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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/30/2026 has been entered.
Status of Claims
Claims 1-20 filed on 03/30/2026 are presently examined. Claims 1 and 11 are amended.
Response to Arguments
Regarding 35 USC 101, Applicant’s arguments filed 03/30/2026 have been fully considered but are unpersuasive. The abstract idea is being performed on generic computer components, which is not significantly more. Applicant’s arguments regarding stochastic optimization are unpersuasive because these limitations are merely mathematical concepts and calculations. The courts have designated mathematical concepts, calculations, and relationships as an abstract idea. Specifically, statistical analysis and distributions are considered abstract ideas. See MPEP 2106.04(a)(2)(I)(C)(i): “performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018)
Regarding 35 USC 103, Applicant’s arguments filed 03/30/2026 have been fully considered but are unpersuasive. Applicant argues Mason and Dubey do not teach the amended independent claims. Examiner respectfully disagrees with the basis of the argument invoking Mason and Dubey. Gusikhin was used in the previous action to teach the stochastic optimization of the energy consumption probability distributions of fleet vehicles. Examiner agrees Mason and Dubey do not teach the stochastic optimization portion of the claims, but they were not used to teach these limitations. As such, Gusikhin in rolled up to the rejections of the independent claims and the 103 rejections are maintained.
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 1-20 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claims 1-10 are directed to a method. Claims 11-20 are directed to a system with executable instructions stored on non-transitory memory media, (i.e. a machine). Therefore, claims 1-20 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 11 recites similar limitations as independent claims 1 and will be used as a representative claim.
Claim 11 is recited below and limitations that recite an abstract idea are emphasized in bolding below:
A system for optimizing operation of a fleet of vehicles, the system comprising:
an optimization network including at least one computer-implemented optimizer configured to execute instructions stored on one or more non-transitory memory media to determine a dispatch and routing plan for a fleet of vehicles, the dispatch and routing plan optimizing a plurality of objectives including delivery objectives and energy or fuel consumption objectives;
provide the dispatch and routing plan to a fleet management system;
receive feedback parameters indicating energy or fuel consumption of the fleet operating according to the dispatch and routing plan;
determine an energy or fuel consumption probability distribution for the fleet in response to the feedback parameters, the energy or fuel consumption probability distribution being modified based on the feedback and a stochastic optimization of at least one candidate distribution performed by a computer-implemented stochastic optimizer; and
determine using the energy or fuel consumption probability distribution an updated dispatch and routing plan for the fleet of vehicles, the updated dispatch and routing plan optimizing the plurality of objectives.
The examiner submits that the bolded limitations “determine a dispatch and routing plan for a fleet of vehicles, the dispatch and routing plan optimizing a plurality of objectives including delivery objectives and energy or fuel consumption objectives” and “determine using the energy or fuel consumption probability distribution an updated dispatch and routing plan for the fleet of vehicles, the updated dispatch and routing plan optimizing the plurality of objectives” constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. The bolded limitations in the context of this claim encompasses a person mentally determining a dispatch and routing plan for a fleet of vehicles which optimizes a plurality of objectives including delivery objectives and energy or fuel consumption objectives, and subsequently using a energy consumption probability distribution in order to update the dispatch and routing plan to optimize the plurality of objectives.
Further, the limitation “determine an energy or fuel consumption probability distribution for the fleet in response to the feedback parameters, the energy or fuel consumption probability distribution being modified based on the feedback and a stochastic optimization of at least one candidate distribution performed by a computer-implemented stochastic optimizer” constitutes a mathematical concept, and specifically mathematical calculations merely performed on a computer. Mathematical concepts and calculations are considered abstract ideas and therefore not eligible. See MPEP 2106.04(a)(2)(I) “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. The Supreme Court has identified a number of concepts falling within this grouping as abstract ideas … It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a ‘‘series of mathematical calculations based on selected information’’ are directed to abstract ideas).” More specifically, statistical analysis and distributions are similarly not eligible and are an abstract idea under the mathematical concepts grouping. See MPEP 2106.04(a)(2)(I)(C)(i): “performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018).” Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A system for optimizing operation of a fleet of vehicles, the system comprising:
an optimization network including at least one computer-implemented optimizer configured to execute instructions stored on one or more non-transitory memory media to determine a dispatch and routing plan for a fleet of vehicles, the dispatch and routing plan optimizing a plurality of objectives including delivery objectives and energy or fuel consumption objectives;
provide the dispatch and routing plan to a fleet management system;
receive feedback parameters indicating energy or fuel consumption of the fleet operating according to the dispatch and routing plan;
determine an energy or fuel consumption probability distribution for the fleet in response to the feedback parameters, the energy or fuel consumption probability distribution being modified based on the feedback and a stochastic optimization of at least one candidate distribution performed by a computer-implemented stochastic optimizer; and
determine using the energy or fuel consumption probability distribution an updated dispatch and routing plan for the fleet of vehicles, the updated dispatch and routing plan optimizing the plurality of objectives.
