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 .
Status of Claims
Due to communications filed 9/29/25, the following is a first action non-final office action. Claims 1-5 are pending in this application and are rejected as follows.
Claim Rejections - 35 USC §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title,
Claims 1-5 are rejected under 35 U.S.C, 101 because the claimed invention is directed to a judicial exception (l.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In addition, the claims 1-5 recite a judicial exception. Under step 2A, Prong One, the claims recite the abstract idea of managing energy resources by analyzing power demand, power generation, electricity pricing, weather information, and consumer usage information to forecast power conditions, determine electricity prices, schedule energy storage operations, and generate consumer-specific demand response schedules. These activities are directed to managing energy supply and consumption using information analysis and decision making processes, which are based on mathematical relationships, forecasting models, and data analysis, and falls into the “Mathematical Calculations” grouping, and organizing activity among energy producers, consumers and resources, which falls into the “certain methods of organizing human activity” category. Thus, the claims recite an abstract idea.
With regard to Step 2A, Prong Two, the claims do not integrate the judicial exception into a practical application because the additional elements, including the virtual power plant system, trading management unit, power management unit, scheduling unit, user terminal, external server, and energy storage system merely perform generic computer functions, such as receiving data, analyzing data, generating information and transmitting schedules, without improving the functioning of a computer or another technology, a particular technological implementation, or an improvement to the operation of a power grid, energy storage system or other technology. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea.
Finally, with respect to Step 2B, the claims do not recite an inventive concept. The additional elements, considered individually and as an ordered combination amount to not more than well-understood, routine and conventional computer activities and therefore do not provide an inventive concept sufficient to transform the judicial exception into patent-eligible subject matter. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Dependent claims 2-5 are also directed to same grouping of “Mathematical Calculations” and “Certain Methods of Organizing Human Activity”. The additional elements of the system of claims 2-5, trading management unit and power exchange of claim 2; notification unit of claim 3; and the scheduling unit/ artificial intelligence unit of claim 5 are additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
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.
Claim(s) 1, 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marhoefer (US 9300141 B2), and further in view of MALDONATO et al (DE 102024126512 A1), and further in view of CHOI (US 20130211614 A1).
As per claim 1, Marhoefer discloses:
A virtual power plant system, (Abstract: Methods and systems provided for creating a scalable building block for a virtual power plant, where individual buildings can incorporate on-site renewable energy assets and energy storage and optimize the acquisition, storage and consumption of energy in accordance with a value hierarchy. Each building block can be aggregated into a virtual power plant);
that analyzes in real time at least one of power demand data, power generation amount data, electricity price data, and distributed power information, ((14) Fourth, information technology can aggregate the collective production and storage capability into a network that forms the equivalent of a virtual power plant. As will be discussed below, this ability to aggregate creates significant value and opportunity within the power grid in the form of ancillary services, arbitrage and peak power supply; (18) In addition to the storage device 104, in which allocation of stored electricity for various purposes occurs dynamically, a static stored electricity amount, such as a pure electric vehicle (PEV) or plug-in hybrid vehicle (PHEV) (collectively, xEV) 115 can also be accommodated in the value hierarchy. For example, if the price of gasoline is relatively high when compared to the price of electricity, then instructions from the thin-client device 106, as determined in the cloud 107, can prioritize charging the xEV's battery before charging the storage device. Alternatively, if the price of gasoline is relatively low when compared to various components of the stored energy value hierarchy, then the cloud 107 would determine that the storage device 104 should be charged before the xEV 115 to the extent that the value hierarchy warrants; (21) (c) reduction or elimination of the capacity charge inherent in real-time pricing schemes; (26) The computer-based algorithms used to optimize a customer's cost savings can also be employed to calculate the optimal amount of renewable energy and storage capacity that should be installed within a building or network of buildings to achieve these savings. In one variation, these algorithms can be run for different configurations of solar and battery capacity using model and/or actual data for load, PV production and real-time rates.);
