selection module 610 may determine the parameters, including the number of available VTOL aircraft 120, battery consumption rate for take-off and landing, battery recharging rate, [0111], [0113], the parameter selection module 610 retrieves 910 current VTOL and routing data, the VTOL data about each of the VTOLs 120 may include: a current location, whether the VTOL is on the ground or air, a maximum battery level);
Regarding claim 6, the combined teachings of Goel and DiCosola teach computer-implemented method of claim 1 (as above). Further, Goel discloses wherein the simulation output comprises an updated flight operations set ([0116], based on the updated routing data, the route optimization subsystem 240 may send routing instructions to some or all of the VTOLs 120 including instructions to direct a VTOL 120 to fly to a particular hub and pick up specified riders, [0066]-[0067], [0073], [0087], calculating an optimum routing for the fleet of VTOLs 120 for each discretized segment of time using a network flow model includes the parameters representing supply and demand of VTOL aircraft at hub i and time t, the capacity of hub i, and the cost of repositioning a VTOL aircraft by arc i).
Regarding claim 7, the combined teachings of Goel and DiCosola teach computer-implemented method of claim 1 (as above). Further, Goel discloses wherein the real-time data comprises data associated with the aircraft ([0111], [0113], the parameter selection module 610 retrieves 910 current VTOL and routing data, the VTOL data about each of the VTOLs 120 may include: a current battery level).
Regarding claim 8, the combined teachings of Goel and DiCosola teach computer-implemented method of claim 7 (as above). Further, Goel discloses wherein the data associated with the aircraft is indicative of a battery level of an aircraft ([0111], [0113], the parameter selection module 610 retrieves 910 current VTOL and routing data, the VTOL data about each of the VTOLs 120 may include: a current battery level).
Regarding claim 9, the combined teachings of Goel and DiCosola teach computer-implemented method of claim 8 (as above). Further, Goel discloses further comprising: computing, based on the data associated with the aircraft, a prediction when the aircraft will need access to a pad for charging at a vertiport ([0116], the route optimization subsystem 240 updates 940 the routing data based on the demand data, based on the updated routing data, the route optimization subsystem 240 may send routing instructions, including the instructions might direct a VTOL 120 to fly to a particular hub, charge its battery for a specified time, pick up specified riders, and perform other suitable activities to optimize the use of the VTOLs in the transport network, [0113], the route may include way points to visit en route, a time to depart, an amount of time to spend charging before departure or after arrival).
Regarding claim 10, the combined teachings of Goel and DiCosola teach computer-implemented method of claim 8 (as above). Further, Goel discloses further comprising: computing, based on the data associated with the aircraft, a predicted amount of charge that the aircraft will need to receive at a vertiport ([0116], the route optimization subsystem 240 updates 940 the routing data based on the demand data, based on the updated routing data, the route optimization subsystem 240 may send routing instructions, including the instructions might direct a VTOL 120 to fly to a particular hub, charge its battery for a specified time, pick up specified riders, and perform other suitable activities to optimize the use of the VTOLs in the transport network, [0113], the route may include way points to visit en route, a time to depart, an amount of time to spend charging before departure or after arrival).
Regarding claims 11-17, these claims are substantially similar to claims 1-5, 8, & 9-10, and therefore, are rejected for the reasons set forth above regarding claims 1-5, 8, & 9-10. While claim 11-17 are directed to a computing system comprising computer storage media storing instructions that are executable by processors to perform operation, Goel discloses a computing system as claimed. [0103], [0111], [0117], fig. 9.
Regarding claims 18-20, these claims are substantially similar to claims 1-3, and therefore, are rejected for the reasons set forth above regarding claims 1-3. While claim 18-20 are directed to a tangible and non-transitory computer storage media comprising instructions executable by one or more processors to perform operations, Goel discloses computer storage media as claimed. [0103], [0111], [0117], fig. 9.
Conclusion
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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623
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