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 .
This is a non-final office action on the merits. Claims 1, 3-11, and 13-20 are currently pending and are addressed below.
The examiner notes that the fundamentals of the rejection are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 11/14/2025 has been entered.
Response to Arguments
Applicant’s arguments with respect to the rejection of claims 1-20 under 35 U.S.C 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1, 3, 6, 11, 13, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Yin Guodong et al. (CN111634195A), hereinafter referred to as Guodong in view of Drako Dean et al. (US2016090005A1), hereinafter referred to as Dean.
Regarding claim 1, Guodong discloses: an apparatus for distributing power of an electric vehicle (see at least Guodong, pg.19, par.10), the apparatus comprising:
a storage configured to store a vehicle speed prediction model in which learning is completed (see at least Guodong, pg.16, par.9-10, which discloses a vehicle speed prediction model which establishes a torque change rate optimization corresponding to any speed and torque range)
and a controller (see at least Guodong, pg.18, par.3, which discloses a fuzzy controller) configured to:
collect vehicle operating information including: efficiencies of the front wheel drive motor and the rear wheel drive motor (see at least Guodong, pg.19, par.9, pg.21, par.2, which discloses the general process of collecting information utilized in the efficiency calculation; pg.20, par.7-8, which discloses calculating operating information such as the efficiencies of the front wheel drive motor and rear wheel drive motor)
predict a vehicle speed for a preset time using the vehicle speed prediction model (see at least Guodong, pg.16, par.9-10, which discloses a vehicle speed prediction model which establishes a torque change rate optimization corresponding to any speed and torque range, this means predict a vehicle speed for a preset time using the vehicle speed prediction mode)
determine wheel power based on the vehicle speed and the collected vehicle operating information (see at least Guodong, pg.20, par.7, discloses the power calculation step based on the movement of the front and rear wheels of the four-wheel drive based on an efficiency calculation, taking into consideration collected vehicle operation information)
and distribute the wheel power to the front wheel drive motor and the rear-wheel-drive motor based on an optimization algorithm to achieve optimal energy consumption efficiency, wherein the controller is configured to determine a torque of the front wheel drive motor and a torque of the rear wheel drive motor based on a dynamic programming (DP) algorithm (see at least Guodong, pg.19, par.6-7, which discloses the general disclosure of the optimization algorithm providing the torque in a dynamic distribution from the perspective of energy saving, implying that wheel power is distributed to the front and rear wheel drive motors as well; pg.20, par.7, discloses the power calculation step based on the movement of the front and rear wheels of the four wheel drive based on an efficiency calculation; pg.21, par.1-2, discloses the specific calculation utilizing the efficiencies of the front and rear wheel motors; pg.22, par.1-2 discloses the optimization algorithm, this all means distribute the wheel power to the front wheel drive motor and the rear-wheel-drive motor based on an optimization algorithm to achieve optimal energy consumption efficiency, wherein the controller is configured to determine a torque of the front wheel drive motor and a torque of the rear wheel drive motor based on a dynamic programming (DP) algorithm)
Guodong is silent on, however, in the same field of endeavor, Dean teaches: revolutions per minute (RPM) of a front wheel drive motor and a rear-wheel-drive motor (see at least Dean, ¶¶ [0012]-[0014], [0075], [0117], which discloses collecting revolutions per minute (rotational speed) of a front wheel drive motor and a rear wheel drive motor)
a state of charge (SOC) of a battery via a vehicle network (see at least Dean, ¶¶ [0125]-[0126], which discloses collecting information such as a state of charge of a battery)
It would have been obvious to a person of ordinary skill in the art to modify Guodong to teach collecting vehicle operation information including revolutions per minute and a state of charge (SOC) of a battery via a vehicle network as taught by Dean. Incorporating the teaching of Dean would allow for an improvement that further enhances the efficiency and accuracy of the optimization calculation to determine a torque of the front wheel drive motor and a torque of the rear wheel drive motor.
