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 .
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The references listed in the Information Disclosure Statement filed on 01/22/2025 have been considered by the examiner (see attached PTO-1449 forms).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a tire cornering stiffness estimation method comprising: obtaining vehicle driving information; and estimating cornering stiffness of a tire based on the vehicle driving information using a bicycle model and a linear tire model, which are vehicle lateral dynamics models,
Claim 12 recites a tire cornering stiffness estimation apparatus comprising: a memory storing one or more programs for estimating tire cornering stiffness; and one or more processors that perform an operation for estimating tire cornering stiffness according to the one or more programs stored in the memory, wherein the one or more processors are configured to perform: obtaining vehicle driving information of a vehicle; and estimating cornering stiffness of a tire based on the vehicle driving information using a bicycle model and a linear tire model, which are vehicle lateral dynamics models.…
Claim 16 recites a road surface condition detection method using a tire cornering stiffness estimation value, the method comprising: obtaining vehicle driving information; and detecting a road surface condition based on cornering stiffness of a tire estimated based on the vehicle driving information.…
and thus grouped as Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations.
These judicial exceptions are not integrated into a practical application because the additional elements, the data gathering step, (claim 1) “obtaining vehicle driving information” (claim 12) “obtaining vehicle driving information of a vehicle” (claim 16) “obtaining vehicle driving information” are mere data gathering that do not add a meaningful limitation to the method as they are insignificant extra-solution activity. Furthermore, the additional elements (claim 12) the “one or more processors” are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions amount to no more than using a computer as a tool to perform an abstract idea. All of which are considered not indicative of integration into a practical application (see “Federal Register / Vol. 84, No. 4/ Monday, January 7, 2019 / Notices” – page 55, second column).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of the data gathering steps are mere data collect steps which fall under insignificant extra solution activity and deemed insufficient to qualify as “significantly more” - see MPEP 2106.05(g). The additional elements of the processors are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and deemed insufficient to qualify as “significantly more” see MPEP 2106.05(f).
Dependent claims 2-11, 13-15 and 17-20 when analyzed as a whole are patent ineligible under 35 U.S.C. §101 because the dependent claims fail to establish that the claims are not directed to an abstract idea as they are directed mathematical concepts and/or mental processes and do not add significantly more to the abstract idea.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 and 10-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Singh [US 2017/0010184 A1 (as submitted in IDS 01/22/2025)].
Regarding claim 1, Singh teaches a tire cornering stiffness estimation method (Abstract) comprising:
obtaining vehicle driving information (sensor-equipped to generate vehicle-based sensor data - 0074) (signals from sensors attached to the vehicle – 0121, vehicle-based sensors - 0122); an
estimating cornering stiffness of a tire based on the vehicle driving information (cornering stiffness estimation using a model-based approach – 0074, 0092, 0097) (Cornering stiffness identifier 194 makes its cornering stiffness determination – 0124, 0126) using a bicycle model and a linear tire model, which are vehicle lateral dynamics models (To build a model based UKF, the nonlinear bicycle model equations and linear tire model equations have been converted to discrete form - 0093, 0098).
Regarding claim 2, Singh teaches the tire cornering stiffness estimation method of claim 1, wherein the vehicle driving information includes a lateral acceleration of a vehicle, a yaw rate of the vehicle, and a longitudinal speed of the vehicle (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance,CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force - 0077-0086) (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance, CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force, front axle cornering stiffness, rear axle cornering stiffness, β sideslip angle, transient state index 0108 - 0120) (longitudinal - 0066).
Regarding claim 3, Singh teaches the tire cornering stiffness estimation method of claim 2, wherein the estimating cornering stiffness includes: obtaining a lateral speed based on the vehicle driving information; obtaining a first tire lateral force based on the vehicle driving information; obtaining a scaling factor for reflecting influence of tire vertical force based on the vehicle driving information; obtaining a slip angle based on the vehicle driving information and the lateral speed; obtaining a second tire lateral force based on the first tire lateral force and the scaling factor (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance,CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force - 0077-0086) (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance, CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force, front axle cornering stiffness, rear axle cornering stiffness, β sideslip angle, transient state index 0108 - 0120); and
obtaining a cornering stiffness estimation value based on the second tire lateral force and the slip angle (conducting the cornering stiffness 194 evaluation and the tire load estimator 188 – 0122).
