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
This is in response to applicant’s filing date of May 28, 2024. Claims 1-15 are currently pending.
Foreign Priority
Acknowledgment is made of applicant’s claim for foreign priority to Application KR10-2021-0167597, filed on November 29, 2021. The certified copy of the application as required by 37 CFR 1.55 has been received.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on May 28, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Prior-Filed Application Priority
Applicant’s claim for the benefit of a prior-filed application, PCT/KR2022/017700 filed on 11/11/2022, under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Title Objection
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Rejections -- 35 U.S.C. § 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Jung et al (US-20200072627-A1)(“Jung”) and Mark Hanchett (US-20220260378-A1)(“Hanchett”).
As per claim 1, Jung discloses charging station information providing server (Figure 2, system 22.) comprising:
a communication interface unit (Jung at Para. [0042] discloses that the database server 28 can communicate with device 10 at Figure 2:” database 28 is located in the illustrated embodiment away from the electric vehicle 26 and the device 10 and is in a communication connection with the device 10.”);
a memory to store instructions (Jung at Para. [0053] discloses storing of computer program to cause a process/computer to perform dedicated functions:” computer program can be stored/distributed on a non-volatile disk, for example on an optical memory or on a semiconductor drive (SSD). A computer program can be distributed with hardware and/or as part of a hardware, for example via the Internet or via wired or wireless communication systems.”); and
a processor configured to execute instructions to receive a route recommendation request including route configuration information from an external device through the communication interface unit (Jung at Figure 1, customized module 20, and Para. [0037] discloses a microprocessor for performing certain functions:” The energy planning unit 1, the charging planning unit 18 and the customization module are designed, for example, together or separately as a processor.” Further, in Para. [0032] a route configuration is determined:” device 10 allows to customize a pre-planned route for passing through a charging station where the energy storage of the electric vehicle can be charged. The determination of at least one customized route is, on the one hand, based on an estimated status of the charge level of the energy storage, i.e. a prediction of the energy consumption of the electric vehicle during the journey along the route”.),
in response to the received route recommendation request (Jung at Figure 5, process for route planning, and Para. [0051] disclosing a response to a recommendation request:” determining S14 an estimated status of the charge level, selecting S18 a charging station and determining S20 at least one customized route. The method can be implemented in particular as a software product or as a computer program product.”) ,
,
select a charging station candidate group based on the direct route using a charging stations election model with an input of condition information for customized recommendation (Jung at Para. [0034] discloses receiving locations where a vehicle can recharge in the course of traversing a route:” locations of charging stations correspond to location information of facilities where the energy storage of the electric vehicle can be recharged. In particular, the locations of charging stations may include coordinates or other corresponding information on the location of charging columns for charging a battery of an electric vehicle.”),
vehicle information (Jung at Para. [0036] discloses receiving vehicle information like capacity and current charge level:” a multi-criteria optimization method can be carried out, via which various objectives are given different weighting. Thus, on the one hand, it can be taken into account that the maximum travel range of the electric vehicle will be sufficient to reach the selected charging station with a safe probability, based on the current and predicted charge level of the energy storage.”),
road condition information (Jung at Para. [0050] discloses using road condition as planning factor:” by taking into account environmental information such as data on temperature, weather, route condition or route traffic, a higher accuracy can be achieved in the prediction of the status of the charge level.”), and
information about charging stations, configure at least one recommended charging route, based on the selected charging station candidates using a route recommendation model (Jung at Paras. [0046]-[0047] disclosing charging station information as basis for route selection:” is understood that the database or other device may contain additional information about the available charging stations. Such additional information can then be considered in the device when selecting the charging station. [0047] For example, it can be taken into account plug types, i.e. interface types that are available for charging the energy storage at a specific charging station. In the field of battery-powered electric vehicles, there are different interfaces and different charging concepts. It will not be useful for the driver of an electric vehicle to stop at a charging station where no corresponding interface is available for charging his own energy storage.), and
transmit the at least one recommended charging route to the external device through the communication interface unit (Jung at Para. [0033] discloses transmitting the charging route:” input interface corresponds in particular to a communication interface for communication with a vehicle information system through which the charge level of the energy storage is transmitted, and also for communication with a navigation unit or a corresponding navigation software through which a planned route is transmitted. The up-to-date charge level can be given, for example, as an absolute energy indication (kWh or kg of hydrogen, etc.) or can be specified as a relative indication (percentage) with respect to a previously known total capacity. The planned route is preferably received in a corresponding data format.”).
