DETIALED 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/17/2025 has been entered.
Status of Claims
Claims 1, 3-16, 18, and 20 of US Application No. 17/859,356, filed on 12/17/2025, are currently pending and have been examined. Claim 1 has been amended, claim 2 has been cancelled, and claims 1, 3-16, 18, and 20 are rejected.
Information Disclosure Statement
The information Disclosure Statement filed on 12/04/2025 has been considered. An initialed copy of form 1449 is enclosed herewith.
Response to Arguments
Applicant's arguments, see REMARKS filed 12/17/2025, with respect to the rejections of claims 1-3, 5 ,6 , 8, 9, 11-13, 15, 19, and 20, under 35 USC §102, have been fully considered but are not persuasive. Therefore, the previous rejections, under 35 USC §102, have been maintained.
With respect to amended claim 1, the Applicant argues:
In the rejection of claim 2 (subject matter of which has been incorporated into claim 1), the Examiner alleges that Engel teaches the wheel drive control target comprises a wheel traction force and/or wheel braking force. Applicant respectfully disagrees.
Paragraph 0038 of Engel discloses, "[t]he computer program product 11 includes an MPC algorithm 13. The MPC algorithm 13 includes a longitudinal dynamic model 14 of the drive train 7 of the motor vehicle 1 and a cost function 15 to be minimized. The processor unit 3 executes the MPC algorithm 13 and thereby predicts a behavior of the motor vehicle 1 based on the longitudinal dynamic model 14, wherein the cost function 15 is minimized. An optimal rotational speed and an optimal torque of the electric machine 8 for calculated points in the prediction horizon result as the output of the optimization by the MPC algorithm 13. For this purpose, the processor unit 3 can determine an input variable for the electric machine 8, enabling the optimal rotational speed and the optimal torque to set in." (emphasis added). These cited portions of Engel merely relate to control of the electric machine (i.e., motor) via an input variable. In other words, the above paragraph of Engel stays firmly at the motor/drivetrain control level, not, e.g., the wheel force output level. Accordingly, Engel does not disclose or fairly suggest that "the wheel drive control target comprises a wheel traction force and/or wheel braking force" (emphasis added), as recited in claim 1.
As anticipation under 35 U.S.C. § 102 requires that each and every element of the claim be disclosed, either expressly or inherently (noting that "inherency may not be established by probabilities or possibilities," Scaltech Inc. v. Retec Tetra, 178 F.3d 1378 (Fed. Cir. 1999)), in a single prior art reference, Akzo N.V v. U.S. Int'l Trade Commission, 808 F.2d 1471 (Fed. Cir. 1986), based on the foregoing, it is submitted that Engel does not anticipate claim 1, nor any claim dependent thereon.
The longitudinal model used in determining the control of the electric motor includes the tractive force that is exerted by the prime mover or the brakes upon the wheels of the motor vehicle. (See at least ¶ [0040]-[0042]) This means the algorithm that determines the control of the motor vehicle, e.g. its output, comprises at least a wheel braking force.
As described in the instant specification “The wheel drive control target may be a control target related to wheel traction force or wheel braking force.” (¶ [0017]) With this definition the output determined from the algorithm to control the motor is a “wheel drive control target” because the control of the motor is related to the wheel traction force or wheel braking force via providing movement to the wheel and regenerative braking.
Thus, the algorithm which comprises a dynamic model, i.e., a wheel braking force, to determine the motor output, i.e., wheel drive control target, teaches “wherein the wheel drive control target comprises a wheel traction force and/or wheel braking force.”
Therefore, the Examiner does not find the above argument persuasive and the rejections under 35 USC §102 are maintained.
The remaining arguments are directed towards the secondary art not remedying the deficiencies of the primary art. However, as provided above, there are no deficiencies to remedy. Therefore, the Examiner finds these arguments unpersuasive.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are:
“…a travel strategy control unit configured to obtain…” in claim 20;
“…a travel assistant unit configured to output a control signal…” in claim 20
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The corresponding structure for the travel strategy control unit is found in ¶ [0198]: “The travel strategy control unit 11 may include a data input and output device, at least one processor configured to perform judgment, calculation, determination, and the like, and a non-transitory memory configured to store an operating system or logic commands, input and output information, etc.”
