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 .
Claim Objections
Claims 1, 11, 12, 15—17, 19 and 21 are objected to because of the following informalities: …optimisation should be amended to optimization…
Appropriate correction is required.
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, 3-8, 10-17, 19-21, 30 and 40 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Daniel (US 2021/0221247) .
Regarding claim 1, Daniel teaches a system for directing energy use within a facility comprising one or more charge stations for charging a fleet of electric vehicles (see para 0204-0205, claim 8; Figs. 5 and 6), the system comprising: an energy optimisation engine configured to receive as input: energy supply data representative of energy supply characteristics over a first designated time period (see local measurements para 0204-0205, claim 8; Figs. 5 and 6); facility operation energy data representative of operational energy consumption characteristics of one or more energy assets associated with the facility over said first designated time period (see local measurements of energy supply energy use by the building or vehicle.; and claim 7); and vehicle operation data representative of electric vehicle operational requirements for one or more of the electric vehicles during a second designated time period (see predicting forward use and charging patterns of electric vehicles at remote sites, and predicting local network performance; claim 8); one or more digital data processors accessible to said energy optimisation engine and configured to compute, for said first designated time period and satisfying a designated energy management condition corresponding at least in part with said energy supply characteristics (see orming an aggregate model of EV use and network performance across a local network using such measurements and forward predictions, and comparing the aggregate model of EV use and network performance to identify a potential issue on a local network where predicted use would exceed the local constraint; claim 8), energy distribution instructions comprising: charging instructions for the one or more charge stations satisfying said vehicle operational requirements (see decision logic to evaluate and schedule a real time adjustment to an EV charging plan to throttle charge rates avoid exceeding the local constraint; communicating the adjusted charging plan to the remote EVs; claim 8); and corresponding facility operation instructions for said energy assets (see forming an aggregate model of EV use and network performance across a local network using such measurements and forward predictions, and comparing the aggregate model of EV use and network performance to identify a potential issue on a local network where predicted use would exceed the local constraint; claim 8); wherein, for said first designated time period, said energy optimisation engine is configured to digitally direct operation of one or more of the charge stations or said energy assets in accordance with said energy distribution instructions (see decision logic to evaluate and schedule a real time adjustment to an EV charging plan to throttle charge rates avoid exceeding the local constraint; communicating the adjusted charging plan to the remote EVs; claim 8).
Regarding claim 3, Daniel teaches wherein said energy assets comprise one or more of an energy consuming device in the facility, an electrical load, or a utility (see claim 7).
Regarding claim 4, Daniel teaches wherein said charging instructions comprise one or more of a charge station activation, a charge station deactivation, a variable rate charge instruction, autonomous vehicle movement instructions, or manual vehicle movement instructions (see Claim 15).
Regarding claim 5, Daniel teaches wherein said energy supply data comprises at least one of demand response data, an energy cost, a time-of-use penalty, an amount of energy, a demand penalty, or a predicted energy consumption (0132-0142).
Regarding claim 6, Daniel teaches wherein said facility operation data comprises at least one of a historical energy consumption, a current energy consumption, or a predicated energy consumption by at least one of said energy assets (see claim 7).
Regarding claim 7, Daniel teacheswherein said vehicle operation data comprises, for at least one of the electric vehicles, at least one of a battery capacity, a battery charge level, a rate of battery charge, a rate of battery discharge, a battery age, a battery temperature, a historical battery discharge rate, a distance to recharge, an expected vehicle weight, or data related to an expected vehicle route, a driving schedule, a driving distance, or driver (see claim 8).
Regarding claim 8, Daniel teaches, wherein said energy distribution instructions comprise instructions to at least one of prioritize charging, discharging, or reschedule charging of at least one of the electric vehicles (see para 0133-0142).
Regarding claim 10, Daniel teaches wherein said energy distribution instructions comprise instructions to discharge at least one battery of the electric vehicles into one or more of a different battery, an energy grid, the facility, or one or more of said energy assets (see claim 3).
Regarding claim 11, Daniel teaches wherein said energy optimisation engine is configured to receive as input electric vehicle charge network data representative of a vehicle charging location that is external to the facility, and wherein said energy optimisation engine is operable to compute said energy distribution instructions in view of said electric vehicle charge network data (see para 0127).