For the following reason(s), the examiner submits that the above underlined additional limitations do not integrate the above-noted abstract idea into a practical application.
Examiner submits that the additional limitation “an optimization network including at least one computer-implemented optimizer configured to execute instructions stored on one or more non-transitory memory media” merely uses a computer (processor, generic computer components) to perform otherwise mental judgements.
Examiner submits that the additional limitation “provide the dispatch and routing plan to a fleet management system” is a pre-solution activity of merely transmitting data within a computer network.
Examiner submits that the additional limitation “receive feedback parameters indicating energy or fuel consumption of the fleet operating according to the dispatch and routing plan” merely performs a pre-solution activity of data gathering.
The examiner submits that these additional limitations merely use a sensors to perform an insignificant extra-solution activity of data gathering, and a computer (processor, generic computer components) to perform otherwise mental judgements is not sufficient to integrate the abstract idea into a practical application.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 11 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a processor or generic computer components to gather data and perform the otherwise mental judgements amounts to nothing more than applying the exception using generic computer components. Generally applying an exception using a generic computer component cannot provide an inventive concept. Further the additional limitations are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, merely use generic computer components in their ordinary capacity to perform an otherwise mental process or judgement, and do not amount to significantly more. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Dependent claims 2-10 and 12-20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application, merely use generic computer components in their ordinary capacity to perform an otherwise mental process or judgement or data gathering, and do not amount to significantly more.
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.
Claims 1-2, 5, 7-12, 15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mason et al. (US 20120022904 A1) in view of Dubey et al. (US 20250030766 A1) and Gusikhin et al. (US 9587954 B2), hereinafter referred to as Mason, Dubey, and Gusikhin, respectively.
Regarding claims 1 and 11, Mason discloses A method of operating a fleet optimization system to optimize operation of a fleet of vehicles, the method comprising operating a computer-implemented fleet optimization network to perform acts of:
determining a dispatch and routing plan for a fleet of vehicles, the dispatch and routing plan optimizing a plurality of objectives including delivery objectives and energy or fuel consumption objectives ([0021] “For each vehicle in the fleet, it can be desirable to reduce the travel distance or transit time to increase the number of stops, deliveries, or the like that may be performed in a given time period … considering routes that reduce fuel or other energy consumption.” [0027] “given two equally feasible routes that would result in on-time deliveries for a delivery company, the routing module 110 may select the route with lowest energy cost”);
providing the dispatch and routing plan to a fleet management system ([0029] “The fleet management module 125 can include functionality for managing vehicles in a fleet.” [0030] “The dispatch module 140 can provide functionality for users of the management devices 135 to assign drivers and vehicles to routes selected by the routing module 110.”);
receiving feedback parameters indicating an energy or fuel consumption of the fleet operating according to the dispatch and routing plan ([0024] “the in-vehicle devices 105 can report information to the vehicle management system 110, such as driver location, speed, energy consumption, and so forth.”).
Mason fails to disclose determining an energy or fuel consumption probability distribution for the fleet in response to the feedback parameters, the energy or fuel consumption probability distribution being modified based on the feedback and at least one candidate distribution; and determining using the energy or fuel consumption probability distribution an updated dispatch and routing plan for the fleet of vehicles, the updated dispatch and routing plan optimizing the plurality of objectives.
Mason fails to disclose determining an energy or fuel consumption probability distribution for the fleet in response to the feedback parameters, the energy or fuel consumption probability distribution being modified based on the feedback and a stochastic optimization of at least one candidate distribution performed by a computer-implemented stochastic optimizer.
