DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is responsive to the RCE filed on 11/25/2025. The claims 1- 8 & 12- 23 are pending, of which the claim(s) 1, 13, & 20 is/are in independent form.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Response to Arguments
Applicant’s arguments with respect to claim(s) (see Remarks, pages 8- 9) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Specifically, examiner notes applicant merely states that the amended limitations are not being disclosed by Ryan in view of Aubin as they applied in final office action. Examiner agrees that Ryan in view of Aubin fails to teach “communicating the respective third mitigating policy, via the input/output interface connected to the processor, to cause an electronic grid controller to initiate one or more third responsive actions at the respective power-grid system.” However, newly discovered Cherian (US 20110106321 A1) teaches this limitation in para. [022, 060-061, fig. 2] as elaborated infra.
Claim Rejections - 35 USC § 103
Claim(s) 1- 3, 13- 15, & 20- 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ryan et al. (WO 2024153341 A1) in view of Aubin et al. (US 20220194447 A1) and further in view of Cherian et al., (US 20110106321 A1).
Ryan and Aubin are references of the record. The combination of Ryan, Aubin, and Cherian is referred as RAC hereinafter.
Regarding claim 1, Ryan teaches an automated method [actions performed by the “control system , e.g. a remote management system” that is implemented by the computer 20 of fig. 2] for managing a charging infrastructure [power network including pluralities of the charging stations 101 of the “geographical area 10 to meet demand at various times” as shown in fig. 1], the method comprising: (page 8, lines 25-38, fig. 1);
determining [“At step 301, the method 30 involves collecting…events of interest to be identified may be those that are associated with large crowds of people”], with a processor [“the controller 20 may be in the form of any suitable computing device, for instance one or more functional units or modules implemented on one or more computer processors”], a set of charging stations [“vehicle (EV) charging stations (or EV chargers) 101” of fig. 1] to be impacted by a traffic event [“any events1 that are expected to have an impact on demand at one or more of the EV chargers 101…could be a sports event or concert where the stadium”] predicted to cause congestion (Page 7, lines 10- 15; page 11, lines 5-20, page 21, lines 27-29);
identifying, with the processor, a plurality of electric vehicles [“bookings or appointments made via a booking system…booking requests for individual EVs”] that need charging in a geographic area [“the geographical area 10” of fig. 1] of the traffic event during the traffic event (Page 9, lines 5- 15, page 10);
for each charging station of the set of charging stations, estimating, with the processor, a respective charging demand [“control system , e.g. a remote management system, may be configured to monitor and predict usage / demand at different EV chargers 101 in the geographical area 10”] during the traffic event (page 9, fig. 1);
selecting, with the processor and based on the respective charging demand, a respective first mitigating policy [a part of the “control signal 202” that causes “EV chargers 101 can then control their batteries' charging/ discharging behaviour” and/or “to prepare such EV chargers peripheral to the location of the event of interest”] for a respective charging station of the set of charging stations, a part of the control signal 202 that causes “control importation of electricity from grids based on the schedule” that was obtained as a solution to the “optimization problem” and/or “meet the increased demand at these times, e.g. by … sourcing more energy from the grid or other EV chargers”] for a respective power-grid system to which the respective charging station is connected, the respective first, being directed at managing the charging infrastructure for the estimated charging demand (pages 9-10, 21);
communicating [“The controller 20 is configured to transmit a control signal 202 to different EV charging stations 101 with respective determined energy control schedules”, “The determined energy control schedule - obtained from a determined solution to the defined optimisation problem - may then be sent/transmitted to one or more of the EV chargers 101 from the controller 20”] the respective first mitigating policy, via an input/output interface connected to the processor, to cause an electronic station controller to initiate one or more first responsive actions at the respective charging station (pages 9- 10, 221);
In summary, Ryan teaches using a “remote management system” 20 (a system with a processor and memory, see fig. 2) to determine various charging demands during a traffic event (“a sports event or concert where the stadium”) at various charging stations 101 of a region 10, identify and select pluralities of the mitigating policies to meet changed power demands due to the traffic events and “transmit a control signal 202 to different EV charging stations 101” (Fig. 1, page 9). That is, Ryan clearly teaches method steps of generating and transmitting a first mitigating policy (part of the command 202 that causes “control their batteries' charging/ discharging behaviour”/ “prepare such EV chargers peripheral”) and a third policy (e.g., command 202 that causes “sourcing more energy from the grid”/ control importation of electricity from grids).
