837
DETAILED ACTIONS
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
The information disclosure statements (IDS) submitted on 06/05/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 08/14/2025.
Status of the Claims
Claims 1-6, 8-17, and 19-20 are pending.
Claims 1,8 and 20 are amended.
Claims 7 and 18 are cancelled.
Response to Arguments
Rejections under 35 USC § 103:
Applicant argument in pages 3-5 in the Remarks filed on 08/14/2025 with respect to the rejection(s) of claims have been fully considered, and are not persuasive. The new rejection is set forth below.
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, 8-17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. (US 20200164703 A1 hereinafter Singh, previously cited) and in further view of Gaunt et al. (US 20070038408 A1, hereinafter Gaunt) and in further view of (US 20160129737 A1, hereinafter Singh"737, previously cited).
Regarding Claim 1, Singh teaches,
A load estimation system for a tire (Singh, Figure 1, [0041], load estimation system 50)
the tire including a pair of sidewalls extending to a circumferential tread and supporting a vehicle (Singh, Figure 1, [0039], Each tire 12 includes a pair of
sidewalls 18 that extend to a circumferential tread 16), the system comprising:
a sensor being mounted to the tire (Singh, Figure 1, [0039], Each tire 12
preferably is equipped with a sensor 24 that is mounted to the tire.);
an inflation pressure of the tire being measured by the sensor (Singh, Figure 1, [0039], The sensor 24 preferably includes a pressure sensor to sense the inflation pressure within a cavity 20 of the tire 12);
a footprint formed by the tread, the footprint including a footprint length wherein the footprint length is measured by the sensor; (Singh, Figure 2, [0043] FIG. 2, a footprint 52 of the tread 16 of the tire 12 (FIG. [0053] The input of footprint measurement 106 includes a raw footprint length 112, which is the footprint centerline length 55 (FIG. 2) as measured by the sensor unit 24 (FIG.1);
a processor in electronic communication with the sensor; (Singh, [0040] The sensor 24 preferably also includes a processor and memory to store tire identification (tire ID) information for each specific tire 12);
a pressure correction module in electronic communication with the processor,
the pressure correction module receiving the measured footprint length, the measured inflation pressure, and the inflation correction factor, wherein the pressure correction module determines an adjusted footprint length (Singh, Figure 11, [0053], The input of footprint measurement 106 includes a raw footprint length 112, which is the footprint centerline length 55 (FIG. 2) as measured by the sensor unit 24 (FIG.1) and corrected for vehicle speed and inflation pressure. The correction for inflation pressure includes inputting a inputting a measured tire pressure as indicated by the sensor 24, and adjusting the measured footprint centerline length 55 to account for any inaccuracies in the measurement value due to vehicle speed and/or improper inflation pressure);
a de-noising module in electronic communication with the processor,
the de-noising module receiving the adjusted footprint length to generate a filtered footprint length (Singh, Figure 11, [0053], an event detection module 114/ a filtering module is employed to select measurement of footprint during straight line driving condition. [0056] In this manner, the raw footprint length 112, which is the footprint centerline length 55 measured by the sensor unit 24 during straight-line driving conditions, as corrected for vehicle speed and inflation pressure, is selected for the footprint measurement 106 input into the tire load estimator
100.);
A wear correction module in electronic communication with the processor (Singh, Figure 11, (0061] Another input parameter for the reference footprint
generator 136 includes a driving/route severity estimator 152. the driving/route severity estimator 152 to determine whether a change in tire wear is expected due to driving and/or route severity),
the wear correction module receiving the filtered footprint length and correcting for wear of the tire to generate a wear-corrected footprint length (Singh, Figure 11, [0057], (0057] “To determine the input of the reference footprint 108 for the tire load estimator 100, a raw footprint length 134 as measured by the sensor unit 24 and selected by the event detection module 114 for straight-line driving conditions
is input into a reference footprint generator 136”. NOTE: Footprint generator 136 is activated when the one of the criteria for activation is met, such as expected tire wear based on driving/ route severity);
a vehicle loading state estimator in electronic communication with the processor and determining a loading state of the vehicle (Singh, Figure 13, (0065] Turning to FIG. 13, as an optional feature, the tire load estimation system 50 may include a vehicle mass estimator 162. The mass estimator 162 includes a model based
observer 164 that receives data through the CAN bus system 116 to indirectly estimate a mass 166 of the vehicle 10”).
