berlDETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Response to Arguments
Applicant argues with respect to claim 1 under 35 U.S.C. 102(a)(1) that Shaotran et al (US 20230004901 A1) fails to disclose all the limitations of independent claim 1. Particularly the applicant argues that Shaotran does not teach the limitation “based on the state-of-charge, the one or more sets of preconditioning time information, the charging time, the one or more sets of charging power information, and the demand placed on the grid” as recited in claim 1, citing Shaotran ¶0064. Shaotran ¶0064 describes the “fleet management system 200 may further include a preconditioning characteristic comparison module 206…the preconditioning characteristic comparison module 206 may be operated to apply a weighting function and/or other metric to the various preconditioning characteristics from the vehicles across the multi-vehicle system 230”. ¶0066 further elaborates “The fleet management system 200 may further optionally include a ranking override module 210. The ranking override module 210 may allow the fleet management system 200 to apply a set of hardcoded rules and/or user input to change a priority of the vehicles relative to the charging station”, which further adds user inputs to weight charging priority of vehicles. Shaotran ¶0066 further teaches “the ranking override module 210 may include a set of rules that increase the priority of certain vehicles based on any of a range of other criteria, including route type, load type, delivery criticality type, payment status (e.g., prioritizing higher-paying charging customers or deprioritizing charging customers with poor payment history), and so on”. The fleet management system 200 as taught by Shaotran does have the ability to include the demand on the grid as metric in the ranking override module 210; however, the examiner agrees with applicant that Shaotran does not explicitly disclose ranking vehicles with demand on the grid being a weighting factor.
Applicant states that the supporting reference Pollack et al (US 20110004358 A1) fails to remedy Shaotran, and does not provide further arguments regarding Pollack. Pollack ¶0149 directly states “the site power flow manager 1010 may allow for optimizing with regard to the overall site electric cost minimization or total cost minimization”. The cost of electricity is directly correlated to the total demand on the power grid, and is further supports in Pollack ¶0174 “A basic cost reduction strategy is to reduce electricity consumption when electricity prices are high”. Thereby the combination of Shatran modified by Pollack does result in ranking vehicles with demand on the power grid being a weighting factor.
Applicant's arguments filed 6 October 2025 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 4-8, 11-15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shaotran et al (US 20230004901 A1) modified by Pollack et al (US 20110004358 A1)
Regarding claim 1, Shaotran teaches a system, comprising: memory (¶0118 "one or more memory components 1408")
storing instructions executed by a processor to: determine a state-of-charge (¶0051 “first battery 114 to be fully or partially recharged at the charging station 104 (e.g., the time required to bring the battery charge to a predetermined value, whether that value is a full charge (i.e., 100% SOC) or a partial charge (e.g., 80% SOC))")
and one or more sets of preconditioning time information from a battery management system of each energy storage system of a plurality of energy storage systems; (¶0044 "disclosed herein are methods for controlling preconditioning in one or more of the vehicles based in the preconditioning ranking and/or queue", ¶0051 " vehicle metrics 190 represented in FIG. 1A include a preconditioning time 192 and a charge time 194")
determine a charging time (¶0051 "charge time 194 may include information associated with a predicted time or required duration for the first battery 114 to be fully or partially recharged at the charging station 104")
and one or more sets of charging power information from a battery charger management system; (¶0051 "the vehicle metrics 190 may include additional information, such as battery temperature, route information, vehicle performance data, and so on")
generate a charging schedule and a preconditioning schedule for the plurality of energy storage systems (¶0065 "vehicle ranking module 208 may be configured to determine a priority ranking of the vehicles of the multi-vehicle system 230 relative to an identified charging station", ¶0064 "the preconditioning characteristic comparison module 206 may be operated to apply a weighting function and/or other metric to the various preconditioning characteristics from the vehicles across the multi-vehicle system 230")
based on the state-of-charge, the one or more sets of preconditioning time information, the charging time, the one or more sets of charging power information, (¶0064 "each vehicle may calculate a return to service metric or time and/or send raw data (e.g., time to charger, charging time, preconditioning time, and so on) for the preconditioning characteristic comparison module 206 to apply the weighting functions and compare the data points across a plurality of vehicles", ¶0065 "vehicle ranking module 208 may rank the vehicles based on an output from the preconditioning characteristic comparison module 206", see below for further detail)
direct the battery management system of each energy storage system of the plurality of energy storage systems to precondition a battery of each energy storage system of the plurality of energy storage systems (¶0051 " The preconditioning time 192 may include information associated with a predicted time or required duration for the first vehicle 110 to precondition the first battery 114 to a target preconditioning temperature")
according to the preconditioning schedule; (¶0065 "vehicle ranking module 208 may rank the vehicles based on an output from the preconditioning characteristic comparison module 206", please see below for further detail)
and direct the battery charger management system to charge the battery of each energy storage system of the plurality of energy storage systems according to the charging schedule. (¶0058 "fleet management system 180 may issue one or more commands to respective vehicles to initiate a preconditioning operation, based on the priority ranking and/or queue position of the respective vehicle relative to the charging station 104", please see below for further detail)
Shaotran ¶0064 and ¶0065 discuss the fleet management system 200's comparison module 206 which compares the preconditioning characteristics of multiple vehicles to priority rank vehicles for charging order, this functions as generating a charging schedule of multiple vehicles based on preconditioning characteristics. The fleet management system creates a priority ranking for the multiple vehicles accessing a charging station via the vehicle ranking module 208, this functions as choosing a charging order based on preconditioning time and charge time of each vehicle in the ranked queue. As the fleet management system charges through the ranked queue, it updates the estimate of when a vehicle will be charged and thereby functionally creates a charging schedule through the vehicle ranking module 208.
