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 .
DETAILED ACTION
This action is in response to the amendment filed December 10, 2025. Claims 1-17 are pending and examined. This action is Final.
Response to Arguments
The claim amendments did remove the claim interpretation since those limitations were removed.
The virtual power plant appears to be a power trading market. (Spec. paragraph 6 “[0006] In order to implement RE100 through recently emerging environmental issues and carbon neutrality, technology for optimizing power trading profits of the virtual power plant by minimizing costs of power use in the virtual power plant and selling surplus power of the virtual power plant in a power trading market to generate power trading profits is desired. ”.) For that reason the claims are still not patentable subject matter.
Examiner’s Note there are and were two separate grounds for 101 rejections for claims 1-12 the first one the Bilski one was not argued in the amendment. Please consider adding a processor to the computing system of claim 1 so that particular ground for 101 can be withdrawn.
Information Disclosure Statement
An initialed and dated copy of Applicant’s IDS form 1449 filed 11/03/2025 is attached to the instant Office action.
Claim objections
Claims 10-12, 14, and 16 are objected to because of the following informalities: they depend from rejected claims. Appropriate correction is required.
Claim Rejections - 35 USC § 101 Utility
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 1-12 are rejected under 35 U.S.C. 101. Based upon consideration of all of the relevant factors with respect to the claim as a whole, these claims are held to claim an abstract idea, and is/are therefore rejected as ineligible subject matter under 35 U.S.C. 101. In light of the recent Supreme Court decision in Bilski v. Kappos, 561 U.S. ___ (2010), the Interim Guidance for Determining Subject Matter Eligibility for Process Claims in View of Bilski v. Kappos provides factors to consider in determining whether a claim is directed to an abstract idea and is therefore not patent-eligible under 35 U.S.C. 101. Factors weighing toward eligibility include:
Recitation of a machine or transformation (either express or inherent).
The claim is directed toward applying a law of nature.
The claim is more than a mere statement of concept.
Factors weighing against eligibility include:
No recitation of a machine or transformation (either express or inherent).
Insufficient recitation of a machine or transformation.
The claim is not directed to an application of a law of nature.
The claim is a mere statement of a general concept.
An example of a method claim that would not qualify as a statutory process would be a claim that recited purely mental steps. Thus, to qualify as a § 101 statutory process, the claim could positively recite the other statutory class (the thing or product) to which it is tied, for example by identifying the apparatus that accomplishes the method steps, or positively recite the subject matter that is being transformed, for example by identifying the material that is being changed to a different state. Furthermore, the use of a particular machine or transformation of a particular article must involve more than insignificant extra-solution activity.
In light of the factors in the Supreme Court decision, Applicant’s method steps do not meet the requirements of 35 U.S.C. 101. The “computing system” is arguably just software the processors from claim 13 could solve this particular rejection but not the other 101 rejection of some of these claims.
Claims 1–17 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
In sum, claims 1–11, 13, 14, 15, and 17 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and do not include an inventive concept that is something “significantly more” than the judicial exception under the January 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process (claims 1–12), and a machine (claims 13–17), where the machine is substantially directed to the subject matter of the process. (See, e.g., MPEP §2106.03). Therefore, we proceed to step 2A, Prong 1.
Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability. Here, the claims recite the abstract idea of operating a virtual power plant by:
obtaining data on constraints of an optimal control model for operating the virtual power plant, wherein the virtual power plant comprises renewable energy use devices including a first electric vehicle using a managed charging method (V1G), a second electric vehicle using a vehicle-to-grid charging method (V2G), an energy storage system (ESS), and a photovoltaic (PV); and
inputting input data, including a variable value, to the optimal control model under the constraints, and deriving an operational schedule of each of the renewable energy use devices using an output value of the optimal control model; and
transmitting control signals to the renewable energy use devices based on the operation schedule so that the renewable energy use devices are controlled to perform at least one of charging, discharging or power generation operations,
wherein the variable value includes schedule data for each time zone of the first electric vehicle, schedule data for each time zone of the second electric vehicle, schedule data for each time zone of the ESS, and schedule data for each time zone of the PV
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: the category of certain methods of organizing human activity, which includes fundamental economic practices or principles and commercial or legal interactions (e.g., the virtual power plant appears to be a power trading market based on (Spec. paragraph 6 “[0006] In order to implement RE100 through recently emerging environmental issues and carbon neutrality, technology for optimizing power trading profits of the virtual power plant by minimizing costs of power use in the virtual power plant and selling surplus power of the virtual power plant in a power trading market to generate power trading profits is desired. ”.).
Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which the claim is directed does not include limitations that integrate the abstract idea into a practical application, since the recited features of the abstract idea are being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See, e.g., MPEP §2106.05(f)). Therefore, the claim is directed to an abstract idea.
Under the 2019 PEG step 2B analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the additional elements, such as: a “computing system” or “processor” do not amount to an innovative concept since, as stated above in the step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming. (See, e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. (See, e.g., MPEP §2106.05 I.A.). Independent claim 13 is nearly identical to independent claim 1 so the analysis for claim 1 also applies to claim 13.
Dependent claims 2–12, and 14-17 have all been considered and do not integrate the abstract idea into a practical application. Dependent claim 2 further defines the abstract idea noted in claim 1 as it describes limiting the renewable energy use devices trading to at least a minimum trading time. Dependent claim 3 further defines the abstract idea noted in claim 1 as it describes limiting the electric vehicles participation to when they are connected to a pre-designated charging device to be in a standby state. Dependent claim 4 further defines the abstract idea noted in claim 1 as it describes limiting the constraints consider priorities given to each power trading market of a plurality of power trading markets. Dependent claim 5 further defines the abstract idea noted in claim 1 as it describes the input data including data on initial battery states of charge of the renewable energy use devices. Dependent claim 6 further defines the abstract idea noted in claim 1 as it describes the input data further includes data on a battery charging amount when the first electric vehicle departs according to a planned schedule of the first electric vehicle or data on a battery charging amount when the second electric vehicle departs according to a planned schedule of the second electric vehicle. Dependent claim 7 further defines the abstract idea noted in claim 1 as it describes the input data further includes a predicted solar energy power generation amount of the PV. Dependent claim 8 further defines the abstract idea noted in claim 1 as it describes the input data further includes predicted power demand for each building included in the virtual power plant. Dependent claim 9 further defines the abstract idea noted in claim 8 as it describes calculating the predicted power demand for each building by inputting a power usage amount during a past pre-designated time, weather information, and date information to a power demand prediction model and using a result value extracted from the power demand prediction model. Dependent claims 10 and 17 further defines the abstract idea noted in claims 1 and 13 as they describes finding, by the optimal control model, the variable value for deriving a minimum value of a difference between a charging cost value and a profit value during a schedule creation period, wherein the charging cost is a charging cost of the first electric vehicle, the second electric vehicle, and the ESS, and wherein the profits are profits of the first electric vehicle, the second electric vehicle, the ESS, and the PV. Dependent claim 11 further defines the abstract idea noted in claim 1 as it describes the constraints include a condition in which it is possible to participate in a power trading market for conducting power trading using the renewable energy use devices every 4 hours, a condition in which each of the renewable energy use devices creates only one schedule for each time zone, a condition in which only a renewable energy use device that has obtained certification in advance among the renewable energy use devices participates in the power trading market, a condition in which the first electric vehicle or the second electric vehicle participates in the power trading market only when it is on standby at an electric vehicle charger (EVC), a condition in which hourly charging and discharging amounts of the renewable energy use devices follow established standards, and a condition in which the renewable energy use devices most preferentially participate in a power trading market that has already successfully bid, and wherein the input data includes data on initial battery states of charge of the renewable energy use devices, data on a battery charging amount when the first electric vehicle or the second electric vehicle departs according to a planned schedule of the first electric vehicle or the second electric vehicle, a predicted solar energy power generation amount of the PV, and predicted power demand for each building included in the virtual power plant. Dependent claims 12 and 16 further defines the optimal control model noted claims 1 and 13 as describes it as a mixed integer linear programming (MILP) model, wherein the method further comprises calculating the predicted power demand for each building included in the virtual power plant and the predicted solar energy power generation amount of the PV by inputting a power usage amount during a past pre-designated time, weather information, and date information to a power demand prediction model and using a result value extracted from the power demand prediction model, and wherein the power demand prediction