DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/23/2026 has been entered.
Claim(s) 1, 6-13, 15, 17-18, and 20 have been amended. Claim(s) 2-5, 16, and 19 have been cancelled. Claims(s) 21-26 have been added. Claim(s) 1, 6-15, 17-18, and 20-26 are pending examination. This action is non-final.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 SUC 103 rejection of independent claims 1, 15, and 20 improper in light of the amendments to the claims. Therefore the claims are allowable over the art. Applicant asserts that the prior art fails to teach, “determine an optimal charging station for the electric vehicle between the trip source location and the trip destination based at least in part on the optimal charging station helping reduce release of pollutants into the air,” for claim 1 and “determining, by the processor, an optimal charging station from among a plurality of charging stations that operate on renewable energy and are located between the trip source location and the trip destination location,” for claims 15 and 20.
Applicant’s arguments with respect to claim(s) 1, 6-15, 17-18, and 21-26 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding applicant’s argument A, the examiner finds it moot. Upon further search and consideration the examiner would reject independent claims with newly cited art. Regarding the new limitations, the examiner would rely on newly cited art Vreeland (US PG Pub 2023/0152108) in combination with previously cited elements of Graham. First Graham already determines the optimal charging locations on a route, [0037] teaches “[o]nce the vehicle's driving range is known…the system controller determines potential re-fueling (ICE based vehicle) or re-charging (EV) stations based on (i) remaining within a preset distance of the intended route and (ii) breaks (e.g., lunch) within the schedule of sufficient time to allow re-fueling/re-charging.” This already shows the system determining an optimal charging location for an EV on a route to be travelled. The only element missing would be the requirement for, “the optimal charging station helping reduce release of pollutants into the air,” and “operate on renewable energy.” As a side note, the examiner is not convinced that the use of an EV wouldn’t broadly teach “helping reduce the release of pollutants” as by their very use there are less pollutants into the air, so this may be an inherent quality of EV chargers. Regardless the introduction of Vreeland would certainly teach this requirement. Broadly speaking Vreeland teaches a system for route planning for a vehicle. This system can, “analyze carbon emissions data for the one or more charging locations,” Abstract. Further [0019]-[0021] teach the system as determining how much of a given charging station’s energy is provided by renewable energy. The inclusion of Vreeland with Graham and Lim would render claims 1, 15, and 20 as obvious. The dependent claims would be rejected due to their dependence on said rejected claims. For further detailed explanation and mapping see the section below titled, “Claims Rejections – 35 USC 103.”
Claim Objections
Claim objected to because of the following informalities:
"reduce release" appears to be improper grammar, it should recite "reduce a release".
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 10-11, 15-17, 20-22 and 24-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graham (US PG Pub 2015/0345984) in view of Lim (US PG Pub 2023/0382269) and Vreeland (US PG Pub 2023/0152108).
Regarding claim 1, Graham teaches a road trip planning system comprising: a transceiver configured to receive a trip information associated with a user, (Figs. 1 and 2 items 119 and 121; and [0025] teach a communication device; [0028] teaches it can receive for a processor user information that is associated with a trip the user is undertaking) wherein the trip information comprises information associated with a trip source location and a trip destination location; ([0031] and [0042] teach the route has beginning and ending points for the user to travel between) and
a processor communicatively coupled with the transceiver, (Fig. 1, item 101; and [0021] teach a processor connected to the communication device) wherein the processor is configured to:
determine that the user is traveling via a first vehicle between the trip source location and the trip destination location, ([0032] teaches the system determining that the user is traveling via a vehicle and determining various information about the vehicle) wherein the first vehicle is an electric vehicle (EV); (Fig. 1 and [0020] teaches the vehicle is an EV)
determine an optimal charging station for the electric vehicle between the trip source location and the trip destination ([0037]-[0038] teach the system determining optimal charging stations for the vehicle to stop at as it travels between locations)
transmit a location information associated with the one or more optimal charging stations to at least one of a user device or a first computing device associated with the electric vehicle. ([0038] teaches the system determining the optimal charger/chargers and displaying a modified route to a user to confirm the possibility of using them on a user device)
Graham does not teach predict an estimated time of arrival for the user at the trip destination location based on a real-time geolocation associated with the electric vehicle; and based at least in part on the optimal charging station helping reduce release of pollutants into the air.