Singh is silent on
wherein the vehicle loading state estimator employs at least one of the measured inflation pressure and the measured footprint length,
and the loading state of the vehicle distinguishes between an empty, half laden, and fully laden vehicle state;
an inflation correction factor being determined from the loading state of the vehicle;
and a vehicle control system in electronic communication with the processor, the vehicle control system receiving the estimated load on the tire.
However, Gaunt teaches wherein the vehicle loading state estimator employs at least one of the measured inflation pressure and the measured footprint length the loading state of the (Gaunt, Figure 1, vehicle mass estimator 112, [0021], a mass estimator 112, TIS control units 114, and tire pressure sensors 116. In practice, these elements may be coupled together using at least one data communication bus 118 or any suitable interconnection architecture, technique, or arrangement. Figure 3, [0042], “The estimated vehicle mass data may be generated using any suitable process or algorithm that indirectly determines mass or loading conditions of the vehicle. process 200 obtains a measured tire pressure for the rear tires of the vehicle (task 206). In addition, process 200 obtains a vehicle/trailer mass estimation value (task 208) for the vehicle and trailer”.) vehicle distinguishes between an empty, half laden, and fully laden vehicle state an inflation correction factor being determined from the loading state of the vehicle (Gaunt, figure 1, figure 3, the TPM system processes the current measured rear tire pressure and the ERAM value to determine whether the current measured rear tire pressure is proper for the current loading conditions. Next, the TPM system notifies the driver by indicating adjustment of the rear tire pressure to either the first placard pressure or the second placard pressure as necessary. If the vehicle is equipped with a TIS, then the TPM system can initiate automatic adjustment
of the tire pressure” (Gaunt, Estimation of error 210, 218, figure 3,)NOTE: the pressure correction is done based on loading state of the vehicle. The loading state could be empty, partially or fully loading condition)
and a vehicle control system in electronic communication with the processor, the vehicle control system receiving the estimated load on the tire (Gaunt, Figure 1, vehicle mass estimator 112, [0021], a mass estimator 112, TIS control units 114, and tire pressure sensors 116. In practice, these elements may be coupled together using at least one data communication bus 118 or any suitable interconnection architecture, technique, or arrangement).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Singh tire load estimation method to incorporate Gaunt’s vehicle load estimation method by incorporation inflation pressure values as taught by Gaunt with the benefit of determining the proper rear tire inflation pressure based upon an estimated rear axle mass value related to the current loading condition. measuring the vehicle loading mass and tire pressure accurately. (Gaunt, [0005], Figure 3-4).
Modified Singh in particularly Gaunt is silent on a load determination model in electronic communication with the processor, the load determination model receiving the wear-corrected footprint length and determining an estimated load on the tire.
However, Singh”737 teaches a load determination model in electronic communication with the processor, the load determination model receiving the wear-corrected footprint length and determining an estimated load on the tire (Singh”737, Figure 7-8, Model 20, [0045], The estimated tire load is achieved by a processor 20 that is ANN programmed to receive as inputs the contact patch length, the
inflation pressure level, the tire rolling speed, and the estimated tire wear state, the artificial neural network operably compensating the measured contact patch length by the tire wear state estimation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Singh and Gaunt tire load estimation method to incorporate Singh”737 neural network model with the benefits of high accuracy in load estimation with validating the use of tire wear state as a compensatory input to contact patch for improved model robustness and improved real-world accurate estimation result. (Singh”737, [0044], Figure 7-9).
