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 .
This second Final Rejection Office Action submitted to correct errors in the 35 U.S.C. §
101 analysis and subsequent withdrawal of rejections. The withdrawal of the 35 U.S.C. § 101 rejections are rescinded and those rejections reinstated.
Response to Arguments
The Amendment filed on April 22, 2026, has been entered. The examiner
acknowledges the cancellation of claim 11.
Claim Rejections 35 U.S.C § 112(b):
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 41, and there is no claim 41, thus Claim 10 does not refer to a system, and is therefore indefinite. This is likely a typographical error in the amending of the claim listing.
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: Review of the analysis of the subject matter eligibility analysis indicates that erroneous conclusions were reached and the invention does not disclose a practical application. In the absence of a practical application, the invention describes abstract ideas implemented in software, run on a processor, and providing output to a user, a classic case of “Apply It.”
MPEP 2106.04(d) describes a practical application as, “An improvement in the functioning of a computer, or an improvement to other technology or technical field; implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In the claims, independent claim 1 currently provides a broad overview of the invention providing an automated training service for workers in the field, or patrolling a beat. The claim describes abstract processes without any practical application improvement to technology or even suggestion of what technology is applied. The earliest mention of technology appears in claim 5, memory storing a machine learning component, but absent detail on how it works or is trained or updated. In general, for ML to be a practical application it must disclose not only training, but feedback into its training from evaluating previous model output (workflows in this case) that was evaluated in the real world. In this way, the model “learns” by comparing its ‘prediction’ with results from exercising the previously provided ‘output.’ Other technical improvements involving ML describe how the model learns and does not “forget” what was done on the previous ‘predictions’ (workflows). Put another way, the ML retaining its individual scenario ‘experience’ so it does not have to continue to re-learn its lessons, would be a technical improvement.
Suggestions to fortify independent claim 1 include, getting the ML components into claim 1, describing the feedback loop from original model output (real world performance) back into the model (results from using the workflow with what was productive and not productive) and some technical detail of how the training was changed. As part of these explanations in the claims, detail of how this is done is required. A high level of technical explanation in the claim can be supported in the specification to document the technical improvement.
In its present state, the claims fail to meet the standard for a practical application, and the rejection of claims under 35 U.S.C. § 101 will not be withdrawn.
Rejections under 35 U.S.C. § 103: Applicant’s amendment dated 4/22/2026 cancels claim 11 and thus no rejections under 35 U.S.C. § 103 remain.
Claim Rejections – 35 U.S.C. § 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 1-3, 5-10 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. The claims, 1-3, 5-10 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, 5-10 are directed to a machine (system), which is a statutory category of invention.
Step 2A
Claims 1-3, 5-10 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 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, (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).
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, 5-10 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-3, 5-10 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, a reinforcement learning (RL) module, a climate prediction module, an electric vehicle charging capacity prediction module, a transfer learning (TL) 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 adds nothing that is not already present when looking at the elements taken individually.
The claims do not amount to a “practical application” of the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, claims 1-3, 5-10 are directed to abstract ideas.
Step 2B
Claims 1-3, 5-10 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea.
The analysis above describes how the claims recite the additional elements beyond those identified above as being directed to an abstract idea, as well as why identified judicial exception(s) are not integrated into a practical application. These findings are hereby incorporated into the analysis of the additional elements when considered both individually and in combination.
For the reasons provided in the analysis in Step 2A, Prong 1, evaluated individually, the additional elements do not amount to significantly more than a judicial exception. Thus, taken alone, the additional elements do not amount to significantly more than a judicial exception.
Evaluating the claim limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. In addition to the factors discussed regarding Step 2A, prong two, there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely amount to instructions to implement the identified abstract ideas on a computer.
Therefore, since there are no limitations in the claims 1-3, 5-10 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, the claims are directed to non-statutory subject matter and are rejected under 35 U.S.C. § 101.
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.
Claims 1-3, 5-10 were previously not rejected by prior art under 35 U.S.C. § 103. Applicant’s amendment dated 4/22/2026 cancels claim 11 and thus no rejections under 35 U.S.C. § 103 remain.
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
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