model is a bidirectional long short-term memory (BLSTM) model. Dependent claim 14 further defines the abstract idea noted in claim 13 as it describes the constraints include a condition in which it is possible to participate in a power trading market for conducting power trading using the renewable energy use devices every 4 hours, a condition in which each of the renewable energy use devices creates only one schedule for each time zone, a condition in which only a renewable energy use device that has obtained certification in advance among the renewable energy use devices participates in the power trading market, a condition in which the first electric vehicle or the second electric vehicle participates in the power trading market only when it is on standby at an electric vehicle charger (EVC), a condition in which hourly charging and discharging amounts of the renewable energy use devices follow established standards, and a condition in which the renewable energy use devices most preferentially participate in a power trading market that has already successfully bid. Dependent claim 15 further defines the abstract idea noted in claim 13 wherein the input data includes data on initial battery states of charge of the renewable energy use devices, data on a battery charging amount when the first electric vehicle or the second electric vehicle departs according to a planned schedule of the first electric vehicle or the second electric vehicle, a predicted solar energy power generation amount of the PV, and predicted power demand for each building included in the virtual power plant.
The additional elements of the dependent claims merely refine and further limit the abstract idea of the independent claims and do not add any feature that is an “inventive concept” which cures the deficiencies of their respective parent claim under the 2019 PEG analysis. None of the dependent claims considered individually, including their respective limitations, include an “inventive concept” of some additional element or combination of elements sufficient to ensure that the claims in practice amount to something “significantly more” than patent-ineligible subject matter to which the claims are directed.
The elements of the instant process steps when taken in combination do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself because the claims do not effect an improvement to another technology or technical field (e.g., the field of computer coding technology is not being improved); the claims do not amount to an improvement to the functioning of an electronic device itself which implements the abstract idea (e.g., the general purpose computer and/or the computer system which implements the process are not made more efficient or technologically improved); the claims do not perform a transformation or reduction of a particular article to a different state or thing (i.e., the claims do not use the abstract idea in the claimed process to bring about a physical change. See, e.g., Diamond v. Diehr, 450 U.S. 175 (1981), where a physical change, and thus patentability, was imparted by the claimed process; contrast, Parker v. Flook, 437 U.S. 584 (1978), where a physical change, and thus patentability, was not imparted by the claimed process); and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment (e.g., simply claiming the use of a computer and/or computer system to implement the abstract idea).
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.
Claims 1- 9, 13, and 15 are rejected under 35 U.S.C. 102(a2) as being anticipated by Rahimi-Klan et al. (USPG 2024/0202,752 A1).
As per claim 1 Rahimi-Klan teaches:
A method for operating a virtual power plant, the method being performed by a computing system, the method comprising:
obtaining data on constraints of an optimal control model for the virtual power plant, wherein the virtual power plant comprises renewable energy use devices including a first electric vehicle using a managed charging method (V1G), a second electric vehicle using a vehicle-to-grid charging method (V2G), an energy storage system (ESS), and a photovoltaic (PV); (see at least Rahimi-Klan paragraphs 14 and 31 “(0014] The subject disclosure is directed to novel software/Saas platforms for data collection and for forecasting short-term loads for power grids, EV-fleet charge demand, and photovoltaic (PV) and wind generation that utilizes blockchain-based transactive energy (TE) technology. The platform can forecast 5-minutes to 1-hour ahead for a grid to provide optimization of resources and scheduling. The platform can be overcome the above-mentioned challenges. The disclosed system is appropriate for the smart grid operators, i.e., power utilities, power distribution companies ( distributor system operator), microgrid operators, smart/green buildings' energy managers, EVSE (Electric Vehicle Supply Equipment) firms and EV-fleet.” “(0031] The disclosed platform is a TE platform within the cloud computing platforms for smart-grids with vehicle-to grid (V2G) and grid-to-vehicle (G2V) services. The invented platform improves the electric distribution network reliability, stability, efficiency. The disclosed platform helps the grid operators make the power network more environment friendly by significantly reducing greenhouse gas via optimal utilization of clean energy resources. Further, the disclosed platform provides financial benefits for the EV drivers, EV serving entities (EVSE), and demand response (DR) aggregators.”)
