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, see Remarks, filed 6/23/2026, with respect to the rejection(s) of the claim(s) 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 Therefore, the rejection has been withdrawn.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 Sun WO 2021/146096.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sun WO 2021/146096 (hereinafter “Sun”).
Regarding claims 1, 9 and 17, Sun discloses a system, comprising: a computer-readable storage medium having executable instructions; and one or more computer processors configured to execute the instructions to provide control in relation to a power system having a plurality of electric vehicles (EVs), wherein the EVs are chargeable with power from the power system (e.g. ¶39, 41, 46, 159; Fig. 3 and 5), the instructions to: receive power system information, the power system information including power demand prediction information relating to predicted demand for power in the power system (e.g. predicted energy need), the power demand prediction information covering a target time period (e.g. charging session) (e.g. ¶11, 51, 108, 143 and 148); receive charging curtailment prediction information relating to the EVs, the charging curtailment prediction information relating to a predicted flexibility in charging EVs (e.g. prioritized/non-prioritized EVSEs/vehicles) while meeting charging goals of the EVs, the charging curtailment prediction information covering the target time period (e.g. ¶25-27 and 141-143); generate power system control information for controlling the power system based on the power system information and the charging curtailment prediction information, the power system control information including EV charging scheduling information (e.g. charge schedule) for use in charging the EVs, the EV charging scheduling information utilizing the predicted flexibility in charging EVs by scheduling charging of EVs to curtail (e.g. reduce load or postponing charging of loads) or to increase an aggregate charging load (e.g. increased power output of prioritized EVSEs via throttling) of the EVs during the target time period (e.g. ¶19-20, 22, 25-27, 31, 35, 51, 10, 108-109 and 143); and control the power system based on the power system control information, including providing the EV charging scheduling information to one or more computing devices for providing charging control to at least some of the EVs (e.g. ¶19-20, 22, 25-27, 31, 35, 51, 10, 108-109 and 143).
Regarding claims 2, 10 and 18, Sun discloses the system according to claim 1, wherein the controlling the power system based on the power system control information comprises performing at least one of: power supply-demand balancing in the power system, and power peak shaving or peak shifting in the power system (e.g. ¶19-20, 22, 25-27, 31, 35, 51, 10, 108-109 and 143), and wherein the controlling the power system is based on the EV charging scheduling information (e.g. ¶19-20, 22, 25-27, 31, 35, 51, 10, 108-109 and 143).
Regarding claims 3, 11 and 19, Sun discloses the system according to claim 1, wherein the target time period includes a predicted upcoming period of higher power demand (e.g. low availability determined by availability model) in the power system (e.g. ¶58, 134-136 and 141-143), and wherein the EV charging scheduling information includes scheduling of charging of EVs outside of the target time period (via postponing charging) to curtail the aggregate charging load of the EVs during the target time period (e.g. ¶19-20, 22, 25-27, 31, 35, 51, 10, 108-109 and 143).
Regarding claims 4, 12 and 20, Sun discloses the system according to claim 1, wherein the charging curtailment prediction information comprises information relating to at least one of: a prediction of an aggregate number of EVs that will be available during the target time period to have their charging curtailed (e.g. ¶19-20, 22, 25-27, 31, 35, 51, 10, 108-109 and 143); and a prediction of an aggregate amount of EV charging load that will be available during the target time period to be curtailed (e.g. ¶19-20, 22, 25-27, 31, 35, 51, 10, 108-109 and 143).
Regarding claims 5 and 13, Sun discloses the system according to claim, the instructions further to: generate the charging curtailment prediction information based on at least one of: historical information comprising at least one of: an aggregate number of EVs that were available during a time period to have their charging curtailed (e.g. ¶19-20, 22, 25-27, 31, 35, 51, 10, 108-109 and 143); an aggregate amount of EV charging load that was available during a time period to be curtailed (e.g. ¶108); EV charging goal information of EVs comprising at least one of: target charging completion date and time information for a given EV, and target EV battery state of charge (SoC) information; and EV charging and use information comprising EV departure date and time information for a given EV, and EV battery state of charge (SoC) information at the time of the EV departure, and at least one of weather information and traffic information (e.g. ¶21, 23 and 51).
Regarding claims 6 and 14, Sun discloses the system according to claim 1, wherein the charging goals of the EVs comprises at least one of: target charging completion date and time information, and target EV battery state of charge (SoC) information (e.g. fully charged) at target charging completion (e.g. ¶27, 36, 97, 103 and 153).
Regarding claims 7 and 15, Sun discloses the system according to claim 1, wherein the power system control information comprises separate control information for each of at least two subsets (e.g. EVSEs) of the power system, and wherein the controlling the power system comprises separately controlling each of the at least two subsets of the power system (e.g. ¶19-20, 22, 25-27, 31, 35, 51, 10, 108-109 and 143).
Regarding claims 8 and 16, Sun discloses the system according to claim 1, wherein at least one of: the scheduling charging of at least some of the EVs outside of the target time period comprises scheduling no charging of the at least some of the EVs during a predicted upcoming period of higher power demand (e.g. ¶19-20, 22, 25-27, 31, 35, 51, 10, 108-109 and 143); and the EV charging scheduling information utilizes the predicted flexibility in charging EVs by scheduling charging of individual EVs of at least some of the EVs during the target time period at a curtailed charging rate that is lower than a charging rate (e.g. cheaper price) that is available to a respective individual EV during the target time period (e.g. ¶108).
Relevant Prior Art
Coleman, Jr et al. U.S. PGPub 2016/0380440 discloses a system, comprising: a computer-readable storage medium having executable instructions; and one or more computer processors configured to execute the instructions to provide control in relation to a power system having a plurality of electric vehicles (EVs), wherein the EVs are chargeable with power from the power system, the instructions to: receive power system information, the power system information including power demand prediction information relating to predicted demand for power in the power system, the power demand prediction information covering a target time period (e.g. abstract; ¶50, 55-59, 76-83 and 91-94; Fig. 1 and 9); receive charging curtailment prediction information relating to the EVs, the charging curtailment prediction information relating to a predicted flexibility in charging EVs while meeting charging goals of the EVs, the charging curtailment prediction information covering the target time period (e.g. abstract; ¶50, 55-59, 76-83 and 91-94; Fig. 1 and 9); generate power system control information for controlling the power system based on the power system information and the charging curtailment prediction information, the power system control information including EV charging scheduling information for use in charging the EVs, the EV charging scheduling information utilizing the predicted flexibility in charging EVs by scheduling charging of EVs to curtail or to increase an aggregate charging load of the EVs during the target time period (e.g. abstract; ¶50, 55-59, 76-83 and 91-94; Fig. 1 and 9); and control the power system based on the power system control information, including providing the EV charging scheduling information to one or more computing devices for providing charging control to at least some of the EVs (e.g. abstract; ¶50, 55-59, 76-83 and 91-94; Fig. 1 and 9).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES R KASENGE whose telephone number is (571)272-3743. The examiner can normally be reached Monday - Friday 7:30am to 4pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CK
September 1, 2026
/CHARLES R KASENGE/Primary Examiner, Art Unit 2116
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