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 .
Response to Amendment
This action is in response to amendments and remarks filed on 06/23/2026. Claim(s) 1, 6, 15, and 20 have been amended. Claim(s) 2-5, 16, and 19 have been cancelled. Claim(s) 1, 6-15, 17-18, and 20-26 are pending examination. The objection of claim 1 is removed in light of the instant amendment. This action is made non-final.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 USC 103 rejection of independent claims 1 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, “transmit the real-time geolocation associated with the electric vehicle to a computing system associated with the optimal charging station, wherein the computing system is configured to control a display screen of a charger at the optimal charging station to be in an unilluminated state until a predefined time duration before the electric vehicle reaches the optimal charging station, as determined based on the real-time geolocation,” for claim 1 and similar language for claim 20.
Applicant asserts that the 35 USC 103 rejection of independent claim 15 improper in light of the amendments to the claim. Therefore the claims are allowable over the art. Applicant asserts that the prior art fails to teach, “determining, by the processor, optimal charging time durations and optimal discharging time durations at the trip destination location, wherein the optimal discharging time durations correspond to time durations when the electric vehicle transfers energy to a grid via the trip destination location.”
Applicant asserts that the 35 USC 103 rejection of dependent claim 23 improper. Therefore the claims are allowable over the art. Applicant asserts that the prior art fails to 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.”
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 Schuchter (US PG Pub 2024/0174109) to teach this limitation. Broadly Schuchter teaches a system for charging EVs, that can act on a signal. [0074] of Schuchter teaches that the system can determine an action to carry out on the basis that a vehicle is nearing it on a route. This action includes, “changing a brightness of a lighting device 11 or of the display device 7, modifying the type and/or frequency of a content displayed on a display device 7,” this would clearly teach the idea of controlling the display screen. This control is in conjunction with a reservation system outlined in [0084]. The combination of Schuchter with Graham, Lim, and Vreeland would render the claims obvious and they would remain rejected under 35 USC 103. Their respective dependent claims would remain rejected for the reasons recited below. For further detailed explanation and mapping see the section below titled, “Claims Rejections – 35 USC 103.”
Regarding applicant’s argument B, 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 Rajabally (US PG Pub 2020/0369175) to teach this limitation. Broadly speaking Rajabally teaches, a system to forecast the charge and storage capacity of EVs. This forecast can be used to determine what is needed/possible at a given charging location. This forecast includes the discharging of EVs to the grid. As seen in [0037]-[0040] the system forecasts what the vehicle’s expected charge and discharge capability is for a given stop. This capability would be analogous to a duration of said action. The combination of Rajabally with Graham, Lim, and Vreeland would render the claims obvious and they would remain rejected under 35 USC 103. Their respective dependent claims would remain rejected for the reasons recited below. For further detailed explanation and mapping see the section below titled, “Claims Rejections – 35 USC 103.”
Regarding applicant’s argument C, 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 Newman (US PG Pub 2018/0188332) to teach this limitation. Broadly speaking Newman teaches a system that ca monitor vehicle data for EVs and determine optimal charging information. This includes the health of a battery, i.e. wear and tear. As seen in [0032], [0046], [0067], and [0110], the system monitors the health of the battery and uses that information in conjunction with its charge data to determine the optimal charging data. This would teach the claim as claimed. For further detailed explanation and mapping see the section below titled, “Claims Rejections – 35 USC 103.”
In light of the above the examiner finds that the claims are rejected under 35 USC 103. Please see the section below for more information.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 15, 17-18, and 25-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Analysis of the claim(s) regarding subject matter eligibility utilizing the 2019 Revised Patent Subject Matter Eligibility Guidance is described below.
STEP 1: STATUTORY CATEGORIES
Claim(s) 15, 17-18, and 25-26 do fall into at least one of the four statutory subject matter categories. Claim 15 and its dependents are directed method which is the statutory category of a process.
STEP 2A: JUDICIAL EXCEPTIONS
PRONG 1: RECITATION OF A JUDICIAL EXCEPTION
The claim(s) recite(s):
- Claim 15 recite(s) an abstract idea belonging to the grouping of mental processes. See table below.
