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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1: Statutory Category
Claim 1 is directed to a system or a method, which falls within the statutory categories of invention.
Step 2A Prong, 1: Recitation of a judicial exception
The claims are directed to operations including:
Receiving EV charging demand information and energy cost information (collecting information);
executing an optimizer for to maximize predicted earnings (analyzing information);
determining a dynamic EV charging price (displaying results);
scheduling charging/discharging of an energy storage system;
transmitting pricing signals; and
charging EVs at the determined price.
These limitations fall under the Abstract Idea grouping of certain methods organizing human activity (commercial interactions such as managing pricing and commerce of EV charging services) and mental processes (price optimization). Both are recognized categories of Abstract Ideas.
These limitations describe collecting information, analyzing information, forecasting future conditions optimizing pricing and resource allocation decisions, and determining pricing for EV charging services. Such activities constitute certain methods of organizing human activity, including commercial interactions, sales activities, business relations, economic practices, and managing personal behavior or relationships between people. The recited optimization of charging prices, charging schedules, energy procurement, and predicted earnings is directed to economic decision-making and resource allocation. Additionally, the recited forecasting, evaluating, and determining operations can be practically performed in the human mind or with a pen and paper and therefore also constitute mental processes.
Step 2A, Prong 2: Integration into a Practical Application
The additional elements (a “computer readable storage medium,” “hardware processors,” “EV charging station,” “ESS”) are recited at a high level of generality as conventional computing components or generic charging hardware. The claims merely use these components as tools to implement the abstract idea. For example, charging an EV at a price determined by the optimizer is still part of the underlying abstract idea of managing commerce/pricing, rather than an improvement to EV charging technology itself.
The claims do not recite any improvement to computer functionality, EV charging technology, charging station hardware, battery technology, renewable energy generation technology, grid operation technology or energy storage technology. The claims do not recite any particular mechanism by which greenhouse gas emissions are reduced, nor do they recite a specific technological improvement to the manner by which EV charging is performed.
Therefore, the claims to not integrate the abstract idea into a practical application.
Step 2B: Inventive Concept
The claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea. Using generic processor to execute optimization, transmitting signals to user devices, and charging prices based on forecasted data are all well-understood, routine and conventional practices in the energy and pricing domains.
Accordingly, the claims are directed to an abstract idea and do not include significantly more.
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.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ayoola (US 20230024900) in view of Lee (US 20240067035) and in further view of Oobayashi (US 20220108248).
Regarding claim 1, Ayoola teaches a system (shown in figure 1 item 100 charging and distributed grid resource adequacy management system) comprising:
a computer-readable storage medium having executable instructions (paragraph [0012] a memory to store executable instructions); and
one or more hardware processors (paragraph [0012] a processor) configured to execute the instructions to: receive electric vehicle (EV) charging demand information (paragraphs [0012], [0013] and [0057] teaches wherein processors receive charging demand interpreted as energy and grid demand data. Paragraph [0015] teaches receiving status on current energy demands);
receive energy cost information for an EV charging station, the energy cost information including current energy cost information and forecasted energy cost information, wherein actual energy costs vary over time (paragraph [0053] teaches wherein energy cost and impact on the energy distribution network is estimated based on cost-benefit analysis framework. Paragraphs [0058] and [0071] teaches wherein the charging network forecasts or predicts when energy costs are at their lowest);
Ayoola does not explicitly teach execute an optimizer for: reducing greenhouse gas emissions (GHG) based on increasing a usage of a renewable energy source for EV charging at the EV charging station; and maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, the usage of the renewable energy source, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price; and charge one or more EVs using the EV charging station at the dynamic price.
Lee teaches reducing greenhouse gas emissions (GHG) based on increasing a usage of a renewable energy source for EV charging at the EV charging station and maximizing a predicted earnings on the EV charging station based on the usage of the renewable energy source (paragraph [0024] discloses “The system may reduce greenhouse gas emissions resulting from electricity generation by increasing the amount of electricity from green sources (e.g., solar collectors or thermal storage) that the charging stations use. Paragraphs [0023] and [0026] discloses maximizing predicted earnings by reducing operating costs by increasing the usage of renewable energy sources).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Lee reference so that the system may reduce instabilities in an electric power grid.
