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 .
Status of Claims
This Office Action is in response to the communication filed on 27 Dec 2023.
Claims 1-10 are being considered on the merits.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 27 Dec 2023, 12 Mar 2025, and 24 Nov 2025, have been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, initialed and dated copies of Applicant's IDS form 1499 are attached to the instant Office action.
Drawings
The drawings filed on 27 Dec 2023 are accepted.
Claim Rejections - 35 USC § 103
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 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.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Youn, et. al. (US 2019/0011970 A1; hereinafter, “Youn”) in view of Bian, et. al. (US 2022/0036392 A1; hereinafter, “Bian”).
Regarding claims 1 and 6, Youn as modified by Bian teaches:
A method of generating energy storage system (ESS) control information, the method comprising: (Bian, para. 0018: “FIG. 1B shows in detail an exemplary system to manage power in buildings. The system includes models detailing the environment, the reward, and actions to be taken. The models are provided to an agent trained using a Q network. The agent retrieves data from energy consumers in a campus building, including HVAC (air conditioning), heaters, and electric vehicles, for example. In the example commercial system, there are one CB and one parking lot, which can be scaled to multiple CBs smoothly. In the CB, there are one heating, ventilation, and air-conditioning (HVAC) system, one electric water heater (EWH), one energy storage system (ESS), one solar panel, and one aggregated base power load”)
A computing device comprising: at least one processor; and a memory configured to load or store a program executed by the at least one processor, wherein the program comprises: (Bian, para. 0048: “All simulations are implemented on a desktop computer with 3.0 GHz Intel Core i5-7400 CPU and 8 GB RAM. The preferred DRL based energy management problem is simulated using Python 3.5, Gurobi 8.0 and Tensorflow 1.8.”)
training a reinforcement training model (Bian, para. 0005: “The current trend of energy management in the distribution system is based on reinforcement learning (RL)”) for reducing peak load of an ESS (Bian, para. 0039: “Moreover, in order to protect substations and transformers, the distribution system operator may perform the load curtailment in the peak hours, where the power exchange through the PCC between the CB and the main grid will be zero”) using power consumption data corresponding to a first period in the past for a building to which the ESS is applied; (Bian, para. 0051: “First, the preferred DQN is perfectly trained based on the 10,000 episodes, where the testing rewards are close to the optimum. Second, the uncertainties associate with the energy management process are highly correlated, which can be traced from one to another. Third, for a certain period, the rewards share a similar shape as shown in FIG. 4-5. This is reasonable since the scenarios are generated based on monthly data which is the same as the period of 30 episodes.” Examiner notes Bian teaches a first period as monthly data i.e. a single period comprising 30 episodes)
generating ESS control information corresponding to the first period in the past by applying, to the trained reinforcement training model, the power consumption data corresponding to the first period in the past for the building; and (Bian, para. 0053 and fig. 6: “An example of test bed can be found in FIG. 6. The test bed models the exemplary Power Grid and Sensor Network where data collected from energy management system (EMS) or phasor measurement unit (PMU) is transmitted through communication networks to the data server. The data server stores and manages the measured data and provides data pipeline to the application server. The pre-trained reinforcement learning model is running on the application server” Examiner notes Bian teaches applying power consumption data to the training model, wherein such data includes monthly and episodic data i.e. daily data)
converting the generated ESS control information corresponding to the first period in the past into ESS control information on a daily basis divided into weekdays and holidays and (Youn, para. 0063: “Next, the past data having a condition similar to that of the specific target date to be predicted is selected (S200). Since temperature-sensitive power consumption characteristics should be similar for similar weather conditions occurring around (e.g., plus or minus fifteen days) the same date of other years, the selected data may be data collected from a prior year for a day corresponding to a date near the target date. Data may be further selected based on whether the specific target date is a holiday, a weekend, a weekday, or a date falling between a holiday and another non-working day.”) applying the ESS control information on a daily basis divided into weekdays and holidays as ESS control information corresponding to a second period at present. (Youn, para. 0087 and fig. 6: “Referring to FIG. 6, to control ESS charge/discharge based on the power demand prediction of FIG. 1, the power demand prediction data for a specified time span is generated using the above-described method (S610). In this case, power demand prediction data may be generated at fifteen-minute intervals for 36 hours from the start of the power demand prediction.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Bian into Yuon. Youn teaches predicting consumer power demand uses power consumption data measured over a long term and a power usage pattern immediately before a target time and for controlling ESS charge/discharge of an ESS based on the predicted power demand; Bian teaches deep reinforcement learning based control determines optimal actions for major components in a commercial building to minimize operation costs while maximizing comprehensive comfort levels of occupants. One of ordinary skill would have been motivated to combine the teachings of Bian into Youn in order to minimizes operation and maintenance costs for the central controller of the CB and maximizes comprehensive comfort levels for the occupants simultaneously (Bian, para. 0007).
