CTFR 18/730,420 CTFR 80694 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This final action is in response to Applicant’s filing dated February 11, 2026. Claims 1-5 and 7-18 are currently pending and have been considered, as provided in more detail below. Claims 1-2, 4, 8-9, 11, 14-16 have been amended. Claim 6 has been cancelled and claim 18 has been newly added. *Examiner Note: Claim language is bolded . Cited References and Applicant’s arguments are italicized . Examiner interpretations are preceded with an asterisk *. Response to Arguments 07-37 AIA Applicant's arguments filed 2/11/26 have been fully considered but they are not persuasive. Applicant’s assertion that “ Lo wenthal fails to disclose or suggest a configuration outputting information about the charging status at future operation timings “ is not commensurate with the scope of the claims, as the claims only require outputting first vehicle information indicating the target vehicle for which the charging plan information of the target vehicle is absent, or for which it is indicated that charging is not completed before the operation timing in the charging plan information. Neither the original nor the amended claims mention future operation timings. The independent claims broadly recite charging-status-related vehicle information associated with deficient or incomplete charging conditions which is taught by Lowenthal, as discussed in detail below. Specifically, Lowenthal discloses outputting charging status related first vehicle information (see at least para. [0054] of Lowenthal which discloses “ the server 180 is programmed to transmit notification messages to subscribers (or to other person(s) as designated by the subscribers), and/or hosts upon the following charge status events ”) associated with deficient or incomplete charging conditions which teaches or suggests outputting information associated with deficient or incomplete charging conditions, or for which it is indicated that charging is not completed before the operation timing in the charging plan information (see at least para. [0042] of Lowenthal which discloses “ receiving notification messages upon certain events occurring … receive a notification message for that event) for the following events: fully charged vehicle, charging has been interrupted (e.g., the charging cord has been removed from the vehicle or has been severed, the station has encountered a power loss, etc.), the utility operating the power grid has caused their charging of the vehicle to be suspended ”, *Examiner interprets the disclosed interrupted charging conditions, suspended charging conditions and vehicle charging-status events of Lowenthal as teaching or suggesting outputting information identifying electric vehicles having deficient or incomplete charging conditions). Under the broadest reasonable interpretation, the claimed “operation timing” broadly encompasses operational scheduling information, charging schedules, vehicle use schedules and timing associated with electric vehicle operations. As discussed in the rejection, Kiessling further teaches such scheduling and operational timing information through fleet-based charging schedules, fixed schedule charging models, charging and discharging schedules and vehicle use schedules. See at least paragraph [0067] of Kiessling. Additionally, Applicant’s arguments improperly attack the cited references individually rather than addressing the combined teachings of the references. The rejection does not rely upon any single reference to disclose every claimed feature and one cannot show non-obviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller , 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). This rejection properly relies on the collective teachings of Kiessling and Lowenthal under 35 U.S.C. §103. Lowenthal is relied upon for teaching outputting charging status related vehicle information associated with deficient or incomplete charging conditions, while Kiessling provides the fleet scheduling and operation timing framework. In view of the arguments presented in response to applicant's remarks and the fact that the existing prior art discloses the claims, as broadly recited, the Examiner maintains the rejections made on October 14, 2025. Applicant’s arguments regarding the rejections under 101 have been considered but they are not persuasive. Applicant asserts “ the amended claim limitations cannot be "performed in the human mind, or by a human using a pen and paper." The amended claim limitations can only be realized by a computer acquiring a huge amount of information, including information indicating the status of a plurality of vehicles and charging plan information, and processing it using specific logic. ” The Examiner respectfully disagrees with this assertion and it is not persuasive because the proper analysis under the 2019 PEG evaluates whether the claim, under its broadest reasonable interpretation, recites concepts that are practically capable of being performed in the human mind, even if a computer may perform the operations more quickly or efficiently. See MPEP 2106.04(a)(2)(III). The amended claims broadly recite acquiring vehicle identification information, acquiring charging plan information and operation timing information, evaluating whether charging plan information exists or whether charging is completed before the operation timing and outputting information identifying vehicles meeting specified criteria. Such activities constitute collecting information, evaluating information, comparing information against criteria and reporting results which are mental processes capable of practical performance in the human mind or with the aid of a pen and paper. The mere recitation of a “plurality” of vehicles or processing larger amounts of information does not remove the claims from the mental process grouping because scaling an otherwise abstract informational analysis using generic computer implementation does not render the claims patent eligible. The claims do not recite any specialized data processing architecture, improved charging hardware or battery-control technology or other technological mechanism that changes the claimed invention from an abstract informational analysis into a technological improvement. Applicant also argues that “t he amended claims require fetching specific data (ID, charging plan, operation timing) from a physical storage and processing it to output specific vehicle information. This constitutes a practical application that improves the technical field of EV fleet management by enabling the identification of specific vehicles that require attention (missing plan or insufficient charge) from a large group, which integrates the abstract idea into a practical application (Step 2A, Prong 2) ”. However, the additional elements of “a computer”, “storage”, “electric vehicles” and “batteries” merely constitutes generic computer implementation and technological envionrment limitation that do not integrate the judicial exception into a practical application. These claims do not recite an improvement or computer functionality, any improvement to electric vehicle charging technology or any particular machine implementation beyond generic data acquisition, evaluation and output operations. The claimed “fetching” of information from storage merely amounts to insignificant extra solution activity using generic storage components and does not impose any meaningful limits on the judicial exception. In this connection, identifying vehicles requiring attention based on scheduling and charging information merely reflects the result of the abstract information analysis itself rather than a technological improvement. Therefore, the rejection under 35 USC 101 is maintained as outlined below . Response to Amendment Regarding the rejection under 35 USC 101, the amendments made to the claims fail to overcome the rejections. The rejection under 35 USC 101 is maintained as outlined below. Regarding the rejection under 35 USC 103, the amendments made to the claims fail to overcome the prior art. The rejection under 35 USC 103 is maintained as outlined below. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-5 and 7-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. 101 Analysis – Step 1 Regarding Step 1 of the Revised Guidance, it must be considered whether the claims are directed to one of the four statutory classes of invention. In the instant case, claims 1-5 and 7-13 and 18 are directed to an electric vehicle management apparatus (i.e., a machine); claims 14-15 are directed to a method for electric vehicle management (i.e., a method) and claims 16-17 are directed to non-transitory computer readable media (i.e., a manufacture/article of manufacture). Therefore, claims 1-5 and 6-17 are within at least one of the four statutory categories (processes, machines, manufactures and compositions of matter. 