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 § 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 1,3,7,9-12,13,15-18, 20 are rejected under 35 U.S.C. 103 as being unpatented over US9873293B2 to Singh et al. (herein after “Singh”) in view of US5452207A to Hrovat et al. (herein after “Hrovat”).
Regarding claim 1, Singh teaches A tire wear state estimation system
comprising: (See Singh A tire wear state estimation system)
a tire mounted sensor being mounted on at least one tire supporting a vehicle,
at least one vehicle mounted sensor being mounted on the vehicle; (See Singh abstract determining the amount of frictional work performed by the tire through the integrated use of tire-mounted, GPS sourced, and vehicle-mounted sensor information)
a vehicle CAN bus in communication with one or more vehicle systems of the vehicle and being in communication with the at least one vehicle mounted sensor; (See Singh CAN-bus accessible sensor inputs)
a processor in communication with the tire mounted sensor and the vehicle CAN bus; (see Singh column 7 From the frictional energy estimation, a tire frictional work estimator 102 makes (Step 3) a frictional work estimation. The frictional work estimation is an input into a processor that also receives information from a GPS system)
a plurality of sub-models executable on the processor, wherein each sub-model causes the processor to at least:
obtain, from the tire mounted sensor, selected tire parameters measured by the tire mounted sensor, including at least one of a temperature of the tire, a pressure of the tire (See Singh column 4 The system 10 and method employed therein uses tire-specific sensor information and vehicle-based inertial sensor information to determine tire forces 22 (F) and sliding velocity (V)), and
identification information of the tire;(See Singh column 4 The subject system uses one or more TPMS sensor(s) and tire identification transponder (tire ID) attached to each tire to gather certain tire-based information such as tire air pressure and tire temperature, as well as a transponder by ID number. The tire-based sensor and tire ID transponder information (collectively referred to herein as “tire-specific information”) are transmitted from each of the tires to a remote processor that conducts the calculations necessary to compute tire forces and sliding velocity 22, the friction energy 24 and total friction work L.)
obtain, from the vehicle CAN bus, selected vehicle parameters retrieved from the at least one vehicle mounted sensor, including at least one of a wheel speed, a vehicle speed (See Singh column 6 The tire rolling radius is estimated from a tire rolling radius estimator 48 based on wheel speed) , an acceleration (See Singh acceleration measurement 34.), a vehicle position, and a vehicle inertia (See Singh columns 4-5 The inertial measurement unit (IMU) provides via the vehicle's CAN-bus a 3-axes of rotation rate measurement and a 3-axes of acceleration measurement 34. )
generate a plurality of sub-model wear state estimates (see Singh at least height estimation 58, see Singh column 6 Mass estimation m is made from mass estimator 52 (RLS) based on a longitudinal dynamics model, see Singh vehicle longitudinal dynamics model, DOF planar model ) , wherein each one of the sub- model wear state estimates corresponds to a respective one of the plurality of sub-models based upon the selected tire parameters and the selected vehicle parameters (See Singh column 6 A 6 axis IMU 42, obtained from CAN-bus vehicle sensor-based sensors or handheld smartphone, provides acceleration and angular velocities ax, ay, az and sensors provide roll rate p, pitch rate q and yaw rate r. )(See Singh column 6 An acceleration bias compensation 50 is made from the a.sub.x, a.sub.y and the chassis, road bank and road grade inputs. Mass estimation m is made from mass estimator 52 (RLS) based on a longitudinal dynamics model.) (see Singh a lateral force estimator to estimate a lateral force on the tire from a planar vehicle model using as inputs measured lateral acceleration,) ;
However, Singh does not expressly disclose or otherwise teach transmit the plurality of sub-model wear state estimates to a supervisory model, a model reliability being determined by execution on the processor for each one of the plurality of sub-models based on the selected tire parameters and the selected vehicle parameters, the supervisory model executable on the processor, wherein the supervisory model causes the processor to at least:
apply the plurality of sub-model wear state estimates and the model reliability for each one of the plurality of sub-models, generate a combined wear state estimate for the at least one tire from the plurality of sub-model wear state estimates and the model reliability for each one of the plurality of sub- models, transmit the combined wear state estimate to a vehicle control system, wherein the vehicle control system includes a control unit configured to adjust parameters of the vehicle in response to the combined wear state estimate. Nevertheless, Hrovat same field of endeavor teaches transmit the plurality of sub-model wear state estimates to a supervisory model; (See Hrovat the weighted individual estimates are then summed together to obtain a final output estimate, )
a model reliability being determined by execution on the processor for each one of the plurality of sub-models based on the selected tire parameters and the selected vehicle parameters (See Hrovat column 3 lines 35-43 the three weighting factors could be W1 =0.1, W2 =0.5 and W3 =0.4. The weighted individual estimates are then summed together to obtain a final output estimate, shown generally by reference numeral 30.); and
the supervisory model executable on the processor, wherein the supervisory model causes the processor to at least:
apply the plurality of sub-model wear state estimates and the model reliability for each one of the plurality of sub-models;(see Hrovat For example, for a 3-model estimation, the three weighting factors could be W1 =0.1, W2 =0.5 and W3 =0.4. The weighted individual estimates are then summed together to obtain a final output estimate, shown generally by reference numeral 30.)