Regarding claims 2 and 7, Youn as modified by Bian teaches:
wherein a date included in the first period in the past includes the same date included in the second period at present. (Youn, para. 0016: “The preset condition may be based on a past date, and the past date may be at least one of a date falling during a time span of fifteen days before and after a date corresponding to the specific time span, and a date similar in type to a date corresponding to the specific time span, in which the type is one of a holiday, a weekend, a weekday, or a date falling between a holiday and another non-working day.” Examiner notes Youn teaches a present condition based on a past date which may be a holiday where holidays sometimes fall on the same day every year).
Regarding claims 3 and 8, Youn as modified by Bian teaches:
wherein the applying of the ESS control information on a daily basis divided into weekdays and holidays as ESS control information corresponding to a second period at present comprises: dividing the generated ESS control information corresponding to the first period in the past into weekday data and holiday data; (Youn, para. 0063: “Data may be further selected based on whether the specific target date is a holiday, a weekend, a weekday, or a date falling between a holiday and another non-working day. That is, similar or analogous conditions may be factored into the selection of data.”)
determining the ESS control information on a daily basis divided into weekdays and holidays by classifying the divided weekday data and the divided holiday data on a daily basis and by deriving an average of the weekday data and the holiday data classified on a daily basis; and (Youn, para. 0103: “The data arrangement unit 310 calculates a difference between a predicted temperature for a specific target date for which the power demand is to be predicted and a temperature at the time of collecting each data point selected by the data selector 200, and arranges the selected data in order of increasing temperature difference. Herein, all the data of one day may be arranged at a time using daily average temperature, meanwhile each data point may be arranged for each time span based on temperature in a time span when each data point is collected.” Examiner notes Youn teaches a daily average temperature for calculating data)
using the ESS control information on a daily basis divided into weekdays and holidays as the ESS control information corresponding to the second period at present. (Youn, para. 0087 and fig. 6: “Referring to FIG. 6, to control ESS charge/discharge based on the power demand prediction of FIG. 1, the power demand prediction data for a specified time span is generated using the above-described method (S610). In this case, power demand prediction data may be generated at fifteen-minute intervals for 36 hours from the start of the power demand prediction.” Examiner notes Youn teaches using the ESS control information data as illustrated in fig. 6.)
Regarding claims 4 and 9, Youn as modified by Bian teaches:
The method of claim 1, further comprising: repeating, at each update cycle, a process of determining ESS control information on daily basis corresponding to a fourth period at present using power consumption data corresponding to a third period in the past immediately after the first period in the past. (Youn, para. 0016: “The preset condition may be based on a past date, and the past date may be at least one of a date falling during a time span of fifteen days before and after a date corresponding to the specific time span, and a date similar in type to a date corresponding to the specific time span, in which the type is one of a holiday, a weekend, a weekday, or a date falling between a holiday and another non-working day.” Examiner notes Youn teaches a present condition based on a past date which may be a holiday where holidays sometimes fall on the same day every year i.e. Christmas Eve and Christmas where Christmas Eve past is period one, Christmas Eve present is period two, Christmas day past is period three and Christmas day present is period four).
Regarding claims 5 and 10, Youn as modified by Bian teaches:
The method of claim 4, wherein a date included in the third period in the past includes the same date included in the fourth period at present. (Youn, para. 0016: “The preset condition may be based on a past date, and the past date may be at least one of a date falling during a time span of fifteen days before and after a date corresponding to the specific time span, and a date similar in type to a date corresponding to the specific time span, in which the type is one of a holiday, a weekend, a weekday, or a date falling between a holiday and another non-working day.” Examiner notes Youn teaches a present condition based on a past date which may be a holiday where holidays sometimes fall on the same day every year i.e. Christmas Eve and Christmas where Christmas Eve past is period one, Christmas Eve present is period two, Christmas day past is period three and Christmas day present is period four).
Prior Art
Thokala (US 2022/0327263 A1) teaches a system of pre-processing to deal with outliers/missing values, followed by synchronization of smart meter data with other sensory data where energy-temperature correlation is calculated to estimate an energy drift using historical power consumptions.
Li, et. al. (US 2019/0312457 A1) teaches a control system for controlling an energy storage system includes a controller including a plurality of layered nodes configured to form an artificial neural network trained to generate a forecasted transmission level load and confidence value for an entire jurisdiction of a utility distribution system
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sally T. Ley whose telephone number is (571)272-3406. The examiner can normally be reached Monday - Thursday, 10:00am - 6:00pm ET.
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/STL/Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
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