Shaotran does not teach identify one or more sets of information which indicate a demand placed on an electric grid, the electric grid to provide electrical power to the battery charger management system, and the demand placed on the electric grid to indicate a total amount of electrical power drawn from the electric grid; generate a charging schedule and a preconditioning schedule for the plurality of energy storage systems based on the demand placed on the electric grid; and direct the battery charger management system to charge the battery of each energy storage system of the plurality of energy storage systems according to the charging schedule to account for the total amount of electrical power drawn from the electric grid. Wherein the italicized portion is taught by Shaotran, as detailed above, and the underlined portion is not taught by Shaotran.
Pollack teaches identify one or more sets of information which indicate a demand placed on an electric grid, the electric grid to provide electrical power to the battery charger management system, and the demand placed on the electric grid to indicate a total amount of electrical power drawn from the electric grid; (¶0181 "distributed energy manager can minimize the total daily cost to provide energy generation by forecasting total system and dispatchable load")
generate a charging schedule for the plurality of energy storage systems based on the demand placed on the electric grid; (¶0089 “ The flow control server 106 includes a connection manager 702 to communicate with electric resources 112, a prediction engine 704 that may include a learning engine 706 and a statistics engine 708, a constraint optimizer 710, and a grid interaction manager 712 to receive grid control signals 714”, ¶0181 “The distributed energy manager schedules dispatchable load to draw power from the grid at times that will minimize cost based on the available generation stack”)
and direct the battery charger management system to charge the battery of each energy storage system of the plurality of energy storage systems according to the charging schedule to account for the total amount of electrical power drawn from the electric grid. (¶0089 “ The flow control server 106 includes a connection manager 702 to communicate with electric resources 112, a prediction engine 704 that may include a learning engine 706 and a statistics engine 708, a constraint optimizer 710, and a grid interaction manager 712 to receive grid control signals 714”, ¶0181 “The distributed energy manager schedules dispatchable load to draw power from the grid at times that will minimize cost based on the available generation stack”)
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to modify the fleet management system 200, as taught by Shaotran, to incorporate demand on the power grid into the ranking system, as taught by Pollack. Shaotran FIG 2 depicts fleet management system 200 to be in communication with charging station 104, and has the ability and structure to include the demand on the grid as metric in the ranking override module 210 via charging station 104. It would be obvious to modify the Shaotran’s fleet management system to include the flow control server 106 as taught by Pollack into the fleet management system to incorporate demand on the power grid into the ranking system. The modification would be obvious because one of ordinary skill in the art would be motivated to optimize how to dispatch the resources under management, that avoid power spikes, and that minimize the total daily cost of providing energy generation.
Similarly as applied to the method claim 8, (Shaotran ¶0040 “a method is disclosed for ranking vehicles according to preconditioning characteristics of the vehicles or otherwise comparing vehicles of the multi-vehicle system to determine the indirect prioritization”)
comprising: determining, by one or more processors coupled with memory (¶0118 “FIG. 14, the computer system 1400 may include…may include one or more processing elements 1402… one or more memory components 1408”).
Similarly as applied to the non-transitory computer-readable medium of claim 15 (¶0118 “FIG. 14, the computer system 1400 may include… one or more memory components 1408”)
storing instructions executed by one or more processors. (¶0118 “FIG. 14, the computer system 1400 may include…may include one or more processing elements 1402”
m)
Regarding claim 4, Shaotran as modified by Pollack teaches the system of claim 1. Shaotran as modified by Pollack further teaches a system further comprising memory storing instructions executed by the processor generate the charging schedule and the preconditioning schedule for the plurality of energy storage systems. (Pollack ¶0181 "distributed energy manager schedules dispatchable load to draw power from the grid at times that will minimize cost based on the available generation stack")
Shaotran as modified by Pollack does not teach a system further comprising memory storing instructions executed by the processor determine information related to a time window related to an impending power disruption event; and based on the information related to the time window related to the impending power disruption event.
