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 .
Priority
The present application is a continuation of US Application number 18/405,250 filed on January 5, 2024 (published as Patent Pub. No. 2024/0321120 on September 26, 2024), which is a continuation of US Application number 17/673,383 filed on February 16, 2022 (issued as Patent No. 11,900,819 on February 13, 2024), which is a continuation of US Application number 16/367,874 filed on March 28, 2019 (issued as Patent No. 11,295,622 on April 5, 2022), which claims the benefit of US Provisional Application number 62/662,189 filed on Apr. 24, 2018.
Status of the Claims
Claims 21-40 are pending in the instant patent application. Claims 1-20 have been canceled. This Non-Final Office Action is in response to the claims filed on 01/17/2025.
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.
Regarding Claims 21-30, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 21-30 are directed to the abstract idea of managing an aviation transport network.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 21, claim 21 recites accessing data corresponding to a status for a fleet of aircraft, the current aircraft status comprising one or more of a current location, a current battery level, and an airborne or grounded indication for each aircraft of the fleet of aircraft; accessing data corresponding to current routes for the fleet of aircraft, the current routes comprising one or more of a destination, a time of arrival, and a number of passengers for each aircraft of the fleet of aircraft; accessing data corresponding to current demand for the fleet of aircraft, the current demand comprising a plurality of requests for transport services from a respective origin to a respective destination; computing estimated future demand for the fleet of aircraft based on the current routes for the fleet of aircraft, the future demand comprising a plurality of predicted requests for future transport services from a respective origin to a respective destination; and computing at least one updated route for the fleet of aircraft based on the current demand and the estimated future demand; and transmitting the at least one updated route to each respective aircraft of the fleet of aircraft.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind (including an observation, evaluation, judgment) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Accordingly, the claim recites an abstract idea and dependent claims 22-30 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim does not recite any elements that would integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 21 includes various elements that are not directed to the abstract idea under 2A. These elements include the generic computing elements described in the Applicant's specification in at least Para 0111-0113. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, recites computer functions that the courts have recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)…at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))).
Therefore, Claim 21 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 31-40, they are directed to a system, however the claims are directed to a judicial exception without significantly more. Claims 31-40 are directed to the abstract idea of managing an aviation transport network.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 31, claim 31 recites accessing data corresponding to a status for a fleet of aircraft, the current aircraft status comprising one or more of a current location, a current battery level, and an airborne or grounded indication for each aircraft of the fleet of aircraft; accessing data corresponding to current routes for the fleet of aircraft, the current routes comprising one or more of a destination, a time of arrival, and a number of passengers for each aircraft of the fleet of aircraft; accessing data corresponding to current demand for the fleet of aircraft, the current demand comprising a plurality of requests for transport services from a respective origin to a respective destination; computing estimated future demand for the fleet of aircraft based on the current routes for the fleet of aircraft, the future demand comprising a plurality of predicted requests for future transport services from a respective origin to a respective destination; and computing at least one updated route for the fleet of aircraft based on the current demand and the estimated future demand; and transmitting the at least one updated route to each respective aircraft of the fleet of aircraft.
These claim limitations fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind (including an observation, evaluation, judgment) and/or with pen/paper. Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Accordingly, the claim recites an abstract idea and dependent claims 32-40 further recite the abstract idea.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of one or more processors and one or more non-transitory computer-readable media. The one or more processors and one or more non-transitory computer-readable media are merely generic computing devices and do not integrate the judicial exception into a practical application.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 31 includes various elements that are not directed to the abstract idea under 2A. These elements include one or more processors, one or more non-transitory computer-readable media the generic computing elements described in the Applicant's specification in at least Para 0111-0113. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. In addition, recites computer functions that the courts have recognized as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)…at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))).
Therefore, Claim 31 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
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.
Claim(s) 21, 25-26, 28-31, 35-36, and 38-40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma (US 2017/0197710 A1) in view of Ludwick et al. (US 2019/0285425 A1).
Regarding Claim 21, Ma teaches of a fleet of aircraft (Ma: Para 0003).