the virtual power plant system comprising:
a trading management unit that calculates power demand and supply on the basis of the electricity price data, the power generation amount data received from a power plant, and weather data received from an external server, ((13) Weather predictions can provide data related to ambient hourly temperature, which is a significant factor in a building's consumption, as well as the hourly availability of solar radiation or wind. A building's hourly electric consumption can be predicted with significant accuracy based on historical factors, weather (e.g., combined heat and humidity), and both demographic and physical considerations that form a “load profile” (e.g., square footage, weekday/weekend/holiday, home office use). With these factors included in the optimization calculus, a building that incorporates renewable energy, grid power and storage can engage in electricity arbitrage and peak power reductions. Power can be acquired and stored when it is cheap, free or even at time when the utility will pay the building owner to take power off the grid. The stored power can then be used later when the real-time price of electricity is high.);
forecasts an appropriate electricity price on the basis of the calculation result, ((24) In one variation, arbitrage is accomplished in a multi-stage optimization process that employs dynamic “smart” individual and aggregate constraints in each stage. Constraints employed in each optimization stage factor in predicted values and relationships among market electricity rates, availability of renewable energy sources, building load, battery characteristics and lifecycle, and other factors);
in which the power generation amount data includes reliable power generation amount data received from a reliable power plant and green power generation amount data received from a green power plant, ((24) In one variation, arbitrage is accomplished in a multi-stage optimization process that employs dynamic “smart” individual and aggregate constraints in each stage. Constraints employed in each optimization stage factor in predicted values and relationships among market electricity rates, availability of renewable energy sources, building load, battery characteristics and lifecycle, and other factors);
a scheduling unit
that an analyzes time-based power consumption pattern and a preference on the basis of power demand data and power consumption data received from a consumer terminal,
(13) Weather predictions can provide data related to ambient hourly temperature, which is a significant factor in a building's consumption, as well as the hourly availability of solar radiation or wind. A building's hourly electric consumption can be predicted with significant accuracy based on historical factors, weather (e.g., combined heat and humidity), and both demographic and physical considerations that form a “load profile” (e.g., square footage, weekday/weekend/holiday, home office use). With these factors included in the optimization calculus, a building that incorporates renewable energy, grid power and storage can engage in electricity arbitrage and peak power reductions. Power can be acquired and stored when it is cheap, free or even at time when the utility will pay the building owner to take power off the grid. The stored power can then be used later when the real-time price of electricity is high; (25) To recover part of the inherent cost difference between real-time rate and flat rate pricing plans, utilities assess a demand or capacity charge, which is essentially a penalty for using electricity during the highest demand hours of the month or year. Utilities typically calculate this charge in one of two ways: (1) the customer's consumption during the highest 5-10 demand hours of the year, compared to a baseline, or (2) the highest interval (15 minute, 30 minute, or one hour) power usage (measured in kilowatts) during each billing cycle.
generates demand forecast data,
(29) FIG. 3 illustrates one variation of how the charge optimization profile for arbitrage is set up for the first hour's calculations. The example of FIG. 3 is based on, a spring day in which solar PV output will exceed building load for a number of hours. Net consumption 301 for each hour is the predicted load of the building minus predicted generation of solar PV. For hours in which PV generation exceeds building load, this value is negative. The predicted total load for the 24 hour period 302 is the sum of the net hourly load. Rates 303 are the published real-time energy cost rate for hour 1 and the day ahead rate for hours 2-24.