Regarding claim 3, Guodong is silent on, however, in the same field of endeavor, Dean teaches: the apparatus of claim 1, wherein the controller is configured to predict the vehicle speed for the preset time by inputting information on a road on which the electric vehicle travels and driving information of the electric vehicle to the vehicle speed prediction model (see at least Dean, ¶¶ [0041], [0056] [0098], [0127]-[0129], which discloses examples of speed determination by inputting information regarding road conditions and driving information to the model)
It would have been obvious to a person of ordinary skill in the art to change Guodong to include the apparatus of claim 1, wherein the controller is configured to predict the vehicle speed for the preset time by inputting information on a road on which the electric vehicle travels and driving information of the electric vehicle to the vehicle speed prediction model. Incorporating the teaching of Dean would allow for an improvement that further enhances the efficiency and accuracy of the optimization calculation to determine a torque of the front wheel drive motor and a torque of the rear-wheel-drive motor.
Regarding claim 6, Guodong discloses: the apparatus of claim 3, wherein the driving information includes at least one of a speed of the electric vehicle, an accelerator pedal position (APS), a brake pedal position (BPS), a driving mode, a driving tendency, a distance from a vehicle in front, or a combination thereof (see at least Guodong, pg.19, par.11, which discloses brake pedal position; par.pg.21, par.1, which discloses a driving mode; pg.21, par.3-5, discloses vehicle speed)
Regarding claim 11, Guodong discloses: a method of distributing power of an electric vehicle (see at least Guodong, pg.19, par.10), the method comprising:
collecting, by a controller, vehicle operating information including: efficiencies of the front wheel drive motor and the rear wheel drive motor (see at least Guodong, pg.19, par.9, pg.21, par.2, which discloses the general process of collecting information utilized in the efficiency calculation; pg.20, par.7-8, which discloses calculating operating information such as the efficiencies of the front wheel drive motor and rear wheel drive motor)
storing, by a storage, a vehicle speed prediction model in which learning is completed (see at least Guodong, pg.16, par.9-10, which discloses a vehicle speed prediction model which establishes a torque change rate optimization corresponding to any speed and torque range)
predicting, by the controller, a vehicle speed for a preset time using the vehicle speed prediction model (see at least Guodong, pg.16, par.9-10, which discloses a vehicle speed prediction model which establishes a torque change rate optimization corresponding to any speed and torque range, this means predicting, by the controller, a vehicle speed for a preset time using the vehicle speed prediction model)
determining, by the controller, wheel power based on the vehicle speed and the collected vehicle operating information (see at least Guodong, pg.20, par.7, discloses the power calculation step based on the movement of the front and rear wheels of the four wheel drive based on an efficiency calculation, taking into consideration collected vehicle operation information)
and distributing, by the controller, the wheel power to a front wheel drive motor and a rear wheel drive motor based on an optimization algorithm to achieve optimal energy consumption efficiency, wherein distributing the wheel power includes determining a torque of the front wheel drive motor and a torque of the rear wheel drive motor based on a dynamic programming (DP) algorithm (see at least Guodong, pg.19, par.6-7, which discloses the general disclosure of the optimization algorithm providing the torque in a dynamic distribution from the perspective of energy saving, implying that wheel power is distributed to the front and rear wheel drive motors as well; pg.20, par.7, discloses the power calculation step based on the movement of the front and rear wheels of the four wheel drive based on an efficiency calculation; pg.21, par.1-2, discloses the specific calculation utilizing the efficiencies of the front and rear wheel motors; pg.22, par.1-2 discloses the optimization algorithm, this all means distribute the wheel power to the front wheel drive motor and the rear-wheel-drive motor based on an optimization algorithm to achieve optimal energy consumption efficiency, wherein the controller is configured to determine a torque of the front wheel drive motor and a torque of the rear wheel drive motor based on a dynamic programming (DP) algorithm)
Guodong is silent on, however, in the same field of endeavor, Dean teaches: revolutions per minute (RPM) of a front wheel drive motor and a rear wheel drive motor (see at least Dean, ¶¶ [0012]-[0014], [0075], [0117], which discloses collecting revolutions per minute (rotational speed) of a front wheel drive motor and a rear wheel drive motor)
and a state of charge (SOC) of a battery via a vehicle network (see at least Dean, ¶¶ [0125]-[0126], which discloses collecting information such as a state of charge of a battery)
It would have been obvious to a person of ordinary skill in the art to modify Guodong to teach collecting vehicle operation information including revolutions per minute and a state of charge (SOC) of a battery via a vehicle network as taught by Dean. Incorporating the teaching of Dean would allow for an improvement that further enhances the efficiency and accuracy of the optimization calculation to determine a torque of the front wheel drive motor and a torque of the rear-wheel-drive motor.