The corresponding structure for the travel assistant unit is found in ¶ [0199]: “the travel assistant unit 12 may include a data input and output device, at least one processor configured to perform judgment, calculation, determination, and the like, and a non-transitory memory configured to store an operating system or logic commands, input and output information, etc.”
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 5, 6, 8, 9, 11, 12, 13, 15, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Engel et al. (US 2022/0371450 A1, “Engel”).
Regarding claim 1, Engel discloses model-based predictive regulation of an electric machine in a drivetrain of a motor vehicle and teaches:
A vehicle driving control method with optimal efficiency, the method comprising: (The method of model predictive control (MPC) was selected in order to find, in any situation under established marginal conditions and constraints, an optimal solution for a "driving efficiency" driving function, which is to provide an efficient driving style. The MPC method is based on a system model that describes the behavior of the system. In addition, the MPC method is based on an objective function or on a cost function that describes an optimization problem and determines which state variables are to be minimized. The state variables for the "driving efficiency" driving function can therefore be, in particular, the vehicle speed or the kinetic energy, the energy remaining in the battery, and the driving time. Energy consumption and driving time are optimized, in particular, on the basis of the uphill grade of the upcoming route and constraints for speed and drive force, and on the basis of the current system state. – See at least ¶ [0008])
obtaining a state variable model of a longitudinal dynamics equation of a vehicle (The longitudinal dynamics model 14 of the motor vehicle 1 can be mathematically expressed as follows:
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- See at least ¶ [0040]) based on a velocity-related state variable (Here, V is the speed of the motor vehicle – See at least ¶ [0041]) and a wheel drive input variable; (Ftrac is the tractive force that is exerted by the prime mover or the brakes upon the wheels of the motor vehicle;– See at least ¶ [0042])
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calculating wheel power using the velocity-related state variable and the wheel drive input variable; (In order to be able to utilize this characteristic map for the optimization, the energy consumption per meter is linearly approximated:
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for all i. – See at least ¶ [0049]; Here, the energy per meter is equivalent to the wheel power, the acceleration is the velocity-related state variable, and Ftrac would be the wheel drive input variable. Examiner further notes that while the velocity-state variable in the previous limitation is velocity and the velocity-state variable in this acceleration is acceleration, their relationship to one another makes them interchangeable.)
calculating battery power using the calculated wheel power; (The cost function 15 includes, as a first term, an electrical energy EBat, i.e., batter power, weighted with a first weighting factor WBat and predicted according to the longitudinal dynamic model, which is provided within a prediction horizon by the battery 9 of the drive train 7 for driving the electric machine 8. – See at least ¶ [0064]; Here, Ebat is the battery power and it is determined according to the longitudinal dynamic model, i.e., using the calculated wheel power.)
