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 .
Response to Arguments
Applicant's arguments filed September 30, 2025 have been fully considered but they are not persuasive.
In response to Applicant's argument on page 10 pertaining to “Cho describes an integrated control system for a vehicle including a friction coefficient calculation unit that calculates friction coefficients of the left side and the right side of the road surface (Abstract). Cho thus describes an estimation of the friction coefficient of the sides of the road, and not the friction between a road surface and a tire of a steered wheel of a vehicle as claimed.”. The Examiner respectfully disagrees.
Cho discloses estimating a coefficient of friction between the wheels of a vehicle and road surface (¶ 45 The friction coefficient calculation unit 10 may be configured to finally calculate the friction coefficient). Cho further discloses, estimating the coefficient is calculated based on one wheel among the front wheels, front left (FL) and front right (FR), or one wheel among the rear wheels, rear left (RL) and rear right (RR) (¶ 45 based on a predetermined one wheel among the front side wheels (FL, FR) and the rear side wheels (RL, RR)). Therefore, in order to calculate the coefficient of friction of the left side wheels and the right side wheels, the coefficient of friction is independently estimated for each wheel, inherently including the steered wheels.
In response to Applicant's argument on page 10 pertaining to “Moreover, Cho does not describe an independent estimation of a friction coefficient for each steered wheel. Indeed, Cho describes the estimation of a left friction coefficient relative to the left wheels of the vehicle and of a right friction coefficient relative to the right wheels of the vehicle ([0032]). The estimation for the two right wheels, and respectively the two left wheels, is thus not independent.”. The Examiner respectfully disagrees.
As mentioned above, Cho discloses estimating the coefficient of friction from any one wheel (¶ 45 based on a predetermined one wheel among the front side wheels (FL, FR) and the rear side wheels (RL, RR)). Cho further discloses independently estimating the coefficient (¶ 46 the friction coefficient calculation unit 10 may be configured to independently calculate and estimate the friction coefficient).
In response to Applicant's argument on page 10 pertaining to “Even in the embodiment where the estimation relates to the front left and right wheels ([0045]), Cho fails to disclose an estimation for each steered wheel, as no estimation is performed for the rear wheels.”. The Examiner respectfully disagrees.
Cho discloses, estimating the coefficient of friction for the rear wheels (¶ 106 – 109 thus estimating the friction coefficient in various traveling situations independently of the left and right; Herein, the left side wheel and the right side wheel mean both the front side wheels and the rear side wheels).
In response to Applicant's argument on page 11 pertaining to “More particularly, the cited references focus on an overall estimation of the road friction, and not on an independent estimation for each steered wheel. This independent estimation for each steered wheel is thus not suggested by the cited documents.”. The Examiner respectfully disagrees.
Singh discloses estimating the coefficient of friction between the wheels of a vehicle and a road surface. Singh (vehicle 34; Col. 5. Ln. 6 coefficient of friction estimation system and methodology), Yamaguchi (¶ 18 estimating a road surface friction state of a road surface on which the vehicle is running), Ono (¶ 3 estimates a road surface friction state using self-aligning torque generated in a tire of a vehicle), and Watanabe (¶ 192 road surface friction coefficients μs corresponding to respective road surface states). Cho discloses, independent estimation of the coefficient of friction for each steered wheel (¶ 106 – 109 thus estimating the friction coefficient in various traveling situations independently of the left and right; Herein, the left side wheel and the right side wheel mean both the front side wheels and the rear side wheels). Therefore the prior art is in the analogous field of independent estimation for each steered wheel.
In response to Applicant's argument on page 11 pertaining to “Turning to new claim 15, as acknowledged by the Office Action, Singh does not disclose a machine learning friction estimation model taking into account a wheel end parameter, an offroad mode and a wiper status. While Yamaguchi discloses the consideration of such parameters, it does so only in the context of vehicle running control ([0041 ], [0087]), not friction estimation. Therefore, the skilled person would not apply the teachings of Yamaguchi to the model of Singh. Moreover, this would require numerous structural modifications of the model of Singh, which are not obvious in themselves.”. The Examiner respectfully disagrees.
