DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after 2013 March 16, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2023 December 25 was filed after the mailing date of the 2023 September 10. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: A title that states the inventive concept of this/these particular CHARGING ALLOCATION DEVICES, METHODS, AND SYSTEMS OF CHARGING STATIONS, which distinguishes it from other CHARGING ALLOCATION DEVICES, METHODS, AND SYSTEMS OF CHARGING STATIONS.
Claim Objections
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim(s) 6, 9 is/are objected to under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim(s) 6, 9 recite(s) the limitation " the one set of charging processes". There is insufficient antecedent basis for this limitation in the claim.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 – 5, 10, 12 – 16, 24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by KIM (US 2022/0111751 A1).
In re claims 1, 12, 24, KIM discloses a charging allocation device of a charging station (FIG. 1A; ¶0032: CVP EV system), comprising a first sensor (battery system 124), a second sensor (performance meter 106, telemetry unit 108), a third sensor (¶¶0010, 0046, 0070: EMS-EV interceptor), a charging module (PCS 126), and a processor (ESS controller 116), wherein
the processor is in communication with the first sensor, the second sensor, the third sensor, and the charging module (¶0034: ESS controller 116 is in communication with BMS 124, telemetry unit 108, EMS-EV, and PCS 126);
the processor is configured to:
determine an electrical storage feature based on first sensing information (¶0050: “… prepares the given amount of power for discharging, considering CVP capacity”), wherein the first sensing information is collected by the first sensor based on a battery of the charging station, and the electrical storage feature at least includes a storage capacity of the battery (¶0063: “Charging that considers the current state of charge (SOC)... together lowers the load on the battery”);
determine an electrical supply feature (¶¶0034–0035: grid rate information) based on second sensing information, wherein the second sensing information is collected by the second sensor based on an electrical grid (¶0034: performance meter 106 data relayed through telemetry unit 108 to ISO/utility authorized channel 110), and the electrical supply feature at least includes an electrical supply cost of the electrical grid (¶¶0033, 0063: Peak Hours, real-time utility rate (RT-RATE));
determine a vehicle demand feature based on third sensing information, wherein the third sensing information is collected by the third sensor based on a charging vehicle, and the vehicle demand feature at least includes a charging demand power of the charging vehicle (¶¶0049, 0071);
determine a charging allocation strategy (¶0060: Adaptive Operation Profile (AOP)) of the charging station during a preset future time period based on the electrical storage feature, the electrical supply feature, the vehicle demand feature, and a charging target (¶¶0041, 0058–0063: AOP determines a charging and discharging schedule for a future time window t to t + w, based on SOC, RT-RATE, and intercepted EV demand, to prevent grid power consumption spikes during Peak Hours); and
control, based on the charging allocation strategy, at least one of the electrical grid (¶0035: ESS charged from grid during off-peak and then dispatches to EV/building) or the battery (¶¶0053, 0075: CVP Bank discharging to charge EV) to charge, through the charging module, a target object according to at least one charging power within the preset future time period (FIG. 2; ¶0041: adaptive power delivery within AOP window).
As to Claim 12, KIM further teaches a method for charging allocation of a charging station, wherein the method is implemented on a computing device having at least one storage device and at least one processor (¶0085: EMS implemented on a computing platform with processor and memory, performing charging allocation across EVSE ports).
As to Claim 24, KIM further teaches a non-transitory computer-readable storage medium, comprising a set of instructions, wherein when executed by a computing device, the set of instructions causes the computing device to perform a method for charging allocation of a charging station (¶¶0086–0087: EMS firmware/software constitutes stored instructions that, when executed by the EMS processor, perform the charging allocation method).
