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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Status of Claims
Claims 1, 3-5, 7, 9-10, 12-13, 15-22, and 24 are pending in this application.
Claims 2, 6, 8, 11, 14, and 23 are cancelled.
Claims 1 and 13 are amended.
Claims 1, 3-5, 7, 9-10, 12-13, 15-22, and 24 are presented for examination.
Response to Amendments
Applicant’s amendments, filed 20 May 2026, with respect to the rejection of claims 1, 3-5, 7, 9-10, and 12 under 35 U.S.C. §101 have been fully considered, and the rejection are withdrawn.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 1, 3-5, 7, 10, 12-13, 15-20, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US Publication 2020/0391721 A1) in view of Telford (US Publication 2023/0322204 A1).
Regarding claim 1, Wang teaches a computer-implemented method for controlling a powertrain of a vehicle comprising: receiving a plurality of optimization variables (Wang: Para. 35, 44; a nonlinear MPC control protocol with SOC terminal cost is provided to optimize fuel consumption in power split output control for hybrid powertrains, while maintaining a desired SOC level; motor torque; engine torque; motor power; battery current; battery open circuit voltage); receiving a cost function representing a vehicle system, wherein the cost function comprises a plurality of weights assigned to the plurality of optimization variables (Wang: Para. 6; adapt the weights on the MPC cost function using dynamic programming techniques and route preview information; MPC weights may optionally be adapted using predicted and historic vehicle data, current state of charge); decomposing the cost function into a main optimization problem and a plurality of sub-problems having shared variables and linking variables (Wang: Para. 12, 44; estimated vehicle velocities for multiple rolling road segments; estimates a respective power request—vehicle axle torque and/or transmission input torque—for each rolling road segment; calculates a minimum cost function of motor power such that total fuel consumption to generate the engine power output for the rolling road segments is minimized); ……….. to compute response values for the linking variables to reduce differences between targets and responses (Wang: Para. 51, 54; vehicle controller transmitting one or more command signals to the engine and motor to output engine torque and motor torque based on the calculated minimum cost function of motor power); and outputting the solution to the vehicle to control the powertrain (Wang: Para. 51, 54; vehicle controller transmitting one or more command signals to the engine and motor to output engine torque and motor torque based on the calculated minimum cost function of motor power).
Wang doesn’t explicitly teach generating a solution by iteratively solving the main optimization problem to set target values for the linking variables and solving each sub-problem.
However Telford, in the same field of endeavor, teaches generating a solution by iteratively solving the main optimization problem to set target values for the linking variables and solving each sub-problem (Wang: Para. 44, 50; calculates a minimum cost function of motor power such that a summation of fuel consumption to generate the engine power outputs for the rolling road segments of the predicted path is minimized).
It would have been obvious to one having ordinary skill in the art to modify the vehicle fuel consumption cost optimization (Wang: Para. 44, 50) with the model-based design simulations (Telford: Para. 190) with a reasonable expectation of success because a model-based design simulation allows the selection of different components for the required range of drive modes and duty cycles of the vehicle (Telford: Para. 190).
Regarding claim 3, Wang teaches the computer-implemented method of claim 1, wherein the optimization variables comprise a plurality of states (Wang: Para. 49; vehicle controller determining, based on the aforesaid path plan data, estimated vehicle velocities for a plurality of rolling road segments of the predicted path; determine an optimal power split between engine power output and motor power output to minimize fuel consumption for the desired trip).
Regarding claim 4, Wang teaches the computer-implemented method of claim 3, wherein the plurality of states comprise at least one of vehicle speed, vehicle distance, gear number, gear dwell time count, battery state-of-charge, battery temperature, engine status, engine on/off dwell time counter, fuel consumption, pre- Diesel Oxidation Catalyst (DOC) temperature, DOC temperature, Diesel Particulate Filter (DPF) temperature, and selective catalytic reduction (SCR) temperature (Wang: Para. 30; inputs may include vehicle speed).
Regarding claim 5, Wang doesn’t explicitly teach wherein the optimization variables comprise a plurality of design parameters.
However Telford, in the same field of endeavor, teaches wherein the optimization variables comprise a plurality of design parameters (Telford: Para. 190; simulation is used for model-based design, which allows the selection of different components for the required range of drive modes and duty cycles of the vehicle).
It would have been obvious to one having ordinary skill in the art to modify the vehicle fuel consumption cost optimization (Wang: Para. 44, 50) with the model-based design simulations (Telford: Para. 190) with a reasonable expectation of success because a model-based design simulation allows the selection of different components for the required range of drive modes and duty cycles of the vehicle (Telford: Para. 190).
