Requirement for Information under 37 CFR § 1.105
Applicant and the assignee of this application are required under 37 CFR 1.105 to provide the following information that the examiner has determined is reasonably necessary to the examination of this application.
The instant application includes an equation in claim 6. Information is requested regarding such equation. Is the claimed equation based upon the work of others, or simply the product of the Applicant’s invention?
The equation of claim 6 is directed towards a function for determining an indicator of demand (DD). Although the broader concept of determining a reward is old and well known in the art, claim 6 of the instant application discloses a specific manner of doing this by summing the agent action reward and the agent state reward. The information requested by the Examiner is required in order to complete the background description in the disclosure by documenting methods of determining the net benefit of a customer order known by those of ordinary skill in the art.
The information is required to document the level of skill and knowledge in the art of analyzing product portfolio in production management.
In response to this requirement, please provide the title, citation and copy of each publication, text, or relevant material, that any of the applicants relied upon to develop the disclosed subject matter that describes the applicant’s invention, particularly as to developing the equations of claim 6. For each document provided, please provide a concise explanation of the reliance placed on that publication in the development of the disclosed subject matter.
In responding to those requirements that require copies of documents, where the document is a bound text or a single article over 50 pages, therequirement may be met by providing copies of those pages that provide theparticular subject matter indicated in the requirement, or where such subjectmatter is not indicated, the subject matter found in applicant's disclosure.
The fee and certification requirements of 37 CFR 1.97 are waived for those documents submitted in reply to this requirement. This waiver extends only to those documents within the scope of this requirement under 37 CFR 1.105 that are included in the applicant’s first complete communication responding to this requirement. Any supplemental replies subsequent to the first communication responding to this requirement and any information disclosures beyond the scope of this requirement under 37 CFR 1.105 are subject to the fee and certification requirements of 37 CFR 1.97.
The applicant is reminded that the reply to this requirement must be made with candor and good faith under 37 CFR 1.56. Where the applicant does not have or cannot readily obtain an item of required information, a statement that the item is unknown or cannot be readily obtained may be accepted as a complete reply to the requirement for that item.
This requirement is an attachment of the enclosed Office action. A complete reply to the enclosed Office action must include a complete reply to this requirement. The time period for reply to this requirement coincides with the time period for reply to the enclosed Office action, which is three (3) months.
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 .
Introduction
The following is a non-final Office Action in response to Applicant’s communications received on February 27, 2026. Claims 1-20 are pending with claims 1-10 under consideration for examination and claims 11-20 being withdrawn as being directed to non-elected invention. Claims 1 is independent.
Election/Restrictions
Applicant’s election without traverse of Invention I, claims 1-10, in the reply filed on February 27, 2026 is acknowledged.
Claims 11-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on February 27, 2026.
Priority
Applicant claims the priority of a Foreign application No. KR 10-2024-0085119, filed on June 28, 2024 is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/21/2024 appears to be in compliance with the provisions of 37 CFR 1.97 and has been entered into record. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1-10 are directed to a method for vehicle charging without tied to a particular machine for performing the steps, which falls outside of the four statutory categories. However, claims 1-10 will be included in Step 2 Analysis for the purpose of compact prosecution.
With respect to claims 1-10, the claims are directed to non-statutory subject matter because the claims are directed to a method without tied to a particular machine in the body of the claims for performing the steps. One factor to consider when determining whether a claim recites a §101 patent eligible process is to determine if the claimed process (1) is tied to a particular machine or; (2) transforms a particular article to a different state or thing. See In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008) (en banc) aff’d, Bilski v. Kappos, 561 U.S. ___, 130 S.Ct. 3218, 95 USPQ2d 1001 (U.S. 2010). (Machine-or-Transformation Test).
