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 final Office Action in response to Applicant’s communications received on June 26, 2026. Claims 1, 3, 5, 6 and 9 have been amended, claims 2 and 7-8 have been canceled.
Currently, claims 1, 3-6 and 9-20 are pending with claims 1, 3-6 and 9-10 under consideration for examination and claims 11-20 being withdrawn as being directed to non-elected invention. Claims 1 is independent.
Response to Amendments
Applicant’s amendments necessitated the new ground(s) of rejection in this Office Action.
Applicant indicated that the equation in claim 6 was independently developed by the Applicant is acknowledged.
Applicant’s amendments to claims 1, 3, 5, 6 and 9 are NOT sufficient to overcome the 35 U.S.C. § 101 rejection as set forth in the previous Office Action. Therefore, the 35 U.S.C. § 101 rejection to claims 1, 3-6 and 9-10 has been maintained.
Response to Arguments
Applicant’s arguments filed on June 26, 2026 have been fully considered but are not persuasive.
In the Remarks on page 11, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that independent claim 1 is no longer directed merely to data analysis, generic business rules, or mathematical calculations. Rather, the claim recites a specific technological solution for controlling the physical deployment and movement of a plurality of mobile charging stations.
In response to Applicant’s argument, the Examiner respectfully disagrees. Merely managing the movement of a plurality of mobile charging stations is still a form of fundamental economic practice and commercial activities, especially when the claim recites the learning data including the actions, charging profiles, movement costs, and rewards. The Specification describes that “Performing the simulation may further include calculating both the charging profile and the moving cost based on the movement of the mobile charging station and the provision of the charging service, and determining the reward based on the calculated charging profile and the moving cost.” (See )18). Thus, the claim falls in the certain methods of organizing human activity grouping.
Further, claim recites “training, by the processor, the multi-agent reinforcement learning model using the generated learning data through respective simulations to maximize the rewards; and upon receipt of an electric vehicle charging request, controlling, by the processor, movement of the plurality of mobile charging stations to locations determined by the trained multi-agent reinforcement learning model to provide charging service.”
Here, the claim recites: training the multi-agent reinforcement learning model to maximize the rewards, however, the trained multi-agent reinforcement learning model is used to provide charging services. Training the multi-agent reinforcement learning model to maximize the rewards but using the multi-agent reinforcement learning model to provide charging service fails to reflect any functioning improvement to the computer itself or other technology. Merely reciting training of a machine learning model without significant details of the training algorithm and retraining the machine learning model with optimized training data. Thus, the claim does not recite an improved way to train a machine learning model and does not purport to improve the machine learning using training data. See Intellectual Ventures, 792 F.3d at 1371.
In the Remarks on page 11, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that even if claim 1 is directed to an abstract idea, the combination of the features of claim 1 provides an inventive concept that is not well-understood, routine, or conventional in the art. The specific combination of “generating, by the processor, electric vehicle charging demand data in an NxM grid environment;” representing….
In response to Applicant’s argument, the Examiner respectfully disagrees. Step 2B is to determine whether any “inventive concept” which can transform the abstract idea into a patent-eligible invention. The “inventive concept” may arise in one or more of the individual claim limitations or in the ordered combination of the limitations. Alice, 134 S. Ct. at 2355. An “inventive concept” that transforms the abstract idea into a patent-eligible invention must be significantly more than the abstract idea itself, and cannot simply be an instruction to implement or apply the abstract idea on a computer. Id. at 2358.
In the present case, beyond the abstract idea, the claims recite the additional element of “a computing device having a processor and a memory” to perform the steps and to train a multi-reinforcement learning model. The Specification describes that “The computing device may include at least one of a processor, a memory, the user interface input device, the user interface output device, and a storage device that communicate through a bus.” (see ¶ 163). These additional elements, in light of the Specification, 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. However, using a 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.
In the Remarks on page 13, Applicant’s arguments that Chen, Yuan, and Wang, either alone or in combination, fail to teach, disclose, or suggest the aforementioned features recited in claim 1, and none of the cited references teach, disclose, or suggest the particular reward architecture recited in amended claims 5 and 6.
