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 .
Status of Claims
Claims 1-3 and 5-18 are pending.
Claim 1 is amended.
Claim 4 is cancelled.
Claims 6-18 are added.
Response to Arguments/Remarks
Rejection under 35 U.S.C. 103
Applicant’s arguments with respect to claims 1-5 have been considered but are moot in view of the new ground(s) of rejection as necessitated by applicant's amendments.
For clarity, regarding the applicant’s disagreement with YAMAMOTO not teaching the features previously disclosed in claim 4 (now part of claim 1), the office respectfully disagrees. The language of claim 4 in dispute was “so that reservations for each energy charging station are distributed”. Given the broadest reasonable interpretation, YAMAMOTO does cover the distribution of reservations in ¶ 0059 when teaching “The utilization rate of the charger is, for example, the ratio of the total time during which the charger is in use and the time during which the charger is reserved, during the time period from a reference time to the current time. The higher the utilization rate, the
more congested the supply point is. The supply point recommended in the cheapest mode of the mode-specified charging plan proposal, or the supply point recommended in the recommendation information of the individually specified charging plan proposal, may be determined so that the congestion situation at each supply point is as equal as possible (for example, so that the utilization rate of each supply point falls within a certain range)”. While YAMAMOTO does not explicitly call for “the distribution of reservations”, YAMAMOTO attempts to equalize the congestion at each supply point based on charging plans, which relies on charger reservation information (¶ 0067). A person having ordinary skill in the art would recognize that equalizing congestion at supply points would mean the distribution of demand and that YAMAMOTO anticipates that charging plans would be implemented to distribute the demand with charger reservations being integral to the regulation of the plan. Given this, the rejection under 35 U.S.C. 103 under YAMAMOTO remains in regards to the limitations in claim 4, now under claim 1.
The office however agrees that YAMAMOTO does not teach the amended narrower limitation of "identifying, based on past history, time periods in which reservations for energy charging stations tend to be concentrated and avoiding the identified time periods".
Please see 35 U.S.C. 103 rejection below.
New Claim(s)
Regarding the new claims 6-18, please see 103 below.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1,3,6,7,9,11,12,13,15,17,18 are rejected under 35 U.S.C. 103 as being unpatentable over SUJAN (US20210018324A1) in view of TAKUMA (WO2022018937A1) in view of YAMAMOTO (JP2020027429A) in further view of SHIMOKAWA (JP2022110819A).
Regarding Claim 1:
SUJAN discloses:
(Currently Amended) An information processing method by an information processing apparatus capable of communicating with one or more vehicles delivering packages, the information processing method comprising: (see at least SUJAN, ¶ 0028, “Route planning server 104 operates to select routes for the electric vehicle that reduce energy use costs and improve operational efficiencies. Route planning server 104 can be implemented in one or more computing devices having processors that execute instructions stored in non-transitory memory. The computing devices can be physically co-located or geographically separate (e.g., located in different data centers). Route planning server 104 includes various components such as a waypoints module 108, a mapping module 110, a vehicle condition module 112, an environment condition module 114, a route calculation module 116, a communication module 118, and a data repository 120. Generally, these components can be implemented in hardware, software, firmware, or any suitable combination thereof.”; ¶ 0036, “Communication module 118 facilitates the transmission of data within route planning server 104 (e.g., between modules 108-116), and between route planning server 104 and external devices (e.g., vehicle device 102). Data repository 120 includes one or more databases that can store any of data 122-144. For example, modules 108-114 may obtain their respective data from external sources and stored them in data repository 120 for later use. In addition to storing data 122-144, data repository 120 stores data generated by route calculation module 116 (e.g., the optimal route). While FIG. 1 shows data repository 120 as residing in route planning server 104, in other embodiments, data repository 120 may be located externally and accessible by route planning server via network 106.”)
acquiring vehicle-related information including (see at least SUJAN, ¶ 0032, “Vehicle condition module 112 is configured to obtain data associated with operating the electric vehicle such as dynamic operating characteristics 132 (e.g., speed, acceleration, yaw rate, wheel slip, braking event, etc.), vehicle state 134 (e.g., state of charge (SOC) of battery, vehicle age, maintenance information, etc.), and vehicle location 136 (e.g., latitude, longitude, etc.). Some or all of data 132-136 can be obtained or estimated using information communicated from vehicle device 102 and/or another device such as a fleet server.”)
