DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-19 were previously pending and subject to a final office action mailed 03/25/2026. Claims 1 and 15 were amended; no claim was cancelled, and no claim was added in a reply filed 06/25/2026. Therefore claims 1-19 are currently pending and subject to the non-final office action below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/13/2026 was considered by the examiner.
Response to Arguments
Applicant's arguments filed 06/25/2026 in regards to 101 rejection have been fully considered but they are not persuasive.
Applicant argues “By this Amendment and Reply, Applicant has amended independent Claims 1 and 15 such that the system "execute[s] the dynamic charge management" and "specif[ies] how the solution is implemented." Accordingly, the claim is not directed towards an abstract idea and is integrated into a practical application. Favorable reconsideration and withdrawal of the rejections of Claims 1-19 under 35 U.S.C. § 101 is respectfully requested.” (remarks p. 8).
Examiner respectfully disagrees. Applicant amended claim 1 to recite “transmit a signal to at least one of the respective electrified fire apparatus or a charging station that is plugged into the respective electrified fire apparatus to charge the respective electrified fire apparatus according to the dynamic charge management based on at least the first information.” However, the amended limitation does not “execute the dynamic charge management” because it is only sending a signal “to charge the electrified fire apparatus according to the dynamic charge management based on at least the first information” (intended use), it does not affirmatively claim the charging of the electrified fire apparatus based on the signal. As such, the amended limitation does not integrate the abstract idea into a practical application. Therefore, the claim is still directed towards an abstract idea without it being integrated into a practical application.
Applicant's arguments filed 06/25/2026 in regards to 103 rejection have been fully considered but they are not persuasive.
Applicant argues “By this Amendment and Reply, Applicant has amended independent Claims 1 and 15. Specifically, independent Claims 1 and 15 have been amended to recite "transmit a signal to at least one of [a] respective electrified fire apparatus or a charging station that is plugged into the respective electrified fire apparatus to charge the respective electrified fire apparatus according to [a] dynamic charge management based on at least first information" where the first information includes "(a) at least one of average response parameters or response requirements for a respective fire department and (b) a location of the respective fire department." The art of record is silent with respect to such dynamic charge management. Accordingly, amended independent Claims 1 and 15 are not rendered obvious by the art of record.” (Remarks p. 9)
Examiner respectfully disagrees. Mangal discloses “transmit a signal to at least one of [a] respective electrified “vehicle” apparatus or a charging station that is plugged into the respective electrified fire apparatus to charge the respective electrified fire apparatus according to [a] dynamic charge management based on first information” with the first information being an operating schedule and the charging needs of each electric vehicle ([0045] In the next step 206, the server utilizes artificial intelligence enabled optimization to schedule the power charging in combination with the power flows to any of the energy assets to achieve the maximized utilization of renewable sources of energy and to minimize the cost of electricity. After the optimization and power flow sequence is generated by the server, in the next step, the server sends the appropriate control signals to each energy asset. The energy asset comprises EV charging stations, solar panel, or stationary batteries. The server monitors the effectiveness of the schedule in step 208. In the next step 210, the server monitors the charging stations to determine if the charging stations are performing as per the control signals sent by the server. If the assets are not performing as per the power requirement from the control signals, then the charging rate is modified in real time to achieve the desired optimization goals. The server therefore measures the outcome of third step and uses artificial intelligence and machine learning to automatically modify algorithms in the previous steps. In the next step, the server utilizes the artificial intelligence-enabled optimization solver to adjust the charging power in combination with the power flows to any of the energy assets to achieve the maximized utilization of renewable sources of energy and to minimize the cost of electricity….[0110] The system generates the charging profile for both continuous and discrete controlled energy resources. The system predicts the energy needed for each charging session of each electric vehicle. The system collects current battery SOC from vehicle's telematic data and performs historical time series data to obtained the energy consumed hourly in the future. The system also extracts information on full capacity of the electric vehicle. The details on the charging time, i.e. starting and ending time of vehicle charging is determined. The system computes the battery SOC till the start of charging session and the energy consumed after the charging session. According to the SOC range, the optimal SOC at the end of charging session is calculated. The starting SOC and ending SOC is compared to obtain the energy needed in this charging session. The result of prediction is then calculated for the EV with details such as starting time, ending time, predicted energy needed. [0111] The system also takes into consideration the operational requirements and the preference of charging schedule as soft constraints, including the available operational time of the fleet operator, the preferred chargers, the preferred charging order for electric vehicles, etc.).
