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 .
Status of Claims
This action is in response to the claims filed 04/15/2026, wherein claims 1 and 20 have been amended, claims 2, 17, and 19 are cancelled. Claims 1, 3-16, 18, and 20 are rejected.
Response to Arguments
Applicant's amendments and arguments, see REMARKS filed 04/15/2026, with respect to the rejections of claims 1-20, under 35 USC §101, have been fully considered and are persuasive. Therefore, the previous rejections, under 35 USC §101, have been withdrawn.
Applicant's amendments and arguments, with respect to the interpretation of claim 20, under 35 USC §112f, have been fully considered and are persuasive. Therefore, the previous interpretations, under 35 USC §112f, have been withdrawn.
Applicant's arguments 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:
First, Engel fails to disclose defining a wheel traction force and/or a wheel braking force as decision variables, as now explicitly required by claim 1. Engel is unequivocally directed to motor-domain optimization, where the control variables are motor torque and/or motor speed. There is no disclosure in Engel of formulating the optimization problem using wheel traction or braking force as decision variables.
The longitudinal dynamic model of the motor vehicle (see ¶ [0040]) of Engel explicitly teaches that the wheel traction force and the wheel braking force is used to calculate wheel power using velocity-related state variables as required by claim 1. The term Ftrac in the equation found in the dynamic model is defined as “the tractive force that is exerted by the prime mover or the brakes upon the wheels of the motor vehicle” (¶[0042])
Therefore, the Examiner does not find the above argument persuasive.
Moreover, Engel fails to disclose calculating battery power with efficiency factors. The claim further requires calculating battery power by reflecting at least one efficiency factor, including motor efficiency and/or battery charging/discharging efficiency. Engel does not disclose such a calculation. While Engel may reference battery-related considerations, it does not disclose calculating battery power based on wheel power while incorporating efficiency factors as claimed. The Examiner's reliance on general battery-related terms in Engel does not satisfy this specific limitation.
As described in ¶ [0064], “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.” Ebat is the battery power and it is weighted by considering the terms of the longitudinal dynamic model as described in ¶ [0040] to accurately calculate the battery power. The longitudinal dynamic model includes the term Fd which is defined as “the drag force of the motor vehicle” (¶ [0045]), i.e., a motor efficiency. Thus, Engel discloses “calculating battery power by reflecting at least one efficiency factor including a motor efficiency in the calculated wheel power” as required by claim 1.
Therefore, the Examiner does not find this argument persuasive.
Furthermore, Engel does not disclose constructing an approximated battery power function. Claim 1 further recites obtaining an approximated battery power function based on the calculated battery power. Engel does not disclose calculating battery power in the claimed manner, or constructing an approximated function based on such calculated battery power. The Examiner appears to equate Engel's cost function with the claimed approximated battery power function. This mapping is improper. A cost function is not equivalent to a function that approximates battery power based on explicitly calculated values. Therefore, this limitation is also absent from Engel.
The cost function as a whole is not cited as teaching these limitations. Rather individual terms within the function teach the limitations. Therefore, this argument is not directed towards the claims as they are mapped and is moot.
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 claims 1 and 20, 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 traction force and/or a wheel braking force as 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 traction force and/or braking force; (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 by reflecting at least one efficiency factor including a motor efficiency and/or a battery charging/discharging efficiency in 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. Further, the longitudinal dynamic model includes the term Fd which is an efficiency factor including a motor efficiency – See at least ¶ [0045])
obtaining an approximated battery power function based on 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])
calculating a minimum solution (The cost function 15 to be minimized can be expressed mathematically as follows:
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) 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 to determine a wheel traction force and/or a wheel braking force; (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])
controlling a motor and/or a brake of the vehicle based on the determined wheel traction force and/or wheel braking force, (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 traction force and/or wheel braking force (
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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]; 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];
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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.L.C./Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
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