However, Lim teaches “predict an estimated time of arrival for the user at the trip destination location based on a real-time geolocation associated with the electric vehicle;” ([0099]-[0100] and [0112] teach the system calculating an estimated travel time from a current location to a destination with an estimated travel time. This would be analogous to a predicted arrival time from a current location as they both would tell an occupant how much longer there is on a trip and both are from the current location of a vehicle)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham with Lim; and have a reasonable expectation of success. Both relate to the control of EVs and EV systems. As Lim teaches in [0132] the temperature of a battery is directly tied to the charging efficiency of the battery. By preconditioning the battery the system is able to charge as efficiently as needed. Lim [0114] also teaches that there is a time required for a best preconditioning of the battery. By ensuring that the time between preconditioning beginning and arrival at a destination is large this ensures that the battery is at the optimal temperature for charging.
The combination of Graham and Lim does not teach based at least in part on the optimal charging station helping reduce release of pollutants into the air.
However, Vreeland teaches “based at least in part on the optimal charging station helping reduce release of pollutants into the air.” ([0018]-[0021] teaches the EV routing system determining a route that, “minimize the carbon emissions along each route,” in relation to which charging stations are selected to be used. This would be analogous to the system determining a charging station based at least in part on reducing the release of pollutants into the air.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim With Vreeland; and have a reasonable expectation of success. All relate to systems that route vehicles and are concerned with the charging of their batteries. As Vreeland teaches in [0018] this routing method allows a user to minimize their carbon emissions based on the charging station selected. Users that do this prevent more pollution from occurring and can continue to be eco-friendly. Additionally, the chargers selected can be greener as well, [0020] teaches that the system can be used to select chargers that have more energy come from renewable resources. All of this allows the recharging of EVs to be greener than an alternative with traditional combustion engines.
Regarding claim 10, Graham teaches road trip planning system of claim 1, wherein the transceiver receives the trip information from the user device or a server. ([0028] teaches the system connecting to a server to receive information associated with a user’s upcoming road trips)
Regarding claim 11, Graham teaches the road trip planning system of claim 1, wherein the processor determines that the user is traveling via the electric vehicle based on user inputs obtained from the user device or inputs obtained from the electric vehicle. ([0021] teaches the user inputting information into the vehicle navigation device and the system navigating using that vehicle, i.e. the first vehicle)
Regarding claim 15, Graham teaches a road trip planning method comprising: determining, by a processor, that a user is traveling via an electric vehicle (Fig. 1 and [0020] teaches the vehicle is an EV) between a trip source location and a trip destination location; (Figs. 1 and 2 items 119 and 121; and [0025] teach a communication device; [0028] teaches it can receive for a processor user information that is associated with a trip the user is undertaking. [0032] teaches the system determining that the user is traveling via a vehicle and determining various information about the vehicle)
monitoring, by the processor, a real-time geolocation associated with the electric vehicle when the electric vehicle is traveling between the trip source location and the trip destination location; ([0030] teaches the system monitoring the vehicle’s current location during the operation of a trip)
determining, by the processor, an optimal charging station from among a plurality of charging stations ([0037]-[0038] teach the system determining optimal charging stations for the vehicle to stop at as it travels between locations)
transmitting, by the processor, an information associated with the optimal charging stations to at least one of a user device or a first computing device associated with the electric vehicle to enable the user use the optimal charging station. ([0038] teaches the system determining the optimal charger/chargers and displaying a modified route to a user to confirm the possibility of using them on a user device. The user would know the location of the optimal device and could travel to it)
Graham does not teach predicting, by the processor, an estimated time of arrival for the user at the trip destination location based on the real-time geolocation; and that operate on renewable energy.
However, Lim teaches “predicting, by the processor, an estimated time of arrival for the user at the trip destination location based on the real-time geolocation;” ([0099]-[0100] and [0112] teach the system calculating an estimated travel time from a current location to a destination with an estimated travel time. This would be analogous to a predicted arrival time from a current location as they both would tell an occupant how much longer there is on a trip and both are from the current location of a vehicle)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim; and have a reasonable expectation of success. Both relate to the control of EVs and EV systems. As Lim teaches in [0132] the temperature of a battery is directly tied to the charging efficiency of the battery. By preconditioning the battery the system is able to charge as efficiently as needed. Lim [0114] also teaches that there is a time required for a best preconditioning of the battery. By ensuring that the time between preconditioning beginning and arrival at a destination is large this ensures that the battery is at the optimal temperature for charging.
The combination of Graham and Lim does not teach that operate on renewable energy.