Singh discloses, a machine learning friction estimation model taking into account wheel parameters (Fig. 1. Table I Artificial neural network (ANN) based), (Fig. 1. Col. 8. Ln. 49 wheel speed), (Fig. 1. Col. 4. Ln. 5 – 6 tire pressure), (Fig. 1. Col. 4. Ln. 9 tire load), (Fig. 1. Col. 6. Ln. 2 wheel force), (Fig. 1. Col. 2. Ln. 4 Aspect ratio). In analogous art, Yamaguchi discloses, estimating the coefficient of friction between the wheels of a vehicle and a road surface (¶ 18 estimating a road surface friction state of a road surface on which the vehicle is running) taking into account an off-road mode (Fig. 1, ¶ 47 judges that the road is a rough road) and a wiper status (Fig. 1, ¶ 48 detecting whether or not the wiper is operating). It would have been obvious to one of ordinary skill in the art to modify Singh and Yamaguchi for the benefit of estimating tire-road friction in a manner that is economical and without spending costs on road facilities [Yamaguchi: ¶ 11].
Claim Rejections - 35 USC § 103
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) 1, 4, 5, 7 – 15 are rejected under 35 U.S.C. 103 as being unpatentable over Singh (US 8,983,749 B) (herein after Singh) in view of Yamaguchi et al (US 2002/0010537 A1) (herein after Yamaguchi), and further in view of Cho (US 2020/0130660 A1) (herein after Cho).
Regarding Claim 1, Singh teaches, method for estimating a friction between a road surface and a tire of a steered wheel of a vehicle (Fig. 2. vehicle 34; Col. 5. Ln. 6 coefficient of friction estimation system and methodology), — the vehicle comprising a steering wheel and a set of sensors comprising wheel end sensors and steering wheel sensors (Fig. 2. bending sensors 14, tire sidewall strain sensor signal(s)) configured to measure signals corresponding to a set of parameters (Fig. 1. Col. 3. Ln. 62 a tire load and a tire slip angle), said signals corresponding respectively to wheel end parameters (Fig. 2. Claim 12, Col. 12. Ln. 11 vehicle sensor-obtained vehicle parameters) of the steered wheel, and to steering wheel parameters, the steering wheel parameters comprising at least a steering wheel torque and a steering wheel angle (Fig. 1. Col. 8. Ln. 48 – 50 steering angle sensor, steering torque), the vehicle further comprising an electronic control unit (Fig. 1. signal processing unit) connected to a communication bus (Fig. 2. CAN Bus 36) configured to convey to the electronic control unit said signals corresponding to the set of parameters, the electronic control unit being configured to run a signal processing module (Fig. 1. signal processor; Examiner interpretation: Fig 2 is a block diagram of Fig 1; see Col 3, Ln 25, 26), and a wheel end friction estimation model (Fig. 2. Col. 5. Ln. 39 – 40 coefficient of friction estimation in an exemplary four-wheeled vehicle 34), the method comprising the following steps implemented by the electronic control unit: -collect the signals, corresponding to the set of parameters, measured by the sensors during a period of time (Fig. 2. Col. 3. Ln. 67 – Col 4. Ln. 2 measuring the load and slip angle of the tire), - process, by the signal processing module, the signals collected to provide processed signal data (Fig. 2. individual wheel force estimations) - provide the processed signal data as input to the wheel end friction estimation model (Fig. 2. friction estimation algorithm), the wheel end friction estimation model being configured to output a friction estimation (Fig. 2. road surface coefficient of friction 44) of the friction between the road surface and the tire of the wheel; —
Singh fails to teach, — the steered wheel being fit with dynamic steering, — wherein the method is performed independently for each steered wheel of the vehicle.
In analogous art, Yamaguchi teaches, — the steered wheel being fit with dynamic steering (Fig. 1, ¶ 84 executing steering to eliminate influences on the behavior of the vehicle by the control), —
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh by combining the vehicle taught by Singh with a vehicle comprising a steered wheel fit with dynamic steering; taught by Yamaguchi for the benefit of estimating tire-road friction in a manner that is economical and without spending costs on road facilities [Yamaguchi: ¶ 11].
