DETAILED ACTION
This action is filed in response to the application filed on 3/20/2024.
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 .
Information Disclosure Statement
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 1/03/2025. This IDS has been considered.
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 1-20 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.
Regarding Claims 1, 8, and 15 the second limitation teaches “receiving, with a controller onboard the vehicle, signals from an onboard sensor system of the vehicle indicative of operating parameters of the vehicle.” Examiner notes it is unclear what signals indicative of operating parameters are and the specification provides no examples or definition for signals indicative of operating parameters. The claimed language is so vague that under the broadest reasonable interpretation of Claim 1 the metes and bounds of the invention are unclear, thus rendering claim 1 indefinite.
Regarding Claims 2, 9, and 16, the claims recite “wherein the operating parameters include a lateral force, a steering torque, a longitudinal speed, a lateral acceleration, a yaw rate, steering angles, and various vehicle parameters.” Examiner notes the phrase “various vehicle parameters” is indefinite as it fails to clearly limit the invention in any manner thus rendering the metes and bounds of the invention unclear.
Claims 3-8, 10-14, and 17-20 are dependent claims and therefore inherit the deficiencies of independent Claims 1, 8, and 15 and are likewise rejected.
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. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. Claim 1, and similarly Claims 8 and 15 recite the following abstract concepts in BOLD of:
A method for estimating a lateral adhesion level indicator of tires for a vehicle traveling on tires, comprising:
receiving, with a controller onboard the vehicle, signals from an onboard sensor system of the vehicle indicative of operating parameters of the vehicle;
processing, with one or more processors of the controller, the signals to estimate a self-aligning torque rate, a lateral force rate, and a slip angle rate;
performing, with the one or more processors of the controller, a state synchronize process to reduce a time mismatch between the lateral force rate and the slip angle rate and thereby provide a synchronized slip angle rate;
performing, with the one or more processors of the controller, a filtering process to provide a lateral slope estimation and a self-aligning torque slope estimation each based on the self-aligning torque rate, the lateral force rate, and the synchronized slip angle rate;
performing, with the one or more processors of the controller, a normalization process to reduce noise associated with the lateral slope estimation and the self-aligning torque slope estimation and thereby produce a normalized lateral slope estimation and a normalized self-aligning torque slope estimation;
classifying, with the one or more processors of the controller, the normalized self-aligning torque slope estimation to obtain a classification; and
performing, with the one or more processors of the controller, an arbitration and fusion process to adjust the normalized lateral slope estimation based on the classification of the normalized self-aligning torque slope estimation to estimate the final lateral adhesion level indicator.
Under Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category because Claim 1 recites a method, Claim 8 recites a system, and Claim 15 recites an apparatus.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps. The step of classifying the self-aligning torque slope can be interpreted as a mental process that can be performed in the human mind, while the step
Of performing a normalization process can be interpreted as performing mathematics. Furthermore, the steps of performing a synchronization process, a filtering process, and an arbitration and fusion process could be considered as performing mathematics or a mental process depending on one's interpretation of the limitation.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Examiner notes the additional elements of a vehicle with sensors and tires suggest a field of use, not a particular machine. Finally, there is nothing in the claims -that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a controller, sensors, and a processor are generic computer elements and not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Additionally, the limitation pertaining to receiving signals from a sensor system recites necessary data gathering and does not integrate the abstract idea into a practical application. The limitation amounts to necessary data gathering and outputting. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Finally, the limitation that teaches processing the signals from the sensors teaches routine conventional activity which is not significantly more than the abstract ideas. See MPEP 2106.05(d) “If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility.”
Claims 2-7, 9-14, and 16-20 further limit the abstract ideas without integrating the abstract concept into a practical , application or including additional limitations that can be considered significantly more than the abstract idea:
Claims 2-5, 7, 9-12, 14, and 16-20 further limit the data gathered which does not integrate the abstract idea into a practical application. The limitation amounts to necessary data gathering and outputting. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
Claims 6 and 13 further limit the mathematics performed in the independent claims without significantly more.
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.
Claims 1-6, 8-13, and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Yasui (DE60305232 T2) in view of Miyashita (JP2007290694 A) and in further view of Kasaiezadeh (US 20180297633 A1), and Kobayashi (WO2023286750 A1).