Marhoefer does not disclose the following limitations, however, MALDONATO et al discloses:
generates a smart contract, (MALDONATO et al: “A unique solution that investigates the economic viability of bidirectional energy trading for electric vehicles (EVs) at public charging stations in the European energy market. By combining various use cases, such as EV battery leasing for demand response (DR) flexibility contracts, frequency containment reserves (FCR), and energy arbitrage, it is possible to create a convenient framework for all contracting parties in the electricity market. The Smart Grid Architecture Model (SGAM) artifact that describes the solution includes all the requirements and features necessary for the solution model to be internationally recognized and implemented”);
a power management unit that generates the distributed power information on the basis of at
least one of the power state data received from an energy storage system, the electricity price data,
the power generation amount data, and the weather data, (MALDONATO et al: “The process further involves the EMSP unit retrieving energy supply and grid demand data from a balancing controller (BRP) unit. This ensures efficient energy management in electric vehicle (EV) charging systems. By providing the EMSP unit with access to real-time information from the BRP unit, charging and discharging processes can be optimized by aligning them with energy prices, grid demand, and grid stability requirements. This interaction increases the effectiveness of the EV as a distributed energy source (DER), contributing to both cost savings for the user and grid stability”);
generates charging and discharging schedule information on the basis of the distributed power information, (MALDONATO et al (DE 102024126512 A1): “The charging and discharging schedule defined by the electric vehicle (EV) refers to a set of personalized instructions, often created by the EV user, that dictate when the EV should charge and when it should feed energy back into the grid. This schedule is determined by a variety of parameters, such as the user's daily routine, energy price forecasts, and grid demand signals”; “Figure 500 further illustrates a System 500 for optimizing charging and discharging schedules for electric vehicles (EVs) 10 in a power grid 20. The EMSP unit 1 receives suggested charging and discharging schedules from each EV 10 connected to the power grid 20, based on the vehicle's needs and available energy data. The BRP unit 2 aggregates the schedules of the connected EVs 10 and optimizes them to maintain balance in the power grid 20 by analyzing real-time supply and demand data. The BRP unit 2 transmits the specifications to the individual EVs 10 via the EMSP unit 1 through communication interfaces 3, allowing the schedules to be adjusted accordingly. The EMSP unit 1 can use a machine learning algorithm to adapt the schedules based on historical energy consumption and predicted grid conditions. The BRP unit 2 communicates with the EMSP unit 1 via a standardized energy management protocol to ensure interoperability with various types of EVs 10 and charging stations 30. A feedback mechanism enables the iterative refinement of the schedules based on real-time conditions in the power grid 20. A user device 4 allows EV users to enter preferences and view updates to their charging schedules and the conditions of the power grid 20. The BRP unit 2 dynamically adjusts the grid-level constraints based on aggregated data from the EVs 10 to ensure optimal energy distribution in the power grid 20”);
and transmits the charging and discharging schedule information to the energy storage system, (MALDONATO et al (DE 102024126512 A1): “EV users can specify precise time windows for charging, such as nighttime when energy prices are lower. Similarly, users can define discharge times when the EV can feed energy back into the grid, often based on financial incentives tied to periods of high demand”; “In another embodiment, the method includes a decentralized optimization process in which each vehicle proposes a charging and discharging schedule based on local information. This information is then aggregated by the EMSP unit and/or the BRP unit”; “The described system provides an efficient framework for integrating an electric vehicle (EV) into the power grid as a bidirectional distributed energy source (DER). Through the use of an electric mobility service provider (EMSP), the system manages real-time data from EV users, the power grid, and energy markets, thus enabling informed decision-making. Regarding charging and discharging schedules, the bidirectional charging station allows the electric vehicle to both charge and discharge energy, making it a flexible asset that can store and supply energy as needed. The Balancing Controller (BRP) unit ensures optimal energy flow between the electric vehicle and the grid by adjusting the flow based on real-time grid conditions, thereby improving grid stability and responsiveness. The communication interface is crucial for transmitting energy price signals and grid demand data between the EMSP unit, the BRP unit, and the electric vehicle to ensure all components work together. This real-time data exchange enables the system to respond to fluctuations in grid demand and energy prices, optimizing the electric vehicle's role as both a consumer and producer of energy”);