Regarding claim 2, combination of Singh, Gaunt and Singh”737 teaches the load estimation system for a tire of Claim 1,
Singh further teaches wherein the tire is a front tire, the sensor is a front sensor being mounted in the front tire, the inflation pressure is a front inflation pressure, and the footprint length is a front footprint length, the system further comprising: a rear tire; a rear sensor being mounted to the rear tire; a rear inflation pressure of the rear tire being measured by the rear sensor; and a rear footprint formed by a tread of the rear tire, the rear footprint including a rear footprint length, wherein the rear footprint length is measured by the rear sensor, wherein the vehicle loading state estimator receives the front and rear measured footprint lengths and the front and rear inflation pressures.(Singh, Figure 11, [0066] In this manner, the tire load estimation system 50 of the present invention accurately and reliably estimates tire load. The tire load estimation system 50 is based upon input from multiple sensors and includes a sensitivity analysis of the tire 12 to arrive at a tire sensitivity 104, a footprint centerline length 106 as adjusted for event detection, a reference footprint setting 108 which incorporates multiple tire and vehicle parameters, and a reference load 110”. Singh teaches that tire load estimation is calculated based on data input from “multiple tires” and vehicle parameter. “Multiple tire” is interpreted as all tires of the car which include front and back tires).
Regarding claim 3, combination of Singh, Gaunt and Singh”737 teaches the load estimation system for a tire of Claim 2,
Singh further teaches wherein the vehicle loading state estimator includes a de-noising module receiving the front measured footprint length and the rear measured footprint length, the de-noising module removing signal noise to generate a filtered front footprint length and a filtered rear footprint length (Singh, Figure 11, [0053], an event detection module 114/ a filtering module is employed to select measurement of footprint during straight line driving condition. [0056] In this manner, the raw footprint length 112, which is the footprint centerline length 55 measured by the sensor unit 24 during straight-line driving conditions, as corrected for vehicle speed and inflation pressure, is selected for the footprint measurement 106 input into the tire load estimator 100.[0066], Figure 11, The tire load estimation system 50 is based upon input from multiple sensors and includes a sensitivity analysis of the tire 12 to arrive at a tire sensitivity 104, a footprint centerline length 106 as adjusted for event detection, a reference footprint setting 108 which incorporates multiple tire and vehicle parameters, and a reference load 110”. Singh teaches that tire load estimation is calculated based on data input from “multiple tires” and vehicle parameter. “Multiple tire” is interpreted as all tires of the car which include front and back tires.).
Regarding claim 4, combination of Singh, Gaunt and Singh”737 teaches the load estimation system for a tire of Claim 3,
Singh further teaches wherein the vehicle loading state estimator includes a ratio estimator that compares the filtered front footprint length to the filtered rear footprint length to determine a footprint length ratio. ([0066], Figure 11, The tire load estimation system 50 is based upon input from multiple sensors and includes a sensitivity analysis of the tire 12 to arrive at a tire sensitivity 104, a footprint centerline length 106 as adjusted for event detection, a reference footprint setting 108 which incorporates multiple tire and vehicle parameters, and a reference load 110.[0065] FIG. 13, as an optional feature, the tire load estimation system 50 may include a vehicle mass estimator 162. The mass estimator 162 includes a model based observer 164 that receives data through the CAN bus system 116 to indirectly estimate a mass 166 of the vehicle 10. The mass estimator 162 employs a feedback loop 168 using the estimated vehicle mass 166 and the tire load 102
from the estimator 100 (FIG. 11) to determine a correction 170 to the tire load 102. Use of the vehicle mass estimator 162 may thus improve the accuracy of the tire load estimation system 50”. Figure 13 clearly shows the correction of tire load consider all four tire parameters. Shing in [0001] teaches that “The invention is directed to a system and method for estimating tire load which, rather than relying on fixed parameters, incorporates multiple tire and vehicle parameters in monitoring a change in the tire footprint length to provide an accurate and reliable estimation of tire load”.). Therefore, sny person skilled in the art will understand that all four tire parameters are being considered to estimate the tire load on a vehicle. The mathematical steps to obtain are known not inventive)
Regarding claim 5, combination of Singh, Gaunt and Singh”737 teaches the load estimation system for a tire of Claim 4,
Singh is silent on wherein the vehicle loading state estimator includes a vehicle loading state estimation classification model, the vehicle loading state estimation classification model receiving the front inflation pressure, the rear inflation pressure, and the footprint length ratio to determine the loading state of the vehicle.