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Claims 1 recites a method for vehicle charging service. More specifically, the claim recites the limitations of “generating an electric vehicle charging demand, detecting a state of a mobile charging station, determining an action of the mobile charging station including moving or waiting based on the electric vehicle charging demand and the state of the mobile charging station, paying a reward as feedback to a result including a charging profit and a moving cost based on the determined action, storing and accumulating the action, the result, and the reward as learning data, training a multi-agent reinforcement learning model for generating an optimal deployment of the mobile charging station by using the accumulated learning data”; the dependent claims further narrowing the limitations of claim 1 including “collecting data with respect to the electric vehicle charging demand, predicting an electric vehicle charging amount, generating an electric vehicle charging demand probability…, generating the electric vehicle charging demand by using the generated electric vehicle charging demand probability model, determining the action through the multi-agent reinforcement learning model, calculating a future value based on the action of the mobile charging station and the electric vehicle charging demand, paying the reward with respect to the action in proportion to the calculated future value, determining the reward based on Equation 1, performing a simulation to generate the electric vehicle charging demand…, calculating the electric vehicle charging demand and a deployment of the mobile charging station, calculating both the charging profit and the moving cost…, determining the reward based on the calculated charging profit and the moving cost, training the multi-agent reinforcement learning model...”. None of the claim limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to methods, that allow user to manage mobile electric vehicle charging service by calculating charging profit and moving cost, and determining the reward based on a mathematic equation. The claims involved with fundamental economic practice and mathematical calculation, which fall within the certain methods of organizing human activity grouping and mathematical calculation grouping. The mere nominal recitation of “training a multi-agent reinforcement learning model” and “predicting an electric vehicle charging amount through an artificial intelligence model” do not take the claims out of the certain methods of organizing human activity grouping and mathematical calculation grouping because: first, making prediction with known data is a fundamental building block of human ingenuity; and second, using a machine learning model is merely adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. The Supreme court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, the claims recite no additional element for performing the steps, when given the broadest reasonable interpretation, a machine is not required in the claim. Even if claim 1 recites the additional elements of “a processor” and “memory” as described in paragraph [0163] of the Specification for performing the steps. These additional elements are recited at a high level of generality and merely invoked as tools to perform the generic computer functions including receiving, storing, manipulating, and transmitting data over a network. Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014); see also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims.”). As to learning per se, such an argument overlooks the entire education system. Reciting machine learning is placing such learning in a computer context, offering no technological implementation details beyond the conceptual idea to use a machine for learning. Further, merely reciting training a multi-agent reinforcement learning model without significant details of the training algorithm, and retraining the multi-agent reinforcement learning model with optimized training data in order to improve the functioning of the machine learning model. However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea, or reflect an improvement to the functioning of a computer itself or another technology. Therefore, the claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, the claims recite no additional element for performing the steps, when given the broadest reasonable interpretation, a machine is not required in the claim. Even if claim 1 recites the additional elements of “a processor” and “memory” as described in paragraph [0163] of the Specification for performing the steps. These additional elements are recited at a high level of generality and merely invoked as tools to perform the generic computer functions including receiving, manipulating, and transmitting data over a network. Taking the claim elements separately and as an ordered combination, the processor, if applicable, at best, may perform the generic computer functions including receiving, storing, manipulating, and transmitting information over a network. However, generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1326-27, 122 USPQ2d 1377, 1379-80 (Fed. Cir. 2017) (claim reciting multiple abstract ideas, i.e., the manipulation of information through a series of mental steps and a mathematical calculation, was held directed to an abstract idea)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1-10 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above. Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 4-10 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al., (CN 112446539, hereinafter: Chen), and in view of Yuan et al., “A Survey of Progress on Cooperative Multi-Agent Reinforcement Learning in Open Environment”, National Key Laboratory for Novel Software Technology, Nanjing University, China, December 2, 2023.