101 Analysis – Step 2A, Prong 1 Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite a judicial exception (subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity). Independent claim 14 includes limitations that recite an abstract idea (bolded below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 14 recites: An electric vehicle management method comprising, by a computer : acquiring, from a storage , vehicle identification information of a plurality of at electric vehicles each incorporated with a battery, and charging plan information indicating a charging schedule period of the electric vehicle associated with the vehicle identification information, in association with each other ; acquiring, from the storage , the vehicle identification information of a plurality of target vehicles each being an electric vehicle for which operation timing is determined, and the operation timing, in association with each other ; and outputting first vehicle information indicating the target vehicle , for which the charging plan information of the target vehicle is absent, or for which it is indicated that charging is not completed before the operation timing in the charging plan information in a state acquired from the storage , from among the plurality of target vehicles. The Examiner submits that the foregoing bolded limitations constitute a judicial exception in terms of “mental process” because under its broadest reasonable interpretation, the claim limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). The independent claim 14 recites the limitations of acquiring vehicle identification information and charging plan information indicating a charging schedule period ; acquiring the vehicle identification information and operation timing is determined, and the operation timing, and outputting first vehicle information indicating the target vehicle indicated that charging is not completed before the operation timing in the charging plan information . The acquiring, indicating and outputting limitations, as drafted, are processes that, under their broadest reasonable interpretation, encompass collecting information, evaluating whether charging information exists, comparing charging completion status relative to operational timing criteria and reporting the results of that evaluation. Such activities are capable of practical performance in the human mind or by a human using pen and paper. The additional recitations of “a computer”; “an electric vehicle” and “a battery” merely provide a technological environment for performing the abstract informational analysis and do not remove the claimed invention from the mental process grouping because the claim does not recite any specific improvement to computer technology, battery technology or electric vehicle operation itself. . Additionally, the acquiring, identification and indication steps, under the broadest reasonable interpretation, covers a process that is practically performed in the human mind. For example, these limitations cover a user acquiring information such as the vehicle ID and data pertaining to the state of the battery. Thus, the claim recites a mental process. 101 Analysis – Step 2A, Prong 2 evaluation: Practical Application - No In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical appli cation. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application. In the instant application, the additional limitations beyond the above-noted abstract ideas are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”): In Claim 14: An electric vehicle management method comprising, by a computer : acquiring, from a storage , vehicle identification information of a plurality of at electric vehicles each incorporated with a battery, and charging plan information indicating a charging schedule period of the electric vehicle associated with the vehicle identification information, in association with each other ; acquiring, from the storage , the vehicle identification information of a plurality of target vehicles each being an electric vehicle for which operation timing is determined, and the operation timing, in association with each other ; and outputting first vehicle information indicating the target vehicle , for which the charging plan information of the target vehicle is absent, or for which it is indicated that charging is not completed before the operation timing in the charging plan information in a state acquired from the storage , from among the plurality of target vehicles. The claim recites the additional element of “ a computer ” “ an electric vehicle ” and “ a battery ”. The method recites a generic computer component but does not require any specific hardware improvement or vehicle control beyond the computer acquiring and outputting data and does not amount to more than the judicial exception. The element of an action is an example of a recitation expressed at a high level of generality without reciting a particular improvement or application. In this connection, the claim does not describe how an action improves the vehicle system. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. 101 Analysis – Step 2B evaluation: Inventive Concept: - No In Step 2B of the 2019 PEG, the claim(s) is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. Thus, claims 14, 1 and 16 are ineligible . 101 Analysis – Dependent Claims Dependent claims 2-5, 7-13; 15, 17 and 18 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application [these dependent claims inherit the abstract idea set forth in claims 1, 14 and 16, respectively. No other technology or action has been recited in claims 2-5, 7-13; 15 and 17 and 18 to integrate the abstract idea into a practical application nor to amount to significantly more than the abstract idea. Thus, claims 2-5, 7-13; 15 and 17 and 18 also do not confer eligibility on the claimed invention and are ineligible for reasons stated above and for similar reasons to claims 1, 14 and 16. Therefore, dependent claims 2-5, 7-13; 15 and 17 and 18 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1, 14, and 16. Therefore, claims 2-5, 7-13; 15 and 17 and 18 are also ineligible under 35 USC §101. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 (i.e., changing from AIA to pre-AIA) 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-3, 5, 7, 10 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kiessling (US 2021/0086647 A1) in view of Lowenthal (US 2010/0211643 A1) . Regarding claim 1, Kiessling discloses at least one memory (Fig. 4, 406 and see at least para. [0055] of Kiessling which discloses a “ memory 406 (i.e., a memory device) ”) configured to store instructions (see at least para. [0059] of Kiessling which discloses “ The instructions may be stored in a memory location, such as the memory ”) ; and at least one processor (Fig. 4, 404 and see at least para. [0055] of Kiessling which describes “ a processor 404 ”) configured to execute the instructions (see at least para. [0058] of Kiessling which discloses “ executable instructions and a processor (e.g., 404) that may execute the instructions ”) to: acquire, from a storage (Fig. 4, 400 and see at least para. [0055] of Kiessling which discloses “ The computer control system 400 can include a computing device 402 having a processor 404 and memory 406 (i.e., a memory device) in electronic communication with each other. The system 400 can also include a data store or database 480 connected to the computing device 402 directly or via a network 408 ”, *Kiessling teaches a computer control system including a processor 404 and a memory 406 for implementing fleet-management and charging scheduling operations. The disclosed system stores and manages vehicle-associated charging schedule information and therefore teaches or at least suggests acquiring vehicle identification information and charging schedule information from stored fleet-management records maintained by the system) , vehicle identification information (see at least para. [0047] of Kiessling which discloses “ identifying the electric vehicle, identifying the fleet or customer to which the electric vehicle belongs ”, *Examiner interprets the disclosed information identifying the electric vehicle and associated fleet/customer information as vehicle identification information because the information identifies particular electric vehicles managed within the fleet-management system) of a plurality of electric vehicles (see at least para. [0026] of Kiessling which discloses “ one or more electric vehicle fleets comprising a plurality of electric vehicles ”) each incorporated with a battery (see at least para. [0038] of Kiessling which discloses “ an electric vehicle may be charging (e.g., storing energy into a battery ”) , and charging plan information (see at least para. [0005] of Kiessling which discloses “ generating a charging method plan for the vehicle ”, *Examiner interprets the disclosed charging method plans generated for identified vehicles and fleets as charging plan information associated with vehicle identification information because the charging schedules are generated and managed for particular electric vehicles within the fleet- management system) indicating a charging schedule period (see at least para. [0028] of Kiessling which discloses “ A charging method plan may comprise one or more charging schedules for one or more electric vehicles. The one or more charging schedules may comprise charging schedules for vehicles ” and see at least para. [0067] of Kiessling which discloses “ A vehicle duty cycle may include a vehicle use schedule, an arrival time at a charging station, or a duration of stay at the charging station. A charging method plan may be selected or generated based on the identified customer or fleet ”, *Examiner interprets the disclosed charging schedules, vehicle use schedules, arrival times and duration of stay at the charging station as charging