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 Arguments
Applicant's arguments filed 12/15/2025 have been fully considered but they are not persuasive.
In response to arguments regarding the 101 rejection, it is submitted that the recitation “by the optimal charging station…charging the battery of the electric vehicle” of claim 1 constitutes a practical application and as such the rejection of claim 1 under 35 USC 101 is withdrawn. However, independent claim 16 does not recite said recitation, and the additional recitations as amended are not a practical application or significantly more than the abstract idea. Therefore the rejection of claim 16 under 35 USC 101 is maintained as described below.
Specification
The disclosure is objected to because of the following informalities: the amendments to the specification filed on 12/15/2025 refer to paragraph numbers of the specification, however, the specification as originally filed does not contain paragraph numbers. Therefore the locations of the amendments to the specifications are not clear. Appropriate correction is required.
Drawings
The drawings were received on 12/15/2025. These drawings are acceptable.
Claim Objections
Claims 1, 3-5, 7, 9-10, 14-16, and 18 are objected to because of the following informalities:
In claim 1, line 43, “a maximum state of charge” should be changed to --the maximum state of charge--.
In claim 1, line 51 “a vehicle-to-grid V2G, energy credit and a grid-to-vehicle G2V, energy cost” should be changed to --a vehicle-to-grid (V2G) energy credit and a grid-to-vehicle (G2V) energy cost--.
In claim 1, lines 76-77, it is not clear how “a minimum total charging response time” is related to “a total charging response time” as recited in lines 61-62. For examination purposes, the recitation is interpreted as a minimum or smallest total charging response time from among all of the total charging response times of the charging stations.
In claim 1, lines 77-78, it is not clear how “a minimum total discharging response time” is related to “a total discharging response time” as recited in lines 65-66. For examination purposes, the recitation is interpreted as a minimum or smallest total discharging response time from among all of the total discharging response times of the charging stations.
In claim 3, lines 10-11, “the lowest energy cost” should be changed to --a lowest energy cost--.
In claim 3, lines 13-14, “a lowest energy cost” should be changed to --the lowest energy cost--.
In claim 4, lines 3-4, it is not clear how the “satisfaction level” is “based on the updated state of charge”. Perhaps the claim should recite it is based on the updated energy stored in the battery after the charging operation.
In claim 4, line 4, the claim should read --total charging response time--.
In claim 5, line 5, it is not clear how the “charging satisfaction level” is “based on the updated state of charge”. Perhaps the claim should recite it is based on the updated energy stored in the battery after the charging operation.
In claim 7, line 5, “the energy discharged…by the electric vehicle” lacks antecedent basis.
In claim 9, lines 2-3, the claim should recite, e.g., --when the decrease…in the present state of charge…for each charging station--.
In claim 9, lines 4-5, “an optimal charging station” should be changed to --the optimal charging station--.
In claim 10, line 1, the claim should recite --the energy available
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--.
In claim 14, line 27, it is not clear which of the plurality of charging stations is referred to in the recitation “the charging station”.
In claim 15, line 8, “an updated energy” should be changed to --the updated energy--.
In claim 15, lines 13-17, it is not clear which of the plurality of charging stations is referred to in the recitation “the charging station”.
In claim 15, line 15, there is no antecedent basis for “the battery of the charging station”.
In claim 15, line 15, it is not clear what is referred to in “an amount of energy delivered”, as the claim lists “constraints in determining the optimal charging station”, and therefore energy would not yet have been delivered.
In claim 15, line 25, it is not clear which of the plurality of charging stations is referred to in the recitation “the charging station”.
In claim 16, lines 65-66, “a maximum state of charge” should be changed to --the maximum state of charge--.
In claim 16, lines 72-73 “a vehicle-to-grid V2G energy credit and a grid-to-vehicle G2V energy cost” should be changed to --a vehicle-to-grid (V2G) energy credit and a grid-to-vehicle (G2V) energy cost--.
In claim 16, line 91, it is not clear how “a minimum total charging response time” is related to “a total charging response time” as recited in lines 79. For examination purposes, the recitation is interpreted as a minimum or smallest total charging response time from among all of the total charging response times of the charging stations.
In claim 16, line 92, it is not clear how “a minimum total discharging response time” is related to “a total discharging response time” as recited in line 82. For examination purposes, the recitation is interpreted as a minimum or smallest total discharging response time from among all of the total discharging response times of the charging stations.
In claim 18, line 7, “searching” should be changed to --search--.
The above mentioned claim objections are not a complete and thorough listing. Applicant is required to revise all of the claims completely, and not just correct the language mentioned. Applicant is requested to verify that the specification provides proper antecedent basis for all amended claim language. Appropriate correction is required.