obtaining an approximated battery power function using the calculated battery power; and (The term function (-Wbat*Ebat(SE)) in the cost function 15 equation is an approximated batter power function, i.e., a weighted battery variable * the energy consumption of the battery – See at least ¶ [0050]-[0064])
outputting a wheel drive control target (Example aspects of the present invention makes it possible that the driver influence is no longer relevant for the energy consumption and the driving time of the motor vehicle, because the electric machine can be controlled by the processor unit based on the input variable, which is determined by executing the MPC algorithm. By the input variable, in particular, an optimal prime mover operating point of the electric machine can be set. As a result, a direct regulation of the optimal speed of the motor vehicle can be carried out – See at least ¶ [0011]) by calculating a minimum solution (The cost function 15 to be minimized can be expressed mathematically as follows:
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) by using the approximated battery power function as an objective function (The cost function 15 includes, as a first term, an electrical energy EBat weighted with a first weighting factor WBat and predicted according to the longitudinal dynamic model, which is provided within a prediction horizon by the battery 9 of the drive train 7 for driving the electric machine 8 – See at least ¶ [0064]) and applying at least one constraint to the objective function. (The cost function 15 has exclusively linear and quadratic terms. As a result, the overall problem has the form of a quadratic optimization with linear constraints and a convex problem results, which can be solved well and quickly, e.g., speed limits torque surges, etc. – See at least ¶ [0063]-[0069])
and performing a brake or motor control of the vehicle based on the outputted wheel drive control target, (Example aspects of the present invention makes it possible that the driver influence is no longer relevant for the energy consumption and the driving time of the motor vehicle, because the electric machine can be controlled by the processor unit based on the input variable, which is determined by executing the MPC algorithm. By the input variable, in particular, an optimal prime mover operating point of the electric machine can be set. As a result, a direct regulation of the optimal speed of the motor vehicle can be carried out – See at least ¶ [0011])
wherein the approximated battery power function comprises a quadratic function of the velocity-related state variable and the wheel drive input variable and (
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the highlighted portion is a quadratic function of the velocity-related state variable and (FA(s1)-FA(s0))^2 is the wheel drive input variable – See at least ¶ [0050])
wherein the wheel drive control target comprises a wheel traction force and/or wheel braking force. (The computer program product 11 includes an MPC algorithm 13. The MPC algorithm 13 includes a longitudinal dynamic model 14 of the drive train 7 of the motor vehicle 1 and a cost function 15 to be minimized. The processor unit 3 executes the MPC algorithm 13 and thereby predicts a behavior of the motor vehicle 1 based on the longitudinal dynamic model 14, wherein the cost function 15 is minimized. An optimal rotational speed and an optimal torque of the electric machine 8 for calculated points in the prediction horizon result as the output of the optimization by the MPC algorithm 13. For this purpose, the processor unit 3 can determine an input variable for the electric machine 8, enabling the optimal rotational speed and the optimal torque to set in – See at least ¶ [0037]; The longitudinal dynamic model 14 of the motor vehicle 1 can be expressed mathematically as follows:
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…F is the tractive force that is exerted by the prime mover or the brakes upon the wheels of the motor vehicle - See at least ¶ [0040] and [0042])
Regarding claim 3, Engel further teaches:
wherein the velocity-related state variable comprises The longitudinal dynamic model 14 of the motor vehicle 1 can be expressed mathematically as follows:
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- See at least ¶ [0040]; the model is further converted from time dependent into distance dependent, which results in the following equation:
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- See at least ¶ [0047]) a square of velocity, (the function Fd(ekin(s)) contains a square of velocity, i.e.,
e
k
i
n
=
1
2
*
m
*
v
2
– See at least ¶ [0047]) and the wheel drive input variable comprises traction force and braking force for a wheel. (Ftrac is the tractive force that is exerted by the prime mover or the brakes upon the wheels of the motor vehicle – See at least ¶ [0042])
Regarding claim 5, Engel further teaches:
wherein the state variable model is defined (The longitudinal dynamic model 14 of the motor vehicle 1 can be expressed mathematically as follows:
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- See at least ¶ [0040]; the model is further converted from time dependent into distance dependent, which results in the following equation:
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) by a relationship between a work done by a wheel (Ftrac is the tractive force that is exerted by the prime mover or the brakes upon the wheels of the motor vehicle – See at least ¶ [0042]) and a change in vehicle kinetic energy under air resistance, (Fd(ekin(s)) is the change in kinetic energy under air resistance – See at least ¶ [0045] and ¶ [0047]) rolling resistance, (F, is the rolling resistance, which is an effect of the deformation of the tires during rolling and depends on the load of the wheels (on the normal force between the wheel and the road) and, thus, on the inclination angle of the road – See at least ¶ [0043]) and gravity resistance for a predetermined distance movement. (F, is the gradient resistance, which describes the longitudinal component of gravity, which acts upon the motor vehicle during operation uphill or downhill, depending on the gradient of the roadway – See at least ¶ [0044])