The suggestion/motivation for combination can be found in the Lee reference in paragraph [0024] wherein grid instabilities are reduced.
Ayoola and Lee do not explicitly teach maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price; charge one or more EVs using the EV charging station at the dynamic price.
Oobayashi teaches maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price (defined in paragraphs [0041] – [0042] discloses wherein predicted earnings or sales are maximized based on acquiring charging demand information. Paragraph [0035] discloses wherein the when the electric power unit price is determined by a dynamic pricing, the RC estimation unit 141 sets the electric power unit price based on the electric power demand forecast); and
charge one or more EVs using the EV charging station at the dynamic price (paragraphs [0045] – [0046] teaches wherein the vehicle is charged based on the dynamic price and when running or operating costs are low. Paragraph [0046] discloses wherein the vehicle is charged at night when the dynamic pricing is low).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola and Lee references with the charging system of the Oobayashi reference so that the system may maximizing the sales volume and reducing opportunity loss.
The suggestion/motivation for combination can be found in the Oobayashi reference in paragraph [0041] wherein maximizing sales volume and reducing opportunity loss is taught.
Regarding claim 2, Ayoola teaches the system of claim 1, wherein the maximizing further comprises determining a rate at which to charge a local energy storage system (ESS) using energy from a power grid, a rate at which to discharge energy from the ESS for charging the one or more EVs, or to neither charge nor discharge the ESS (Paragraph [0054] teaches wherein a charge and discharge rates are determined to provide charge to vehicle from a grid and bidirectionally in reverse).
Regarding claim 3, Ayoola teaches the system of claim 1, wherein the one or more hardware processors are configured to execute the instructions to: generate forecasted on-site power generation information for one or more power generation sources of the EV charging station, wherein the maximizing predicted earnings is also based on the forecasted on-site power generation information (paragraph [0088 teaches wherein the optimization module predicts the earning or revenue based on the use of the energy grid. Paragraph [0093] teaches wherein the predicted earning or revenue is determined by a system which provides for optimization at a local and global level by predicting and forecasting the power generation interpreted as the charging state of the electrical charging station and grid sources).
Regarding claim 4, Ayoola teaches the system of claim 1, wherein the one or more hardware processors are configured to execute the instructions to: transmit a signal to electronic devices of EV users indicative of the dynamic optimized EV charging price (paragraph [0054] teaches wherein charging and pricing data is exchanged on an mobile device application. Pparagraph [0136] teaches wherein real-time price signals are provided to the users within a specific region).
Regarding claim 5, Ayoola teaches the system of claim 1, wherein the EV charging demand information includes current EV charging demand information and forecasted EV charging demand information (paragraphs [0015], [0092] and [0093] teaches wherein charging demand information includes current and predicted charge, interpreted as, energy demands).
Regarding claim 6, Ayoola teaches the system of claim 1, wherein the optimizer maximizes predicted earnings over a time horizon window, wherein the EV charging demand information comprises forecasted EV charging demand information (paragraphs [0015], [0092] and [0093] teaches wherein charging demand information includes current, predicted and forcasted charge, interpreted as, energy demands). .
Regarding claim 7, Ayoola teaches the system of claim 6, wherein the optimizer is configured for maximizing predicted earnings in the time horizon window by optimizing at least one of: EV charging price at points in time in the time horizon window, and a schedule in the time horizon window for charging a local energy storage system (ESS) using energy from a power grid, and for discharging energy from the ESS for charging the one or more EVs (paragraph [0078] teaches wherein optimizer executed maximum predicted earnings during a time horizon window, interpreted as a time period. The time period allows for managing power during peak power consumptions period or when market rates are low for charging and discharging to the local grid) .
Regarding claim 8, Ayoola teaches the system of claim 1, wherein the forecasted EV charging demand information is based at least in part on one or more of forecasted weather information and forecasted traffic information (paragraph [0091] teaches wherein forecasted weather and traffic data is used to determine demand information).