However, Gaunt teaches wherein the vehicle loading state estimator (Gaunt, Figure 1, 3) includes a vehicle loading state estimation classification model, the vehicle loading state estimation classification model receiving the front inflation pressure, the rear inflation pressure (Gaunt, Figure 4, [0017] In a practical deployment, the TPM system utilizes suitably configured processing logic and processing algorithms
that will notify or instruct the driver to adjust the rear tire pressures based upon an estimated rear axle mass value for the vehicle and the measured tire pressure. In practice, the TPM system can leverage various inputs, data, and
signals obtained from other electronic control modules ("ECUs") in the vehicle”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Singh tire load estimation method to incorporate Gaunt’s vehicle load estimation method by incorporation inflation pressure values as taught by Gaunt with the benefit of determining the proper rear tire inflation pressure based upon an estimated rear axle mass value related to the current loading condition. measuring the vehicle loading mass and tire pressure accurately. (Gaunt, [0005], Figure 3-4).
Both Singh and Gaunt are silent on, and the footprint length ratio.
However, Singh”737 teaches the footprint length ratio (Singh”737, Figure 7-8, Model 20, [0036] FIG. 8 is a schematic representation of an ANN having as inputs the tire rolling speed, the tire inflation pressure, the contact patch length, and the tire wear state as a compensating factor [0045], The estimated tire load is achieved by a processor 20 that is ANN programmed to receive as inputs the contact patch length, the inflation pressure level, the tire rolling speed, and the estimated tire wear state, the artificial neural network operably compensating the measured contact patch length by the tire wear state estimation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Singh and Gaunt tire load estimation method to incorporate Singh”737 neural network model with the benefits of high accuracy in load estimation with validating the use of tire wear state as a compensatory input to contact patch for improved model robustness and improved real-world accurate estimation result. (Singh”737, [0044], Figure 7-9).
Regarding claim 6, combination of Singh, Gaunt and Singh”737 teaches the load estimation system for a tire of Claim 5,
Singh is silent on wherein the vehicle loading state estimation classification model employs a multinomial logistic regression classification methodology.
However, Singh”737teaches wherein the vehicle loading state estimation classification model employs a multinomial logistic regression classification methodology. (Singh”737, Figure 8, 9 [0044] FIG. 8 shows diagrammatically the ANN network receiving as inputs the tire rolling speed, the tire inflation
pressure, the contact patch length, and the compensatory tire wear state. The ANN operably processes the inputs to yield a tire vertical load estimation compensated by tire wear state"NOTE: Machine learning with plural outcome is known as multinomial regression.
.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Singh and Gaunt tire load estimation method to incorporate Singh”737 neural network model with the benefits of high accuracy in load estimation with validating the use of tire wear state as a compensatory input to contact patch for improved model robustness and improved real-world accurate estimation result. (Singh”737, [0044], Figure 7-9).
Regarding claim 8, combination of Singh, Gaunt and Singh”737 teaches the load estimation system for a tire of Claim 1,
Singh further teaches further comprising: at least one of a lookup table and a database in electronic communication with the processor; and an inflation sensitivity being stored in the at least one of the lookup table and the database (Singh, Figure 11, [0051] Turning now to FIG. 11, aspects of the tire load estimation system 50 of the present invention are shown. The system 50 includes a tire load estimator 100, which outputs an estimation of tire load 102. The estimator 100 takes into account a predetermined or precalibrated sensitivity 104, a footprint measurement 106, a reference footprint 108 and a reference load 110)
Singh is silent on wherein the inflation correction factor is determined from the inflation sensitivity.
