DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/8/2026 has been entered.
Response to Amendment
Claims 1, 3, 5-9, 11-16, 18, and 20 remain pending in the application along with added new claims 21-25, and claims 2, 4, 10, 17, and 19 have been canceled. Applicant’s amendments to the Claims have overcome every and claim objection and 102 rejection previously set forth in the Final Office Action mailed 10/17/25.
Response to Arguments
Applicant's arguments filed 1/8/2026 have been fully considered but they are not persuasive.
Applicant submits on page 9 of Remarks that Cella does not disclose all recited elements of claim 1, particularly “a first model to simulate one or more operating characteristics of the charging site” and “a second model to simulate charging of one or more batteries,” where the actions (of the simulated charging environment) include “one or more simulated charging sessions of the one or more batteries based on one or more combinations of the one or more operating characteristics.”
The examiner submits that Cella describes a first model in the form a second neural network (¶[612]) which is used to process vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities. A first neural network describes the second model in that it is used to process inputs relating to charge or fuel states of the plurality of vehicles. Parameters include routing, battery state, charge value, and charge demand (¶[388]). These neural networks 4520 and 4522 operate in conjunction and through updates with the charging plan 4512 (Fig. 45) which is based on charging sessions.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 5-9, 11-16, 18, and 20-25are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cella (US 20200192350 A1).
Regarding independent claim 1, Cella teaches a vehicle charging system (Fig. 45) comprising:
one or more processors and a memory (¶0806: system includes computer readable storage media executed on processors) storing computer-executable instructions that, when executed, cause the one or more processors to:
aggregate available first data associated with states of multiple vehicles (¶0018: classified states of a vehicle) and available second data associated with a charging site and associated with charging the multiple vehicles at the charging site (¶0201: “operational state and energy consumption information from at least one of a plurality of network-enabled vehicles is gathered in real time” is associated with recharging stations via the vehicle transportation system which includes vehicle information) (¶0203: recharging infrastructure comprises recharging stations);
execute a pre-trained learning model to apply a charging policy to the available data to charge the multiple vehicles at the charging site based at least on the available first data and the available second data, the pre-trained learning model configured to take actions and to observe effects of the actions in a simulated charging environment to obtain the charging policy (¶[612]: neural networks serve as models from which a charging plan is developed), wherein the simulated charging environment includes (i) a first model to simulate one or more operating characteristics of the charging site (¶[612]: “a second neural network 4520 of the hybrid neural network is used to process inputs relating to charging or refueling infrastructure and the like. The second neural network 4520 may process vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities within the target energy renewal region to determine at least one parameter 4514 of a charge infrastructure operational plan 4512 that facilitates access by the at least one of the plurality vehicles to renewal energy in the target energy renewal region 4516”) and (ii) a second model to simulate charging of one or more batteries of the multiple vehicles based on the one or more operating characteristics (¶[612]: “A first neural network 4522 of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles (directly received from the vehicles or through the vehicle information port 4532) … the first neural network 4522 may process inputs comprising vehicle route and stored energy state information for a plurality of vehicles to predict for at least one of the plurality of vehicles a target energy renewal region.”), and wherein the actions taken include one or more simulated charging sessions of the one or more batteries based on one or more combinations of the one or more operating characteristics (¶[612]); and
provide, via a charger of the vehicle charging system, power to charge the multiple vehicles in accordance with the charging policy (¶[203]: “the artificial intelligence system provides a recharging plan that accommodates near-term charging needs for the plurality of rechargeable vehicles based on the optimized at least one parameter”).
Regarding claim 3, Cella teaches the vehicle charging system of claim 1, including a simulated charging environment (¶0203: artificial intelligence system) modeled to include at least one power source chosen from an Alternating Current (AC) station and a Direct Current (DC) power cabinet, wherein the at least one power source is modeled as a set of output channels and a set of dispensers, and wherein each output channel of the set of output channels supplies one or more dispensers of the set of dispensers (models including DC and AC outlets arranged in parallel or in series fall within the scope of the recharging stations of Cella as is common in the layouts of Electric Vehicle Service Equipment).