In re claims 2 – 3, 13 – 14, KIM discloses wherein to determine the charging allocation strategy of the charging station during the preset future time period based on the electrical storage feature, the electrical supply feature, the vehicle demand feature, and the charging target, the processor is further configured to (FIG. 2; ¶¶0058–0064: ESS controller evaluates SOC, RT-RATE, EV demand inputs to determine how power is routed):
determine a first allocation strategy (SOC-gated BESS dispatch logic) when the charging target is a first target (BESS-dispatch optimization condition), the charging allocation strategy including the first allocation strategy (AOP evaluates SOC and RT-RATE to determine which dispatch mode applies), the first allocation strategy is determined by (FIG. 1B, FIG. 2; ¶¶0050–0053, 0058–0064):
in response to determining that an output power of the battery is greater than the charging demand power, controlling the battery to charge the charging vehicle (¶¶0050–0053: BESS supplies EV load when SOC is sufficient), and in response to determining that the storage capacity is less than a preset value, at least controlling the electrical grid to charge the battery (¶¶0035, 0063: BESS charged from grid during off-peak hours when SOC falls below threshold); and
in response to determining that the output power of the battery is less than the charging demand power, controlling the electrical grid and the battery to charge the charging vehicle (¶¶0035, 0050: grid supplements BESS to cover EV demand when CVP capacity insufficient), and in response to determining that the storage capacity is less than the preset value, at least controlling the electrical grid to charge the battery (¶¶0035, 0063).
As to Claims 3, 14, KIM further teaches determining a second allocation strategy (interval-division dispatch logic) when the charging target is a second target (Utility Tariff-based cost-minimization objective), the charging allocation strategy including the second allocation strategy (FIG. 2; ¶¶0033, 0041, 0058), the second allocation strategy is determined by:
dividing the preset future time period into at least two charging intervals based on the electrical supply feature (RT-RATE / Utility Tariff), wherein the at least two charging intervals at least include a low-cost charging interval (off-peak hours) and a high-cost charging interval (Peak Hours).
In re claims 4 – 5, 15 – 16, KIM discloses wherein the electrical supply feature further includes a future electrical supply cost feature (Peak Hours), the low-cost charging interval includes at least one first sub-interval (¶0059: “…(AERS™) use 1 second interval data analysis to determine exact amount of dispatching energy”), and the processor is further configured to:
determine the at least one first sub-interval based on at least one of the storage capacity and the future electrical supply cost feature (¶0063: each sub-interval determined by SOC and RT-RATE), wherein each first sub-interval of the at least one first sub-interval corresponds to one set of charging processes (¶0059: each 1-second window), respectively.
As to Claim 5, 16, KIM further teaches the high-cost charging interval includes at least one second sub-interval (¶0059: “…(AERS™) use 1 second interval data analysis to determine exact amount of dispatching energy”), the processor is further configured to:
determine the at least one second sub-interval based on at least one of the storage capacity and the future electrical supply cost feature (¶0063: each sub-interval determined by SOC and RT-RATE), wherein each second sub-interval of the at least one second sub-interval corresponds to one set of charging processes (¶0059: each 1-second window), respectively.
In re claim 10, KIM discloses wherein the processor is further configured to:
determine a predicted charging demand power during the preset future time period (¶0060: “… predict power consumption/power required for charging for the next
t
+
w
by reading the power usage in real time at the current time
t
”);
determine a target completion degree and an estimated total cost corresponding to each candidate charging target based on the predicted charging demand power, wherein the target completion degree refers to a degree of coverage of the charging allocation strategy corresponding to a certain candidate charging target to the candidate charging target (¶¶0050, 0063: degree to which CVP capacity covers predicted EV demand; RT-RATE cost per dispatch mode); and
determine the charging target based on the target completion degree and the estimated total cost (¶¶0050, 0063: AOP dispatch mode selection jointly weighs coverage capacity and RT-RATE cost).
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 non-obviousness.
Claim(s) 6 – 9, 17 – 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over KIM (US 2022/0111751 A1), and further in view of MCGRATH (US 2017/0190256 A1).
In re claim 6, 9, 17, KIM discloses wherein the one set of charging processes and second allocation strategy (per Claim 17) at least includes a corresponding power supplementary condition, the power supplementary condition at least includes a first and second (per Claim 9) preset capacity threshold of the battery (¶0050, ¶0063: SOC threshold for grid-to-battery supplementation).
KIM is silent to the processor is further configured to: determine a first and second evaluation value corresponding to at least one candidate capacity threshold based on a target completion degree and an estimated total cost corresponding to the at least one candidate capacity threshold, wherein the at least one candidate capacity threshold is determined based on historical operating data of the charging station; and determine the first and second preset capacity threshold based on the first evaluation value.