and generates a consumer-customized schedule on the basis of the demand forecast data, (MALDONATO et al (DE 102024126512 A1): This customized schedule is then submitted to the energy service provider (ESP), which optimizes energy management based on the EV user's preferences. The charging and discharging plan defined by the electric vehicle can offer a high degree of flexibility, enabling electric vehicle users to control their energy consumption while benefiting from cost-efficient charging and grid participation. The charging and discharging schedule defined by the electric vehicle (EV) refers to a set of personalized instructions, often created by the EV user, that dictate when the EV should charge and when it should feed energy back into the grid. This schedule is determined by a variety of parameters, such as the user's daily routine, energy price forecasts, and grid demand signals. EV users can specify precise time windows for charging, such as nighttime when energy prices are lower. Similarly, users can define discharge times when the EV can feed energy back into the grid, often based on financial incentives tied to periods of high demand. This customized schedule is then submitted to the energy service provider (ESP), which optimizes energy management based on the EV user's preferences. The charging and discharging plan defined by the electric vehicle can offer a high degree of flexibility, enabling electric vehicle users to control their energy consumption while benefiting from cost-efficient charging and grid participation).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the above limitations as taught by MALDONATO et al in the systems of Marhoefer, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Marhoefer does not disclose the following limitations, however, CHOI (US 20130211614 A1) discloses:
and provides the result of the analysis in order to enable an individual consumer to perform self-demand response (Self-DR) using a user terminal, (CHOI: [0003] Exemplary embodiments relate to an apparatus and method for controlling a smart appliance using a smart terminal to induce a consumer to reduce energy consumption in response to a demand response signal from an energy utility).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the above limitations as taught by CHOI in the systems of Marhoefer, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 4, Marhoefer discloses:
the power demand data includes peak period demand data and off-peak period demand data,
the electricity price data includes price data during the peak period and price data during the
off-peak period, ((29) The ability to aggregate stored power has additional benefits during peak demand periods. Although building owners realize an individual benefit by using battery or solar power instead of the grid during peak price periods, utilities are willing to pay a premium beyond the market price to companies that can aggregate and control peak demand reductions. The additional value of the peak power incentive is generally based on multiplying the number of “super” peak kilowatt-hours avoided by an incentive rate. This results in initial revenues to the aggregator and is on top of the arbitrage cost savings realized by the building owner as reflected in the monthly electricity bill; (11) The inclusion of some type of energy storage, such as a battery, can be integrated into a DSM scheme in a way that provides opportunity for both arbitrage based on differences in peak and off-peak rates and reductions to peak demand charges. In typical optimization schemes, the battery stores excess capacity from renewable sources like solar and wind but this is not typically integrated into an overall optimization calculus based on predictive factors.
Marhoefer does not disclose the following, however, MALDONATO et al discloses:
wherein the scheduling unit generates consumer-customized schedule information on the basis of the power demand data and the electricity price data, the customer-customized schedule information includes a schedule for reducing costs during the peak period, (MALDONATO et al (DE 102024126512 A1): This customized schedule is then submitted to the energy service provider (ESP), which optimizes energy management based on the EV user's preferences. The charging and discharging plan defined by the electric vehicle can offer a high degree of flexibility, enabling electric vehicle users to control their energy consumption while benefiting from cost-efficient charging and grid participation. The charging and discharging schedule defined by the electric vehicle (EV) refers to a set of personalized instructions, often created by the EV user, that dictate when the EV should charge and when it should feed energy back into the grid. This schedule is determined by a variety of parameters, such as the user's daily routine, energy price forecasts, and grid demand signals. EV users can specify precise time windows for charging, such as nighttime when energy prices are lower. Similarly, users can define discharge times when the EV can feed energy back into the grid, often based on financial incentives tied to periods of high demand. This customized schedule is then submitted to the energy service provider (ESP), which optimizes energy management based on the EV user's preferences. The charging and discharging plan defined by the electric vehicle can offer a high degree of flexibility, enabling electric vehicle users to control their energy consumption while benefiting from cost-efficient charging and grid participation).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the above limitations as taught by MALDONATO et al in the systems of Marhoefer, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5, Marhoefer does not disclose:
wherein the scheduling unit generates the customer-customized schedule information using a power trading optimization algorithm of an artificial intelligence unit.