generate a combined wear state estimate (see Hrovat final output in column 3) for the at least one tire from the plurality of sub-model wear state estimates and the model reliability for each one of the plurality of sub- models (See Hrovat column 3 lines 35-43 the three weighting factors could be W1 =0.1, W2 =0.5 and W3 =0.4. The weighted individual estimates are then summed together to obtain a final output estimate, shown generally by reference numeral 30.); and
transmit the combined wear state estimate to a vehicle control system, wherein the vehicle control system includes a control unit configured to adjust parameters of the vehicle in response to the combined wear state estimate. (See Singh column 1 It is accordingly desirable to achieve a system and method that accurately and reliably measures tire wear state and communicates wear state to vehicle operators and/or to vehicle operating systems such as braking and stability control systems.)
(See Hrovat column 9 Once the final output is obtained, vehicle operation can be controlled so as to maintain vehicle wheel traction with the road surface.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Hrovat’s torque estimation using multiple models in order to allow to estimate a single torque quantity, such as wheel torque, utilizing multiple models (see Hrovat column 1).
Regarding claim 3, Singh and Hrovat remain applied to claim 1. Singh and Hrovat both teach wherein plurality of sub-models includes a rolling radius-based wear state estimator. (see Singh claim 1 tire wear rate estimator, column 5 Tire rolling radius (48). Tire rolling radius may be obtained through the estimation of tire effective radius using information from a tire-attached TPMS module. U.S. Patent Publication No. 2014/0114558, filed Oct. 19, 2012, published Apr. 24, 2014, and entitled VEHICLE WEIGHT AND CENTER OF GRAVITY ESTIMATION SYSTEM AND METHOD teaches an acceptable approach and is incorporated by reference herein in its entirety.)(Hrovat effective tire radius is used for the calculation see Eqs(1) –(2))
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Hrovat’s torque estimation using multiple models in order to allow to estimate a single torque quantity, such as wheel torque, utilizing multiple models (see Hrovat column 1).
Regarding claim 7, Singh and Hrovat remain applied to claim 1. However, Singh does not teach wherein the model reliability for the rolling radius-based wear state estimator is generated by inferring a plurality of correlations. Nevertheless, Hrovat same field of endeavor teaches wherein the model reliability for the rolling radius-based wear state estimator is generated by inferring a plurality of correlations (See Hrovat equations (1)-(8)).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Hrovat’s torque estimation using multiple models in order to allow to estimate a single torque quantity, such as wheel torque, utilizing multiple models (see Hrovat column 1).
Regarding claim 9, Singh and Hrovat remain applied to claim 1. Singh teaches wherein the plurality of sub- models includes a slip based wear state estimator. (See Singh column 7 The expressions for determining the sliding forces are as shown in FIG. 9 where w is the normalized slip with respect to the limit slips. F.sub.x and F.sub.y are the longitudinal and lateral tire forces estimated under combined slip conditions. Sliding velocities V.sub.sx and V.sub.sy are determined from the expressions shown and friction energy is then calculated from the expressions for E.sub.x and E.sub.y. The kinematics of sliding velocity will be understood from the model 90 shown in FIG. 9, identifying the wheel/tire velocity components. Contact patch (p) velocity may be determined from the expressions shown).