schedule periods because such information defines periods associated with charging operations for electric vehicles) of the electric vehicle associated with the vehicle identification information (see at least para. [0005] of Kiessling which discloses “ identifying a customer vehicle fleet to which the vehicle belongs, wherein the customer vehicle fleet comprises one or more charging metrics ” and see at least para. [0067] of Kiessling which discloses “ The one or more charging metrics may include number of vehicles, type of vehicles, target state of charge, vehicle duty cycle, vehicle range, vehicle battery size, number and availability of parking stations, power output limits of charging stations, and other parameters. A vehicle duty cycle may include a vehicle use schedule, an arrival time at a charging station, or a duration of stay at the charging station ”, *Examiner interprets the disclosed charging plans generated for identified vehicles and fleets as being associated with vehicle identification information because the charging schedules are generated and managed for identified electric vehicles within the fleet-management system) , in association with each other; acquire, from the storage, the vehicle identification information of a plurality of target vehicles each being an electric vehicle (see at least para. [0026] of Kiessling which discloses “ one or more electric vehicle fleets comprising a plurality of electric vehicles ”) for which operation timing is determined (see at least para. [0067] of Kiessling which discloses “ a charging method plan may be based on a first-in, first-out (FIFO) model or fixed schedule charging model. The charging method plan may be a fleet-based charging method plan. For example, the charging method plan may coordinate between a plurality of electric vehicles or a plurality of charging depots. The optimizer system may generate a charging and discharging schedule for the electric vehicle ” and see at least para. [0055] of Kiessling which discloses a memory 406 and database 480 associated with the fleet-management system, as discussed above) , and the operation timing (see at least para. [0067] of Kiessling, as discussed above because Examiner interprets the disclosed charging schedules, fixed schedule charging models, coordinated fleet charging operations and vehicle scheduling information as teaching or suggesting operation timing associated with electric vehicles) , in association with each other (see at least para. [0067] of Kiessling which discloses “ a method 500 for implementing a fleet-based charging method plan comprising one or more charging schedules (e.g., consumption management plans), and updating one or more charging schedules upon arrival of an electric vehicle at a charging station. At block 510, the optimizer system (e.g., 120) may receive a notification that an electric vehicle (e.g., 151) has arrived at a charging station (e.g., 166). The notification may be sent from the electric vehicle to, for example, the optimizer system or the charging depot (e.g., 165). In some embodiments, the notification may be sent over a network. The electric vehicle may be identified upon arrival at the charging station. The optimizer system may identify a customer or a fleet to which the vehicle belongs at block 520” , *Examiner interprets the disclosed identification of electric vehicles together with associated charging schedules and fleet scheduling information as teaching vehicle identification information and operation timing information in association with each other ) ; in a state acquired from the storage, from among the plurality of target vehicles (see at least para. [0055] of Kiessling which discloses “ The memory 406 can therefore comprise a scheduling logic 410 configured to determine one or more charging strategies for one or more electric vehicle fleets and one or more charging depots. A vehicle to grid revenue logic 420 may be configured to increase revenue received from ancillary service providers from providing power to the ancillary service providers ”; *Examiner interprets Kiessling as teaching retrieving and evaluating stored fleet- management information maintained in memory/database systems for multiple electric vehicles, which teaches or at least suggests the claimed “state acquired from the storage, from among the plurality of target vehicles) . Kiessling may not explicitly disclose outputting first vehicle information indicating the target vehicle for which the charging plan information of the target vehicle is absent, or for which it is indicated that charging is not completed before the operation timing in the charging plan information. However, in the same field of endeavor, Lowenthal discloses outputting charging status related first vehicle information (see at least para. [0054] of Lowenthal which discloses “ the server 180 is programmed to transmit notification messages to subscribers (or to other person(s) as designated by the subscribers), and/or hosts upon the following charge status events ”) associated with deficient or incomplete charging conditions such as indicating the target vehicle for which the charging plan information of the target vehicle is absent , or for which it is indicated that charging is not completed before the operation timing in the charging plan information (see at least para. [0042] of Lowenthal which discloses “ receiving notification messages upon certain events occurring … receive a notification message for that event) for the following events: fully charged vehicle, charging has been interrupted (e.g., the charging cord has been removed from the vehicle or has been severed, the station has encountered a power loss, etc.), the utility operating the power grid has caused their charging of the vehicle to be suspended ”, *Examiner interprets the disclosed interrupted charging conditions, suspended charging conditions and vehicle charging-status events of Lowenthal as teaching or suggesting outputting information identifying electric vehicles having deficient or incomplete charging conditions) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the electric vehicle management apparatus of Kiessling to modify the electric vehicle management apparatus of Kiessling to output information identifying vehicles having deficient charging conditions relative to scheduled vehicle operation timing using the charging-status output techniques taught by Lowenthal, with a reasonable expectation of success in order to improve fleet operational readiness, facilitate management of charging operations for multiple electric vehicles and prevent deployment of insufficiently charged electric vehicles. See para. [0042] of Lowenthal for motivation. Regarding claim 2, Kiessling, as modified by Lowenthal discloses wherein the at least one processor is further configured to execute the instructions (see at least para. [0058] of Kiessling which discloses “ a processor (e.g., 404) that may execute the instructions to provide for electric vehicle charging optimization and control. The control system 400 includes a processor 404/central processing unit (CPU, also “processor” and “computer processor” herein), which can be a single core or multi core processor, or a plurality of processors for parallel processing. Any kind and/or number of processor may be present, including one or more central processing unit(s) (CPUs), graphics processing units (GPUs), other computer processors, mobile processors, digital signal processors (DSPs), microprocessors, computer chips, and/or processing units configured to execute machine-language instructions and process data. The computer system also includes memory 406 or a memory location (e.g., random-access memory, read-only memory, flash memory) and can further include an electronic storage unit (e.g., hard disk), a communication interface (e.g., a network adapter) for communicating with one or more other systems or components connected to the network 408, and peripheral devices, such as a cache, other memory, data storage and/or electronic display adapters ”) to output second vehicle information indicating an electric vehicle in which the charging schedule period is set (see at least para. [0091] of Kiessling which discloses “ the charging schedule is updated in response to a change in state of another vehicle in the fleet. The charging schedule may be updated as described with respect to FIG. 2. The charging schedule may be updated one or more times while the electric vehicle is plugged into the charging station. The charging schedule may be updated based on a change in a state of charge of the electric vehicle, a deviation from a nominal state of charge of the electric vehicle, or a deviation from 100% accuracy. A charging method plan may be a fleet-based charging method plan. Charging strategies may be coordinated between a plurality of depots based on power metrics and ancillary services to increase overall gross contribution, as described with respect to FIG. 2 and FIG. 3. The charging schedule may be updated based on one or more charging strategies coordinated between the plurality of depots ”) . Regarding claim 3, Kiessling, as modified by Lowenthal discloses wherein the at least one processor is further configured to execute the instructions to cause to display (see at least para. [0066] of Kiessling which discloses “ The computer system can include or be in communication with