Pollack further teaches a system further comprising memory storing instructions executed by the processor determine information related to a time window related to an impending power disruption event; (¶0094 "communicative power flow controller 806 also includes Ethernet and information processing components, such as a processor 810 or microcontroller and an associated Ethernet media access control (MAC) address 812; volatile random access memory 814, nonvolatile memory 816 or data storage, an interface")
and based on the information related to the time window related to the impending power disruption event generate the charging schedule. (¶0149 "the site power flow manager 1010 may allow for optimizing with regard to the overall site electric cost minimization or total cost minimization, or to recharge in the greenest, most efficient meaner")
It would be obvious to one of ordinary skill in the art, before the effective filing date, to modify the system as taught by Shaotran determine information related to a time window related to an impending power disruption event as taught by Pollack for the purpose of optimizing how to dispatch the resources under management, that avoid power spikes, and that minimize the total daily cost of providing energy generation.
Similarly as applied to a method and a non-transitory computer-readable medium for dependent claim 11 and 18 respectively, wherein Shaotran as modified by Pollack teaches the method of claim 8 and the non-transitory computer-readable medium of claim 15.
Regarding claim 5, Shaotran as modified by Pollack teaches the system of claim 1. Shaotran as modified by Pollack further teaches a system wherein the state-of-charge and the one or more sets of preconditioning time information are determined from the battery management system of each energy storage system of the plurality of energy storage systems (Shaotran ¶0051 “first battery 114 to be fully or partially recharged at the charging station 104 (e.g., the time required to bring the battery charge to a predetermined value, whether that value is a full charge (i.e., 100% SOC) or a partial charge (e.g., 80% SOC))")
via a wireless communication connection prior to each energy storage system of the plurality of energy storage systems being physically/electrically connected to a charger system associated with the battery charger management system. (Shaotran ¶0124 "network interface 1410 includes one or more communication protocols, such as, but not limited to WIFI, Ethernet, Bluetooth, and so on")
Shaotran ¶0053 states "first vehicle 110, the second vehicle 130, the third vehicle 150, and charging station 104 may include a communications component", which is implemented by network 170. Network 170 is described in 10055 "sensor(s) 174 may more generally be any other sensor that provides supplemental information to the network 170 associated with battery preconditioning, vehicles, vehicle environment, and so on", which is how preconditioning time is determined.
Similarly as applied to a method and a non-transitory computer-readable medium for dependent claim 12 and 19 respectively, wherein Shaotran as modified by Pollack teaches the method of claim 8 and the non-transitory computer-readable medium of claim 15.
Regarding claim 6, Shaotran as modified by Pollack teaches the system of claim 1. Shaotran as modified by Pollack further teaches a system wherein the state-of-charge and the one or more sets of preconditioning time information determined from the battery management system of each energy storage system of the plurality of energy storage systems via a wired communication connection subsequent to each energy storage system of the plurality of energy storage systems being physically/electrically connected to a charger system associated with the battery charger management system. (Shaotran ¶0124 "network interface 1410 includes one or more communication protocols, such as, but not limited to WIFI, Ethernet, Bluetooth, and so on")
Shaotran ¶0124 specifies that the network may be, but is not limited to, WiFi, Ethernet, Bluetooth, and so on. Ethernet is well known in the art of communication to be a wired technology, forming a wired communication connection.
Similarly as applied to a method and a non-transitory computer-readable medium for dependent claim 13 and 20 respectively, wherein Shaotran as modified by Pollack teaches the method of claim 8 and the non-transitory computer-readable medium of claim 15.
Regarding claim 7, Shaotran as modified by Pollack teaches the system of claim 1. Shaotran as modified by Pollack further teaches a system further comprising memory storing instructions executed by the processor to: send a notification to at least one energy storage system of the plurality of energy storage systems, wherein the notification comprises an indication of the charging schedule and the preconditioning schedule. (¶0054 "The user device 172 may include a display or screen that allows a user to receive information, including visual representations of the preconditioning characteristics of other vehicles, rankings or queues of vehicles at a given charging station").
Shaotran ¶0054 describes a user device which can receive information regarding the preconditioning characteristics and charging schedule information of at least one of the plurality of energy storage systems.
Similarly as applied to a method for dependent claim 14, wherein Shaotran as modified by Pollack teaches the method of claim 8.
Prior Art not relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited by Examiner attached to this correspondence.
Langston et al (US 20220185135 A1) teaches a system for preconditioning vehicle batteries prior to charging based on a scheduling queue through an external server.
Vismara et al (US 20220048398 A1) teaches a method of multiple vehicle charging management using battery preconditioning and a server based scheduling system.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LISA M KOTOWSKI whose telephone number is (571)270-3771. The examiner can normally be reached Monday-Friday 8a-5p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Taelor Kim can be reached at (571) 270-7166. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LISA KOTOWSKI/Examiner, Art Unit 2859
/TAELOR KIM/Supervisory Patent Examiner, Art Unit 2859