However, Singh”737 teaches the inflation sensitivity being correlated to the vehicle loading state classification, [0045], “The estimated tire load is achieved by a processor 20 that is ANN programmed to receive as inputs the contact patch length, the inflation pressure level, the tire rolling speed, and the estimated tire wear state, the artificial neural network operably compensating the measured contact patch length by the tire wear state estimation”. It is known in the art that ANN can be used both as regression classification tool.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Singh tire load estimation method to incorporate Singh”737 neural network model with the benefits of high accuracy in load estimation with validating the use of tire wear state as a compensatory input to
contact patch for improved model robustness and improved real-world accurate estimation result. (Singh”737, [0044], Figure 7-9).
Regarding claim 9, combination of Singh, Gaunt and Singh”737 teaches the load estimation system for a tire of Claim 1,
Singh further teaches wherein the de-noising module includes an event filter (Singh, Figure 11, Event detection data filtering 114),
the event filter receiving a steering wheel angle of the vehicle from a controlled area network bus of the vehicle to ensure that only footprint length measurements during straight-line travel of the vehicle are analyzed (Singh, Figure 11, Steering Wheel Angle 118 received from vehicle CAN BUS 116. 0054] With additional reference to FIG. 12, the event detection module 114 receives inputs from vehicle-based sensors through the vehicle CAN bus 116. More particularly, the CAN bus 116 provides data on the angle of the vehicle steering wheel, indicated at 118[0053], an event detection module 114/ a filtering module is employed to select measurement of footprint during straight line driving condition.).
Regarding claim 10 combination of Singh, Gaunt and Singh”737 teaches the load estimation system for a tire of Claim 9,
Singh further teaches wherein the de-noising module includes a de-noising algorithm to filter the adjusted footprint length data. (Singh, Figure 11, [0053], an event detection module 114/ a filtering module is employed to select measurement of footprint during straight line driving condition. [0056] In this manner, the raw footprint length 112, which is the footprint centerline length 55 measured by the sensor unit 24 during straight-line driving conditions, as corrected for vehicle speed and inflation pressure, is selected for the footprint measurement 106 input into the tire load estimator 100.)
Regarding claim 11, combination of Singh, Gaunt and Singh”737 teaches the load estimation system for a tire of Claim 10,
Singh further teaches wherein the de-noising algorithm includes a recursive least square algorithm with a forgetting factor. (Singh, Figure 11, While any known selection technique may be employed in the filtering module 132, a heuristic computation using bivariate gaussian functions is preferred,
as bivariate gaussian functions enable the filtering module to be less sensitive to measurement noise as compared to techniques employing "fixed" thresholds)
Regarding claim 12, combination of Singh, Gaunt and Singh”737 teaches load estimation system for a tire of Claim 10,
Singh further teaches wherein the de-noising module includes a smoothing module, the smoothing module receiving the adjusted footprint length from the de-noising algorithm to generate the filtered footprint length. (Singh, “Figure 11, While any known selection technique may be employed in the filtering module 132, a heuristic computation using bivariate gaussian functions is preferred, as bivariate gaussian functions enable the filtering module to be less sensitive to measurement noise as compared to techniques employing "fixed" thresholds”. Any statistical analysis may be used to smooth out data. It is not an inventive concept. The specification recites in [0041] that smoothing module is an exponential weighted average filter.)
Regarding claim 13, combination of Singh, Gaunt and Singh”737 teaches load estimation system for a tire of Claim 12,
wherein the smoothing module employs an exponential weighted average filter. (Singh, “Figure 11, While any known selection technique may be employed in the filtering module 132, a heuristic computation using bivariate gaussian functions is preferred, as bivariate gaussian functions enable the filtering module to be less sensitive to measurement noise as compared to techniques employing "fixed" thresholds”. Any statistical analysis may be used to smooth out data. It is not an inventive concept).