While Jung discloses an optimized routing method that may include different objectives, such as a short travel time, a fast journey, a known preference of the user, a favorable price for charging the energy storage, etc. are considered. Jung does not explicitly disclose first determining a search for a direct route, based on the route configuration information.
Hanchett in the same field of endeavor for planning a route where potential charging waypoints are considered discloses a system that plans a route that takes into consideration the weight of the electric vehicle, the weight of a trailer pulled by electric vehicle, if any, atmospheric conditions, the gradient (e.g., grade of road) of the potential routes and/or time to destination in addition to range, proximity of recharging stations and rate of consumption of energy from the battery. See Abstract and Figures 1 & 3.
In particular, Hanchett discloses a process for route selection where a search for a direct route, based on the route configuration information (Hanchett at Para. [0057] discloses identifying a direct route to a destination before adding charging and other factors into the determination:” the driver of the electric vehicle 110 wants to travel from starting location city A to destination city B. ST104 on paper, as best shown in FIG. 4, appears to be the most direct and shortest route between city A and city B.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system for route planning of electric vehicles as taught by Jung with the method that identifies the feasibility of routes for the electric vehicle as taught by Hanchett with a reasonable expectation of success in order for the one or more route can be identified based on the proximity of recharging stations and rate of consumption of energy from the battery in an electric vehicle. The teaching suggestion/motivation to combine is that by identifying route with associated charging station, increase in the accuracy of a route for travel can be improved as taught by Hanchett at Para. [0005].
As per claim 2, Jung and Hanchett disclose a charging station information providing server of claim 1, wherein the processor executes the instructions to search for an optimal travel route that does not pass through a charging station from a departure point to a destination included in the route configuration information as the direct route (Hanchett at Para. [0057] discloses different routes that are optimized for time and do not include charging stations:” US highway 912 (“US912”) and state highway 104 (“ST104”), as best seen in FIG. 4, may be used to illustrate how routes are identified and assessed. In this example, the driver of the electric vehicle 110 wants to travel from starting location city A to destination city B. ST104 on paper, as best shown in FIG. 4, appears to be the most direct and shortest route between city A and city B.”), based on a scheduled departure time (Hanchett at Para. [0022] discloses using a plurality of parameters such as departure/start time:” electric vehicle 110 travels a route, the electric vehicle 110 may capture and record data for storage in the database 180. Capture data may include information such as starting location, start time, destination, end time, route, present time, date, temperature, present charge on battery, amount of energy expended, rate of energy usage along the route, road condition (e.g., traction), grade (e.g., gradient), environmental factors, vehicle weight, trailer weight, identity of the driver, and driving characteristics of the driver.”).
As per claim 3, Jung and Hanchett disclose a charging station information providing server of claim 1, wherein the processor executes the instructions to search for a charging station candidate group near the direct route using the charging station selection model, search for an optimal route passing through each charging station in the searched charging station candidate group (Hanchett at Para. [0051] identifies all the charging station from a departure to a destination point to select the optimal route for the vehicle based on various parameters:” map data 310 further includes the charging station locations 318. The charging station locations 318 includes information regarding the geographic location of charging stations where the electric vehicle 110 may charge the battery 116. The charging station locations 318 identifies the roads that may access the various charging stations. The charging station locations 318 may further identify the type and capacity of the charging equipment located at a charging station thereby providing information as to how long it may take to recharge the battery 116.”),
compare a difference in time or distance required between partial sections of the optimal route and the direct route corresponding to the optimal route (Hanchett at Para. [0019] discloses that time and distance are used to evaluate/compared routes:” evaluating the routes, the system may use factors such as the weight (e.g., load, curb, gross) of the electric vehicle; the weight of a trailer (e.g., load, curb, gross) pulled by the electric vehicle; atmospheric conditions; distance, grade, and elevation changes between starting location and destination; predicted rate of energy usage from the battery along a potential route, and the location of charging stations along the route.”), and
select the charging station candidate group by removing the charging station of which the difference does not satisfy a predetermined standard from the charging station candidate group (Jung at Para. [0047] discloses taking into account compatibility between the charging station and the vehicle:” it can be taken into account plug types, i.e. interface types that are available for charging the energy storage at a specific charging station. In the field of battery-powered electric vehicles, there are different interfaces and different charging concepts. It will not be useful for the driver of an electric vehicle to stop at a charging station where no corresponding interface is available for charging his own energy storage.”).