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 16 and 18-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without reciting additional elements that integrate the judicial exception into a practical application. Moreover, the claims do not appear to recite additional elements that amount to significantly more than the judicial exception.
Claim 16 recites a system for assigning an electric vehicle to a charging station. The recited steps for assigning the electric vehicle to the charging station are directed to mental choices or evaluations or mathematical calculations that are part of the abstract idea, and are thus considered a Judicial Exception (YES for Step 2A, prong one). The “plurality of charging stations”, “fog-and-cloud computing framework”, the steps of receiving information from the electric vehicle, the steps of receiving information from each charging station, and the step of transmitting one of a charging command and a discharging command to an optimal charging station are considered additional elements. The “plurality of charging stations” are recited at a high level of generality, and merely indicates the field of use. The “fog-and-cloud computing framework” are merely generic computer components (e.g., recited at a high level of generality); and the steps of receiving/transmitting information can be considered receiving or transmitting data over a network, and appear to be insignificant extra-solution activity and well‐understood, routine, and conventional functions because they are claimed in a merely generic manner (e.g., at a high level of generality). The recited steps do no more than automate the processes that a user can perform to determine and communicate control instructions. Thus, the claim as a whole, including the additional elements, does not integrate the recited judicial exception into a practical application (NO for Step 2A, prong two).
Finally, the additional elements are not sufficient to amount to significantly more than the judicial exception because the “plurality of charging stations” are recited at a high level of generality and merely indicates the field of use, “fog-and-cloud computing framework” are generic computer components, and the steps of receiving/transmitting are recited at a high level of generality, and are well-known, routine, and conventional computer functions (NO for Step 2B).
Claims 18-19 do not appear to make the claims eligible for reasons similar to those noted above and are therefore also rejected under 35 USC 101.
Allowable Subject Matter
Claims 1-12, 14-16, and 18-19 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 and the claim “objections” set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 1:
RAJMOHAN (US PG Pub 2022/0089056; previously cited) discloses a method for assigning an electric vehicle to a charging station (¶ 0012: dynamically identify an optimal charging station such that the user's vehicle is charged within a certain radius of the user; ¶ 0013: comprehensive optimization of user requirements to locate an optimal charging station….to select and subsequently navigate to the selected optimal charging station), comprising:
arranging a plurality of charging stations distributed among a plurality of locations (¶ 0021: charge optimization program 110 connects one or more charging stations (e.g., solar, traditional, mobile, etc.) to a central system).
receiving, by [a computing device] (108, Fig. 1) (¶ 0016: server computer 108 can represent a server computing system utilizing multiple computer as a server system, such as in a cloud computing environment), a request from an electric vehicle EVi for a charging station assignment at a time Ti, where i=1, 2, . . . , I (¶ 0045: charge optimization program 110 receives a request to charge from one or more electric vehicles needing charge; ¶ 0048: charge optimization program 110 routes the received request to the dynamically determined, optimal charging station by interfacing with an existing navigation service to transmit instructions to the requesting vehicle);
receiving, by the [computing device], a position of the electric vehicle EVi (¶ 0052: Charge optimization program 110 calculates the edge to be the distance between the vehicle (e.g., node) to respective charging stations (e.g., another node) and selects nodes that can fulfill (e.g., meet) user requirements and is along the navigation route (e.g., via graph matching). The distance is between node (vehicle) to node (charging station). The actual distance can be leveraged using GPS systems) and a route of the electric vehicle EVi (¶ 0021: charge optimization program 110 includes nodes on the left side that represent vehicles connected to the central system node, with each vehicle node having a set of attributes associated with the vehicle and user such as vehicle trip information, type of trip (e.g., leisure, business, etc.), progress of trip, current route information (e.g., navigation information));
receiving, by the [computing device], a present state of charge
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, a threshold state of charge
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and a maximum state of charge
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of a battery of the electric vehicle EVi (¶ 0021: with each vehicle node having a set of attributes associated with the vehicle and user such … battery charge level of the vehicle; ¶ 0028: user information (e.g., components of user device, vehicle components, user requirements (e.g., charging need, available time, charging capacity of the vehicle…);
identifying, by the [computing device], among the plurality of charging stations, a number S of charging stations CSs, for s=1, 2, . . . , S, along the route of the electric vehicle EVi (¶ 0022: Charge optimization program 110 calculates the edge to be the distance between the vehicle to the charging stations that can fulfill (e.g., meet) user requirements and is along the navigation route);