a position of each vehicle, (see at least SUJAN, ¶ 0010, “According to another embodiment, the present disclosure provides a computing device, such as a server, for route planning for an electric vehicle. The computing device includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to obtain waypoint data indicating a plurality of waypoint locations for an electric vehicle. The processor also generates a plurality of route segments to connect each of the plurality of waypoint locations on a map. The processor further calculates an optimal route for the electric vehicle to visit each of the plurality of waypoint locations by evaluating the plurality of route segments. In response to detecting changes occurring in conditions associated with each of the plurality of route segments, the processor recalculates the optimal route for the electric vehicle to visit each of the plurality of waypoint locations. Additionally, the processor monitors whether the conditions associated with each of the plurality of route segments have changed.”)
an energy charge amount of each vehicle, (see at least SUJAN, ¶ 0032)
a travel condition of each vehicle, (see at least SUJAN, ¶ 0032)
a temperature, (see at least SUJAN, ¶ 0011, “In a further aspect, evaluating the plurality of route segments includes minimizing a total energy consumed by the electric vehicle to travel the plurality of route segments while completing the visit to each of the plurality of waypoint locations within a target time. Minimizing the total energy consumed by the electric vehicle includes determining a length, a road characteristic, and a speed limit for each of the plurality of route segments, where minimizing the total energy consumed by the electric vehicle to travel the length of each of the plurality of route segments is based on one or more dynamic operating characteristics of the electric vehicle, and the road characteristic and the speed limit for each of the plurality of route segments. Minimizing the total energy is further based on the conditions associated with each of the plurality of route segments including one or more of a road condition, a traffic condition, and a weather condition. Minimizing the total energy is further based on a state of the electric vehicle. The speed limit for each of the plurality of route segments is based on either a marked speed limit or an effective speed limit due to the road condition and/or the traffic condition.”)
delivery destination information on the package on each vehicle, (see at least SUJAN, ¶ 0030, “Waypoints module 108 is configured to obtain waypoint data 122. Waypoint data 122 may be provided by a fleet server, for example. Waypoint data 122 includes a plurality of waypoints or stopping points for the electric vehicle such as a starting location, an ending location, and one or more intermediate waypoint locations. Waypoint module 108 is also configured to obtain waypoint prioritization data 124, if available, that indicates which waypoint (or groups of waypoints) has a higher stopping priority and thus should be visited first or earlier than the other waypoints. Some or all of waypoint data 122 can be provided to mapping module 110 for use in generating maps.”; ¶ 0032)
energy charging station information near a delivery route of each vehicle, and/or (see at least SUJAN, ¶ 0031, “Mapping module 110 is configured to obtain map data 126. Map data 126 may be provided by a mapping server, for example. Map data 126 includes various characteristics of a road such as road terrain information (e.g., grade, curvature, etc.) and road parameter information (e.g., speed limit, road length, etc.). In one embodiment, mapping module 110 generates a map using received waypoint data 122 and map data 126. The map can be in a grid format showing the various networks of roads, highways, bridges, etc. Mapping module 110 is also configured to obtain other mapping information such as charging locations 128 that indicate one or more sites to charge the electric vehicle, and ZEZ locations 130 that indicate one or more areas where access by vehicles with internal combustion engines is restricted or deterred with the aim of improving the air quality in the areas. In some embodiments, map data 126 includes charging locations 128 and ZEZ locations 130.”; ¶ 0044, “In scenarios where the electric vehicle is a pure electric vehicle, additional factors such as charging locations (e.g., 128) may be considered in calculating the optimal route. Here, the onboard energy storage system (e.g., battery) of the electric vehicle may be designed to be smaller, but the electric vehicle must pick up energy along the way at the appropriate charging locations. In one embodiment, the map data obtained at block 204 includes charging locations for the electric vehicle. As such, generating the plurality of route segments at block 206 includes generating route segments that connect each of the plurality of waypoint locations in view of the charging locations. Similarly, calculating the optimal route by evaluating the plurality of route segments at block 208 includes minimizing a total energy consumed by the electric vehicle to travel the plurality of route segments while considering the charging opportunities available at the charging locations and completing the visit to each of the plurality of waypoint locations within a target time.”)