inputting input data, including a variable value, to the optimal control model under the constraints, and deriving an operation schedule of each of the renewable energy use devices using an output value of the optimal control model; (see at least Rahimi-Klan paragraph 22 “(0022] The platform leverages novel algorithms and software/Saas platforms for data collection, communication, and data engineering services. The platform collects data from Advanced Metering Infrastructure (AMI) comprising smart meters, IoT devices, and also the weather data collection devices. The platform provides Deep Neural Network based Predictive Analytics SaaS for short-term (5-minute to one-hour) prediction of electric grid's load, EV charging demand, PV and Wind power generation forecasts.”) and
transmitting control signals to the renewable energy use devices based on the operation schedule so that the renewable energy use devices are controlled to perform at least one of charging, discharging, or power generation operations, wherein the variable value includes schedule data for each time zone of the first electric vehicle, schedule data for each time zone of the second electric vehicle, schedule data for each time zone of the ESS, and schedule data for each time zone of the PV. (see at least Rahimi-Klan paragraphs 23 and 60 “(0023] The distributed energy resources management system (DERMS) software platform is used to model and optimally control/dispatch a group of distributed energy resource (DER) assets such as wind turbines, PV solar panels, battery energy storage systems (BESS), demand response (DR), and fleet of electric vehicles (EVs) to deliver vital grid services to help power utilities (PUs) to achieve mission-critical outcomes.” “(0060] In the exemplary embodiment, features of train and test data would incorporate historical data and data that would form the basis for machine learning training. As the exemplary artificial neural network relates to PV data, the test data correspond to the particular analysis needs. In this embodiment, historical electrical vehicle demand/generation data can include start time, duration, energy, and EV type data. Additionally, the features of train/test data can further include temporal price tariffs, calendar data, and forecasted electric load data.”)
As per claim 2 Rahimi-Klan teaches:
The method of claim 1, wherein the constraints express that it is possible to participate in a power trading market for conducting power trading using the renewable energy use devices only for a pre-designated minimum trading time or more. (see at least Rahimi-Klan paragraph 52 “(0052] In an exemplary embodiment, the framework can be utilized as a part of a software as a service (SaaS) model for short term prediction of PV generation, where the prediction can be produced from five minutes to one hour in accordance to schedule. In various other embodiments, the framework can be adapted to support predictive analytics SaaS models over a variety of time intervals.” It is being applied over requested time intervals.)
As per claim 3 Rahimi-Klan teaches:
The method of claim 1, wherein the constraints include a condition in which the first electric vehicle or the second electric vehicle participates in a power trading market only when it is connected to a pre-designated charging device to be in a standby state. (see at least Rahimi-Klan paragraph 60 The historical electrical vehicle demand/generation data should include this because a vehicle at a public supercharger would hardly be expected to be able to provide power rather than needing to be charged as fast as practically possible.)
As per claim 4 Rahimi-Klan teaches:
The method of claim 1, wherein the constraints consider priorities given to each power trading market of a plurality of power trading markets. (see at least Rahimi-Klan paragraph 36 “(0036] The platform is enabled through innovative math modeling. In various embodiments, the disclosed software platform engine is capable of OPF analysis with different objective functions. In some embodiments, the functions can include energy cost ( or price tariff) minimization for the grid stake holders minimizing the network losses, peak-shaving, volt-var optimization (VVO), and network load curtailment minimization during network contingencies. … The network information and key parameters within the CSV reports can include bus/node voltages, line currents, line power flows, bus/node power injection/consumption, nodal energy prices (DLMP), EV charging/discharging power/energy, greenhouse gas (GHG) reduction, lines and network power losses.”)