Regarding claim 10, Singh and Hrovat remain applied to claim 1. Singh teaches wherein the slip based wear state estimator includes a tire slip calculator, and the tire slip calculator receives the selected tire parameters and the selected vehicle parameters to calculate the slip of the at least one tire. (see Singh column 6 Sliding results in heat build-up in the tire and wear (abrasion) with wear accelerated at higher temperatures, A parabolic pressure distribution is assumed. F.sub.total is equal to F.sub.adhesion plus F.sub.sliding, see column 7 The expression for the normalized slip with respect to slip limits [ψ] is shown. It will be appreciated that λ.sub.max and α.sub.max define the point of full sliding which is affected by tire temperature, inflation pressure and tire construction properties).
Regarding claim 12, Singh and Hrovat remain applied to claim 1. Singh teaches wherein the slip based sensitivity parameters include at least one of a loading state of the vehicle, inflation pressure conditions, a global positioning system status, an ambient temperature of the at least one tire, and a road surface condition. (See Singh claim 8 wherein the abrasion compensation parameters include tire-specific construction characteristics, road-surface characteristics, ambient temperature characteristics, and road surface interfacial contaminant condition characteristics.).
Regarding claim 13, Singh and Hrovat remain applied to claim 1. Singh teaches wherein the model reliability for the slip based wear state estimator is inferred through a plurality of correlations. (See Singh lateral force and longitudinal force vs. slip ratio figure 10, FIG. 8A are graphs of normalized force vs. slip ratio demonstrating an identification of a sliding zone and showing total force, adhesion force and sliding force graphs. See figure 8B equation).
Regarding claim 15, Singh and Hrovat remain applied to claim 1. Singh teaches wherein the plurality of sub- models includes a frictional energy based wear state estimator (See Singh determines frictional energy from the tire force).
Regarding claim 16, Singh and Hrovat remain applied to claim 1. Singh teaches wherein the frictional energy based wear state estimator includes a frictional energy calculator (See Singh abstract determines frictional energy from the tire force), and the frictional energy calculator receives the selected tire parameters and the selected vehicle parameters to calculate a frictional energy of the at least one tire. (see Singh column 7 FIG. 9 explains the methodology in estimating the sliding friction energy. As summarized, the frictional energy (E.sub.x, E.sub.y) is calculated based on the sliding forces and sliding velocities of the contact patch. T).
Regarding claim 17, Singh and Hrovat remain applied to claim 1. Singh teaches wherein the model reliability for the frictional energy based wear state estimator includes a frictional energy based reliability score function that scores frictional energy based sensitivity parameters to generate the model reliability score for the frictional energy based wear state estimator. (See Singh column 4 The system 10 and method employed therein uses tire-specific sensor information and vehicle-based inertial sensor information to determine tire forces 22 (F) and sliding velocity (V)… total friction work L, In addition to tire-specific information above discussed, abradability factor Ab changes as the result of ambient factors such as pavement characteristics (e.g. smoothness, grading zone, flackiness, etc. of the road surface), ambient air and road temperature and the presence and concentration of interfacial contaminants such as water, dust, mud, etc. on the road surface. Such information).