Regarding Claim 1, Yasui teaches a method for estimating a lateral adhesion level indicator of tires for a vehicle traveling on tires (e.g. see [Pg. 2 paragraph 1] “The present invention relates to a device for estimating a Soil adhesion factor and in particular a device for estimate a coefficient of adhesion that indicates the degree of adhesion of a tire on a Road surface in lateral Indicates direction of a vehicle wheel”), comprising:
receiving, with a controller onboard the vehicle, signals from an onboard sensor system of the vehicle indicative of operating parameters of the vehicle (e.g. see [pg. 10 paragraph 6] “a steering torque Tstr acting on a steering shaft 2 with a steering wheel operated by a driver 1 is applied, determined by a steering torque sensor TS and an electric motor 3 is controlled in response to the detected steering torque Tstr to the front wheels FL and FR via a speed-reducing gear 4 and about rack and pinion 5 to control, so that the steering operation of the driver is supported”);
processing, with one or more processors of the controller (e.g. see [pg. 13 paragraph 4] “these control units ECU1-ECU5 are connected to the communication bus through a communication unit connected to the CPU, ROM or RAM for communication. Accordingly, the for Each control system transmits required information through other control systems”), the signals to estimate a self-aligning torque rate (e.g. see [pg. 4 paragraph 1] “Accordingly, the torque Fy .Math. e .sub.n becomes a restoring torque (Tsa) acting in such a direction as to decrease the slip angle α and call it a self-restoring torque”), a lateral force rate, and a slip angle rate (e.g. see [pg. 5 paragraph 3] “the device further comprises a wheel factor estimator for the appraisal of at least one wheel factor including a lateral force and a slip angle, which are applied to the wheel due to the state quantity”),
performing , with the one or more processors of the controller, an arbitration and fusion process to adjust the normalized lateral slope estimation based on the classification of the normalized self-aligning torque slope estimation to estimate the final lateral adhesion level indicator. (e.g. see [pg. 4 paragraph 3] “As described above, by monitoring the change of the tire slip (e .sub.n ), the degree of adhesion of the tire in its lateral direction can be determined. And besides, the change of the tire slip (e .sub.n ) results in a return torque Tsa, which can serve to estimate an adhesion factor indicating a degree of adhesion of the tire in its lateral direction, eg, with respect to a front wheel (hereinafter simply referred to as an adhesion factor). With regard to the adhesion factor, this can be estimated based on a limitation of the lateral force for the road friction, as described”).
Yasui does not explicitly disclose performing, with the one or more processors of the controller, a state synchronize process to reduce a time mismatch between the lateral force rate and the slip angle rate and thereby provide a synchronized slip angle rate,
In the same field of endeavor, Miyashita teaches performing, with the one or more processors of the controller, a state synchronize process to reduce a time mismatch between the lateral force (e.g. see [0023] “The calculation device 10 receives input of measurement data of transient lateral force Fy self-aligning torque (hereinafter simply referred to as torque) Mz and longitudinal force Fx, given slip angle and slip ratio as time series data. Based on the tire dynamics model described later, it calculates the value of the delay time constant (transient response parameter) that characterizes the transient response characteristics of the tire. It also uses this delay time constant and the values of each of the tire dynamics element parameters that constitute the tire dynamics model to calculate time series data of transient lateral force Fy, torque Mz, and longitudinal force Fx)
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the lateral slope and self-aligning torque of Yasui with the state synchronization method of Miyashita for the purpose of estimating lateral adhesion with the advantage of organizing the collected data in order to increase the accuracy of the adhesion determination.
Yasui as modified by Miyashita does not explicitly disclose performing, with the one or more processors of the controller, a filtering process to provide a lateral slope estimation and a self-aligning torque slope estimation each based on the self-aligning torque rate, the lateral force rate, and the synchronized slip angle rate.
In the same field of endeavor, Kasaiezadeh teaches performing, with the one or more processors of the controller, a filtering process to provide a lateral slope estimation and a self-aligning torque slope estimation each based on the self-aligning torque rate, the lateral force rate, and the synchronized slip angle rate (e.g. see [0057] “In various embodiments, the pneumatic trail estimation module 512 uses a Kalman filter, a least squares method (e.g. a recursive least squares method), or other averaging or filtration based algorithms to determine the slope between estimate SAT values and estimated axle lateral force values, thereby to estimate pneumatic trail”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the lateral slope and self-aligning torque of Yasui with the filtering and classification of Kasaiezadeh for the purpose of estimating lateral adhesion with the advantage of filtering the collected data in order to increase the accuracy of the adhesion determination.
