DETAILED 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
Regarding the rejection of claims 1, 3, 12-13, 15, and 18 under 35 U.S.C. 102(a)(2), applicant has amended independent claims 1, 13, and 18 and argues against the prior art of record Gadh et al (US 20130179061 A1). Claim 1 has been amended to recite “wherein predicting the peak recharge time interval comprises analyzing the SoC data to predict which of the EVs within the threshold distance will stop at the charging stations for recharging”, arguing that the emphasized portion is not disclosed by Gadh. Applicant argues Gadh “describes a power grid expert system for grid balancing and prediction of peak and off-peak hours, but is limited to general grid-level management functions for demands of large numbers of EVs” and does not predict operations of individual EV’s. However, Gadh FIG 4 (as described in ¶0097) depicts a user interface on a personal smartphone in communication with a server including charge status (state of charge) and charging prices of nearby charging stations. FIG 5 expands the user interface to include user preferences which are described in ¶0099 as “Among such preferences may be maxima and minima for: total cost allowed for charging, charging time allowed, and desired EV travel range”. The user preferences as taught by Gadh do include an EV travel range, but do not explicitly disclose picking a charging station within a threshold distance. Similarly Gadh FIG 6 depicts the plurality of charging stations in communication with a central server (as described in ¶0100), which assigns a specific vehicle 610 to a specific charging station 608. Gadh ¶0100 discloses “central server 602 may control simultaneous charging of many vehicles, based on users' profiles, selections of charge modes, allowable charging costs, and allowable time limits for charging”, which does not account the vehicle’s distance from the charging station as a parameter.
Applicant's arguments filed 08 December 2025 have been fully considered and they are 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, 12-13, 15, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gadh et al (US 20130179061 A1) modified by Malik et al (US 20190092177 A1)
Regarding claim 1, Gadh teaches a non-transitory machine readable medium having machine executable instructions comprising: (¶0353 “these computer program instructions, such as embodied in computer-readable program code logic, may also be stored in a computer-readable memory that can direct a computer or other programmable processing apparatus to function in a particular manner”)
a power needs engine that: predicts a peak recharge time interval for charging points of charging stations (¶0064 "[FIG 1, WINSmartEV architecture 100] power grid expert system 120 (perhaps elsewhere referred to as an "aggregator") is provided to address grid balancing, grid management and prediction of peak and off-
peak hours to store excess capacity, and/or demands for large numbers of EVs charging efficiently,
economically and safely.")
based on state of charge (SoC) data for electric vehicles (EVs) that are within a threshold distance, wherein the SoC data characterizes an SoC of batteries of the EVs, (¶0057 "Smart Energizing 108 is the management of EV 114 battery charging rates and extent of the charge backfill station 112 based on some or all data, including, but not limited to: grid stability, energy cost, vehicle location, battery status, driver preferences, and driving patterns")
wherein predicting the peak recharge time interval (¶0064 "[FIG 1 WINSmartEV architecture 100] power grid expert system 120 (perhaps elsewhere referred to as an "aggregator") is provided to address grid balancing, grid management and prediction of peak and off-peak hours to store excess capacity, and/or demands for large numbers of EVs charging efficiently, economically and safely.", ¶0116 “the gateway 910 also dispatches the command from the central controller to the charging station 904. The central controller decides the charging capacity of each area by the information provided by the energy provider and the status of each charging area")
comprises analyzing the SoC data to predict which of the EVs [within the threshold distance] will stop at the charging stations for recharging; (FIG 1 WINSmartEV architecture 100 comprising client portal 110a featuring a state of charge, ¶0057 "Smart Energizing 108 is the management of EV 114 battery charging rates and extent of the charge backfill station 112 based on some or all data, including, but not limited to: grid stability, energy cost, vehicle location, battery status, driver preferences, and driving patterns")
and a charge control module that: creates and/or updates charging schedules for the charging points of the charging stations based on a charge time and the peak recharge time interval for the charging points of the charging stations; (¶0116 "collectors 906 search and join an available mesh network automatically after startup and play the role of controllers to decide the charging/discharging process for the charging stations")
and provides the charging schedules to computing platforms of the charging points of the charging stations, (¶0116 " the gateway 910 also dispatches the command from the central controller to the charging station 904. The central controller decides the charging capacity of each area by the information provided by the energy provider and the status of each charging area")
wherein the computing platforms cause batteries of the charging points to charge and discharge according to a corresponding charging schedule of the charging schedules. (¶0116 "collectors 906 search and join an available mesh network automatically after startup and play the role of controllers to decide the charging/discharging process for the charging stations")
Gadh FIG 4 (as described in ¶0097) depicts a user interface on a personal smartphone in communication with a server including charge status (state of charge) and charging prices of nearby charging stations. FIG 5 expands the user interface to include user preferences which are described in ¶0099 as “Among such preferences may be maxima and minima for: total cost allowed for charging, charging time allowed, and desired EV travel range”. The user preferences as taught by Gadh do include an EV travel range, but do not explicitly disclose picking a charging station within a threshold distance. Further the state of charge is indicative of miles remaining until recharge, which could function as a threshold distance. Gadh has the architecture and ability to set, but does not explicitly disclose, using a threshold distance as a metric for predicting when an EV will stop at a charging station for recharging.