Regarding claim 1, Chen discloses a multi-agent reinforcement learning-based mobile electric vehicle charging service method, the method comprising:
generating an electric vehicle charging demand (see pg. 4, ¶ 8; pg. 6, ¶ 3-6, ¶ 10; pg. 11, ¶ 3-5; pg. 15, ¶ 9-10);
detecting a state of a mobile charging station (see pg. 3, ¶ 9; pg. 10, ¶ 5-11; pg. 20, ¶ 2);
determining an action of the mobile charging station including moving or waiting based on the electric vehicle charging demand and the state of the mobile charging station (see pg. 4, ¶ 2-4; pg. 9, ¶ 8 to pg. 10, ¶ 3; pg. 11, ¶ 8-10; pg. 12, 9-10).
Chen discloses obtaining the charging moving cost and waiting time cost corresponding to each vehicle, and determining a comprehensive cost corresponding to each group of addressing schemes (see pg. 3, ¶ 2-4).
Chen does not explicitly disclose the following limitations; however, Yuan in an analogous for training a team of agents to cooperatively achieve tasks discloses
a multi-agent reinforcement learning (see Title; pg. 9, § 2.2.3; pg. 27, § 4.2)
paying a reward as feedback to a result including a charging profit and a moving cost based on the determined action (see pg. 3, § 2.1 to § 2.1.1).
storing and accumulating the action, the result, and the reward as learning data (see pg. 2, ¶ 3 to pg. 3, ¶ 1; pg. 5, ¶ 5; pg. 18, § 3.3.1); and
training a multi-agent reinforcement learning model for generating an optimal deployment of the mobile charging station by using the accumulated learning data (see Abstract; pg. 9, § 2.2.3; pg. 11, § 2.2.4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the elements in the system of Chen to include teaching of Yuan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, enabling better informed decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 4, Chen does not explicitly disclose the following limitations; however, Yuan discloses the method of claim 1, wherein determining the action of the mobile charging station comprises determining the action through the multi-agent reinforcement learning model including a Deep Q Network, a Dueling Q Network, and an Actor-Critic Model (see pg. 5, ¶ 5; pg. 8, § 2-1-4; pg. 17, § 3.2.3; pg. 39, [70]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the elements in the system of Chen to include teaching of Yuan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, enabling better informed decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 5, Chen discloses the method of claim 1, wherein paying the reward comprises:
calculating a future value based on the action of the mobile charging station and the electric vehicle charging demand, respectively (see pg. 10, ¶ 6-11; pg. 14, ¶ 6-8; pg. 12, ¶ 9 to pg. 13, ¶ 3).
Chen does not explicitly disclose the following limitations; however, Yuan discloses
paying the reward with respect to the action in proportion to the calculated future value (see pg. 3, § 2.1 to § 2.1.1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the elements in the system of Chen to include teaching of Yuan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, enabling better informed decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6, Chen does not explicitly disclose the following limitations; however, Yuan discloses the method of claim 1, wherein paying the reward comprises determining the reward based on Equation 1:
RAgent = RAction + RState
wherein agent is an electric vehicle charging station, RAgent is the reward obtained by respective agents, RAction is an agent action reward, and RState is an agent state reward, and
wherein RAction is proportional to a charging service providing profit, and inversely proportional to an agent moving cost, and RState is proportional to a number of remaining electric vehicles for charging and inversely proportional to a number of agents.