Regarding claim 12, Daniel teaches wherein said energy optimisation engine is operable to compute said energy distribution instructions in accordance with an artificial intelligence process (see para 0127).
Regarding claim 13, Daniel teaches wherein said one or more digital data processors are further configured to automatically compute delivery management recommendations based at least in part on said vehicle operational requirements and said energy distribution instructions (see para 0191-0199).
Regarding claim 14, Daniel teaches wherein said one or more digital data processors are further operable to automatically compute vehicle charge requirements based at least in part on said vehicle operational requirements (see claim 8).
Regarding claim 15, Daniel teaches wherein said energy optimisation engine is configured to receive as input charging authorization for authorizing said operation of the charge stations (see para 0133-0141).
Regarding claim 16, Daniel teaches wherein said electric vehicle charge optimisation engine is configured to receive as input asset direction authorization for authorizing said operation of the energy assets in the facility (see para 0133-0141).
Regarding claim 17, Daniel teaches wherein said energy optimisation engine is configured to digitally direct operation of said one or more of the charge stations or said energy assets via a graphical user interface (GUI) displaying to a user data related to said energy distribution instructions (see para 0133-0138 and 0158-0166); and
wherein said GUI is configured to receive as input execution data related to said energy distribution instructions to thereby implement at least a portion of said energy distribution instructions (see para 0133-0138 and 0158-0166).
18. (canceled)
Regarding claim 19, Daniel teaches wherein said energy optimisation engine is configured to access one or more of a local server or a cloud-based server to receive as input therefrom one or more of said energy supply data, said facility operation data, said vehicle operation data, said first or said second time period, or said designated energy management condition (see para 0014 and 0015).
Regarding claim 20, Daniel teaches further comprising one or more of said local server or said cloud-based server (see para 0014-0015).
Regarding claim 21, Daniel teaches a method for directing energy use within a facility comprising one or more charging stations for charging a fleet of electric vehicles, the method digitally executed by an energy optimisation engine via one or more digital data processors and comprising: receiving as input: energy supply data representative of energy supply characteristics over a first designated time period; facility operation energy data representative of operational energy consumption characteristics of one or more energy assets associated with the facility over said first designated time period; and vehicle operation data representative of electric vehicle operational requirements for one or more of the electric vehicles during a second designated time period; computing, for said first designated time period and satisfying a designated energy management condition corresponding at least in part with said energy supply characteristics, energy distribution instructions comprising: charging instructions for the one or more charge stations satisfying said vehicle operational requirements; and corresponding facility operation instructions for said energy assets; and for said first designated time period, digitally directing operation of one or more of the charge stations or said energy assets in accordance with said energy distribution instructions (please see the rejection of claim 1).
Regarding claim 30, Daniel teaches, wherein said energy distribution instructions comprise instructions to discharge at least one battery of the electric vehicles into one or more of another battery, an energy grid, the facility, another vehicle, or one or more of said energy assets (see claim 3).
Regarding claim 41, Daniel teaches a system for directing energy use for a facility associated with one or more charge stations for charging an electric vehicles, the system comprising: a communications interface interfacing a digital processing resource with an external energy management interface; one or more digital data processors configured to: receive as input, in association with a designated time period: energy supply data representative of energy supply characteristics; facility operation energy data representative of energy usage requirements by at least one energy asset associated with the facility; and vehicle operation data representative of electric vehicle operational requirements; compute for said designated time period a designated energy distribution regime at least in part based on said vehicle operation data, said facility operation energy data, and said energy supply data; via said communications interface, digitally direct operation of the one or more charge stations and said energy asset at least in part in accordance with said energy distribution regime (Please see the rejection of claim 1).
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Daniel.
Regar4ding
Regarding claim 2, Daniel teaches the system of claim 1.
Yet does not disclose wherein said first and second designated time periods correspond to at least partially overlapping time periods.
Nevertheless, The claim would have been obvious because a particular known technique of designating when to assign time periods in is system is recognized as part of the ordinary capabilities of one skilled in the art.
Conclusion
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/ELIM ORTIZ/ Primary Examiner, Art Unit 2836
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