Regarding claim 6, Engel further teaches:
wherein the objective function further comprises a target-velocity-following function. (Speed limits are hard limits for the optimization that are not permitted to be exceeded. A slight exceedance of the speed limits is always permissible in reality and tends to be the normal case primarily during transitions from one speed zone into a second zone. In dynamic surroundings, where speed limits shift from one computing cycle to the next computing cycle, it can happen, in the case of very hard limits, that a valid solution for a speed profile can no longer be found. In order to increase the stability of the computational algorithm, a soft constraint is introduced into the cost function 15. A slack variable Varslack, i.e., a target-velocity-following function, weighted with a weighting factor Wslack becomes active in a predefined narrow range before the hard speed limit is reached – See at least ¶ [0069])
Regarding claim 8, Engel further teaches:
wherein the at least one constraint comprises a traveling velocity band constraint. (A slack variable Varslack weighted with a weighting factor Wslack becomes active in a predefined narrow range, i.e., a traveling velocity band, before the hard speed limit is reached. Solutions that are situated very close to this speed limit are evaluated as poorer, i.e., solutions, the speed trajectory of which maintains a certain distance to the hard limit – See at least ¶ [0069])
Regarding claim 9, Engel further teaches:
wherein the at least one constraint comprises a motor constraint according to a vehicle velocity. (In order to ensure comfortable driving, one further term is introduced in the cost function 15 for penalizing torque surges, namely Wtemstart (FA(s₁)-FA(S))2 – See at least ¶ [0067]-[0068] FA is the drive force that is provided by the electric machine, transmitted by a transmission at a constant ratio, and applied at a wheel of the motor vehicle – See at least ¶ [0055] Here, Wtemstart is being minimized, i.e., is a constraint, and it consists of Fa which is a drive force provided by the electric motors. Therefore, it is a constraint that comprises a motor constraint. In the exemplary embodiment shown, the motor vehicle 1 has a fixed ratio between the electric machine 8 and the road on which the motor vehicle 1 moves. As a result, the rotational speed of the electric machine 8 can be directly converted into a speed of the motor vehicle 1 or even into a kinetic energy of the motor vehicle 1 – See at least ¶ [0049]; Because the motor speed can be directly converted into the vehicle speed, then the motor constraint is according the vehicle velocity.)
Regarding claim 11, Engel further teaches:
wherein the at least one constraint comprises a safe velocity constraint according to a road curvature. (The route data can include, for example, uphill grade information, curve information, and information about speed limits. Moreover, a curve curvature can be converted, via a maximum permissible lateral acceleration, into a speed limit for the motor vehicle 1 – See at least ¶ [0039])
Regarding claim 12, Engel further teaches:
wherein the obtaining of a state variable model, the calculating of wheel power, the calculating of battery power, the obtaining of an approximated battery power function, and the outputting of a wheel drive control target are performed for a set forward prediction horizon. (The longitudinal model, i.e., obtain of a state variable model, is performed over a prediction horizon – See at least ¶ [0039]; the minimization of the cost function, i.e., calculating wheel power, battery power, obtaining approximated battery power function, and outputting of a wheel control command, also occur over a prediction horizon – See at least ¶ [0038] and [0050]-[0066] )
Regarding claim 13, Engel further teaches:
wherein the set forward prediction horizon is divided into a plurality of distance-based or time-based steps, the minimum solution is calculated for all of the plurality of distance-based or time-based steps, and the wheel drive control target is obtained from a minimum solution corresponding to a first step among the plurality of distance-based or time-based steps. (The detection unit 6 can measure current state variables of the motor vehicle 1, record appropriate data, and supply the current state variables and data to the MPC algorithm 13. In this way, route data from an electronic map can be updated, in particular cyclically, for a prediction horizon (for example, 400 m) ahead of the motor vehicle 1 – See at least ¶ [0039]; Examiner notes that the “s” variable in the cost function is the distance, i.e., the function is a distance-based function and the target is obtained for distance intervals – See at least ¶ [0050]-[0064])
Regarding claim 15, Engel further teaches:
wherein the set forward prediction horizon is constant. The detection unit 6 can measure current state variables of the motor vehicle 1, record appropriate data, and supply the current state variables and data to the MPC algorithm 13. In this way, route data from an electronic map can be updated, in particular cyclically, for a prediction horizon (for example, 400 m) ahead of the motor vehicle 1 – See at least ¶ [0039];
Regarding claim 20, Engel further teaches:
