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 Claims
This action is in reply to the response filed on April 14, 2026.
Claims 1-3 and 5-7 are currently pending and have been examined.
Claim 4 has been canceled by the applicant.
This action is made FINAL.
The examiner would like to note that this application is being handled by examiner Christine Huynh.
Response to Amendment
The amendment filed April 14, 2026 has been entered. Claims 1-3 and 5-7 remain pending in the application. Applicant’s amendments to the claims have overcome the claim objection set forth in the Non-Final Office Action mailed February 2, 2026.
Response to Arguments
Applicant’s arguments, see pages 4-5, filed April 14, 2026, with respect to the 35 U.S.C. 112(a) and 112(b) rejections have been fully considered and are persuasive. The112(a) and 112(b) rejections of claims 1-7 has been withdrawn.
Applicant’s arguments with respect to claim(s) 1 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. With respect to the art rejections, the applicant’s amendment has overcome the current 35 U.S.C. 103 rejection. However, upon further search and consideration, the amended claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over Mangal et al. (US 20220410750 A1) in view of Shi (US 11398000 B2) and Sartipizadeh et al. (US 20230256855 A1). In addition, the amended limitations “(f) a surrogate module predicting, based on the operational parameters, energy consumption of each vehicle in the fleet of electric vehicles along routes” and “wherein the electrical energy costs include energy price per kwh and electricity demand charges” are still rejected using the prior art Mangal et al. (US 20220410750 A1), as Mangal teaches (“The system is used for driving trip prediction to predict the potential driving route. The machine learning model utilizes telematics data coming from the fleet management system to predict arrival time of the electric vehicle at a charging station. The system performs energy consumption prediction of the electric vehicle to forecast the amount of energy the electric vehicle consumes based on the real-time and historical telematics data.” See Mangal [0013], “FIG. 2 is a flow chart diagram showing a method for management of charging of electric vehicles in accordance with an embodiment of the present invention. In the first step 202, the historical and real-time data from the fleet telematics and the charging stations are received. In step 204, the EV's energy consumption prediction method then determines how much energy is needed by each vehicle and by what time.” See Mangal [0044]), where the energy consumption of a singular electric vehicle in a fleet can be predicted, and (“As shown in FIG. 10, the useful data set information 1002 such as charging data, consuming data and battery SOC data is collected for the vehicle and the charging station. Similarly, price information, demand prices are fetched from utility billing systems. The system then converts and integrates useful information based on the primary information… During the optimization process 1006, the system considers the pricing information for different time interval and creates charging profile for EV1 and EV2. The charging time for EV1 is determined for 8 to 10 am with each hour charging 40 kWh of energy. The charging profile 1008 for EV2 is determined as 10 to 12 am with each hour charging 50 kWh of energy to the EV2.” See Mangal [0122], “As per the data, the energy cost was $1913.90 and Demand Charge was $3317.2 before the plan was taken. The total cost before the optimization plan was $5230.91. After the optimization plan had been taken, the energy cost was reduced to $1894.80 and demand charge was $2658.14, making total cost of $4553.01. Comparing the cost before and after the optimization, there is a substantial reduction of 12.88% in cost saving associated with charging the vehicles.” See Mangal [0123]), where these examples show that the optimization of an operational task such as charging an electric vehicle includes calculations of energy price per kwh and a demand charge. Therefore, the claims as amended are rejected under 35 U.S.C. 103 as being unpatentable over Mangal et al. (US 20220410750 A1) in view of Shi (US 11398000 B2) and Sartipizadeh et al. (US 20230256855 A1). See detailed rejection below.
Dependent claims are rejected for the same reasons as listed above due to dependency. See detailed rejection below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 1-3 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mangal et al. (US 20220410750 A1) in view of Shi (US 11398000 B2), which was provided in the IDS sent on July 15, 2025, and Sartipizadeh et al. (US 20230256855 A1).
Regarding claims 1-3 and 5:
With respect to claim 1, Mangal teaches:
(a) electric vehicle charging stations; (“The plurality of data source comprises charging stations… The plurality of energy assets comprises EV charging stations…” [0009])
(b) …electric energy storage batteries; (“The plurality of data source comprises… battery energy storage systems… The plurality of energy assets comprises EV charging stations, renewable energy source and battery energy storage systems.” [0009]), which teaches battery energy storage systems.