However, while Mangal does not explicitly state that the first information includes “a) at least one of average response parameters or response requirements for a respective fire department and (b) a location of the respective fire department”, this disclosure is actually supplied by ESRI references which states that “"Some GIS map layers that fire departments use include Streets, Parcels Fire hydrants Utility networks Topography, Lakes and rivers Commercial and government buildings, Fire station locations Police station locations Hospital locations ", page 3, "This allows users to identify a station location, specify a travel time, and run a network analysis. The result will be displayed by an irregular polygon around the station that illustrates where the fire apparatus could travel in any direction for the specified time. This type of analysis can be performed on a single station or simultaneously on all stations to analyze gaps in coverage, establish run orders, and more. ", page 9-12, "There are three National Fire Protection Association (NFPA) standards that contain time requirements that influence the delivery of fire and emergency medical services.).
Therefore, a person of ordinary skill in the art would understand that it would have been obvious to include the station location, the travel time and the result of the network analysis in the first information of Mangal in order to meet the charging needs of every vehicle. As such, the combination teaches the amended limitation.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1/15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “acquire first information regarding (a) at least one of average response parameters or response requirements for a respective fire department and (b) a location of the respective fire department; acquire second information regarding at least one of average costs or environmental impacts for operating one or more fire apparatuses associated with the respective fire department, wherein the second information includes energy consumption information associated with a subsystem of the one or more fire apparatuses, and wherein acquiring the second information includes receiving the energy consumption information from the subsystem; and providing one or more results for a respective electrified fire apparatus based on the first information and the second information, the one or more results including at least one of: (a) an energy cost savings output providing a projected energy cost savings over a lifespan of the respective electrified fire apparatus by owning and operating the respective electrified fire apparatus using dynamic charge management rather than owning and operating an equivalent internal combustion engine (ICE) variant thereof; (b) a CO2e savings output providing a projected CO2e emissions savings over the lifespan of the respective electrified fire apparatus by owning and operating the respective electrified fire apparatus using dynamic charge management rather than owning and operating an equivalent ICE variant; or (c) an emissions output providing a projected amount or percentage of emission generated by the respective electrified fire apparatus using the dynamic charge management over the lifespan of the respective electrified fire apparatus relative to the equivalent ICE variant.”
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of providing analytics on the electrification of a fire department which falls under a method of organizing a human activity and mental processes. That is, the method allows fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and processes that can be performed in the human mind.
This judicial exception is not integrated into a practical application. In particular, the claim recites “a results graphical user interface for display, “one or more processing circuits including one or more memory devices coupled to one or more processors” and “dynamic charge management”, “transmit a signal to at least one of the respective electrified fire apparatus or a charging station that is plugged into the respective electrified fire apparatus to charge the respective electrified fire apparatus according to the dynamic charge management based on at least the first information “(claim 1), “a non-transitory computer-readable medium”, “one or more processors”, “a results graphical user interface for display” and “dynamic charge management” and “ transmit a signal to at least one of the respective electrified fire apparatus or a charging station that is plugged into the respective electrified fire apparatus to charge the respective electrified fire apparatus according to the dynamic charge management based on at least the first information. “(claim 15). Each of the additional limitations is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are nothing more than mere instructions to apply the exception on a general computer.
Dependent claim 2 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claim 3/16 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“wherein at least a portion of the first information is automatically acquired without user input based on the at least one of the telematics data, the GPS data, or the vehicle data” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 4/17 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“wherein at least a portion of the second information is automatically acquired without user input based on the location” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claims 5-11 and 18-19 are also directed to an abstract idea without significantly more because they further narrow the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claim 12 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“a charger sizing tool graphical user interface for display” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 13 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“a charger sizing tool graphical user interface for display” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-2, 5, 7-12, 15 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Andrew Burnham, “User Guide for Afleet Tool 2020”, published by anl.gov in 2020, hereinafter “Burnham” and “GIS for Fire station locations and response protocol”, published by ESRI in January 2007, hereinafter “ESRI” in further view of “E-One vector becomes Arizona’s first all-electric north American style fire truck” published by firehouse.com on January 4, 2024, hereinafter “eOne” in further view of Mangal (US 2022/0410750).