A vehicle driving control device comprising: (IG. 1 shows a motor vehicle 1, for example, a passenger car. The motor vehicle 1 includes a system 2 for the model predictive control of an electric machine of a drive train of the motor vehicle 1. The system 2 in the exemplary embodiment shown includes a processor unit 3, a memory unit 4, a communication interface 5, and a detection unit 6 for detecting state data related to the motor vehicle 1. The motor vehicle 1 also includes a drive train 7, which can include, for example, an electric machine 8, which can be operated as a motor and as a generator, a battery 9, and a transmission 10 – See at least ¶ [0036])
a travel strategy control unit configured to obtain a state variable model of a longitudinal dynamics equation of a vehicle (The longitudinal dynamics model 14 of the motor vehicle 1 can be mathematically expressed as follows:
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- See at least ¶ [0040]) based on a velocity-related state variable (Here, V is the speed of the motor vehicle – See at least ¶ [0041]) and a wheel drive input variable, (Ftrac is the tractive force that is exerted by the prime mover or the brakes upon the wheels of the motor vehicle;– See at least ¶ [0042]) to calculate wheel power using the velocity-related state variable and the wheel drive input variable, (In order to be able to utilize this characteristic map for the optimization, the energy consumption per meter is linearly approximated:
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for all i. – See at least ¶ [0049]; Here, the energy per meter is equivalent to the wheel power, the acceleration is the velocity-related state variable, and Ftrac would be the wheel drive input variable. Examiner further notes that while the velocity-state variable in the previous limitation is velocity and the velocity-state variable in this acceleration is acceleration, their relationship to one another makes them interchangeable.) to calculate battery power using the calculated wheel power, (The cost function 15 includes, as a first term, an electrical energy EBat, i.e., batter power, weighted with a first weighting factor WBat and predicted according to the longitudinal dynamic model, which is provided within a prediction horizon by the battery 9 of the drive train 7 for driving the electric machine 8. – See at least ¶ [0064]; Here, Ebat is the battery power and it is determined according to the longitudinal dynamic model, i.e., using the calculated wheel power.) to obtain an approximated battery power function using the calculated battery power, (The term function (-Wbat*Ebat(SE)) in the cost function 15 equation is an approximated batter power function, i.e., a weighted battery variable * the energy consumption of the battery – See at least ¶ [0050]-[0064]) and to output a wheel drive control target, (Example aspects of the present invention makes it possible that the driver influence is no longer relevant for the energy consumption and the driving time of the motor vehicle, because the electric machine can be controlled by the processor unit based on the input variable, which is determined by executing the MPC algorithm. By the input variable, in particular, an optimal prime mover operating point of the electric machine can be set. As a result, a direct regulation of the optimal speed of the motor vehicle can be carried out – See at least ¶ [0011]; Examiner further notes that the vehicle may be a fully autonomous vehicle and therefore has a wheel drive control target – See at least ¶ [0014]) which comprises a wheel traction force and/or wheel braking force, by calculating a minimum solution (The cost function 15 to be minimized can be expressed mathematically as follows:
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) by using the approximated battery power function as an objective function (The cost function 15 includes, as a first term, an electrical energy EBat weighted with a first weighting factor WBat and predicted according to the longitudinal dynamic model, which is provided within a prediction horizon by the battery 9 of the drive train 7 for driving the electric machine 8 – See at least ¶ [0064]) and applying at least one constraint to the objective function and (The cost function 15 has exclusively linear and quadratic terms. As a result, the overall problem has the form of a quadratic optimization with linear constraints and a convex problem results, which can be solved well and quickly, e.g., speed limits torque surges, etc. – See at least ¶ [0063]-[0069])
a travel assistant unit configured to output a control signal so as to control a motor and a brake with the wheel traction force and and/or wheel braking force derived from the minimum solution, (Example aspects of the present invention makes it possible that the driver influence is no longer relevant for the energy consumption and the driving time of the motor vehicle, because the electric machine can be controlled by the processor unit based on the input variable, which is determined by executing the MPC algorithm. By the input variable, in particular, an optimal prime mover operating point of the electric machine can be set. As a result, a direct regulation of the optimal speed of the motor vehicle can be carried out – See at least ¶ [0011])
wherein the approximated battery power function comprises a quadratic function of the velocity-related state variable and the wheel drive input variable. (
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the highlighted portion is a quadratic function of the velocity-related state variable and (FA(s1)-FA(s0))^2 is the wheel drive input variable – See at least ¶ [0050])
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.