Regarding claim 13, Guodong is silent on, however, in the same field of endeavor, Dean teaches: the method of claim 11, wherein the predicting of the vehicle speed includes: inputting information on a road on which the electric vehicle travels and driving information of the electric vehicle to the vehicle speed prediction model (see at least Dean, ¶¶ [0041], [0056] [0098], [0127]-[0129], which discloses examples of speed determination by inputting information regarding road conditions and driving information to the model)
It would have been obvious to a person of ordinary skill in the art to change Guodong to include the apparatus of claim 1, wherein the controller is configured to predict the vehicle speed for the preset time by inputting information on a road on which the electric vehicle travels and driving information of the electric vehicle to the vehicle speed prediction model. Incorporating the teaching of Dean would allow for an improvement that further enhances the efficiency and accuracy of the optimization calculation to determine a torque of the front wheel drive motor and a torque of the rear-wheel-drive motor.
Regarding claim 16, Guodong discloses: the method of claim 13, wherein the driving information includes at least one of a speed of the electric vehicle, an accelerator pedal position (APS), a brake pedal position (BPS), a driving mode, a driving tendency, a distance from a vehicle in front, or a combination thereof (see at least Guodong, pg.19, par.11, which discloses brake pedal position; par.pg.21, par.1, which discloses a driving mode; pg.21, par.3-5, discloses vehicle speed).
Claims 3-4 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over modified Guodong in further view of Liu Kuan et al. (US20200114926A1), hereinafter referred to as Kuan.
Regarding claim 4, modified Guodong is silent on, however, in the same field of endeavor, Kuan teaches: the apparatus of claim 3, wherein the information on the road includes at least one of slope information of a road located in front of the electric vehicle, information on a traffic light located on the road, a predicted average speed for each section, or a combination thereof (see at least Kuan, ¶¶ [0006], discloses extracted features of the plurality of time series datasets used as road data in the prediction including, road curvature, a location of a traffic light via timing; [0042]-[0043] discloses specific example of extracted features being included as road information when predicting a vehicle’s future velocities according to extracted features, [0056] discloses specific traffic information relative to the vehicle’s route)
It would have been obvious to a person of ordinary skill in the art to further change modified Guodong to include wherein the information on the road includes at least one of slope information of a road located in front of the electric vehicle, information on a traffic light located on the road, a predicted average speed for each section, or a combination thereof. Incorporating the teaching into modified Guodong would further improve the base invention by application of real-world inputs to predict future vehicle velocities while driving, which may be incorporated to further enhance the optimization calculation.
Regarding claim 5, modified Guodong is silent on, however, in the same field of endeavor, Kuan teaches: the apparatus of claim 4, wherein the traffic light information includes at least one of a location of the traffic light and a signal period of the traffic light (see at least Kuan, ¶¶ [0006] discloses the extracted feature of a traffic light included as road information utilizing a vehicle’s acceleration/deceleration profile determined by a traffic light, specifically via time series and general location)
It would have been obvious to a person of ordinary skill in the art to change modified Guodong to include the apparatus of claim 4, wherein the traffic light information includes at least one of a location of the traffic light and a signal period of the traffic light. Incorporating the teaching into modified Guodong would further improve the base invention by application of real-world inputs to predict future vehicle velocities while driving, which may be incorporated to further enhance the optimization calculation.