Regarding claim 9, Ayoola teaches the system of claim 1, wherein the one or more hardware processors are configured to execute the instructions to: receive EV charging demand information for each of a plurality of EV charging stations (paragraphs [0012], [0013] and [0057] teaches wherein processors receive charging demand interpreted as energy and grid demand data. Paragraph [0015] teaches receiving status on current energy demands);
receive energy cost information for each of the plurality of EV charging stations (paragraph [0053] teaches wherein energy cost and impact on the energy distribution network is estimated based on cost-benefit analysis framework. Paragraphs [0058] and [0071] teaches wherein the charging network forecasts or predicts when energy costs are at their lowest);
Ayoola does not explicitly teach execute an optimizer for: reducing greenhouse gas emissions (GHG) based on increasing a usage of a renewable energy source for EV charging at the EV charging station; and maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, the usage of the renewable energy source, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price; and charge one or more EVs using the EV charging station at the dynamic price.
Lee teaches reducing greenhouse gas emissions (GHG) based on increasing a usage of a renewable energy source for EV charging at the EV charging station and maximizing a predicted earnings on the EV charging station based on the usage of the renewable energy source (paragraph [0024] discloses “The system may reduce greenhouse gas emissions resulting from electricity generation by increasing the amount of electricity from green sources (e.g., solar collectors or thermal storage) that the charging stations use. Paragraphs [0023] and [0026] discloses maximizing predicted earnings by reducing operating costs by increasing the usage of renewable energy sources).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Lee reference so that the system may reduce instabilities in an electric power grid.
The suggestion/motivation for combination can be found in the Lee reference in paragraph [0024] wherein grid instabilities are reduced.
Ayoola and Lee do not explicitly teach maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price; charge one or more EVs using the EV charging station at the dynamic price.
Oobayashi teaches maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price (defined in paragraphs [0041] – [0042] discloses wherein predicted earnings or sales are maximized based on acquiring charging demand information. Paragraph [0035] discloses wherein the when the electric power unit price is determined by a dynamic pricing, the RC estimation unit 141 sets the electric power unit price based on the electric power demand forecast); and
charge one or more EVs using the EV charging station at the dynamic price (paragraphs [0045] – [0046] teaches wherein the vehicle is charged based on the dynamic price and when running or operating costs are low. Paragraph [0046] discloses wherein the vehicle is charged at night when the dynamic pricing is low).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola and Lee references with the charging system of the Oobayashi reference so that the system may maximizing the sales volume and reducing opportunity loss.
The suggestion/motivation for combination can be found in the Oobayashi reference in paragraph [0041] wherein maximizing sales volume and reducing opportunity loss is taught.
Regarding claim 10, Ayoola teaches a method (shown in figure 1 item 100 charging and distributed grid resource adequacy management system) comprising:
at one or more electronic devices each having one or more hardware processors and computer- readable memory (paragraph [0041] teaches wherein a process is provided on an electronic device such as a phone with an app): receiving electric vehicle (EV) charging demand information (paragraphs [0012], [0013] and [0057] teaches wherein processors receive charging demand interpreted as energy and grid demand data. Paragraph [0015] teaches receiving status on current energy demands);
receiving energy cost information for an EV charging station, the energy cost information including current energy cost information and forecasted energy cost information, wherein actual energy costs vary over time (paragraph [0053] teaches wherein energy cost and impact on the energy distribution network is estimated based on cost-benefit analysis framework. Paragraphs [0058] and [0071] teaches wherein the charging network forecasts or predicts when energy costs are at their lowest).
Ayoola does not explicitly teach execute an optimizer for: reducing greenhouse gas emissions (GHG) based on increasing a usage of a renewable energy source for EV charging at the EV charging station; and maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, the usage of the renewable energy source, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price; and charge one or more EVs using the EV charging station at the dynamic price.