Gadh does not teach [wherein predicting the peak recharge time interval comprises analyzing the SoC data to predict which of the EVs] within the threshold distance [will stop at the charging stations for recharging.]
Malik teaches [wherein predicting the peak recharge time interval comprises analyzing the SoC data to predict which of the EVs] within the threshold distance [will stop at the charging stations for recharging.] (¶0043 “the smart charge application 118 may utilize the GPS 210 of the EV 102 to determine the location of the EV 102”, ¶0053 “station determinant module 402 may determine that the EV 102 is connected to the charging station 112 and may communicate with the GPS 210 of the EV 102 to determine current GPS locational coordinates of the EV 102. Upon determining the GPS locational coordinates of the EV 102, the station determinant module 402 may access the saved charging station list to query the list for GPS locational coordinates that are within a predetermined locational range of the current GPS locational coordinates”)
Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to modify the non-transitory medium as taught by Gadh wherein EVs within a threshold distance will stop at a designated charging station for recharging as taught by Malik. Gadh, as discussed above for FIGs 4-6, has the structures capable of determining location and state of charge; it would be a logical next step to set a threshold distance into the model to determine when and where to recharge. The modification would be obvious because one of ordinary skill in the art would be motivated to prevent the vehicle from running out of charge during use thereby improving user experience and preventing users from being stranded between charging stations.
Similarly for claim 13 as applied to a system for charging and discharging batteries (¶0064 "[FIG 1, WINSmartEV architecture 100] power grid expert system 120 (perhaps elsewhere referred to as an "aggregator", FIG 2 power grid expert system 120 connected to Database 118 and internet 202)
Similarly for claim 18 as applied to a method for charging and discharging batteries comprising: predicting, by a charging server. (FIG 2A database 118 and power grid expert system 120 as connected to internet 202)
Regarding claim 3, Gadh as modified by Malik teaches the medium of claim 1. Gadh as modified by Malik further teaches wherein the power needs engine comprises a machine learning (ML) model tuned with the SoC data. (Gadh ¶0057 "Smart Energizing 108 is the management of EV 114 battery charging rates and extent of the charge backfill station 112 based on some or all data, including, but not limited to: grid stability, energy cost, vehicle location, battery status, driver preferences, and driving patterns")
Gadh FIG 1 depicts the Smart Energizing 108 as being a component of the WINSmartEV architecture in communication with EV114a and collecting real-time data such as battery status including SOC.
Similarly for claim 15 as applied to a system for charging and discharging batteries, Gadh as modified by Malik teaches the system for charging and discharging batteries of claim 13.