As per claim 4, Jung and Hanchett disclose a charging station information providing server of claim 1, wherein the processor executes the instructions to calculate target charging amount required to reach a destination from a departure point included in the route configuration information (Hanchett at Para. [0021] discloses that factor such as total energy is included in the determination:” criteria for selection may include total energy use and amount of time needed to traverse the route. Other factors may include potential stress (e.g., wear-and-tear) on the systems of the electric vehicle, safety factors and/or even scenery along a route.”),
calculate a reachable range of a vehicle, based on a remaining battery capacity of the vehicle, predict a travel time and the remaining battery capacity to each charging station belonging to the charging station candidate group within the reachable range (Hanchett at Para. [0064] discloses travel time and battery capacity for completing a route to a destination:” the system determines that the electric vehicle 110 must start the trip with the battery 116 fully charged, must stop at both charging stations R460 and R462, and at each charging station should charge to at least 80% of the charge capacity of the battery 116. If the driver does not comply with the identified charging requirements, the electric vehicle 110 will not be able to traverse the route because the battery 116 will be fully depleted before reaching a charging station or the destination city B.”),
predict appropriate charging amount at each charging station and the charging time and cost corresponding to the appropriate charging amount, based on charging efficiency (Hanchett at Para. [0072] discloses various charging scenarios for completing a trip:” the system instructs the driver, via the display 118, to not take ST104 and to stop for full charging at charging stations R422 and R426 in addition to charging to at least 50% at charging station R428 or at least 35% at charging station R430. The system further informs the driver that it is best to not start the trip with less than 80% charge on the battery 116.”), and
configure the at least one recommended charging route by comparing a cumulative charge amount after charging the appropriate charge amount with the target charge amount, and by completing or continuing to configure a recommended charging route (Hanchett at Para. [0066] discloses the target charge per station to complete a trip:” times shown in FIG. 8 include the time required to charge at charging stations along the way. The driver will need to charge the battery 116 at two, possibly three charging stations along US912, but since they are much closer together than on ST104, the driver will be able to choose which charging stations to stop at merely by watching the gauge showing remaining charge on the battery and range.”).
As per claim 5, Jung and Hanchett disclose a charging station information providing server of claim 4, wherein the processor executes the instructions to, when the cumulative charge amount exceeds the target charge amount, obtain the remaining battery capacity at the destination and a total time and total cost corresponding to the completed recommended charging route (Jung at Para. [0008] discloses taking charge time and cost as factors:” a sufficient number of sufficiently long charging stops must be scheduled, on the other hand, other criteria must be taken into account, such as, the shortest possible travel time, low cost, and personal preferences of the driver.”), and
when the cumulative charge amount is less than or equal to the target charge amount, continue to configure the recommended charging route until the destination is reached (Hanchett at Para. [0004] discloses using a combination of factors such as charge amount per station and battery capacity to select a route:” system may identify various routes and assess each route in accordance with the above information. Range information and energy usage may be based on energy required by the traction motors to traverse a route of a particular length and/or gradient. Estimates for evaluating and comparing various routes may further include energy usage by the electric vehicle in accordance with the weight of the vehicle and/or the weight of the vehicle and a trailer pulled by the vehicle. The system may evaluate identified routes in accordance with any of the above factors or combination thereof.”).