calculating, by the [computing device], a travelling distance di→s of the electric vehicle EVi to each charging station CSs (¶ 0052: Charge optimization program 110 calculates the edge to be the distance between the vehicle (e.g., node) to respective charging stations (e.g., another node) and selects nodes that can fulfill (e.g., meet) user requirements and is along the navigation route (e.g., via graph matching). The distance is between node (vehicle) to node (charging station). The actual distance can be leveraged using GPS systems; ¶ 0055: distance from the user vehicle to a charging station);
calculating, by the [computing device], a travelling time
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for the electric vehicle EVi to travel from the position to each charging station CSs (¶ 0023: determine the optimal charging station for individual electric vehicles while optimizing the travel time; ¶ 0056: Charge optimization program 110 can then determine the optimal charging station for individual electric vehicles by dynamically and iteratively calculating confidence values to optimize the travel time);
receiving, by the [computing device], a grid-to-vehicle G2V, energy cost from each charging station CSs; (¶ 0022: each charging station node including attributes associated with respective charging locations such as total charging capacity of station, current charge available at the station, price of charging per unit; ¶ 0025: charge optimization program 110 can configure dynamic pricing of charging stations (e.g., price increase or incentives depends on demand/supply));
receiving, by the [computing device], from each charging station CSs, a service charging time
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to charge the battery of the electric vehicle EVi (¶ 0023: determine the optimal charging station for individual electric vehicles while optimizing the…. charging time; ¶ 0056: determine the optimal charging station for individual electric vehicles by dynamically and iteratively calculating confidence values to optimize the… charging time);
receiving, by the [computing device], an energy available
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at each charging station CSs (¶ 0013: allocate and route vehicles to one or more charging stations (e.g., existing charging stations, solar charging stations, mobile charging stations, etc.) based on capacity (e.g., available capacity to charge a vehicle); ¶ 0022: each charging station node including attributes associated with respective charging locations such as total charging capacity of station, current charge available at the station; ¶ 0055: constraints of the each respective charging station (e.g., ability to charge, number of charging stalls, at the charging station, available power, etc.));
determining, by the [computing device], an optimal charging station, CSopt, based on the G2V energy cost of each charging station CSs, the amount of energy available
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at each charging station CSs, and a maximum amount of energy to be delivered to the battery of the electric vehicle EVi (¶ 0041: Charging recommendation module 226 dynamically determines optimal charging locations that satisfy user requirements; ¶ 0046: Information can also include attribute information for respective vehicles and charging stations. As mentioned before attribute information can include…. battery charge level of the vehicle, …. price constraints, etc. while examples of charging station information (e.g., attributes) include total charging capacity of station, current charge available at the station, price of charging per unit; ¶ 0047: charge optimization program 110 dynamically determines one or more optimal charging stations).
assigning the optimal charging station CSopt to the electric vehicle EVi (¶ 0020: charge optimization program 110 to dynamically determine an optimal charging station and subsequently route the user to the determined optimal charging station);
transmitting, by the [computing device], a route to the optimal charging station CSopt to the electric vehicle EVi (¶ 0012: generating routes to an optimal charge station based on a user requirements; ¶ 0048: charge optimization program 110 navigates or routes the requesting vehicle to the dynamically determined, optimal charging station); and
calculating, by the [computing device], an updated energy
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stored in the battery of the electric vehicle EVi (¶ 0037: Vehicle attribute monitor 210 works with vehicle communication module 212 to send attribute information to charge optimization program 110. Vehicle attribute monitor 210 monitors changes in attribute information…., battery charge level of the vehicle).
DHAWAN (US Patent 11,616,259; previously cited) discloses a fog-and-cloud computing framework including a cloud-based computing platform and a plurality of fog servers (col 8, ll. 23-25: the cloud-computing platform executes on information processed by at least one of an edge network and a fog network; col 23, ll. 33-35: the smart battery management platform executes on information processed by a fog network; col 6, l. 67 – col 7, l. 1: a blockchain network that includes a plurality of charging station nodes; col 7, ll. 13-16: the process is configured for managing the rechargeable energy storage battery system via one of a battery charging station and a battery service station; col 8, ll. 50-51: the process further comprises locating a nearest charging station; col 23, ll. 27-28: smart battery management platform 120 that executes in the cloud); and
arranging a software-define networking (SDN) controller in the cloud-based computing platform (col 15, 1. 65 - col 16, 1. 4: The chain of network interactions, communications, events, etc., may be stored in libraries/repositories of the Software Defined Networking (SDN). APIs may be derived based on the characteristics of each of the interactions, communications, events, etc., being mapped to characteristics of APIs (also stored in libraries/repositories of the SDN architecture)).