Regarding claim 5, Cella teaches the vehicle charging system of claim 1, wherein the available first data includes at least one data type chosen from energy rate data, carbon emissions data, and renewable energy source data, and wherein the simulated charging environment includes a charging infrastructure modeled to include at least one object chosen from an object modeling energy rates, an object modeling carbon emissions, and an object modeling renewable energy sources (¶0203: the artificial intelligence system optimizes energy parameters ; ¶0293: parameters include reduced carbon footprint).
Regarding claim 6, Cella teaches the vehicle charging system of claim 1, wherein the simulated charging environment includes the multiple vehicles modeled to include at least one object chosen from an arrival time of a respective vehicle of the multiple vehicles, a state of charge of a battery of the respective vehicle, a charge curve for the battery of the respective vehicle, details of the battery of the respective vehicle, a departure time of the respective vehicle, or a minimum required charge of the respective vehicle (¶0204: artificial system optimizes electricity usage, recharge time, duration of time for recharging, value of charging, and battery charge state ; ¶0293: rider experience parameter includes desired arrival times).
Regarding claim 7, Cella teaches the vehicle charging system of claim 1, wherein the pre-trained learning model includes a Policy Gradient Algorithm (PGA) configured to train a neural network based on at least one set of data chosen from simulated data and a cache of data collected from one or more charging sites (¶0391: parameters for charging are integrated with a system for robotic process automation (RPA). “Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision …”).
Regarding independent claim 8, Cella teaches a method for vehicle charging comprising:
aggregating, by one or more processors coupled with memory, available first data associated with states of multiple vehicles (¶0018: classified states of a vehicle) and available second data associated with a charging site and associated with charging the multiple vehicles at the charging site (¶0201: “operational state and energy consumption information from at least one of a plurality of network-enabled vehicles is gathered in real time” is associated with recharging stations via the vehicle transportation system which includes vehicle information) (¶0203: recharging infrastructure comprises recharging stations);
executing, by the one or more processors (¶0806: system includes computer readable storage media executed on processors), a pre-trained learning model to apply a charging policy to charge the multiple vehicles at the charging site based at least on the available first data and available second data, the pre-trained learning model configured to take actions and to observe effects of the actions in a simulated charging environment to obtain the charging policy (¶[612]: neural networks serve as models from which a charging plan is developed), wherein the simulated charging environment includes (i) a first model to simulate one or more operating characteristics of the charging site (¶[612]: “a second neural network 4520 of the hybrid neural network is used to process inputs relating to charging or refueling infrastructure and the like. The second neural network 4520 may process vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities within the target energy renewal region to determine at least one parameter 4514 of a charge infrastructure operational plan 4512 that facilitates access by the at least one of the plurality vehicles to renewal energy in the target energy renewal region 4516”) and (ii) a second model to simulate charging of one or more batteries of the multiple vehicles based on the one or more operating characteristics (¶[612]: “A first neural network 4522 of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles (directly received from the vehicles or through the vehicle information port 4532) … the first neural network 4522 may process inputs comprising vehicle route and stored energy state information for a plurality of vehicles to predict for at least one of the plurality of vehicles a target energy renewal region.”), and wherein the actions taken include one or more simulated charging sessions of the one or more batteries based on one or more combinations of the one or more operating characteristics (¶[612]); and
providing, by the one or more processors, via a charger, power to charge the multiple vehicles in accordance with the charging policy (¶[203]: “the artificial intelligence system provides a recharging plan that accommodates near-term charging needs for the plurality of rechargeable vehicles based on the optimized at least one parameter”).
Regarding claim 9, Cella teaches the method of claim 8, wherein the simulated charging environment (¶0203: artificial intelligence system) includes a charging infrastructure modeled to include at least one power source chosen from an Alternating Current (AC) station and a Direct Current (DC) power cabinet, wherein the at least one power source is modeled as a set of output channels and a set of dispensers, and wherein each output channel of the set of output channels supplies one or more dispensers of the set of dispensers (models including DC and AC outlets arranged in parallel or in series fall within the scope of the recharging stations of Cella as is common in the layouts of Electric Vehicle Service Equipment).