road congestion information; (see at least SUJAN, ¶ 0011)
generating, from the vehicle-related information, (see at least SUJAN, ¶ 0032)
plans for delivery routes and/or (see at least SUJAN, ¶ 0003, “Route optimization has applications in vehicle routing. For example, package delivery companies select routes for their vehicles to pick up and drop off packages at various destinations. The routes are optimized to maximize the number of deliveries or minimize the fuel consumption based on a multitude of factors such as a number of turns in a given route, a number of intersections, speed limits, bridge crossings, and the like. Most of these optimized routes, however, are computed in advance and therefore cannot respond to real-time circumstances that may affect the current operation.”; ¶ 0004, “For an emerging future, where electric vehicles are poised to replace vehicles powered by internal combustion engines, package delivery companies and other vehicle fleet operators will face additional challenges in route optimization including minimizing energy usage, exploiting charging opportunities, meeting zero emission zone (ZEZ) requirements in urban environments, etc. Without taking these considerations into account, any potential solution may result in increased costs (e.g., oversized batteries), limited charging strategies, and route plans that largely mimic those used by conventional vehicles. Accordingly, there remains a need to develop new approaches for optimizing route planning for electric vehicles.”; ¶ 0006, “In a further aspect, calculating the optimal route by evaluating the plurality of route segments includes minimizing a total energy consumed by the electric vehicle to travel the plurality of route segments while completing the visit to each of the plurality of waypoint locations within a target time. Minimizing the total energy consumed by the electric vehicle includes determining a length, a road characteristic, and a speed limit for each of the plurality of route segments, where minimizing the total energy consumed by the electric vehicle to travel the length of each of the plurality of route segments is based on one or more dynamic operating characteristics of the electric vehicle, and the road characteristic and the speed limit for each of the plurality of route segments. Minimizing the total energy is further based on the conditions associated with each of the plurality of route segments including one or more of a road condition, a traffic condition, and a weather condition. Minimizing the total energy is further based on a state of the electric vehicle. The speed limit for each of the plurality of route segments is based on either a marked speed limit or an effective speed limit due to the road condition and/or the traffic condition.”)
energy charge timings with highest energy efficiency; and (see at least SUJAN, ¶ 0006; ¶ 0045, “As an illustration, FIG. 9 shows map 300 with example charging locations 322 along with the plurality of waypoint locations 304. The objective is to find an optimal route that will minimize the total energy used by the electric vehicle while factoring in the charging locations and charging times needed to complete the mission on time. The calculated optimal route in FIG. 9 may not be the same as optimal route 308 in FIG. 5. Also, there will be more energy consumed by the electric vehicle operating in the environment of FIG. 9 because the electric vehicle will need to make one or more detours to pick up the needed energy along the way.”)
commanding the one or more vehicles to execute the plans, (see at least SUJAN, ¶ 0013, “In still another aspect, the map is generated to display the plurality of waypoint locations using the waypoint data and map data. The map data includes charging locations for the electric vehicle. As such, the processor generates the plurality of route segments to connect each of the plurality of waypoint locations in view of the charging locations. Similarly, the processor calculates the optimal route by minimizing a total energy consumed by the electric vehicle to travel the plurality of route segments while considering charging opportunities at the charging locations and completing the visit to each of the plurality of waypoint locations within a target time.”; ¶ 0027, “Vehicle device 102 can be any computing device associated with the electric vehicle that receives route information from route planning server 104 and performs navigation of the electric vehicle based on the route information. In one example, vehicle device 102 is an in-vehicle device (e.g., a navigation device) installed in the electric vehicle. In another example, vehicle device 102 is a user device (e.g., a mobile device) connected to the electric vehicle. While only one vehicle device 102 is shown in FIG. 1, it will be understood that route planning server 104 may communicate route information to any number of vehicle devices 102 associated with a fleet of electric vehicles.”)
wherein the generating the plans includes planning the delivery routes and/or the energy charge timings (see at least SUJAN, ¶ 0003, “Route optimization has applications in vehicle routing. For example, package delivery companies select routes for their vehicles to pick up and drop off packages at various destinations. The routes are optimized to maximize the number of deliveries or minimize the fuel consumption based on a multitude of factors such as a number of turns in a given route, a number of intersections, speed limits, bridge crossings, and the like. Most of these optimized routes, however, are computed in advance and therefore cannot respond to real-time circumstances that may affect the current operation.”; ¶ 0004, “For an emerging future, where electric vehicles are poised to replace vehicles powered by internal combustion engines, package delivery companies and other vehicle fleet operators will face additional challenges in route optimization including minimizing energy usage, exploiting charging opportunities, meeting zero emission zone (ZEZ) requirements in urban environments, etc. Without taking these considerations into account, any potential solution may result in increased costs (e.g., oversized batteries), limited charging strategies, and route plans that largely mimic those used by conventional vehicles. Accordingly, there remains a need to develop new approaches for optimizing route planning for electric vehicles.”; ¶ 0006; ¶ 0045)
SUJAN does not disclose, but TAKUMA teaches:
a weight of a package on each vehicle, (see at least TAKUMA, ¶ 0019, “The delivery information storage unit 4 stores delivery information as the first information. The delivery information includes information related to delivery, such as the distance to the delivery destination, the number and weight of packages, etc. This information relates to, for example, the power consumption in delivery by electric vehicles.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the vehicle condition module of SUJAN to incorporate the delivery information regarding package weights of TAKUMA to yield an effective electric delivery vehicle route planner that accounts for the weight of the delivered load when determining range of travel.