Singh in view of Yamaguchi fail to teach, — wherein the method is performed independently for each steered wheel of the vehicle.
In analogous art, Cho teaches, — wherein the method is performed independently for each steered wheel of the vehicle (Fig. 5, ¶ 45 The friction coefficient calculation unit 10 may be configured to finally calculate the friction coefficient, based on a predetermined one wheel among the front side wheels (FL, FR) and the rear side wheels (RL, RR),).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi by combining the wheel taught by Singh in view of Yamaguchi with a wheel wherein, the method is performed independently for each steered wheel of the vehicle; taught by Cho for the benefit of performing a more stable braking operation even when the left side and right side wheel coefficients of friction are different [Cho: ¶ 10].
Regarding Claim 4, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 1, which this claim depends on.
Singh further teaches, method according to claim 1, wherein the wheel end friction estimation model is a physics-based friction estimation model (Fig. 1. Col. 5. Ln. 60 model based design) and wherein the wheel end parameters comprise a wheel end speed (Fig. 1. Col. 8. Ln. 49 wheel speed), a wheel end tire pressure (Fig. 1. Col. 4. Ln. 5 – 6 tire pressure), — a wheel end tire normal load (Fig. 1. Col. 4. Ln. 9 tire load), a wheel end torque (Fig. 1. Col. 6. Ln. 2 wheel force), a wheel end tire size (Fig. 1. Col. 2. Ln. 4 Aspect ratio), and wherein in the step of providing the processed signal data as input to the wheel end friction estimation model, the wheel end friction estimation model is configured to output a physics-based friction estimation of the friction (Fig. 2, estimation of road surface coefficient of friction 44) between the road surface and the tire of the wheel.
Yamaguchi further teaches, — a wheel end alignment parameter (Fig. 1, straight running state detecting circuit 13) —
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi in view of Cho by combining the vehicle taught by Singh in view of Yamaguchi in view of Cho with a vehicle comprising a wheel end alignment parameter; taught by Yamaguchi for the benefit of estimating tire-road friction in a manner that is economical and without spending costs on road facilities [Yamaguchi: ¶ 11].
Regarding Claim 5, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 1, which this claim depends on.
Singh further teaches, method according to claim 1, wherein the wheel end friction estimation model is a machine learning friction estimation model (Fig. 1. Table I Artificial neural network (ANN) based), and wherein the set of sensors comprises other vehicle sensors configured to measure signals corresponding to other vehicle parameters, the other vehicle parameters comprising a vehicle speed (Fig. 1, Col. 8. Ln. 61 vehicle speed), and wherein the wheel end parameters comprise a wheel end speed (Fig. 1. Col. 8. Ln. 49 wheel speed), a wheel end tire pressure (Fig. 1. Col. 4. Ln. 5 – 6 tire pressure), —, a wheel end tire normal load (Fig. 1. Col. 4. Ln. 9 tire load), a wheel end torque (Fig. 1. Col. 6. Ln. 2 wheel force), a wheel end tire size (Fig. 1. Col. 2. Ln. 4 Aspect ratio), and wherein in the step of providing the processed signal data as input to the wheel end friction estimation model, the wheel end friction estimation model is configured to output a machine learning friction estimation of the friction (Fig. 2, estimation of road surface coefficient of friction 44) between the road surface and the tire of the wheel.
Yamaguchi further teaches, — a wheel end alignment parameter (Fig. 1, straight running state detecting circuit 13), —
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi in view of Cho by combining the vehicle taught by Singh in view of Yamaguchi in view of Cho with a vehicle comprising a wheel end alignment parameter; taught by Yamaguchi for the benefit of estimating tire-road friction in a manner that is economical and without spending costs on road facilities [Yamaguchi: ¶ 11]
Regarding Claim 7, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 5, which this claim depends on.