Claim(s) 4 is rejected under 35 U.S.C. 103 as being unpatentable over Engel, as applied to claim 1, and in further view of Yu et al. (Electrical Motor Products, “Yu”)
Regarding claim 4, Engel does not explicitly teach wherein the calculating of battery power is performed by multiplying the calculated wheel power by at least one of motor efficiency, battery charging and discharging efficiency, and reducer efficiency. However, Yu discloses testing methods for electrical motors and teaches:
wherein the calculating of battery power is performed by multiplying the calculated wheel power by at least one of motor efficiency, battery charging and discharging efficiency, and reducer efficiency. (Motor efficiency is a measure of the effectiveness with which electrical energy is converted to mechanical energy. Motor efficiency can be directly expressed as the ratio of power output to power input (see equation 6.1). The efficiency computed from this ratio is known as the direct efficiency. Alternatively, motor efficiency is indirectly determined by using the losses and the input power (see equation 6.2):
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where Pin and Pout are the input and output powers of the motor, respectively, and Ploss is the total losses in the motor. – See at least §6.1. Examiner notes that the power in comes from a battery in the case of an electric motor and therefore the battery power may be calculated by multiplying the output power (wheel power) by the motor efficiency.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the model-based predictive regulation of an electric machine in a drivetrain of a motor vehicle of Engel to provide for the motor efficiency expression, as taught in Yu, because the ratio of power has a known relationship of
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.
Claim(s) 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Engel, as applied to claim 1, and in further view of Mohan et al. (US 2020/0164745 A1, “Mohan”).
Regarding claim 7, Engel discloses a velocity constraint, but does not explicitly disclose wherein the at least one constraint comprises an average velocity constraint. However, Mohan discloses methods and systems for cruise control velocity tracking and teaches:
wherein the at least one constraint comprises an average velocity constraint. (In some examples, the leader function may enforce bounds, i.e., constraints, to ensure that an average vehicle velocity after applying the MPCC - based control is within the predetermined interval about the set-point velocity. That is, the leader function may enforce bounds to ensure that the potential torque commands of the outcomes will all result in a vehicle velocity or an average vehicle velocity that is within the predetermined interval about the set-point velocity. In cases where the bounds ensure that the average vehicle velocity is within the predetermined interval about the set-point velocity, the average velocity may be based on a velocity filter , such as velocity filter 326 – See at least ¶ [0097])
In summary, Engel discloses controlling a vehicle using model predictive control that includes a velocity constraint. Engel does not explicitly disclose that the velocity constraint is an average velocity. However, Mohan discloses model predictive cruise control and teaches providing constraints based on the average velocity.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the model-based predictive regulation of an electric machine in a drivetrain of a motor vehicle of Engel to provide for the methods and systems for cruise control tracking, as taught in Mohan, to solve for the torque command in a strategic manner which results in improved efficiency. (At Mohan ¶ (0007))
Regarding claim 18, Engel does not explicitly disclose, but Mohan further teaches:
further comprising receiving a selection of a cruise control travel mode by a user. (As one example, the vehicle may enter the cruise control mode upon receiving a cruise control request from a vehicle operator – See at least ¶ [0089])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the model-based predictive regulation of an electric machine in a drivetrain of a motor vehicle of Engel to provide for the methods and systems for cruise control tracking, as taught in Mohan, to solve for the torque command in a strategic manner which results in improved efficiency. (At Mohan ¶ (0007))
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Engel, as applied to claim 1, and in further view of Jia. (US 2020/0031349 A1, “Jia”).