As per claim 6, Jung and Hanchett disclose a charging station information providing server of claim 4, wherein the processor executes the instructions to adjust the target charging amount, depending on the vehicle information and a deployment of available charging stations at the destination (Hanchett at Para. [0066] discloses using charging time and available number of stations in route is used as selection factor:” time required to charge at charging stations along the way. The driver will need to charge the battery 116 at two, possibly three charging stations along US912, but since they are much closer together than on ST104, the driver will be able to choose which charging stations to stop at merely by watching the gauge showing remaining charge on the battery and range.”).
As per claim 7, Jung and Hanchett disclose a charging station information providing server of claim 4, wherein the processor executes the instructions to determine the appropriate charging amount, based on the charger output of each charging station (Hanchett at Para. [0051] discloses collecting and using charging information for each station in a route:” charging station locations 318 includes information regarding the geographic location of charging stations where the electric vehicle 110 may charge the battery 116. The charging station locations 318 identifies the roads that may access the various charging stations. The charging station locations 318 may further identify the type and capacity of the charging equipment located at a charging station thereby providing information as to how long it may take to recharge the battery 116. The charging station locations 318 may further include information regarding the throughput (e.g., vehicles charged per hour) of a charging station thereby providing further information as to how long it may take recharge the battery 116 at that particular location.”),
charging efficiency characteristics depending on a battery capacity of the vehicle (Hanchett at Para. [0020] discloses battery capacity of the vehicle as a planning factor:” the server 170 may need to receive data from the vehicle computer 112 such as, for example, date of the trip, starting location, destination, vehicle weight, trailer weight, traction motor size and power usage, battery type, battery capacity, current amount of energy stored in the battery, and the identity of the driver of an electric vehicle 110.”), and
a charging limit indicating an upper charging limit (Hanchett at Para. [0020] discloses an upper charging limit, i.e., capacity for the battery:” battery type, battery capacity, current amount of energy stored in the battery, and the identity of the driver of an electric vehicle 110.”).
As per claim 8, Jung discloses a method of providing a recommended charging route performed by a charging station information providing server (Figure 5), the method comprising:
receiving a route recommendation request including route configuration information from an external device (Jung at Figure 5, process for route planning, and Para. [0051] disclosing a response to a recommendation request:” determining S14 an estimated status of the charge level, selecting S18 a charging station and determining S20 at least one customized route. The method can be implemented in particular as a software product or as a computer program product.”);
in response to the received route recommendation request (Figure 5) , ;
selecting a charging station candidate group based on the direct route using a charging station selection model with an input of condition information for customized recommendation, vehicle information, road condition information, and information about charging stations (Jung at Para. [0034] discloses receiving locations where a vehicle can recharge in the course of traversing a route:” locations of charging stations correspond to location information of facilities where the energy storage of the electric vehicle can be recharged. In particular, the locations of charging stations may include coordinates or other corresponding information on the location of charging columns for charging a battery of an electric vehicle.”);
configuring at least one recommended charging route, based on the selected charging station candidate group using a route recommendation model (Jung at Paras. [0046]-[0047] disclosing charging station information as basis for route selection:” is understood that the database or other device may contain additional information about the available charging stations. Such additional information can then be considered in the device when selecting the charging station. [0047] For example, it can be taken into account plug types, i.e. interface types that are available for charging the energy storage at a specific charging station. In the field of battery-powered electric vehicles, there are different interfaces and different charging concepts. It will not be useful for the driver of an electric vehicle to stop at a charging station where no corresponding interface is available for charging his own energy storage.); and
transmitting the at least one recommended charging route to the external device (Jung at Para. [0033] discloses transmitting the charging route:” input interface corresponds in particular to a communication interface for communication with a vehicle information system through which the charge level of the energy storage is transmitted, and also for communication with a navigation unit or a corresponding navigation software through which a planned route is transmitted. The up-to-date charge level can be given, for example, as an absolute energy indication (kWh or kg of hydrogen, etc.) or can be specified as a relative indication (percentage) with respect to a previously known total capacity. The planned route is preferably received in a corresponding data format.”).