As per claim 5 Rahimi-Klan teaches:
The method of claim 1, wherein the input data further includes data on initial battery states of charge of the renewable energy use devices. (see at least Rahimi-Klan paragraph 60 “In this embodiment, historical electrical vehicle demand/generation data can include start time, duration, energy, and EV type data.” The “energy” would be how much charge the vehicle currently has which is why this is ”demand/generation data” since the vehicle can provide some power before at a higher price before it is recharged at what is expected to be a lower demand time when prices should be lower.)
As per claim 6 Rahimi-Klan teaches:
The method of claim 1, wherein the input data further includes data on a battery charging amount when the first electric vehicle departs according to a planned schedule of the first electric vehicle or data on a battery charging amount when the second electric vehicle departs according to a planned schedule of the second electric vehicle. (see at least Rahimi-Klan paragraph 60 “In this embodiment, historical electrical vehicle demand/generation data can include start time, duration, energy, and EV type data.” The “energy” would be how much charge the vehicle currently has which is why this is ”demand/generation data” since the vehicle can provide some power before at a higher price before it is recharged at what is expected to be a lower demand time when prices should be lower.)
As per claim 7 Rahimi-Klan teaches:
The method of claim 1, wherein the input data further includes a predicted solar energy power generation amount of the PV. (see at least Rahimi-Klan paragraph 52 “(0052] In an exemplary embodiment, the framework can be utilized as a part of a software as a service (SaaS) model for short term prediction of PV generation, where the prediction can be produced from five minutes to one hour in accordance to schedule. In various other embodiments, the framework can be adapted to support predictive analytics SaaS models over a variety of time intervals.”)
As per claim 8 Rahimi-Klan teaches:
The method of claim 1, wherein the input data further includes predicted power demand for each building included in the virtual power plant. (see at least Rahimi-Klan paragraph 45 “(0045] For each of the data inputs, the disclosed system provides a number of methods for obtain these inputs. In an exemplary embodiment, the platform collects data from advanced metering infrastructure (AMI) comprising smart meters, internet of things (IOT) devices, and weather collection devices. These meters and devices can be connected through the metering infrastructure with the other components of the disclosed platform through cloud computing network, in some embodiments. The connection methodology can be modified and adapted for application specific infrastructure demands.” A building is an application specific infrastructure demand.)
As per claim 9 Rahimi-Klan teaches:
The method of claim 8, further comprising: calculating the predicted power demand for each building by inputting a power usage amount during a past pre- designated time, weather information, and date information to a power demand prediction model and using a result value extracted from the power demand prediction model. (see at least Rahimi-Klan paragraphs 30 and 104 “(0030] The platform leverages a hybrid neural network model with long short-term memory (LSTM) and deep feedforward network (DFNN) for forecasting electric load and PV generation by using historical, meteorological and irradiation parameters.” “(0104] Supported embodiments include any of the foregoing methods, wherein the at least one artificial neural network is configured for electric load forecast.”)