As per claim 1/15, Burnham discloses an electrified apparatus system comprising:
one or more processing circuits including one or more memory devices coupled to one or more processors, the one or more memory devices configured to store instructions thereon that, when executed by the one or more processors, cause the one or more processors to (page 4-6, Burnham discloses Excel sheets executed by a computer):
acquire first information regarding annual vehicle miles (page 4-6);
acquire second information regarding at least one of average costs or environmental impacts for operating one or more ice vehicles associated with the respective department (at least page 4-6, the Afleet tool allows the user to enters information on the cost of ownerships of the conventional vehicles (i.e. maintenance, fuel, etc.); and
provide a results graphical user interface for display providing one or more results for a respective electrified apparatus based on the first information and the second information, the one or more results including at least one of:
(a) an energy cost savings output providing a projected energy cost savings over a lifespan of the respective electrified apparatus by owning and operating the respective electrified apparatus rather than an equivalent internal combustion engine (ICE) variant thereof (page 9);
(b) a CO2e savings output providing a projected CO2e emissions savings over the lifespan of the respective electrified apparatus by owning and operating the respective electrified apparatus rather than the equivalent ICE variant; or
(c) an emissions output providing a projected amount or percentage of emission generated by the respective electrified apparatus over the lifespan of the respective electrified apparatus relative to the equivalent ICE variant (page 15-16, the sheets show a comparison between EV and conventional vehicles in term of emissions).
However, Burnham does not disclose but ESRI discloses acquire first information regarding (a) at least one of average response parameters or response requirements for a respective fire department and (b) a location of the respective fire department (page 1, “Some GIS map layers that fire departments use include Streets Parcels Fire hydrants Utility networks Topography Lakes and rivers Commercial and government buildings Fire station locations Police station locations Hospital locations “, page 3, “This allows users to identify a station location, specify a travel time, and run a network analysis. The result will be displayed by an irregular polygon around the station that illustrates where the fire apparatus could travel in any direction for the specified time. This type of analysis can be performed on a single station or simultaneously on all stations to analyze gaps in coverage, establish run orders, and more. “, 9-12, “There are three National Fire Protection Association (NFPA) standards that contain time requirements that influence the delivery of fire and emergency medical services. These are NFPA 1221, Standard for the Installation, Maintenance, and Use of Emergency Services Communications Systems; NFPA 1710, Standard for the Organization and Deployment of Fire Suppression Operations, Emergency Medical Operations, and Special Operations to the Public by Career Fire Departments; and NFPA 1720, Standard for the Organization and Deployment of Fire Suppression Operations, Emergency Medical Operations, and Special Operations to the Public by Volunteer Fire Departments).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by ESRI in the teaching of Burnham, in order for the tool was designed to be used by stakeholders of DOE’s Clean Cities program to assist state and regional air quality officials with developing ozone precursor and carbon monoxide emission reduction strategies for use in State Implementation Plans (please see Burnham page 1).
However, Burnham does not explicitly disclose but eOne discloses an electrified fire apparatus (page 1).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by eOne in the teaching of Burnham, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
However, Burnham does not disclose but Mangal discloses wherein the second information includes energy consumption information associated with a subsystem of the one or more fire apparatuses, and wherein acquiring the second information includes receiving the energy consumption information from the subsystem (abstract, “The system receives live data and historical data feeds from charging stations, fleet telematics, meteorological services, traffic management, mobile application, fleet dashboard, renewable source of energy, battery energy storage system, and the electric utility grid. The system utilizes machine learning algorithms to predict energy usage and optimize the charging schedule of electric vehicle. The system uses real time data to generate electric vehicle trip condition training feature for predicting the remaining driving range.”, paragraph 29, “The system provides artificial intelligence based smart charging management of electric vehicles in a fleet. The data sources from where the historical and live data are received comprises charging stations, fleet telematics, meteorological services, traffic management, mobile application, fleet dashboard, renewable source of energy, battery energy storage system, electric utility grid, etc. The data received from the charging station comprises three phase energy information on real-time charging power, current and voltage for each phase. It also provides the total energy that has been charged for the specific charger up to now. The telematics data includes every second or every minute information of the vehicle as it is being driven or parked or being charged. The information comprises energy being consumed or recovered or idled or charged; the instantaneous power consumed to drive the vehicle, the instantaneous power fed from the regenerating brakes to the battery in the vehicle, the instantaneous power received from the charger; acceleration/deceleration, the speed of the vehicle, the frequency of braking, odometer, GPS information including latitude, longitude, and altitude; the state of charge of the battery in the vehicle, battery voltage and current, battery temperature; weight of the vehicle, and other variables that are related with the vehicle.”); dynamic charge management as the central operational process that the outputs are based on (abstract, “he system utilizes machine learning algorithms to predict energy usage and optimize the charging schedule of electric vehicle.”, paragraph 8, “perform optimization and generates a power flow sequence; send control signals to each of a plurality of energy assets; monitor and determine the plurality of energy assets are performing as per the control signals”).