Regarding claim 4, Singh teaches the tire cornering stiffness estimation method of claim 3, wherein the obtaining a lateral speed includes calculating the lateral speed vy based on the lateral acceleration ay, the yaw rate r, and the longitudinal speed vx (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance,CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force - 0077-0086) (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance, CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force, front axle cornering stiffness, rear axle cornering stiffness, β sideslip angle, transient state index 0108 - 0120).
Regarding claim 5, Singh teaches the tire cornering stiffness estimation method of claim 4, wherein the obtaining a first tire lateral force includes:calculating a first front wheel tire lateral force Fyf based on the lateral acceleration ay, the yaw rate r, a total length L between front and rear wheels of the vehicle, a z-axis moment of inertia Iz, a mass m of the vehicle, and a rear wheel length lr between a center of the front and rear wheels and the rear wheel;calculating a first rear tire lateral force Fyr based on the lateral acceleration ay, the yaw rate r, the total length L, the z-axis moment of inertia Iz, the mass m, and a front wheel length if between the center of the front and rear wheels and the front wheel (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance,CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force - 0077-0086) (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance, CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force, front axle cornering stiffness, rear axle cornering stiffness, β sideslip angle, transient state index 0108 - 0120); and obtaining the first tire lateral force including the first front tire lateral force Fyf and the first rear wheel tire lateral force Fyr (0087, 0125).
Regarding claim 10, Singh teaches the tire cornering stiffness estimation method of claim 2, wherein the estimating cornering stiffness of a tire includes: estimating the cornering stiffness (estimation performance of actual cornering stiffness) when a preset driving condition is satisfied; and maintaining a previously estimated cornering stiffness when the driving condition is not satisfied (estimation performance – 0105-0107).
Regarding claim 11, Singh teaches the tire cornering stiffness estimation method of claim 10, wherein the driving condition includes a case where an absolute value of variation of the longitudinal speed vx is less than a preset first reference value and an absolute value of the lateral acceleration ay is less than a preset second reference value (transient state index, lateral acceleration – 0076, 0102).
Regarding claim 12, Singh teaches a tire cornering stiffness estimation (Abstract) apparatus comprising:
a memory storing one or more programs for estimating tire cornering stiffness; and one or more processors (a data processor – 0122) that perform an operation for estimating tire cornering stiffness according to the one or more programs stored in the memory, wherein the one or more processors are configured to perform:
obtaining vehicle driving information (sensor-equipped to generate vehicle-based sensor data - 0074) (signals from sensors attached to the vehicle – 0121, vehicle-based sensors - 0122); an
estimating cornering stiffness of a tire based on the vehicle driving information ( cornering stiffness estimation using a model-based approach – 0074, 0092, 0097) (Cornering stiffness identifier 194 makes its cornering stiffness determination – 0124, 0126) using a bicycle model and a linear tire model, which are vehicle lateral dynamics models (To build a model based UKF, the nonlinear bicycle model equations and linear tire model equations have been converted to discrete form - 0093, 0098).
Regarding claim 13, Singh teaches the tire cornering stiffness estimation apparatus of claim 12, wherein the one or more processors are further configured to perform controlling at least one of a steering system, a braking system, or a suspension system of the vehicle by using the estimated cornering stiffness of the tire (corrective control action is instituted, resulting in control of differential wheel braking – 0067, satisfactory steering and stability of a vehicle - 0071) (cornering stiffness estimates are input into the vehicle's control unit 198 with the sideslip angle β for vehicle control systems such as steering, suspension and/or braking - 0128).
Regarding claim 14, Singh teaches the tire cornering stiffness estimation apparatus of claim 12, wherein the vehicle driving information includes a lateral acceleration of the vehicle, a yaw rate of the vehicle, and a longitudinal speed of the vehicle (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance,CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force - 0077-0086) (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance, CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force, front axle cornering stiffness, rear axle cornering stiffness, β sideslip angle, transient state index 0108 - 0120).