While Ryan focuses on making increased power available at the charging station(s) predicted to have higher power demand, it does not teach lowering the power consumption for its charge seeking vehicles coming at the charging stations 101s. Furthermore, while Ryan teaches of determining the third mitigating policy and initiating one or more third responsive actions, it still fails to clarify this third policy is communicated to an electronic grid controller as claimed. Simply put, as shown above with strikethrough emphasis, Rayan does not teach:
[1] selecting, a respective second mitigating policy for a respective electric vehicle of the plurality of electric vehicles, and
communicating the respective second mitigating policy, via the input/output interface connected to the processor, to cause an electronic vehicle controller to initiate one or more second responsive actions at the respective electric vehicle; and
[2] communicating the respective third mitigating policy, via the input/output interface connected to the processor, to cause an electronic grid controller to initiate one or more third responsive actions at the respective power-grid system.
However, Ryan’s 1st deficiency is cured by Aubin and 2nd deficiency is cured by Cherian.
Aubin relates to a method executed by a server device 10 for managing electrical energy consumption [“the highest electrical energy consumer after the vehicle propulsion system”] of a set of passenger transport vehicles 100s for a geographic area to balance power production and power demand and cures 1st deficiency of Ryan (Abstract, fig. 1, [008]). More specifically, Aubin teaches an automated method for managing a charging infrastructure, the method comprising:
identifying [“selecting vehicles from among the vehicles of the fleet 20”], with a processor [processor of the management device 10], a plurality of electric vehicles that need charging in a geographic area of a traffic event during the traffic event (Fig. 1, [073-076, 0128-0129]);
selecting, with the processor and based on a charging demand [“the electrical energy consumption of the set of electricity consumers 50”] during a traffic event, a second mitigating policy [“the management device sends a request for reduction of the electricity consumption to the vehicles of the set”] for a respective electric vehicle of the plurality of electric vehicles, the second mitigating policy being directed at managing [“improving the management of electrical energy used” by the vehicles means managing the charging infrastructure because vehicles air conditioner and propulsion system receives electric energy from the charging stations. If the power consumption is reduced, it takes longer for the vehicles to request for new charging session as can be clear to PHOSITA] the charging infrastructure for the estimated charging demand ([008, 027-028, 075], fig. 4); and
communicating [“sending a request for reducing the electrical energy consumption provided for the vehicles of the set”] the respective second mitigating policy, via the input/output interface connected to the processor, to cause an electronic vehicle controller to initiate one or more second responsive actions at the respective electric vehicle ([088, 0108, 0124]). Furthermore, PHOSITA knows that as the power consumption of the vehicles goes down due to request to reduce power consumption at the AC units of the vehicles, so does the charging power demand at the nearby charging stations.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Aubin and Ryan because they both related to a management server communicating signals with subset of power requesting electric vehicles during a traffic event (high power demand situation) to help balance power supply and demand in a power network of a geographical region and (2) modify the Ryan’s “remote management system” to include the steps of determine and communicate a second mitigation policy to lower the power consumption at the electric vehicles from Aubin. Doing so would help to reduce the value of the determined overall power consumption at the charging stations and the vehicles during peak hours with traffic event thereby avoiding being penalized for large power consumption (Aubin [008]).
Ryan in view of Aubin still fails to teach [2] communicating the respective third mitigating policy, via the input/output interface connected to the processor, to cause an electronic grid controller to initiate one or more third responsive actions at the respective power-grid system but this deficiency is cured by Cherian but this deficiency is cured by Cherian.
