DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 3-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 7 recite, in the preamble, “a method of estimating longitudinal stiffness.” However, none of the recited method steps correspond to this activity. This leaves it unclear as to what, if anything, is required to read on this limitation. Claim 12 does not have this issue.
The dependent claims are rejected based on their dependency from Claims 1 and 7.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-12, and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) the abstract idea of a mathematical algorithm (i.e., use of Kalman filter mathematics) for estimating longitudinal stiffness and/or traction torque of a tire of a vehicle.
This judicial exception is not integrated into a practical application because no improvement to the operation of the underlying vehicle is realized through the performance of the algorithm.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recited processor, machine-readable storage medium, and instructions amount to the recitation of general-purpose computer elements for implementing the algorithm through use of a general-purpose computer (see Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014)).
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, 7, 8, 10, 12, 14, 15, 18, 19, and 21 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al. (US 20150375719 A1)[hereinafter “Zhang”].
Regarding Claim 1, Zhang discloses a method of estimating longitudinal stiffness [Paragraph [0034] – “A wheel-specific slip curve estimation is preferably used for determining the parameters of the tire characteristics. This is carried out in particular by estimation according to a recursive least squares method. During said estimation, the maximum available coefficients of friction and tire stiffnesses are advantageously included in the calculation.”] to calculate a traction torque of a tire of a vehicle [See Fig. 1 (element 2) and Paragraphs [0088]-[0089] – “2) “μ Slip Characteristic Estimator”, λ,μ-Curve Estimator” Component
Using the offset-corrected longitudinal acceleration of the vehicle a.sub.x,sensor,corr, the coefficient of friction used μ.sub.used can be calculated and the coefficient of friction can be divided among the four wheels depending on the type of drive (two-wheel drive, all-wheel-center differential). Based on a parameterized tire characteristic and using the coefficients of friction used μ.sub.used,i and the calculated slips λ.sub.i of the wheels from 4, the parameters c.sub.0, c.sub.1 and c.sub.2 of the reference characteristic are determined by a least squares method and the maximum coefficients of friction μ.sub.max, i are also determined therefrom. The model coefficients of friction μ.sub.model, i and the associated peripheral wheel forces
F.sub.x,i=F.sub.n,i.Math.μ.sub.model, i
can be derived from the characteristic obtained depending on the slips.” The wheel forces reading on “traction torque” because torque is rotational force and wheel slips are considered in the force determination.], comprising:
calculating a parameter vector using a first extended Kalman filter [See Fig. 1 and Paragraph [0083] – “At the lower level 300 there are two EKF filters 3, which estimate the slip-affected angular speeds ω.sub.FL and ω.sub.FR at the front axle and ω.sub.RL and ω.sub.RR at the rear axle.”];
calculating a state vector using a second extended Kalman filter [See Fig. 1 and Paragraph [0083] – “At the lower level 300 there are two EKF filters 3, which estimate the slip-affected angular speeds ω.sub.FL and ω.sub.FR at the front axle and ω.sub.RL and ω.sub.RR at the rear axle.”]; and
calculating the traction torque [See Fig. 1 (element 2) and Paragraphs [0088]-[0089] – “2) “μ Slip Characteristic Estimator”, λ,μ-Curve Estimator” ComponentUsing the offset-corrected longitudinal acceleration of the vehicle a.sub.x,sensor,corr, the coefficient of friction used μ.sub.used can be calculated and the coefficient of friction can be divided among the four wheels depending on the type of drive (two-wheel drive, all-wheel-center differential). Based on a parameterized tire characteristic and using the coefficients of friction used μ.sub.used,i and the calculated slips λ.sub.i of the wheels from 4, the parameters c.sub.0, c.sub.1 and c.sub.2 of the reference characteristic are determined by a least squares method and the maximum coefficients of friction μ.sub.max, i are also determined therefrom. The model coefficients of friction μ.sub.model, i and the associated peripheral wheel forces