Regarding claim 14, modified Guodong is silent on, however, in the same field of endeavor, Kuan teaches: the method of claim 13, wherein the information on the road includes at least one of slope information of a road located in front of the electric vehicle, information on a traffic light located on the road, a predicted average speed for each section, or a combination thereof (see at least Kuan, ¶¶ [0006], discloses extracted features of the plurality of time series datasets used as road data in the prediction including, road curvature, a location of a traffic light via timing; [0042]-[0043] discloses specific example of extracted features being included as road information when predicting a vehicle’s future velocities according to extracted features, [0056] discloses specific traffic information relative to the vehicle’s route)
It would have been obvious to a person of ordinary skill in the art to further change modified Guodong to include wherein the information on the road includes at least one of slope information of a road located in front of the electric vehicle, information on a traffic light located on the road, a predicted average speed for each section, or a combination thereof. Incorporating the teaching into modified Guodong would further improve the base invention by application of real-world inputs to predict future vehicle velocities while driving, which may be incorporated to further enhance the optimization calculation.
Regarding claim 15, modified Guodong is silent on, however, in the same field of endeavor, Kuan teaches: the method of claim 14, wherein the traffic light information includes at least one of a location of the traffic light or a signal period of the traffic light (see at least Kuan, ¶¶ [0006] discloses the extracted feature of a traffic light included as road information utilizing a vehicle’s acceleration/deceleration profile determined by a traffic light, specifically via time series and general location)
It would have been obvious to a person of ordinary skill in the art to change modified Guodong to include wherein the traffic light information includes at least one of a location of the traffic light and a signal period of the traffic light. Incorporating the teaching into modified Guodong would further improve the base invention by application of real-world inputs to predict future vehicle velocities while driving, which may be incorporated to further enhance the optimization calculation.
10. Claims 7-9 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over further modified Guodong in view of Kodama Hiroyuki et al. (US20090063000A1), hereinafter referred to as Hiroyuki.
Regarding claim 7, further modified Guodong is silent on, however, in the same field of endeavor, Hiroyuki teaches: the apparatus of claim 1, wherein the controller is configured to:
determine an acceleration per second based on the vehicle speed for the preset time when a road on which the electric vehicle is scheduled to travel is flat (see at least Hiroyuki, ¶¶ [0068] discloses the general calculation of a required acceleration value depending on a timed control cycle )
determine a force on a flat road by multiplying the acceleration by a weight of the electric vehicle (¶¶ [0086]-[0088] discloses the calculation of force of gravity relative to acceleration of a vehicle traveling along a direction; jerk acceleration is multiplied by Fx)
determine a wheel torque by multiplying the force by a tire radius
determine the wheel power by multiplying the wheel torque by a wheel angular velocity (see at least Hiroyuki, Fig.17 discloses a block diagram detailing a process for calculating power-train torque; [0066]-[0070], [0088]-[0091] discloses wheel torque calculation (power-train torque) steps in depth and the relative factors in consideration)
It would have been obvious to a person of ordinary skill in the art to change further modified Guodong to include: determine an acceleration per second based on the vehicle speed for the preset time when a road on which the electric vehicle is scheduled to travel is flat, determine a force on a flat road by multiplying the acceleration by a weight of the electric vehicle, and determine a wheel torque by multiplying the force by a tire radius, and determine the wheel power by multiplying the wheel torque by a wheel angular velocity as taught by Hiroyuki. The examiner would like to note that inputs indicative of an acceleration profile of a vehicle along a certain route, within a predetermined period of time is disclosed in further modified Guodong (Kuan). However, a specific instance of acceleration being calculated is not explicitly stated. Implementing this determination taught by Hiroyuki would allow for improvement of the base device, the electronic unit with learning model capability, to further improve its predictions of vehicle speed/velocity. Specifically, the vehicle acceleration will be able to be controlled in a feedback operation which allows for the prediction model to fine tune predicted future velocities according to a more in-depth acceleration profile.