However, Dubey teaches determining an energy or fuel consumption probability distribution for the fleet in response to the feedback parameters ([claim 1] “forecasting energy consumption by vehicles in a mixed-vehicle fleet … features indicative of energy consumption and a marginal probability distribution over each of the identified features” [0008] “the system stores route data identifying vehicle routes … and energy consumption data indicative of energy consumed by each of the vehicles.”), the energy or fuel consumption probability distribution being modified based on the feedback and at least one candidate distribution ([0029] “with reference to FIG. 3D, the energy consumption module 261 uses the dynamic data 274 to calculate the past energy consumption 260 of each of the vehicles 120.” [0046] “the feature selection module 401 identifies features 400 of past trips for use as part of the training data 278 that is used to train the neural network(s) 500 … the energy consumption forecasting system 200 is also used to predict future energy consumption.” [0055] “At each training iteration … loss is calculated between the predicted target and true target for each vehicle class. The gradient of the loss is then propagated back through the neural network(s) 500.” The machine learning system is trained iteratively based on the feedback of energy use of the fleet as well as the predicted energy use target of the fleet of vehicles.); determining using the energy or fuel consumption probability distribution an updated dispatch and routing plan for the fleet of vehicles, the updated dispatch and routing plan optimizing the plurality of objectives ([0029] “the past energy consumption 260 … form training data 278” [0030] “the neural network(s) 500 are trained, using the training data 278, to identify the forecasted energy consumption 260′ of each vehicle 120 using the features 401 identified by the feature selection module 400.” [0071] “the optimization engine 280 identifies the vehicle assignments with the minimum total predicted energy consumption 260′ (or minimum total energy cost) to perform each transit trip 720”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mason with Dubey’s teaching of recording of and determining of a probability distribution of the energy consumption of various vehicle types on routes and the subsequent forecasting of energy use on future routes. One would be motivated, with reasonable expectation of success, to generate a probability distribution of energy consumption of fleet vehicles in order to provide accurate, dynamic, and data-driven prediction ability for energy use in fleet vehicles ([0005] “accurate, dynamic, data-driven prediction of the trip-level energy use of each of a number of types of vehicles in a mixed-vehicle fleet.”). Dubey similarly fails to explicitly teach a stochastic optimizer.
However, Gusikhin teaches determining an energy or fuel consumption probability distribution for the fleet in response to the feedback parameters ([column 12, line 61 through column 13 line 52] these paragraphs outline the energy consumption probability distribution generation steps. Energy consumption estimation is done after driving scenarios are simulated. Energy consumption estimation is based on traffic simulation and powertrain simulation. Once energy consumption and powertrain simulations are complete, the data sets from the plurality of simulations are analyzed to determine statistical accuracy and consistent results.),
the energy or fuel consumption probability distribution being modified based on the feedback and a stochastic optimization of at least one candidate distribution performed by a computer-implemented stochastic optimizer ([column 13 line 53 through column 14 line 18] these paragraphs describe how vehicles will use the generated energy consumption powertrain models and the vehicles provide feedback into the system of energy rated data which is used to modify the subsequently generated models to improve the system. [column 1, lines 7-9] “fuel optimization in vehicles by taking into consideration of selecting a robust route based on the stochastic variability.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mason with Gusikhin’s teaching of stochastic statistical analysis of energy consumption for a vehicle routing system. One would be motivated, with reasonable expectation of success, to use the energy consumption probability determined through stochastic variability analysis in order to optimize energy consumption and travel time (Gusikhin [column 9, lines 13-14] “optimize energy consumption as well as minimize travel time”).
Regarding claims 2 and 12, Mason discloses The method of claim 1, comprising determining a fleet resource plan for the fleet of vehicles, the fleet resource plan defining a number of vehicles of the fleet and powertrain attributes of said vehicles ([0008] “determining one or more sets of a vehicle, a driver, and a subset of the plurality of destinations configured to reduce a total amount of energy use by the vehicles to visit the plurality of destinations.” [0028] “The type of vehicle used can also factor into route selection … gasoline powered vehicles … electric vehicles, hybrid vehicles, and alternative fuel vehicles.”).
Regarding claims 5 and 15, Mason discloses The method of claim 1, wherein the feedback parameters indicate route travel parameters of the fleet operating according to the dispatch and routing plan ([0104] “The dynamic re-routing process 900 illustrates one embodiment in which new optimal routes can be generated in real time based on changes to the energy use factors” [0105] “The dynamic re-routing process 900 begins at decision block 905, where the routing module 200 waits for new information to be received that may affect the cost (e.g., energy use estimate) of the current identified route.”).