Lee teaches reducing greenhouse gas emissions (GHG) based on increasing a usage of a renewable energy source for EV charging at the EV charging station and maximizing a predicted earnings on the EV charging station based on the usage of the renewable energy source (paragraph [0024] discloses “The system may reduce greenhouse gas emissions resulting from electricity generation by increasing the amount of electricity from green sources (e.g., solar collectors or thermal storage) that the charging stations use. Paragraphs [0023] and [0026] discloses maximizing predicted earnings by reducing operating costs by increasing the usage of renewable energy sources).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Lee reference so that the system may reduce instabilities in an electric power grid.
The suggestion/motivation for combination can be found in the Lee reference in paragraph [0024] wherein grid instabilities are reduced.
Ayoola and Lee do not explicitly teach maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price; charge one or more EVs using the EV charging station at the dynamic price.
Oobayashi teaches maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price (defined in paragraphs [0041] – [0042] discloses wherein predicted earnings or sales are maximized based on acquiring charging demand information. Paragraph [0035] discloses wherein the when the electric power unit price is determined by a dynamic pricing, the RC estimation unit 141 sets the electric power unit price based on the electric power demand forecast); and
charge one or more EVs using the EV charging station at the dynamic price (paragraphs [0045] – [0046] teaches wherein the vehicle is charged based on the dynamic price and when running or operating costs are low. Paragraph [0046] discloses wherein the vehicle is charged at night when the dynamic pricing is low).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola and Lee references with the charging system of the Oobayashi reference so that the system may maximizing the sales volume and reducing opportunity loss.
The suggestion/motivation for combination can be found in the Oobayashi reference in paragraph [0041] wherein maximizing sales volume and reducing opportunity loss is taught.
Regarding claim 11, Ayoola teaches the method of claim 10, wherein the maximizing further comprises determining a rate at which to charge a local energy storage system (ESS) using energy from a power grid, a rate at which to discharge energy from the ESS for charging the one or more EVs, or to neither charge nor discharge the ESS (Paragraph [0054] teaches wherein a charge and discharge rates are determined to provide charge to vehicle from a grid and bidirectionally in reverse).
Regarding claim 12, Ayoola teaches the method of claim 10, further comprising: generating forecasted on-site power generation information for one or more power generation sources of the EV charging station, wherein the maximizing predicted earnings is also based on the forecasted on-site power generation information (paragraph [0088 teaches wherein the optimization module predicts the earning or revenue based on the use of the energy grid. Paragraph [0093] teaches wherein the predicted earning or revenue is determined by a system which provides for optimization at a local and global level by predicting and forecasting the power generation interpreted as the charging state of the electrical charging station and grid sources).
Regarding claim 13, Ayoola teaches the method of claim 10, further comprising: transmitting a signal to electronic devices of EV users indicative of the dynamic optimized EV charging price (paragraph [0054] teaches wherein charging and pricing data is exchanged on an mobile device application. Pparagraph [0136] teaches wherein real-time price signals are provided to the users within a specific region).
Regarding claim 14, Ayoola teaches the method of claim 10, wherein the EV charging demand information includes current EV charging demand information and forecasted EV charging demand information (paragraphs [0015], [0092] and [0093] teaches wherein charging demand information includes current and predicted charge, interpreted as, energy demands).
Regarding claim 15, Ayoola teaches the method of claim 10, wherein the optimizer maximizes predicted earnings over a time horizon window, wherein the EV charging demand information comprises forecasted EV charging demand information (paragraphs [0015], [0092] and [0093] teaches wherein charging demand information includes current and predicted charge, interpreted as, energy demands).
Regarding claim 16, Ayoola teaches the method of claim 15, wherein the optimizer is configured for maximizing predicted earnings in the time horizon window by optimizing at least one of: EV charging price at points in time in the time horizon window, and a schedule in the time horizon window for charging a local energy storage system (ESS) using energy from a power grid, and for discharging energy from the ESS for charging the one or more EVs (paragraph [0078] teaches wherein optimizer executed maximum predicted earnings during a time horizon window, interpreted as a time period. The time period allows for managing power during peak power consumptions period or when market rates are low for charging and discharging to the local grid) .