Yasui as modified by Miyashita and Kasaiezadeh does not explicitly disclose performing, with the one or more processors of the controller, a normalization process to reduce noise associated with the lateral slope estimation and the self-aligning torque slope estimation and thereby produce a normalized lateral slope estimation and a normalized self-aligning torque slope estimation; classifying, with the one or more processors of the controller, the normalized self-aligning torque slope estimation to obtain a classification.
In the same field of endeavor, Kobayashi teaches disclose performing, with the one or more processors of the controller, a normalization process to reduce noise associated with the lateral slope estimation and the self-aligning torque slope estimation and thereby produce a normalized lateral slope estimation and a normalized self-aligning torque slope estimation (e.g. see [pg. 16 paragraph 7] “Therefore, the normalized self-aligning torque stiffness can be approximated by the following equation (37) using the normalized cornering stiffness C .sub.yα (i.e. normalized lateral slope)(denoted as Wa in the above embodiment)”);
classifying, with the one or more processors of the controller, the normalized self-aligning torque slope estimation to obtain a classification (e.g. see [pg. 2 last paragraph] “Tire design information is a concept that includes data on tire specifications and data indicating characteristics related to the force generated by the tire. Examples of tire specification data include data indicating the structure, shape, and material of a tire. An example of the data indicating characteristics related to tire force generation includes data indicating tire characteristics such as cornering stiffness, road surface friction coefficient, and self-aligning torque,” and [pg. 3 paragraph 4] “By inputting parameters related to tire design information into the tire model, the analysis device 1 identifies a tire model that reflects the tire design information, and uses the identified tire model to perform vehicle simulation with the vehicle model. Specifically, the analysis device 1 converts the tire design information into tire model parameters in the tire information conversion unit 10, and identifies the tire model using the converted parameters in the tire model identification unit 20”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the lateral slope and self-aligning torque of Yasui with the normalization and classification method of Kobayashi for the purpose of estimating lateral adhesion with the advantage of eliminating noise and organizing the data.
Regarding Claim 2, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claim 1. Yasui further discloses wherein the operating parameters include a lateral force (e.g. see [pg. 4 paragraph 1] “in this case, a lateral force Fy generated on the entire contact surface is
equal to the product of a deformed surface of the tread in its lateral direction”), a steering torque (e.g. see [pg. 5 paragraph 3] “steering factor measuring device to determine at least one steering factor, including one Steering torque”), a longitudinal speed (e.g. see [pg. 6 paragraph 2] “Apparatus may further comprise a speed measuring device for the Determining the speed of the vehicle and a filter device for setting a cutoff frequency in accordance with the speed measuring device”), a lateral acceleration, a yaw rate (e.g. see [pg. 11 paragraph 1] “the front side force Fyf can be determined from the results obtained by the lateral acceleration measuring unit M7 and the yaw measuring unit M8”), steering angles, and various vehicle parameters (e.g. see pg. 11 last paragraph] “Therefore, the signals detected by the steering angle measuring unit M4, the lateral acceleration measuring unit M7 and the yaw rate measuring unit M8 are supplied to a slip angle estimating unit M9y together with a signal detected by a vehicle speed estimating unit M9x”).
Regarding Claim 3, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claim 1. Yasui further discloses wherein processing the signals to estimate the self-aligning torque rate is based on a self-aligning torque of the tires (e.g. see [pg. 4 paragraph 1] “Accordingly, the torque Fy .Math. e .sub.n becomes a restoring torque (Tsa) acting in such a direction as to decrease the slip angle α and call it a self-restoring torque.”), a lumped mass of a steering system of the vehicle (e.g. see [pg. 19 paragraph 9] “In this regard, the dynamic characteristic of the electric steering apparatus is represented by the following differential equation ( 16 ): where M .sub.r is the mass of the housing and J .sub.m is the motor inertia”).
Yasui does not explicitly disclose wherein processing the signals to estimate the self-aligning torque rate is based on a total torque received from a controller area network of the vehicle, a position and a velocity of the tires, and a lumped dampening of the vehicle.