F.sub.x,i=F.sub.n,i.Math.μ.sub.model, i
can be derived from the characteristic obtained depending on the slips.” The wheel forces reading on “traction torque” because torque is rotational force and wheel slips are considered in the force determination.] as a function of the parameter vector and the state vector [See Fig. 1, output from 3 feeds into 4 which feeds into 2.Paragraphs [0092]-[0093] – “4) “Slip Calculation” ComponentThe wheel slips λ.sub.i are calculated using the estimated vehicle speed V.sub.ref from 7 and the estimated wheel revolution rates {tilde over (ω)}.sub.i from 3.”],
wherein calculating the parameter vector using the first extended Kalman filter further comprises:
calculating a predicted parameter vector [See Fig. 1 and Paragraph [0083] – “At the lower level 300 there are two EKF filters 3, which estimate the slip-affected angular speeds ω.sub.FL and ω.sub.FR at the front axle and ω.sub.RL and ω.sub.RR at the rear axle.”] using a time-delayed parameter value [Fig. 1, Fusion filter 7 produces “vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref,” which are time-delayed in that they are produced later in the process before being fed as inputs to “Revolution Rate Estimator” Component 3.];
calculating the parameter vector using the predicted parameter vector [Fig. 1, Fusion filter 7 produces “vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref,” which are fed as inputs to “Revolution Rate Estimator” Component 3.Paragraphs [0090]-[0091] – “3) “Revolution Rate Estimator” ComponentBy analysis of the engine torque signal T.sub.eng, the revolution rate signals ω.sub.i, the estimated vehicle acceleration a.sub.ref from 7, of the estimated gradient angle γ.sub.ref from 1, the determined peripheral tire forces F.sub.x, i and the tire properties (slip curve) from 2, the noise-reduced wheel speeds {tilde over (ω)}.sub.i are determined by means of a stochastic estimating method (e.g. Extended Kalman Filter) taking into account the drive train model (incl. the tire dynamics model).”]; and
generating the time-delayed parameter value [Paragraphs [0098]-[0099] – “7) “Fusion Filter” ComponentIn a stochastic fusion filter (e.g. Kalman filter), the vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref are estimated”] based on the parameter vector [See Fig. 1, the output from 3 is used by 4, 2, 6, 5, and finally 7.].
Regarding Claim 3, Zhang discloses that calculating the parameter vector further comprises using a system input vector to calculate the parameter vector [Fig. 1 – 9, 11, and 12], the system input vector comprising at least one of: an angular velocity of a front tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Front axle wheel revolution rate” section of 3.], an angular velocity of a rear tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Rear axle wheel revolution rate” section of 3.], a steering wheel angle, an effective tire radius of the front tire, or an effective tire radius of the rear tire.
Regarding Claim 5, Zhang discloses that calculating the parameter vector further comprises using a system measurement vector to calculate the parameter vector, the system measurement vector comprising at least one of: a longitudinal acceleration of the vehicle, a lateral acceleration of the vehicle, or a yaw rate of the vehicle [Paragraph [0064] – “The electronic controller receives signals from vehicle sensors that are connected thereto or contained within the controller, such as wheel revolution rate sensors, a yaw rate sensor or a longitudinal acceleration sensor and processes said signals in a microcontroller. Because of the internal digital signal processing, discrete Kalman filters are therefore particularly advantageous for the fusion of states.”].
