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 patent application filed on October 7, 2024.
Claims 1-7 are currently pending and have been examined.
This action is made Non-FINAL.
The examiner would like to note that this application is being handled by examiner Christine Huynh.
Claim Objections
Claims 1-3 are objected to because of the following informalities: Claim 1 recites a “a surrogate module”, but claims 2-3 recite “the surrogate model”. It is unclear if the surrogate model recited in claims 2-3 are the same as the surrogate module of claim 1. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 1, which recites “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”, where “forecasting module” is a generic placeholder, “predicting operational parameters including electricity prices, weather variables, power production of the solar panels, and emission factors of the electrical power distribution grid” is the functional language, and no structural modifier is stated in the claims or specification.
Claim 1, which recites “a surrogate module predicting, based on the operational parameters, energy consumption of the fleet of electric vehicles”, where “surrogate module” is a generic placeholder, “predicting, based on the operational parameters, energy consumption of the fleet of electric vehicles” is the functional language, and no structural modifier is stated in the claims or specification.
Claim 1, which recites “an optimization module adapted to compute, based on the operational parameters and the energy consumption, optimal operational tasks for the fleet of electric vehicles and for the electric energy storage batteries”, where “optimization module” is a generic placeholder, “to compute, based on the operational parameters and the energy consumption, optimal operational tasks for the fleet of electric vehicles and for the electric energy storage batteries” is the functional language, and no structural modifier is stated in the claims or specification.
Claim 1, which recites “a communications subsystem adapted to communicate the operational tasks to the fleet of electric vehicles and to the electric energy storage batteries”, where “communications subsystem” is a generic placeholder, “to communicate the operational tasks to the fleet of electric vehicles and to the electric energy storage batteries” is the functional language, and no structural modifier is stated in the claims or specification.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites “a forecasting module”, “a surrogate module”, “an optimization module”, and “a communications subsystem”, but the specification fails to describe the claimed invention in sufficient detail to establish that the inventor or joint inventor(s) had possession of the claimed invention as of the application's filing date.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention. Evidence that claims 1-7 fail(s) to correspond in scope with that which the inventor or a joint inventor, or for pre-AIA applications the applicant regards as the invention can be found in the reply filed October 7, 2024. In that paper, the inventor or a joint inventor, or for pre-AIA applications the applicant has stated generic placeholder terms such as “a forecasting module”, “a surrogate module”, “an optimization module”, and “a communications subsystem”, and this statement indicates that the invention is different from what is defined in the claim(s) because the specification fails to describe the claimed invention in sufficient detail to establish that the inventor or joint inventor(s) had possession of the claimed invention as of the application's filing date.
Claim limitation “a forecasting module”, “a surrogate module”, “an optimization module”, and “a communications subsystem”, invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. There is insufficient disclosure of the corresponding structure as the disclosure is devoid of any structure that performs the functions of the “a forecasting module”, “a surrogate module”, “an optimization module”, and “a communications subsystem”. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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) 1-5 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.
Regarding claims 1-5:
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]
(c) solar panels; (“The plurality of data source comprises… renewable energy source, such as solar photovoltaic...” [0009])
wherein the electric vehicle charging stations, the electric energy storage batteries, 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.
(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 the fleet of electric vehicles; (“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” (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 and for the electric energy storage batteries, (“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]), where the machine learning system is used to compute optimal operational tasks, such as charging schedules for the electric vehicles.
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 and emissions of the electrical power distribution grid; (“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.
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 operational tasks to the fleet of electric vehicles and to the electric energy storage batteries; (“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.
With respect to claim 2, Mangal in combination with Shi, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal and Shi teaches an electric vehicle fleet operations system of claim 1. Mangal further teaches:
wherein the surrogate model 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, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal and Shi teaches an electric vehicle fleet operations system of claim 1. Mangal further teaches:
wherein the surrogate model 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 4, Mangal in combination with Shi, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal and Shi teaches an electric vehicle fleet operations system of claim 1. Mangal further teaches:
wherein the operational tasks for the fleet of electric vehicles include charging schedules and route assignments; (“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 tasks for the electric vehicles including charging schedules and an assigned route.
With respect to claim 5, Mangal in combination with Shi, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal and Shi teaches an electric vehicle fleet operations system of claim 1. Mangal further teaches:
wherein the operational tasks for the electric energy storage batteries include charging and discharging schedules; (“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. The battery energy storage system communicates to the server about the state of the battery energy storage system and the information comprises total capacity of the battery in kilowatt-hour (kWh), real SOC of the battery, historical charging and discharging profiles of the battery, etc.” [0042], “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.” [0045], which shows tasks include charging and discharging schedules for the electric energy storage batteries.
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) and Ju et al. (US 20240227611 A1).
Regarding claims 6:
With respect to claim 6, Mangal in combination with Shi, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal and Shi 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’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) and Parvania et al. (US 20240017635 A1).
Regarding claims 7:
With respect to claim 7, Mangal in combination with Shi, as shown in the rejection above, discloses the limitations of claim 1. The combination of Mangal and Shi 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’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
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Kraeling et al. (US 20230339354 A1) is pertinent because (“the controllers or systems described herein may have a local data collection system deployed and may use machine learning to enable derivation-based learning outcomes. The controllers may learn from and make decisions on a set of data (including data provided by the various sensors), by making data-driven predictions and adapting according to the set of data. In embodiments, machine learning may involve performing a plurality of machine learning tasks by machine learning systems, such as supervised learning, unsupervised learning, and reinforcement learning” [0063]) which pertains to optimization of charging stations for electric vehicles using reinforcement learning.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Christine N Huynh whose telephone number is (571)272-9980. The examiner can normally be reached Monday - Friday 8 am - 4 pm.
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/CHRISTINE NGUYEN HUYNH/Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662