However, Mangal does not teach a stationary battery energy storage system (BESS), but Sartipizadeh teaches, (“In addition to one or more of the connected fleet vehicles, the plurality of power sources may include fixed, stationary batteries. The charging strategy may include charging at least some of the fleet vehicles using power from the fixed, stationary batteries.” [0007]), which includes a stationary or fixed battery system for a fleet of electrical vehicles.
Mangal further teaches:
(c) solar panels; (“The plurality of data source comprises… renewable energy source, such as solar photovoltaic...” [0009])
wherein the electric vehicle charging stations, the stationary BESS, and the solar panels are connected to each other and to an electrical power distribution grid; (“One other information that may affect EV charging optimization is energy production data from on-side renewable sources of energy, such as the solar panels, and the battery energy storage system that provides information on capacity of battery, state of charge of the battery, charging and discharging profile of the battery. The system is also in communication with electric utility grids that provides information on demand response programs and electricity pricing information.” [0031], “Electric vehicle receives electricity from, or provides electricity to, an electric grid 108 at a charging station.” [0034], “The server 102 is in communication with energy generation system and battery energy storage systems and electric utility grid. The energy renewable generation system 122, such as the solar panels, provides energy production data from on-side generation which includes the amount of power being generated historically and in real time.” [0042), where the system that includes charging stations, batteries, and solar panels are connected to the electrical power distribution grid.
However, Mangal does not teach a stationary battery energy storage system (BESS), but Sartipizadeh teaches (“In addition to one or more of the connected fleet vehicles, the plurality of power sources may include fixed, stationary batteries. The charging strategy may include charging at least some of the fleet vehicles using power from the fixed, stationary batteries.” [0007], “The external power source 136 may be an electrical power distribution network or grid as provided by an electric utility company, or an alternate power source such as a photovoltaic (solar) system, wind generation system, fixed/stationary batteries, or batteries of other connected fleet vehicles, for example.” [0022]), which includes a stationary or fixed battery system for a fleet of electrical vehicles.
Mangal further teaches:
(d) a fleet of electric vehicles adapted to charge using the electric vehicle charging stations; (“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... The data received from the charging station comprises three phase energy information on real-time charging power, current and voltage for each phase” [0029]), which shows a fleet of electric vehicles that charge using charge stations.
(e) a forecasting module predicting operational parameters including electricity prices, weather variables, power production of the solar panels, and emission factors of the electrical power distribution grid; (“The present invention proposes a method that uses artificial intelligence (AI) based machine learning (ML) algorithms in a server to predict energy usage and optimize the charging schedule. The server is connected to a network that receives historical and live data from multiple sources. 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.” [0029], “The charging station 106 sends and receives data associated with the charging of electric vehicle, the battery capacity of the electric vehicle, the power capacity of the charging station, the current energy stored in the electric vehicle, the rate of charging of the charging station and the electric vehicle, the price of electricity received from a power grid” [0036]), where energy usage and charging schedules can be predicted using collected data including weather information and charging station information like electricity prices and usage.
(f) a surrogate module predicting, based on the operational parameters, energy consumption of each vehicle in the fleet of electric vehicles along routes; (“The machine learning model utilizes telematics data coming from the fleet management system to predict arrival time of the electric vehicle at a charging station. The system performs energy consumption prediction of the electric vehicle to forecast the amount of energy the electric vehicle consumes based on the real-time and historical telematics data.” [0013], “The fleet dashboard is a management tool that enables the fleet operator to visualize real time vehicle status, such as status of charge (SOC), remaining driving range, speeds, GPS locations, etc. as well as optimized charging plans and schedules, estimated times for completion of charging, vehicle's driving routes, the arrival time of the vehicle, and the potential energy consumptions and predicted driving ranges are predicted using the developed machine learning methods such as deep learning, neural network, decision tree, random forest, multiple regression, support vector machine and clustering/classifications algorithms.” [0032], “perform the energy consumption prediction of the EVs to forecast how much energy the electric vehicle will consume based on the real-time and historical telematics data. The system utilizes machine learning module that will use input from the vehicle database and output the expected energy consumption of the vehicle. It can provide the continuous energy consumption forecast of each EV in a fleet for up to 24 hours.” [0101], “The system is used for driving trip prediction to predict the potential driving route. The machine learning model utilizes telematics data coming from the fleet management system to predict arrival time of the electric vehicle at a charging station. The system performs energy consumption prediction of the electric vehicle to forecast the amount of energy the electric vehicle consumes based on the real-time and historical telematics data.” See Mangal [0013], “FIG. 2 is a flow chart diagram showing a method for management of charging of electric vehicles in accordance with an embodiment of the present invention. In the first step 202, the historical and real-time data from the fleet telematics and the charging stations are received. In step 204, the EV's energy consumption prediction method then determines how much energy is needed by each vehicle and by what time.” See Mangal [0044]), where the energy consumption of a singular electric vehicle in a fleet can be predicted, and machine learning is used to predict energy consumption of the fleet of electric vehicles. The surrogate module can use “linear regression models, polynomial regression, Gaussian Processes, neural networks, or support vector regression” (see instant claim 3), which is comparable to the machine learning used in Mangal, which can include methods such as deep learning, neural network, decision tree, random forest, multiple regression, support vector machine and clustering/classifications algorithms.