However, Ma does not explicitly disclose the limitations of Claim 21 which state accessing data corresponding to a status for a fleet of aircraft, the current aircraft status comprising one or more of a current location, a current battery level, and an airborne or grounded indication for each aircraft of the fleet of aircraft; accessing data corresponding to current routes for the fleet of aircraft, the current routes comprising one or more of a destination, a time of arrival, and a number of passengers for each aircraft of the fleet of aircraft; accessing data corresponding to current demand for the fleet of aircraft, the current demand comprising a plurality of requests for transport services from a respective origin to a respective destination; computing estimated future demand for the fleet of aircraft based on the current routes for the fleet of aircraft, the future demand comprising a plurality of predicted requests for future transport services from a respective origin to a respective destination; and computing at least one updated route for the fleet of aircraft based on the current demand and the estimated future demand; and transmitting the at least one updated route to each respective aircraft of the fleet of aircraft.
Ludwick though, with the teachings of Ma, teaches of
accessing data corresponding to a status for a fleet of aircraft, the current aircraft status comprising one or more of a current location, a current battery level, and an airborne or grounded indication for each aircraft of the fleet of aircraft (Ludwick: Para 0036 via Positioning system 170 may be used by computing devices 110 in order to determine the vehicle's relative or absolute position on a map or on the earth. For example, the position system 170 may include a GPS receiver to determine the device's latitude, longitude and/or altitude position. Other location systems such as laser-based localization systems, inertial-aided GPS, or camera-based localization may also be used to identify the location of the vehicle. The location of the vehicle may include m absolute geographical location, such as latitude, longitude, and altitude as well as relative location information, such as location relative to other cars immediately around it which can often be determined with less noise that absolute geographical location);
accessing data corresponding to current routes for the fleet of aircraft, the current routes comprising one or more of a destination, a time of arrival, and a number of passengers for each aircraft of the fleet of aircraft (Ludwick: Para 0051, 0053 via The current vehicle task may further specify service type and number of passengers… The demand data may indicate current aod/or expected demand for trips by the vehicles of the fleet. For example, the demand data may include current demand for service, or in other words, the number of trips that re currently in progress or being requested by users. FIG. 5C shows some example current demand data in a table 500C that may be sent from users 322, 332, 342, 352. The demand data may specify service type requested, for example, service type for users 322, 332, and 342 is taxi, service type for user 352 is car pool. The demand data may further specify a number of passengers requesting the trip, for example, user 322 is requesting a trip for four passengers, user 332 is requesting a trip for one passenger, while users 342 and 352 are each currently on a trip with another passenger. The demand data may include pickup locations and destination locations);
accessing data corresponding to current demand for the fleet of aircraft, the current demand comprising a plurality of requests for transport services from a respective origin to a respective destination (Ludwick: Para 0053 via The demand data may indicate current aod/or expected demand for trips by the vehicles of the fleet. For example, the demand data may include current demand for service, or in other words, the number of trips that re currently in progress or being requested by users. FIG. 5C shows some example current demand data in a table 500C that may be sent from users 322, 332, 342, 352. The demand data may specify service type requested, for example, service type for users 322, 332, and 342 is taxi, service type for user 352 is car pool. The demand data may further specify a number of passengers requesting the trip, for example, user 322 is requesting a trip for four passengers, user 332 is requesting a trip for one passenger, while users 342 and 352 are each currently on a trip with another passenger. The demand data may include pickup locations and destination locations);
computing estimated future demand for the fleet of aircraft based on the current routes for the fleet of aircraft, the future demand comprising a plurality of predicted requests for future transport services from a respective origin to a respective destination (Ludwick: Para 0053, 0060 via The demand data may indicate current and/or expected demand for trips by the vehicles of the fleet. For example, the demand data may include current demand for service, or in other words, the number of trips that re currently in progress or being requested by users. FIG. 5C shows some example current demand data in a table 500C that may be sent from users 322, 332, 342, 352. The demand data may specify service type requested, for example, service type for users 322, 332, and 342 is taxi, service type for user 352 is car pool. The demand data may further specify a number of passengers requesting the trip, for example, user 322 is requesting a trip for four passengers, user 332 is requesting a trip for one passenger, while users 342 and 352 are each currently on a trip with another passenger. The demand data may include pickup locations and destination locations… The server computing devices 310 may aggregate requests from many users to determine a current distribution of high and low demand areas or make projections of future distribution of high and low demand areas. The server computing devices 310 may use past demand data to build a future demand model based on parameters such as date and time. The server computing devices 310 may use triangulating signals, such as traffic on the road or events in the area, to predict future demand. The server computing devices 310 may also use pre-demand signals, such as the number of people opening a trip-requesting app, to predict future demand); and