Yamaguchi further teaches, method according to claim 5, wherein the other vehicle parameters further comprise at least one of an off-road mode (Fig. 1, ¶ 47 judges that the road is a rough road) and a wiper status (Fig. 1, ¶ 48 detecting whether or not the wiper is operating).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi in view of Cho by combining the vehicle taught by Singh in view of Yamaguchi in view of Cho with a vehicle comprising other vehicle parameters, wherein the other vehicle parameters further comprise at least one of an off-road mode and a wiper status; taught by Yamaguchi for the benefit of estimating tire-road friction in a manner that is economical and without spending costs on road facilities [Yamaguchi: ¶ 11].
Regarding Claim 8, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 4, which this claim depends on.
Singh further teaches, method according to claim 4, wherein the wheel end friction estimation model comprises the physics-based friction estimation model and the machine learning friction estimation model (Fig. 1. Col. 5. Ln. 66 – 67 incorporate MATLAB algorithms into models and export simulation results to MATLAB for further analysis), and wherein the step of providing the processed signal data as input to the wheel end friction estimation model is performed with the physics-based friction estimation model (Fig. 1. Col. 5. Ln. 60 model based design) to provide the physics-based friction estimation and the step of providing the processed signal data as input to the wheel end friction estimation model is further performed with the machine learning friction estimation model (Fig. 1. Table I Artificial neural network (ANN) based) to provide the machine learning friction estimation, and wherein the friction estimation is a combination of the physics-based friction estimation and of the machine learning friction estimation (Fig. 1. Col. 5. Ln. 66 – 67 incorporate MATLAB algorithms into models and export simulation results to MATLAB for further analysis).
Regarding Claim 9, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 1, which this claim depends on.
Singh further teaches, method according to claim 1, further comprising a step of communication of the friction estimation to other electronic control units of the vehicle through the communication bus (Fig. 1. Col. 9. Ln. 7 – 9 friction coefficient estimation approach which makes use of sensor information from an intelligent tire and vehicle CAN bus information).
Regarding Claim 10, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 1, which this claim depends on.
Yamaguchi further teaches, method according to claim 1, further comprising a step of verification (Fig. 1, ¶ 76 changes the tire parameters) implemented before the step of communication of the friction estimation to other electronic control units (Fig. 1, ¶ 76 tire parameters, are used for controlling the running of the vehicle, which includes, ABS (Automatic Braking System)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi in view of Cho by combining the vehicle taught by Singh in view of Yamaguchi in view of Cho with a vehicle further comprising, a step of verification implemented before the step of communication of the friction estimation to other electronic control units; taught by Yamaguchi for the benefit of estimating tire-road friction in a manner that is economical and without spending costs on road facilities [Yamaguchi: ¶ 11].
Regarding Claim 11, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 1, which this claim depends on.
Singh further teaches, — the step of verification resulting in a consolidated friction estimation (Fig. 1. Col. 9. Ln. 18 – 21 Thereafter, a sensor fusion approach is used to combine the intelligent tire and vehicle information and make an estimate of the tire-road friction coefficient) which is further communicated to other electronic control units of the vehicle through the communication bus.
Yamaguchi further teaches, method according to claim 1, wherein the step of verification is based on another friction estimation obtained from an Electronic Braking System Control Unit of the vehicle, and/or from an Engine Electronic control Unit of the vehicle (Fig. 1, ¶ 76 tire parameters, are used for controlling the running of the vehicle, which includes, ABS (Automatic Braking System)), —.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi in view of Cho by combining the vehicle taught by Singh in view of Yamaguchi in view of Cho with a vehicle comprising a method wherein the step of verification is based on another friction estimation obtained from an Electronic Braking System Control Unit of the vehicle, and/or from an Engine Electronic control Unit of the vehicle; taught by Yamaguchi for the benefit of estimating tire-road friction in a manner that is economical and without spending costs on road facilities [Yamaguchi: ¶ 11].
Regarding Claim 12, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 1, which this claim depends on.