Regarding claim 17, Ayoola teaches the method of claim 10, wherein the forecasted EV charging demand information is based at least in part on one or more of forecasted weather information and forecasted traffic information (paragraph [0091] teaches wherein forecasted weather and traffic data is used to determine demand information).
Regarding claim 18, Ayoola teaches the method of claim 10, comprising: receiving EV charging demand information for each of a plurality of EV charging stations (paragraphs [0012], [0013] and [0057] teaches wherein processors receive charging demand interpreted as energy and grid demand data. Paragraph [0015] teaches receiving status on current energy demands);
receiving energy cost information for each of the plurality of EV charging stations (paragraph [0053] teaches wherein energy cost and impact on the energy distribution network is estimated based on cost-benefit analysis framework. Paragraphs [0058] and [0071] teaches wherein the charging network forecasts or predicts when energy costs are at their lowest).
Ayoola does not explicitly teach execute an optimizer for: reducing greenhouse gas emissions (GHG) based on increasing a usage of a renewable energy source for EV charging at the EV charging station; and maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, the usage of the renewable energy source, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price; and charge one or more EVs using the EV charging station at the dynamic price.
Lee teaches reducing greenhouse gas emissions (GHG) based on increasing a usage of a renewable energy source for EV charging at the EV charging station and maximizing a predicted earnings on the EV charging station based on the usage of the renewable energy source (paragraph [0024] discloses “The system may reduce greenhouse gas emissions resulting from electricity generation by increasing the amount of electricity from green sources (e.g., solar collectors or thermal storage) that the charging stations use. Paragraphs [0023] and [0026] discloses maximizing predicted earnings by reducing operating costs by increasing the usage of renewable energy sources).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Lee reference so that the system may reduce instabilities in an electric power grid.
The suggestion/motivation for combination can be found in the Lee reference in paragraph [0024] wherein grid instabilities are reduced.
Ayoola and Lee do not explicitly teach maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price; charge one or more EVs using the EV charging station at the dynamic price.
Oobayashi teaches maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price (defined in paragraphs [0041] – [0042] discloses wherein predicted earnings or sales are maximized based on acquiring charging demand information. Paragraph [0035] discloses wherein the when the electric power unit price is determined by a dynamic pricing, the RC estimation unit 141 sets the electric power unit price based on the electric power demand forecast); and
charge one or more EVs using the EV charging station at the dynamic price (paragraphs [0045] – [0046] teaches wherein the vehicle is charged based on the dynamic price and when running or operating costs are low. Paragraph [0046] discloses wherein the vehicle is charged at night when the dynamic pricing is low).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola and Lee references with the charging system of the Oobayashi reference so that the system may maximizing the sales volume and reducing opportunity loss.
The suggestion/motivation for combination can be found in the Oobayashi reference in paragraph [0041] wherein maximizing sales volume and reducing opportunity loss is taught.
Regarding claim 19, Ayoola teaches a non-transitory computer-readable medium having computer-readable instructions stored thereon, the computer-readable instructions executable by at least one processor to cause the performance of operations (paragraph [0041] teaches wherein a process is provided on an electronic device such as a phone with an app) comprising:
receiving electric vehicle (EV) charging demand information (paragraphs [0012], [0013] and [0057] teaches wherein processors receive charging demand interpreted as energy and grid demand data. Paragraph [0015] teaches receiving status on current energy demands);
receiving energy cost information for an EV charging station, the energy cost information including current energy cost information and forecasted energy cost information, wherein actual energy costs vary over time (paragraph [0053] teaches wherein energy cost and impact on the energy distribution network is estimated based on cost-benefit analysis framework. Paragraphs [0058] and [0071] teaches wherein the charging network forecasts or predicts when energy costs are at their lowest).
Ayoola does not explicitly teach execute an optimizer for: reducing greenhouse gas emissions (GHG) based on increasing a usage of a renewable energy source for EV charging at the EV charging station; and maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, the usage of the renewable energy source, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price; and charge one or more EVs using the EV charging station at the dynamic price.