an electronic display ”) or print the first vehicle information and the second vehicle information in a different mode from each other (see at least para. [0058] of Kiessling which discloses “ peripheral devices, such as a cache, other memory, data storage and/or electronic display adapters. Any type or kind of memory may be present (e.g., read-only memory (ROM), random access memory (RAM), solid state drive (SSD), and secure digital card (SD card). While a single box is depicted as memory 406, any number of memory devices may be present. The memory 406 may be in communication with (e.g., electrically connected to) processor 404. The memory, storage unit, interface and peripheral devices are in communication with the processor through a communication bus (solid lines), such as a motherboard “ and see at least para. [0091] of Kiessling which discloses “ the charging schedule is updated in response to a change in state of another vehicle in the fleet. The charging schedule may be updated as described with respect to FIG. 2. The charging schedule may be updated one or more times while the electric vehicle is plugged into the charging station. The charging schedule may be updated based on a change in a state of charge of the electric vehicle, a deviation from a nominal state of charge of the electric vehicle, or a deviation from 100% accuracy. A charging method plan may be a fleet-based charging method plan ”, *Examiner notes that since the limitation is recited in the alternative, only one of the limitations is required, i.e., the display) . Regarding claim 5, Kiessling, as modified by Lowenthal discloses further comprising wherein the at least one processor is further configured to execute the instructions to: acquire charging state information indicating a charging state of the electric vehicle at predetermined timing (see at least para. [0084] of Kiessling which discloses “ the consumption of the loads as determined over a predetermined period of time while those measured loads have remained within one standard deviation of a larger group of consumption values (e.g., measurements from a preceding time period) ”) ; and determine whether a predetermined output is required by using a charging state indicated by the charging state information (see at least para. [0034] of Kiessling which discloses “ the customer may select the factors, or the weights assigned to different factors based on a predetermined selection of charging categories or charging strategies”, *Examiner interprets the predetermined charging categories to be predetermined output) and a charging state at the predetermined timing in the charging plan information, and output the predetermined information according to a result of the determination (see at least para. [0043]-[0044] and see para. [0055] of Kiessling which discloses “ The power output accuracy logic 440 may be configured to adjust a charging method plan to increase power output accuracy or to approach 100% accuracy. The power cost minimization logic 450 may be configured to adjust a charging method plan to decrease the deviation of an actual state of charge trajectory from the state of charge nominal trajectory ”) . Regarding claim 7, Kiessling, as modified by Lowenthal discloses wherein the charging state includes at least two of a state in which a charging connector is unconnected, a state in which a charging connector is connected, a state in which charging is being performed, and a state in which charging is completed (see at least para. [0044] of Kiessling which discloses “ the optimizer system may assess a current state of charge of one or more electric vehicles. The optimizer system may compare the current state of charge to a target state of charge or a predicted state of charge. The optimizer system may determine if action should be taken to correct for a discrepancy between a current state of charge, a target state of charge, or a predicted state of charge. The optimizer system may provide an instruction to take a corrective action at step 270. For example, the optimizer system may instruct a charging depot to restart a charger. Steps 260, 270, and 280 may be performed in any order. In some cases, steps 260, 270, and 280 may occur simultaneously ”) . Regarding claim 10, Kiessling, as modified by Lowenthal discloses wherein the at least one processor is further configured to execute the instructions to output information serving as an output target to a terminal of a driver of the electric vehicle serving as the information target, or a terminal of a maintenance worker of the electric vehicle (see at least para. [0004] of Kiessling which discloses “ distinct charging strategies for a plurality of charging depots, providing flexibility to the depot and fleet operators. Machine learning may be implemented to increase efficiency of cross-depot scheduling based on correlation between charging depots, charging metrics, and cost of power due to electrical grid supply and demand ”) Regarding amended claim 14, Kiessling discloses An electric vehicle management method comprising, by a computer (see at least para. [0008] of Kiessling which discloses “ a computer ”) : acquiring, from a storage (Fig. 4, 400 and see at least para. [0055] of Kiessling which discloses “ The computer control system 400 can include a computing device 402 having a processor 404 and memory 406 (i.e., a memory device) in electronic communication with each other. The system 400 can also include a data store or database 480 connected to the computing device 402 directly or via a network 408 ”, *Kiessling teaches a computer control system including a processor 404 and a memory 406 for implementing fleet-management and charging scheduling operations. The disclosed system stores and manages vehicle-associated charging schedule information and therefore teaches or at least suggests acquiring vehicle identification information and charging schedule information from stored fleet-management records maintained by the system) vehicle identification information (see at least para. [0047] of Kiessling which discloses “ identifying the electric vehicle, identifying the fleet or customer to which the electric vehicle belongs ”, *Examiner interprets the disclosed information identifying the electric vehicle and associated fleet/customer information as vehicle identification information because the information identifies particular electric vehicles managed within the fleet-management system) of a plurality of vehicles (see at least para. [0026] of Kiessling which discloses “ one or more electric vehicle fleets comprising a plurality of electric vehicles ”) each incorporated with a battery (see at least para. [0038] of Kiessling which discloses “ an electric vehicle may be charging (e.g., storing energy into a battery ”) , and charging plan information (see at least para. [0005] of Kiessling which discloses “ generating a charging method plan for the vehicle ”, *Examiner interprets the disclosed charging method plans generated for identified vehicles and fleets as charging plan information associated with vehicle identification information because the charging schedules are generated and managed for particular electric vehicles within the fleet-management system) indicating a charging schedule period (see at least para. [0028] of Kiessling which discloses “ A charging method plan may comprise one or more charging schedules for one or more electric vehicles. The one or more charging schedules may comprise charging schedules for vehicles ” and see at least para. [0067] of Kiessling which discloses “ A vehicle duty cycle may include a vehicle use schedule, an arrival time at a charging station, or a duration of stay at the charging station. A charging method plan may be selected or generated based on the identified customer or fleet ”, *Examiner interprets the disclosed charging schedules, vehicle use schedules, arrival times and duration of stay at the charging station as charging schedule periods because such information defines periods associated with charging operations for electric vehicles) of the electric vehicle associated with the vehicle identification information (see at least para. [0005] of Kiessling which discloses “ identifying a customer vehicle fleet to which the vehicle belongs, wherein the customer vehicle fleet comprises one or more charging metrics ” and see at least para. [0067] of Kiessling which discloses “ The one or more charging metrics may include number of vehicles, type of vehicles, target state of charge, vehicle duty cycle, vehicle range, vehicle battery size, number and availability of parking stations, power output limits of charging stations, and other parameters. A vehicle duty cycle may include a vehicle use schedule, an arrival time at a charging station, or a duration of stay at the charging station ”, *Examiner interprets the disclosed charging plans generated for identified vehicles and fleets as being associated with vehicle identification information because the charging schedules are generated and managed for identified electric vehicles within the fleet-management system) , in association with each other; acquiring, from the storage, the vehicle identification information of a plurality of target vehicles each being an electric vehicle (see at least para. [0026] of Kiessling which discloses “ one or more electric vehicle fleets comprising a plurality of electric vehicles ”) for which operation timing is determined (see at least para. [0067] of Kiessling which discloses “ a charging method plan may be based on a first-in, first-out (FIFO) model or fixed schedule charging model. The charging method plan may be a fleet-based charging method plan. For example, the charging method plan may coordinate between a plurality of electric vehicles or a plurality of charging depots. The optimizer system may generate a charging and discharging schedule for the electric vehicle ” and see at least para. [0055] of Kiessling which discloses a memory 406 and database 480 associated with the fleet-management system, as discussed above) , and the operation timing (see at least para. [0067] of Kiessling, as discussed above because Examiner interprets the disclosed charging schedules, fixed schedule charging models, coordinated fleet charging operations and vehicle scheduling information as teaching or suggesting operation timing associated with electric vehicles) , in association with each other (see at least para. [0067] of Kiessling which discloses “ a method 500 for implementing a fleet-based charging method plan comprising one or more charging schedules (e.g., consumption management plans), and updating one or more charging schedules upon arrival of an electric vehicle at a charging station. At block 510, the optimizer system (e.g., 120) may receive a notification that an electric vehicle (e.g., 151) has arrived at a charging station (e.g., 166). The notification may be sent from the electric vehicle to, for example, the optimizer system or the charging depot (e.g., 165). In some embodiments, the notification may be sent over a network. The electric vehicle may be identified upon arrival at the charging station. The optimizer system may identify a customer or a fleet to which the vehicle belongs at block 520” , *Examiner interprets the disclosed identification of electric vehicles together with associated charging schedules and fleet scheduling information as teaching vehicle identification information and operation timing information in association with each other) ; in a state acquired from the storage, from among the plurality of target vehicles (see at least para. [0055] of Kiessling which discloses “ The memory 406 can therefore comprise a scheduling logic 410 configured to determine one or more charging strategies for one or more electric vehicle fleets and one or more charging depots. A vehicle to grid revenue logic 420 may be configured to increase revenue received from ancillary service providers from providing power to the ancillary service providers ”; *Examiner interprets Kiessling as teaching retrieving and evaluating stored fleet-management information maintained in memory/database systems for multiple electric vehicles, which teaches or at least suggests the claimed “state acquired from the storage, from among the plurality of target vehicles) . Kiessling may not explicitly disclose outputting first vehicle information indicating the target vehicle, for which the charging plan information of the target vehicle is absent, or for which it is indicated that charging is not completed before the operation timing in the charging plan information However, in the same field of endeavor, Lowenthal discloses outputting charging status related first vehicle information (see at least para. [0054] of Lowenthal which discloses “ the server 180 is programmed to transmit notification messages to subscribers (or to other person(s) as designated by the subscribers), and/or hosts upon the following charge status events ”) associated with deficient or incomplete charging conditions such as indicating the target vehicle for which the charging plan information of the target vehicle is absent , or for which it is indicated that charging is not completed before the operation timing in the charging plan information (see at least para. [0042] of Lowenthal which discloses “ receiving notification messages upon certain events occurring … receive a notification message for that event) for the following events: fully charged vehicle, charging has been interrupted (e.g., the charging cord has been removed from the vehicle or has been severed, the station has encountered a power loss, etc.), the utility operating the power grid has caused their charging of the vehicle to be suspended ”, *Examiner interprets the disclosed interrupted charging conditions, suspended charging conditions and vehicle charging-status events of Lowenthal as teaching or suggesting outputting information identifying electric vehicles having deficient or incomplete charging conditions) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the electric vehicle management apparatus of Kiessling to modify the electric vehicle management apparatus of Kiessling to output information identifying vehicles having deficient charging conditions relative to scheduled vehicle operation timing using the charging-status output techniques taught by Lowenthal, with a reasonable expectation of success in order to improve fleet operational readiness, facilitate management of charging operations for multiple electric vehicles and prevent deployment of insufficiently charged electric vehicles. See para. [0042] of Lowenthal for motivation. Regarding claim 15, Kiessling, as modified by Lowenthal discloses further comprising, by the computer (see at least para. [0008] of Kiessling which discloses “ a computer ”) : acquiring charging state information indicating a charging state of the electric vehicle at predetermined timing (see at least para. [0084] of Kiessling which discloses “ the consumption of the loads as determined over a predetermined period of time while those measured loads have remained within one standard deviation of a larger group of consumption values (e.g., measurements from a preceding time period) ”) ; and determining whether a predetermined output is required by using a charging state indicated by the charging state information (see at least para. [0034] of Kiessling which discloses “ the customer may select the factors, or the weights assigned to different factors based on a predetermined selection of charging categories or charging strategies ”, *Examiner interprets the predetermined charging categories to be predetermined output) and a charging state at the predetermined timing in the charging plan information, and outputting the predetermined information according to a result of the determination (see at least para. [0043]-[0044] and see at least para. [0055] of Kiessling which discloses “ The power output accuracy logic 440 may be configured to adjust a charging method plan to increase power output accuracy or to approach 100% accuracy. The power cost minimization logic 450 may be configured to adjust a charging method plan to decrease the deviation of an actual state of charge trajectory from the state of charge nominal trajectory ”) . Regarding amended claim 16, Kiessling discloses A non-transitory computer-readable storage medium (see at least para. [0009] of Kiessling which discloses “ a non-transitory computer readable medium comprising machine executable code that, upon execution by one or more processors ”) storing a program causing a computer to perform: acquiring, from a storage (Fig. 4, 400 and see at least para. [0055] of Kiessling which discloses “ The computer control system 400 can include a computing device 402 having a processor 404 and memory 406 (i.e., a memory device) in electronic communication with each other. The system 400 can also include a data store or database 480 connected to the computing device 402 directly or via a network 408 ”, *Kiessling teaches a computer control system including a processor 404 and a memory 406 for implementing fleet-management and charging scheduling operations. The disclosed system stores and manages vehicle-associated charging schedule information and therefore teaches or at least suggests acquiring vehicle identification information and charging schedule information from stored fleet-management records maintained by the system) , vehicle identification information (see at least para. [0047] of Kiessling which discloses “ identifying the electric vehicle, identifying the fleet or customer to which the electric vehicle belongs ”, *Examiner interprets the disclosed information identifying the electric vehicle and associated fleet/customer information as vehicle identification information because the information identifies particular electric vehicles managed within the fleet-management system) of a plurality of electric vehicles (see at least para. [0026] of Kiessling which discloses “ one or more electric vehicle fleets comprising a plurality of electric vehicles ”) each incorporated with a battery (see at least para. [0038] of Kiessling which discloses “ an electric vehicle may be charging (e.g., storing energy into a battery ”) , and charging plan information (see at least para. [0005] of Kiessling which discloses “ generating a charging method plan for the vehicle”, *Examiner interprets the disclosed charging method plans generated for identified vehicles and fleets as charging plan information associated with vehicle identification information because the charging schedules are generated and managed for particular electric vehicles within the fleet-management system) indicating a charging schedule period (see at least para. [0028] of Kiessling which