Regarding claim 11, Cella teaches the method of claim 9, wherein the AC power station is modeled as a single object that includes the set of output channels and the set of dispensers, and wherein a number of the set of output channels is equal to a number of the set of dispensers (each AC power station modeled as a single object including a set of dispensers equal to the number of the set of output channels falls within the scope of Cella).
Regarding claim 12, Cella teaches the method of claim 8, wherein the available first data includes at least one data type chosen from energy rate data, carbon emissions data, and renewable energy source data, and wherein the simulated charging environment includes a charging infrastructure modeled to include at least one object chosen from an object modeling energy rates, an object modeling carbon emissions, and an object modeling renewable energy sources (¶0203: the artificial intelligence system optimizes energy parameters ; ¶0293: parameters include reduced carbon footprint).
Regarding claim 13, Cella teaches the method of claim 8, wherein the simulated charging environment includes the multiple vehicles modeled to include at least one object chosen from an arrival time of a respective vehicle of the multiple vehicles, a state of charge of a battery of the respective vehicle, a charge curve for the battery of the respective vehicle, details of the battery of the respective vehicle, a departure time of the respective vehicle, or a minimum required charge of the respective vehicle (¶0204: artificial system optimizes electricity usage, recharge time, duration of time for recharging, value of charging, and battery charge state ; ¶0293: rider experience parameter includes desired arrival times).
Regarding claim 14, Cella teaches the method of claim 8, wherein the pre-trained learning model includes a Policy Gradient Algorithm (PGA) configured to train a neural network based on at least one set of data chosen from simulated data and a cache of data collected from one or more charging sites (¶0391: parameters for charging are integrated with a system for robotic process automation (RPA). “Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision …”).
Regarding independent claim 15, Cella teaches a method for vehicle charging using reinforcement learning comprising:
training, by one or more processors coupled with memory (¶0806: system includes computer readable storage media executed on processors), a learning model (Fig. 45 and ¶[203]: artificial intelligence system) to (i) obtain a charging policy (recharging plan) (ii) take actions (applying feedback) and (iii) observe effects of the actions taken in a simulated charging environment using at least one set of data chosen from simulated data and a cache of data collected for charging multiple vehicles at one or more charging sites (¶[391): “artificial intelligence/machine learning system may be trained on a training set of data”);
aggregating, by the one or more processors, available first data associated with states of the multiple vehicles (¶0018: classified states of a vehicle) and available second data associated with a charging site and associated with charging the multiple vehicles at the charging site (¶0201: “operational state and energy consumption information from at least one of a plurality of network-enabled vehicles is gathered in real time” ” is associated with recharging stations via the vehicle transportation system which includes vehicle information) (¶0203: recharging infrastructure comprises recharging stations);
executing, by the one or more processors, the learning model to apply the charging policy to the available data to charge the multiple vehicles at the charging site based at least on the available first data and the available second data and within the simulated charging environment (¶[612]: neural networks serve as models from which a charging plan is developed), wherein the simulated charging environment includes (i) a first model to simulate one or more operating characteristics of the charging site (¶[612]: “a second neural network 4520 of the hybrid neural network is used to process inputs relating to charging or refueling infrastructure and the like. The second neural network 4520 may process vehicle energy renewal infrastructure usage and demand information for vehicle energy renewal infrastructure facilities within the target energy renewal region to determine at least one parameter 4514 of a charge infrastructure operational plan 4512 that facilitates access by the at least one of the plurality vehicles to renewal energy in the target energy renewal region 4516”) and (ii) a second model to simulate charging of one or more batteries of the multiple vehicles based on the one or more operating characteristics (¶[612]: “A first neural network 4522 of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles (directly received from the vehicles or through the vehicle information port 4532) … the first neural network 4522 may process inputs comprising vehicle route and stored energy state information for a plurality of vehicles to predict for at least one of the plurality of vehicles a target energy renewal region.”), and wherein the actions taken include one or more simulated charging sessions of the one or more batteries based on one or more combinations of the one or more operating characteristics (¶[612]); and
providing, by the one or more processors, via a charger, power to charge the multiple vehicles in accordance with the charging policy (¶[203]: “the artificial intelligence system provides a recharging plan that accommodates near-term charging needs for the plurality of rechargeable vehicles based on the optimized at least one parameter”).