SUJAN in view of TAKUMA does not disclose, but YAMAMOTO teaches:
so that reservations for each energy charging station among a plurality of energy charging stations are distributed (see at least YAMAMOTO, ¶ 0057, “The charging planning unit 71 may cooperate with a general EMS (energy management system), for example the EMS of each charger, to create a charging plan that uses a general charging planning method to level out each EMS, based on the EV charging plan (energy supply plan) as described above. Alternatively, a charging plan may be created using multi-objective optimization that enables shortening the user's waiting time, leveling the energy of each charger, or both. When creating a proposed charging plan, as an example, at least one of the following is used: the EV's departure point, destination, the route the EV will take, information on supply points that the EV can reach with its current battery remaining charge, the predicted arrival time of the EV at each supply point, the reservation status at each supply point, and conditions specified by each user (user-specified conditions).”; ¶ 0059, “Furthermore, the charging planning unit 71 may take into consideration the congestion state (whether the waiting time is long or short) at each supply point. As the congestion status, the utilization rate of the chargers at each supply point (if there are multiple chargers at a supply point, the average utilization rate of the multiple chargers may be used). The utilization rate of the charger is, for example, the ratio of the total time during which the charger is in use and the time during which the charger is reserved, during the time period from a reference time to the current time. The higher the utilization rate, the more congested the supply point is. The supply point recommended in the cheapest mode of the mode-specified charging plan proposal, or the supply point recommended in the recommendation information of the individually specified charging plan proposal, may be determined so that the congestion situation at each supply point is as equal as possible (for example, so that the utilization rate of each supply point falls within a certain range).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, with a reasonable expectation of success, the route planning and electric delivery vehicle of SUJAN in view of TAKUMA to include the service area reservation system with the utilization rate tracking and “multi-objective optimization” of YAMAMOTO to yield a more efficient recharging station that distributes traffic based on travel, reduces congestion, and minimizes wait times for the delivery drivers.
SUJAN in view of TAKUMA in view of YAMAMOTO does not disclose, but SHIMOKAWA teaches:
by identifying, based on (see at least SHIMOKAWA, ¶ 0113, "The charging planning unit 148 is a management unit that creates a charging plan for each travel ID registered by the service usage registration unit 144 .The charging planning unit 148 calculates the possible driving distance using the power consumption prediction unit 132a from the information for each driving ID registered in the service usage registration unit 144 (EV's departure point, destination, remaining battery power at the time of departure).Then, based on the prediction result of the charging facility congestion prediction unit 116d, a charging spot with a low degree of congestion within the driving distance is determined as are commended charging spot. Then, a recommended EV charging plan (including charging locations, charging times, etc.) is provided.) to create a The created charging plan is stored in the charging plan DB 191, and is also used as an EV charging plan (including charging locations, charging times, etc.) recommended to the service user. ) is provided.")