However, Vreeland teaches “[charging stations] that operate on renewable energy” ([0018]-[0021] teaches the EV routing system determining a route that, “minimize the carbon emissions along each route,” in relation to which charging stations are selected to be used. [0021] in particular teaches the system determining the amount of renewable energy provided to each charging station for the user to select)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim With Vreeland; and have a reasonable expectation of success. All relate to systems that route vehicles and are concerned with the charging of their batteries. As Vreeland teaches in [0018] this routing method allows a user to minimize their carbon emissions based on the charging station selected. Users that do this prevent more pollution from occurring and can continue to be eco-friendly. Additionally, the chargers selected can be greener as well, [0020] teaches that the system can be used to select chargers that have more energy come from renewable resources. All of this allows the recharging of EVs to be greener than an alternative with traditional combustion engines.
Regarding claim 17, Graham teaches the road trip planning method of claim 15 further comprising: estimating an expected time of vehicle travel commencement between the trip source location and the trip destination location based on a planned departure time from the trip source location; ([0048] teaches the system determining a departure time and determining an expected advanced time for preparing the vehicle) and
transmitting a command signal to the electric vehicle to cause a vehicle pre-conditioning at a predefined time duration before the expected time, wherein the vehicle pre-conditioning comprises controlling a vehicle battery temperature. ([0048] teaches the vehicle receiving instructions to prepare the vehicle for a departure. This includes prepping a variety of vehicle systems such as battery temp, cabin temp, lights, etc.)
Regarding claim 20, Graham teaches a non-transitory computer-readable storage medium having instructions stored thereupon which, when executed by a processor, cause the processor to: ([0021] teaches a non-transitory memory storing instructions thereon that can be executed by a processor) determine that a user is traveling via an electric vehicle (Fig. 1 and [0020] teaches the vehicle is an EV) between a trip source location and a trip destination location; (Figs. 1 and 2 items 119 and 121; and [0025] teach a communication device; [0028] teaches it can receive for a processor user information that is associated with a trip the user is undertaking. [0032] teaches the system determining that the user is traveling via a vehicle and determining various information about the vehicle)
monitor a real-time geolocation associated with the electric vehicle when the electric vehicle is traveling between the trip source location and the trip destination location; ([0030] teaches the system monitoring the vehicle’s current location during the operation of a trip)
determine an optimal charging station from among a plurality of charging stations ([0037]-[0038] teach the system determining optimal charging stations for the vehicle to stop at as it travels between locations)
Graham does not teach predict an estimated time of arrival for the user at the trip destination location based on the real-time vehicle geolocation; that operate on renewable energy; reserve a charger at the optimal charging station for the electric vehicle enroute, based on at least one of the real-time geolocation or the estimated time of arrival.
However, Lim teaches “predict an estimated time of arrival for the user at the trip destination location based on the real-time vehicle geolocation;” ([0099]-[0100] and [0112] teach the system calculating an estimated travel time from a current location to a destination with an estimated travel time. This would be analogous to a predicted arrival time from a current location as they both would tell an occupant how much longer there is on a trip and both are from the current location of a vehicle)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim; and have a reasonable expectation of success. Both relate to the control of EVs and EV systems. As Lim teaches in [0132] the temperature of a battery is directly tied to the charging efficiency of the battery. By preconditioning the battery the system is able to charge as efficiently as needed. Lim [0114] also teaches that there is a time required for a best preconditioning of the battery. By ensuring that the time between preconditioning beginning and arrival at a destination is large this ensures that the battery is at the optimal temperature for charging.
The combination of Graham and Lim does not teach that operate on renewable energy; reserve a charger at the optimal charging station for the electric vehicle enroute, based on at least one of the real-time geolocation or the estimated time of arrival.
However, Vreeland teaches “[charging stations] that operate on renewable energy” ([0018]-[0021] teaches the EV routing system determining a route that, “minimize the carbon emissions along each route,” in relation to which charging stations are selected to be used. [0021] in particular teaches the system determining the amount of renewable energy provided to each charging station for the user to select) and “reserve a charger at the optimal charging station for the electric vehicle enroute, based on at least one of the real-time geolocation or the estimated time of arrival.” ([0027] teaches that the computing system can determine a charger at the optimal charging station and then transmitting a reservation to the station for a given charger)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim With Vreeland; and have a reasonable expectation of success. All relate to systems that route vehicles and are concerned with the charging of their batteries. As Vreeland teaches in [0018] this routing method allows a user to minimize their carbon emissions based on the charging station selected. Users that do this prevent more pollution from occurring and can continue to be eco-friendly. Additionally, the chargers selected can be greener as well, [0020] teaches that the system can be used to select chargers that have more energy come from renewable resources. All of this allows the recharging of EVs to be greener than an alternative with traditional combustion engines.