Claim 15
Notes
A road trip planning method comprising:
determining, by a processor, that a user is traveling via an electric vehicle between a trip source location and a trip destination location;
Mental step of determination
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;
Mental step of monitoring
predicting, by the processor, an estimated time of arrival for the user at the trip destination location based on the real-time geolocation;
Mental step of predicting
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;
Mental step of determination
transmitting, by the processor, an information associated with the optimal charging station to at least one of user device or a first computing device associated with the electric vehicle to enable the user use the optimal charging station; and
Generic data transmission step
determining, by the processor, optimal charging time durations and optimal discharging time durations at the trip destination location, wherein the optimal discharging time durations correspond to time durations when the electric vehicle transfers energy to a grid via the trip destination location.
Mental step of determination
The subject matter of claim 15 is clearly a mental process. A person with a generic computing device or generic computing environment could achieve the result of the claim. The steps of the claims are merely determining that a person is traveling, monitoring data, and predicting progress of the trip. This is then used to determine a stop for the vehicle and transmitting generic data elements. The final determination step is merely determining a time for something to occur. A person could reasonably achieve this result.
- Claim 25 recite(s) an abstract idea belonging to the grouping of mental processes. Claim 25 recites, “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.” A person with a generic computing device could determine the optimal station to charge at based on a release of pollutants, i.e. charging at a solar powered stop.
- Claim 26 recite(s) an abstract idea belonging to the grouping of mental processes. Claim 26 recites, “wherein determining the optimal charging station is based on determining a pollution level of an area in which the optimal charging station is located.” A person with a generic computing device could determine the optimal station to charge at based on a pollution level of an area. This could be achieved with generic computer searching.
PRONG 2: INTEGRATION INTO A PRACTICAL APPLICATION
The additional element(s) recited in the claim(s) beyond the judicial exception are generic data transmission steps. The additional element(s) do not integrate the judicial exception into a practical application because the additional element(s) do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception and add insignificant extra-solution activity to the judicial exception. The computer elements are merely used as a tool to perform the abstract idea, and the use of the judicial exception is generally linked to the particular technological environment of vehicle routing without using the judicial exception in some other meaningful way (MPEP 2106.04(d)).
STEP 2B: INVENTIVE CONCEPT/SIGNIFICANTLY MORE
The additional elements recited in the claim(s) are not sufficient to amount to significantly more than the judicial exception because they do not add more than insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), and the computer functions of receiving and transmitting data have been recognized by the courts as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(d)). Further, the additional elements of a “memory” and a “processor” recited in the claim(s) are well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality (MPEP2106.05 (d)).
Based on the above analysis, claim(s) 15, 17-18, and 25-26 is/are not eligible subject matter and is/are rejected under 35 U.S.C 101.
A note on overcoming the rejection. Applicant can overcome the rejection by integrating the invention into a practical application. This can be achieved by amending the claim to add similar language to claims 1 and 20, i.e. controlling the display of the charging device. It appears that claims 17 and 18 each contain a control step as well, pre-conditioning a battery and/or activating a user comfort device. Applicant is invited to request an interview for further explanation of their options.
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, 6, 10-11, 20-22 and 24 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) Vreeland, (US PG Pub 2023/0152108) Tremblay, (US PG Pub 2025/0091476), and Schuchter (US PG Pub 2024/0174109).
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) and
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 a 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 a 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.
The combination of Graham, Lim, and Vreeland does not teach transmit the real-time geolocation associated with the electric vehicle to a computing system associated with the optimal charging station, wherein the computing system is configured to control a display screen of a charger at the optimal charging station to be in an unilluminated state until a predefined time duration before the electric vehicle reaches the optimal charging station, as determined based on the real-time geolocation.
However, Tremblay teaches “transmit the real-time geolocation associated with 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 “as determined based on the real-time geolocation.” ([0161] teaches the “CSOC,” controlling the chargers based on received vehicle information, i.e. real-time geolocation.)
