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 .
Response to Arguments
2. The Amendment filed on December 11, 2025, has been entered. The examiner acknowledges the amendments to claim 1, the cancellation of claim 4, and the addition of claim 11.
Claim Rejections 35 U.S.C § 112(b):
3. Applicant’s amendments to claim 1 that specify structure for the “modules” stated in the claims now renders claims 1-3, 5-9, and 11 not indefinite, thus rejection under 35 U.S.C § 112(b) for these claims is withdrawn.
4. Claim 10 is 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.
Claim 10 states, The system for optimizing electric vehicles…according to claim 4, where claim 4 has been cancelled with the current amendment, and thus Claim 10 does not refer to a system, and is therefore indefinite.
Since claim 10 fails 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, and has been determined to be indefinite, it is rejected under 35 U.S.C. § 112(b).
Rejections under 35 U.S.C. § 101: Applicant argues for reconsideration of rejections under § 101 in view of the amendments. Examiner agrees noting that employing additional elements to collect and process additional data applied to machine learning modules, employing reinforcement learning and transfer learning to optimize energy and environmental impacts while increasing overall charging capacity, does improve the technical field and amounts to a practical application.
In view of the above, the rejections under 35 U.S.C. § 101 will be withdrawn.
Rejections under 35 U.S.C. § 102 and 103: Applicant’s arguments in favor of claims 1-2, 5-6 are compelling in view of the amendments to the claims and the rejections of these claims will be withdrawn.
Claim Rejections – 35 U.S.C. § 101
5. 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 1-3, 5-11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. The claims, 1-3, 5-11 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 1-3, 5-11 are directed to a machine (system), which is a statutory category of invention.
Step 2A
Claims 1-3, 5-11 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, optimizing electric vehicle charging schedules and improving the accuracy of predicting greenhouse gas emissions resulting from the electric vehicle charging.
Claim 1 discloses a method, comprising: A system for optimizing electric vehicle charging schedules and improving the accuracy of predicting greenhouse gas emissions comprising:
inputting usage data of at least one charging facility, so as to optimize the charging behavior of the charging facility, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion),
and establish a charging schedule based on the reduction of greenhouse gas emissions is given as,
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(mathematical formulas or equations, economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion), inputting charging data and climate conditions of the charging facility, so as to adjust the charging schedule according to the interactivity of the charging data and the climate conditions; (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion),
inputting meteorological data collected at the location of the charging facility, so as to predict a meteorological change occurred at the location of the charging facility according to a time sequence, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion),
and integrate the climate conditions to adjust the charging schedule; (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion),
inputting charging demand data of the charging facility, so as to predict the charging demand of the charging facility according to the time sequence, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion),
and generate a charging prediction information accordingly; (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion),
integrating the outputs using the charging prediction information as a supplementary variable, and predicting the reduction of greenhouse gas emissions; (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion), and
adjust the charging schedule according to the usage data of the charging facility, the charging data, the meteorological data, the charging demand data, the charging prediction information, the supplementary variable and predicted greenhouse gas reduction, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion).
Additional limitations employ the system where usage data refers to at least one of capacity charging rate remaining capacity charging price and facility emissions – (claim 2), where charging data refers to wither failure rate or facility charging price- (claim 3), where the charging schedule is established by:
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(mathematical formulas or equations, economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 4), where a learning function defines a state space and an action parameter, the state space is a combination of the charging data and the corresponding climate condition, the action parameter is for an adjustment of the charging schedule, and the learning function responds to the amount greenhouse gas reduction when the action parameter is executed and the state space is presented, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 5), where the learning function defines a reward factor and a discount factor, (economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 6), where the reward function is defined as:
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(mathematical formulas or equations, economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 7), where the learning function is defined as:
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(mathematical formulas or equations, economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 8), where the learning function updates the reinforcement learning by:
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(mathematical formulas or equations, economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 9), and where a transfer learning function is defined by:
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(mathematical formulas or equations, economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 10), and optimizing charging schedules and improving greenhouse gas emissions based on the optimization function
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Inputting charging data and climate conditions, meteorological data to dynamically adjust the charging schedule, generate charging prediction information, adjust the charging schedule according to the data and predict greenhouse gas reduction, ((mathematical formulas or equations, economic principles and practices calculating costs, following rules or instructions, observation, evaluation, judgement, opinion – claim 11).