Regarding claim 7, Wang teaches the computer-implemented method claim 1, wherein the optimization variables further comprise a plurality of control variables (Wang: Para. 35; motor torque; engine torque; battery current; battery open circuit voltage).
Regarding claim 10, Wang teaches the computer-implemented method of claim 1, wherein the cost function is a function that comprises values representing fuel, battery energy, and emissions (Wang: Para. 8, 51; AI-enhanced MPC powertrain control architectures and methodologies, increased fuel economy and reduced emissions are realized with minimal additional cost and reduced powertrain calibration time; method translates engine torque and motor torque, subject to SOC and battery power constraints, as well as a single motor torque min/max constraint).
Regarding claim 12, Wang doesn’t explicitly teach wherein the solution to the cost function comprises a design- space optimization.
However Telford, in the same field of endeavor, teaches wherein the solution to the cost function comprises a design- space optimization (Telford: Para. 256; model-based dynamic multivariate analysis get and control system optimisation; dynamically explore the entire design space; arrive at an instantaneous set of variable settings that best meet the prevailing performance requirements of the vehicle while satisfying the defined energy sub-system constraints).
It would have been obvious to one having ordinary skill in the art to modify the vehicle fuel consumption cost optimization (Wang: Para. 44, 50) with the model-based design simulations (Telford: Para. 190) with a reasonable expectation of success because a model-based design simulation allows the selection of different components for the required range of drive modes and duty cycles of the vehicle (Telford: Para. 190).
Regarding claim 13, Wang teaches a system for controlling a powertrain of a vehicle. the system comprising: a vehicle powertrain (Wang: Para. 16; electric-drive motor vehicle with a hybrid powertrain); and a computing device in operable communication with the vehicle powertrain, wherein the computing device comprises a processor and a memory, the memory having computer-executable instructions stored thereon that, when executed by the processor, cause the processor to: (Wang: Para. 57; machine readable instructions for execution by: (a) a processor) receive a plurality of optimization variables (Wang: Para. 35, 44; a nonlinear MPC control protocol with SOC terminal cost is provided to optimize fuel consumption in power split output control for hybrid powertrains, while maintaining a desired SOC level; motor torque; engine torque; motor power; battery current; battery open circuit voltage); receive a cost function representing a vehicle system, wherein the cost function comprises a plurality of weights assigned to the plurality of optimization variables (Wang: Para. 6; adapt the weights on the MPC cost function using dynamic programming techniques and route preview information; MPC weights may optionally be adapted using predicted and historic vehicle data, current state of charge); decompose the cost function into a main optimization problem and a plurality of sub-problems having shared variables and linking variables (Wang: Para. 12, 44; estimated vehicle velocities for multiple rolling road segments; estimates a respective power request—vehicle axle torque and/or transmission input torque—for each rolling road segment; calculates a minimum cost function of motor power such that total fuel consumption to generate the engine power output for the rolling road segments is minimized); ……….. to compute response values for the linking variables to reduce differences between targets and responses (Wang: Para. 51, 54; vehicle controller transmitting one or more command signals to the engine and motor to output engine torque and motor torque based on the calculated minimum cost function of motor power); and output the solution to the vehicle to control the powertrain (Wang: Para. 51, 54; vehicle controller transmitting one or more command signals to the engine and motor to output engine torque and motor torque based on the calculated minimum cost function of motor power).
Wang doesn’t explicitly teach generate a solution by iteratively solving the main optimization problem to set target values for the linking variables and solving each sub-problem.
However Telford, in the same field of endeavor, teaches generating a solution by iteratively solving the main optimization problem to set target values for the linking variables and solving each sub-problem (Telford: Para. 212; model-based control layer which computes the optimal power flow for vehicle powertrain sub-systems by iteratively solving a constrained optimization problem using available data sources).
It would have been obvious to one having ordinary skill in the art to modify the vehicle fuel consumption cost optimization (Wang: Para. 44, 50) with the model-based design simulations (Telford: Para. 190) with a reasonable expectation of success because a model-based design simulation allows the selection of different components for the required range of drive modes and duty cycles of the vehicle (Telford: Para. 190).
Regarding claim 15, Wang teaches the system of claim 14, wherein the optimization variables comprise a plurality of states (Wang: Para. 49; vehicle controller determining, based on the aforesaid path plan data, estimated vehicle velocities for a plurality of rolling road segments of the predicted path; determine an optimal power split between engine power output and motor power output to minimize fuel consumption for the desired trip).