Regarding claim 18, Singh and Hrovat remain applied to claim 1. Singh teaches wherein the frictional energy based sensitivity parameters include at least one of an ambient temperature of the at least one tire, a road surface condition, and a road roughness condition. (see Singh column 7 a tire frictional energy estimator 101 makes (Step 2) a fictional energy estimation. From the frictional energy estimation, a tire frictional work estimator 102 makes (Step 3) a frictional work estimation. The frictional work estimation is an input into a processor that also receives information from a GPS system. The GPS information include ambient weather condition, a road roughness determination and ambient temperature)
Regarding claim 20, Singh teaches A tire wear state estimation system comprising: (See Singh A tire wear state estimation system )
a tire mounted sensor being mounted on at least one tire supporting a vehicle, at least one vehicle mounted sensor being mounted on the vehicle;
a vehicle CAN bus in communication with one or more vehicle systems of the vehicle and being in communication with the at least one vehicle mounted sensor; (See Singh abstract determining the amount of frictional work performed by the tire through the integrated use of tire-mounted, GPS sourced, and vehicle-mounted sensor information)
a processor in communication with the tire mounted sensor (See Singh CAN-bus accessible sensor inputs) and the vehicle CAN bus(see Singh column 7 From the frictional energy estimation, a tire frictional work estimator 102 makes (Step 3) a frictional work estimation. The frictional work estimation is an input into a processor that also receives information from a GPS system)
; a plurality of sub-models executable on the processor, wherein each sub-model causes the processor to at least:
obtain, from the tire mounted sensor, selected tire parameters measured by the tire mounted sensor
, including at least one of a temperature of the tire, a pressure of the tire(See Singh column 4 The system 10 and method employed therein uses tire-specific sensor information and vehicle-based inertial sensor information to determine tire forces 22 (F) and sliding velocity (V)), and
identification information of the tire;(See Singh 1 column 4 The subject system uses one or more TPMS sensor(s) and tire identification transponder (tire ID) attached to each tire to gather certain tire-based information such as tire air pressure and tire temperature, as well as a transponder by ID number. The tire-based sensor and tire ID transponder information (collectively referred to herein as “tire-specific information”) are transmitted from each of the tires to a remote processor that conducts the calculations necessary to compute tire forces and sliding velocity 22, the friction energy 24 and total friction work L.)
obtain, from the vehicle CAN bus, selected vehicle parameters retrieved from the at least one vehicle mounted sensor, including at least one of a wheel speed, a vehicle speed(See Singh column 6 The tire rolling radius is estimated from a tire rolling radius estimator 48 based on wheel speed) ,, an acceleration(See Singh acceleration measurement 34.), a vehicle position, and a vehicle inertia(See Singh columns 4-5 The inertial measurement unit (IMU) provides via the vehicle's CAN-bus a 3-axes of rotation rate measurement and a 3-axes of acceleration measurement 34. );
generate a plurality of sub-model wear state estimates(see Singh at least height estimation 58, see Singh column 6 Mass estimation m is made from mass estimator 52 (RLS) based on a longitudinal dynamics model, see Singh vehicle longitudinal dynamics model, DOF planar model ), wherein each one of the sub- model wear state estimates corresponds to a respective one of the plurality of sub-models based upon the selected tire parameters and the selected vehicle parameters(See Singh column 6 A 6 axis IMU 42, obtained from CAN-bus vehicle sensor-based sensors or handheld smartphone, provides acceleration and angular velocities ax, ay, az and sensors provide roll rate p, pitch rate q and yaw rate r. )(See column 6 An acceleration bias compensation 50 is made from the a.sub.x, a.sub.y and the chassis, road bank and road grade inputs. Mass estimation m is made from mass estimator 52 (RLS) based on a longitudinal dynamics model.).
However, Singh does not expressly disclose or otherwise teach transmit the plurality of sub-model wear state estimates to a supervisory model, a model reliability being determined by execution on the processor for each one of the plurality of sub-models based on the selected tire parameters and the selected vehicle parameters, the supervisory model executable on the processor, wherein the supervisory model causes the processor to at least:
apply the plurality of sub-model wear state estimates and the model reliability for each one of the plurality of sub-models, generate a combined wear state estimate for the at least one tire from the plurality of sub-model wear state estimates and the model reliability for each one of the plurality of sub- models, transmit the combined wear state estimate to a vehicle control system, wherein the vehicle control system includes a control unit configured to adjust parameters of the vehicle in response to the combined wear state estimate. Nevertheless, Hrovat same field of endeavor teaches transmit the plurality of sub-model wear state estimates to a supervisory model; (See Hrovat the weighted individual estimates are then summed together to obtain a final output estimate, )
a model reliability being determined by execution on the processor for each one of the plurality of sub-models based on the selected tire parameters and the selected vehicle parameters (See Hrovat column 3 lines 35-43 the three weighting factors could be W1 =0.1, W2 =0.5 and W3 =0.4. The weighted individual estimates are then summed together to obtain a final output estimate, shown generally by reference numeral 30.); and
the supervisory model executable on the processor, wherein the supervisory model causes the processor to at least:
apply the plurality of sub-model wear state estimates and the model reliability for each one of the plurality of sub-models; ;(see Hrovat For example, for a 3-model estimation, the three weighting factors could be W1 =0.1, W2 =0.5 and W3 =0.4. The weighted individual estimates are then summed together to obtain a final output estimate, shown generally by reference numeral 30.)