The claimed limitations listed employ abstract ideas involving mathematical formulas or equations, methods of organizing human behavior including economic principles and practices calculating costs, following rules or instructions, and mental processes involving observation, evaluation, judgement, and opinion.
Thus, the concepts set forth in claims 1-3, 5-11 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-3, 5-11 recite additional limitations which are hardware or software elements such as based on transfer learning and deep reinforcement learning, one or more processors and one or more storage devices having stored thereon instructions that are configured to be executable by the one or more processors to implement:
a mixed-integer linear programming (MILP) optimization module, for, wherein the mixed-integer linear programming (MILP) optimization module is based on a mixed-integer linear programming (MILP) function f(x) to establish the charging schedule, and the mixed-integer linear programming (MILP) function f(x) is given in Equation (1):
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where, Cemissions(t) is the carbon emissions from power generation of the charging facility at the charging time t; Xijt is a decision variable, which is a non-negative variable for each an electric vehicle i at the charging time t of a charging station j; Pt is the charging price of the charging facility; and Yjt is a function of whether or not the charging station j is used at the charging time t.
a reinforcement learning (RL) module, a climate prediction module, for, let the reinforcement learning (RL) module dynamically adjust, an electric vehicle charging capacity prediction module, a transfer learning (TL) module, for integrating the outputs of the mixed integer linear programming (MILP) optimization module and the reinforcement learning (RL) module, an integration and control module, linked to the mixed-integer linear programming (MILP) optimization module, the reinforcement learning (RL) module, the climate prediction module, the electric vehicle charging capacity prediction module and the transfer learning (TL) module, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination indicates the application of science and engineering employing additional elements to collect and process additional data applied to machine learning modules employing reinforcement learning and transfer learning to optimize energy and environmental impacts while increasing overall charging capacity. This does improve the technical field and amounts to a practical application.
Therefore, since the limitations in the claims transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims 1-3, 5-11 are directed to statutory subject matter and are not rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 11 is rejected under 35 U.S.C. § 103 as being taught by Bhimani, (US-
20230196234-A1), in view of Joshi, “Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems.”
Regarding Claim 11, A system for optimizing electric vehicle charging schedules and improving the accuracy of predicting greenhouse gas emissions based on transfer learning and deep reinforcement learning, comprising one or more processors and one or more
storage devices having stored thereon instructions that are configured to be executable by the one or more processors, Bhimani teaches (Techniques as described herein can be used to improve overall carbon footprints of electricity or energy consumption by electric vehicles by leveraging artificial intelligence (AI) or machine learning(ML) techniques to predict or forecast energy or electricity demands and greenhouse or carbon dioxide gas emissions associated with the demands and by using predictions or forecasts of the demands and the emissions to schedule electric vehicle charging events to satisfy the demands and influence electricity or energy consumption behaviors to minimize the emissions. to implement:
a mixed-integer linear programming (MILP) optimization module, for inputting usage data of at least one charging facility, so as to optimize the charging behavior of the charging facility, Bhimani does not teach, Joshi teaches, (The unit control (UC) problem of microgrids is modeled as MILP, which obtains the UC decision variables at each optimization time step, [p.1517]), and establish a charging schedule based on the reduction of greenhouse gas emissions, (Specific portions or intervals within the available charging time may be used by the charging schedule generator to meet electric energy demands such that greenhouse or carbon gas emissions associated with these charging events are minimized, [ ] the system may comprise a charging schedule generator to schedule, concentrate, consolidate and/or optimize charging events for some or all of the electric vehicles FIG. 1A, [0065]), wherein the mixed-integer linear programming (MILP) optimization module is based on a mixed-integer linear programming (MILP) function f(x) to establish the charging schedule, and the mixed integer linear programming (MILP) function f(x) is given in Equation (6):
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where, Cemissions(t) is the carbon emissions from power generation of the charging facility at the charging time t; Xijt is a decision variable, which is a nonnegative variable for each an electric vehicle i at the charging time t of a charging station j; Pt is the charging price of the charging facility; Bhimani teaches, (scheduling the charging time duration for battery charging within a specific candidate time duration that is selected from among the plurality of candidate time durations, [claim 1], wherein a predicted greenhouse gas emission for the electricity grid to charge the electric vehicle in the specific candidate time duration is lower than all other predicted greenhouse gas emissions for the electricity grid to charge the electric vehicle in all other candidate time durations in the plurality of candidate time durations, [claim 2], and the system (102) can cause the electric vehicle to be charged during specific daytime hours(if possible) when utility cost for electricity is still cheap, but as the sun is up, the electricity can be generated using solar energy that prevents or reduces greenhouse or carbon gas emissions across the grid used to charge the electric vehicle, [0076]).