computing at least one updated route for the fleet of aircraft based on the current demand and the estimated future demand (Ludwick: Para 0021, 0023, 0044 via a server system may be configured as a dispatch system to determine a next vehicle task for a vehicle in the fleet based on vehicle data, charger data, and demand data. The vehicle tasks may include, but me not limited to, recharging, continuing to recharge, stopping recharging, powering off, or servicing a next trip… heuristics may be designed based on predictions of a likelihood that the vehicle will be closer to an available charger upon completing the current trip than upon completing the next trip… the server computing devices 310 may be configured to receive, update and use the vehicle data, the charger data, and demand data to make predictions, and determine a next vehicle task for the vehicles in the fleet. The vehicle tasks may include, but are not limited to, recharging, continuing to recharge, stopping recharging, powering off, or servicing a next trip. The server computing devices 310 may be further configured to determine a next vehicle task based on heuristics. The server computing devices 310 may be configured to direct each of the vehicles in the fleet to a respective next vehicle task); and
transmitting the at least one updated route to each respective aircraft of the fleet of aircraft (Ludwick: Para 0044, 0082 via the server computing devices 310 may be configured to receive, update and use the vehicle data, the charger data, and demand data to make predictions, and determine a next vehicle task for the vehicles in the fleet. The vehicle tasks may include, but are not limited to, recharging, continuing to recharge, stopping recharging, powering off, or servicing a next trip. The server computing devices 310 may be further configured to determine a next vehicle task based on heuristics. The server computing devices 310 may be configured to direct each of the vehicles in the fleet to a respective next vehicle task…Heuristics may also be designed as fleet-wide rules for identifying next tasks, rather than specific to individual vehicles. For example, the server computing devices 310 may monitor the states of all the vehicles of the fleet and all the heuristics—fleet-wide and individual, and adjust the individual vehicle tasks and the individual heuristics according to fleet-wide goals set by fleet-wide heuristics. For example, to save energy, a heuristic may require a staggered start time for the fleet. For another example, a heuristic may require that vehicles in the fleet to recharge before peak demand hours such that, during peak demand hours, a minimum mileage capacity across the fleet may be maintained to minimize lost trips).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ma with the teachings of Ludwick in order to have accessing data corresponding to a status for a fleet of aircraft, the current aircraft status comprising one or more of a current location, a current battery level, and an airborne or grounded indication for each aircraft of the fleet of aircraft; accessing data corresponding to current routes for the fleet of aircraft, the current routes comprising one or more of a destination, a time of arrival, and a number of passengers for each aircraft of the fleet of aircraft; accessing data corresponding to current demand for the fleet of aircraft, the current demand comprising a plurality of requests for transport services from a respective origin to a respective destination; computing estimated future demand for the fleet of aircraft based on the current routes for the fleet of aircraft, the future demand comprising a plurality of predicted requests for future transport services from a respective origin to a respective destination; and computing at least one updated route for the fleet of aircraft based on the current demand and the estimated future demand; and transmitting the at least one updated route to each respective aircraft of the fleet of aircraft. The motivations behind this being to incorporate the teachings of managing a fleet of vehicles to provide transportation services as taught by Ludwick. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 22, the combination of Ma/Ludwick teaches the limitation of Claim 22 which states
wherein computing the estimated future demand comprises computing the estimated future demand with a model trained on routes serviced by the fleet of aircraft (Ludwick: Para 0060 via The demand data may be sent from the user computing devices. For example, referring to FIGS. 4 and 5C, user 322 may enter a new trip request using user input devices 326 of client computing device 320, client computing device 320 then sends the trip request to server computing devices 310. The client computing device 320 may send updates on the request to the server computing devices 310, for example, if user 322 is on the trip or if the trip is eventually completed. The server computing devices 310 may aggregate requests from many users to determine a current distribution of high and low demand areas or make projections of future distribution of high and low demand areas. The server computing devices 310 may use past demand data to build a future demand model based on parameters such as date and time. The server computing devices 310 may use triangulating signals, such as traffic on the road or events in the area, to predict future demand. The server computing devices 310 may also use pre-demand signals, such as the number of people opening a trip-requesting app, to predict future demand)
Regarding Claim 25, the combination of Ma/Ludwick teaches the limitation of Claim 25 which states
wherein accessing the data corresponding to the status for the fleet of aircraft comprises receiving the status for an aircraft of the fleet of aircraft from the aircraft via a wireless network (Ludwick: Para 0042 via The network 360, and intervening nodes, may include various configurations and protocols including short range communication protocols such as Bluetooth, Bluetooth LE, the Internet, World Wide Web, Internet, virtual private networks, wide area networks, local networks, private networks using communication protocols proprietary to one or more companies, Ethernet, WiFi and HTTP, and various combinations of the foregoing. Such communication may be facilitated by any device capable of transmitting data to and from other computing devices, such as modems and wireless interfaces).