past history, (see at least SHIMOKAWA, ¶ 0044, "Next, in step S4, the charging facility congestion prediction unit 116 predicts the future congestion status of charging facility C at predetermined time intervals based on SA/PA vacancy information, current usage status information of charging facility C, and past statistical data of charging facility C. The method for predicting the congestion status of charging facilities is based on current usage status information for each charging facility C, and takes into account the date, day of the week, and time period when congestion will occur based on past statistical value data for that charging facility C. Furthermore, the seat availability information for the SA/PA at that time is also taken into account and a comprehensive decision is made. Here, the predetermined time, which is the interval for predicting the congestion state, is, for example, every 30 minutes or every hour. The time intervals of the predicted times do not have to be constant. ")
time periods in which reservations for the plurality of energy charging stations tend to be concentrated, and (see at least SHIMOKAWA, ¶ 0044)
avoiding the identified time periods. (see at least SHIMOKAWA, ¶ 0113, "The charging planning unit 148 is a management unit that creates a charging plan for each travel ID registered by the service usage registration unit 144 .The charging planning unit 148 calculates the possible driving distance using the power consumption prediction unit 132a from the information for each driving ID registered in the service usage registration unit 144 (EV's departure point, destination, remaining battery power at the time of departure).Then, based on the prediction result of the charging facility congestion prediction unit 116d, a charging spot with a low degree of congestion within the driving distance is determined as are commended charging spot. Then, a recommended EV charging plan (including charging locations, charging times, etc.) is provided.) to create a The created charging plan is stored in the charging plan DB 191, and is also used as an EV charging plan (including charging locations, charging times, etc.) recommended to the service user. ) is provided.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify, with a reasonable expectation of success, the route planning for electric delivery vehicles with service area reservation system and energy planning within SUJAN in view of TAKUMA in further view of YAMAMOTO to include planning based on charging statistical and prior facility vacancy rates within SHIMOKAW to yield an efficient charge planning system which can spread the charging demand of delivery vehicles among multiple charging locations based on statistical demand at certain periods of time.
Regarding claim 3:
SUJAN in view of TAKUMA in further view of YAMAMOTO in further view of SHIMOKAW discloses the limitations within claim 1 and further discloses:
when the one or more vehicles include a battery electric vehicle. (see at least SUJAN, ¶ 0004, “For an emerging future, where electric vehicles are poised to replace vehicles powered by internal combustion engines, package delivery companies and other vehicle fleet operators will face additional challenges in route optimization including minimizing energy usage, exploiting charging opportunities, meeting zero emission zone (ZEZ) requirements in urban environments, etc. Without taking these considerations into account, any potential solution may result in increased costs (e.g., oversized batteries), limited charging strategies, and route plans that largely mimic those used by conventional vehicles. Accordingly, there remains a need to develop new approaches for optimizing route planning for electric vehicles.”)
SUJAN in view of TAKUMA does not disclose, but YAMAMOTO teaches:
reserving, based on the plans, (see at least YAMAMOTO, ¶ 0002, "An information provision system for EVs (Electric Vehicles) is a system that refers to map information when an EV uses a highway (toll road), accurately predicts the reachable range of the EV, and recommends and reserves service/parking areas (SA/PA) that the user should use. It is necessary to create a plan to efficiently use service areas and parking areas with charging facilities so that EVs do not run out of power.")
a fast charging station (see at least YAMAMOTO, ¶ 0049; “The I/P fee management unit 31 executes I/P related to fees based on the I/P plan for each user generated by the I/P calculation unit 51 . The I/P fee management unit 31 is connected to an EV charging fee payment system 91 via wire or wirelessly. The fee payment system 91 may be disposed inside the reservation management device 101 as a component of the reservation management device 101 . The I/P fee management unit 31 transmits to the fee settlement system 91 instruction data indicating the fee discount rate or whether or not a discount is applied, as well as other data required for calculating the fee (such as the amount of charge), for users who have been granted a fee-related I/P (e.g., I003, P001, etc. in FIG. 10) based on the I/P plan for each user created by the I/P calculation unit 51. The fee settlement system 91 determines the amount to be charged to the user (charging fee) in accordance with the data received from the I/P fee management unit 31 . The charging fee per unit of electric energy may be managed by the fee payment system 91 or by the reservation management device 101. In this case, the fee may be the same or different for each supply point and each charger, as an example. For example, a fast charger may be more expensive than a normal charger. In addition, the charge may be dynamically changed depending on the time of charging and congestion.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, with a reasonable expectation of success, the route planning and electric delivery vehicle of SUJAN in view of TAKUMA in further view of YAMAMOTO in further view of SHIMOKAW to include the service area reservation system with optional fast chargers of YAMAMOTO to yield a more efficient recharging station that minimizes wait times for the delivery drivers.