MCGRATH discloses the processor is further configured to:
determine a first and second evaluation value (FIG. 3A: minimum energy EMIN) corresponding to at least one candidate capacity threshold based on a target completion degree (¶0036: route completion ability) and an estimated total cost corresponding to the at least one candidate capacity threshold (¶¶0037–0038: demand billing rate exposure), wherein the at least one candidate capacity threshold is determined based on historical operating data of the charging station (¶0032: EMAX determined from historic energy consumption data); and
determine the first and second preset capacity threshold based on the first and second evaluation value (FIG. 3A; ¶¶0036–0038: EMIN determined from evaluation value).
It would have been obvious for a person having ordinary skill in the art (PHOSITA) to incorporate MCGRATH 's EMAX / EMIN capacity threshold determination methodology into KIM's CVP-EV system to provide a principled, data-driven method for selecting the SOC threshold governing grid supplementation during each charging process.
In re claim 7 – 8, 18 – 19, KIM does not expressly disclose the processor is further configured to: determine a third allocation strategy in response to determining that the charging target is a third target, the charging allocation strategy including the third allocation strategy, the third allocation strategy is determined by: dividing the preset future time period into at least three charging intervals based on the electrical supply feature, wherein the at least three charging intervals at least include a mid-cost charging interval (per Claims 7, 18), the mid-cost charging interval includes at least one third sub-interval, the processor is further configured to: determine the at least one third sub-interval based on at least one of the storage capacity and the future electrical supply cost feature, wherein each third sub-interval of the at least one third sub-interval corresponds to one set of charging processes, respectively (per Claims 8, 19).
MCGRATH discloses the processor is further configured to:
determine a third allocation strategy (¶0024: three-tier cost optimization logic) in response to determining that the charging target is a third target (¶0024: third rate tier), the charging allocation strategy including the third allocation strategy, the third allocation strategy is determined by:
dividing the preset future time period into at least three charging intervals based on the electrical supply feature, wherein the at least three charging intervals at least include a mid-cost charging interval (¶0024: intermediate rate tier between first and second rates);
the mid-cost charging interval includes at least one third sub-interval (¶0033: 15-minute reference windows within the mid-cost interval), the processor is further configured to:
determine the at least one third sub-interval based on at least one of the storage capacity and the future electrical supply cost feature, wherein each third sub-interval of the at least one third sub-interval corresponds to one set of charging processes, respectively (¶0033: 15-minute windows determined against tariff schedule).
It would have been obvious for a PHOSITA to incorporate MCGRATH 's three-tier electricity rate structure into KIM's Utility Tariff-based scheduling to subdivide the charging time horizon into three cost-differentiated intervals, enabling more granular dispatch decisions that avoid triggering elevated rate tiers and further reducing utility costs.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over KIM (US 2022/0111751 A1), and further in view of BRIDGES (US 2019/0061535 A1).
In re claim 11, KIM discloses wherein the processor is further configured to: determine the predicted charging demand power through a prediction model (¶0060: ESS controller predicts future charging demand via AOP).
KIM does not expressly disclose a prediction model based on historical charging demand powers of vehicles at a plurality of historical time points, historical vehicle queuing information of the vehicles at the plurality of historical time points, and location information of the current charging station, wherein the prediction model is a machine learning model.
BRIDGES discloses a prediction model based on historical charging demand powers of vehicles at a plurality of historical time points (prediction engine 704; ¶0107: historical system data used to optimize performance), historical vehicle queuing information of the vehicles at the plurality of historical time points (¶¶0081, 0180: tidal connection trends; vehicles plugged in the longest tracked by site power flow manager), and location information of the current charging station (¶¶0235–0241: unique network IDs / smart meter IDs), wherein the prediction model is a machine learning model (learning engine 706).
It would have been obvious for a PHOSITA to incorporate BRIDGES’ learning-engine-based prediction model, historical vehicle connection tracking, and station location determination into KIM's CVP-EV system to predict future charging demand using machine learning on historical charging and queuing data, enabling proactive candidate-target evaluation and optimal dispatch selection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHANN DJANAL-MANN whose telephone number is (571)272-4697. The examiner can normally be reached Monday - Friday 8:00 - 17:00.
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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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/D. JOHANN DJANAL-MANN/Examiner, Art Unit 2859
/DREW A DUNN/Supervisory Patent Examiner, Art Unit 2859
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