Regarding claim 15, Singh teaches the tire cornering stiffness estimation apparatus of claim 14, wherein the one or more processors perform: obtaining a lateral speed based on the vehicle driving information; obtaining a first tire lateral force based on the vehicle driving information; obtaining a scaling factor for reflecting influence of tire vertical force based on the vehicle driving information; obtaining a slip angle based on the vehicle driving information and the lateral speed; obtaining a second tire lateral force based on the first tire lateral force and the scaling factor (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance,CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force - 0077-0086) (steering wheel angle, lateral acceleration, yaw rate, CoG to front axle distance, CoG to rear axle distance, yaw moment of inertia, front axle lateral force, rear axle lateral force, front axle cornering stiffness, rear axle cornering stiffness, β sideslip angle, transient state index 0108 - 0120); and
obtaining a cornering stiffness estimation value based on the second tire lateral force and the slip angle (conducting the cornering stiffness 194 evaluation and the tire load estimator 188 – 0122).
Regarding claim 16, Singh teaches a road surface condition detection method using a tire cornering stiffness estimation value, the method comprising:
obtaining vehicle driving information (sensor-equipped to generate vehicle-based sensor data - 0074) (signals from sensors attached to the vehicle – 0121, vehicle-based sensors - 0122); and
detecting a road surface condition (road bank angle, tire road conditions – 0075) (tire-road friction coefficient – 0128) based on cornering stiffness of a tire estimated based on the vehicle driving information ( cornering stiffness estimation using a model-based approach – 0074, 0092, 0097) (Cornering stiffness identifier 194 makes its cornering stiffness determination – 0124, 0126).
Regarding claim 17, Singh teaches the road surface condition detection method of claim 16, wherein the detecting a road surface condition includes:
obtaining a cornering stiffness estimation value ( cornering stiffness estimation using a model-based approach – 0074, 0092, 0097) (Cornering stiffness identifier 194 makes its cornering stiffness determination – 0124, 0126) based on the vehicle driving information using a bicycle model and a linear tire model, which are vehicle lateral dynamics models (To build a model based UKF, the nonlinear bicycle model equations and linear tire model equations have been converted to discrete form - 0093, 0098); and
obtaining the road surface condition corresponding to the cornering stiffness estimation value by using cornering stiffness information in which road surface conditions are mapped for respective cornering stiffness ranges (shown graphically, error range 0105, 0106).
Regarding claim 18, Singh teaches the road surface condition detection method of claim 17, wherein the obtaining a road surface condition includes:
obtaining a cornering stiffness range corresponding to the cornering stiffness estimation value from the cornering stiffness information (cornering stiffness and error range - 0105-0107); and
obtaining a road surface condition (road bank angle, tire road conditions – 0075) (tire-road friction coefficient – 0128) corresponding to the obtained cornering stiffness range as the road surface condition corresponding to the cornering stiffness estimation value (cornering stiffness and error range - 0105-0107).
Regarding claim 19, Singh teaches the road surface condition detection method of claim 17, wherein in the cornering stiffness information, the road surface conditions are mapped for the respective cornering stiffness ranges using a boundary reference value set based on a physical phenomenon of a road surface and a boundary adjustment value changeable based on vehicle characteristics (shown graphically, error range 0105, 0106) (cornering stiffness and error range - 0105-0107).
Regarding claim 20, Singh teaches the road surface condition detection method of claim 16, wherein the detecting a road surface condition includes:
detecting the road surface condition (road bank angle, tire road conditions – 0075) (tire-road friction coefficient – 0128) when a preset driving condition is satisfied; and maintaining a previously detected road surface condition when the driving condition is not satisfied ( tire-road conditions - 0075) (worn front and rear tire condition – 0107).
Allowable Subject Matter
Claims 6-9 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
Claim 6 is objected to because the closest prior art, Singh [US 2017/0010184 A1], fails to anticipate or render obvious obtaining a scaling factor includes obtaining the scaling factor kscale corresponding to the lateral acceleration ay using scaling factor information in which scaling factors are mapped for respective lateral accelerations, in combination with all other limitations in the claim(s) as defined by applicant.
Relevant Prior Art / Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Varunjikar et al. (US Patent Application Publication 2020/0331520 A1) discloses a system and method for using a bicycle model calculator and a linear tire model for determining lateral force properties;
Ryu et al. (US Patent Application Publication 2010/0131144 A1) discloses a kinematic estimator for vehicle lateral velocity using force tables;
Deng et al. (US Patent Application Publication 2010/0100360 A1) discloses a model-based road surface condition identification using linear modeling.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICKY GO whose telephone number is (571)270-3340. The examiner can normally be reached on Monday through Friday from 9:00 a.m. to 5:30 p.m.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M. Vazquez can be reached on (571) 272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RICKY GO/Primary Examiner, Art Unit 2857