Singh further teaches, computer program comprising a set of instructions executable on a computer or a processing unit (Fig. 1. Col. 5. Ln. 56 mathematical simulation tool), the set of instructions being configured to implement the method according to claim 1, when the instructions are executed by the computer or the processing unit.
Regarding Claim 13, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 12, which this claim depends on.
Singh further teaches, electronic control unit (Fig. 1. signal processing unit) installed on a vehicle, the electronic control unit being configured to communicate with a communication bus (Fig. 2. CAN Bus 36) communicate with a communication bus (Fig. 2. CAN Bus 36) of the vehicle so as to collect during a period of time signals time series corresponding to a set of parameters (Fig. 1. Col. 3. Ln. 62 a tire load and a tire slip angle), said signals corresponding respectively at least to wheel end parameters (Fig. 2. Claim 12, Col. 12. Ln. 11 vehicle sensor-obtained vehicle parameters) relative to at least one wheel of the vehicle and to steering wheel parameters (Fig. 1. Col. 8. Ln. 48 – 50 steering angle sensor, steering torque) relative to a steering system of the vehicle, the electronic control unit further comprising a processing unit and a memory unit (Fig. 1. memory), the memory unit comprising the computer program according to claim 12 and the electronic control unit being further configured to run a signal processing module (Fig. 1. signal processor; Examiner interpretation: Fig 2 is a block diagram of Fig 1; see Col 3, Ln 25, 26), and a wheel end friction estimation model (Fig. 2. Col. 5. Ln. 39 – 40 coefficient of friction estimation in an exemplary four-wheeled vehicle 34) when the processing unit executes said computer program, the wheel end friction estimation model (Fig. 2. friction estimation algorithm) being configured to output a friction estimation (Fig. 2. road surface coefficient of friction 44), the friction estimation being at least one of a physics-based friction estimation (Fig. 1. Col. 5. Ln. 60 model based design), a machine learning friction estimation (Fig. 1. Table I Artificial neural network (ANN) based), and a combination of the physics based friction estimation and the machine learning friction estimation (Fig. 1. Col. 5. Ln. 66 – 67 incorporate MATLAB algorithms into models and export simulation results to MATLAB for further analysis), of the friction between the road surface and the tire of the wheel.
Singh fails to teach, — a steering system of the vehicle —
Yamaguchi further teaches, — a steering system of the vehicle (Fig. 1, ¶ 84 executing steering to eliminate influences on the behavior of the vehicle by the control) —
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi in view of Cho by combining the vehicle taught by Singh in view of Yamaguchi in view of Cho with a steering system of the vehicle, the electronic control unit being configured to communicate with a communication bus; taught by Yamaguchi for the benefit of estimating tire-road friction in a manner that is economical and without spending costs on road facilities [Yamaguchi: ¶ 11].
Regarding Claim 14, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 13, which this claim depends on.
Singh further teaches, vehicle comprising the electronic control unit according to claim 13. (Fig. 2. vehicle 34).