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.
The combination of Graham, Lim, Vreeland, and Tremblay does not wherein the computing system is configured to control a display screen of a charger at the optimal charging station to be in an unilluminated state until a predefined time duration before the electric vehicle reaches the optimal charging station.
However, Schuchter teaches “wherein the computing system is configured to control a display screen of a charger at the optimal charging station to be in an unilluminated state until a predefined time duration before the electric vehicle reaches the optimal charging station.” ([0074] teaches that the system can determine an action to carry out on the basis that a vehicle is nearing it on a route. This action includes, “changing a brightness of a lighting device 11 or of the display device 7, modifying the type and/or frequency of a content displayed on a display device 7,” This control is in conjunction with a reservation system outlined in [0084]. The system can keep the screen dimmed until the approach of the vehicle is detected, [0050].)
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 Trembley, witch Schuchter; and have a reasonable expectation of success. All relate to the control systems of electronic vehicles. As Schuchter teaches in [0043], the reservations of a charging station along a route allows for the user to know that there is one for them at a particular stop. By transmitting this signal and controlling the display an errant outside user will not accidentally block a reserved charger. ‘
Regarding claim 6, 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 computing system associated with the optimal charging station is further configured to control operating conditions of one or more charges at the optimal charging station based on the real-time geolocation associated with the electric vehicle.
However, Trembley teaches “wherein the computing system associated with the optimal charging station is further configured to control operating conditions of one or more charges at the optimal charging station based on the real-time geolocation associated with the electric vehicle..” ([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.
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 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 and transmit the real-time geolocation associated with the electric vehicle to a computing system associated with the optimal charging station, wherein the computing system is configured to control a display screen of a charger at the optimal charging station to be in an unilluminated state until a predefined time duration before the electric vehicle reaches the optimal charging station, as determined based on the real-time geolocation.
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 and transmit the real-time geolocation associated with the electric vehicle to a computing system associated with the optimal charging station, wherein the computing system is configured to control a display screen of a charger at the optimal charging station to be in an unilluminated state until a predefined time duration before the electric vehicle reaches the optimal charging station, as determined based on the real-time geolocation.
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.
The combination of Graham, Lim, and Vreeland does not teach transmit the real-time geolocation associated with the electric vehicle to a computing system associated with the optimal charging station, wherein the computing system is configured to control a display screen of a charger at the optimal charging station to be in an unilluminated state until a predefined time duration before the electric vehicle reaches the optimal charging station, as determined based on the real-time geolocation.
However, Tremblay teaches “transmit the real-time geolocation associated with 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 “as determined based on the real-time geolocation.” ([0161] teaches the “CSOC,” controlling the chargers based on received vehicle information, i.e. real-time geolocation.)
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.
The combination of Graham, Lim, Vreeland, and Tremblay does not wherein the computing system is configured to control a display screen of a charger at the optimal charging station to be in an unilluminated state until a predefined time duration before the electric vehicle reaches the optimal charging station.
However, Schuchter teaches “wherein the computing system is configured to control a display screen of a charger at the optimal charging station to be in an unilluminated state until a predefined time duration before the electric vehicle reaches the optimal charging station.” ([0074] teaches that the system can determine an action to carry out on the basis that a vehicle is nearing it on a route. This action includes, “changing a brightness of a lighting device 11 or of the display device 7, modifying the type and/or frequency of a content displayed on a display device 7,” This control is in conjunction with a reservation system outlined in [0084]. The system can keep the screen dimmed until the approach of the vehicle is detected, [0050].)
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 Trembley, witch Schuchter; and have a reasonable expectation of success. All relate to the control systems of electronic vehicles. As Schuchter teaches in [0043], the reservations of a charging station along a route allows for the user to know that there is one for them at a particular stop. By transmitting this signal and controlling the display an errant outside user will not accidentally block a reserved charger.
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.
Claim(s) 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graham, Lim, Vreeland, Tremblay, and Schuchter 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, Tremblay, and Schuchter 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, Tremblay, and Schuchter 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, Tremblay, and Schuchter teaches the road trip planning system of claim 7.