Regarding claim 12, Gadh as modified by Malik teaches the medium of claim 1. Gadh as modified by Malik further teaches wherein the power needs engine predicts a low usage time period for a particular charging station, (Gadh ¶0064 " power grid expert system 120 (perhaps elsewhere referred to as an "aggregator") is provided to address grid balancing, grid management and prediction of peak and off-peak hours to store excess capacity, and/or demands for large numbers of EVs charging efficiently, economically and safely.")
and the charge control module creates and/or updates a respective charging schedule of the charging schedules based on the low usage period of time, (Gadh ¶0116 "collectors 906 search and join an available mesh network automatically after startup and play the role of controllers to decide the charging/discharging process for the charging stations")
and a computing platform of charging points of the particular charging station causes a corresponding battery of a corresponding charge point to discharge (Gadh ¶0116 "collectors 906 search and join an available mesh network automatically after startup and play the role of controllers to decide the charging/discharging process for the charging stations")
to at or below a threshold charge level until the low usage period of time has expired, (Gadh ¶0061 "Real-time information about available charging stations, type of charger capacities, ability to both charge and backfill (grid tying), type of battery in the current EV, and dynamic pricing from the utility will affect the maximum and minimum limits for user selectable parameters of charge time and price")
wherein the charging schedule for the charging points of a particular charging station of the charging stations specifies a schedule to charge and discharge the corresponding batteries of the charging points based on peak usage time intervals for a power grid. (Gadh ¶0116 "collectors 906 search and join an available mesh network automatically after startup and play the role of controllers to decide the charging/discharging process for the charging stations").
Claim(s) 2-11, 14, 16-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gadh as modified by Malik and further in view of Sham et al (US 20190275893 A1)
Regarding claim 2, Gadh as modified by Malik teaches the medium of claim 1. Gadh as modified by Malik does not teach wherein the peak recharge time interval for a particular charging station of the charging stations is a time interval where at least 70% of charging points at the particular charging station are expected to be coupled to a respective EV of the EVs.
Sham teaches wherein the peak recharge time interval for a particular charging station of the charging stations is a time interval where at least 70% of charging points at the particular charging station are expected to be coupled to a respective EV of the EVs. (¶0030 "The grid profile estimates a future load on at least a portion of the power grid as a function of historically received grid capacity information, and the station profile estimates a future station availability as a function of historically received station capacity information").
Sham ¶0034 "The station capacity information can provide any suitable indication of availability of one or more EV charging stations 102 for EV charging", if a particular charging station is coupled to a respective EV during the peak recharge time interval then it has 100% occupancy. Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to modify the medium as taught by Gadh modified by Malik wherein the peak recharge time interval for a particular charging station of the charging stations is a time interval where at least 70% of charging points at the particular charging station are expected to be coupled to a respective EV of the EVs as taught by Sham. The modification would be obvious because one of ordinary skill in the art would be motivated to optimize the pairing between available EV charging stations and requested charging times.
Similarly for claim 14 as applied to a system for charging and discharging batteries, Gadh as modified by Malik teaches the system for charging and discharging batteries of claim 13.
Regarding claim 4, Gadh as modified by Malik teaches the medium of claim 3. Gadh as modified by Malik does not teach wherein the prediction of the peak recharge time interval for a particular charging station of the charging stations is based on a calculated likelihood that a subset of the EVs will recharge at the particular charging station.
Sham teaches wherein the prediction of the peak recharge time interval for a particular charging station of the charging stations is based on a calculated likelihood that a subset of the EVs will recharge at the particular charging station. (¶0030 "The grid profile estimates a future load on at least a portion of the power grid as a function of historically received grid capacity information, and the station profile estimates a future station availability as a function of historically received station capacity information)
Sham FIG 1 depicts multiple charging stations 102a-102e, described in ¶0022 "information regarding the given EV charging station 102 can include availability of the given EV charging station 102". Sham is able to determine if each charging stations 102 has future availability, and would also be capable of determining a subset of the charging stations 102 have future availability. Therefor it would be obvious to one of ordinary skill in the art, before the effective filing date, to modify the non-transitory machine readable medium having machine executable instructions as taught by Gadh, wherein the prediction of the peak recharge time interval for a particular charging station of the charging stations is based on a calculated likelihood that a subset of the EVs will recharge at the particular charging station as taught by Sham. The modification would be obvious because one of ordinary skill in the art would be motivated to optimize the pairing between available EV charging stations and requested charging times
Similarly for claim 16 as applied to a system for charging and discharging batteries, Gadh as modified by Malik teaches the system for charging and discharging batteries of claim 15.