In the same field of endeavor, Kasaiezadeh teaches wherein processing the signals to estimate the self-aligning torque rate is based on a total torque received from a controller area network of the vehicle (e.g. see [0037] “The sensors 130 sense one or more of the following vehicle parameters and generate corresponding control signals: lateral acceleration, longitudinal acceleration, yaw rate, EPS torque, steering angle, etc. In various embodiments, the sensors 130 communicate the signals directly to the control module 120 and/or may communicate the signals to other control modules (not shown) which, in turn, communicate data from the signals to the control module 120 over a communication bus (not shown) or other communication means.”),
a position and a velocity of the tires (e.g. see [0064] “In one embodiment, the tire slip angle estimation module 516 is configured to map longitudinal and lateral velocity as measured in the sensor system 200 to each tire. For example, the tire slip angle estimation module is configured to estimate tire slip angle using value obtained through sensor system 200 including lateral velocity, longitudinal velocity, yaw rate The following equations are known for front axle and rear axle slip angle estimation”),
and a lumped dampening of the vehicle (e.g. see [0062] “The steering correction calculation module 518 is configured to calculate a steering correction 528, specifically a steering torque reducing factor, so that the automated vehicle control system 520, through the actuator system 400, works with reduced steering torque. This feature of the present disclosure allows for the vehicle 100 to come out of lateral tire force saturation condition and also allows for mitigation of adverse consequences of vehicle instability caused by the lateral tire force saturation condition.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the signal processed variables of Yasui with the additional variables of Kasaiezadeh for the purpose of estimating the self-aligning torque with the advantage of additional data to ensure the determined torque rate is accurate.
Regarding Claim 4, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claim 1. Yasui further discloses wherein processing the signals to estimate the lateral force rate is based on lateral forces of the tires (e.g. see [pg. 11 paragraph 1] “ front side force Fyf, estimated by a side force estimation unit M9, which serves as wheel factor estimation device. The front side force Fyf can be determined from the results obtained by the lateral acceleration measuring unit M7 and the yaw measuring unit M8 in accordance with the following equation: where "Lr" is a distance from the center of gravity to the rear axle, "m" is the vehicle mass, "L" is the wheelbase, "Iz" is the yaw inertia moment, and dy / dt is a derived yaw measure value”, vertical forces of the tires (e.g. see [pg. 15 paragraph 3] “the program goes to the step 304 in which a vertical load on each wheel (wheel load) is calculated based on the lateral acceleration”), and a steering road wheel angle (e.g. see [pg. 13 paragraph 3] “Further, a stop switch ST, which turns on when the brake pedal BP is depressed and turns off when the brake pedal BP is released, provides a steering angle sensor SS for detecting a steering angle θh of the front wheels FL and FR”).
Regarding Claim 5, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claim 1. Yasui further discloses wherein processing the signals to estimate the slip angle rate is based on a longitudinal speed, a lateral acceleration, a yaw rate, one or more vehicle parameters, and a steering wheel angle (e.g. see [pg. 11 last paragraph] “Therefore, the signals detected by the steering angle measuring unit M4, the lateral acceleration measuring unit M7 and the yaw rate measuring unit M8 are supplied to a slip angle estimating unit M9y together with a signal detected by a vehicle speed estimating unit M9x. Corresponding to the skew angle estimation unit M9y, a vehicle skew angular velocity dβ / dt is initially determined based on the yaw rate, the lateral acceleration and the lateral acceleration Vehicle speed calculated and then integrated to obtain a vehicle slip angle β”).
Regarding Claim 6, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claim 1. Yasui does not explicitly disclose wherein performing the filtering process to provide the lateral slope estimation and the self-aligning torque slope estimation includes using a recursive least square estimator or a Kalman filter.
In the same field of endeavor, Kasaiezadeh teaches wherein performing the filtering process to provide the lateral slope estimation and the self-aligning torque slope estimation includes using a recursive least square estimator or a Kalman filter (e.g. see [0057] “In various embodiments, the pneumatic trail estimation module 512 uses a Kalman filter, a least squares method (e.g. a recursive least squares method), or other averaging or filtration based algorithms to determine the slope between estimate SAT values and estimated axle lateral force values, thereby to estimate pneumatic trail”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the lateral slope and self-aligning torque of Yasui with the filtering and classification of Kasaiezadeh for the purpose of estimating lateral adhesion with the advantage of filtering the collected data in order to increase the accuracy of the adhesion determination.