While Jung discloses an optimized routing method that may include different objectives, such as a short travel time, a fast journey, a known preference of the user, a favorable price for charging the energy storage, etc. are considered. Jung does not explicitly disclose a process for searching for a direct route, based on the route configuration information.
Hanchett in the same field of endeavor for planning a route where potential charging waypoints are considered discloses a system that plans a route that takes into consideration the weight of the electric vehicle, the weight of a trailer pulled by electric vehicle, if any, atmospheric conditions, the gradient (e.g., grade of road) of the potential routes and/or time to destination in addition to range, proximity of recharging stations and rate of consumption of energy from the battery. See Abstract and Figures 1 & 3.
In particular, Hanchett discloses a process for route selection by searching for a direct route, based on the route configuration information (Hanchett at Para. [0057] discloses identifying a direct route to a destination before adding charging and other factors into the determination:” the driver of the electric vehicle 110 wants to travel from starting location city A to destination city B. ST104 on paper, as best shown in FIG. 4, appears to be the most direct and shortest route between city A and city B.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system for route planning of electric vehicles as taught by Jung with the method that identifies the feasibility of routes for the electric vehicle as taught by Hanchett with a reasonable expectation of success in order for the one or more route can be identified based on the proximity of recharging stations and rate of consumption of energy from the battery in an electric vehicle. The teaching suggestion/motivation to combine is that by identifying route with associated charging station, increase in the accuracy of a route for travel can be improved as taught by Hanchett at Para. [0005].
As per claim 9, Jung and Hanchett disclose a method of claim 8, wherein the searching for the direct route includes searching for an optimal travel route that does not pass through a charging station from a departure point to a destination included in the route configuration information (Hanchett at Para. [0057] discloses different routes that are optimized for time and do not include charging stations:” US highway 912 (“US912”) and state highway 104 (“ST104”), as best seen in FIG. 4, may be used to illustrate how routes are identified and assessed. In this example, the driver of the electric vehicle 110 wants to travel from starting location city A to destination city B. ST104 on paper, as best shown in FIG. 4, appears to be the most direct and shortest route between city A and city B.”), based on a scheduled departure time (Hanchett at Para. [0022] discloses using a plurality of parameters such as departure/start time:” electric vehicle 110 travels a route, the electric vehicle 110 may capture and record data for storage in the database 180. Capture data may include information such as starting location, start time, destination, end time, route, present time, date, temperature, present charge on battery, amount of energy expended, rate of energy usage along the route, road condition (e.g., traction), grade (e.g., gradient), environmental factors, vehicle weight, trailer weight, identity of the driver, and driving characteristics of the driver.”).
As per claim 10, Jung and Hanchett disclose a method of claim 8, wherein the selecting of the charging station candidates includes searching for a charging station candidate group near the direct route using the charging station selection model (Hanchett at Para. [0051] identifies all the charging station from a departure to a destination point to select the optimal route for the vehicle based on various parameters:” map data 310 further includes the charging station locations 318. The charging station locations 318 includes information regarding the geographic location of charging stations where the electric vehicle 110 may charge the battery 116. The charging station locations 318 identifies the roads that may access the various charging stations. The charging station locations 318 may further identify the type and capacity of the charging equipment located at a charging station thereby providing information as to how long it may take to recharge the battery 116.”),
comparing a difference in time or distance required between partial sections of the optimal route and the direct route corresponding to the optimal route (Hanchett at Para. [0019] discloses that time and distance are used to evaluate/compared routes:” evaluating the routes, the system may use factors such as the weight (e.g., load, curb, gross) of the electric vehicle; the weight of a trailer (e.g., load, curb, gross) pulled by the electric vehicle; atmospheric conditions; distance, grade, and elevation changes between starting location and destination; predicted rate of energy usage from the battery along a potential route, and the location of charging stations along the route.”),
searching for an optimal route passing through each charging station belonging to the searched charging station candidate group (Jung at Para. [0047] discloses taking into account compatibility between the charging station and the vehicle:” it can be taken into account plug types, i.e. interface types that are available for charging the energy storage at a specific charging station. In the field of battery-powered electric vehicles, there are different interfaces and different charging concepts. It will not be useful for the driver of an electric vehicle to stop at a charging station where no corresponding interface is available for charging his own energy storage.”). and
removing a charging station of which the difference does not satisfy a predetermined standard from the charging station candidate group (Jung at Para. [0047] discloses taking into account compatibility between the charging station and the vehicle:” it can be taken into account plug types, i.e. interface types that are available for charging the energy storage at a specific charging station. In the field of battery-powered electric vehicles, there are different interfaces and different charging concepts. It will not be useful for the driver of an electric vehicle to stop at a charging station where no corresponding interface is available for charging his own energy storage.”).