(see pg. 3, § 2.1.1)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the elements in the system of Chen to include teaching of Yuan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, enabling better informed decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 7, Chen does not explicitly disclose the following limitations; however, Yuan discloses the method of claim 1, wherein accumulating as the learning data comprises:
performing a simulation to generate the electric vehicle charging demand over time in an N x M grid environment and to generate the reward based on a movement of the mobile charging station and provision of a charging service, for accumulation of the learning data (see pg. 2, ¶ 3 to pg. 3, ¶ 2; pg. 26, ¶ 3-4; pg. 30, ¶ 3). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the elements in the system of Chen to include teaching of Yuan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, enabling better informed decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 8, Chen does not explicitly disclose the following limitations; however, Yuan discloses the method of claim 7, wherein performing the simulation comprises:
calculating the electric vehicle charging demand and a deployment of the mobile charging stations as a 2-dimension matrix matching the N x M grid environment (see pg. 30, § 4.2.2; pg. 33, § 4.2.7). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the elements in the system of Chen to include teaching of Yuan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, enabling better informed decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 9, Chen discloses the method of claim 7, wherein performing the simulation further comprises:
calculating both the charging profit and the moving cost based on the movement of the mobile charging station and the provision of the charging service (see pg. 2, last ¶ to pg. 3, ¶ 2; pg. 11, ¶ 5); and
determining the reward based on the calculated charging profit and the moving cost (see (pg. 3, ¶ 3-7; pg. 11, ¶ 3-10).
Regarding claim 10, Chen does not explicitly disclose the following limitations; however, Yuan discloses the method of claim 9, wherein training the multi-agent reinforcement learning model further comprises training the multi-agent reinforcement learning model in a direction to maximize the reward through repetitive simulation (see pg. 3, § 2.1.1; pg. 4, § 2.1.2; pg. 32, ¶ 1). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the elements in the system of Chen to include teaching of Yuan in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more accurate computation, enabling better informed decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Chen and in view of Yuan as applied to claims 1, 4-10 above, and further in view of Wang et al., (CN 110222907, hereinafter: Wang).
Regarding claim 2, Yuan discloses that the research focuses in the field of cooperative MARL is to improve the coordination efficiency of the system and to promote the application of artificial intelligence in real world (see Abstract).
Chen and Yuan do not explicitly the following limitations; however, Wang in an analogous art for EV charging station planning discloses method of claim 1, wherein generating the electric vehicle charging demand comprises:
collecting data with respect to the electric vehicle charging demand and a traffic amount (see Abstract; pg. 2, ¶ 3, ¶ 6-7; pg. 3, ¶ 2; pg. 5, ¶ 1);
predicting an electric vehicle charging amount through an artificial intelligence model based on the collected data (see pg. 3, ¶ 2; pg. 4, ¶ 6-9);
generating an electric vehicle charging demand probability model based on the predicted electric vehicle charging amount (see pg. 6, ¶ 15 to pg. 7, ¶ 4); and
generating the electric vehicle charging demand by using the generated electric vehicle charging demand probability model (see pg. 11, ¶ 5-6; pg. 15, ¶ 4-5).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the elements in the system of Chen and in view of Yuan to include teaching of Wang in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3, Chen and Yuan do not explicitly the following limitations; however, Wang discloses the method of claim 2, wherein the electric vehicle charging demand probability model comprises a Poisson distribution model (see pg. 8, ¶ 3, ¶ 7, ¶ 10; pg. 11, last ¶ to pg. 12, ¶ 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the elements in the system of Chen and in view of Yuan to include teaching of Wang in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more optimal solution, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Paik et al., (WO 2022186445) discloses a method for providing electric vehicle charging service based on service call information including charging state information of the vehicle, the charging demand amount and the charging request time.
Tani et al., (JP 2022072202) discloses a power feeding system comprising a mobile charging station, a power generator for generating electricity using hydrogen and oxygen stored in the storage part.
Nabizada et al., (US 2025/0214473) discloses a charging system for providing electrical interconnect between the charging site and an electric vehicle.
Tan et al., (US 2019/0014488) discloses a method for deep reinforcement learning using deep learning network, deep Q-learning network, dueling network, or any other applicable network.
Praneeth et al., “Scheduling of EV Charging Station for Demand Response Support to Utility”, 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP).
Qureshi et al., “Dynamic Pricing Based Mobile Charging Service for Electric Vehicle Charging”, Department of Electrical Engineering Indian Institute of Technology, Delhi, Inda. 2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation.
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/PAN G CHOY/Primary Examiner, Art Unit 3624