a reinforcement learning (RL) module, for inputting charging data and climate conditions of the charging facility, so as to adjust the charging schedule according to
the interactivity of the charging data and the climate conditions; (The system (102) or the electricity demand and carbon prediction models (112) therein can be used to extract input features from raw data (or history data) collected from the grid(s) (104), the electric vehicles (108), weather information sources, etc., and use these input features to generate estimations, predictions or forecasts of current or future electricity demands at various levels such as global, regional, grid, locale and/or vehicle levels, [0081]), a climate prediction module, for inputting meteorological data collected at the location of the charging facility, so as to predict a meteorological change occurred at the location of the charging facility according to a time sequence, and integrate the climate conditions to let the reinforcement learning (RL) module dynamically adjust the charging schedule; (the system (102) or the prediction models (112) therein may schedule these charging events to specific times when relatively clean energy production is predicted or forecasted to be online in the next few days for the purpose of reduce individual or overall carbon footprint of electric vehicles. For example, the system (102) can incorporate weather information to determine that a relatively large amount of electricity may be produced from solar energy sources at specific times and steer or select charging schedules for electric vehicles with a relatively large charging time windows to these specific times to minimize emissions associated with charging these electric vehicles, [0086]),
an electric vehicle charging capacity prediction module, for inputting charging demand data of the charging facility, so as to predict the charging demand of the charging facility according to the time sequence, and generate a charging prediction information accordingly; (The AI/ML techniques enable the demand and emission prediction models to process large-scale raw data (e.g., in real time, concurrently, etc.) collected from the electricity grids and electric vehicles. These electricity grids may be deployed in many national or transnational geographic region and used to satisfy electricity demands from numerous (e.g., millions of, etc.) electric vehicles operating in these regions, [0038]),
a transfer learning (TL) module, for integrating the outputs of the mixed integer linear programming (MILP) optimization module and the reinforcement learning (RL) module, using the charging prediction information as a supplementary variable, and predicting the reduction of greenhouse gas emissions; and an integration and control module, linked to the mixed-integer linear programming (MILP) optimization module, the reinforcement learning (RL) module, the climate prediction module, the electric vehicle charging capacity prediction module and the transfer learning (TL) module, so as to adjust the charging schedule according to the usage data of the charging facility, the charging data, the meteorological data, the
charging demand data, the charging prediction information, the supplementary variable and predicted greenhouse gas reduction, Bhimani teaches, (the system (102) may comprise a charging schedule generator (114) to schedule, concentrate, consolidate and/or optimize charging events for some or all of the electric vehicles (108 of FIG. 1A). For example, the charging schedule generator (114) may fit or adjust charging durations for some electric vehicles within relatively large available charging time durations before these electric vehicles resume driving operations, when drivers/owners of these electric vehicles have provided permissions to the system (102) to schedule charging events. Specific portions or intervals within the available charging time may be used by the charging schedule generator (114)to meet electric energy demands such that greenhouse or carbon gas emissions associated with these charging events are minimized.
Bhimani and Joshi are both considered to be analogous to the claimed invention because they are both in the field of optimized EV charging. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the emissions prediction techniques of Bhimani with the AI and ML techniques for microgrid energy management systems of Joshi to achieve greater performance and efficiency in managing energy resources, Joshi, [Abstract].
Conclusion
THIS ACTION IS MADE FINAL. 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.