transmit a signal to at least one of the respective electrified fire apparatus or a charging station that is plugged into the respective electrified apparatus to charge the respective electrified apparatus according to the dynamic charge management based on at least the first information ([0045] In the next step 206, the server utilizes artificial intelligence enabled optimization to schedule the power charging in combination with the power flows to any of the energy assets to achieve the maximized utilization of renewable sources of energy and to minimize the cost of electricity. After the optimization and power flow sequence is generated by the server, in the next step, the server sends the appropriate control signals to each energy asset. The energy asset comprises EV charging stations, solar panel, or stationary batteries. The server monitors the effectiveness of the schedule in step 208. In the next step 210, the server monitors the charging stations to determine if the charging stations are performing as per the control signals sent by the server. If the assets are not performing as per the power requirement from the control signals, then the charging rate is modified in real time to achieve the desired optimization goals. The server therefore measures the outcome of third step and uses artificial intelligence and machine learning to automatically modify algorithms in the previous steps. In the next step, the server utilizes the artificial intelligence-enabled optimization solver to adjust the charging power in combination with the power flows to any of the energy assets to achieve the maximized utilization of renewable sources of energy and to minimize the cost of electricity….[0110] The system generates the charging profile for both continuous and discrete controlled energy resources. The system predicts the energy needed for each charging session of each electric vehicle. The system collects current battery SOC from vehicle's telematic data and performs historical time series data to obtained the energy consumed hourly in the future. The system also extracts information on full capacity of the electric vehicle. The details on the charging time, i.e. starting and ending time of vehicle charging is determined. The system computes the battery SOC till the start of charging session and the energy consumed after the charging session. According to the SOC range, the optimal SOC at the end of charging session is calculated. The starting SOC and ending SOC is compared to obtain the energy needed in this charging session. The result of prediction is then calculated for the EV with details such as starting time, ending time, predicted energy needed. [0111] The system also takes into consideration the operational requirements and the preference of charging schedule as soft constraints, including the available operational time of the fleet operator, the preferred chargers, the preferred charging order for electric vehicles, etc.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Mangal in the teaching of Burnham, in order to provide an artificial intelligence-based system for management of electric vehicles fleet (Mangal, abstract).
As per claim 2/19, Afleet discloses wherein the instructions cause the one or more processors to acquire a user input regarding the respective electrified fire apparatus that the respective fire department owns or is considering purchasing, and wherein the one or more results are additionally based on the user input (page 14-16, the user inputs the data about the EV vehicles).
As per claim 5, Afleet discloses wherein the second information includes a current electricity cost per kilowatt-hour at the location and a current fuel cost per gallon at the location (page 4-10, the user inputs into the tool the current electricity cost per kilowatt hour and fuel cost at the location), wherein the one or more results include the energy cost savings output, and wherein the projected energy cost savings is estimated based on a projected electricity usage over the lifespan of the respective electrified fire apparatus versus a projected fuel usage of the equivalent ICE variant over the lifespan based on (a) the at least one of the average response parameters or response requirements or miles, (b) the current electricity cost per kilowatt-hour at the location, and (c) the current fuel cost per gallon at the location (page 4-10, 23-27, the system outputs the cost of ownership over the lifespan of the vehicle comparison between ICE and EV vehicles based on the estimated miles, electricity cost and fuel cost).