(g) an optimization module adapted to compute, based on the operational parameters and the energy consumption, optimal operational tasks for the fleet of electric vehicles…, wherein the optimal operational tasks comprise assignments of routes to the electric vehicles in the fleet, scheduling vehicle charging, …, (“The processed and merged data will be fed into the AI/ML system and yield the predictions and optimization strategies based on historical and real-time information. This AI/ML system provides information of the electric vehicle and electric chargers for fleet operators to visualize, analyze, and make decisions on vehicle charging schedules. This AI/ML system has features including but not limited to remaining mileage prediction, driver behavior classification and charging schedule optimization.” [0033], “In a fleet operation, vehicles often have routine trips and routes, for example, transit bus and delivery trucks use the similar routes on their trips. Prediction of the departure time, arriving time and range of the trips are crucial to estimate the energy needed for the electric vehicle to complete these trips. Subsequently, the estimated energy needed can be used for optimizing the electric vehicle charging schedules.” [0088]), where the machine learning system is used to compute optimal operational tasks, such as assigning routes and determining charging schedules for the electric vehicles.
However, Mangal does not teach optimizing operational tasks for the stationary BESS and scheduling charging and discharging of the electric storage batteries in the stationary BESS, but Sartipizadeh teaches (“FIG. 2 is a block diagram of an EV fleet smart management system 200, which includes a centralized optimization-based controller 202 that manages one or more EV fleet depots 204 to provide requested services 206 and control power interactions with an associated grid/microgrid 208 and among fleet EVs 240 and power storage units 246 for optimal charge scheduling and dispatching. Controller 202 communicates with fleet charging stations 242 and EVs 240 for optimization 230 of the overall selected service, monetary, and energy utilization objectives. Service objectives may include routine fleet requirements such as routing and dispatching while the main monetary and energy objectives to be considered are described in greater detail herein. As generally described herein, requested services include destinations or routes 270 for designated EVs 240.” [0026], “Controller 202 may also use grid historical data, predicted power factor, and utility rate information 214, which may also include rate schedules and surcharges associated with connected loading and associated power factor of connected loads to perform an associated grid/microgrid demand analysis 226. Based on the available information, controller 202 may maximize the use of off-peak and low-rate energy hours for charging the fleet BEVs 240, as well as fixed/stationary energy units 246. Similarly, controller 202 may predict, estimate, or otherwise determine the effect on the grid/microgrid power factor of connecting charging loads, which are primarily capacitive in nature, and may be subject to surcharges by the utility operator if the power factor is below a designated threshold and/or outside of a predetermined range of unity.” [0029]), which shows assignments of routes to the electric vehicles in the fleet, scheduling vehicle charging, and scheduling charging and discharging of the electric storage batteries in the stationary BESS.