discloses “ A charging method plan may comprise one or more charging schedules for one or more electric vehicles. The one or more charging schedules may comprise charging schedules for vehicles ” and see at least para. [0067] of Kiessling which discloses “ A vehicle duty cycle may include a vehicle use schedule, an arrival time at a charging station, or a duration of stay at the charging station. A charging method plan may be selected or generated based on the identified customer or fleet ”, *Examiner interprets the disclosed charging schedules, vehicle use schedules, arrival times and duration of stay at the charging station as charging schedule periods because such information defines periods associated with charging operations for electric vehicles) of the electric vehicle associated with the vehicle identification information (see at least para. [0005] of Kiessling which discloses “ identifying a customer vehicle fleet to which the vehicle belongs, wherein the customer vehicle fleet comprises one or more charging metrics ” and see at least para. [0067] of Kiessling which discloses “ The one or more charging metrics may include number of vehicles, type of vehicles, target state of charge, vehicle duty cycle, vehicle range, vehicle battery size, number and availability of parking stations, power output limits of charging stations, and other parameters. A vehicle duty cycle may include a vehicle use schedule, an arrival time at a charging station, or a duration of stay at the charging station ”, *Examiner interprets the disclosed charging plans generated for identified vehicles and fleets as being associated with vehicle identification information because the charging schedules are generated and managed for identified electric vehicles within the fleet-management system) , in association with each other; acquiring, from the storage, the vehicle identification information of a plurality of target vehicles each being an electric vehicle (see at least para. [0026] of Kiessling which discloses “ one or more electric vehicle fleets comprising a plurality of electric vehicles ”) for which operation timing is determined (see at least para. [0067] of Kiessling which discloses “ a charging method plan may be based on a first-in, first-out (FIFO) model or fixed schedule charging model. The charging method plan may be a fleet-based charging method plan. For example, the charging method plan may coordinate between a plurality of electric vehicles or a plurality of charging depots. The optimizer system may generate a charging and discharging schedule for the electric vehicle ” and see at least para. [0055] of Kiessling which discloses a memory 406 and database 480 associated with the fleet-management system, as discussed above) , and the operation timing (see at least para. [0067] of Kiessling, as discussed above because Examiner interprets the disclosed charging schedules, fixed schedule charging models, coordinated fleet charging operations and vehicle scheduling information as teaching or suggesting operation timing associated with electric vehicles) , in association with each other (see at least para. [0067] of Kiessling which discloses “ a method 500 for implementing a fleet-based charging method plan comprising one or more charging schedules (e.g., consumption management plans), and updating one or more charging schedules upon arrival of an electric vehicle at a charging station. At block 510, the optimizer system (e.g., 120) may receive a notification that an electric vehicle (e.g., 151) has arrived at a charging station (e.g., 166). The notification may be sent from the electric vehicle to, for example, the optimizer system or the charging depot (e.g., 165). In some embodiments, the notification may be sent over a network. The electric vehicle may be identified upon arrival at the charging station. The optimizer system may identify a customer or a fleet to which the vehicle belongs at block 520” , *Examiner interprets the disclosed identification of electric vehicles together with associated charging schedules and fleet scheduling information as teaching vehicle identification information and operation timing information in association with each other ) ; in a state acquired from the storage, from among the plurality of target vehicles (see at least para. [0055] of Kiessling which discloses “ The memory 406 can therefore comprise a scheduling logic 410 configured to determine one or more charging strategies for one or more electric vehicle fleets and one or more charging depots. A vehicle to grid revenue logic 420 may be configured to increase revenue received from ancillary service providers from providing power to the ancillary service providers ”; *Examiner interprets Kiessling as teaching retrieving and evaluating stored fleet-management information maintained in memory/database systems for multiple electric vehicles, which teaches or at least suggests the claimed “state acquired from the storage, from among the plurality of target vehicles) . Kiessling may not explicitly disclose outputting first vehicle information indicating the target vehicle, for which the charging plan information of the target vehicle is absent, or for which it is indicated that charging is not completed before the operation timing in the charging plan information However, in the same field of endeavor, Lowenthal discloses outputting charging status related first vehicle information (see at least para. [0054] of Lowenthal which discloses “ the server 180 is programmed to transmit notification messages to subscribers (or to other person(s) as designated by the subscribers), and/or hosts upon the following charge status events ”) associated with deficient or incomplete charging conditions such as indicating the target vehicle for which the charging plan information of the target vehicle is absent, or for which it is indicated that charging is not completed before the operation timing in the charging plan information (see at least para. [0042] of Lowenthal which discloses “ receiving notification messages upon certain events occurring … receive a notification message for that event) for the following events: fully charged vehicle, charging has been interrupted (e.g., the charging cord has been removed from the vehicle or has been severed, the station has encountered a power loss, etc.), the utility operating the power grid has caused their charging of the vehicle to be suspended ”, *Examiner interprets the disclosed interrupted charging conditions, suspended charging conditions and vehicle charging-status events of Lowenthal as teaching or suggesting outputting information identifying electric vehicles having deficient or incomplete charging conditions) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the electric vehicle management apparatus of Kiessling to modify the electric vehicle management apparatus of Kiessling to output information identifying vehicles having deficient charging conditions relative to scheduled vehicle operation timing using the charging-status output techniques taught by Lowenthal, with a reasonable expectation of success in order to improve fleet operational readiness, facilitate management of charging operations for multiple electric vehicles and prevent deployment of insufficiently charged electric vehicles. See para. [0042] of Lowenthal for motivation. Regarding claim 17, Kiessling, as modified by Lowenthal discloses wherein the program causes the computer to further perform (see at least para. [0059] of Kiessling which discloses “ a program or software. The instructions may be stored in a memory location, such as the memory. The instructions can be directed to the CPU, which can subsequently program or otherwise configure the CPU to implement methods ”) : acquiring charging state information indicating a charging state of the electric vehicle (see at least para. [0044] of Kiessling which discloses “ the optimizer system may assess a current state of charge of one or more electric vehicles. The optimizer system may compare the current state of charge to a target state of charge or a predicted state of charge. The optimizer system may determine if action should be taken to correct for a discrepancy between a current state of charge, a target state of charge, or a predicted state of charge ”) at predetermined timing (see at least para. [0084] of Kiessling which discloses “ the steady state consumption can be determined as the consumption of the loads as determined over a predetermined period of time ”) ; and determining whether a predetermined output is required by using a charging state indicated by the charging state information (see at least para. [0042] of Kiessling which discloses “ An event update from a charging station may comprise arrival or departure of an electric vehicle, a state of charge of the electric vehicle, a charging rate, a power output limit, and power metrics ”) and a charging state at the predetermined timing in the charging plan information, and outputting the predetermined information according to a result of the determination (see a least para. [0033] of Kiessling which discloses “ an overall power consumption rate of change of the site can be used to make determinations as to the charging or power draw rate for one or more electric vehicles. In instances where the power consumption rate is increasing (e.g., a positive-sloped trend), the vehicle charging rates