Lee teaches reducing greenhouse gas emissions (GHG) based on increasing a usage of a renewable energy source for EV charging at the EV charging station and maximizing a predicted earnings on the EV charging station based on the usage of the renewable energy source (paragraph [0024] discloses “The system may reduce greenhouse gas emissions resulting from electricity generation by increasing the amount of electricity from green sources (e.g., solar collectors or thermal storage) that the charging stations use. Paragraphs [0023] and [0026] discloses maximizing predicted earnings by reducing operating costs by increasing the usage of renewable energy sources).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola reference with the charging system of the Lee reference so that the system may reduce instabilities in an electric power grid.
The suggestion/motivation for combination can be found in the Lee reference in paragraph [0024] wherein grid instabilities are reduced.
Ayoola and Lee do not explicitly teach maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price; charge one or more EVs using the EV charging station at the dynamic price.
Oobayashi teaches maximizing a predicted earnings on the EV charging at the EV charging station, the predicted earnings being based on the EV charging demand information, and the energy cost information, wherein the maximizing comprises determining a dynamic EV charging price (defined in paragraphs [0041] – [0042] discloses wherein predicted earnings or sales are maximized based on acquiring charging demand information. Paragraph [0035] discloses wherein the when the electric power unit price is determined by a dynamic pricing, the RC estimation unit 141 sets the electric power unit price based on the electric power demand forecast); and
charge one or more EVs using the EV charging station at the dynamic price (paragraphs [0045] – [0046] teaches wherein the vehicle is charged based on the dynamic price and when running or operating costs are low. Paragraph [0046] discloses wherein the vehicle is charged at night when the dynamic pricing is low).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the charging system of the Ayoola and Lee references with the charging system of the Oobayashi reference so that the system may maximizing the sales volume and reducing opportunity loss.
The suggestion/motivation for combination can be found in the Oobayashi reference in paragraph [0041] wherein maximizing sales volume and reducing opportunity loss is taught.
Regarding claim 20, Ayoola teaches the non-transitory computer-readable medium of claim 19, wherein the maximizing further comprises determining a rate at which to charge a local energy storage system (ESS) using energy from a power grid, a rate at which to discharge energy from the ESS for charging the one or more EVs, or to neither charge nor discharge the ESS Paragraph [0054] teaches wherein a charge and discharge rates are determined to provide charge to vehicle from a grid and bidirectionally in reverse).
Response to Arguments
Applicant's arguments filed Arguments/Remarks 12/19/2025 have been fully considered but they are not persuasive.
Claim rejections under 35 USC 101
The applicant argues that the claims are not directed to an Abstract Idea. This argument is
not persuasive. As disclosed above, the claims recite receiving EV charging demand information, receiving current and forecasted energy cost information, executing an optimizer for reducing greenhouse gas emissions and predicted earnings, determining a dynamic EV charging price based on the received information, and charging one or more EVs using the dynamic EC charging price. The claims further recite forecasting demand, forecasting on-site power generation, determining charging and discharging schedules for an energy storage system, and transmitting pricing information to others. These limitations recite collecting information, analyzing information, displaying information and price optimization. These activities constitute mental processes because such observations, judgments and decision making can be performed in the human mind or with pen and paper.
The claims do not recite any improvement to computer functionality, EV charging technology, charging station hardware, battery technology, renewable energy generation technology, grid operation technology or energy storage technology. The claims do not recite any particular mechanism by which greenhouse gas emissions are reduced, nor do they recite a specific technological improvement to the manner by which EV charging is performed.
Thus, this argument is not persuasive.
Claim rejections under 35 USC 102
Applicant’s arguments, see Arguments/Remarks filed 12/19/2025 with respect to the rejection(s) of claim(s) 1-20 under Ayoola have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ayoola, Lee and Oobayashi.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXIS B PACHECO whose telephone number is (571)272-5979. The examiner can normally be reached M-F 9:00 - 5:30.
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ALEXIS BOATENG PACHECO
Primary Examiner
Art Unit 2859
/ALEXIS B PACHECO/Primary Examiner, Art Unit 2859
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