Regarding Claim 26, the combination of Ma/Ludwick teaches the limitation of Claim 26 which states
wherein accessing the data corresponding to the status for the fleet of aircraft comprises estimating the status for the aircraft of the fleet of aircraft based on a last known status for the aircraft and the current route for the aircraft (Ludwick: Para 0021, 0023, 0044 via The technology relates to systems and methods of directing a fleet of vehicles. For instance, a server system may be configured as a dispatch system to determine a next vehicle task for a vehicle in the fleet based on vehicle data, charger data, and demand data. The vehicle tasks may include, but me not limited to, recharging, continuing to recharge, stopping recharging, powering off, or servicing a next trip…The dispatch system may use the vehicle data, the charger data, the demand data, and predictions in combination with heuristic data to determine a next vehicle task for the vehicle, which may include recharging, continue recharging, stop recharging powering off, or servicing a next trip. For example, as a safety measure, the dispatch system may use a heuristic that identifies a threshold absolute minimum such that the vehicle must be directed to recharge once the threshold absolute minimum is reached. In other embodiments, heuristics may be designed based on predictions of a likelihood that the vehicle will be closer to an available charger upon completing the current trip than upon completing the next trip…the server computing devices 310 may be configured to receive, update and use the vehicle data, the charger data, and demand data to make predictions, and determine a next vehicle task for the vehicles in the fleet. The vehicle tasks may include, but are not limited to, recharging, continuing to recharge, stopping recharging, powering off, or servicing a next trip. The server computing devices 310 may be further configured to determine a next vehicle task based on heuristics. The server computing devices 310 may be configured to direct each of the vehicles in the fleet to a respective next vehicle task).
Regarding Claim 28, the combination of Ma/Ludwick teaches the limitation of Claim 28 which states
wherein the data corresponding to the current demand for the fleet of aircraft comprises a set of transport requests received from user client devices (Ludwick: Para 0053 via The demand data may indicate current aod/or expected demand for trips by the vehicles of the fleet. For example, the demand data may include current demand for service, or in other words, the number of trips that re currently in progress or being requested by users. FIG. 5C shows some example current demand data in a table 500C that may be sent from users 322, 332, 342, 352. The demand data may specify service type requested, for example, service type for users 322, 332, and 342 is taxi, service type for user 352 is car pool. The demand data may further specify a number of passengers requesting the trip, for example, user 322 is requesting a trip for four passengers, user 332 is requesting a trip for one passenger, while users 342 and 352 are each currently on a trip with another passenger. The demand data may include pickup locations and destination locations…).
Regarding Claim 29, the combination of Ma/Ludwick teaches the limitation of Claim 29 which states
wherein computing the estimated future demand for the fleet of aircraft comprises computing the estimated future demand for the fleet of aircraft within a time period that is no greater than four hours (Ludwick: Para 0053 via the demand data may also include predictions of future demand for trips, such as in the next 10 min, next 30 min, next hour, or for a particular time on a particular day. Current and/or future demand data may be predicted based on historical data collected for similar times and days).