Regarding claim 8, further modified Guodong is silent on, however, in the same field of endeavor, Hiroyuki teaches: the apparatus of claim 1, wherein the controller is configured to:
determine an acceleration per second based on the vehicle speed for the preset time when a road on which the electric vehicle is scheduled to travel is an uphill road (see at least Hiroyuki, ¶¶ [0103]-[0108] discloses the determination of acceleration threshold values based on the current velocity within a time; upper and lower limiting values of acceleration are relative to the vehicle control portion conditions such as speed/velocity)
determine a force on a flat road by multiplying the acceleration by a weight of the electric vehicle (see at least Hiroyuki, ¶¶ [0099]-[0103] discloses calculation of Fx under the condition that a vehicle is traveling along a road)
determine an acceleration torque by multiplying the force on the flat road by a tire radius (see at least Hiroyuki, ¶¶ [0089] discloses the acceleration “jerk” torque calculation considering factors such as Fx and the radius of the vehicle’s tires)
determine a force on the uphill road (see at least Hiroyuki, ¶¶ [0088] discloses the general calculation of a force on a road with consideration of road gradient, positive or negative (slope); [0099]-[0103] discloses an example calculation of a vehicle traveling an uphill road)
determine a gradient torque by multiplying the force on the uphill road by the tire radius (see at least Hiroyuki, ¶¶ [0089] discloses the acceleration “jerk” torque calculation considering factors such as Fx and the radius of the vehicle’s tires)
determine the wheel power by multiplying a result of adding the acceleration torque and the gradient torque by a wheel angular velocity (see at least Hiroyuki, Fig.17 discloses a block diagram detailing a process for calculating power-train torque; [0066]-[0070], [0088]-[0091] discloses wheel torque calculation (power-train torque) steps in depth and the relative factors in consideration; [0099]-[0103])
It would have been obvious to a person of ordinary skill in the art to change further modified Guodong to include determine an acceleration per second based on the vehicle speed for the preset time when a road on which the electric vehicle is scheduled to travel is an uphill road, determine a force on a flat road by multiplying the acceleration by a weight of the electric vehicle, determine an acceleration torque by multiplying the force on the flat road by a tire radius, determine a force on the uphill road, determine a gradient torque by multiplying the force on the uphill road by the tire radius, and determine the wheel power by multiplying a result of adding the acceleration torque and the gradient torque by a wheel angular velocity as taught by Hiroyuki. The examiner would like to note that inputs indicative of an acceleration profile of a vehicle along a certain route, within a predetermined period of time is disclosed in further modified Guodong (Kuan). However, a specific instance of acceleration being calculated is not explicitly stated. Implementing this determination taught by Hiroyuki would allow for improvement of the base device, the electronic unit with learning model capability, to further improve its predictions of vehicle speed/velocity. Specifically, the vehicle acceleration will be able to be controlled in a feedback operation which allows for the prediction model to fine tune.
Regarding claim 9, further modified Guodong is silent on, however, in the same field of endeavor, Hiroyuki teaches: the apparatus of claim 1, wherein the controller is configured to:
determine an acceleration per second based on the vehicle speed for the preset time when a road on which the electric vehicle is scheduled to travel is a downhill road (see at least Hiroyuki, ¶¶ [0088] discloses the general calculation of a force on an road with consideration of road gradient, positive or negative (slope); [0103]-[0108] discloses the determination of acceleration threshold values based on the current velocity within a time; upper and lower limiting values of acceleration are relative to the vehicle control portion conditions such as speed/velocity)
determine a force on a flat road by multiplying the acceleration by a weight of the electric vehicle (see at least Hiroyuki, ¶¶ [0099]-[0103] discloses calculation of Fx under the condition that a vehicle is traveling along a road)
determine an acceleration torque by multiplying the force on the flat road by a tire radius (see at least Hiroyuki, ¶¶ [0089] discloses the acceleration “jerk” torque calculation considering factors such as Fx and the radius of the vehicle’s tires)
determine a force on the downhill road (see at least Hiroyuki, ¶¶ [0099]-[0103] discloses calculation of Fx under the condition that a vehicle is traveling along a road relative to a positive/negative road gradient)
determine a gradient torque by multiplying the force on the downhill road by the tire radius (see at least Hiroyuki, ¶¶ [0089] discloses the acceleration “jerk” torque calculation considering factors such as Fx and the radius of the vehicle’s tires)
determine the wheel power by multiplying a result of adding the acceleration torque and the gradient torque by a wheel angular velocity (see at least Hiroyuki, Fig.17 discloses a block diagram detailing a process for calculating power-train torque; [0066]-[0070], [0088]-[0091] discloses wheel torque calculation (power-train torque) steps in depth and the relative factors in consideration; [0099]-[0103])
It would have been obvious to a person of ordinary skill in the art to change further modified Guodong to include determine an acceleration per second based on the vehicle speed for the preset time when a road on which the electric vehicle is scheduled to travel is a downhill road, determine a force on a flat road by multiplying the acceleration by a weight of the electric vehicle, determine an acceleration torque by multiplying the force on the flat road by a tire radius, determine a force on the downhill road, determine a gradient torque by multiplying the force on the downhill road by the tire radius, and determine the wheel power by multiplying a result of adding the acceleration torque and the gradient torque by a wheel angular velocity as taught by Hiroyuki. Further implementing this method of calculation taught by Hiroyuki would allow for improvement of the base device, the electronic unit with learning model capability, to further improve its predictions of vehicle speed/velocity. Specifically, the vehicle acceleration will be able to be controlled in a feedback operation, a target acceleration according to a road gradient, conditions such as uphill, or downhill, and can be further adapted. Thus, this determination would be considered in the wheel power distributed to the transmission.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over further modified Guodong in view of Lu Xingyu et al. (CN111216722A), hereinafter referred to as Xingyu.