Regarding claim 21, the combination of Graham and Lim teach the road trip planning system of claim 1.
The combination of Graham and Lim does not teach wherein determining the optimal charging station is based on determining a pollution level of an area in which the optimal charging station is located.
However, Vreeland teaches “wherein determining the optimal charging station is based on determining a pollution level of an area in which the optimal charging station is located.” ([0018]-[0021] teaches the system using the emissions around a charger as a basis for determining the optimal charger for an EV on a trip)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim With Vreeland; and have a reasonable expectation of success. All relate to systems that route vehicles and are concerned with the charging of their batteries. As Vreeland teaches in [0018] this routing method allows a user to minimize their carbon emissions based on the charging station selected. Users that do this prevent more pollution from occurring and can continue to be eco-friendly. Additionally, the chargers selected can be greener as well, [0020] teaches that the system can be used to select chargers that have more energy come from renewable resources. All of this allows the recharging of EVs to be greener than an alternative with traditional combustion engines.
Regarding claim 22, the combination of Graham and Lim teach the road trip planning system of claim 1.
The combination of Graham and Lim does not teach wherein the processor is further configured to determine the optimal charging station for the electric vehicle based in further part on determining that a first pollution level at a first location associated with the optimal charging station is higher than a second pollution level at a second location associated with a second charging station.
However, Vreeland teaches “wherein the processor is further configured to determine the optimal charging station for the electric vehicle based in further part on determining that a first pollution level at a first location associated with the optimal charging station is higher than a second pollution level at a second location associated with a second charging station.” ([0022] teaches the system comparing the emissions levels at multiple charging locations and determining the best charger for a given vehicle to use based on a positive net effect)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim With Vreeland; and have a reasonable expectation of success. All relate to systems that route vehicles and are concerned with the charging of their batteries. As Vreeland teaches in [0018] this routing method allows a user to minimize their carbon emissions based on the charging station selected. Users that do this prevent more pollution from occurring and can continue to be eco-friendly. Additionally, the chargers selected can be greener as well, [0020] teaches that the system can be used to select chargers that have more energy come from renewable resources. All of this allows the recharging of EVs to be greener than an alternative with traditional combustion engines.
Regarding claim 24, the combination of Graham and Lim teach the road trip planning system of claim 1.
The combination of Graham and Lim does not teach wherein the processor is further configured to determine the optimal charging station for the electric vehicle based in further part on determining that the optimal charging station operates on renewable energy.
However, Vreeland teaches “wherein the processor is further configured to determine the optimal charging station for the electric vehicle based in further part on determining that the optimal charging station operates on renewable energy.” ([0018]-[0021] teaches the EV routing system determining a route that, “minimize the carbon emissions along each route,” in relation to which charging stations are selected to be used. [0021] in particular teaches the system determining the amount of renewable energy provided to each charging station for the user to select)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim With Vreeland; and have a reasonable expectation of success. All relate to systems that route vehicles and are concerned with the charging of their batteries. As Vreeland teaches in [0018] this routing method allows a user to minimize their carbon emissions based on the charging station selected. Users that do this prevent more pollution from occurring and can continue to be eco-friendly. Additionally, the chargers selected can be greener as well, [0020] teaches that the system can be used to select chargers that have more energy come from renewable resources. All of this allows the recharging of EVs to be greener than an alternative with traditional combustion engines.
Regarding claim 25, the combination of Graham and Lim teach the road trip planning method of claim 15.
The combination of Graham and Lim does not teach wherein determining the optimal charging station is based on determining that the optimal charging station reduces a release of pollutants into the air based on operating on a renewable energy power generation system.
However, Vreeland teaches “wherein determining the optimal charging station is based on determining that the optimal charging station reduces a release of pollutants into the air based on operating on a renewable energy power generation system.” ([0018]-[0021] teaches the EV routing system determining a route that, “minimize the carbon emissions along each route,” in relation to which charging stations are selected to be used. [0021] in particular teaches the system determining the amount of renewable energy provided to each charging station for the user to select. The use of the renewable energy would result in the system reducing the release of pollutants.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim With Vreeland; and have a reasonable expectation of success. All relate to systems that route vehicles and are concerned with the charging of their batteries. As Vreeland teaches in [0018] this routing method allows a user to minimize their carbon emissions based on the charging station selected. Users that do this prevent more pollution from occurring and can continue to be eco-friendly. Additionally, the chargers selected can be greener as well, [0020] teaches that the system can be used to select chargers that have more energy come from renewable resources. All of this allows the recharging of EVs to be greener than an alternative with traditional combustion engines.