Regarding Claims 8 and 15, Yasui teaches a system for a vehicle(e.g. see [Pg. 2 paragraph 1] “The present invention relates to a device for estimating a Soil adhesion factor and in particular a device for estimate a coefficient of adhesion that indicates the degree of adhesion of a tire on a Road surface in lateral Indicates direction of a vehicle wheel”), comprising: a sensor system configured to sense observable conditions of an environment exterior to the vehicle, an interior environment of the vehicle, and/or a condition of one or more components of the vehicle (e.g. see [pg. 13 paragraph 3] “is in 11 Wheel speed sensors WS1 to WS4 are connected to the wheels FL, FR, RL and RR, respectively, which are connected to the electronic control unit ECU, the signals giving pulses which are proportional to the rotational speed of each wheel, ie a wheel speed signal is transmitted to the electronic control unit ECU supplied. Further, a stop switch ST, which turns on when the brake pedal BP is depressed and turns off when the brake pedal BP is released, provides a steering angle sensor SS for detecting a steering angle θh of the front wheels FL and FR, a longitudinal acceleration sensor XG for detecting a vehicle longitudinal acceleration Gx , a lateral acceleration sensor YG for detecting a vehicle lateral acceleration Gy, a yaw rate sensor YS for determining a yaw rate γ of the vehicle, steering torque sensor TS, rotational angle sensor RS for detecting a rotational angle of the EPS motor 3 etc. These components are electrically connected to the electronic control unit ECU”); and a controller configured to, with one or more processors(e.g. see [pg. 13 paragraph 4] “these control units ECU1-ECU5 are connected to the communication bus through a communication unit connected to the CPU, ROM or RAM for communication. Accordingly, the for Each control system transmits required information through other control systems”):
receive signals from the sensor system indicative of operating parameters of the vehicle while traveling on tires; (e.g. see [pg. 10 paragraph 6] “a steering torque Tstr acting on a steering shaft 2 with a steering wheel operated by a driver 1 is applied, determined by a steering torque sensor TS and an electric motor 3 is controlled in response to the detected steering torque Tstr to the front wheels FL and FR via a speed-reducing gear 4 and about rack and pinion 5 to control, so that the steering operation of the driver is supported”);
process the signals to estimate a self-aligning torque rate (e.g. see [pg. 4 paragraph 1] “Accordingly, the torque Fy .Math. e .sub.n becomes a restoring torque (Tsa) acting in such a direction as to decrease the slip angle α and call it a self-restoring torque”), a lateral force rate, and a slip angle rate (e.g. see [pg. 5 paragraph 3] “the device further comprises a wheel factor estimator for the appraisal of at least one wheel factor including a lateral force and a slip angle, which are applied to the wheel due to the state quantity”),
perform an arbitration and fusion process to adjust the normalized lateral slope estimation based on the classification of the normalized self-aligning torque slope estimation to estimate the final lateral adhesion level indicator. (e.g. see [pg. 4 paragraph 3] “As described above, by monitoring the change of the tire slip (e .sub.n ), the degree of adhesion of the tire in its lateral direction can be determined. And besides, the change of the tire slip (e .sub.n ) results in a return torque Tsa, which can serve to estimate an adhesion factor indicating a degree of adhesion of the tire in its lateral direction, eg, with respect to a front wheel (hereinafter simply referred to as an adhesion factor). With regard to the adhesion factor, this can be estimated based on a limitation of the lateral force for the road friction, as described”).
Yasui does not explicitly disclose perform a state synchronize process to reduce a time mismatch between the lateral force rate and the slip angle rate and thereby provide a synchronized slip angle rate,
In the same field of endeavor, Miyashita teaches perform a state synchronize process to reduce a time mismatch between the lateral force (e.g. see [0023] “The calculation device 10 receives input of measurement data of transient lateral force Fy self-aligning torque (hereinafter simply referred to as torque) Mz and longitudinal force Fx, given slip angle and slip ratio as time series data. Based on the tire dynamics model described later, it calculates the value of the delay time constant (transient response parameter) that characterizes the transient response characteristics of the tire. It also uses this delay time constant and the values of each of the tire dynamics element parameters that constitute the tire dynamics model to calculate time series data of transient lateral force Fy, torque Mz, and longitudinal force Fx)
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the lateral slope and self-aligning torque of Yasui with the state synchronization method of Miyashita for the purpose of estimating lateral adhesion with the advantage of organizing the collected data in order to increase the accuracy of the adhesion determination.