However, Burnham does not disclose but ESRI discloses the at least one of the average response parameters or response requirements (9-12, “There are three National Fire Protection Association (NFPA) standards that contain time requirements that influence the delivery of fire and emergency medical services. These are NFPA 1221, Standard for the Installation, Maintenance, and Use of Emergency Services Communications Systems; NFPA 1710, Standard for the Organization and Deployment of Fire Suppression Operations, Emergency Medical Operations, and Special Operations to the Public by Career Fire Departments; and NFPA 1720, Standard for the Organization and Deployment of Fire Suppression Operations, Emergency Medical Operations, and Special Operations to the Public by Volunteer Fire Departments)(please see claim 1 rejection for combination rationale).
As per claim 7, Burnham in view of ESRI and eOne disclose all the limitation of claim 5. Burnham discloses wherein the energy cost savings output includes a graph including a first cost curve associated with the respective electrified fire apparatus over the lifespan, a second cost curve associated with the equivalent ICE variant over the lifespan, and a cost savings curve associated with cost savings from owning and operating the respective electrified fire apparatus relative to the equivalent ICE variant over the lifespan (page 27-29, the system outputs a graph showing the cost of ownership of the gasoline vehicles vs. EV vehicles, savings curve is not per se named but the cumulative TCO graph visualizes the differences).
As per claim 8, Burnham discloses wherein the one or more results include the CO2e savings output (page 29-30, graphs show the savings in emission and GHG).
As per claim 9, Burnham in view of ESRI and eOne disclose all the limitation of claim 8. Burnham discloses herein the one or more results include the energy cost savings output and the CO2e savings output, and wherein the energy cost savings output and the CO2e savings output are provided via a bar graph (page 29-30, graphs show the savings in energy and emission/GHG as bar graphs).
As per claim 10, Burnham discloses wherein the one or more results include the emissions output (page 27-30).
As per claim 11/18, Burnham discloses wherein the one or more results include the energy cost savings output, the CO2e savings output, and the emissions output (page 27-30).
As per claim 12, Burnham discloses wherein the instructions provide a charger sizing tool graphical user interface for display that facilitates evaluating charger sizes to identify a suitable charging station for the respective electrified fire apparatus based on individual needs of the respective fire department (page 11-13, the tool offers a charging input in order to determine how many chargers will be needed based on the use requirements).
Claim(s) 3-4 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnham in view of ESRI, eOne and Mangal, as disclosed in the rejection of claim 1, in further view of Arsenault (WO 2022/087716), hereinafter “Ars”.
As per claim 3/16, Burnham does not disclose but Ars discloses wherein the instructions cause the one or more processors to acquire at least one of telematics data, GPS data, or vehicle data associated with the respective fire department, and wherein at least a portion of the first information is automatically acquired without user input based on the at least one of the telematics data, the GPS data, or the vehicle data (paragraph 62, “Location detection module 221 may be connected to, or form part of, data acquisition module 218. Location detection module 221 is configured to determine the physical location of a vehicle over time, and may be implemented using any known technology, including, but not limited, to Global Positioning System (GPS), WiFi positioning systems (WPS), Near Field Communication (NFC), Radio-Frequency Identification (RFID), 6954729 Bluetooth Low Energy (BLE) beacons, Quick Response (QR) codes. As will be appreciated by the skilled reader, any other suitable technology may be used. As will be appreciated by the skilled reader, once the physical location of a vehicle is established over a period of time with sufficient granularity, it is possible to determine not only the route of the vocational vehicle, but also the speed and acceleration of the vocational vehicle, as well as the time a vocational vehicle spends at particular locations, each of which can be determined by the systems and methods disclosed herein.”)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation(s) above as taught by Ars in the teaching of Burnham, in order to implement the process of electrification in a simple and predictable manner (please see Ars paragraph 12).