generate a combined wear state estimate(see Hrovat final output in column 3) for the at least one tire from the plurality of sub-model wear state estimates and the model reliability for each one of the plurality of sub- models(See Hrovat column 3 lines 35-43 the three weighting factors could be W1 =0.1, W2 =0.5 and W3 =0.4. The weighted individual estimates are then summed together to obtain a final output estimate, shown generally by reference numeral 30.); and
transmit the combined wear state estimate to a device, wherein an operator of the vehicle actuates the vehicle in response to the combined tire wear state estimate(See Hrovat column 9 Once the final output is obtained, vehicle operation can be controlled so as to maintain vehicle wheel traction with the road surface.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Hrovat’s torque estimation using multiple models in order to allow to estimate a single torque quantity, such as wheel torque, utilizing multiple models (see Hrovat column 1).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatented over Singh in view of Hrovat and US 8600917 B1 to Schimert et al. (herein after “Schimert”).
Regarding claim 18, Singh and Hrovat remain applied to claim 1. Singh does not expressly mention or otherwise teach wherein the supervisory model executes a Bayesian inference to determine a probability distribution over the plurality of sub- models in generating the combined wear state estimate. Nevertheless, Schimert same field of endeavor teaches wherein the supervisory model executes a Bayesian inference to determine a probability distribution over the plurality of sub- models in generating the combined wear state estimate (See Schimert column 6 The goal of recursive Bayesian estimation is to estimate an unknown probability density function over time using observations and a mathematical process model. A Bayes filter uses information about noise and system dynamics to reduce uncertainty from noisy observations. ) .
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Schimert’s Bayesian probability distribution execution over the plurality of sub-models to estimate tire wear in order to allow to utilize for fault detection and prognosis(see Schimert column 4) and to monitor for sudden changes and reject outliers, and adapt the model after these events (see Schimert abstract).
Claims 5,8 and 14 are rejected under 35 U.S.C. 103 as being unpatented over Singh in view of Hrovat and JP 2005174355 A to Yano et al. (herein after “Yano”).
Regarding claim 5, Singh and Hrovat remain applied to claim 1. Singh does not expressly mention or otherwise teach wherein the model reliability for the rolling radius based wear state estimator includes a rolling radius reliability score function that scores rolling radius sensitivity parameters to generate the model reliability score for the rolling radius based wear state estimator. Nevertheless, Yano same field of endeavor teaches wherein the model reliability for the rolling radius based wear state estimator includes a rolling radius reliability score function that scores rolling radius sensitivity parameters to generate the model reliability score for the rolling radius based wear state estimator (see Yano at least para[0023] The adjustment of this normal equation is made by using the variance of the sale price/new car price for the same group to determine the difference in wear and tear caused by factors that are unknown or cannot be set at the time of sale.) .
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Yano’s reliability score function for rolling radius in order to allow to obtain the theoretical residual value rate (see para[0030]) and to determine the difference in wear and tear caused by factors that are unknown or cannot be set at the time of sale (see para[0023]).
Regarding claim 8, Singh and Hrovat remain applied to claim 1. Singh teaches a correlation between a rolling radius of the at least one tire to a vehicle load (See Singh FIG. 6A is a graph showing rolling radius sensitivity to tire load), and a correlation of a grade of a road on which the vehicle travels (See Singh column 1 lines 62-65 a road grade angle input ) .