Regarding claim 14, combination of Singh, Gaunt and Singh”737 teaches load estimation system for a tire of Claim 1,
Singh further teaches the wear correction module includes a direct current block filter, the direct current block filter separating a signal for the filtered footprint length into a direct current component that carries a load dependency and a drift component that carries a wear dependency (Singh, Figure 11, (0061] Another input parameter for the reference footprint generator 136 includes a driving/route severity estimator 152. the driving/route severity estimator 152 to determine whether a change in tire wear is expected due to driving and/or route severity. Figure 11, [0057], (0057] “To determine the input of the reference footprint 108 for the tire load estimator 100, a raw footprint length 134 as measured by the sensor unit 24 and selected by the event detection module 114 for straight-line driving conditions
is input into a reference footprint generator 136”)
Regarding claim 14, combination of Singh, Gaunt and Singh”737 teaches load estimation system for a tire of Claim 15,
Singh further teaches wherein the wear correction module removes the drift component from the filtered footprint length to generate the wear- corrected footprint length. (Singh, Figure 11, (0061] Another input parameter for the reference footprint generator 136 includes a driving/route severity estimator 152. the driving/route severity estimator 152 to determine whether a change in tire wear is expected due to driving and/or route severity. Figure 11, [0057], (0057] “To determine the input of the reference footprint 108 for the tire load estimator 100, a raw footprint length 134 as measured by the sensor unit 24 and selected by the event detection module 114 for straight-line driving conditions is input into a reference footprint generator 136”).
Regarding claim 16, combination of Singh, Gaunt and Singh”737 teaches load estimation system for a tire of Claim 1,
Singh is silent on wherein the wear determination model employs a regression model.
However, Singh”737 teaches wherein the wear determination model employs a regression model (Singh”737, Figure 7-8, Model 20, [0036] FIG. 8 is a schematic representation of an ANN having as inputs the tire rolling speed, the tire inflation pressure, the contact patch length, and the tire wear state as a compensating factor [0045], The estimated tire load is achieved by a processor 20 that is ANN programmed to receive as inputs the contact patch length, the inflation pressure level, the tire rolling speed, and the estimated tire wear state, the artificial neural network operably compensating the measured contact patch length by the tire wear state estimation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Singh tire load estimation method to incorporate Singh”737 neural network model with the benefits of high accuracy in load estimation with validating the use of tire wear state as a compensatory input to
contact patch for improved model robustness and improved real-world accurate estimation result. (Singh”737, [0044], Figure 7-9).
Regarding claim 17, combination of Singh, Gaunt and Singh”737 teaches load estimation system for a tire of Claim 16,
Singh is silent on wherein the regression model includes a linear regression model.
However, Singh”737 teaches wherein the regression model includes a linear regression model. [0045], “The estimated tire load is achieved by a processor 20 that is ANN programmed to receive as inputs the contact patch length, the inflation pressure level, the tire rolling speed, and the estimated tire wear state, the artificial neural network operably compensating the measured contact patch length by the tire wear state estimation”. It is known in the art that ANN can be used both as regression classification tool. See evidence reference “Artificial Neural Network”, Steven Walczak et al. cited above and examiner uploaded a copy “.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Singh tire load estimation method to incorporate Singh”737 neural network model with the benefits of high accuracy in load estimation with validating the use of tire wear state as a compensatory input to
contact patch for improved model robustness and improved real-world accurate estimation result. (Singh”737, [0044], Figure 7-9).
Regarding claim 19, combination of Singh, Gaunt and Singh”737 teaches load estimation system for a tire of Claim 1,
Singh further teaches wherein the processor includes at least one of a vehicle-mounted processor and a processor in a cloud-based computing system (Singh, Figure [0042] Aspects of the tire load estimation system 50 preferably are executed on a processor that is accessible through the vehicle CAN bus. Use of such a processor, and accompanying memory, enables input of data from the
tire-based sensor 24, data from certain vehicle-based sensors to be described below, and data from a lookup table or a database that is stored in a suitable storage medium and is in electronic communication with the processor).