Regarding claim 10, further modified Guodong discloses: the apparatus of claim 1, wherein the controller is configured to:
determine a force on a flat road by multiplying the acceleration by a weight of the electric vehicle (see at least Hiroyuki, ¶¶ [0099]-[0103] discloses calculation of Fx under the condition that a vehicle is traveling along a road)
determine an acceleration torque by multiplying the force on the flat road by a tire radius (see at least Hiroyuki, ¶¶ [0089] discloses the acceleration “jerk” torque calculation considering factors such as Fx and the radius of the vehicle’s tires)
determine a force on the uphill road (see at least Hiroyuki, ¶¶ [0099]-[0103] discloses calculation of Fx under the condition that a vehicle is traveling along a road relative to a positive/negative road gradient)
determine a first gradient torque by multiplying the force on the uphill road by the tire radius, determine a force on the downhill road (see at least Hiroyuki, ¶¶ [0089] discloses the acceleration “jerk” torque calculation considering factors such as Fx relative to a road gradient and the radius of the vehicle’s tires)
Further modified Guodong is silent on, however, in the same field of endeavor, Xingyu teaches: determine an acceleration per second based on the vehicle speed for the preset time when a road on which the electric vehicle is scheduled to travel is a complex road of an uphill road and a downhill road (see at least Xingyu, pg.18, par.14-25 which discloses the condition of a complex road consisting of transitioning between an uphill to a downhill gradient; pg.16-17 discloses the calculation steps of various components such as acceleration, force, and velocity in accordance to the condition (uphill or downhill) then considers the combination of those values relative to the complex condition)
determine a second gradient torque by multiplying the force on the downhill road by the tire radius (see at least Xingyu pg.16, par.6 which discloses a second gradient torque, the condition of uphill torque relative to the force and radius)
determine the wheel power by multiplying a result of adding the acceleration torque, the first gradient torque and the second gradient torque by a wheel angular velocity (see at least Xingyu, pg.16-17 which discloses the calculation of throttle adjustment (directly related to torque and power delivered to wheels of the vehicle) through consideration of the complex conditional factors of a first (uphill or downhill) paired with a second gradient calculation by wheel angular velocity)
It would have been obvious to a person of ordinary skill in the art to change further modified Guodong to include determine an acceleration per second based on the vehicle speed for the preset time when a road on which the electric vehicle is scheduled to travel is a complex road of an uphill road and a downhill road, determine a second gradient torque by multiplying the force on the downhill road by the tire radius, and determine the wheel power by multiplying a result of adding the acceleration torque, the first gradient torque and the second gradient torque by a wheel angular velocity as taught by Xingyu. The examiner would like to note that further modified Guodong discloses processes of determining wheel power (and related factors) in light of uphill and downhill road gradients, however it does not explicitly disclose a condition where both are considered as a complex road, however if the same implementation of calculations by Xingyu) are applied, it would be reasonable to assume it was possible. The implementation of this teaching by Xingyu would allow for a further improvement of the base device discussed in modified Xingyu. As mentioned, conditions of uphill and downhill gradients are already taken into consideration, the modification would allow for the complex condition of an uphill and downhill roadway to be considered by the vehicle prediction model, expanding the range of wheel power.