Mangal further teaches:
wherein the optimization module is adapted to determine optimal operational tasks by solving an optimization problem to minimize an objective function that selects the optimal operational tasks to simultaneously minimize electrical energy costs …, wherein the electrical energy costs include energy price per kwh and electricity demand charges; (“The optimization step comprises scheduling the power charging in combination with the power flows to any of the plurality of energy asset to achieve the maximized utilization of renewable source of energy and minimized electric bill while satisfying vehicle's energy need for the fleet operations.” [0010], “The system of the present invention manages, monitors, schedules and controls the energy and power flow into the electric vehicles to satisfy the objectives of the fleet operator. In an embodiment of the present invention, the objective of the present invention is to minimize the bill cost associated with charging while satisfying the energy need for fleet operation. This is achieved via a combination of minimization of demand charges and optimization around the Time-Of-Use (TOU) pricing considering the previous and future charging performances in the billing cycle. The system takes into consideration different parameters associated with electric vehicles, energy resources, and grid distribution to create strict constraints, including the predicted energy consumption of the next working period for electric vehicles, the predicted arrival and departure time of electric vehicles, the energy required for electric vehicles, real-time battery state of charge of electric vehicles, power capacity and usage restrictions from the energy resources, bill information and charges levied for electricity at the different time period from the grid, the peak power in the current billing cycle so far, etc.” [0107], “The electric vehicle has its power source in form of a battery, solar panels, fuel cells or an electric generator to convert fuel to electricity. Replacement of petroleum-based vehicles with the electric vehicles serves an important source of reducing carbon footprint and other pollutants emission.” [0004]), where the machine learning system is used to compute optimizes operational tasks to minimize electrical energy costs. By optimizing the usage of an electric charging system, then emissions of the electrical power distribution grid are minimized. Thus, it would have been obvious to a person of ordinary skill in the art where the optimization module is used to select the optimal operational tasks to simultaneously minimize electrical energy costs and emissions of the electrical power distribution grid in an attempt to provide an improved system or method, as a person with ordinary skill has good reason to pursue the known options within his or her technical grasp. In turn, because the product as claimed has the properties predicted by the prior art, it would have been obvious to make the system or product where the emissions of the electrical power distribution grid are minimized. Mangal further teaches, (“As shown in FIG. 10, the useful data set information 1002 such as charging data, consuming data and battery SOC data is collected for the vehicle and the charging station. Similarly, price information, demand prices are fetched from utility billing systems. The system then converts and integrates useful information based on the primary information… During the optimization process 1006, the system considers the pricing information for different time interval and creates charging profile for EV1 and EV2. The charging time for EV1 is determined for 8 to 10 am with each hour charging 40 kWh of energy. The charging profile 1008 for EV2 is determined as 10 to 12 am with each hour charging 50 kWh of energy to the EV2.” See Mangal [0122], “As per the data, the energy cost was $1913.90 and Demand Charge was $3317.2 before the plan was taken. The total cost before the optimization plan was $5230.91. After the optimization plan had been taken, the energy cost was reduced to $1894.80 and demand charge was $2658.14, making total cost of $4553.01. Comparing the cost before and after the optimization, there is a substantial reduction of 12.88% in cost saving associated with charging the vehicles.” See Mangal [0123]), where these examples show that the optimization of an operational task such as charging an electric vehicle includes calculations of energy price per kwh and a demand charge.
However, while Mangal teaches determining optimal operational tasks by solving an optimization problem that determines operational tasks to minimize electrical energy costs and therefore lower emissions of the electrical power distribution grid, Mangal does not directly teach determining optimal operational tasks by solving an optimization problem that determines operational tasks to minimize emissions of the electrical power distribution grid, but Shi teaches, (“Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging…” (column 11, lines 38-48), “Still referring to FIG. 4, computing device 104 may generate a power output recommendation minimizing a function of carbon output and cost. This approach may offer a singular advantage over existing resource optimization and control strategies, which only respond to the price signals with an objective of reducing energy costs” (column 23, lines 35-40)).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Mangal’s smart charging management with Shi’s carbon emission minimization because (“Deep learning for real-time/online optimization and control may be used to minimize emission impacts while maximizing efficiency benefits under uncertainties of the ambient environment and user behaviors.” See (column 2, lines 47-51)).
Mangal further teaches:
(h) a communications subsystem adapted to communicate the optimal operational tasks to the fleet of electric vehicles…; (“Another source to which server is connected through the network is vehicle telematics 110. The vehicle telematics 110 provides information about the electric vehicle as it is being driven around, or when it is parked, or when it is being charged. The communication between the server 102 and EV telematics 110 is a continuous data stream and the data stream includes information such as, the energy being consumed, the instantaneous power consumed to drive the vehicle, the instantaneous power fed from the regenerating brakes to the battery in the vehicle, acceleration/deceleration, the SOC of the battery 112 in the vehicle, the speed of the vehicle, the frequency of braking and other variables, etc.” [0037]), where the fleet of electric vehicles communicate information from the batteries.