Regarding Claim 20, Singh teaches
A method for estimating the load of a tire, (Singh, Figure 1, [0041], load estimation)
the tire including a pair of sidewalls extending to a circumferential tread and supporting a vehicle (Singh, Figure 1, [0039], Each tire 12 includes a pair of
sidewalls 18 that extend to a circumferential tread 16), the system comprising:
a sensor being mounted to the tire (Singh, Figure 1, [0039], Each tire 12
preferably is equipped with a sensor 24 that is mounted to the tire.);
an inflation pressure of the tire being measured by the sensor (Singh, Figure 1, [0039], The sensor 24 preferably includes a pressure sensor to sense the inflation pressure within a cavity 20 of the tire 12);
a footprint formed by the tread, the footprint including a footprint length wherein the footprint length is measured by the sensor; (Singh, Figure 2, [0043] FIG. 2, a footprint 52 of the tread 16 of the tire 12 (FIG. [0053] The input of footprint measurement 106 includes a raw footprint length 112, which is the footprint centerline length 55 (FIG. 2) as measured by the sensor unit 24 (FIG.1);
a processor in electronic communication with the sensor; (Singh, [0040] The sensor 24 preferably also includes a processor and memory to store tire identification (tire ID) information for each specific tire 12);
a pressure correction module in electronic communication with the processor,
the pressure correction module receiving the measured footprint length, the measured inflation pressure, and the inflation correction factor, wherein the pressure correction module determines an adjusted footprint length (Singh, Figure 11, [0053], The input of footprint measurement 106 includes a raw footprint length 112, which is the footprint centerline length 55 (FIG. 2) as measured by the sensor unit 24 (FIG.1) and corrected for vehicle speed and inflation pressure. The correction for inflation pressure includes inputting a inputting a measured tire pressure as indicated by the sensor 24, and adjusting the measured footprint centerline length 55 to account for any inaccuracies in the measurement value due to vehicle speed and/or improper inflation pressure);
a de-noising module in electronic communication with the processor,
the de-noising module receiving the adjusted footprint length to generate a filtered footprint length(Singh, Figure 11, [0053], an event detection module 114/ a filtering module is employed to select measurement of footprint during straight line driving condition. [0056] In this manner, the raw footprint length 112, which is the footprint centerline length 55 measured by the sensor unit 24 during straight-line driving conditions, as corrected for vehicle speed and inflation pressure, is selected for the footprint measurement 106 input into the tire load estimator
100.);
a wear correction module in electronic communication with the processor (Singh, Figure 11, (0061] Another input parameter for the reference footprint
generator 136 includes a driving/route severity estimator 152. the driving/route severity estimator 152 to determine whether a change in tire wear is expected due to driving and/or route severity),
the wear correction module receiving the filtered footprint length and correcting for wear of the tire to generate a wear-corrected footprint length (Singh, Figure 11, [0057], (0057] “To determine the input of the reference footprint 108 for the tire load estimator 100, a raw footprint length 134 as measured by the sensor unit 24 and selected by the event detection module 114 for straight-line driving conditions
is input into a reference footprint generator 136”. NOTE: Footprint generator 136 is activated when the one of the criteria for activation is met, such as expected tire wear based on driving/ route severity);
a vehicle loading state estimator in electronic communication with the processor and determining a loading state of the vehicle (Singh, Figure 13, (0065] Turning to FIG. 13, as an optional feature, the tire load estimation system 50 may include a vehicle mass estimator 162. The mass estimator 162 includes a model based
observer 164 that receives data through the CAN bus system 116 to indirectly estimate a mass 166 of the vehicle 10”).
Sing is silent on
wherein the loading state of the vehicle distinguishes between an empty, half laden, and fully laden vehicle state;
an inflation correction factor being determined from the loading state of the vehicle;
and a vehicle control system in electronic communication with the processor, the vehicle control system receiving the estimated load on the tire.