As per claim 13 Rahimi-Klan teaches:
An operation server of a virtual power plant, comprising:
one or more processors;
a memory configured to store one or more instructions; and
a communication interface, wherein the one or more processors are configured, (see at least Rahimi-Klan paragraph 85 “(0085] The illustrated computing system 500 includes one or more processing devices 510, one or more memory devices 512, one or more communication devices 514, one or more input/output (I/0) devices 516, and one or more mass storage devices 518, all coupled to each other through an interconnect 520. The interconnect 520 can be or include one or more conductive traces, buses, point-to-point connections, controllers, adapters, and/or other conventional connection devices. Each of the processing devices 510 controls, at least in part, the overall operation of the processing of the computing system 500 and can be or include, for example, one or more general-purpose programmable microprocessors, digital signal processors (DSPs), mobile application processors, microcontrollers, application-specific integrated circuits (ASICs), programmable gate arrays (PGAs), or the like, or a combination of such devices.”) by executing the stored one or more instructions, to:
perform an operation of obtaining data on constraints of an optimal control model for operating the virtual power plant, wherein the virtual power plant comprises renewable energy use devices including a first electric vehicle using a managed charging method (V1G), a second electric vehicle using a vehicle-to-grid charging method (V2G), an energy storage system (ESS), and a photovoltaic (PV); (see at least Rahimi-Klan paragraphs 14 and 31 “(0014] The subject disclosure is directed to novel software/Saas platforms for data collection and for forecasting short-term loads for power grids, EV-fleet charge demand, and photovoltaic (PV) and wind generation that utilizes blockchain-based transactive energy (TE) technology. The platform can forecast 5-minutes to 1-hour ahead for a grid to provide optimization of resources and scheduling. The platform can be overcome the above-mentioned challenges. The disclosed system is appropriate for the smart grid operators, i.e., power utilities, power distribution companies ( distributor system operator), microgrid operators, smart/green buildings' energy managers, EVSE (Electric Vehicle Supply Equipment) firms and EV-fleet.” “(0031] The disclosed platform is a TE platform within the cloud computing platforms for smart-grids with vehicle-to grid (V2G) and grid-to-vehicle (G2V) services. The invented platform improves the electric distribution network reliability, stability, efficiency. The disclosed platform helps the grid operators make the power network more environment friendly by significantly reducing greenhouse gas via optimal utilization of clean energy resources. Further, the disclosed platform provides financial benefits for the EV drivers, EV serving entities (EVSE), and demand response (DR) aggregators.”)
perform an operation of inputting input data including a variable value to the optimal control model under the constraints, and deriving an operation schedule of each of the renewable energy use devices using an output value of the optimal control model; (see at least Rahimi-Klan paragraph 22 “(0022] The platform leverages novel algorithms and software/Saas platforms for data collection, communication, and data engineering services. The platform collects data from Advanced Metering Infrastructure (AMI) comprising smart meters, IoT devices, and also the weather data collection devices. The platform provides Deep Neural Network based Predictive Analytics SaaS for short-term (5-minute to one-hour) prediction of electric grid's load, EV charging demand, PV and Wind power generation forecasts.”) and
perform an operation of transmitting control signals to the renewable energy use devices based on the operation schedule so that the renewable energy use devices are controlled to perform at least one of charging, discharging, or power generation operations, wherein the variable value includes schedule data for each time zone of the first electric vehicle, schedule data for each time zone of the second electric vehicle, schedule data for each time zone of the ESS, and schedule data for each time zone of the PV. (see at least Rahimi-Klan paragraphs 23 and 60 “(0023] The distributed energy resources management system (DERMS) software platform is used to model and optimally control/dispatch a group of distributed energy resource (DER) assets such as wind turbines, PV solar panels, battery energy storage systems (BESS), demand response (DR), and fleet of electric vehicles (EVs) to deliver vital grid services to help power utilities (PUs) to achieve mission-critical outcomes.” “(0060] In the exemplary embodiment, features of train and test data would incorporate historical data and data that would form the basis for machine learning training. As the exemplary artificial neural network relates to PV data, the test data correspond to the particular analysis needs. In this embodiment, historical electrical vehicle demand/generation data can include start time, duration, energy, and EV type data. Additionally, the features of train/test data can further include temporal price tariffs, calendar data, and forecasted electric load data.”)