Similarly for claim 19 as applied to a system for charging and discharging batteries, Gadh as modified by Malik teaches the system for charging and discharging batteries of claim 18.
Regarding claim 5, Gadh as modified by Malik and Sham teaches the medium of claim 4. Gadh as modified by Malik and Sham wherein [the likelihood that the subset of the EVs will recharge at the particular charging station is based on] data characterizing driving habits of drivers of the EVs. (Gadh ¶0072 "EV usage information and electric grid status may be collected wirelessly to determine better efficient and economic charging operation of the EVs. This information may be used to form the basis of "customer intelligence" whereby the activities and preferences of a user are stored and analyzed in a "Customer Behavior" 124 module.", depicted in FIG 1)
The Customer Behavior module 124, as taught by Gadh, modifies the WINSmartEV instructions based on customer activities which includes driving habits of drivers of the EVs.
Regarding claim 6, Gadh as modified by Malik and Sham teaches the medium of claim 4. Gadh as modified by Malik and Sham wherein the SoC data includes data characterizing a location of the EVs. (¶0057 "Smart Energizing 108 is the management of EV 114 battery charging rates and extent of the charge backfill station 112 based on some or all data, including, but not limited to: grid stability, energy cost, vehicle location, battery status, driver preferences, and driving patterns")
Regarding claim 7, Gadh as modified by Malik and Sham teaches the medium of claim 6. Gadh as modified by Malik and Sham wherein the SoC data includes data characterizing a route for a subset of the EVs. (¶0097 FIG. 4, which is a diagram 400 showing a smart phone 402 displaying a map 404 of nearby charging stations 406, and a nearby charging station pushing real-time charging information to the smart phone 402 resulting in a cost per kWh 408").
Applicant specification ¶0017 "if the given charging station is situated along a route to a predetermine destination of an EV, that EV is more likely to stop for charging at the given station than another station", wherein determining which charging station to request is based on proximity to the vehicle and the vehicle's intended route.
Regarding claim 8, Gadh as modified by Malik and Sham teaches the medium of claim 2. Gadh as modified by Malik and Sham further teaches wherein the charge control module receives power data identifying a peak usage time interval for a power grid, (Gadh ¶0064 " power grid expert system 120 (perhaps elsewhere referred to as an "aggregator") is provided to address grid balancing, grid management and prediction of peak and off- peak hours to store excess capacity, and/or demands for large numbers of EVs charging efficiently, economically and safely.")
and responsive to the power data the charge control module updates and/or creates the charge control schedules based on the peak usage time interval of the power grid (Gadh ¶0116 "collectors 906 search and join an available mesh network automatically after startup and play the role of controllers to decide the charging/discharging process for the charging stations")
such that the computing platforms of the charging points are configured to cause the corresponding batteries of the charging points to discharge to the power grid during time intervals that are off-peak recharge time intervals and the peak usage time interval. (Gadh ¶0102 "FIG. 7, which is a photograph of a WINSmartEV test bed 700 for a low powered electric vehicle setup. The WINSmartEV test bed allows for the analysis of electric vehicle battery management in order to provide smart scheduling for consumer charging of electric vehicles and smart discharging for vehicle-to-grid implementation")
Regarding claim 9, Gadh as modified by Malik and Sham teaches the medium of claim 8. Gadh as modified by Malik and Sham further teaches wherein the charge control module receives an indication of a grid event, and responsive to the grid event, (¶0223 "WINSmartEV utilizes various wireless communication protocols to collect data such as Vehicle ID, status of charge, battery temperature, power usage in kW, voltage and amperage and to send control signals to connect and disconnect a charger based on various inputs such as grid capacity, user preference, time of use, and demand respond events")
the charge control module provides a discharge command to the computing platforms of the charging stations to support the power grid for a duration of the grid event. (¶0102 "The WINSmartEV test bed allows for different load conditions to be tested with varying bidirectional power flow and multiple communications technologies to analyze various scenarios of electric vehicle charging, discharging, and grid backfilling")
Regarding claim 10, Gadh as modified by Malik and Sham teaches the medium of claim 9. Gadh as modified by Malik and Sham further teaches wherein the charge control module provides the discharge commands to the computing platforms of the charging points of the charging stations in a round-robin order, such that each charging point in a first charging station of the charging stations is provided the discharge command prior to providing the discharge commands to each charging point in a second charging station of the charging stations. (Gadh FIG 2B depicts the WINSmartEV information flow including a control center which dispatches instructions to each of the charging points of the charging stations).