Regarding Claim 7, Zhang discloses a method of estimating longitudinal stiffness [Paragraph [0034] – “A wheel-specific slip curve estimation is preferably used for determining the parameters of the tire characteristics. This is carried out in particular by estimation according to a recursive least squares method. During said estimation, the maximum available coefficients of friction and tire stiffnesses are advantageously included in the calculation.”] to calculate a traction torque of a tire of a vehicle [See Fig. 1 (element 2) and Paragraphs [0088]-[0089] – “2) “μ Slip Characteristic Estimator”, λ,μ-Curve Estimator” Component
Using the offset-corrected longitudinal acceleration of the vehicle a.sub.x,sensor,corr, the coefficient of friction used μ.sub.used can be calculated and the coefficient of friction can be divided among the four wheels depending on the type of drive (two-wheel drive, all-wheel-center differential). Based on a parameterized tire characteristic and using the coefficients of friction used μ.sub.used,i and the calculated slips λ.sub.i of the wheels from 4, the parameters c.sub.0, c.sub.1 and c.sub.2 of the reference characteristic are determined by a least squares method and the maximum coefficients of friction μ.sub.max, i are also determined therefrom. The model coefficients of friction μ.sub.model, i and the associated peripheral wheel forces
F.sub.x,i=F.sub.n,i.Math.μ.sub.model, i
can be derived from the characteristic obtained depending on the slips.” The wheel forces reading on “traction torque” because torque is rotational force and wheel slips are considered in the force determination.], comprising:
calculating a parameter vector using a first extended Kalman filter [See Fig. 1 and Paragraph [0083] – “At the lower level 300 there are two EKF filters 3, which estimate the slip-affected angular speeds ω.sub.FL and ω.sub.FR at the front axle and ω.sub.RL and ω.sub.RR at the rear axle.”];
calculating a state vector using a second extended Kalman filter [See Fig. 1 and Paragraph [0083] – “At the lower level 300 there are two EKF filters 3, which estimate the slip-affected angular speeds ω.sub.FL and ω.sub.FR at the front axle and ω.sub.RL and ω.sub.RR at the rear axle.”]; and
calculating the traction torque [See Fig. 1 (element 2) and Paragraphs [0088]-[0089] – “2) “μ Slip Characteristic Estimator”, λ,μ-Curve Estimator” ComponentUsing the offset-corrected longitudinal acceleration of the vehicle a.sub.x,sensor,corr, the coefficient of friction used μ.sub.used can be calculated and the coefficient of friction can be divided among the four wheels depending on the type of drive (two-wheel drive, all-wheel-center differential). Based on a parameterized tire characteristic and using the coefficients of friction used μ.sub.used,i and the calculated slips λ.sub.i of the wheels from 4, the parameters c.sub.0, c.sub.1 and c.sub.2 of the reference characteristic are determined by a least squares method and the maximum coefficients of friction μ.sub.max, i are also determined therefrom. The model coefficients of friction μ.sub.model, i and the associated peripheral wheel forces
F.sub.x,i=F.sub.n,i.Math.μ.sub.model, i
can be derived from the characteristic obtained depending on the slips.” The wheel forces reading on “traction torque” because torque is rotational force and wheel slips are considered in the force determination.] as a function of the parameter vector and the state vector [See Fig. 1, output from 3 feeds into 4 which feeds into 2.Paragraphs [0092]-[0093] – “4) “Slip Calculation” ComponentThe wheel slips λ.sub.i are calculated using the estimated vehicle speed V.sub.ref from 7 and the estimated wheel revolution rates {tilde over (ω)}.sub.i from 3.”],
wherein calculating the state vector using the second extended Kalman filter further comprises:
calculating a predicted state vector [See Fig. 1 and Paragraph [0083] – “At the lower level 300 there are two EKF filters 3, which estimate the slip-affected angular speeds ω.sub.FL and ω.sub.FR at the front axle and ω.sub.RL and ω.sub.RR at the rear axle.”] using a time-delayed state value [Fig. 1, Fusion filter 7 produces “vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref,” which are time-delayed in that they are produced later in the process before being fed as inputs to “Revolution Rate Estimator” Component 3.];
calculating the state vector using the predicted state vector [Fig. 1, Fusion filter 7 produces “vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref,” which are fed as inputs to “Revolution Rate Estimator” Component 3.Paragraphs [0090]-[0091] – “3) “Revolution Rate Estimator” ComponentBy analysis of the engine torque signal T.sub.eng, the revolution rate signals ω.sub.i, the estimated vehicle acceleration a.sub.ref from 7, of the estimated gradient angle γ.sub.ref from 1, the determined peripheral tire forces F.sub.x, i and the tire properties (slip curve) from 2, the noise-reduced wheel speeds {tilde over (ω)}.sub.i are determined by means of a stochastic estimating method (e.g. Extended Kalman Filter) taking into account the drive train model (incl. the tire dynamics model).”]; and
generating the time-delayed state value [Paragraphs [0098]-[0099] – “7) “Fusion Filter” ComponentIn a stochastic fusion filter (e.g. Kalman filter), the vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref are estimated”] based on the state vector [See Fig. 1, the output from 3 is used by 4, 2, 6, 5, and finally 7.].