Regarding claims 7 and 17, Mason discloses The method of claim 2, wherein the act of determining the fleet resource plan includes determining the number of vehicles in the fleet and the powertrain attributes of said vehicles to optimize a second plurality of objectives including one or more of total operational cost of the fleet and total productivity of the fleet ([0008] “determining one or more sets of a vehicle, a driver, and a subset of the plurality of destinations configured to reduce a total amount of energy use by the vehicles to visit the plurality of destinations.” [0028] “The type of vehicle used can also factor into route selection … gasoline powered vehicles … electric vehicles, hybrid vehicles, and alternative fuel vehicles.” [0050] “routes that include multiple legs between intermediate stops, an energy use cost can be calculated for each leg and the energy use costs for the legs can be summed to generate a total route energy use cost.”).
Regarding claims 8 and 18, Mason discloses The method of claim 2, wherein the act of determining the fleet resource plan includes determining at least one of connectivity and automation features for vehicles in the fleet and tire attributes for vehicles in the fleet (0074] “the cost of a route can depend on monetary factors other than energy. Such factors can include … estimated wear-and-tear or maintenance information (e.g., tire replacement costs, brake replacement costs)”).
Regarding claims 9 and 19, Mason discloses The method of claim 1, wherein the act of determining the dispatch and routing plan accounts for one or more of energy resource infrastructure parameters, vehicle powertrain parameters, and vehicle delivery loads ([0008] “determining one or more sets of a vehicle, a driver, and a subset of the plurality of destinations configured to reduce a total amount of energy use by the vehicles to visit the plurality of destinations.” [0028] “The type of vehicle used can also factor into route selection … gasoline powered vehicles … electric vehicles, hybrid vehicles, and alternative fuel vehicles.” [0050] “routes that include multiple legs between intermediate stops, an energy use cost can be calculated for each leg and the energy use costs for the legs can be summed to generate a total route energy use cost.”).
Regarding claims 10 and 20, Mason fails to disclose The method of claim 1, wherein the act of determining an energy or fuel consumption probability distribution for the fleet in response to the feedback parameters is performed by a stochastic optimizer.
However, Gusikhin teaches determining an energy or fuel consumption probability distribution for the fleet in response to the feedback parameters is performed by a stochastic optimizer ([column 1, lines 7-9] “fuel optimization in vehicles by taking into consideration of selecting a robust route based on the stochastic variability.” [column 13, lines 36-38] “determine if the estimated energy consumption and time variables are statistically accurate” Gusikhin determines a probability of energy consumption for routes based on the stochastic variability for energy optimization.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mason with Gusikhin’s teaching of stochastic statistical analysis of energy consumption for a vehicle routing system. One would be motivated, with reasonable expectation of success, to use the energy consumption probability determined through stochastic variability analysis in order to optimize energy consumption and travel time (Gusikhin [column 9, lines 13-14] “optimize energy consumption as well as minimize travel time”).
Claims 3-4 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Mason in view of Dubey and Gusikhin as applied to claims 2 and 12 above, further in view of Balali et al. (US 20230045381 A1), hereinafter referred to as Balali.
Regarding claims 3 and 13, Mason discloses storing and using historical data for the consideration of longer term data sets that impact real-time routing ([0043] “the parameter database 240 can store historical or real-time traffic information.” [0080] “best-guess estimates based on historical data may have to be used for stop times.” [claim 1] “driver profile comprises information regarding past driver behavior that is reflective of energy use”).
Mason fails to disclose The method of claim 2, wherein the act of determining the dispatch and routing plan is performed by a first optimizer configured over a first time range and the act of determining a fleet resource plan is performed by a second optimizer over a second time range greater than the first time range.
However, Balali teaches the act of determining the dispatch and routing plan is performed by a first optimizer configured over a first time range ([0024] “the optimized charging schedule of each EV is determined based on the required energy demand for each travel shift” [0105] “FIG. 3B presents an example of energy demand estimation to initialize the charging schedule optimization problem. Rows are representing EV ids, and columns are representing hours of a day.”) and the act of determining a fleet resource plan is performed by a second optimizer over a second time range greater than the first time range ([0028] long term horizon for electrification, short term time horizon for charging scheduling [0158] “FIG. 5 presents an example of EVSE infrastructure recommendation over 5 years of optimization horizon, which is based on the projected maximum number of EVs that exist in a fleet.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mason with Balali’s teaching of a hourly/daily/weekly travel route charging schedule for a fleet of vehicles and a yearly electric vehicle quantity optimizer based on the collected energy demand over the period of time. One would be motivated, with reasonable expectation of success, to optimize the charging schedule for the first shorter period of time in order to minimize total energy cost of the fleet and ensure the EVs have required energy for shifts (Balali [0024] “The main objective of this model is to provide optimal charging rates in order to minimize the total energy cost, while ensuring that all the EVs have their required energy before starting their next travel shift.”). Further, one would be motivated, with reasonable expectation of success, to optimize the number of electric vehicles in the fleet for the second longer period of time in order to minimize total cost of ownership and provide socio- and economic benefits ([Balali [0094] “One objective of the PredictEV Fleet algorithm is to electrify the current fleet while minimizing cost and providing socio and economic benefits” [0095] “Electric vehicle fleet composition seeks to minimize the total cost of ownership, either lease or finance, and total cost of charge.”).