Regarding claim 6:
SUJAN in view of TAKUMA in further view of YAMAMOTO in further view of SHIMOKAW discloses the limitations within claim 1 and SUJAN does not disclose, but SHIMOKAW teaches:
the generating the plans includes determining the plans from a table configured to (see at least SUJAN, ¶ 0037, "The past charger information DB154 is a database that stores information on the past usage status of each charging facility C provided at an SA/PA or the like (performance data such as the ID of the charger used and the time period of use, and related statistical data). "; ¶ 0039, "The charging facility congestion prediction information DB156 is a database that stores congestion prediction information for charging facility C predicted by the charging facility congestion prediction unit 116 based on SA/PA occupancy information, past usage status information for each charging facility C, and current usage status information for each charging facility C. "; ¶ 0103, "The user DB 188 is a database that holds information registered by the user ID registration unit 143 and the service usage registration unit 144 . The virtual EV DB 189 is a database that stores information about the virtual EVs managed by the virtual EV management unit 145 . Here, the virtual EVs are EVs that are not using the service of this embodiment, and the status of these EVs is registered by estimating that a certain number of EVs are running. The charging plan DB 191 is a database that stores charging plan data created by the charging plan unit 148 in order to provide recommended charging locations to service users.")
store correspondences between the vehicle-related information and the plans. (see at least SUJAN, ¶ 0031, "The charging facility congestion prediction unit 116 is a prediction unit that predicts the congestion status of the charging facility C at a predetermined time in the future (e.g., 30 minutes from now) based on past congestion status information of the charging facility C stored in the past charger information DB 154, current usage status information of the charging facility C stored in the current charger information DB 153, and current congestion status information of the SA/PA stored in the rest facility occupancy information DB 155. "; ¶ 0113, "The charging planning unit 148 is a management unit that creates a charging plan for each travel ID registered by the service usage registration unit 144 .The charging planning unit 148 calculates the possible driving distance using the power consumption prediction unit 132a from the information for each driving ID registered in the service usage registration unit 144 (EV's departure point, destination, remaining battery power at the time of departure).Then, based on the prediction result of the charging facility congestion prediction unit 116d, a charging spot with a low degree of congestion within the driving distance is determined as are commended charging spot. Then, a recommended EV charging plan (including charging locations, charging times, etc.) is provided.) to create a The created charging plan is stored in the charging plan DB 191, and is also used as an EV charging plan (including charging locations, charging times, etc.) recommended to the service user. ) is provided.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, with a reasonable expectation of success, the route planning for electric delivery vehicles with service area reservation system and statistical station history within SUJAN in view of TAKUMA in further view of YAMAMOTO in further view of SHIMOKAW to include a database that stores charging plans, station vacancy history, and vehicle travel information within SHIMOKAW to yield an efficient charge planning system which can recall previously computed data to avoid constant recalculations.
Regarding claim 7:
With regards to claim 7, this claim is the information processing apparatus claim to method claim 1 and is substantially similar to claim 1 and is therefore rejected using the same references and rationale.
Regarding claim 9:
With regards to claim 9, this claim is substantially similar to claim 3 and is therefore rejected using the same references and rationale.
Regarding claim 11:
With regards to claim 11, this claim is substantially similar to claim 6 and is therefore rejected using the same references and rationale.
Regarding claim 12:
SUJAN in view of TAKUMA in further view of YAMAMOTO in further view of SHIMOKAW discloses the limitations within claim 7 and SUJAN further discloses:
the information processing apparatus is a server configured to assist a provider in providing a service, installed in a facility dedicated to the provider or in a shared facility including a data center, or mounted in the one or more vehicles. (see at least SUJAN, ¶ 0010, “According to another embodiment, the present disclosure provides a computing device, such as a server, for route planning for an electric vehicle. The computing device includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to obtain waypoint data indicating a plurality of waypoint locations for an electric vehicle. The processor also generates a plurality of route segments to connect each of the plurality of waypoint locations on a map. The processor further calculates an optimal route for the electric vehicle to visit each of the plurality of waypoint locations by evaluating the plurality of route segments. In response to detecting changes occurring in conditions associated with each of the plurality of route segments, the processor recalculates the optimal route for the electric vehicle to visit each of the plurality of waypoint locations. Additionally, the processor monitors whether the conditions associated with each of the plurality of route segments have changed.”; ¶ 0027, “Vehicle device 102 can be any computing device associated with the electric vehicle that receives route information from route planning server 104 and performs navigation of the electric vehicle based on the route information. In one example, vehicle device 102 is an in-vehicle device (e.g., a navigation device) installed in the electric vehicle. In another example, vehicle device 102 is a user device (e.g., a mobile device) connected to the electric vehicle. While only one vehicle device 102 is shown in FIG. 1, it will be understood that route planning server 104 may communicate route information to any number of vehicle devices 102 associated with a fleet of electric vehicles.”; ¶ 0028, “Route planning server 104 operates to select routes for the electric vehicle that reduce energy use costs and improve operational efficiencies. Route planning server 104 can be implemented in one or more computing devices having processors that execute instructions stored in non-transitory memory. The computing devices can be physically co-located or geographically separate (e.g., located in different data centers). Route planning server 104 includes various components such as a waypoints module 108, a mapping module 110, a vehicle condition module 112, an environment condition module 114, a route calculation module 116, a communication module 118, and a data repository 120. Generally, these components can be implemented in hardware, software, firmware, or any suitable combination thereof.”)