Yasui as modified by Miyashita does not explicitly disclose perform a filtering process to provide a lateral slope estimation and a self-aligning torque slope estimation each based on the self-aligning torque rate, the lateral force rate, and the synchronized slip angle rate.
In the same field of endeavor, Kasaiezadeh teaches perform a filtering process to provide a lateral slope estimation and a self-aligning torque slope estimation each based on the self-aligning torque rate, the lateral force rate, and the synchronized slip angle rate (e.g. see [0057] “In various embodiments, the pneumatic trail estimation module 512 uses a Kalman filter, a least squares method (e.g. a recursive least squares method), or other averaging or filtration based algorithms to determine the slope between estimate SAT values and estimated axle lateral force values, thereby to estimate pneumatic trail”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the lateral slope and self-aligning torque of Yasui with the filtering and classification of Kasaiezadeh for the purpose of estimating lateral adhesion with the advantage of filtering the collected data in order to increase the accuracy of the adhesion determination.
Yasui as modified by Miyashita and Kasaiezadeh does not explicitly disclose perform a normalization process to reduce noise associated with the lateral slope estimation and the self-aligning torque slope estimation and thereby produce a normalized lateral slope estimation and a normalized self-aligning torque slope estimation; classify the normalized self-aligning torque slope estimation to obtain a classification.
In the same field of endeavor, Kobayashi teaches disclose perform a normalization process to reduce noise associated with the lateral slope estimation and the self-aligning torque slope estimation and thereby produce a normalized lateral slope estimation and a normalized self-aligning torque slope estimation (e.g. see [pg. 16 paragraph 7] “Therefore, the normalized self-aligning torque stiffness can be approximated by the following equation (37) using the normalized cornering stiffness C .sub.yα (i.e. normalized lateral slope)(denoted as Wa in the above embodiment)”);
classify the normalized self-aligning torque slope estimation to obtain a classification (e.g. see [pg. 2 last paragraph] “Tire design information is a concept that includes data on tire specifications and data indicating characteristics related to the force generated by the tire. Examples of tire specification data include data indicating the structure, shape, and material of a tire. An example of the data indicating characteristics related to tire force generation includes data indicating tire characteristics such as cornering stiffness, road surface friction coefficient, and self-aligning torque,” and [pg. 3 paragraph 4] “By inputting parameters related to tire design information into the tire model, the analysis device 1 identifies a tire model that reflects the tire design information, and uses the identified tire model to perform vehicle simulation with the vehicle model. Specifically, the analysis device 1 converts the tire design information into tire model parameters in the tire information conversion unit 10, and identifies the tire model using the converted parameters in the tire model identification unit 20”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the lateral slope and self-aligning torque of Yasui with the normalization and classification method of Kobayashi for the purpose of estimating lateral adhesion with the advantage of eliminating noise and organizing the data.
Regarding Claims 9 and 16, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claim 8 and 15. Yasui further discloses wherein the operating parameters include a lateral force (e.g. see [pg. 4 paragraph 1] “in this case, a lateral force Fy generated on the entire contact surface is
equal to the product of a deformed surface of the tread in its lateral direction”), a steering torque (e.g. see [pg. 5 paragraph 3] “steering factor measuring device to determine at least one steering factor, including one Steering torque”), a longitudinal speed (e.g. see [pg. 6 paragraph 2] “Apparatus may further comprise a speed measuring device for the Determining the speed of the vehicle and a filter device for setting a cutoff frequency in accordance with the speed measuring device”), a lateral acceleration, a yaw rate (e.g. see [pg. 11 paragraph 1] “the front side force Fyf can be determined from the results obtained by the lateral acceleration measuring unit M7 and the yaw measuring unit M8”), steering angles, and various vehicle parameters (e.g. see pg. 11 last paragraph] “Therefore, the signals detected by the steering angle measuring unit M4, the lateral acceleration measuring unit M7 and the yaw rate measuring unit M8 are supplied to a slip angle estimating unit M9y together with a signal detected by a vehicle speed estimating unit M9x”).
