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 .
Drawings
The drawings were received on 06/05/2026. These drawings are acceptable.
Claims Status
Claims 1-15 are currently pending, claims 1-2, 4-5 and 13 are currently amended.
Response to Arguments
Applicant’s arguments, see remarks, filed 06/05/2026, with respect to the rejection(s) of claim(s) Claim 1 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of GALBRAITH (US 2024/0157836 A1).
Regarding the rejection of claims 1-15 are 35 U.S.C. 101, applicant’s amendments overcome the previous rejection, the rejection is hereby withdrawn.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-3, 6-8 and 12-13 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by GALBRAITH et al. (US 2024/0157836 A1, hereinafter GALBRAITH).
Regarding claim 1, GALBRAITH discloses a computer-implemented method, comprising:
performing a simulation of power generation by one or more power sources of a decentralized energy system during a simulation duration (See Fig.1, discloses a plurality of decentralized energy systems 102 and 104, each comprising its own local power generation systems 107 and 111. Fig.2 discloses charging station A [210] comprising power generation systems 216-224. And further comprising EV management system 201. Fig.4 and Par.88, disclose performing a forecast of onsite power generation 432),
obtaining a vehicle schedule defining presence time windows during the simulation duration, one or more electric vehicles being connected to one or more charging stations of the decentralized energy system during the presence time windows (See Fig.4 and Par.90, disclose a forecaster for EV charging demand, using user choices and behavior, i.e. historical usage and selected schedules),
performing an optimization of a charging strategy of the one or more charging stations based on the power generation during the simulation duration and further based on the vehicle schedule (See Fig.4, discloses an optimizer which takes as input the forecasted on-site generation of power 430, the forecasted expected energy cost 418 and the forecasted charging demand 412. Pars.76-79, 81 and Fig.4, Items# 438-442 disclose generating different types of optimizations based on the data i.e. maximizing earnings on EV charging at one EV charging station or across multiple EV charging stations, ensure grid stability using hard constraints, minimize carbon emissions from electricity generation, maximize utilization of renewable energy, and maximize generation of carbon offsets and/or renewable energy credits); and
charging or discharging the one or more electric vehicles according to the optimized charging strategy (See Fig.2 and Par.57, disclose the chargers 212 are in communication with EV charging management system 201 to implement the optimization strategy).
Regarding claim 2, GALBRAITH discloses the computer-implemented method of claim 1 as discussed above, further comprising:
when performing the simulation of the power generation:
performing an optimization of the power generation by the one or more power sources (See Par.57, discloses EV charging management system 201 controls power sources such as EV batteries to discharge to the grid), the optimization of the power generation being coupled with the optimization of the charging strategy (See Fig.4, Steps#438-442, disclose different optimization strategies. Par.69 discloses the optimization may include one vehicle being controlled to discharge to the system V2G/V2B while another is simultaneously controlled to charge).
Regarding claim 3, GALBRAITH discloses the computer-implemented method of claim 2 as discussed above, wherein the optimization of the power generation and the optimization of the charging strategy are executed interleaved for rolling time increments within the simulation duration (See Pars.99 and 133, disclose that optimization is updated by taking in new price data, therefore the process of updating the optimization strategy overlaps with the power generation control as new data causes control of the charging sources to be changed since a change in pricing would influence the decision to discharge the vehicle to grid or to building).
Regarding claims 6-7, GALBRAITH discloses the computer-implemented method of claim 1 as discussed above, wherein the optimization of the charging strategy sets charging time windows during the presence time windows, the one or more electric vehicles being charged during the charging time windows and not being charged outside of the charging time windows (See Pars.38, 100 and 130, disclose the system may determine efficient times or schedules for charging EVs controlling charging to be within certain times to manage costs, also Pars.12, 61 and 69, discloses EV management system 201 controlling charging by transmitting charging schedule to optimize costs).