As per claim 4/17, Burnham does not disclose but Ars discloses wherein at least a portion of the second information is automatically acquired without user input based on the location (paragraph 13, “The system for electrification of a fleet of vehicles comprises a processor and at least one non-transitory memory containing instructions which when executed by the processor cause the system to receive positional information relating to the position of one or more vehicles in the fleet over time and receive energy consumption information relating to the energy consumed by the one or more vehicles in the fleet over time.”, page 60, “In some embodiments, fuel consumption monitoring module 217 forms part of the wireless chargeable unit 209 and is configured to collect operational information relating to the range extender 214. Examples of such information include various fuel/energy consumption statistics. The operational information relating to the range extender 214 is then sent to data acquisition module 218 for subsequent use, as described in more detail herein.”, paragraph 63, “As will be described in more detail below, during the initial use of the systems and methods described herein, some or all of the vehicles in a fleet may be fuel-based vocational vehicles. That is to say that, prior to the electrification of a site, the systems and methods described herein may be configured to collect vocational vehicle information in order to establish an initial energy distribution system.”)(please see claim 3 rejection for combination rationale).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnham in view of ESRI, eOne and Mangal, as disclosed in the rejection of claim 5, in further view of Priya Lavappa, “Energy Price Indices and discount factors for life Cycle Cost Analysis – 2021 Annual Supplement to NIST Handbook 135” published by NIST in 2021, hereinafter “Lavappa”.
As per claim 6, Burnham does not disclose but Lavappa discloses wherein the projected energy cost savings is estimated based on (d) a first inflation rate for the current electricity cost per kilowatt-hour and (e) a second, different inflation rate for the current fuel cost per gallon (page 31, “Tables Cb-1 through Cb-5 present the projected average fuel price escalation rates (percentage change compounded annually) for selected periods from 2021 to 2051 for the four Census regions and for the overall United States, page 54, “Table S-1. Projected fuel price indices with assumed general price inflation rates of 2%, 3%, 4%, and 5%, by end-use sector and fuel type.)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Lavappa in the teaching of Burnham, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim(s) 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnham in view of ESRI, eOne and Mangal, as disclosed in the rejection of claim 12, in further view of Andy Burnham, “Afleet tool 2023 updates” published by Argonne national laboratory on August 9, 2023, hereinafter “Andy” and “how to estimate Your EV’s charging time”, published by chargie.com on March 9, 2023, hereinafter “Chargie”.
As per claim 13, Burnham does not disclose but Andy discloses wherein the charger sizing tool graphical user interface includes a first input area associated with an average energy usage during or an average state of charge following an average call for the respective fire department (page 10, “daily EV mileage and EV electricity use” would result in the amount of energy used per trip/call) and a second input area that facilitates entering or selecting a size of a charger (page 10, “charger rating”), wherein the instructions cause the one or more processors to:
acquire third information via the first input area (page 10);
acquire fourth information via the second input area (page 10).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Andy in the teaching of Burnham, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
However, Burnham in view of Andy does not disclose but Chargie discloses provide a recovery time output based on the third information and the fourth information, the recovery time output including a value indicating an amount of time it would take to recover energy depleted from the respective electrified fire apparatus following the average call for the respective fire department based on the size of the charger (page 1, the system discloses the formula to calculate how long it take to charge the EV vehicle based on charger rating and energy used).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Chargie in the teaching of Burnham in view of Andy, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 14, Burnham in view of ESRI, eOne, Mangal, Andy and Chargie disclose all the limitation of claim 13. Burnham does not disclose wherein the instructions cause the one or more processors to automatically populate the third information into the first input area based on at least one of the first information or the second information. However, Andy discloses entering the number of miles and energy usage per mile (page 10)(please see claim 13 rejection for combination rationale).
However, Burnham does not disclose but ESRI discloses obtaining the number of miles used per call from at least the first information or the second information (page 2-4, “Each street line segment between intersections contains attribute information such as road type, distance, and travel speeds (miles or kilometers per hour). This allows users to identify a station location, specify a travel time, and run a network analysis.” Page 14, “GIS simulates the real road network of the area being analyzed. A high degree of accuracy is ensured by using actual travel distances, vehicle speeds, time delays for roadway conditions (e.g., congestion, turning radius, weather, hills), accounting for one-way or unusable roadways, and implementing user-defined risk factors.”)(please see claim 1 rejection for combination rationale)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OMAR ZEROUAL whose telephone number is (571)272-7255. The examiner can normally be reached Flex schedule.
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3629
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