Regarding Claims 10 and 17, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claims 8 and 15. Yasui further discloses wherein the controller is configured to, by the one or more processors, process the signals to estimate the self-aligning torque rate is based on a self-aligning torque of the tires (e.g. see [pg. 4 paragraph 1] “Accordingly, the torque Fy .Math. e .sub.n becomes a restoring torque (Tsa) acting in such a direction as to decrease the slip angle α and call it a self-restoring torque.”), a lumped mass of a steering system of the vehicle (e.g. see [pg. 19 paragraph 9] “In this regard, the dynamic characteristic of the electric steering apparatus is represented by the following differential equation ( 16 ): where M .sub.r is the mass of the housing and J .sub.m is the motor inertia”).
Yasui does not explicitly disclose wherein processing the signals to estimate the self-aligning torque rate is based on a total torque received from a controller area network of the vehicle, a position and a velocity of the tires, and a lumped dampening of the vehicle.
In the same field of endeavor, Kasaiezadeh teaches wherein the controller is configured to, by the one or more processors, process the signals to estimate the self-aligning torque rate is based on a total torque received from a controller area network of the vehicle (e.g. see [0037] “The sensors 130 sense one or more of the following vehicle parameters and generate corresponding control signals: lateral acceleration, longitudinal acceleration, yaw rate, EPS torque, steering angle, etc. In various embodiments, the sensors 130 communicate the signals directly to the control module 120 and/or may communicate the signals to other control modules (not shown) which, in turn, communicate data from the signals to the control module 120 over a communication bus (not shown) or other communication means.”),
a position and a velocity of the tires (e.g. see [0064] “In one embodiment, the tire slip angle estimation module 516 is configured to map longitudinal and lateral velocity as measured in the sensor system 200 to each tire. For example, the tire slip angle estimation module is configured to estimate tire slip angle using value obtained through sensor system 200 including lateral velocity, longitudinal velocity, yaw rate The following equations are known for front axle and rear axle slip angle estimation”),
and a lumped dampening of the vehicle (e.g. see [0062] “The steering correction calculation module 518 is configured to calculate a steering correction 528, specifically a steering torque reducing factor, so that the automated vehicle control system 520, through the actuator system 400, works with reduced steering torque. This feature of the present disclosure allows for the vehicle 100 to come out of lateral tire force saturation condition and also allows for mitigation of adverse consequences of vehicle instability caused by the lateral tire force saturation condition.”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the signal processed variables of Yasui with the additional variables of Kasaiezadeh for the purpose of estimating the self-aligning torque with the advantage of additional data to ensure the determined torque rate is accurate.
Regarding Claims 11 and 18, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claim 8 and 15. Yasui further discloses wherein the controller is configured to, by the one or more processors, process the signals to estimate the lateral force rate is based on lateral forces of the tires (e.g. see [pg. 11 paragraph 1] “ front side force Fyf, estimated by a side force estimation unit M9, which serves as wheel factor estimation device. The front side force Fyf can be determined from the results obtained by the lateral acceleration measuring unit M7 and the yaw measuring unit M8 in accordance with the following equation: where "Lr" is a distance from the center of gravity to the rear axle, "m" is the vehicle mass, "L" is the wheelbase, "Iz" is the yaw inertia moment, and dy / dt is a derived yaw measure value”, vertical forces of the tires (e.g. see [pg. 15 paragraph 3] “the program goes to the step 304 in which a vertical load on each wheel (wheel load) is calculated based on the lateral acceleration”), and a steering road wheel angle (e.g. see [pg. 13 paragraph 3] “Further, a stop switch ST, which turns on when the brake pedal BP is depressed and turns off when the brake pedal BP is released, provides a steering angle sensor SS for detecting a steering angle θh of the front wheels FL and FR”).
Regarding Claims 12 and 19, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claim 8 and 15. Yasui further discloses wherein the controller is configured to, by the one or more processors, process the signals to estimate the slip angle rate is based on a longitudinal speed, a lateral acceleration, a yaw rate, one or more vehicle parameters, and a steering wheel angle (e.g. see [pg. 11 last paragraph] “Therefore, the signals detected by the steering angle measuring unit M4, the lateral acceleration measuring unit M7 and the yaw rate measuring unit M8 are supplied to a slip angle estimating unit M9y together with a signal detected by a vehicle speed estimating unit M9x. Corresponding to the skew angle estimation unit M9y, a vehicle skew angular velocity dβ / dt is initially determined based on the yaw rate, the lateral acceleration and the lateral acceleration Vehicle speed calculated and then integrated to obtain a vehicle slip angle β”).