Regarding claim 8, GALBRAITH discloses the computer-implemented method of claim 1 as discussed above, wherein the vehicle schedule further defines a target state of charge of the one or more electric vehicles at an end of the presence time windows (See Par.90, discloses the charging demand information taken into account to perform optimization include amount of charge needed in kilowatts. Par.57 discloses that charger 212 provides information of the current SOC to the EV charging management 201. The examiner explains that the target SOC is current SOC+ charge demand. Par.57 also discloses controlling charging starting and stopping time based on the SOC data i.e. when the vehicle is charged to the target level).
Regarding claim 12, GALBRAITH discloses the computer-implemented method of claim 1 as discussed above, wherein the vehicle schedule further defines an initial state of charge of the one or more electric vehicles at a beginning of the presence time windows (See Fig.4 and Par.90, disclose a forecaster for EV charging demand, using user choices and behavior, i.e. historical usage and selected schedules. Par.57, discloses that vehicle SOC information is transmitted from the charger 212 to EV management system 201).
Regarding claim 13, GALBRAITH discloses the computer-implemented method of claim 1 as discussed above, wherein the vehicle schedule further defines whether the one or more electric vehicles can discharge electric energy into the decentralized energy system through the one or more charging station (See Par.56, disclose in some circumstances power may flow from the vehicle to another vehicle or to a building or grid and Par.57, discloses the EV management system controls the V2G/V2B power draw. Par.69 discloses controlling a V2X capable vehicle. The examiner explains that it is implicit that the vehicle capability is shared with the charger 212 which shares it with the EV charging management system 201 such that it takes account of which vehicle can be discharged as a power source to the grid).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over GALBRAITH in view of FAIRLIE (US 2005/0165511 A1, hereinafter FAIRLIE).
Regarding claim 4, GALBRAITH discloses the computer-implemented method of claim 2 as discussed above, GALBRAITH further discloses wherein the optimization of the power generation minimizes an output value of a goal function (See Par.77, discloses optimizer 404 may comprise one or more trained optimizers, which may be configured to solve one or more optimization problems related to the EV charging system, for example by minimizing or maximizing an objective function and Par.78, discloses maximizing earnings on EVs charging and charging efficiency).
However, GALBRAITH does not disclose the goal function penalizing emission of pollutants by the one or more power sources of the decentral energy system.
FAIRLIE discloses an energy network comprising one or more power sources of a decentral energy system (See Fig.1, Items#58, 62, 66 and 70, disclose a plurality of power generation sources), wherein the optimization of the power generation minimizes an output value of a goal function (See Par.126, discloses adjusting the availability of the power station having a reduced amount of pollutants i.e. minimizing pollutants), the goal function penalizing emission of pollutants by the one or more power sources of the decentral energy system (See Par.126, discloses including an emission penalty).
GALBRAITH and FAIRLIE are analogous art since they both deal with power networks.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention disclosed by GALBRAITH with the teachings of FAIRLIE by adding a penalty for emission of pollutants to the goal function for the benefit of combating climate change by reducing greenhouse gases.
Regarding claim 5, GALBRAITH discloses the computer-implemented method of claim 2 as discussed above, GALBRAITH further discloses wherein the optimization of the power generation minimizes an output value of a goal function (See Par.77, discloses optimizer 404 may comprise one or more trained optimizers, which may be configured to solve one or more optimization problems related to the EV charging system, for example by minimizing or maximizing an objective function and Par.78 discloses minimizing cost of charging EVs).
However, GALBRAITH does not disclose the goal function penalizing electric power injected into the decentral energy system from a supplier grid.
FAIRLIE discloses an energy network comprising one or more power sources of a decentral energy system (See Fig.1, Items#58, 62, 66 and 70, disclose a plurality of power generation sources), wherein the optimization of the power generation minimizes an output value of a goal function (See Par.126, discloses adjusting the availability of the power station having a reduced amount of pollutants i.e. minimizing pollutants), the goal function penalizing electric power injected into the decentral energy system from a supplier grid. (See Par.126, discloses penalizing electric power being injected when the power supplier has higher emissions).