Regarding claims 4 and 14, Mason fails to disclose The method of claim 3, wherein at least one of (a) the first time range is weekly or more frequently, and (b) the second time range is monthly or less frequently.
However, Balali teaches at least one of (a) the first time range is weekly or more frequently ([0023] “The main inputs to the algorithm include the 1) current mix of the fleet, 2) expected travel shifts information such as average and maximum distance travel for a defined period (such as per weekday or weekend) [0105] “FIG. 3B presents an example of energy demand estimation to initialize the charging schedule optimization problem. Rows are representing EV ids, and columns are representing hours of a day.”), and (b) the second time range is monthly or less frequently ([0158] “FIG. 5 presents an example of EVSE infrastructure recommendation over 5 years of optimization horizon, which is based on the projected maximum number of EVs that exist in a fleet.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mason with Balali’s teaching of a hourly/daily/weekly travel route charging schedule for a fleet of vehicles and a yearly electric vehicle quantity optimizer based on the collected energy demand over the period of time. One would be motivated, with reasonable expectation of success, to optimize the charging schedule for the first shorter period of time in order to minimize total energy cost of the fleet and ensure the EVs have required energy for shifts (Balali [0024] “The main objective of this model is to provide optimal charging rates in order to minimize the total energy cost, while ensuring that all the EVs have their required energy before starting their next travel shift.”). Further, one would be motivated, with reasonable expectation of success, to optimize the number of electric vehicles in the fleet for the second longer period of time in order to minimize total cost of ownership and provide socio- and economic benefits ([Balali [0094] “One objective of the PredictEV Fleet algorithm is to electrify the current fleet while minimizing cost and providing socio and economic benefits” [0095] “Electric vehicle fleet composition seeks to minimize the total cost of ownership, either lease or finance, and total cost of charge.”).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mason in view of Dubey and Gusikhin as applied to claims 5 and 15 above, further in view of Hellgren et al. (US 20240220896 A1), hereinafter referred to as Hellgren.
Regarding claims 6 and 16, Mason discloses The method of claim 5, wherein the route travel parameters of the fleet include determining if vehicles have sufficient fuel or battery capacity to complete the route if re-routed ([0106] “The centralized dispatch center may advantageously be able to determine if vehicles have sufficient fuel or battery capacity to complete the route if re-routed.”). Mason fails to disclose The method of claim 5, wherein the route travel parameters of the fleet include actual routes traveled by vehicles of the fleet and an indication of success or failure of missions corresponding to the actual routes traveled.
However, Hellgren teaches the route travel parameters of the fleet include actual routes traveled by vehicles of the fleet and an indication of success or failure of missions corresponding to the actual routes traveled ([0007] “the object is reached with a method for controlling a plurality of vehicles performing missions along a respective route … determining a mission completion deviation comprising a deviation of an actual number of completed missions from the desired number of completed missions” [0042] “a set of balance parameter values may be calculated upon an establishment of an actual mission completion deviation.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Mason with Hellgren’s teaching of recording mission completions of actual routes travelled by fleet vehicle. One would be motivated, with reasonable expectation of success, to record mission completions of actual routes travelled by fleet vehicles and generate balance parameters as a function of the successful route completions in order to optimize the tradeoff between vehicle operating cost and progress (Hellgren [0123] “The bank of balance parameter values may be provided by a function fw that maps mission completion deviations, normalized times for the mission completion deviations, and optionally vehicle individual progress deviations, to balance parameter values.” [0034] “an optimal vehicle speed … in dependence on respective the second balance parameter value. Thereby, an optimal tradeoff between the vehicle operating cost and the progress”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK R HEIM whose telephone number is (571)270-0120. The examiner can normally be reached M-F 9-6 EST.
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/M.R.H./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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