DOW (US PG PUB 2023/0408273; previously cited) discloses receiving, by the [computing device], a vehicle-to-grid V2G, energy credit (¶ 0024: selecting of the optimal charging station may include determining an available charging/discharging time period and determining a charging station leading to an optimal fee based on a charging/discharging fee rate of each of the identified charging stations of the determined available charging/discharging time period; ¶ 0029: deposit or withdrawal of a fee to or from a user account has been completed for the discharging or the charging performed in the corresponding time period);
receiving, by the [computing device], from each charging station CSs a service discharging time
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to discharge the battery of the electric vehicle EVi (¶ 0024: selecting of the optimal charging station may include determining an available charging/discharging time period; ¶ 0027: determining of the charging/discharging schedule may include determining a discharging time period leading to a maximum fee based on the charging/discharging fee rate policy of the selected charging station; ¶ 0124: perform the discharging scheduling by dynamically determining a discharging time period leading to a maximum discharging fee based on the fee rate policy information corresponding to the corresponding area code); and
determining, by the [computing device], an optimal charging station, CSopt, based on the V2G energy credit of each charging station CSs and a maximum amount of energy to be delivered to each charging station CSs (¶ 0024, 0027, 0029, 0124: see above).
IWAMURA (US PG Pub 2015/0298565; previously cited) discloses determining, by the [computing device], a decrease in the present charge of the battery of the electric vehicle EVi, based on the travelling distance di→s to each charging station CSs (¶ 0238-0239);
receiving, by the [computing device], from each charging station CSs, a wait time
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to access a charger (¶ 0038: control center uses a unit for scheduling the use of the charging stations, to shorten the total time required for the charging (the sum of the charge waiting time and the time required for the charging); ¶ 0206: control center 1 determines the charging station as the guidance destination for each EV as described above, and thus generates a guidance schedule covering all the guidance target EVs. The control center 1 selects from a plurality of guidance schedules, the guidance schedule in which the operation rate of the charging stations is high and the waiting time before the charging starts is shortest as a whole; ¶ 0257: third rule is that the waiting time of each EV is minimized);
calculating, by the [computing device], a total charging response time
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for the battery of the electric vehicle EVi to charge at each charging station CSs, based on the travelling time
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, the service charging time
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, and the wait time
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(¶ 0038: control center uses a unit for scheduling the use of the charging stations, to shorten the total time required for the charging (the sum of the charge waiting time and the time required for the charging). The control center detects a charging station with which the shortest total time required for the charging can be achieved, based on use and reservation states of the charging stations; ¶ 0234: charging station data selection unit 154 calculates an evaluation value S for setting the priority on the basis of a preference parameter of the EV user, stored in the preference DB 157, through the following linear Formula (6). S=A1×T1 + A2×T2 + A3×T3 (6) (A1 + A2 + A3 = 1)); ¶ 0235: T1 represents a charging time. T2 represents a time required for moving from the current location to the charging station; ¶ 0236: charging station data selection unit 154 sequentially selects data on the charging station starting from that with the smallest evaluation value S); and
determining, by the [computing device], an optimal charging station, CSopt, based on a minimum total charging response time
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at each charging station CSs (¶ 0038, 0234-0236: see above).
SUN (US PG Pub 2021/0213848; previously cited) discloses receiving, by the [computing device], a notice from the electric vehicle EVi that it has arrived at the optimal charging station CSopt (¶ 0018: determining a check in at the EVSE for the reserved charging session; ¶ 0070: determining a check in can include generating, by a charger management system, a code (e.g., access codes, authentication codes, etc.), wherein the code includes a data sequence uniquely identifying the electric vehicle; causing the electric vehicle to communicate the code to the EVSE upon plugin of an EVSE into an electric vehicle; and wherein the check in is successful upon receiving, by the EVSE, the code as generated; ¶ 0093: Determining one or more check ins is preferably performed by a reservation system (e.g., a centralized reservation system; a reservation system receiving check in indications from user devices, electric vehicles);
then, transmitting, by the [computing device], one of a charging command and a discharging command to the optimal charging station CSopt; by the optimal charging station CSopt, charging the battery of the electric vehicle EVi to a maximum state of charge
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or discharging the battery of the EVi to a threshold state of charge
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(¶ 0018: causing the EVSE to charge the first electric vehicle during the scheduled time period based on the integration with the EVSE (e.g., in response to determining the check in of the first electric vehicle at the EVSE; ¶ 0098: initiation of the charging session can include charging the vehicle according to a set of power output parameters until at least one of the following occurs: a battery of the vehicle is fully charged); ¶ 0104: Controlling (e.g., remotely controlling; etc.) one or more EVSEs can include issuing one or more of: start charging commands (e.g., a “remoteStart” API call through OCPI, where the API call can result in a control signal being forwarded by the network to one or more EVSEs; for the EVSE to begin charging of an electric vehicle; such as in response to determining a check in of a user for a reserved charging session associated with the EVSE; etc.); stop charging commands (e.g., a “remoteStop” API call through OCPI, where the API call can result in a control signal being forwarded by the network to one or more EVSEs; for the EVSE to stop charging of an electric vehicle; such as in response to a current time reaching a reservation end time; such as in response to a fully charged state of the electric vehicle).