However, Singh does not expressly mention or otherwise teach a correlation of a global positioning system speed to a wheel speed of the vehicle. Nevertheless, Hrovat same field of endeavor teaches a correlation of a global positioning system speed to a wheel speed of the vehicle, (see Hrovat at least column 6 As illustrated in FIG. 8, the vehicle dynamics model 90 is based on a non-driven wheel speed Nwnd input (RPM) and an optional input for noise for sensitivity studies. Block 92 represents an optional filter, preferably implemented to multiply the wheel speed by "1". The non-driven wheel speed is then multiplied by 2πr/60 (i.e. 0.10476 for tire radius r=1 ft) at block 94, to convert RPM to radians/S, or ft/S. At block 96, the wheel velocity is passed through a differentiator to obtain vehicle acceleration. At block 98, the acceleration is multiplied by the quantity "95.238", an estimation of the total vehicle mass. The output of block 98 is the first estimate of wheel torque, τA.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Hrovat’s torque estimation using multiple models in order to allow to estimate a single torque quantity, such as wheel torque, utilizing multiple models (see Hrovat column 1).
However, Singh does not expressly mention or otherwise teach wherein
the plurality of correlations includes at least one of a correlation of a rolling radius of the at least one tire to a mileage of the vehicle. Nevertheless, Yano same filed of endeavor teaches wherein the plurality of correlations includes
at least one of a correlation of a rolling radius of the at least one tire to a mileage of the vehicle (See Yano para[0005] The vehicle resale price analysis system of the present invention as described in claim 42 is a vehicle resale price analysis system that predicts information regarding the sale price, residual value, or residual value rate of a vehicle before resale from the correlation between at least the sale price and the mileage at the time of resale of a vehicle that has already been resold, and is characterized in that it displays a graph with the mileage as one axis and the sale price or residual value rate obtained by dividing the sale price by the new car price as the other axis, and displays actual data of the mileage and the sale price or residual value rate of the vehicle that has already been resold on the graph, and displays the correlation between the mileage and the sale price or residual value rate.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Yano’s reliability score function for rolling radius in order to allow to obtain the theoretical residual value rate (see para[0030]) and to determine the difference in wear and tear caused by factors that are unknown or cannot be set at the time of sale (see para[0023]).
Regarding claim 14, Singh and Hrovat remain applied to claim 1. Singh teaches a correlation between a global positioning system speed to wheel speeds of the vehicle, (see Singh column 6 The tire rolling radius is estimated from a tire rolling radius estimator 48 based on wheel speed. A tire longitudinal force estimator 46 estimates tire longitudinal forces from the wheel speed)
a correlation of a slip of the at least one tire to a temperature of the at least one tire, a correlation of surface characteristics of a road on which the vehicle travels,(See Singh column 4 abradability factor Ab changes as the result of ambient factors such as pavement characteristics (e.g. smoothness, grading zone, flackiness, etc. of the road surface), ambient air and road temperature and the presence and concentration of interfacial contaminants such as water, dust, mud, etc. on the road surface.) and
a correlation of a roughness of a road on which the vehicle travels (See Singh column 7 The GPS information include ambient weather condition, a road roughness determination ).
However, Singh does not expressly mention or otherwise teach wherein the plurality of correlations includes at least one of a correlation between a slip of the at least one tire and a mileage of the vehicle. Nevertheless, Yano same field of endeavor teaches wherein the plurality of correlations includes at least one of a correlation between a slip of the at least one tire and a mileage of the vehicle(See Yano para[0005] The vehicle resale price analysis system of the present invention as described in claim 42 is a vehicle resale price analysis system that predicts information regarding the sale price, residual value, or residual value rate of a vehicle before resale from the correlation between at least the sale price and the mileage at the time of resale of a vehicle that has already been resold, and is characterized in that it displays a graph with the mileage as one axis and the sale price or residual value rate obtained by dividing the sale price by the new car price as the other axis, and displays actual data of the mileage and the sale price or residual value rate of the vehicle that has already been resold on the graph, and displays the correlation between the mileage and the sale price or residual value rate.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Yano’s reliability score function for rolling radius in order to allow to obtain the theoretical residual value rate (see para[0030]) and to determine the difference in wear and tear caused by factors that are unknown or cannot be set at the time of sale (see para[0023]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatented over Singh in view of Hrovat and US9663115B2 to Kanwar Bharat Singh (herein after “Kanwar”).