Regarding Claim 15, Singh teaches, method for estimating a friction between a road surface and a tire of a steered wheel of a vehicle (Fig. 2. vehicle 34; Col. 5. Ln. 6 coefficient of friction estimation system and methodology), — the vehicle comprising a steering wheel and a set of sensors comprising wheel end sensors and steering wheel sensors (Fig. 2. bending sensors 14, tire sidewall strain sensor signal(s)) configured to measure signals corresponding to a set of parameters (Fig. 1. Col. 3. Ln. 62 a tire load and a tire slip angle), said signals corresponding respectively to wheel end parameters (Fig. 2. Claim 12, Col. 12. Ln. 11 vehicle sensor-obtained vehicle parameters) of the steered wheel, and to steering wheel parameters, the steering wheel parameters comprising at least a steering wheel torque and a steering wheel angle (Fig. 1. Col. 8. Ln. 48 – 50 steering angle sensor, steering torque), the vehicle further comprising an electronic control unit (Fig. 1. signal processing unit) connected to a communication bus (Fig. 2. CAN Bus 36) configured to convey to the electronic control unit said signals corresponding to the set of parameters, the electronic control unit being configured to run a signal processing module (Fig. 1. signal processor; “Fig 2 is a block diagram of Fig 1; see Col 3, Ln 25, 26”), and a wheel end friction estimation model (Fig. 2. Col. 5. Ln. 39 – 40 coefficient of friction estimation in an exemplary four-wheeled vehicle 34), the method comprising the following steps implemented by the electronic control unit: - collect the signals, corresponding to the set of parameters, measured by the sensors during a period of time (Fig. 2. Col. 3. Ln. 67 – Col 4. Ln. 2 measuring the load and slip angle of the tire); - process, by the signal processing module, the signals collected to provide processed signal data (Fig. 2. individual wheel force estimations) - provide the processed signal data as input to the wheel end friction estimation model (Fig. 2. friction estimation algorithm), the wheel end friction estimation model being configured to output a friction estimation (Fig. 2. road surface coefficient of friction 44) of the friction between the road surface and the tire of the wheel; — wherein the wheel end friction estimation model is a machine learning friction estimation model (Fig. 1. Table I Artificial neural network (ANN) based), and wherein the set of sensors comprises other vehicle sensors configured to measure signals corresponding to other vehicle parameters, the other vehicle parameters comprising a vehicle speed (Fig. 1, Col. 8. Ln. 61 vehicle speed), and wherein the wheel end parameters comprise a wheel end speed (Fig. 1. Col. 8. Ln. 49 wheel speed), a wheel end tire pressure (Fig. 1. Col. 4. Ln. 5 – 6 tire pressure), — a wheel end tire normal load (Fig. 1. Col. 4. Ln. 9 tire load), a wheel end torque (Fig. 1. Col. 6. Ln. 2 wheel force), a wheel end tire size (Fig. 1. Col. 2. Ln. 4 Aspect ratio), and wherein in the step of providing the processed signal data as input to the wheel end friction estimation model, the wheel end friction estimation model is configured to output a machine learning friction estimation of the friction (Fig. 2, estimation of road surface coefficient of friction 44) between the road surface and the tire of the wheel, —.
Singh fails to teach, — the steered wheel being fit with dynamic steering, — wherein the method is performed independently for each steered wheel of the vehicle, — a wheel end alignment parameter, — wherein the other vehicle parameters further comprise at least one of an off-road mode and a wiper status.
In analogous art, Yamaguchi teaches, — the steered wheel being fit with dynamic steering (Fig. 1, ¶ 84 executing steering to eliminate influences on the behavior of the vehicle by the control), — a wheel end alignment parameter (Fig. 1, straight running state detecting circuit 13), — wherein the other vehicle parameters further comprise at least one of an off-road mode (Fig. 1, ¶ 47 judges that the road is a rough road) and a wiper status (Fig. 1, ¶ 48 detecting whether or not the wiper is operating).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh by combining the vehicle taught by Singh with a vehicle comprising, — the steered wheel being fit with dynamic steering, a wheel end alignment parameter, wherein the other vehicle parameters further comprise at least one of an off-road mode and a wiper status; taught by Yamaguchi for the benefit of estimating tire-road friction in a manner that is economical and without spending costs on road facilities [Yamaguchi: ¶ 11].
Singh in view of Yamaguchi fail to teach, — wherein the method is performed independently for each steered wheel of the vehicle, —
In analogous art, Cho teaches, — wherein the method is performed independently for each steered wheel of the vehicle (Fig. 5, ¶ 45 The friction coefficient calculation unit 10 may be configured to finally calculate the friction coefficient, based on a predetermined one wheel among the front side wheels (FL, FR) and the rear side wheels (RL, RR)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi by combining the wheel taught by Singh in view of Yamaguchi with a wheel wherein, the method is performed independently for each steered wheel of the vehicle; taught by Cho for the benefit of performing a more stable braking operation even when the left side and right side wheel coefficients of friction are different [Cho: ¶ 10].