As per claim 11, Jung and Hanchett disclose a method of claim 8, wherein the configuring of the at least one recommended charging route includes calculating a target charging amount required to reach a destination from a departure point included in the route configuration information (Hanchett at Para. [0021] discloses that factor such as total energy is included in the determination:” criteria for selection may include total energy use and amount of time needed to traverse the route. Other factors may include potential stress (e.g., wear-and-tear) on the systems of the electric vehicle, safety factors and/or even scenery along a route.”);
calculating a reachable range of a vehicle, based on a remaining battery capacity of the vehicle (Hanchett at Para. [0064] discloses travel time and battery capacity for completing a route to a destination:” the system determines that the electric vehicle 110 must start the trip with the battery 116 fully charged, must stop at both charging stations R460 and R462, and at each charging station should charge to at least 80% of the charge capacity of the battery 116. If the driver does not comply with the identified charging requirements, the electric vehicle 110 will not be able to traverse the route because the battery 116 will be fully depleted before reaching a charging station or the destination city B.”);
predicting travel time and the remaining battery capacity to each charging station belonging to the charging station candidate group within the reachable range (Hanchett at Para. [0072] discloses various charging scenarios for completing a trip:” the system instructs the driver, via the display 118, to not take ST104 and to stop for full charging at charging stations R422 and R426 in addition to charging to at least 50% at charging station R428 or at least 35% at charging station R430. The system further informs the driver that it is best to not start the trip with less than 80% charge on the battery 116.”);
based on charging efficiency, predicting an appropriate charging amount at each charging station, and a charging time and cost corresponding to the appropriate charging amount (Hanchett at Para. [0020] discloses battery capacity of the vehicle as a planning factor:” the server 170 may need to receive data from the vehicle computer 112 such as, for example, date of the trip, starting location, destination, vehicle weight, trailer weight, traction motor size and power usage, battery type, battery capacity, current amount of energy stored in the battery, and the identity of the driver of an electric vehicle 110.”); and
completing or continuing to configure the recommended charging route by comparing the cumulative charge amount after charging the appropriate charge amount with the target charge amount (Hanchett at Para. [0066] discloses the target charge per station to complete a trip:” times shown in FIG. 8 include the time required to charge at charging stations along the way. The driver will need to charge the battery 116 at two, possibly three charging stations along US912, but since they are much closer together than on ST104, the driver will be able to choose which charging stations to stop at merely by watching the gauge showing remaining charge on the battery and range.”).
As per claim 12, Jung and Hanchett disclose a method of claim 11, wherein the completing or continuing to configure the recommended charging route includes, when the cumulative charge amount exceeds the target charge amount, obtaining the remaining battery capacity at the destination and a total time and total cost corresponding to the completed recommended charging route (Jung at Para. [0008] discloses taking charge time and cost as factors:” a sufficient number of sufficiently long charging stops must be scheduled, on the other hand, other criteria must be taken into account, such as, the shortest possible travel time, low cost, and personal preferences of the driver.”), and when the cumulative charge amount is less than or equal to the target charge amount, repeating configuring the recommended charging route until the destination is reached (Hanchett at Para. [0004] discloses using a combination of factors such as charge amount per station and battery capacity to select a route:” system may identify various routes and assess each route in accordance with the above information. Range information and energy usage may be based on energy required by the traction motors to traverse a route of a particular length and/or gradient. Estimates for evaluating and comparing various routes may further include energy usage by the electric vehicle in accordance with the weight of the vehicle and/or the weight of the vehicle and a trailer pulled by the vehicle. The system may evaluate identified routes in accordance with any of the above factors or combination thereof.”).