However, Mangal does not teach a stationary battery energy storage system (BESS), but Sartipizadeh teaches (“In addition to one or more of the connected fleet vehicles, the plurality of power sources may include fixed, stationary batteries. The charging strategy may include charging at least some of the fleet vehicles using power from the fixed, stationary batteries.” [0007], “FIG. 3 is a block diagram illustrating operation of an EV fleet smart management system 300 to provide optimized dispatching, charge scheduling, and energy interactions among fleet vehicles and energy sources. Referring to FIGS. 2 and 3, optimization algorithm 230 of controller 202 may control various resources of the system to prioritize or achieve competing goals depending on the particular circumstances. In a first example, controller 202 may control resources including dispatching vehicles and scheduling charging based on minimizing energy expenses so that any required charging is performed from a surplus or idle EV and/or from fixed/stationary battery storage of the fleet depot 350 considering effect on battery health/life and SOC.” [0035), which includes a stationary or fixed battery system for a fleet of electrical vehicles incorporated into the communicated optimal operational task.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Mangal-Shi’s smart charging management with Sartipizadeh’s stationary battery system because (“Stored energy from higher-SOC EVs 240 or fixed/stationary batteries 246 may be used to charge lower-SOC EVs during the peak hours or when subject to power factor surcharges to reduce associated energy charges from the grid/microgrid operator.” See Sartipizadeh [0029]), to provide another source of power for an electric vehicle fleet.
With respect to claim 2, Mangal in combination with Shi and Sartipizadeh, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal, Shi, and Sartipizadeh teaches an electric vehicle fleet operations system of claim 1. Shi further teaches:
wherein the surrogate module uses a Gaussian Process-based surrogate model comprising a probabilistic model that infers a distribution over data points based on known input-output values; (“Optimization engine 228 may be used to generate recommended courses of action for optimization of carbon output and/or costs and may produce inputs to forecast models 224. Results of optimization may be used as control decisions that are dispatched to energy resources such as power generators, local grid monitors, or other devices and/or entities making decisions affecting power generation and/or power consumption parameters in local grid. Power consumption parameters may include, without limitation, customers' energy storage, electric vehicle charging…” (column 11, lines 38-48), “Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression.” (column 17, lines 40-41)), where the machine learning used for optimization can use a Gaussian Process.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Mangal’s smart charging management with Shi’s carbon emission minimization because (“Deep learning for real-time/online optimization and control may be used to minimize emission impacts while maximizing efficiency benefits under uncertainties of the ambient environment and user behaviors.” See (column 2, lines 47-51)).
With respect to claim 3, Mangal in combination with Shi and Sartipizadeh, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal, Shi, and Sartipizadeh teaches an electric vehicle fleet operations system of claim 1. Mangal further teaches:
wherein the surrogate module uses linear regression models, polynomial regression, Gaussian Processes, neural networks, or support vector regression; (“The machine learning model utilizes telematics data coming from the fleet management system to predict arrival time of the electric vehicle at a charging station. The system performs energy consumption prediction of the electric vehicle to forecast the amount of energy the electric vehicle consumes based on the real-time and historical telematics data.” [0013], “The fleet dashboard is a management tool that enables the fleet operator to visualize real time vehicle status, such as status of charge (SOC), remaining driving range, speeds, GPS locations, etc. as well as optimized charging plans and schedules, estimated times for completion of charging, vehicle's driving routes, the arrival time of the vehicle, and the potential energy consumptions and predicted driving ranges are predicted using the developed machine learning methods such as deep learning, neural network, decision tree, random forest, multiple regression, support vector machine and clustering/classifications algorithms.” [0032], “perform the energy consumption prediction of the EVs to forecast how much energy the electric vehicle will consume based on the real-time and historical telematics data. The system utilizes machine learning module that will use input from the vehicle database and output the expected energy consumption of the vehicle. It can provide the continuous energy consumption forecast of each EV in a fleet for up to 24 hours.” [0101]), which shows that machine learning is used to predict energy consumption of the fleet of electric vehicles. The surrogate module can use “linear regression models, polynomial regression, Gaussian Processes, neural networks, or support vector regression”, which is comparable to the machine learning used in Mangal, which can include methods such as deep learning, neural network, decision tree, random forest, multiple regression, support vector machine and clustering/classifications algorithms.