Regarding claim 26, the combination of Graham and Lim teach the road trip planning method of claim 15.
The combination of Graham and Lim does not teach wherein determining the optimal charging station is based on determining a pollution level of an area in which the optimal charging station is located.
However, Vreeland teaches “wherein determining the optimal charging station is based on determining a pollution level of an area in which the optimal charging station is located.” ([0018]-[0021] teaches the system using the emissions around a charger as a basis for determining the optimal charger for an EV on a trip)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim With Vreeland; and have a reasonable expectation of success. All relate to systems that route vehicles and are concerned with the charging of their batteries. As Vreeland teaches in [0018] this routing method allows a user to minimize their carbon emissions based on the charging station selected. Users that do this prevent more pollution from occurring and can continue to be eco-friendly. Additionally, the chargers selected can be greener as well, [0020] teaches that the system can be used to select chargers that have more energy come from renewable resources. All of this allows the recharging of EVs to be greener than an alternative with traditional combustion engines.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graham, Lim, and Vreeland in view of Tremblay (US PG Pub 2025/0091476).
Regarding claim 6, the combination of Graham, Lim, and Vreeland teaches the road trip planning system of claim 5.
The combination of Graham, Lim, and Vreeland does not teach wherein the processor is further configured to transmit real-time geolocation information of the electric vehicle to a computing system associated with the optimal charging station, wherein the computing system is configured to control operating conditions of one or more chargers at the optimal charging station.
However, Trembley teaches “wherein the processor is further configured to transmit real-time geolocation information of the electric vehicle to a computing system associated with the optimal charging station,” ([0159]-[0160] taches the user system transmitting a real-time location to the ”CSOC,” which is a computer system associated with a charging device) and “wherein the computing system is configured to control operating conditions of one or more chargers at the optimal charging station.” ([0161] teaches the “CSOC,” controlling the chargers based on received vehicle information in the condition verification. This optimizes charging for the vehicle.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham, Lim, and Vreeland with Trembley; and have a reasonable expectation of success. All relate to the control systems of electronic vehicles. As [0003]-[0007] of Tremblay teaches there is a range anxiety of users with EVs, users are afraid of running out of power. The ability to send signals to chargers helps this. Further [0161] teaches that the system can receive users’ location and provide controls to the computer controlling the charger. This allows for the computer to optimize the charging for the vehicle, this can include the amount of charge to provide by the charging system. This eliminates the range anxiety as users can be sure their vehicle has enough power for a trip.
Claim(s) 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graham, Lim, Vreeland, and Tremblay in view of Kim (US PG Pub 2021/0310818).
Regarding claim 7, Graham teaches the road trip planning system of claim 6, wherein the processor is further configured to: obtain vehicle information associated with the electric vehicle; ([0032]-[0037] teach the system obtaining vehicle information)
determine an ([0037]-[0038] teach the system determining the best locations to charge a vehicle based on the time to stop/time to leave, possible breaks, vehicle location and destination, as well as vehicle/charger information, while not explicitly described as optimal it would be assumed to be optimized) and
The combination of Graham, Lim, Vreeland, and Tremblay does not teach an optimal amount of energy to be transferred to the electric vehicle (Emphasis added) and transmit information associated with the optimal amount of energy to at least the electric vehicle.
However, Kim teaches “an optimal amount of energy to be transferred to the electric vehicle at each charging station of the one or more optimal charging stations based on the charging station information, the vehicle information, the planned departure time, the planned arrival time, and the real-time vehicle geolocation;” ([0004], [0048]-[0049], and [0060] teach determining the optimal charging amount for a vehicle based on vehicle information and charger information, this includes vehicle status, charger status, locations to be travelled to) and “transmit information associated with the optimal amount of energy to at least one of the electric vehicle or computing systems associated with the one or more optimal charging stations.” ([0060] teaches providing the user with optimal charging information from the charging location)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham, Lim, Vreeland, and Tremblay with Kim; and have a reasonable expectation of success. All relate to the control of vehicles. Charging EVs is a big concern. As Kim teaches in [0003]-[0004] there is a need to optimize vehicle charging on a route. As the price per unit of energy can be variable, a user wants to ensure that there is no excess stoppages/charging. Optimizing this routing/charging provides a great advantage for a user.