Regarding claim 16, Wang teaches the system of claim 15, wherein the plurality of states comprise at least one of vehicle speed, vehicle distance, gear number, gear dwell time count, battery state-of-charge, battery temperature, engine status, engine on/off dwell time counter, fuel consumption, pre- Diesel Oxidation Catalyst (DOC) temperature. DOC temperature, Diesel Particulate Filter (DPF) temperature, and selective catalytic reduction (SCR) temperature (Wang: Para. 30; inputs may include vehicle speed).
Regarding claim 17, Wang doesn’t explicitly teach wherein the optimization variables comprise a plurality of design parameters.
However Telford, in the same field of endeavor, teaches wherein the optimization variables comprise a plurality of design parameters (Telford: Para. 190; simulation is used for model-based design, which allows the selection of different components for the required range of drive modes and duty cycles of the vehicle).
It would have been obvious to one having ordinary skill in the art to modify the vehicle fuel consumption cost optimization (Wang: Para. 44, 50) with the model-based design simulations (Telford: Para. 190) with a reasonable expectation of success because a model-based design simulation allows the selection of different components for the required range of drive modes and duty cycles of the vehicle (Telford: Para. 190).
Regarding claim 18, Wang teaches the system of claim 13, wherein the optimization variables comprise a plurality of continuous and discrete variables (Wang: Para. 30, 41; inputs may include vehicle speed and acceleration data, speed limit data, traffic light status and location data, road gradient data, stop sign location data, traffic flow data, geospatial data, road and lane-level data, vehicle dynamics data; a lumped thermal mass and the engine exhaust variables may act as static maps of engine operating conditions).
Regarding claim 19, Wang teaches the system of claim 13, wherein the optimization variables comprise a plurality of control variables (Wang: Para. 35; motor torque; engine torque; battery current; battery open circuit voltage).
Regarding claim 20, Wang teaches the system of claim 19, wherein the control variables comprise at least one of vehicle acceleration, gear shift command, torque split, and engine switch (Wang: Para. 6, 28; minimizing fuel consumption through electric-assisted propulsion or engine load-up and load-down shifting; power transmission may use differential gearing to achieve selectively variable torque and speed ratios).
Regarding claim 22, Wang teaches the system of claim 13, wherein the cost function is a function that comprises values representing fuel, battery energy, and emissions (Wang: Para. 8, 51; AI-enhanced MPC powertrain control architectures and methodologies, increased fuel economy and reduced emissions are realized with minimal additional cost and reduced powertrain calibration time; method translates engine torque and motor torque, subject to SOC and battery power constraints, as well as a single motor torque min/max constraint).
Regarding claim 24, Wang doesn’t explicitly teach wherein the solution to the cost function comprises a design-space optimization.
However Telford, in the same field of endeavor, teaches wherein the solution to the cost function comprises a design-space optimization (Telford: Para. 256; model-based dynamic multivariate analysis get and control system optimisation; dynamically explore the entire design space; arrive at an instantaneous set of variable settings that best meet the prevailing performance requirements of the vehicle while satisfying the defined energy sub-system constraints).
It would have been obvious to one having ordinary skill in the art to modify the vehicle fuel consumption cost optimization (Wang: Para. 44, 50) with the model-based design simulations (Telford: Para. 190) with a reasonable expectation of success because a model-based design simulation allows the selection of different components for the required range of drive modes and duty cycles of the vehicle (Telford: Para. 190).
Claims 9 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US Publication 2020/0391721 A1) in view of Genter et al. (US Publication 2023/0278651 A1).
Regarding claim 9, Wang doesn’t explicitly teach wherein the optimization variables comprise a plurality of design parameters, and wherein the design parameters comprise number of battery cells in series (Ns), number of battery cells in parallel (Np), scaling factor for a genset power, and genset selection between diesel and compressed natural gas (CNG).
However Genter, in the same field of endeavor, teaches wherein the optimization variables comprise a plurality of design parameters, and wherein the design parameters comprise number of battery cells in series (Ns), number of battery cells in parallel (Np), scaling factor for a genset power, and genset selection between diesel and compressed natural gas (CNG) (Genter: Para. 6, 43, 50, 95, 109; optimize the regenerative braking split for optimal vehicle/powertrain level; cells connected in series and parallel to achieve the total voltage and current requirements; operation of the genset; engines as described herein may be compression-ignited diesel internal combustion engines or spark-ignited internal combustion engine, and may be fueled by at least one of gasoline, ethanol, methanol, hydrogen, CNG).
