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 2A Prong, 1: Recitation of a judicial exception
The claims are directed to operations including:
receiving demand information and energy cost information;
executing an optimizer to maximize predicted earnings;
determining a dynamic optimized EV charging price;
scheduling charging/discharging of an energy storage system;
transmitting pricing signals; and
charging EVs at the optimized price.
These limitations describe mathematical concepts (optimization, forecasting,
predicting) and methods organizing human activity (managing pricing and commerce of EV charging services). Both are recognized categories of Abstract Ideas.
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.
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 § 112
1. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term, “maximize predicted earnings” is vague and subjective. It is unclear what constitutes “maximizing,” what baseline is used for comparison , or how “predicted earnings” are measured. The scope of protection is uncertain.
The term, “dynamic optimized EV charging price” lacks clarity as to what is “optimized” and according to what criteria. The phrase could encompass multiple, different pricing strategies without improving metes and bounds.
The phrase “time horizon window” in claims 6, 7, 15 and 16 is indefinite because it is not clear what constitutes such a window (eg minutes, hours, days, variable intervals).
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)(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.
1. Claims 1 – 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ayoola (US 20230024900).
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);
execute an optimizer for maximizing predicted earnings on EV charging at the EV charging station based on the EV charging demand information and the energy cost information, wherein the maximizing comprises determining a dynamic optimized EV charging price (paragraphs [0012] and [0013] teaches wherein an optimizer, interpreted as a processor executes maximizing predicted earnings, interpreted as maximum future revenue based on energy cost and charge demand. This information is then used to predict a dynamic real time price for providing charging services. Paragraphs [0093] [0097] teaches real-time micro-grid optimization via price signals and predicting optimal prices. Paragraphs [0100] [0131] and [0134] teaches wherein a processor is executed using to determine predicted earnings, interpreted as maximum revenue); and
charge one or more EVs using the EV charging station at the dynamic optimized price (paragraphs [0073]-[0074] wherein charging is provided to vehicles via a fast-charging outlet. Paragraph [0097] teaches wherein the predicted optimal price signal may be used to drive the movement (e.g., charge or discharge) of energy from one point to another within a given region, local market, or concatenated market).
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);
execute the optimizer for maximizing predicted combined earnings on EV charging at the plurality of EV charging stations based on the EV charging demand information and the energy cost information, wherein the maximizing comprises determining a dynamic optimized EV charging price at each of the plurality of charging stations (paragraphs [0012] and [0013] teaches wherein an optimizer, interpreted as a processor executes maximizing predicted earnings, interpreted as maximum future revenue based on energy cost and charge demand. This information is then used to predict a dynamic real time price for providing charging services. Paragraphs [0093] [0097] teaches real-time micro-grid optimization via price signals and predicting optimal prices. Paragraphs [0100] [0131] and [0134] teaches wherein a processor is executed using to determine predicted earnings, interpreted as maximum revenue); and
charge one or more EVs using at least one of the EV charging stations at the respective dynamic optimized price for that EV charging station (paragraphs [0073]-[0074] wherein charging is provided to vehicles via a fast-charging outlet. Paragraph [0097] teaches wherein the predicted optimal price signal may be used to drive the movement (e.g., charge or discharge) of energy from one point to another within a given region, local market, or concatenated market).
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);
executing an optimizer for maximizing predicted earnings on EV charging at the EV charging station based on the EV charging demand information and the energy cost information, wherein the maximizing comprises determining a dynamic optimized EV charging price (paragraphs [0012] and [0013] teaches wherein an optimizer, interpreted as a processor executes maximizing predicted earnings, interpreted as maximum future revenue based on energy cost and charge demand. This information is then used to predict a dynamic real time price for providing charging services. Paragraphs [0093] [0097] teaches real-time micro-grid optimization via price signals and predicting optimal prices. Paragraphs [0100] [0131] and [0134] teaches wherein a processor is executed using to determine predicted earnings, interpreted as maximum revenue); and
charging one or more EVs using the EV charging station at the dynamic optimized price (paragraphs [0073]-[0074] wherein charging is provided to vehicles via a fast-charging outlet. Paragraph [0097] teaches wherein the predicted optimal price signal may be used to drive the movement (e.g., charge or discharge) of energy from one point to another within a given region, local market, or concatenated market).
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; executing the optimizer for maximizing predicted combined earnings on EV charging at the plurality of EV charging stations based on the EV charging demand information and the energy cost information, wherein the maximizing comprises determining a dynamic optimized EV charging price at each of the plurality of 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); and
charging one or more EVs using at least one of the EV charging stations at the respective dynamic optimized price for that EV charging station (paragraphs [0073]-[0074] wherein charging is provided to vehicles via a fast-charging outlet. Paragraph [0097] teaches wherein the predicted optimal price signal may be used to drive the movement (e.g., charge or discharge) of energy from one point to another within a given region, local market, or concatenated market).
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);
executing an optimizer for maximizing predicted earnings on EV charging at the EV charging station based on the EV charging demand information and the energy cost information, wherein the maximizing comprises determining a dynamic optimized EV charging price (paragraphs [0012] and [0013] teaches wherein an optimizer, interpreted as a processor executes maximizing predicted earnings, interpreted as maximum future revenue based on energy cost and charge demand. This information is then used to predict a dynamic real time price for providing charging services. Paragraphs [0093] [0097] teaches real-time micro-grid optimization via price signals and predicting optimal prices. Paragraphs [0100] [0131] and [0134] teaches wherein a processor is executed using to determine predicted earnings, interpreted as maximum revenue); and
charging one or more EVs using the EV charging station at the dynamic optimized price (paragraphs [0073]-[0074] wherein charging is provided to vehicles via a fast-charging outlet. Paragraph [0097] teaches wherein the predicted optimal price signal may be used to drive the movement (e.g., charge or discharge) of energy from one point to another within a given region, local market, or concatenated market).
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).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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ALEXIS BOATENG PACHECO
Primary Examiner
Art Unit 2859
/ALEXIS B PACHECO/Primary Examiner, Art Unit 2859