However, Gaunt teaches wherein the vehicle loading state estimator employs at least one of the measured inflation pressure and the measured footprint length the loading state of the (Gaunt, Figure 1, vehicle mass estimator 112, [0021], a mass estimator 112, TIS control units 114, and tire pressure sensors 116. In practice, these elements may be coupled together using at least one data communication bus 118 or any suitable interconnection architecture, technique, or arrangement. Figure 3, [0042], “The estimated vehicle mass data may be generated using any suitable process or algorithm that indirectly determines mass or loading conditions of the vehicle. process 200 obtains a measured tire pressure for the rear tires of the vehicle (task 206). In addition, process 200 obtains a vehicle/trailer mass estimation value (task 208) for the vehicle and trailer”.) vehicle distinguishes between an empty, half laden, and fully laden vehicle state an inflation correction factor being determined from the loading state of the vehicle (Gaunt, figure 1, figure 3, the TPM system processes the current measured rear tire pressure and the ERAM value to determine whether the current measured rear tire pressure is proper for the current loading conditions. Next, the TPM system notifies the driver by indicating adjustment of the rear tire pressure to either the first placard pressure or the second placard pressure as necessary. If the vehicle is equipped with a TIS, then the TPM system can initiate automatic adjustment
of the tire pressure” (Gaunt, Estimation of error 210, 218, figure 3,)NOTE: the pressure correction is done based on loading state of the vehicle. The loading state could be empty, partially or fully loading condition)
and a vehicle control system in electronic communication with the processor, the vehicle control system receiving the estimated load on the tire (Gaunt, Figure 1, vehicle mass estimator 112, [0021], a mass estimator 112, TIS control units 114, and tire pressure sensors 116. In practice, these elements may be coupled together using at least one data communication bus 118 or any suitable interconnection architecture, technique, or arrangement).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Singh tire load estimation method to incorporate Gaunt’s vehicle load estimation method by incorporation inflation pressure values as taught by Gaunt with the benefit of determining the proper rear tire inflation pressure based upon an estimated rear axle mass value related to the current loading condition. measuring the vehicle loading mass and tire pressure accurately. (Gaunt, [0005], Figure 3-4).
Modified Singh in particularly Gaunt is silent on a load determination model in electronic communication with the processor, the load determination model receiving the wear-corrected footprint length and determining an estimated load on the tire.
However, Singh”737 teaches a load determination model in electronic communication with the processor, the load determination model receiving the wear-corrected footprint length and determining an estimated load on the tire (Singh”737, Figure 7-8, Model 20, [0045], The estimated tire load is achieved by a processor 20 that is ANN programmed to receive as inputs the contact patch length, the
inflation pressure level, the tire rolling speed, and the estimated tire wear state, the artificial neural network operably compensating the measured contact patch length by the tire wear state estimation).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Singh and Gaunt tire load estimation method to incorporate Singh”737 neural network model with the benefits of high accuracy in load estimation with validating the use of tire wear state as a compensatory input to contact patch for improved model robustness and improved real-world accurate estimation result. (Singh”737, [0044], Figure 7-9).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ludwig Seethler (DE 102017208213 A1), recites “The invention relates to a tire pressure control system of a vehicle with an arrangement for changing, that is to reduce or increase the pressure prevailing in the vehicle tire inflation pressure, wherein an electronic control unit, the tire inflation pressure at a sensed at vehicle standstill rash of a vehicle body relative to the road or to a wheel or the wheels of the vehicle or to a wheel load determined during vehicle standstill. The adaptation preferably takes place on the basis of stored data which describe a relationship between the weight or loading state of the vehicle body, which is estimated on the basis of the ride height of the vehicle body, and a tire inflation pressure adapted thereto”. (Abstract).
Ga et al (KR 2021-0045571 A) recites “A vehicle information monitoring device according to an embodiment of the present invention includes: a sensor unit including at least one sensor for collecting vehicle information; and a control unit that determines whether a vehicle is in a stopped state based on vehicle information and determines whether to estimate the weight of the vehicle and whether to diagnose a failure of at least one sensor according to a determination result. When determining the failure of an acceleration sensor, the device can accurately determine vehicle tire pressure conditions and can prevent the vehicle from being erroneously controlled by estimating the weight of the vehicle quickly and accurately” (abstract)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DILARA SULTANA/Examiner, Art Unit 2858
/PARESH PATEL/Primary Examiner, Art Unit 2858 October 23, 2025