As per claim 13, Jung and Hanchett disclose a method of claim 11, wherein the calculating of the target charging amount further includes adjusting the target charging amount depending on the vehicle information and whether an available charging station is located at the destination (Hanchett at Para. [0066] discloses using charging time and available number of stations in route is used as selection factor:” time required to charge at charging stations along the way. The driver will need to charge the battery 116 at two, possibly three charging stations along US912, but since they are much closer together than on ST104, the driver will be able to choose which charging stations to stop at merely by watching the gauge showing remaining charge on the battery and range.”).
As per claim 14, Jung and Hanchett disclose a method of claim 11, wherein the appropriate charging amount is determined, based on a charger output of each charging station (Hanchett at Para. [0051] discloses collecting and using charging information for each station in a route:” charging station locations 318 includes information regarding the geographic location of charging stations where the electric vehicle 110 may charge the battery 116. The charging station locations 318 identifies the roads that may access the various charging stations. The charging station locations 318 may further identify the type and capacity of the charging equipment located at a charging station thereby providing information as to how long it may take to recharge the battery 116. The charging station locations 318 may further include information regarding the throughput (e.g., vehicles charged per hour) of a charging station thereby providing further information as to how long it may take recharge the battery 116 at that particular location.”) and the charging efficiency characteristics depending on the battery capacity of the vehicle and a charging limit indicating an upper charging limit (Hanchett at Para. [0020] discloses an upper charging limit, i.e., capacity for the battery:” battery type, battery capacity, current amount of energy stored in the battery, and the identity of the driver of an electric vehicle 110.”).
As per claim 15 charging station information providing application stored in a medium and configured to cause at least one processor of a terminal device to perform a method of providing a recommended charging route (Figure 2, especially database 28.), wherein the method of providing the recommended charging route comprises
detecting an input of a route recommendation request including route configuration information in a user interface upon execution of a charging station information providing application (Jung at Figure 5, process for route planning, and Para. [0051] disclosing a response to a recommendation request:” determining S14 an estimated status of the charge level, selecting S18 a charging station and determining S20 at least one customized route. The method can be implemented in particular as a software product or as a computer program product.”), transmitting the route recommendation request to a charging station information providing server, in response to the transmitting of the route recommendation request, receiving, from the charging station information providing server, at least one recommended charging route (Jung at Para. [0033] discloses transmitting the charging route:” input interface corresponds in particular to a communication interface for communication with a vehicle information system through which the charge level of the energy storage is transmitted, and also for communication with a navigation unit or a corresponding navigation software through which a planned route is transmitted. The up-to-date charge level can be given, for example, as an absolute energy indication (kWh or kg of hydrogen, etc.) or can be specified as a relative indication (percentage) with respect to a previously known total capacity. The planned route is preferably received in a corresponding data format.”), wherein the at least one recommended charging route is configured by the charging station information providing server (Jung at Figure 2 & 5.) , selecting a charging station candidate group based on the direct route using a charging station selection model with an input of condition information for customized recommendation, vehicle information, road condition information, and information about charging stations (Jung at Para. [0034] discloses receiving locations where a vehicle can recharge in the course of traversing a route:” locations of charging stations correspond to location information of facilities where the energy storage of the electric vehicle can be recharged. In particular, the locations of charging stations may include coordinates or other corresponding information on the location of charging columns for charging a battery of an electric vehicle.”), and configuring the at least one recommended charging route, based on the selected charging station candidate group using a route recommendation model, and changing and outputting the user interface, based on the received at least one recommended charging route (Jung at Paras. [0046]-[0047] disclosing charging station information as basis for route selection:” is understood that the database or other device may contain additional information about the available charging stations. Such additional information can then be considered in the device when selecting the charging station. [0047] For example, it can be taken into account plug types, i.e. interface types that are available for charging the energy storage at a specific charging station. In the field of battery-powered electric vehicles, there are different interfaces and different charging concepts. It will not be useful for the driver of an electric vehicle to stop at a charging station where no corresponding interface is available for charging his own energy storage.).