Regarding Claim 8, Zhang discloses that calculating the state vector further comprises using a system input vector to calculate the state vector [Fig. 1 – 9, 11, and 12], the system input vector comprising at least one of: an angular velocity of a front tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Front axle wheel revolution rate” section of 3.], an angular velocity of a rear tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Rear axle wheel revolution rate” section of 3.], a steering wheel angle, an effective tire radius of the front tire, or an effective tire radius of the rear tire.
Regarding Claim 10, Zhang discloses that calculating the state vector further comprises using a system measurement vector to calculate the state vector, the system measurement vector comprising at least one of: a longitudinal acceleration of the vehicle, a lateral acceleration of the vehicle, or a yaw rate of the vehicle [Paragraph [0064] – “The electronic controller receives signals from vehicle sensors that are connected thereto or contained within the controller, such as wheel revolution rate sensors, a yaw rate sensor or a longitudinal acceleration sensor and processes said signals in a microcontroller. Because of the internal digital signal processing, discrete Kalman filters are therefore particularly advantageous for the fusion of states.”].
Regarding Claim 12, Zhang discloses a system for calculating traction torque of a tire of a vehicle [See Fig. 1 (element 2) and Paragraphs [0088]-[0089] – “2) “μ Slip Characteristic Estimator”, λ,μ-Curve Estimator” Component
Using the offset-corrected longitudinal acceleration of the vehicle a.sub.x,sensor,corr, the coefficient of friction used μ.sub.used can be calculated and the coefficient of friction can be divided among the four wheels depending on the type of drive (two-wheel drive, all-wheel-center differential). Based on a parameterized tire characteristic and using the coefficients of friction used μ.sub.used,i and the calculated slips λ.sub.i of the wheels from 4, the parameters c.sub.0, c.sub.1 and c.sub.2 of the reference characteristic are determined by a least squares method and the maximum coefficients of friction μ.sub.max, i are also determined therefrom. The model coefficients of friction μ.sub.model, i and the associated peripheral wheel forces
F.sub.x,i=F.sub.n,i.Math.μ.sub.model, i
can be derived from the characteristic obtained depending on the slips.” The wheel forces reading on “traction torque” because torque is rotational force and wheel slips are considered in the force determination.], comprising:
a processor [Paragraphs [0054] and [0058], “computer system”]; and
a machine-readable storage medium storing instructions that, when executed by the processor [Paragraphs [0054] and [0058], “RAM/ROM Resources” and “processing”], cause the processor to:
calculate a parameter vector using a first extended Kalman filter [See Fig. 1 and Paragraph [0083] – “At the lower level 300 there are two EKF filters 3, which estimate the slip-affected angular speeds ω.sub.FL and ω.sub.FR at the front axle and ω.sub.RL and ω.sub.RR at the rear axle.”];
calculate a state vector using a second extended Kalman filter [See Fig. 1 and Paragraph [0083] – “At the lower level 300 there are two EKF filters 3, which estimate the slip-affected angular speeds ω.sub.FL and ω.sub.FR at the front axle and ω.sub.RL and ω.sub.RR at the rear axle.”];
calculate a longitudinal stiffness of the tire [See Fig. 1 and Paragraph [0088] – “2) “μ Slip Characteristic Estimator”, λ,μ-Curve Estimator” Component”Paragraph [0034] – “A wheel-specific slip curve estimation is preferably used for determining the parameters of the tire characteristics. This is carried out in particular by estimation according to a recursive least squares method. During said estimation, the maximum available coefficients of friction and tire stiffnesses are advantageously included in the calculation.”] as a function of the parameter vector and the state vector [See Fig. 1, output from 3 feeds into 4 which feeds into 2 where the curve is estimated.]; and
calculate the traction torque of the tire [See Fig. 1 (element 2) and Paragraphs [0088]-[0089] – “2) “μ Slip Characteristic Estimator”, λ,μ-Curve Estimator” Component