Regarding Claim 13, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claim 8. Yasui does not explicitly wherein the controller is configured to, by the one or more processors, perform the filtering process to provide the lateral slope estimation and the self-aligning torque slope estimation includes using a recursive least square estimator or a Kalman filter.
In the same field of endeavor, Kasaiezadeh teaches wherein the controller is configured to, by the one or more processors, perform the filtering process to provide the lateral slope estimation and the self-aligning torque slope estimation includes using a recursive least square estimator or a Kalman filter (e.g. see [0057] “In various embodiments, the pneumatic trail estimation module 512 uses a Kalman filter, a least squares method (e.g. a recursive least squares method), or other averaging or filtration based algorithms to determine the slope between estimate SAT values and estimated axle lateral force values, thereby to estimate pneumatic trail”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the lateral slope and self-aligning torque of Yasui with the filtering and classification of Kasaiezadeh for the purpose of estimating lateral adhesion with the advantage of filtering the collected data in order to increase the accuracy of the adhesion determination.
Claims 7,14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yasui (DE60305232 T2) in view of Miyashita (JP2007290694 A) and in further view of Kasaiezadeh (US 20180297633 A1), Kobayashi (WO2023286750 A1), and Matsuda (JP2013007703A).
Regarding Claim 7, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claim 1. While Yasui and Kobayashi both teach considering road conditions (e.g. see Yasui [pg. 15 paragraph 3] “Therefore, it is possible to calculate the road friction coefficient μ by using an inflection point of the effective restoring torque,” and Kobayashi [pg. 2 last paragraph] “An example of the data indicating characteristics related to tire force generation includes data indicating tire characteristics such as cornering stiffness, road surface friction coefficient, and self-aligning torque,” Yasui does not explicitly disclose wherein performing the normalization process to reduce the noise associated with the lateral slope estimation and the self-aligning torque slope estimation includes consideration of road conditions.
In the same field of endeavor, Matsuda teaches discloses wherein performing the normalization process to reduce the noise associated with the lateral slope estimation and the self-aligning torque slope estimation includes consideration of road conditions (e.g. see [0011] “as shown in equation (4), the coefficient multiplied by M<sub>ｚ</sub>/F<sub>ｙ</sub>, which corresponds to the lateral force lever arm, is a function of φ. Here, according to equation (3) above, information on the road surface friction coefficient μ is necessary to calculate φ,” and [0055] “the denominator of the normalized self-aligning torque includes the tire contact length l that we are trying to estimate and the road surface friction coefficient μ (i.e. road conditions), which is not easy to estimate”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the consideration of road conditions of Yasui with the normalization process of Kobayashi and Matsuda for the purpose of filtering the gathered data with the advantage of additional data to enhance the accuracy of the data normalization.
Regarding Claims 14 and 20, Yasui, Miyashita, Kasaiezadeh, and Kobayashi teach the limitations of Claims 8 and 15. While Yasui and Kobayashi both teach considering road conditions (e.g. see Yasui [pg. 15 paragraph 3] “Therefore, it is possible to calculate the road friction coefficient μ by using an inflection point of the effective restoring torque,” and Kobayashi [pg. 2 last paragraph] “An example of the data indicating characteristics related to tire force generation includes data indicating tire characteristics such as cornering stiffness, road surface friction coefficient, and self-aligning torque,” Yasui does not explicitly disclose wherein the controller is configured to, by the one or more processors, perform the normalization process to reduce the noise associated with the lateral slope estimation and the self-aligning torque slope estimation includes consideration of road conditions.
In the same field of endeavor, Matsuda teaches discloses wherein the controller is configured to, by the one or more processors, perform the normalization process to reduce the noise associated with the lateral slope estimation and the self-aligning torque slope estimation includes consideration of road conditions (e.g. see [0011] “as shown in equation (4), the coefficient multiplied by M<sub>ｚ</sub>/F<sub>ｙ</sub>, which corresponds to the lateral force lever arm, is a function of φ. Here, according to equation (3) above, information on the road surface friction coefficient μ is necessary to calculate φ,” and [0055] “the denominator of the normalized self-aligning torque includes the tire contact length l that we are trying to estimate and the road surface friction coefficient μ (i.e. road conditions), which is not easy to estimate”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the consideration of road conditions of Yasui with the normalization process of Kobayashi and Matsuda for the purpose of filtering the gathered data with the advantage of additional data to enhance the accuracy of the data normalization.
Conclusion
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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