The combination of Graham, Lim, Vreeland, Tremblay, and Schuchter 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, Vreeland, Tremblay, and Schuchter 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 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graham, Lim, Vreeland, Tremblay, and Schuchter in view of Fang (CN-114646134).
Regarding claim 12, the combination of Graham, Lim, Vreeland, Tremblay, and Schuchter teaches the road trip planning system of claim 1.
The combination of Graham, Lim, Vreeland, Tremblay, and Schuchter 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, Lim, Vreeland, Tremblay, and Schuchter 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, Vreeland, Tremblay, and Schuchter in view of Majima (US PG Pub 2020/0132494).
Regarding claim 13, the combination of Graham, Lim, Vreeland, Tremblay, and Schuchter teaches the road trip planning system of claim 1.
The combination of Graham, Lim, Vreeland, Tremblay, and Schuchter 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, Vreeland, Tremblay, and Schuchter 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, Vreeland, Tremblay, and Schuchter teaches the road trip planning system of claim 13.
The combination of Graham, Lim, Vreeland, Tremblay, and Schuchter 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, Vreeland, Tremblay, and Schuchter 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 Newman (US PG Pub 2018/0188332).
Regarding claim 23, the combination of Graham, Lim, Vreeland, Tremblay, and Schuchter teaches the road trip planning system of claim 1.
The combination of Graham, Lim, Vreeland, Tremblay, and Schuchter 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, Newman 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.” ([0032], [0046], [0067], and [0110], the system monitors the health of the battery and uses that information in conjunction with its charge data to determine the optimal charging information, including timing and amount)
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, Tremblay, and Schuchter with Newman; and have a reasonable expectation of success. All relate to vehicle charging and control systems. As Newman teaches in [0003], the charging of an EV is a process that people want to be fast and safe. By ensuring that the system is aware of the health state of the battery, the system can ensure that the battery is charged the correct amount. The charger will not charge the battery too much or too fast, this ensures a safer operating environment.
Claim(s) 15, 17, and 25-26 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) Vreeland, and Rajabally (US PG Pub 2020/0369175).
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) and
determining, by the processor, optimal charging time durations and ([0005], [0037], and at least [0040],teach the system determining an optimal time to recharge the vehicle for a given length of time)
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 and determining, by the processor, optimal discharging time durations, wherein the optimal discharging time durations correspond to time durations when the electric vehicle transfers energy to a grid via the trip destination location.
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 and optimal discharging time durations, wherein the optimal discharging time durations correspond to time durations when the electric vehicle transfers energy to a grid via the trip destination location.
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.
The combination of Graham, Lim, and Vreeland does not teach optimal discharging time durations, wherein the optimal discharging time durations correspond to time durations when the electric vehicle transfers energy to a grid via the trip destination location.
However, Rajabally teaches “optimal discharging time durations…wherein the optimal discharging time durations correspond to time durations when the electric vehicle transfers energy to a grid via the trip destination location.” ([0037]-[0040] teach the system forecasting what the vehicle’s expected charge and discharge capability is for a given stop. This capability would be analogous to a duration of said action. This discharge can be used to transfer charge power to the grid at the stop of 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 Rajabally; and have a reasonable expectation of success. All relate to systems that route vehicles and are concerned with the charging of their batteries. As Rajabally teaches in [0003]-[0005] the transfer of power from a vehicle to the grid allows for the vehicle to provide power as needed to the grid. This would allow the charging station to request additional power from vehicles in order to provide power for other systems. As seen in [0007] forecasting this information allows for the charging bays to be filled effectively.
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 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) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Graham, Lim, Vreeland, and Rajabally in view of Fang (CN-114646134).
Regarding claim 18, the combination of Graham, Lim, Vreeland, and Rajabally teaches the road trip planning method of claim 15.
The combination of Graham, Lim, Vreeland, and Rajabally 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, Lim, Vreeland, and Rajabally 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.
Conclusion
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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