Regarding Claim 30, the combination of Ma/Ludwick teaches the limitation of Claim 30 which states
wherein computing the at least one updated route for the fleet of aircraft comprises computing a plurality of updated routes for the fleet of aircraft based on both actual transport requests of the current demand and predicted transport requests of the estimated future demand (Ludwick: Para 0021, 0023, 0044 via a server system may be configured as a dispatch system to determine a next vehicle task for a vehicle in the fleet based on vehicle data, charger data, and demand data. The vehicle tasks may include, but me not limited to, recharging, continuing to recharge, stopping recharging, powering off, or servicing a next trip… heuristics may be designed based on predictions of a likelihood that the vehicle will be closer to an available charger upon completing the current trip than upon completing the next trip… the server computing devices 310 may be configured to receive, update and use the vehicle data, the charger data, and demand data to make predictions, and determine a next vehicle task for the vehicles in the fleet. The vehicle tasks may include, but are not limited to, recharging, continuing to recharge, stopping recharging, powering off, or servicing a next trip. The server computing devices 310 may be further configured to determine a next vehicle task based on heuristics. The server computing devices 310 may be configured to direct each of the vehicles in the fleet to a respective next vehicle task).
Regarding Claims 31-32, 35-36 and 38-40, they are analogous to Claims 21-22, 25-26 and 28-30 respectively and are rejected for the same reasons. See also Ludwick: Para 0027.
Claim(s) 23 and 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma (US 2017/0197710 A1) in view of Ludwick et al. (US 2019/0285425 A1) further in view of Mason et al. (US 2012/0022904 A1).
Regarding Claim 23, while Ma/Ludwick teaches the limitations of Claim 21, it does not explicitly disclose the limitations of Claim 23 which state wherein the at least one updated route reduces total power usage of the fleet of aircraft relative to the current routes for the fleet of aircraft after an unexpected failure in another mode of transport.
Mason though, with the teachings of Ma/Ludwick, teaches of
wherein the at least one updated route reduces total power usage of the fleet of aircraft relative to the current routes for the fleet of aircraft after an unexpected failure in another mode of transport (Mason: Para 0045, 0050 via With reference to FIG. 3, an embodiment of an energy use selection process 300 executable by the routing module 110 or 200 is illustrated. In certain embodiments, the energy use selection process 300 advantageously selects routes to reduce energy use, which can reduce energy costs and improve the environment. In some embodiments, consideration of energy use can be overlaid on top of routes selected for shortest distance and/or shortest estimated transit time to provide a route that meets time deadlines and that reduces energy use at the same time… the route calculation module 225 can analyze the feasible routes based on an energy use cost function and overlay the energy-related costs on each route. In some embodiments, the overlay of routes includes calculating or otherwise identifying an energy use cost based on the cost function for each of the feasible routes. The energy use cost function can be an objective function or the like that is based on one or more energy use factors such as terrain or elevation, vehicle characteristics, driver characteristics, road conditions, traffic, speed limits, stop time, turn information, traffic information, and weather information, distance, estimated stop or idling time, driver profile data (e.g., driver average miles per gallon), and the like. The cost function can compute a value for each route or route leg that represents the energy cost of that route. This energy cost can be an actual monetary cost or some arbitrary value other than a monetary value).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ma/Ludwick with the teachings of Mason in order to have wherein the at least one updated route reduces total power usage of the fleet of aircraft relative to the current routes for the fleet of aircraft after an unexpected failure in another mode of transport. The motivations behind this being to incorporate the teachings of criteria based route optimization as taught by Mason. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 33, it is analogous to Claim 23 and is rejected for the same reasons.
Claim(s) 24 and 34 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma (US 2017/0197710 A1) in view of Ludwick et al. (US 2019/0285425 A1) further in view of Alonso-Mora et al. (US 2018/0224866 A1).
Regarding Claim 24, while Ma/Ludwick teaches the limitations of Claim 21, it does not explicitly disclose the limitations of Claim 24 which state wherein accessing the data corresponding to the status for the fleet of aircraft, accessing the data corresponding to the current routes for the fleet of aircraft, accessing the data corresponding to the current demand for the fleet of aircraft, computing the estimated future demand for the fleet of aircraft, and computing the at least one updated route for the fleet of aircraft are repeated no less than every five minutes such that the at least one updated route reduces total power usage of the fleet of aircraft relative to the current routes for the fleet of aircraft after an increase in requests for transport services for the fleet of aircraft.