may be reduced, even if the overall consumption average may be below a desired threshold. This reductions can help to ensure that the overall consumption for a time period of the charging depot does not exceed a threshold, and can provide a gap that can account for non-vehicle related power consuming devices. Additionally or alternatively, a standard deviation or other historical comparison value may be used to predict and update the charging rates for the vehicles ”) . Regarding claim 18, Kiessling, as modified by Lowenthal discloses wherein the at least one processor (Fig. 4, 404 and see at least para. [0055] of Kiessling which describes “ a processor 404 ”) is further configured to execute the instructions (see at least para. [0058] of Kiessling which discloses “ executable instructions and a processor (e.g., 404) that may execute the instructions ”) to output the first vehicle information (see at least para. [0054] of Lowenthal which discloses “ the server 180 is programmed to transmit notification messages to subscribers (or to other person(s) as designated by the subscribers), and/or hosts upon the following charge status events ”) associated with deficient or incomplete charging conditions) indicating the target vehicle for which it is indicated that charging is not completed before the operation timing in the charging plan information (see at least para. [0042] of Lowenthal which discloses “ receiving notification messages upon certain events occurring … receive a notification message for that event) for the following events: fully charged vehicle, charging has been interrupted (e.g., the charging cord has been removed from the vehicle or has been severed, the station has encountered a power loss, etc.), the utility operating the power grid has caused their charging of the vehicle to be suspended ”, *Examiner interprets the disclosed interrupted charging conditions, suspended charging conditions and vehicle charging-status events of Lowenthal as teaching or suggesting outputting information identifying electric vehicles having deficient or incomplete charging conditions) in a state acquired from the storage among the plurality of target vehicles (see at least para. [0055] of Kiessling which discloses “ The memory 406 can therefore comprise a scheduling logic 410 configured to determine one or more charging strategies for one or more electric vehicle fleets and one or more charging depots. A vehicle to grid revenue logic 420 may be configured to increase revenue received from ancillary service providers from providing power to the ancillary service providers ”; *Examiner interprets Kiessling as teaching retrieving and evaluating stored fleet-management information maintained in memory/database systems for multiple electric vehicles, which teaches or at least suggests the claimed “state acquired from the storage, from among the plurality of target vehicles) . 07-21-aia AIA Claim s 4 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Kiessling (US 2021/0086647 A1) in view of Lowenthal (US 2010/0211643 A1) and further in view of Hendrix (US 2012/033049 4 A1) . Regarding claim 4, Kiessling, as modified by Lowenthal discloses wherein the at least one processor (Fig. 4, 404 and see at least para. [0055] of Kiessling which describes “ a processor 404 ”) is further configured to execute the instructions (see at least para. [0058] of Kiessling which discloses “ executable instructions and a processor (e.g., 404) that may execute the instructions ”) . Kiessling, as modified by Lowenthal may not explicitly disclose to output a table indicating a state of each of the plurality of electric vehicles, and wherein, in the table, the first vehicle information and the second vehicle information are indicated in a same row or a same column. However, in the same field of endeavor, Hendrix discloses to output a table (see at least para. [0079] of Hendrix which discloses “ Reports 470 may be organized and displayed in any useful format, such as in a table, chart, list, etc. ”) indicating a state of each of the plurality of electric vehicles, and, in the table, the first vehicle information and the second vehicle information are indicated in a same row or a same column (see at least para. [0079] of Hendrix which discloses “ FIG. 17, user interface 410 may be used to generate standard and/or custom reports 470 of user-selected data available through FMS 400. Reports 470 may report metrics such as system energy consumption, peak demand hours charged, energy consumed per vehicle, energy consumed per driver, total energy cost, and total energy supplied by system 100. In an embodiment, reports 470 and accompanying preferred settings/configurations may be saved and accessed by user interface 410 for simple report generation. Reports 470 may be organized and displayed in any useful format, such as in a table, chart, list, etc.). In an embodiment, FMS 400 may store, sort, and report performance data for an electric vehicle 110, possibly by combining charging data with on-board diagnostics data from electric vehicle 110 ”, *Examiner interprets the table to be an output of a report of multiple electric vehicles with rows and columns) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Kiessling, as modified by Lowenthal, to output a table indicating a state of each of the plurality of electric vehicles, and, in the table, the first vehicle information and the second vehicle information are indicated in a same row or a same column , as taught in Hendrix with a reasonable expectation of success in order to clearly and effectively display vehicle information for each electric vehicle that is managed by the apparatus. See para. [0077] and [0079] of Hendrix for motivation. Regarding claim 8, Kiessling, as modified by Lowenthal discloses wherein the at least one processor (Fig. 4, 404 and see at least para. [0055] of Kiessling which describes “ a processor 404 ”) is further configured to execute the instructions (see at least para. [0058] of Kiessling which discloses “ executable instructions and a processor (e.g., 404) that may execute the instructions ”). Kiessling, as modified by Lowenthal may not explicitly disclose to output a table indicating a state of the electric vehicle, wherein one item included in the table is the charging state, wherein the predetermined information is a display mode in the charging state, and, when a difference between a charging state indicated by the charging state information, and a charging state at the predetermined timing in the charging plan information does not satisfy a criterion, make the display mode different with respect to a case where the difference satisfies a criterion. However, in the same field of endeavor, Hendrix discloses to output a table (see at least para. [0079] of Hendrix which discloses “ Reports 470 may be organized and displayed in any useful format, such as in a table, chart, list, etc. ”) indicating a state of the electric vehicle, wherein one item included in the table is the charging state, wherein the predetermined information is a display mode in the charging state, and, when a difference between a charging state indicated by the charging state information (see at least para. [0013] of Hendrix which discloses “ the computer-executable instructions may further comprise computer code to calculate performance, mileage, and maintenance data for the one or more of the plurality of electric vehicle ”, *Examiner interprets maintenance data to be the charging state information) , and a charging state at the predetermined timing in the charging plan information does not satisfy a criterion, to make the display mode different with respect to a case where the difference satisfies a criterion (see at least para. [0079] of Hendrix which discloses “ user interface 410 may be used to generate standard and/or custom reports 470 of user-selected data available through FMS 400. Reports 470 may report metrics such as system energy consumption, peak demand hours charged, energy consumed per vehicle, energy consumed per driver, total energy cost, and total energy supplied by system 100. In an embodiment, reports 470 and accompanying preferred settings/configurations may be saved and accessed by user interface 410 for simple report generation. Reports 470 may be organized and displayed in any useful format, such as in a table, chart, list, etc.). In an embodiment, FMS 400 may store, sort, and report performance data for an electric vehicle 110, possibly by combining charging data with on-board diagnostics data from electric vehicle 110 ”) It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Kiessling, as modified by Lowenthal, to output a table indicating a state of the electric vehicle, wherein one item included in the table is the charging state, wherein the predetermined information is a display mode in the charging state, and, when a difference between a charging state indicated by the charging state information, and a charging state at the predetermined timing in the charging plan information does not satisfy a criterion, make the display mode different with respect to a case where the difference satisfies a criterion, as taught in Hendrix with a reasonable expectation of success in order to clearly and effectively display vehicle information for each electric vehicle that is managed by the apparatus. See para. [0077] and [0079] of Hendrix for motivation. Regarding claim 9, Kiessling, as modified by Lowenthal and further modified by Hendrix discloses wherein the table indicates a state of each of a plurality of electric vehicles (see at least para. [0079] of Hendrix which discloses “ any useful format, such as in a table, chart, list, etc.). In an embodiment, FMS 400 may store, sort, and report performance data for an electric vehicle 110 ”) . 