Regarding claim 10, Engel does not explicitly teach wherein the at least one constraint comprises a safe distance constraint from a preceding vehicle. However, Jia discloses adaptive cruise control and teaches:
wherein the at least one constraint comprises a safe distance constraint from a preceding vehicle. (In addition, the driving cost optimization means may be configured to use a maximum host vehicle speed, a maximum host vehicle traction force and the minimum safe inter-vehicle distance defined by the safe distance defining means as hard constraints in cost optimization – See at least ¶ [0076])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the model-based predictive regulation of an electric machine in a drivetrain of a motor vehicle of Engel to provide for the adaptive cruise control, as taught in Jia, so an improved vehicle behavior of the host vehicle is obtained while avoiding unexpected behavior of the host vehicle in real traffic situation for surrounding vehicles. (At Jia ¶ (0076))
Claim(s) 14 is rejected under 35 U.S.C. 103 as being unpatentable over Engel, as applied to claim 12, and in further view of Havens et al. (US 2020/0142405 A1, “Havens”).
Regarding claim 14, Engel does not explicitly teach wherein the set forward prediction horizon is defined as a range of time distance or traveling distance. However, Havens discloses systems and methods for dynamic predictive control of autonomous vehicles and teaches:
wherein the set forward prediction horizon is defined as a range of time distance or traveling distance. (The MPC 330 may model the movement of the semi-truck 200 over a prediction horizon using the dynamic linear time-invariant model 331. In one embodiment, the prediction horizon may be about 3-4 seconds, however, the prediction horizon may be a shorter or longer length of time in other embodiments – See at least ¶ [0058])
In summary, Engel discloses a prediction horizon for determining the trajectory of a vehicle using MPC. Engel does not explicitly teach that the horizon is defined as a range of time or traveling distance. However, Havens discloses systems and methods for dynamic predictive control of autonomous vehicles and teaches that the prediction horizon is dynamic and can consist of a range of time, e.g., 3-4 seconds.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the model-based predictive regulation of an electric machine in a drivetrain of a motor vehicle of Engel to provide for the systems and methods for dynamic predictive control of autonomous vehicles, as taught in Havens, to improve predictive control for autonomous driving (At Havens ¶ (0001))
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Engel and, as applied to claim 12, and in further view of Yoshizawa et al. (US 2019/0092187 A1, “Yoshizawa”).
Regarding claim 16, Engel does not explicitly teach wherein, when the output wheel braking force is less than or equal to a predetermined value, all of the output wheel braking force is distributed to regenerative braking force, and when the output wheel braking force exceeds the predetermined value, the output wheel braking force is distributed to regenerative braking force and mechanical braking force. However, Yoshizawa discloses vehicle control system and vehicle control method and teaches:
wherein, when the output wheel braking force is less than or equal to a predetermined value, all of the output wheel braking force is distributed to regenerative braking force, and when the output wheel braking force exceeds the predetermined value, the output wheel braking force is distributed to regenerative braking force and mechanical braking force. (Fig. 2 provides a mapping of regenerative braking and friction braking based on brake stroke amount. The map provides a relationship that for specific stroke length(s), i.e., a threshold value, the regenerative braking will be used and for values above that threshold regenerative and friction braking will be used.)
In summary, Engel discloses regenerative braking including low speeds. Engel does not explicitly teach wherein, when the output wheel braking force is less than or equal to a predetermined value, all of the output wheel braking force is distributed to regenerative braking force, and when the output wheel braking force exceeds the predetermined value, the output wheel braking force is distributed to regenerative braking force and mechanical braking force. However, Yoshizawa discloses vehicle control system and vehicle control method and teaches providing regenerative braking up to a specific threshold and then providing friction braking with regenerative braking.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the model-based predictive regulation of an electric machine in a drivetrain of a motor vehicle of Engel and to provide for the systems and methods for the vehicle control system and vehicle control method, as taught in Yoshizawa, to provide a novel and improved vehicle control system and vehicle control method that are capable of controlling regenerative braking force reliably even in the case where a control parameter of a motor generator cannot be acquired. (At Yoshizawa ¶ (0012))
Conclusion
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/C.L.C./Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662