The prior art fails to disclose the combination of:
“determining, by the cloud-based computing platform, a decrease
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in the present state of charge
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of the battery of the electric vehicle EVi, based on the travelling distance di→s to each charging station CSs;
calculating, by the cloud-based computing platform, an updated state of charge
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of the battery of the electric vehicle by subtracting the decrease in the present state of charge
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from the present state of charge
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;
calculating, by the cloud-based computing platform, an amount of energy required
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to charge the battery of the electric vehicle EVi at each charging station CSs based on a difference between a maximum state of charge
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of the EVi and the updated state of charge
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and multiplying the difference by an energy rating
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of the battery of the electric vehicle EVi;
when the updated state of charge
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is greater than the threshold state of charge
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, calculating an amount of available energy
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to discharge from the battery to the charging station CSs by multiplying a difference between the updated state of charge
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and the threshold state of charge
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by the energy rating
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”;
“calculating, by the cloud-based computing platform, a total discharging response time
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for the battery of the electric vehicle EVi to discharge at each charging station CSs, based on the travelling time
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, the service discharging time
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, and the wait time
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”; and
“determining, by the cloud-based computing platform, an optimal charging station, CSopt, based at least on one of the amount of energy required
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to charge the battery of the electric vehicle EVi at each charging station CSs and the amount of energy available
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to discharge the battery of the electric vehicle EVi; further based on…. a minimum total discharging response time
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at each charging station CSs”.
It would not have been obvious to modify the prior art to arrive at the claimed invention.
Claims 2-12 & 14-15 are dependent from claim 1 and are therefore allowable for the same reasons as independent claim 1.
Regarding claim 16, the prior art teaches a system for assigning an electric vehicle to a charging station (see rejection of claim 16 in the office action with mail date 7/1/2025, and also the relevant portions of the prior art references in the reasons for the indication of allowable subject matter of claim 1 above) but fails to disclose the combination of:
“determine a decrease
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in the present state of charge
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of the battery of the electric vehicle EVi, based on the travelling distance di→s to each charging station CSs;
calculate an updated state of charge
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of the battery of the electric vehicle by subtracting the decrease in the present state of charge
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from the present state of charge
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;
calculate an amount of energy required
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to charge the battery of the electric vehicle EVi at each charging station CSs based on a difference between a maximum state of charge
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of the EVi and the updated state of charge
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and multiplying the difference by an energy rating
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of the battery of the electric vehicle EVi;
when the updated state of charge
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is greater than the threshold state of charge
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, calculate an amount of available energy
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to discharge from the battery to the charging station CSs by multiplying a difference between the updated state of charge
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and the threshold state of charge
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by the energy rating
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”;
“calculate a total discharging response time
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for the battery of the electric vehicle EVi to discharge at each charging station CSs, based on the travelling time
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→
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, the service discharging time
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and the wait time
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”;
“determine the optimal charging station CSopt based at least on one of the amount of energy required
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to charge the battery of the electric vehicle EVi at each charging station CSs and the amount of energy available
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to discharge battery of the electric vehicle EVi; further based on …. a minimum total discharging response time
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at each charging station CSs”; and
“calculate a satisfaction level of the EVi after one of charging and discharging based on the updated state of charge
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and the total charging response time
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at the optimal charging station CSopt”.
It would not have been obvious to modify the prior art to arrive at the claimed invention.
Claims 18-19 are dependent from claim 16 and are therefore allowable for the same reasons as independent claim 16.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANUEL HERNANDEZ whose telephone number is (571)270-7916. The examiner can normally be reached Monday-Friday 9a-5p ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Drew Dunn can be reached at (571) 272-2312. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manuel Hernandez/Examiner, Art Unit 2859 3/28/2026
/TAELOR KIM/Supervisory Patent Examiner, Art Unit 2859