While Jung discloses an optimized routing method that may include different objectives, such as a short travel time, a fast journey, a known preference of the user, a favorable price for charging the energy storage, etc. are considered. Jung does not explicitly disclose first determining a search for a direct route, based on the route configuration information.
Hanchett in the same field of endeavor for planning a route where potential charging waypoints are considered discloses a system that plans a route that takes into consideration the weight of the electric vehicle, the weight of a trailer pulled by electric vehicle, if any, atmospheric conditions, the gradient (e.g., grade of road) of the potential routes and/or time to destination in addition to range, proximity of recharging stations and rate of consumption of energy from the battery. See Abstract and Figures 1 & 3.
In particular, Hanchett discloses a process for route selection where a search for a direct route, based on the route configuration information (Hanchett at Para. [0057] discloses identifying a direct route to a destination before adding charging and other factors into the determination:” the driver of the electric vehicle 110 wants to travel from starting location city A to destination city B. ST104 on paper, as best shown in FIG. 4, appears to be the most direct and shortest route between city A and city B.”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system for route planning of electric vehicles as taught by Jung with the method that identifies the feasibility of routes for the electric vehicle as taught by Hanchett with a reasonable expectation of success in order for the one or more route can be identified based on the proximity of recharging stations and rate of consumption of energy from the battery in an electric vehicle. The teaching suggestion/motivation to combine is that by identifying route with associated charging station, increase in the accuracy of a route for travel can be improved as taught by Hanchett at Para. [0005].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
MAEDA; Eri Izumi et al. (US-20230152111-A1) SYSTEMS AND METHODS FOR SELECTING A CHARGING ENTITY BASED ON OCCUPANCY STATUS;
TAKAKI; Kentarou (US-20220136850-A1) PROVISION TARGET SEARCH METHOD, PROVISION TARGET DISPLAY METHOD, PROVISION-TARGET-SEARCHING APPARATUS, PROVISION-TARGET-DISPLAYING APPARATUS, AND PROVISION-TARGET-SEARCHING SYSTEM;
KIM; Yun Jae et al. (US-20210389144-A1) USER INTERFACES FOR CUSTOMIZED NAVIGATION ROUTES;
Pedersen; Robert D. (US-20180238698-A1) Systems And Methods Using Artificial Intelligence For Routing Electric Vehicles;
YOU; Hyun Jong et al. (US-20170343366-A1) VEHICLE SYSTEM AND NAVIGATION PATH SELECTING METHOD OF THE SAME;
INOUE; Hirofumi et al. (US-20170010116-A1) VEHICLE INFORMATION PROVIDING DEVICE;
KIYAMA; Noboru et al. (US-20140163877-A1) NAVIGATION SYSTEM FOR ELECTRIC VEHICLE;
Hiruta; Tomoaki et al. (US-8751077-B2) Route planning device and route planning system;
TAKIZAWA DAIJIRO (JP-2011232241-A) NAVIGATION SYSTEM FOR ELECTRIC VEHICLE AND ELECTRIC VEHICLE;
UESUGI; Kenichiro (US-20110193522-A1) OPERATION MANAGING SERVER FOR CHARGING STATIONS AND OPERATION MANAGING SYSTEM FOR CHARGING STATIONS;
ABE TAKAAKI et al. (JP-2008087719-A) HYBRID VEHICLE AND CONTROL METHOD THEREFOR.
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/ELLIS B. RAMIREZ/Examiner, Art Unit 3658