Claim(s) 2, 3 are rejected under 35 U.S.C. 103 as being unpatentable over Singh (US 8,983,749 B) (herein after Singh) in view of Yamaguchi et al (US 2002/0010537 A1) (herein after Yamaguchi) in view of Cho (US 2020/0130660 A1) (herein after Cho), and further in view of Ono et al (US 2005/0005691 A1) (herein after Ono).
Regarding Claim 2, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 1, which this claim depends on.
Singh in view of Yamaguchi in view of Cho fail to teach, method according to claim 1, wherein the steering wheel torque is measured by a dynamic steering motor.
In analogous art, Ono teaches, method according to claim 1, wherein the steering wheel torque is measured by a dynamic steering motor (Fig. 1, ¶ 47 electric motor which is used in the electric power steering device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi in view of Cho by combining the vehicle taught by Singh in view of Yamaguchi in view of Cho with a vehicle comprising a steering wheel, wherein the steering wheel torque is measured by a dynamic steering motor taught by Ono for the benefit of estimating tire-road friction accurately without being influenced by road surface disturbance [Ono: ¶ 10].
Regarding Claim 3, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 1, which this claim depends on.
Singh in view of Yamaguchi in view of Cho fails to teach, method according to claim 1 wherein the processing step comprises removing noise from the signals measured during the period of time and/or a transformation in a frequency domain of the signals measured during the period of time.
In analogous art, Ono teaches, wherein the processing step comprises removing noise from the signals measured during the period of time and/or a transformation in a frequency domain of the signals measured during the period of time (Fig. 2, ¶ 81 a variation component such as noise due to road surface disturbance, low pass filter 28 removes the variation component).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi in view of Cho by combining the vehicle taught by Singh in view of Yamaguchi in view of Cho with a vehicle with a processing step comprising removing noise from the signals measured during the period of time and/or a transformation in a frequency domain of the signals measured during the period of time; taught by Ono for the benefit of estimating tire-road friction accurately without being influenced by road surface disturbance [Ono: ¶ 10].
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Singh (US 8,983,749 B) (herein after Singh) in view of Yamaguchi et al (US 2002/0010537 A1) (herein after Yamaguchi) in view of Cho (US 2020/0130660 A1) (herein after Cho), and further in view of Watanabe et al (US 2004/0138831 A1) (herein after Watanabe).
Regarding Claim 6, Singh in view of Yamaguchi in view of Cho teaches the limitations of claim 5, which this claim depends on.
Singh further teaches, — and/or a wheel end temperature measured by a temperature sensor placed on the wheel (Fig. 1. Col. 4. Ln. 67 – Col 5, Ln 1 a temperature sensor that measures tire temperature).
Singh in view of Yamaguchi in view of Cho fail to teach, method according to claim 5, wherein the wheel end parameters further comprise a wheel end sound level measured by a microphone sensor placed on the wheel and/or a wheel end temperature measured by a temperature sensor placed on the wheel.
In analogous art, Watanabe teaches, wherein the wheel end parameters further comprise a wheel end sound level measured by a microphone sensor placed on the wheel (Fig. 1, ¶ 117 for detecting a sound that is generated by a tire) —.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Singh in view of Yamaguchi in view of Cho by combining the vehicle taught by Singh in view of Yamaguchi in view of Cho with a vehicle comprising wheel end parameters, wherein the wheel end parameters further comprise a wheel end sound level measured by a microphone sensor placed on the wheel and/or a wheel end temperature measured by a temperature sensor placed on the wheel; taught by Watanabe for the benefit of estimating tire-road friction more accurately by taking wheel rotation speed into account [Watanabe: ¶ 21].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Takenaka et al (US 2012/0029783 A1) teaches, method for estimating a friction between a road surface and a tire of a steered wheel of a vehicle (Fig. 3, ¶ 177 the controller 20 also has a function of sequentially estimating a friction coefficient or the like of the road surface).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH O. NYAMOGO whose telephone number is (469)295-9276. The examiner can normally be reached 9:00 A to 5:00 P CT.
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/JOSEPH O. NYAMOGO/
Examiner
Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 12/30/2025