Regarding claim 4, Singh and Hrovat remain applied to claim 1. Singh teaches wherein the rolling radius based wear state estimator includes a rolling radius calculator (See Singh claim 2 tire rolling radius estimator, column 6 The tire rolling radius is estimated from a tire rolling radius estimator 48 based on wheel speed.). However, Singh does not teach the rolling radius calculator receives the selected tire parameters and the selected vehicle parameters to calculate a change in a radius of the at least one tire. Nevertheless, Kanwar same field of endeavor teaches the rolling radius calculator receives the selected tire parameters and the selected vehicle parameters to calculate a change in a radius of the at least one tire (See Kanwar column 6 lines 44-48 Tire rolling radius is thus shown to be a function of load, pressure, speed and tire wear state with increasing load and decreasing tread depth acting to decrease rolling radius and increasing pressure and increasing speed acting to increase rolling radius.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Kanwar’s use tire and vehicle parameters to calculate a change in a radius of the at least one tire in order to allow to achieve such a robust system that accurately and reliably measures tire forces in vehicle-supporting tires in real time during vehicle operation (see Column 1).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatented over Singh in view of Hrovat, Yano and Kanwar.
Regarding claim 6, Singh, Hrovat and Yano remain applied to claim 5. Singh and Kanwar combinedly teach wherein the rolling radius sensitivity parameters include at least one of a loading state of the vehicle (See Kanwar FIG. 6A is a graph showing rolling radius sensitivity to tire load.), inflation pressure conditions (See Kanwar FIG. 6B is a graph showing rolling radius sensitivity to tire inflation pressure.), a road grade state (See Singh 2 column 1 lines 62-65 a road grade angle input; ) , and a global positioning system status (See Kanwar The subject system force estimate is made without using global positioning system (GPS) or suspension.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Kanwar’s use tire and vehicle parameters to calculate a change in a radius of the at least one tire in order to allow to achieve such a robust system that accurately and reliably measures tire forces in vehicle-supporting tires in real time during vehicle operation (see Column 1).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatented over Singh in view of Hrovat, Yano and US9739689B2 to Kanwar 2 (herein after “Kanwar 2”).
Regarding claim 19, Singh and Hrovat remain applied to claim 1. Singh teaches wherein the plurality of sub- models includes at least one of a braking stiffness based wear state estimator (see Singh claim 17 wherein the tire-specific information comprises tire characteristics taken from the group consisting of tire inflation pressure, tire temperature, tire material composition hardness, tire material composition molecular structure, tire material composition elongation at break, tire material composition wear resistance, tire material composition degree of vulcanization, tire material composition carbon black content.), a footprint length based wear state estimator (see Singh “Lateral edges” means a line tangent to the axially outermost tread contact patch or footprint as measured under normal load and tire inflation, the lines being parallel to the equatorial centerplane.)
and a tire wear state estimator based on analysis of parameter combinations including at least one of weather (Singh claim 3), and tire construction (Singh claim 8 The tire wear estimation system of claim 7, wherein the abrasion compensation parameters include tire-specific construction characteristics, road-surface characteristics, ambient temperature characteristics, and road surface interfacial contaminant condition characteristics.).
Nevertheless, Yano teaches a tire wear state estimator based on analysis of parameter combinations including at least one of tire mileage (See Yano para[0005]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Yano’s reliability score function for rolling radius in order to allow to obtain the theoretical residual value rate (see para[0030]) and to determine the difference in wear and tear caused by factors that are unknown or cannot be set at the time of sale (see para[0023]).
Nevertheless, Kanwar 2 same field of endeavor teaches, wherein the plurality of sub- models includes at least one of a vibration based wear state estimator, a cornering stiffness based wear state estimator (See Kanwar 2 a tire cornering stiffness estimator employing a model operable to generate a tire cornering stiffness estimation based upon the hub accelerometer signal and adapted by the tire-specific information.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Singh’s Indirect tire wear state prediction system and method with Kanwar 2’s vibration based wear state estimator in order to allow to optimize control commands (active front/rear steering input, yaw control command) to achieve vehicle stability and safety without degrading driver intentions (see column 1).
Conclusion
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott A Browne can be reached at 5712700151. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/NAZIA AFRIN/ Examiner, Art Unit 3666
/SCOTT A BROWNE/ Supervisory Patent Examiner, Art Unit 3666
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