Using the offset-corrected longitudinal acceleration of the vehicle a.sub.x,sensor,corr, the coefficient of friction used μ.sub.used can be calculated and the coefficient of friction can be divided among the four wheels depending on the type of drive (two-wheel drive, all-wheel-center differential). Based on a parameterized tire characteristic and using the coefficients of friction used μ.sub.used,i and the calculated slips λ.sub.i of the wheels from 4, the parameters c.sub.0, c.sub.1 and c.sub.2 of the reference characteristic are determined by a least squares method and the maximum coefficients of friction μ.sub.max, i are also determined therefrom. The model coefficients of friction μ.sub.model, i and the associated peripheral wheel forces
F.sub.x,i=F.sub.n,i.Math.μ.sub.model, i
can be derived from the characteristic obtained depending on the slips.” The wheel forces reading on “traction torque” because torque is rotational force and wheel slips are considered in the force determination.] based on the longitudinal stiffness of the tire [Paragraph [0034] – “A wheel-specific slip curve estimation is preferably used for determining the parameters of the tire characteristics. This is carried out in particular by estimation according to a recursive least squares method. During said estimation, the maximum available coefficients of friction and tire stiffnesses are advantageously included in the calculation.”],
wherein calculating the parameter vector using the first extended Kalman filter further comprises:
calculating a predicted parameter vector [See Fig. 1 and Paragraph [0083] – “At the lower level 300 there are two EKF filters 3, which estimate the slip-affected angular speeds ω.sub.FL and ω.sub.FR at the front axle and ω.sub.RL and ω.sub.RR at the rear axle.”] using a time-delayed parameter value [Fig. 1, Fusion filter 7 produces “vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref,” which are time-delayed in that they are produced later in the process before being fed as inputs to “Revolution Rate Estimator” Component 3.];
calculating the parameter vector using the predicted parameter vector [Fig. 1, Fusion filter 7 produces “vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref,” which are fed as inputs to “Revolution Rate Estimator” Component 3.Paragraphs [0090]-[0091] – “3) “Revolution Rate Estimator” ComponentBy analysis of the engine torque signal T.sub.eng, the revolution rate signals ω.sub.i, the estimated vehicle acceleration a.sub.ref from 7, of the estimated gradient angle γ.sub.ref from 1, the determined peripheral tire forces F.sub.x, i and the tire properties (slip curve) from 2, the noise-reduced wheel speeds {tilde over (ω)}.sub.i are determined by means of a stochastic estimating method (e.g. Extended Kalman Filter) taking into account the drive train model (incl. the tire dynamics model).”]; and
generating the time-delayed parameter value [Paragraphs [0098]-[0099] – “7) “Fusion Filter” ComponentIn a stochastic fusion filter (e.g. Kalman filter), the vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref are estimated”] based on the parameter vector [See Fig. 1, the output from 3 is used by 4, 2, 6, 5, and finally 7.].
Regarding Claim 14, Zhang discloses that calculating the parameter vector further comprises using a system input vector to calculate the parameter vector [Fig. 1 – 9, 11, and 12], the system input vector comprising at least one of: an angular velocity of a front tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Front axle wheel revolution rate” section of 3.], an angular velocity of a rear tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Rear axle wheel revolution rate” section of 3.], a steering wheel angle, an effective tire radius of the front tire, or an effective tire radius of the rear tire.
Regarding Claim 15, Zhang discloses that calculating the parameter vector further comprises using a system measurement vector to calculate the parameter vector, the system measurement vector comprising at least one of: a longitudinal acceleration of the vehicle, a lateral acceleration of the vehicle, or a yaw rate of the vehicle [Paragraph [0064] – “The electronic controller receives signals from vehicle sensors that are connected thereto or contained within the controller, such as wheel revolution rate sensors, a yaw rate sensor or a longitudinal acceleration sensor and processes said signals in a microcontroller. Because of the internal digital signal processing, discrete Kalman filters are therefore particularly advantageous for the fusion of states.”].