Regarding claim 8, the combination of Graham, Lim, Vreeland and Tremblay teaches the road trip planning system of claim 7.
The combination of Graham, Lim, Vreeland and Tremblay does not teach wherein the charging station information comprises at least one of an expected emission rate associated with the optimal charging station for different times of a day, an expected per unit energy price at the optimal charging station for different times of a day, wear and tear information associated with one or more components of the optimal charging station, or an energy output capacity information associated with each charger of the optimal charging station.
However, Kim teaches “wherein the charging station information comprises at least one of an expected emission rate associated with the optimal charging station for different times of a day, an expected per unit energy price at the optimal charging station for different times of a day, wear and tear information associated with one or more components of the optimal charging station, or an energy output capacity information associated with each charger of the optimal charging station.” (Fig. 4 and [0052]-[0060] teach determining the optimal charging based on charger information which includes the price to charge as a price per unit energy)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham, Lim, and Fang with Kim; and have a reasonable expectation of success. All relate to the control of vehicles. Charging EVs is a big concern. As Kim teaches in [0003]-[0004] there is a need to optimize vehicle charging on a route. As the price per unit of energy can be variable, a user wants to ensure that there is no excess stoppages/charging. Optimizing this routing/charging provides a great advantage for a user.
Regarding claim 9, Graham teaches the road trip planning system of claim 7, wherein the vehicle information comprises at least one of an energy receiving capacity information associated with the electric vehicle, or a wear and tear information associated with one or more components of the electric vehicle. ([0032] and [0039] teach the system determining the battery charge level which would impact the capacity of energy received from the charging station, i.e. the vehicle’s energy receiving capacity)
Claim(s) 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graham, Lim, and Vreeland in view of Fang (CN-114646134).
Regarding claim 12, the combination of Graham, Lim, and Vreeland teaches the road trip planning system of claim 1.
The combination of Graham, Lim, and Vreeland does not teach transmit the information associated with the estimated time of arrival to a second computing device located at the trip destination location, wherein the second computing device activates one or more user comfort devices at the trip destination location based on the information associated with the estimated time of arrival and wherein the trip destination location is a house, an office or a hotel, and wherein the one or more user comfort devices comprises at least one of a heating, ventilation, and air conditioning (HVAC) system, a light, a television, or electric equipment located at a room associated with the user.
However, Fang teaches “transmit the information associated with the estimated time of arrival to a second computing device located at the trip destination location,” ([n0057] teaches the system transmitting information based on its location to a computing device associated with a destination) and “wherein the second computing device activates one or more user comfort devices at the trip destination location based on the information associated with the estimated time of arrival and wherein the trip destination location is a house, an office or a hotel,” ([n0057] teaches the turning on an air conditioner at the destination) and “wherein the trip destination location is a house, an office or a hotel,” ([n0057] teaches the destination as a home, office, or other location) and “wherein the one or more user comfort devices comprises at least one of a heating, ventilation, and air conditioning (HVAC) system, a light, a television, or electric equipment located at a room associated with the user.” ([n0057] teaches the comfort device as a HVAC or other air handling system)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim with Fang; and have a reasonable expectation of success. All teach control systems of vehicles with the option to transmit information from the vehicle to other devices. This can include transmitting instructions/instructional messages. As Fang teaches in [n0002]-[n0004] the ability to control a comfort device from a remote vehicle allows for the user to arrive to a destination in the most comfortable way possible. It prevents energy waste by not having to have a comfort device run constantly, but allows for user comfort by allowing the device to turn on with enough time as needed to reach optimal comfort.
Regarding claim 18, the combination of Graham, Lim, and Vreeland teaches the road trip planning method of claim 15.
The combination of Graham, Lim, and Vreeland does not teach transmitting, by the processor, the estimated time of arrival to a second computing device located at the trip destination location, wherein the trip destination location is a house, an office or a hotel, and wherein the second computing device activates one or more user comfort devices at the trip destination location based on the information associated with the estimated time of arrival.