Regarding claim 16, Cella teaches the method of claim 15, wherein the learning model includes a Policy Gradient Algorithm (PGA), the PGA including a neural network that includes a set of parameters that define the charging policy, the set of parameters being updated based on trajectories obtained by the actions taken and the effects observed s given a reward function and an objective function (¶0391: parameters for charging are integrated with a system for robotic process automation (RPA). “Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision …”).
Regarding claim 18, Cella teaches the method of claim 15, wherein the simulated charging environment (¶0203: artificial intelligence system) includes a charging infrastructure modeled to include at least one power source chosen from an Alternating Current (AC) station and a Direct Current (DC) power cabinet, wherein the at least one power source is modeled as a set of output channels and a set of dispensers, and wherein each output channel of the set of output channels supplies one or more dispensers of the set of dispensers (models including DC and AC outlets arranged in parallel or in series fall within the scope of the recharging stations of Cella as is common in the layouts of Electric Vehicle Service Equipment).
Regarding claim 20, Cella teaches the method of claim 18, wherein the AC power station is modeled as a single object that includes the set of output channels and the set of dispensers, and wherein a number of the set of output channels is equal to a number of the set of dispensers (each AC power station modeled as a single object including a set of dispensers equal to the number of the set of output channels falls within the scope of Cella).
Regarding claim 21, Cella teaches the vehicle charging system of claim 1, wherein the one or more simulated charging sessions include simulating multiple adjustments to capacity and load distributions across multiple dispensers of the charging site (Fig. 45 and ¶[612, 624, 388]: the second neural network 4620 is used to process inputs relating to charging or refueling infrastructure and the like. Infrastructure control parameters include routing, battery state, charge value, and charge demand ).
Regarding claim 22, Cella teaches the vehicle charging system of claim 1, wherein simulating the charging of the one or more batteries includes simulating a sequential charging of the one or more batteries (Fig. 45 and ¶[810-811, 612]: “The acts performed as part of the method may be ordered in any suitable way.” “A first neural network 4522 of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles”).
Regarding claim 23, Cella teaches the method of claim 8, wherein the one or more simulated charging sessions include simulating multiple adjustments to capacity and load distributions across multiple dispensers of the charging site (Fig. 45 and ¶[612, 624, 388]: the second neural network 4620 is used to process inputs relating to charging or refueling infrastructure and the like. Infrastructure control parameters include routing, battery state, charge value, and charge demand ).
Regarding claim 24, Cella teaches the method of claim 8, wherein simulating the charging of the one or more batteries includes simulating a sequential charging of the one or more batteries (Fig. 45 and ¶[810-811, 612]: “The acts performed as part of the method may be ordered in any suitable way.” “A first neural network 4522 of the hybrid neural network may be used to process inputs relating to charge or fuel states of the plurality of vehicles”).
Regarding claim 25, Cella teaches the method of claim 15, wherein the one or more simulated charging sessions include simulating multiple adjustments to capacity and load distributions across multiple dispensers of the charging site (Fig. 45 and ¶[612, 624, 388]: the second neural network 4620 is used to process inputs relating to charging or refueling infrastructure and the like. Infrastructure control parameters include routing, battery state, charge value, and charge demand ).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Von Novak et al. (US 20150102775 A1, published 2015-04-16) teaches a vehicle charging system comprising a processor (Fig. 2 and ¶0022: controller 208) and employing a charging policy (¶0034-0035).
Chang et al. (US 20140210408 A1, published 2014-07-31) teaches EVSEs of different makes and models connected in a series.
Mishra (“L1 and L2 EV Charger Electric Vehicle Service Equipment Design Considerations,” Texas Instruments, Revised June 2021) teaches power configurations for electric vehicle service equipment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ryu-Sung Peter Weinmann whose telephone number is (703)756-5964. The examiner can normally be reached Monday-Friday 9am-5pm ET.
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/Ryu-Sung P. Weinmann/Examiner, Art Unit 2859 March 19, 2026
/JULIAN D HUFFMAN/Supervisory Patent Examiner, Art Unit 2859