However, MALDONATO et al discloses:
“The transmission of the optimized charging and discharging schedule from the EMSP unit to the BRP unit for aggregation enables effective coordination between electric vehicles and the grid. This interaction allows the balancing operator to aggregate the schedules of multiple electric vehicles, thereby improving the accuracy of energy supply and demand forecasts. By incorporating the optimized schedules into its operations, the balancing operator can manage energy resources more effectively, particularly during periods of high demand or fluctuating renewable energy availability. Aggregating the schedules allows the balancing operator to balance energy flows within its network, reducing reliance on reserve power sources and optimizing grid performance. The transmission of these schedules also enables the balancing operator to fine-tune its energy trading strategies and make informed decisions about when to buy or sell energy based on EV resource availability.”, “The prediction module can be implemented as an AI unit. The AI unit can be implemented in an executable program using common programming languages and processed on a processing unit. The implementation of the AI unit can include data collection and data preparation. This preparation can involve gathering training data containing EV10-related data and/or parameters, grid20-related data and/or parameters, and/or energy market-related data and/or parameters. The implementation of the AI unit can also include model development and training. Model development can involve selecting a suitable machine learning model (e.g., neural network, random forest, etc.) that is trained on the provided training datasets. During a training phase, the AI unit can learn to recognize specific, relevant, and/or predefined patterns between the provided data and identified/determined real-time data.”
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the above limitations as taught by MALDONATO et al in the systems of Marhoefer, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marhoefer (US 9300141 B2), and further in view of MALDONATO et al (DE 102024126512 A1), and further in view of CHOI (US 20130211614 A1), and further in view of MU et al (CN 116137008 A).
As per claim 2, Marhoefer does not disclose: wherein the trading management unit receives the electricity price data from a power exchange and transmits the smart contract to the power exchange.
However, MU et al (CN 116137008 A), discloses: (FIG. 1 is a diagram showing a structure of a contract management system according to one embodiment of the present invention. As an example, the contract management system 1 shown in FIG. 1 is used as the electric vehicle of the vehicle 5 of the user U and the electric power company V, and using the vehicle 5 of the battery power exchange related to the contract related intelligent contract (smart contract) for management. In the contract, as a contract condition, comprising a user U from the power network 7 of the power company V to the vehicle 5 charging the power purchase unit price, and from the vehicle 5 to the power network 7 discharging the power sales unit price).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the above limitations as taught by MU et al in the systems of Marhoefer, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Marhoefer (US 9300141 B2), and further in view of MALDONATO et al (DE 102024126512 A1), and further in view of CHOI (US 20130211614 A1), and further in view of ELLICE-FLINT et al (WO 2014197931 A1).
As per claim 3, Marhoefer does not disclose:
an emergency response unit that detects in real time whether an emergency state has
occurred; and a notification unit that transmits an emergency notification to an administrator and the
consumer terminal when the emergency state occurs, wherein the emergency state includes at least one of a power plant failure, a surge in power demand, and a worsening weather condition.
However, ELLICE-FLINT et al (WO 2014197931 A1) discloses:
“If the local on-site power is not available the STS FLOW = 2 is set, for example, indicating the decoupling of power consuming devices in the second electrical network. Figure 10 shows how the demand response impacts the end-users load profile. The open circles map a forecasted end-user load profile across a period of approximately 48 hours (for example, load points 1025, 1030 & 103 ). The vertical bars indicate where demand response is required within the second electrical network (for example regions labelled as J 005, 1010, 1015) by modulating power flow into the second electrical network from the first electrical network as initiated by the control device. j 00134] Referring now to Figure 11, there is shown a graph of a comparison between the end-user load profile forecast 1105 relative to the regional supply network demand forecast 1205 over a 48 hour period This graph highlights a failure of current electricity network management systems in that an end-user's local peak load profile 1105 forecast of the second electrical network may differ in timing from the peak regional demand profile 1205 forecast of the first electrical network)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to include the above limitations as taught by ELLICE-FLINT et al in the systems of Marhoefer, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Akiba Robinson whose telephone number is 571-272-6734 and email is Akiba.Robinsonboyce@USPTO.gov. The examiner can normally be reached on Monday-Thursday 6:30am-4:30pm.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner's supervisor, Nathan Uber can be reached on 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Any inquiry of a general nature or relating to the status of this application or proceeding should be directed to the receptionist whose telephone number is (703) 305-3900.
July 29, 2026
/Akiba K Robinson/
Primary Examiner, Art Unit
Prosecution Insights