Applicant’s arguments have been fully considered and are persuasive. The closest prior art of Chen et al., (CN 112446539), Yuan et al., and Wang et al., (CN 110222907) fail to teach or suggest the features of “generating an elect vehicle charging demand probability model based on the electric vehicle charging demand information and the traffic information, generating electric vehicle charging demand data in an NxM grid environment by using the electric vehicle charging demand probability model, representing a plurality of electric vehicle charging demand matrix and a mobile charging station state matrix, respectively, corresponding to the NxM grid environment; and detecting a state of each mobile charging station of the plurality of mobile charging station, determining an action for each mobile charging of the plurality of mobile charging stations, the action including movement, waiting, or charging service provision based on the electric vehicle charging demand matrix, the mobile charging station state matrix, and action information associated with other mobile charging stations of the plurality mobile charging station,” as amended in claim 1. Therefore, the 35 U.S.C. § 103 rejection set forth in the previous Office Action is withdrawn.
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, 3-6 and 9-10 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, 3-6 and 9-10 are directed to a method for mobile electric vehicle charging service, which falls within the statutory category of a process.
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 “collecting electric vehicle charging demand information and traffic information, generating an electric vehicle charging demand probability model based on the collected information, generating electric vehicle charging demand data in an NxM grid environment, representing a plurality of electric vehicle charging demands and state of a plurality of mobile charging stations as electric vehicle charging demand matrix detecting a state of a mobile charging station, determining an action for each mobile charging station including moving or waiting based on the electric vehicle charging demand and the state of the mobile charging station, performing a simulation in the NxM grad environment to generate learning data including the actions, charging profits, movement costs and rewards, training a multi-agent reinforcement learning model using the generated learning data, upon receipt of an electric vehicle charging request, controlling movement of the plurality of mobile charging station to locations determined by the trained multi-agent reinforcement learning model to provide charging service.” The dependent claims further narrowing the limitations of claim 1 including “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 as feedback to a result including a charging profit and a movement cost based on the determined action, 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 the additional element of “a computing device having a processor and a memory” to perform the steps and to train the multi-reinforcement learning model. The Specification describes that “the computing device may include at least one of a processor, a memory, the user interface input device, the user interface output device, and a storage device that communicate through a bus.” (see ¶ 163). These additional elements, and in light of the Specification, 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. 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.”). Further, merely reciting training a multi-agent reinforcement learning model to maximize reward and using the trained multi-agent reinforcement learning model to provide charging service without significant details of the training algorithm, and without retraining the multi-agent reinforcement learning model with optimized training data fail to reflect any improvement to the functioning of the multi-agent reinforcement learning model. 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. See Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). 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. 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 the additional element of “a computing device having a processor and a memory” to perform the steps and to train the multi-reinforcement learning model. The Specification describes that “the computing device may include at least one of a processor, a memory, the user interface input device, the user interface output device, and a storage device that communicate through a bus.” (see ¶ 163). These additional elements, and in light of the Specification, 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 additional elements, 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, 3-6 and 9-10 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al., (WO 2024187458) discloses a method for managing operation of electric vehicle charging station to provide electric vehicle members with charging reservation and charging service between a plurality of power supply station.
Shin (US 2023/0044838) discloses a mobile electric vehicle charging system that generates electric power required to drive a vehicle and distribute electric power to a bidirectional power converter and the mobile charge.
Lee et al., (US 2022/0012647) discloses a charging station management server includes a communication device that communicates with an electric vehicle and a power grid management serve tor reserving a charging station based on driving information and information about the charging station.
Huang et al., (CN 110126666 B) discloses a charging station group control system based on cloud platform comprises charging station background control adopts grid selecting method to charge scheduling the charging station.
Meng et al., (CN 117261662 A) discloses a method for predicting load in charging station of electric vehicle and calculating the queuing time of the electric vehicle in the station.
Lin et al., (CN 118277673) discloses a method for providing charging station intelligent recommendation based on GPS and artificial intelligence through performing characteristic extraction on the information of the device to be charged at a plurality of predetermined time points.
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.
Wang et al., “Towards Accessible Shared Autonomous Electric Mobility with Dynamic Deadlines”, IEEE Transactions on Mobile Computing, Vol. 23, No. 1, January 2024.
Zhang et al., “Optimal Planning of PEV Charging Station With Single Output Multiple Cables Charging Spots”, IEEE Transactions on Smart Grid, Vol., 8, No. 5, September 2017.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAN CHOY whose telephone number is (571)270-7038. The examiner can normally be reached 5/4/9 compressed work schedule.
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/PAN G CHOY/Primary Examiner, Art Unit 3624
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