GALBRAITH and FAIRLIE are analogous art since they both deal with power networks.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention disclosed by GALBRAITH with the teachings of FAIRLIE by adding a penalty to the goal function for injecting electric power from the supplier grid when the power supplier generates pollutant emissions for the benefit of combating climate change by reducing greenhouse gases.
Claim(s) 9-10 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over GALBRAITH in view of KUMAR et al. (US 11,485,249 B2, hereinafter KUMAR).
Regarding claims 9-10, GALBRAITH discloses the computer-implemented method of claim 8 as discussed above, However, GALBRAITH does not disclose wherein the vehicle schedule further defines a penalty associated with not reaching the target state of charge at the end of the presence time windows.
KUMAR discloses control of electric vehicle charging infrastructure comprising a penalty associated with not reaching the target state of charge at the end of the presence time windows (See Col.5, lines 4-14, disclose input data including required SOC upon departure and an unfulfillment penalty. Regarding claim 10 is it implicit that when a penalty is set, the program aims to minimize the penalty to ensure that a vehicle has sufficient charge to reach its destination).
GALBRAITH and KUMAR are analogous art since they both deal with vehicle charging systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention disclosed by GALBRAITH with the teachings of KUMAR by adding a penalty for not reaching the target state of charge at the end of the presence time or the benefit of ensuring the vehicle has sufficient capacity to reach its destination.
Regarding claim 15, GALBRAITH discloses the computer-implemented method of claim 1 as discussed above, wherein the optimization of the charging strategy is based on a goal function (See Par.77, discloses optimizer 404 may comprise one or more trained optimizers, which may be configured to solve one or more optimization problems related to the EV charging system, for example by minimizing or maximizing an objective function and Par.78 discloses minimizing cost of charging EVs. Fig.4, 438-440, discloses optimizations with different goals).
However, GALBRAITH does not disclose wherein the goal function comprises multiple objectives, wherein relative weighting factors of the multiple objectives are set based on a user input.
GALBRAITH discloses control of electric vehicle charging infrastructure comprising an optimization based on a goal function, wherein the goal function comprises multiple objectives, wherein relative weighting factors of the multiple objectives are set based on a user input (See Col.15, line 62 to Col.16, line 5, disclose optimization comprising two objectives, weighted sum of annualized costs and end user convenience and the weights may be adjusted by the user).
GALBRAITH and KUMAR are analogous art since they both deal with vehicle charging systems.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention disclosed by GALBRAITH with the teachings of KUMAR such that the goal function comprises multiple objectives, wherein relative weighting factors of the multiple objectives are set based on a user input for the benefit of allowing the user to change the charging goal based on their priority (cost VS convenience).
Claim(s) 11 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over GALBRAITH in view of LOGHAVI et al. (US 2020/0254897 A1, hereinafter LOGHAVI).
Regarding claims 11 and 14, GALBRAITH discloses the computer-implemented method of claim 1 as discussed above, However, GALBRAITH does not disclose wherein the optimization of the charging strategy maximizes or minimizes an output value of a goal function, the goal function penalizing larger charging rates or discharging rates of the one or more charging station.
LOGHAVI discloses a system and method for charging a fleet of electric vehicles, comprising an optimization algorithm with a goal function to minimize battery deterioration, the goal function penalizing larger charging rates (See Par.60, discloses the optimization algorithm may associate a penalty for a higher charging rate).
GALBRAITH and LOGHAVI are analogous art since they both deal with battery charging.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the invention disclosed GALBRAITH with the teachings of LOGHAVI by adding a penalty to larger charging rates for the benefit of protecting the vehicle battery against deterioration.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMED H OMAR whose telephone number is (571)270-7165. The examiner can normally be reached 10:00 am -7:00 PM EST.
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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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/AHMED H OMAR/ Primary Examiner, Art Unit 2859
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