Applicant specification ¶0054 states "sending discharge commands to the charging points 108 of the J number of charging stations 120 on a round robin (rotating) discharge command, the inconvenience of delayed charging of the EV battery 124 of coupled EVs 112 is curtailed", indicating that the batteries are discharged to the grid on a rotating basis. A rotating basis would mean some of the batteries are discharging while others are charging. The medium as taught by Gadh as modified by Sham controls each electric vehicle coupled to a charging point individually, and thereby would be able to discharge electric vehicles on a rotating basis.
Regarding claim 11, Gadh as modified by Malik and Sham teaches the medium of claim 10. Gadh as modified by Malik and Sham further teaches wherein responsive to the discharge commands, the computing platforms of the charging points cause the corresponding battery and an EV battery to discharge to the grid for a duration specified in the discharge commands. (FIG 2B depicts the WINSmartEV information flow including a control center which dispatches instructions to each of the charging points of the charging stations).
Gadh ¶0064 " power grid expert system 120 (perhaps elsewhere referred to as an "aggregator") is provided to address grid balancing, grid management and prediction of peak and off-peak hours to store excess capacity, and/or demands for large numbers of EVs charging efficiently, economically and safely.", which updates the WINSmartEV controls based on peak and off-peak hours in tandem with the electric vehicles SOC to determine when to charge or discharge the battery including duration of discharge.
Regarding claim 17, Gadh as modified by Malik teaches the system of claim 13. Gadh as modified by Malik does not teach wherein the charging server predicts a total available power for a power grid as a function of time based on the SoC data and the peak recharge time.
Sham teaches wherein the charging server predicts a total available power for a power grid as a function of time based on the SoC data and the peak recharge time. (¶0030 " the EV charging server 108 can be further in communication with multiple EVs (including the requesting EV) via the communication network(s) 106… at least one of the EV charging stations 102 is identified as having at least the threshold associated grid capacity for charging of the requesting EV during the charging timeframe as a function of the grid profile, and/or the at least one of the EV charging stations 102 is identified as available for charging of the requesting EV during the charging timeframe as a function of the station profile").
It would be obvious to one of ordinary skill in the art, before the effective filling date, to modify the system for charging and discharging batteries as taught by Gadh, wherein the charging server predicts a total available power for a power grid as a function of time based on the SoC data and the peak recharge time as taught by Sham. The modification would be obvious because one of ordinary skill in the art would be motivated to optimize the pairing between available EV charging stations and requested charging times.
Regarding claim 20, Gadh as modified by Malik and Sham teaches the method of claim 19. Gadh as modified by Malik and Sham teaches further comprising predicting, by the charging server, a low usage time period for a particular charging station of the charging stations, (Gadh ¶0116 "collectors 906 search and join an available mesh network automatically after startup and play the role of controllers to decide the charging/discharging process for the charging stations")
and the charge control module charging server creates and/or updates a respective charging schedule of the charging schedules based on the low usage period of time, (Gadh¶0061 Real-time information about available charging stations, type of charger capacities, ability to both charge and backfill (grid tying), type of battery in the current EV, and dynamic pricing from the utility will affect the maximum and minimum limits for user selectable parameters of charge time and price")
and a computing platform of charging points of the particular charging station causes a corresponding battery to discharge to at or below a threshold charge level until the low usage period of time has expired. (Gadh ¶0061 " Real-time information about available charging stations, type of charger capacities, ability to both charge and backfill (grid tying), type of battery in the current EV, and dynamic pricing from the utility will affect the maximum and minimum limits for user selectable parameters of charge time and price")
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.
Wang et al (US 20210284043 A1) teaches a server for routing and charge scheduling a fleet of electric vehicles using machine learning.
Sujan et al (US 20220348105 A1) teaches a server system for charge scheduling of a fleet of electric vehicles to optimize peak power performance and minimize costs.
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