Alonso-Mora though, with the teachings of Ma/Ludwick, teaches of
wherein accessing the data corresponding to the status for the fleet of aircraft, accessing the data corresponding to the current routes for the fleet of aircraft, accessing the data corresponding to the current demand for the fleet of aircraft, computing the estimated future demand for the fleet of aircraft, and computing the at least one updated route for the fleet of aircraft are repeated no less than every five minutes such that the at least one updated route reduces total power usage of the fleet of aircraft relative to the current routes for the fleet of aircraft after an increase in requests for transport services for the fleet of aircraft (Alonso-Mora: Para 0026, 0032, 0200 via The system described herein operates in real time and is particularly well suited for use with autonomous vehicle fleets that can continuously reroute based on real-time requests. It can also rebalance idle vehicles to areas with high demand and is general enough to be applied to other multivehicle, multitask assignment problems… In accordance with a further aspect of the concepts described herein, a method for controlling and continuously rerouting a fleet of vehicles based up on real-time ride requests, includes (a) receiving current requests for rides from one or more vehicles within a fleet of vehicles within a window; (b) generating a pairwise request-vehicle shareability graph (RV-graph); (c) generating a generating a request-trip-vehicle graph (RTV-graph) of trips and the vehicles that can serve them; (d) solving an integer linear program (ILP) to determine an assignment of vehicles to trips; and (e) assigning specific vehicles from the fleet of vehicle to specific trips… For real-time fleet management, the method can be applied to continuous discovery and assignment of incoming requests. The described approach is to perform batch assignment of the requests to the fleet of vehicles within a defined period of time (and preferably a short time span, for example every 30 seconds). Problem 1 is invoked with the predicted state of the fleet at the assignment time and the cumulated requests. Requests that have not been picked-up by a vehicle within the previous assignment round are kept in the pool for assignment).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ma/Ludwick with the teachings of Alonso-Mora in order to have wherein accessing the data corresponding to the status for the fleet of aircraft, accessing the data corresponding to the current routes for the fleet of aircraft, accessing the data corresponding to the current demand for the fleet of aircraft, computing the estimated future demand for the fleet of aircraft, and computing the at least one updated route for the fleet of aircraft are repeated no less than every five minutes such that the at least one updated route reduces total power usage of the fleet of aircraft relative to the current routes for the fleet of aircraft after an increase in requests for transport services for the fleet of aircraft. The motivations behind this being to incorporate the teachings of efficient vehicle routing and request assignment as taught by Alonso-Mora. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 34, it is analogous to Claim 24 and is rejected for the same reasons.
Claim(s) 27 and 37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ma (US 2017/0197710 A1) in view of Ludwick et al. (US 2019/0285425 A1) further in view of Chen et al. (US 2019/0033084 A1).
Regarding Claim 27, while the combination of Ma/Ludwick teaches the limitations of Claim 21, it does not explicitly disclose the limitations of Claim 27 which state wherein accessing the data corresponding to the current routes for the fleet of aircraft comprises accessing the data corresponding to the current routes for the fleet of aircraft from a routing data store located offboard the fleet of aircraft.
Chen though, with the teachings of Ma/Ludwick, teaches of
wherein accessing the data corresponding to the current routes for the fleet of aircraft comprises accessing the data corresponding to the current routes for the fleet of aircraft from a routing data store located offboard the fleet of aircraft (Chen: Para 0103 via storage device 1108 stores candidate point data 114, service provider data 116, active route data 118, and map data 120).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ma/Ludwick with the teachings of Chen in order to have wherein accessing the data corresponding to the current routes for the fleet of aircraft comprises accessing the data corresponding to the current routes for the fleet of aircraft from a routing data store located offboard the fleet of aircraft. The motivations behind this being to incorporate the teachings of providing vehicle routing guidance to dynamically determined start and end locations as taught by Chen. Furthermore, in addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 37, it is analogous to Claim 27 and is rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Barker et al. (US 2010/0185486 A1)
Klein et al. (US 2017/0169366 A1)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYRONE E SINGLETARY whose telephone number is (571)272-1684. The examiner can normally be reached 9 - 5:30.
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/T.E.S./ Examiner, Art Unit 3625
/BETH V BOSWELL/ Supervisory Patent Examiner, Art Unit 3625