However, Fang teaches “transmitting, by the processor, the estimated time of arrival to a second computing device located at the trip destination location,,” ([n0057] teaches the system transmitting information based on its location to a computing device associated with a destination) and “wherein the trip destination location is a house, an office or a hotel,” ([n0057] teaches the destination as a home, office, or other location) and “wherein the second computing device activates one or more user comfort devices at the trip destination location based on the information associated with the estimated time of arrival and wherein the trip destination location is a house, an office or a hotel.” ([n0057] teaches the turning on an air conditioner at the destination)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham and Lim with Fang; and have a reasonable expectation of success. All teach control systems of vehicles with the option to transmit information from the vehicle to other devices. This can include transmitting instructions/instructional messages. As Fang teaches in [n0002]-[n0004] the ability to control a comfort device from a remote vehicle allows for the user to arrive to a destination in the most comfortable way possible. It prevents energy waste by not having to have a comfort device run constantly, but allows for user comfort by allowing the device to turn on with enough time as needed to reach optimal comfort.
Claim(s) 13 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graham, Lim, and Vreeland in view of Majima (US PG Pub 2020/0132494).
Regarding claim 13, the combination of Graham, Lim, and Vreeland teaches the road trip planning system of claim 1.
The combination of Graham, Lim, and Vreeland does not teach wherein the processor is further configured to: determine that a second vehicle is available to travel on a trip portion between the trip source location and the trip destination location and the electric vehicle is configured to travel a remaining trip portion between the trip source location and the trip destination location; transmit a request to the user device to travel between the trip source location and the trip destination location by using the second vehicle for the trip portion and the electric vehicle for the remaining trip portion; obtain a user confirmation responsive to transmitting the request; transmit a signal to a server to reserve the second vehicle and the electric vehicle for the user; and transmit a reservation confirmation message to the user device, responsive to transmitting the signal to the server.
However, Majima teaches “wherein the processor is further configured to: determine that a second vehicle is available to travel on a trip portion between the trip source location and the trip destination location and the electric vehicle is configured to travel a remaining trip portion between the trip source location and the trip destination location;” (Fig. 8A and [0205]-[0206] teach a system capable of multi-modal route planning, this would allow for the user to travel multiple different legs of the trip using a first and second mode of transit. [0241] further teaches this as the system can use both public transit and car rentals) “transmit a request to the user device to travel between the trip source location and the trip destination location by using the second vehicle for the trip portion and the electric vehicle for the remaining trip portion;” (Figs. 8A and 8B and [204]-[0206] and [0254]-[0255] teaches the system sending responsive to the user that there is an option to select a multi-modal route) “obtain a user confirmation responsive to transmitting the request;” ([0255] teaches a button confirming that a user has selected a specific multi-modal route. Fig. 13A item 1303 and [0295]-[0299] teach the user selecting a seat on a mode of public transit) “transmit a signal to a server to reserve the second vehicle and the electric vehicle for the user;” ([0299]-[0300] teach the system transmitting a request to purchase a ticket for a second vehicle [0268] further teaches that rentals/reservations can be made for a series of vehicles) and “transmit a reservation confirmation message to the user device, responsive to transmitting the signal to the server.” ([0306] teaches displaying the purchased pass of the second vehicle on the user’s device. As this confirms that the user can ride the second vehicle it is seen as analogous to confirming the reservation)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham, Lim, and Vreeland with Majima; and have a reasonable expectation of success. All relate to vehicle control systems. They provide wireless communications between a vehicle and user devices/servers. As Majima teaches in [0025]-[0027] the use of user data in conjunction with travelling can provide an optimal multi-modal route. This usage of data in routing ensures that a user’s preferences are met and that the system as a whole can provide the most efficient route. Using a second vehicle for part of a route can allow a user to rest, travel to locations that may be difficult to reach via a different mode of transit, and conserve energy as a whole as more energy efficient transit systems are used.
Regarding claim 14, the combination of Graham, Lim, and Vreeland teaches the road trip planning system of claim 13.
The combination of Graham, Lim, and Vreeland does not teach wherein the second vehicle is a train.
However, Majima teaches “wherein the second vehicle is a train.” ([0186]-[0187] teaches the multi-modal routing device can select as train as a possible second transport method)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham, Lim, and Vreeland with Majima; and have a reasonable expectation of success. All relate to vehicle control systems. They provide wireless communications between a vehicle and user devices/servers. As Majima teaches in [0025]-[0027] the use of user data in conjunction with travelling can provide an optimal multi-modal route. This usage of data in routing ensures that a user’s preferences are met and that the system as a whole can provide the most efficient route. Using a second vehicle for part of a route can allow a user to rest, travel to locations that may be difficult to reach via a different mode of transit, and conserve energy as a whole as more energy efficient transit systems are used. This can include train travel.