As per claim 15 Rahimi-Klan teaches:
The operation server of claim 13, wherein the input data includes data on initial battery states of charge of the renewable energy use devices, data on a battery charging amount when the first electric vehicle or the second electric vehicle departs according to a planned schedule of the first electric vehicle or the second electric vehicle, (see at least Rahimi-Klan paragraph 60 “In this embodiment, historical electrical vehicle demand/generation data can include start time, duration, energy, and EV type data.” The “energy” would be how much charge the vehicle currently has which is why this is ”demand/generation data” since the vehicle can provide some power before at a higher price before it is recharged at what is expected to be a lower demand time when prices should be lower.) a predicted solar energy power generation amount of the PV, . (see at least Rahimi-Klan paragraph 52 “(0052] In an exemplary embodiment, the framework can be utilized as a part of a software as a service (SaaS) model for short term prediction of PV generation, where the prediction can be produced from five minutes to one hour in accordance to schedule. In various other embodiments, the framework can be adapted to support predictive analytics SaaS models over a variety of time intervals.”) and predicted power demand for each building included in the virtual power plant. (see at least Rahimi-Klan paragraphs 30 and 104 “(0030] The platform leverages a hybrid neural network model with long short-term memory (LSTM) and deep feedforward network (DFNN) for forecasting electric load and PV generation by using historical, meteorological and irradiation parameters.” “(0104] Supported embodiments include any of the foregoing methods, wherein the at least one artificial neural network is configured for electric load forecast.”)
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 of this title, 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 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Rahimi-Klan et al. (US 2024/0202752 A1) in view of Ooka et al. (US 2023/0402,848 A1).
As per claims 10 and 17 while Rahimi-Klan does teach battery energy storage systems (BESS) (see at least Rahimi-Klan paragraph 23 “(0023] The distributed energy resources management system (DERMS) software platform is used to model and optimally control/dispatch a group of distributed energy resource (DER) assets such as wind turbines, PY solar panels, battery energy storage systems (BESS), demand response (DR), and fleet of electric vehicles (EVs) to deliver vital grid services to help power utilities (PUs) to achieve mission-critical outcomes.”) as well as vehicle-to-grid (V2G) and grid-to-vehicle (G2V) services (see at least Rahimi-Klan paragraph 31 “The disclosed platform is a TE platform within the cloud computing platforms for smart-grids with vehicle-to-grid (V2G) and grid-to-vehicle (G2V) services.) which would allow a vehicle to provide power to the grid as well as being charged from the grid it is not explicit about why both of those services could be useful but Ooka does teach that the use of energy storage systems to generate profit. (see at least Ooka paragraph 48 “(0048] Optimization component 113 may include optimizing the hybrid system ( e.g., hybrid system 108) operation for threshold gain (e.g., selected, or preferred level of profit) while taking uncertainties ( e.g., variability of the data or forecasted information) into account. Threshold gain as used herein may correspond to a calculated difference in value, such as a calculated profit. The hybrid resource may act as one unit against the grid operation and may be able to follow market dispatch instructions at any moment. This may be accomplished by predicting future renewable energy generation uncertainties, predicting ancillary services dispatch uncertainties, or predicting or keeping enough energy in storage at the right times. The optimization engine (e.g., optimization component 113) may take the data from of DP component 111 or forecast information from forecasting component 112 (e.g., renewable energy generation forecast, market value forecast, or some combination thereof) to find an proposed value strategy for energy services and ancillary services offering based on ISO market rules, wherein ISO market rules may differ based on the ISO, region, etc. In an example, an ISO may specify ISO market rules in a set of documents called a business practice manual (8PM). Proposed value as used herein may correspond to a calculated optimal bidding or bid for a service. In some examples, the proposed value may refer to a bid corresponding to the threshold operational value. The results ( e.g., the proposed values) may be saved in the data storage system 116. Some system constraints or proposed value preferences can come from previously defined user inputs associated with a device (e.g., device 102). In some examples, threshold gain may be any value that corresponds or correlates to a calculated increase in profit associated with the operation of hybrid system 108. In some examples, the proposed value may be any bid value for services (e.g., energy services, or ancillary services, among other things) offered. In an example, the highest level of profit may correspond to the selected threshold operational value.”) Therefore it would have been obvious for the elements of the power grid to be allocated by the profits they would produce since it is solving a known problem in a known way with an expectation of success.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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