Cherian relates to using a central/remote management computer [“enterprise control module 275”, analogous to Ryan’s controller 20] in communication with pluralities of the local/regional computers (items 225/215) in a distributed power grid comprising pluralities of the end power loads [“large commercial enterprises and residential loads 250”, analogous to Ryan’s charging stations 101s] to safely and reliably control power production, distribution, storage, and consumption (Fig. 2, [057, 059]). Specifically, Cherian teaches an automated method for managing a charging infrastructure, the method comprising:
selecting, with a processor [processor used by “the enterprise control module 275”], third mitigating policy [“The enterprise control module can issue commands to one or more regional control modules to increase power production or decrease consumption as well as reroute excess power”] for a respective power-grid system and communicating the respective third mitigating policy, via the input/output interface connected to the processor, to cause an electronic grid controller [“to one or more regional control modules”] to initiate one or more third responsive actions at the respective power-grid system ([022, 069, 0102]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Cherian and Ryan in view of Aubin because they both related to managing changing power demand at various subsections of a power network and (2) modify the method/system of Ryan in view of Aubin to have its the determined/selected third mitigating policy being directly communicated to an electronic grid controller (from the processor of the controller 20 to initiate third responsive actions) at the respective power-grid system. Doing so would implement the “sourcing more energy from the grid”/ “control importation of electricity from grids” actions discussed in Ryan’s system in faster rate and more reliable manner and also provide smooth control over a large number of assets (Ryan, page 9; Cherian, [022, 069]). Accordingly, when Ryan is modified with Aubin and Cherian as discussed above, the modified Ryan discloses each limitation and renders invention of the claim obvious to PHOSITA.
Regarding claim 2, RAC teaches/suggests the automated method of claim 1, wherein the determining comprises filtering [e.g., “events of interest to be identified”, “the identified events may be labelled with specific time windows”], with the processor, traffic-event related information received through the input/output interface (Ryan, Pages 12-13).
Regarding claim 3, RAC teaches/suggests the automated method of claim 2, wherein the determining further comprises collecting [“preliminary database of events may be collected”] the traffic-event related information from one or more information sources selected from the group of information sources consisting of: historical traffic data; real-time traffic data; information about weather events capable of impacting traffic in a geographic area of the traffic event, and information about anticipatable or predictable high traffic events (Ryan, pages 12-13).
Regarding claims 13- 15, RAC teaches/suggests inventions of these claims for the similar reasons set forth above in method claims 1- 3 respectively since they are system claim having similar subject matter.
Please note that the controller 20 of fig. 2 (“A control system, e.g. a remote management system,”) is mapped with claimed “a system” with processor and a memory and “geographic area 10” as claimed “charging infrastructure” having multiple charging stations.
Regarding claims 20, RAC teaches invention of this claim for the same rationale set forth above in claim 1 since it is a non-transitory computer readable storage medium claim having similar subject matter.
Regarding claim 21, RAC teaches the automated method of claim 1, wherein the one or more first responsive actions is/are selected from the group of actions consisting of: preparing [“the energy control schedule may be determined to prepare such EV chargers peripheral to the location of”] charging equipment to meet the respective charging demand; preparing cooling equipment to deliver to the charging equipment a cooling capacity corresponding to the respective charging demand; charging [“control their batteries' charging/ discharging behaviour”] stationary storage batteries for mitigating estimated power-demand spikes corresponding to the respective charging demand; and charging an inventory of swappable battery packs to meet estimated battery-swap demand corresponding to the respective charging demand (Ryan, page 21).
Regarding claim 22, RAC teaches the automated method of claim 1, wherein the one or more second responsive actions is/are selected from the group of actions consisting of: enacting an extended range mode; shutting down one or more nonessential units, circuits, devices, and accessories; changing a vehicle regeneration setpoint; changing a vehicle ride height; thermally preconditioning a vehicle battery; indicating trip infeasibility; and suggesting a different route (Aubin [0109, 0123-0124]).
Regarding claim 23, RAC teaches the automated method of claim 1, wherein the one or more third responsive actions is/are selected from the group of actions consisting of: preparing [“increase power production or decrease consumption as well as reroute excess power.”] the power-grid system for the estimated charging demand; reallocating a portion of a present power load to charging stationary storage batteries at the respective charging station; and increasing generator output to a level sufficient for the estimated demand in the geographic area (Ryan Page 21, lines 8-12 & Cherian [022, 069]).
Claim(s) 4-8, 12, & 16- 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over RAC (as in claim 1 & 13), and further in view of North et al. (US 20150226572 A1, reference of the record).
The combination of Ryan, Aubin, Cherian (RAC) and North is referred as RACN hereinafter.
Regarding claim 4, RAC teaches the automated method of claim 1 as set forth above.
However, RAC may not teach matching, with the processor, each electric vehicle of the plurality of electric vehicles with a corresponding charging station of the set of charging stations for charging thereat, the matching being performed to accommodate charging needs of individual electric vehicles of the plurality of electric vehicles; and communicating results of the matching, via the input/output interface, to the individual electric vehicles.