With respect to claim 5, Mangal in combination with Shi and Sartipizadeh, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal, Shi, and Sartipizadeh teaches an electric vehicle fleet operations system of claim 1. Mangal does not teach, but Sartipizadeh teaches:
wherein the operational tasks for the stationary BESS include charging and discharging schedules; “Controller 202 may also use grid historical data, predicted power factor, and utility rate information 214, which may also include rate schedules and surcharges associated with connected loading and associated power factor of connected loads to perform an associated grid/microgrid demand analysis 226. Based on the available information, controller 202 may maximize the use of off-peak and low-rate energy hours for charging the fleet BEVs 240, as well as fixed/stationary energy units 246. Similarly, controller 202 may predict, estimate, or otherwise determine the effect on the grid/microgrid power factor of connecting charging loads, which are primarily capacitive in nature, and may be subject to surcharges by the utility operator if the power factor is below a designated threshold and/or outside of a predetermined range of unity.” [0029]), which shows tasks include charging and discharging schedules for the stationary batteries.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Mangal-Shi’s smart charging management with Sartipizadeh’s stationary battery system because (“Stored energy from higher-SOC EVs 240 or fixed/stationary batteries 246 may be used to charge lower-SOC EVs during the peak hours or when subject to power factor surcharges to reduce associated energy charges from the grid/microgrid operator.” See Sartipizadeh [0029]), to provide another source of power for an electric vehicle fleet.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mangal et al. (US 20220410750 A1) in view of Shi (US 11398000 B2), Sartipizadeh et al. (US 20230256855 A1), and Ju et al. (US 20240227611 A1).
Regarding claims 6:
With respect to claim 6, Mangal in combination with Shi and Sartipizadeh, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal, Shi, and Sartipizadeh teaches an electric vehicle fleet operations system of claim 1. Mangal does not teach, but Ju teaches:
wherein solving the optimization problem to minimize the objective function uses mixed integer linear programming; (“the method and apparatus formulate the operation and planning of the MCCS as a mixed-integer linear programming (MILP) problem, enabling in-depth optimizations on charger assignments, plug-in/out schedules, charging power, and facility planning of the charging station.” [0001], “determining an optimal combination of fixed chargers and robotic chargers to maximize the profit of the charging station with required service capacity. In other words, given a collection of typical daily charging demands, considering different capital costs and operation costs of the two types of chargers, a planning model is designed to solve a best portfolio of fixed chargers and robotic chargers. Both the operation model and the planning model can be reformulated as a mixed-integer linear programming (MILP) problem” [0033]), where a mixed integer linear programing can be used to optimize charging schedules, which is comparable to the optimization of operational tasks.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Mangal-Shi- Sartipizadeh’s smart charging management with Ju’s mixed integer linear programming because (“There is a need to alleviate the overstay issue and to enhance the service capacity and efficiency of charging stations.” [0008], and “generating, with respect to an optimization horizon including the time step and a plurality of subsequent time steps, a charging demand forecast; and solving, with respect to the optimization horizon, an optimal operation solution, based on the first charging demand, the second charging demand, and the charging demand forecast.” [0009]), which can be done using mixed integer linear programing.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mangal et al. (US 20220410750 A1) in view of Shi (US 11398000 B2), Sartipizadeh et al. (US 20230256855 A1), and Parvania et al. (US 20240017635 A1).
Regarding claims 7:
With respect to claim 7, Mangal in combination with Shi and Sartipizadeh, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal, Shi, and Sartipizadeh teaches an electric vehicle fleet operations system of claim 1. Mangal does not teach, but Parvania teaches:
wherein solving the optimization problem to minimize the objective function uses Reinforcement Learning-based methods; (“Optimal operation of a power distribution system requires solving a combinatorial optimization problem over a time horizon, and is often subject to uncertainties due to consumer behavior, renewable generation, and equipment failure… On the other hand, in at least one embodiment, the capabilities of Deep Reinforcement Learning (DRL) in solving stochastic and high dimensional problems may provide a fast and scalable alternative for solving large-scale operational problems.” [0023]), where optimizing electric vehicle charging based upon locations of the vehicles, locations of charging stations, and the associated power distribution systems can be done using a reinforcement learning.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the instant application to have combined Mangal-Shi- Sartipizadeh’s smart charging management with Parvania’s reinforcement learning because (“the capabilities of Deep Reinforcement Learning (DRL) in solving stochastic and high dimensional problems may provide a fast and scalable alternative for solving large-scale operational problems.” See Parvania [0023]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CHRISTINE NGUYEN HUYNH/Examiner, Art Unit 3662
/Madison R. Inserra/Primary Examiner, Art Unit 3662
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