Claim 1, is not rejected by prior art under 35 U.S.C. § 103. Dependent claims 2-3, 5-6,
are not rejected because of their inherent dependency on independent claim 1. Claims 7-10 were not rejected under 35 U.S.C. § 103 in the earlier Office Action.
The closest prior art to the invention includes Bhimani, (US 20240034180 A1) “Method for Reducing Carbon Footprint Leveraging A Cost Function for Focused Optimization,” Ju (US 20240227611 A1), “Method and Apparatus for Operating Electric Vehicle Charging Infrastructure,” and Galbraith, (US 20240343149 A1), “Systems and Methods for Optimal Control of Electrical Vehicle Fleet and Charging Infrastructure.”
Regarding claim 1, Claim 1 discloses a method, comprising: A system for optimizing electric vehicle charging schedules and improving the accuracy of predicting greenhouse gas emissions based on transfer learning and deep reinforcement learning, comprising one or more processors and one or more storage devices having stored thereon instructions that are configured to be executable by the one or more processors, Bhimani teaches improving the carbon footprint of EVs, and an optimized schedule for battery operations based on cost using AI and ML, to implement:
a mixed-integer linear programming (MILP) optimization module, for inputting usage data of at least one charging facility, so as to optimize the charging behavior of the charging facility and establish a charging schedule based on the reduction of greenhouse gas emissions, Bhimani employs multi-variate prediction models and neural nets to capture trends and factors through optimization models, wherein the mixed-integer linear programming (MILP) optimization module is based on a mixed-integer linear programming (MILP) function f(x) to establish the charging schedule, and the mixed-integer linear programming (MILP) function f(x) is given in Equation (1): Bhimani does not teach these specifics, the closest art is Ju and Galbraith, neither teaching the claim as recited,
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where, Cemissions(t) is the carbon emissions from power generation of the charging facility at the charging time t; Xijt is a decision variable, which is a non-negative variable for each an electric vehicle i at the charging time t of a charging station j; Pt is the charging price of the charging facility; and Yjt is a function of whether or not the charging station j is used at the charging time t.
a reinforcement learning (RL) module, for inputting charging data and climate
conditions of the charging facility, so as to adjust the charging schedule according to
the interactivity of the charging data and the climate conditions; Bhimani teaches a charging and scheduling generator incorporating charging and climate conditions,
a climate prediction module, for inputting meteorological data collected at the
location of the charging facility, so as to predict a meteorological change occurred at
the location of the charging facility according to a time sequence, and integrate the
climate conditions to let the reinforcement learning (RL) module dynamically adjust
the charging schedule;
an electric vehicle charging capacity prediction module, for inputting charging
demand data of the charging facility, so as to predict the charging demand of the charging facility according to the time sequence, and generate a charging prediction
information accordingly; Bhimani teaches generating a plurality of candidate charging schedules, each of which begins at a respective start time and finishes at a respective end time within the specific time window with charging predictions,
a transfer learning (TL) module, for integrating the outputs of the mixed integer linear programming (MILP) optimization module and the reinforcement
learning (RL) module, using the charging prediction information as a supplementary variable, and predicting the reduction of greenhouse gas emissions; and
an integration and control module, linked to the mixed-integer linear programming (MILP) optimization module, the reinforcement learning (RL) module, the climate prediction module, the electric vehicle charging capacity prediction module and the transfer learning (TL) module, so as to adjust the charging schedule according to the usage data of the charging facility, the charging data, the meteorological data, the charging demand data, the charging prediction information, the supplementary variable and predicted greenhouse gas reduction, Bhimani teaches a system for generating charging and transfer schedules while concentrating, consolidating and/or optimizing electricity charging, energy/power transfer events for some or all of the electric vehicles, while the schedule generator (may fit or adjust charging and/or transfer durations for some electric vehicles; the system can extract input features from raw data collected from the grids, and weather information sources, and predictions of forecasts of current or future electricity demand.
None of the prior art alone or in combination teaches the claimed invention as recited in the claims wherein the novelty is in the combination of all the limitations and not in a single limitation.
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703)756-1822. The examiner can normally be reached M-F 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624