North relates to a computer system [item 125] identifying and displaying to vehicle users most suitable charging stations where the users can go to charge their electric vehicles (Abstract [011, 034], Figs. 1- 2). More specifically, North teaches an automated method for managing a charging infrastructure comprising:
identifying [“the request module 210 is configured and/or programmed to receive a request to find an available charging station”], with the processor, a plurality of electric vehicles that need charging in a geographic area of the traffic event during the traffic event ([036]);
matching [“operation 630…automatically selects a charging station that is a best match for the electric vehicle”], with the processor, each electric vehicle of the plurality of electric vehicles with a corresponding charging station of the set of charging stations for charging thereat, the matching being performed to accommodate charging needs of individual electric vehicles of the plurality of electric vehicles; and communicating [“operation 640, the vehicle routing system 125 presents information to the electric vehicle that indicates the selected charging station”] results of the matching, via the input/output interface, to the individual electric vehicles ([015, 044, 046, 070-071]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine North and RAC because they both related to an automated system identifying a plurality of vehicles that need charging in a geographic area having multiple charging stations and (2) modify the system/method of RAC to include missing limitations from North. Doing so would allow the charge requesting vehicles surrounding the events of the Ryan (in RAC) to be presented with best charging station that will be predicted to be available once the power requesting vehicle(s) arrive(s) at the respective charging station(s) and will minimize the waiting time in a queue before recharging can be commenced (North [038] & Ryan Page 2). Furthermore, North teaches what other types of the information (state of charge, location, etc.) that can be collected and analyzed at the server to determine status of the vehicles and improve the control of the charging stations North, [047]). Accordingly, the combination of RAC and North (RAN) teaches each limitation and renders invention of this claim obvious to PHOSITA.
Regarding claim 5, RACN teaches the automated method of claim 4, wherein the identifying comprises receiving geolocation information, via the input/output interface, from a vehicle navigation system (Ryan page 9; North [0078], Fig. 7).
Regarding claim 6, RACN teaches the automated method of claim 4, wherein the identifying comprises retrieving vehicle information of the individual electric vehicles, via the input/output interface, from a database (North [042, 060]).
Regarding claim 7, RACN teaches the automated method of claim 4, wherein the matching is performed using powertrain information [“information associated with a current geographical route traveled by the electric vehicle” indicate which direction the power train is headed. The powertrain information covers every possible type of the information for the powertrain of the vehicle] of the individual electric vehicles (North [042] & Ryan page 9).
Regarding claim 8, RACN teaches the automated method of claim 4, wherein the matching is performed using driver information [vehicle information provides information about the driver] corresponding to the individual electric vehicles and route information [“accesses context information for a current trip”] corresponding to the individual electric vehicles (North, [041-042, 077]; Ryan page 9).
Regarding claim 12, RACN teaches/suggests the automated method of claim 4, wherein the matching includes estimating times of arrival [“a current location of an electric vehicle 410 at a given point in time” like information can be used to estimate times of arrival as can be clear to PHOSITA] of the individual electric vehicles to different ones of the set of charging stations ([054-055]).
Regarding claims 16- 18, RACN teaches/suggests inventions of these claims for the similar reasons set forth above.
Regarding claim 19, RACN teaches/suggests the system of claim 16, wherein the matching is performed using one or more of the following:
driver information corresponding to the individual electric vehicles; route information [e.g., “information associated with the parameters of a current trip traveled by the electric vehicle 130”] corresponding to the individual electric vehicles; iterative adjustment of vehicle policies for the individual electric vehicles; and iterative adjustment of the respective first mitigating policy of at least one charging station of the set of charging stations (North, [070-071]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTOSH R. POUDEL whose telephone number is (571)272-2347. The examiner can normally be reached Monday - Friday (8:30 am - 5:00 pm).
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
1 Events of Ryan are similar to applicant’s events discussed in spec, para. 014 (“such as sporting events, concerts, and evacuations”)
2 Lines 8- 25: “The determined energy control schedule - obtained from a determined solution to the defined optimisation problem - may then be sent/transmitted to one or more of the EV chargers 101 from the controller 20. The EV chargers 101 can then control their batteries' charging/ discharging behaviour and control importation of electricity from grids based on the schedule….other EV chargers in the vicinity of an event may see increased usage as a result of the event (e.g. because nearby chargers are inaccessible) and, as such, the energy control schedule may be determined to prepare such EV chargers peripheral to the location of the event of interest (e.g. within a threshold distance) for such increased usage.”