It would have been obvious to one having ordinary skill in the art to modify the vehicle fuel consumption cost optimization (Wang: Para. 44, 50) with vehicle component options (Genter: Para. 95) with a reasonable expectation of success because the tractor unit may be configured in any suitable configuration with optimized regenerative braking split for optimal vehicle/powertrain level operation to ensure efficient operation (Genter: Para. 43, 95, 109).
Regarding claim 21, Wang doesn’t explicitly teach wherein the optimization variables comprise a plurality of design parameters. and wherein the design parameters comprise number of battery cells in series (Ns), number of battery cells in parallel (Np), scaling factor for a genset power, and genset selection between diesel and compressed natural gas (CNG).
However Genter, in the same field of endeavor, teaches wherein the optimization variables comprise a plurality of design parameters. and wherein the design parameters comprise number of battery cells in series (Ns), number of battery cells in parallel (Np), scaling factor for a genset power, and genset selection between diesel and compressed natural gas (CNG) (Genter: Para. 6, 43, 50, 95, 109; optimize the regenerative braking split for optimal vehicle/powertrain level; cells connected in series and parallel to achieve the total voltage and current requirements; operation of the genset; engines as described herein may be compression-ignited diesel internal combustion engines or spark-ignited internal combustion engine, and may be fueled by at least one of gasoline, ethanol, methanol, hydrogen, CNG).
It would have been obvious to one having ordinary skill in the art to modify the vehicle fuel consumption cost optimization (Wang: Para. 44, 50) with vehicle component options (Genter: Para. 95) with a reasonable expectation of success because the tractor unit may be configured in any suitable configuration with optimized regenerative braking split for optimal vehicle/powertrain level operation to ensure efficient operation (Genter: Para. 43, 95, 109).
Response to Arguments
Applicant’s arguments with respect to the rejection of claims 1-4, 7, 10, 13-16, 18-20, and 22 under 35 U.S.C. §102 and have been fully considered, and are not persuasive.
The applicant’s attorney argues that Wang does not teach a “main optimization problem and a plurality of control sub-problems having shared variables and linking variables”, “iteratively solving the main optimization problem to set target values for linking variables and solving each sub-problem to compute response values for the linking variables to reduce differences between targets and responses”, and “outputting the solution to the vehicle to control the powertrain.”
In response to the applicant’s argument above, this argument is moot because it does not apply to the reference in this rejection.
Applicant’s arguments with respect to the rejection of claims 5, 9, 12, 17, 21, and 24 under 35 U.S.C. §103 and have been fully considered and are not persuasive.
The applicant’s attorney argues that Telford does not teach a “main optimization problem and a plurality of control sub-problems having shared variables and linking variables.”
In response to the applicant’s argument above, Telford teaches a model-based control layer which computes the optimal power flow for vehicle powertrain sub-systems by iteratively solving a constrained optimization problem (Telford: Para. 212). Sub systems of the vehicle’s powertrain are an example of a plurality of sub-problems that are part of the main optimization problem of the optimal power flow for a vehicle powertrain. At minimum there are the basic control factors of battery state of charge, fuel, torque, and time for the multiple power sources (Telford: Para. 190). There are variables in the system that are similar and different for each power source. It is obvious to one of ordinary skill in the art that there will be linking variables for a vehicle powertrain cost optimization problem.
The applicant next argues that Telford does not teach “iteratively solving the main optimization problem to set target values for linking variables and solving each sub-problem to compute response values for the linking variables to reduce differences between targets and responses.”
In response to the applicant’s argument above, Telford teaches a model-based control layer which computes the optimal power flow for vehicle powertrain sub-systems by iteratively solving a constrained optimization problem (Telford: Para. 212). Sub systems of the vehicle’s powertrain are an example of a plurality of sub-problems that are part of the main optimization problem of the optimal power flow for a vehicle powertrain. At minimum there are the basic control factors of battery state of charge, fuel, torque, and time for the multiple power sources (Telford: Para. 190). There are variables in the system that are similar and different for each power source. It is obvious to one of ordinary skill in the art that there will be linking variables for a vehicle powertrain cost optimization problem.
The applicant’s arguments have failed to point out the distinguishing characteristics of the amended claim language over the prior art. For the above reasons, Wang’s powertrain dynamic control in view of Telford’s iteratively solving the sub-systems optimization problem reads on applicant’s vehicle powertrains control. The rejection is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAURA E LINHARDT whose telephone number is (571) 272-8325. The examiner can normally be reached on M-TR, M-F: 8am-4pm.
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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