Regarding Claim 18, Zhang discloses that calculating the state vector using the second extended Kalman filter further comprises:
calculating a predicted state vector [See Fig. 1 and Paragraph [0083] – “At the lower level 300 there are two EKF filters 3, which estimate the slip-affected angular speeds ω.sub.FL and ω.sub.FR at the front axle and ω.sub.RL and ω.sub.RR at the rear axle.”] using a time-delayed state value [Fig. 1, Fusion filter 7 produces “vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref,” which are time-delayed in that they are produced later in the process before being fed as inputs to “Revolution Rate Estimator” Component 3.];
calculating the state vector using the predicted state vector [Fig. 1, Fusion filter 7 produces “vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref,” which are fed as inputs to “Revolution Rate Estimator” Component 3.Paragraphs [0090]-[0091] – “3) “Revolution Rate Estimator” ComponentBy analysis of the engine torque signal T.sub.eng, the revolution rate signals ω.sub.i, the estimated vehicle acceleration a.sub.ref from 7, of the estimated gradient angle γ.sub.ref from 1, the determined peripheral tire forces F.sub.x, i and the tire properties (slip curve) from 2, the noise-reduced wheel speeds {tilde over (ω)}.sub.i are determined by means of a stochastic estimating method (e.g. Extended Kalman Filter) taking into account the drive train model (incl. the tire dynamics model).”]; and
generating the time-delayed state value [Paragraphs [0098]-[0099] – “7) “Fusion Filter” ComponentIn a stochastic fusion filter (e.g. Kalman filter), the vehicle speed V.sub.ref and the vehicle acceleration a.sub.ref are estimated”] based on the state vector [See Fig. 1, the output from 3 is used by 4, 2, 6, 5, and finally 7.].
Regarding Claim 19, Zhang discloses that calculating the state vector further comprises using a system input vector to calculate the state vector [Fig. 1 – 9, 11, and 12], the system input vector comprising at least one of: an angular velocity of a front tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Front axle wheel revolution rate” section of 3.], an angular velocity of a rear tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Rear axle wheel revolution rate” section of 3.], a steering wheel angle, an effective tire radius of the front tire, or an effective tire radius of the rear tire.
Regarding Claim 21, Zhang discloses that calculating the state vector further comprises using a system measurement vector to calculate the state vector, the system measurement vector comprising at least one of: a longitudinal acceleration of the vehicle, a lateral acceleration of the vehicle, or a yaw rate of the vehicle [Paragraph [0064] – “The electronic controller receives signals from vehicle sensors that are connected thereto or contained within the controller, such as wheel revolution rate sensors, a yaw rate sensor or a longitudinal acceleration sensor and processes said signals in a microcontroller. Because of the internal digital signal processing, discrete Kalman filters are therefore particularly advantageous for the fusion of states.”].
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) 4, 6, 9, 11, 16, 17, 20, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20150375719 A1)[hereinafter “Zhang”] and Johansson (US 20190226841 A1).
Regarding Claim 4, Zhang discloses that the system input vector comprises each of: the angular velocity of a front tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Front axle wheel revolution rate” section of 3.] and the angular velocity of a rear tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Rear axle wheel revolution rate” section of 3.], but fails to disclose that the system input vector comprises each of: the steering wheel angle, the effective tire radius of the front tire, and the effective tire radius of the rear tire.
However, Johansson discloses the use of the steering wheel angle, the effective tire radius of the front tire, and the effective tire radius of the rear tire in characterizing the wheels of a vehicle [See Paragraphs [0031], [0034], and [0035]]. It would have been obvious to make use of such parameters in order to better characterize and control the vehicle.
Regarding Claim 6, Zhang discloses that the system measurement vector comprises each of: the longitudinal acceleration of the vehicle and the yaw rate of the vehicle [Paragraph [0064] – “The electronic controller receives signals from vehicle sensors that are connected thereto or contained within the controller, such as wheel revolution rate sensors, a yaw rate sensor or a longitudinal acceleration sensor and processes said signals in a microcontroller. Because of the internal digital signal processing, discrete Kalman filters are therefore particularly advantageous for the fusion of states.”], but fails to disclose that the system measurement vector comprises the lateral acceleration of the vehicle.
However, Johansson discloses the use of lateral acceleration of the vehicle in characterizing the wheels of a vehicle [See Paragraph [0031]]. It would have been obvious to make use of such a parameter in order to better characterize and control the vehicle.
Regarding Claim 9, Zhang discloses that the system input vector comprises each of: the angular velocity of a front tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Front axle wheel revolution rate” section of 3.] and the angular velocity of a rear tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Rear axle wheel revolution rate” section of 3.], but fails to disclose that the system input vector comprises each of: the steering wheel angle, the effective tire radius of the front tire, and the effective tire radius of the rear tire.
However, Johansson discloses the use of the steering wheel angle, the effective tire radius of the front tire, and the effective tire radius of the rear tire in characterizing the wheels of a vehicle [See Paragraphs [0031], [0034], and [0035]]. It would have been obvious to make use of such parameters in order to better characterize and control the vehicle.