Regarding claim 13:
With regards to claim 13, this claim is the non-transitory computer readable medium claim to method claim 1 and is substantially similar to claim 1 and is therefore rejected using the same references and rationale.
Regarding claim 15:
With regards to claim 15, this claim is substantially similar to claim 3 and is therefore rejected using the same references and rationale.
Regarding claim 17:
With regards to claim 17, this claim is substantially similar to claim 6 and is therefore rejected using the same references and rationale.
Regarding claim 18:
With regards to claim 18, this claim is substantially similar to claim 12 and is therefore rejected using the same references and rationale.
Claims 2, 5, 8, 10, 14, 16 are rejected under 35 U.S.C. 103 as being unpatentable over SUJAN (US20210018324A1) in view of TAKUMA (WO2022018937A1) in view of YAMAMOTO (JP2020027429A) in further view of SHIMOKAWA (JP2022110819A) in further view of YAMANOUCHI (JP2010070255A).
Regarding claim 2:
SUJAN in view of TAKUMA in further view of YAMAMOTO in further view of SHIMOKAW discloses the limitations within claim 1 and SUJAN does not disclose, but YAMAMOTO teaches:
reserving, based on the plans, (see at least YAMAMOTO, ¶ 0002, “An information provision system for EVs (Electric Vehicles) is a system that refers to map information when an EV uses a highway (toll road), accurately predicts the reachable range of the EV, and recommends and reserves service/parking areas (SA/PA) that the user should use. It is necessary to create a plan to efficiently use service areas and parking areas with charging facilities so that EVs do not run out of power.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, with a reasonable expectation of success, the route planning of SUJAN in view of TAKUMA in further view of YAMAMOTO in further view of SHIMOKAW to include the service area reservation system of YAMAMOTO to yield a more efficient recharging station that minimizes wait times for the delivery drivers.
SUJAN in view of YAMAMOTO does not disclose, but YAMANOUCHI teaches:
a hydrogen refueling station and (see at least YAMANOUCHI, ¶ 0053, “In the above embodiment, the case where oil liquid is supplied to a fuel tank of a vehicle has been described as an example, but the present invention is not limited to this. It goes without saying that the present invention can also be applied to cases where liquefied gas such as liquefied natural gas (LNG), liquefied petroleum gas (LPG), dimethyl ether (DME), compressed natural gas (CNG), or hydrogen is filled into a fuel cell vehicle.”)
a qualified person to handle hydrogen refueling (see at least YAMANOUCHI, ¶ 0012, “According to the present invention, upon receiving a detection signal from the proxy target detection means, the fuel supply station worker can be quickly notified that the proxy target (person wishing to have fuel supplied on behalf of the fuel supplier) has arrived. This means that the worker can automatically recognize that the proxy target has arrived when the vehicle arrives, without the driver having to take the trouble to notify the worker. This allows the worker to quickly accept the supply on behalf of the fuel supplier, and even if the driver wishes to have fuel supplied on behalf of the fuel supplier, the driver does not have to go to the office, but simply moves the vehicle directly in front of the fuel supply device and waits, thereby significantly reducing the burden on the driver.”; ¶ 0022, “Now, in the case of the present invention, when a detection signal is received from the detection camera 30 (substitution target detection means), the management computer 130 lights up or flashes the substitution button 134a on the screen 132 and emits a buzzer sound to notify the operator that the substitution target has arrived. The operator also immediately issues a notice through the broadcast microphone 160 to the attendant at the fueling area to refuel the vehicle in his place. As a result, the attendant at the fueling area instructs (guides) the vehicle to be substituted that has arrived at the fueling station 20 to move to an unused lane.”; ¶ 0037, “In this way, since it is possible to detect that the driver is a person eligible for substitution when the vehicle arrives at the fuel supply station 20, the driver can receive substitute refueling by stopping the vehicle in the substitute refueling dedicated area 72 as instructed by the attendant without getting out of the vehicle, which significantly reduces the burden on the driver and also significantly shortens the waiting time until a worker arrives.”; ¶ 0053)
when the one or more vehicles include a fuel cell electric vehicle. (see at least YAMANOUCHI, ¶ 0053)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, with a reasonable expectation of success, the scheduled routing of recharging in SUJAN in view of TAKUMA in further view of YAMAMOTO in further view of SHIMOKAW with the full-service station with automatic staff notification and hydrogen taught within YAMANOUCHI to effectively yield a recharge/refuel station with scheduled full-service for delivery drivers with electric or hydrogen powered vehicles.