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graham, Lim, and Vreeland in view of Bregman (US PG Pub 2025/0296461).
Regarding claim 23, the combination of Graham, Lim, and Vreeland teaches the road trip planning system of claim 1.
The combination of Graham, Lim, and Vreeland does not teach wherein the processor is further configured to determine the optimal charging station for the electric vehicle based in further part on wear and tear information associated with one or more batteries and/or one or more components of the optimal charging station and/or the electric vehicle.
However, Bregman teaches “wherein the processor is further configured to determine the optimal charging station for the electric vehicle based in further part on wear and tear information associated with one or more batteries and/or one or more components of the optimal charging station and/or the electric vehicle.” ([0057]-[0059] teaches the system determining the equipment at a given charging station and ensures that the equipment is compatible with the vehicle charging port.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Graham, Lim, and Vreeland with Bregman; and have a reasonable expectation of success. All relate to vehicle charging and control systems. As Bregman teaches in [0061] using a matching algorithm that ensures that a user is sent to compatible charging locations prevents wasted travel time and time spent not charging. By ensuring compatibility of the charging station before routing it prevents energy expenditure that cannot be easily recovered.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Aviv (US PG Pub 2024/0361137) teaches a method for planning an optimal geographical route by an electric car from origin to destination locations, using an objective of minimizing estimated time, minimizing distance to travel, or minimizing charging price, while arriving at all user-defined multiple locations along the route. The method uses time or trip sensitive data and non-time/trip sensitive data, the data include environmental factors (such as roads map, charging stations location, and weather), car related factors, and driver or user factors. The optimal route planning may be based on Bayesian network or optimization, such as by using a Travelling Salesman Problem (TSP), a Linear Programming (LP) problem, using unsupervised clustering such as K-Means clustering or algorithm, or using casual inference methodology or process that is based on Bayesian inference or Frequentist statistical inference. Any data item used may be obtained from a database, a server, or from a local or remote sensor.
Feldman (US PG Pub 2024/0142256) teaches techniques for activating charging stations for an EV driver are disclosed. A charging station authorization system of an electric mobility service provider (eMSP) may receive information from an EV driver describing an electric vehicle (EV), preferences, constraints and trip information. The charging station authorization system may determine one or more routes for the trip that include use of charging stations owned or operated by Charge Point Operators (CPOs) during the trip and fit within the constraints. The charging station authorization system may order, based on costs of using charging stations, the routes, and/or recommend use of a route. Based on a EV driver selection and/or authorization, the charging station authorization system may send a request to one or more CPO computing systems to authorize charging by the EV driver of an EV at charging stations along the selected.
Ropel (US PG Pub 2024/0142247) teaches a dynamic routing system, comprising a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise a first receiving component that receives information of a trip comprising a destination and departure information, and an optimal routing component that determines an optimal routing based on current conditions, user preferences for charging stations, primary user's driving habits, battery's state of health, financial impact, and availability of charging stations at the time of receiving the trip information. At the time of trip set up, the system can make reservations for charging at the charging station requiring a reservation. During the trip, if the system requires a change to the reservation, the system establishes communication with the charging station and adjusts the reservation.
Nikulin (US PG Pub 2018/0143029) teaches a system for vehicles that detects and gathers information about of a vehicle to determine a route that accounts for charging a vehicle. The system is configured to obtain remaining charge of a battery, identify present occupants of the vehicle, obtain characteristics of the occupants, identify charging stations located between present location of the vehicle and the specified destination, obtain characteristics of the charging stations, and determining a route for the vehicle to the specified destination based on the remaining charge of the battery, the characteristics of the present occupants, and the characteristics of the charging stations. The system can also determine routes without a specified destination. This includes alerting for low charge and providing range estimates so that the vehicle remains within range of a charging station.
Ellison (US PG Pub 2014/0129139) teaches systems and methods are described for determining an optimal path and/or route to a destination for a vehicle. Embodiments of the systems and methods disclosed herein facilitate intelligent route planning to a desired destination by a vehicle. In certain embodiments, a path and/or route to a desired destination is determined that accounts for vehicle charging and/or refueling requirements. Disclosed systems and methods may further generate and distribute reservation information ensuring availability of vehicle charging and/or refueling stations along a selected route. Further embodiments disclosed herein may implement information targeting services in connection with intelligent route planning.
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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