07-21-aia AIA Claim s 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kiessling (US 2021/0086647 A1) in view of Lowenthal (US 2010/0211643 A1) and further in view of Shoa Hassani Lashidani (US 2019/0170827 A1) . Regarding claim 11, Kiessling, as modified by Lowenthal discloses further comprising wherein the at least one processor (Fig. 4, 404 and see at least para. [0055] of Kiessling which describes “ a processor 404 ”) is further configured to execute the instructions (see at least para. [0058] of Kiessling which discloses “ executable instructions and a processor (e.g., 404) that may execute the instructions ”) . Kiessling, as modified by Lowenthal, may not explicitly disclose to: acquire State of Health (SOH) of the electric vehicle; and further output the SOH. However, in the same field of endeavor, Shoa Hassani Lashidani discloses to: acquire State of Health (SOH) (see at least para. [0121] of Shoa Hassani Lashidani which discloses “ acquired parameters wherein the first parameter relates to the battery initial SoC; and a second parameter comprises either the duration of charging period, or directly relates to SoH ”) of the electric vehicle; and further output the SOH (see at least para. [0006] of Shoa Hassani Lashidani which discloses “ output the calculated value of the SoH on the user interface ” and see at least para. [0087] of Shoa Hassani Lashidani which discloses “ The user interface 220 also includes an output display, which indicates the SoH as determined by the charger 200 ”) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Kiessling, as modified by Lowenthal, to: acquire SOH of the electric vehicle; and further output the SOH , as taught in Shoa Hassani Lashidani with a reasonable expectation of success in order to monitor and keep track of vehicle state information regarding the charging and battery performance. See para. [0121] of Shoa Hassani Lashidani for motivation. Regarding claim 12, Kiessling, as modified by Lowenthal and further modified by Shoa Hassani Lashidani discloses wherein the at least one processor is further configured to execute the instructions to output information indicating the electric vehicle in which a deterioration (see at least para. [0027] of Shoa Hassani Lashidani which discloses “ as a battery's SoH deteriorates, the maximum OCV to which the battery can be charged declines ”) state of the SOH satisfies a criterion (see at least para. [0066] of Shoa Hassani Lashidani which discloses “ the filter obtains the initial OCV, which is used in step 51 to estimate the initial SoC. In step 102, an initial SoH range is obtained, based on the SoH classification into which the battery has been classed. For example, a good SoH is in the range >90%; a medium SoH is in the range 70-90%; and a poor range is <70% ” and see at least para. [0119] of Shoa Hassani Lashidani which discloses “ the battery information is obtained prior and during charging. In some embodiments the apparatus comprises additional electronics, wherein the battery discharge response and/or small signal frequency response is extracted from the battery and employed to evaluate the condition (i.e. poor, medium or good SoH) of the battery ”) . Regarding claim 13, Kiessling, as modified by Lowenthal and further modified by Shoa Hassani Lashidani discloses wherein a deterioration (see at least para. [0027] of Shoa Hassani Lashidani which discloses “ as a battery's SoH deteriorates, the maximum OCV to which the battery can be charged declines ” state of the SOH includes a lowering amount of the SOH during a unit period (see at least para. [0038]-[0039] of Shoa Hassani Lashidani which discloses “ the SoH is determined or calculated more accurately than the initial classification. In some cases, the SoH is one of the states of the Kalman filter used for determining the SoC, and it is determined by reading its value from the Kalman filter ” and “ In other cases, instead of determining the SoH at step 16, charging of the battery continues in step 17 of FIG. 3. At a later point in time, in step 18, a second SoC data point is obtained (SoC 2 ), which, for example, may be the SoC when the battery is fully charged, i.e. 100%, or when the battery is has been charged more but not completely. The amount of charge (Q actual ) the battery takes between the SoC of the two data points, divided by the expected charge that the battery should take if its capacity is equal to its rated capacity, results in the SoH of the battery ”). Additional Prior Art 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Sarlashkar (US 2021/0223326A1) which discloses that the battery SOH is not an absolute measurement but is instead a metric that reflects the general condition of a battery and its ability to deliver the specified performance compared with a new battery. There is no single accepted definition of the SOH. Instead, it is a subjective measure in that may be derived from a variety of different measurable battery performance parameters interpreted according to a selected set of rules. Often, however, SOH is identified as a percentage of the performance capability of a new battery. Using this approach, a battery will have a SOH of 100% at the time of manufacture and the SOH will decrease over time and use. In some instances, a battery with a specific SOH, e.g. 50% or 80%, may be considered in jeopardy of imminent failure. A system 600 includes a battery 602 and a battery management system (BMS) 604 is also disclosed including a SOH monitor 606. An electrical power supply 608, such as wall outlet providing line voltage (120 VAC), a battery source such as a vehicle battery, etc. may be coupled to the battery 602 through the BMS 604. A battery load 610, such as an electric vehicle drive system, a laptop computer, cell phone, a power tool, medical device, etc., may be coupled to the battery 602 through the BMS 604. Yamamoto (US 2021/0178927 A1) discloses a rechargeable battery evaluation device including: a charging controller configured to switch charging current of a rechargeable battery being charged with first current to second current; and a first state estimator configured to estimate a state of the rechargeable battery based on a feature of change in at least one of a voltage and a charging amount of the rechargeable battery due to switching from the charging current to the second current . Conclusion 07-40 AIA 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 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANA IVEY whose telephone number is (313)446-4896. The examiner can normally be reached 9-5:30 EST Monday-Friday. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /DANA D IVEY/Examiner, Art Unit 3662 /D.D.I/May 25, 2026 /JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662 Application/Control Number: 18/730,420 Page 2 Art Unit: 3662 Application/Control Number: 18/730,420 Page 3 Art Unit: 3662 Application/Control Number: 18/730,420 Page 4 Art Unit: 3662 Application/Control Number: 18/730,420 Page 5 Art Unit: 3662 Application/Control Number: 18/730,420 Page 6 Art Unit: 3662 Application/Control Number: 18/730,420 Page 7 Art Unit: 3662 Application/Control Number: 18/730,420 Page 8 Art Unit: 3662 Application/Control Number: 18/730,420 Page 9 Art Unit: 3662 Application/Control Number: 18/730,420 Page 10 Art Unit: 3662 Application/Control Number: 18/730,420 Page 11 Art Unit: 3662 Application/Control Number: 18/730,420 Page 12 Art Unit: 3662 Application/Control Number: 18/730,420 Page 13 Art Unit: 3662 Application/Control Number: 18/730,420 Page 14 Art Unit: 3662 Application/Control Number: 18/730,420 Page 15 Art Unit: 3662 Application/Control Number: 18/730,420 Page 16 Art Unit: 3662 Application/Control Number: 18/730,420 Page 17 Art Unit: 3662 Application/Control Number: 18/730,420 Page 18 Art Unit: 3662 Application/Control Number: 18/730,420 Page 19 Art Unit: 3662 Application/Control Number: 18/730,420 Page 20 Art Unit: 3662 Application/Control Number: 18/730,420 Page 21 Art Unit: 3662 Application/Control Number: 18/730,420 Page 22 Art Unit: 3662 Application/Control Number: 18/730,420 Page 23 Art Unit: 3662 Application/Control Number: 18/730,420 Page 25 Art Unit: 3662 Application/Control Number: 18/730,420 Page 26 Art Unit: 3662 Application/Control Number: 18/730,420 Page 27 Art Unit: 3662 Application/Control Number: 18/730,420 Page 28 Art Unit: 3662 Application/Control Number: 18/730,420 Page 30 Art Unit: 3662 Application/Control Number: 18/730,420 Page 31 Art Unit: 3662 Application/Control Number: 18/730,420 Page 32 Art Unit: 3662 Application/Control Number: 18/730,420 Page 33 Art Unit: 3662 Application/Control Number: 18/730,420 Page 34 Art Unit: 3662 Application/Control Number: 18/730,420 Page 35 Art Unit: 3662 Application/Control Number: 18/730,420 Page 36 Art Unit: 3662 Application/Control Number: 18/730,420 Page 37 Art Unit: 3662 Application/Control Number: 18/730,420 Page 38 Art Unit: 3662 Application/Control Number: 18/730,420 Page 39 Art Unit: 3662
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