EXAMINERS NOTE: Even though YAMANOUCHI does not explicitly name hydrogen delivery vehicles, it is well known in the art that delivery vehicles can be powered via gas, electric or hydrogen fuel.
Regarding claim 5:
SUJAN in view of TAKUMA in further view of YAMAMOTO in further view of SHIMOKAW discloses the limitations within claim 1 and SUJAN further discloses:
charging of energy includes: (see at least SUJAN, ¶ 0045, “As an illustration, FIG. 9 shows map 300 with example charging locations 322 along with the plurality of waypoint locations 304. The objective is to find an optimal route that will minimize the total energy used by the electric vehicle while factoring in the charging locations and charging times needed to complete the mission on time. The calculated optimal route in FIG. 9 may not be the same as optimal route 308 in FIG. 5. Also, there will be more energy consumed by the electric vehicle operating in the environment of FIG. 9 because the electric vehicle will need to make one or more detours to pick up the needed energy along the way.”)
storing electricity and/or (see at least SUJAN, ¶ 0004, “For an emerging future, where electric vehicles are poised to replace vehicles powered by internal combustion engines, package delivery companies and other vehicle fleet operators will face additional challenges in route optimization including minimizing energy usage, exploiting charging opportunities, meeting zero emission zone (ZEZ) requirements in urban environments, etc. Without taking these considerations into account, any potential solution may result in increased costs (e.g., oversized batteries), limited charging strategies, and route plans that largely mimic those used by conventional vehicles. Accordingly, there remains a need to develop new approaches for optimizing route planning for electric vehicles.”)
SUJAN does not disclose, but YAMANOUCHI discloses:
refueling hydrogen. (see at least YAMANOUCHI, ¶ 0012, “According to the present invention, upon receiving a detection signal from the proxy target detection means, the fuel supply station worker can be quickly notified that the proxy target (person wishing to have fuel supplied on behalf of the fuel supplier) has arrived. This means that the worker can automatically recognize that the proxy target has arrived when the vehicle arrives, without the driver having to take the trouble to notify the worker. This allows the worker to quickly accept the supply on behalf of the fuel supplier, and even if the driver wishes to have fuel supplied on behalf of the fuel supplier, the driver does not have to go to the office, but simply moves the vehicle directly in front of the fuel supply device and waits, thereby significantly reducing the burden on the driver.”; ¶ 0053, “In the above embodiment, the case where oil liquid is supplied to a fuel tank of a vehicle has been described as an example, but the present invention is not limited to this. It goes without saying that the present invention can also be applied to cases where liquefied gas such as liquefied natural gas (LNG), liquefied petroleum gas (LPG), dimethyl ether (DME), compressed natural gas (CNG), or hydrogen is filled into a fuel cell vehicle.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, with a reasonable expectation of success, the scheduled routing of electric recharging in SUJAN in view of TAKUMA in further view of YAMAMOTO in further view of SHIMOKAW with the hydrogen charging proposed within YAMANOUCHI to effectively yield a route planning system with the ability to account for electric and hydrogen charging necessities.
Regarding claim 8:
With regards to claim 8, this claim is substantially similar to claim 2 and is therefore rejected using the same references and rationale.
Regarding claim 10:
With regards to claim 10, this claim is substantially similar to claim 5 and is therefore rejected using the same references and rationale.
Regarding claim 14:
With regards to claim 14, this claim is substantially similar to claim 2 and is therefore rejected using the same references and rationale.
Regarding claim 16:
With regards to claim 16, this claim is substantially similar to claim 5 and is therefore rejected using the same references and rationale.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAFAEL VELASQUEZ VANEGAS whose telephone number is (571)272-6999. The examiner can normally be reached M-F 8 - 4.
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/RAFAEL VELASQUEZ VANEGAS/Patent Examiner, Art Unit 3667
/VIVEK D KOPPIKAR/Supervisory Patent Examiner, Art Unit 3667
October 7, 2025