Regarding claim 20, further modified Gudong discloses: the method of claim 11, wherein the determining of the wheel power includes:
determining a force on a flat road by multiplying the acceleration by a weight of the electric vehicle (see at least Hiroyuki, ¶¶ [0099]-[0103] discloses calculation of Fx under the condition that a vehicle is traveling along a road)
determining an acceleration torque by multiplying the force on the flat road by a tire radius (see at least Hiroyuki, ¶¶ [0089] discloses the acceleration “jerk” torque calculation considering factors such as Fx and the radius of the vehicle’s tires)
determining a force on the uphill road (see at least Hiroyuki, ¶¶ [0099]-[0103] discloses calculation of Fx under the condition that a vehicle is traveling along a road relative to a positive/negative road gradient)
determining a first gradient torque by multiplying the force on the uphill road by the tire radius (see at least Hiroyuki, ¶¶ [0089] discloses the acceleration “jerk” torque calculation considering factors such as Fx relative to a road gradient and the radius of the vehicle’s tires)
determining a force on the downhill road (see at least Hiroyuki, ¶¶ [0099]-[0103] discloses calculation of Fx under the condition that a vehicle is traveling along a road relative to a positive/negative road gradient)
Further modified Guodong is silent on, however, in the same field of endeavor, Xingyu teaches: determining an acceleration per second based on the vehicle speed for the preset time when a road on which the electric vehicle is scheduled to travel is a complex road of an uphill road and a downhill road (see at least Xingyu, pg.18, par.14-25 which discloses the condition of a complex road consisting of transitioning between an uphill to a downhill gradient; pg.16-17 discloses the calculation steps of various components such as acceleration, force, and velocity in accordance to the condition (uphill or downhill) then considers the combination of those values relative to the complex condition)
determine a second gradient torque by multiplying the force on the downhill road by the tire radius (see at least Xingyu pg.16, par.6 which discloses a second gradient torque, the condition of uphill torque relative to the force and radius)
determining the wheel power by multiplying a result of adding the acceleration torque, the first gradient torque and the second gradient torque by a wheel angular velocity (see at least Xingyu, pg.16-17 which discloses the calculation of throttle adjustment (directly related to torque and power delivered to wheels of the vehicle) through consideration of the complex conditional factors of a first (uphill or downhill) paired with a second gradient calculation by wheel angular velocity)
It would have been obvious to a person of ordinary skill in the art to change further modified Guodong to include determining an acceleration per second based on the vehicle speed for the preset time when a road on which the electric vehicle is scheduled to travel is a complex road of an uphill road and a downhill road, determine a second gradient torque by multiplying the force on the downhill road by the tire radius, and determining the wheel power by multiplying a result of adding the acceleration torque, the first gradient torque and the second gradient torque by a wheel angular velocity as taught by Xingyu. The examiner would like to note that modified Kuan discloses processes of determining wheel power (and related factors) in light of uphill and downhill road gradients, however it does not explicitly disclose a condition where both are considered as a complex road, however if the same implementation of calculations by Xingyu) are applied, it would be reasonable to assume it was possible. The implementation of this teaching by Xingyu would allow for a further improvement of the base device discussed in modified Xingyu. As mentioned, conditions of uphill and downhill gradients are already taken into consideration, the modification would allow for the complex condition of an uphill and downhill roadway to be considered by the vehicle prediction model, expanding the range of wheel power.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIRSTEN JADE M SANTOS whose telephone number is (571)272-7442. The examiner can normally be reached Monday: 8:00 am - 4:00 pm, 6:00-8:00 pm (+ with flex).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rachid Bendidi can be reached at (571) 272-4896. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KIRSTEN JADE M SANTOS/Examiner, Art Unit 3664
/RACHID BENDIDI/Supervisory Patent Examiner, Art Unit 3664
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