Regarding Claim 11, Zhang discloses that the system measurement vector comprises each of: the longitudinal acceleration of the vehicle and the yaw rate of the vehicle [Paragraph [0064] – “The electronic controller receives signals from vehicle sensors that are connected thereto or contained within the controller, such as wheel revolution rate sensors, a yaw rate sensor or a longitudinal acceleration sensor and processes said signals in a microcontroller. Because of the internal digital signal processing, discrete Kalman filters are therefore particularly advantageous for the fusion of states.”], but fails to disclose that the system measurement vector comprises the lateral acceleration of the vehicle.
However, Johansson discloses the use of lateral acceleration of the vehicle in characterizing the wheels of a vehicle [See Paragraph [0031]]. It would have been obvious to make use of such a parameter in order to better characterize and control the vehicle.
Regarding Claim 16, Zhang discloses that the system input vector comprises each of: the angular velocity of a front tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Front axle wheel revolution rate” section of 3.] and the angular velocity of a rear tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Rear axle wheel revolution rate” section of 3.], but fails to disclose that the system input vector comprises each of: the steering wheel angle, the effective tire radius of the front tire, and the effective tire radius of the rear tire.
However, Johansson discloses the use of the steering wheel angle, the effective tire radius of the front tire, and the effective tire radius of the rear tire in characterizing the wheels of a vehicle [See Paragraphs [0031], [0034], and [0035]]. It would have been obvious to make use of such parameters in order to better characterize and control the vehicle.
Regarding Claim 17, Zhang discloses that the system measurement vector comprises each of: the longitudinal acceleration of the vehicle and the yaw rate of the vehicle [Paragraph [0064] – “The electronic controller receives signals from vehicle sensors that are connected thereto or contained within the controller, such as wheel revolution rate sensors, a yaw rate sensor or a longitudinal acceleration sensor and processes said signals in a microcontroller. Because of the internal digital signal processing, discrete Kalman filters are therefore particularly advantageous for the fusion of states.”], but fails to disclose that the system measurement vector comprises the lateral acceleration of the vehicle.
However, Johansson discloses the use of lateral acceleration of the vehicle in characterizing the wheels of a vehicle [See Paragraph [0031]]. It would have been obvious to make use of such a parameter in order to better characterize and control the vehicle.
Regarding Claim 20, Zhang discloses that the system input vector comprises each of: the angular velocity of a front tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Front axle wheel revolution rate” section of 3.] and the angular velocity of a rear tire [Fig. 1 – 9, “measured wheel speeds ω.sub.i”Inherently corresponding to a front tire per the use by the “Rear axle wheel revolution rate” section of 3.], but fails to disclose that the system input vector comprises each of: the steering wheel angle, the effective tire radius of the front tire, and the effective tire radius of the rear tire.
However, Johansson discloses the use of the steering wheel angle, the effective tire radius of the front tire, and the effective tire radius of the rear tire in characterizing the wheels of a vehicle [See Paragraphs [0031], [0034], and [0035]]. It would have been obvious to make use of such parameters in order to better characterize and control the vehicle.
Regarding Claim 22, Zhang discloses that the system measurement vector comprises each of: the longitudinal acceleration of the vehicle and the yaw rate of the vehicle [Paragraph [0064] – “The electronic controller receives signals from vehicle sensors that are connected thereto or contained within the controller, such as wheel revolution rate sensors, a yaw rate sensor or a longitudinal acceleration sensor and processes said signals in a microcontroller. Because of the internal digital signal processing, discrete Kalman filters are therefore particularly advantageous for the fusion of states.”], but fails to disclose that the system measurement vector comprises the lateral acceleration of the vehicle.
However, Johansson discloses the use of lateral acceleration of the vehicle in characterizing the wheels of a vehicle [See Paragraph [0031]]. It would have been obvious to make use of such a parameter in order to better characterize and control the vehicle.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Tan et al., Vehicle State Estimation of Steer by Wire System Based on Multi Sensor Fusion, IEEE, 2017
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US 20160059735 A1 – Dual Kalman Filter For Torsional Damping Of Electric Traction Drives
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857