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 .
Status of Claims
This Office Action is in response to the application filed on 11/21/2023. Claim(s) 1-20 are presently pending and are examined in this first action on the merits (FAOM).
Priority
Examiner acknowledges Applicant’s claim to priority based on Application PRO 63/209,114 filed 06/10/2021.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 11/21/2023 has been considered by the Examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-11 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Emi Ueda JP2020008528 (“Ueda”) in view of Ho Jong Lee et. al. US20210101603A (“Lee”)
As per Claim 1,
Ueda discloses,
A method for measuring a vibration characteristic of a non-pneumatic tire (see at least [0001] measuring method and a measuring device for measuring a vibration characteristic of a tire, and [0028] When the tire 100 is an airless tire, since the rim 110 corresponds to a hub connected to the spokes, the rim assembling step S1 may be included in a part of the manufacturing process of the tire 100).
receiving, by a computing device, tire-road contact acceleration data from an accelerometer, the accelerometer being secured to a spoke of a non-pneumatic tire within a sector of the tire (see at least [0072] 5: Evaluation unit (calculation unit), [0034] When the tire 100 is an airless tire, the acceleration sensor 41 may be provided on a spoke portion or a hub portion of the tire 100 and [0033] The detection step S4 is a step in which the detection unit 4 detects the vibration transmitted to the tire 100 or the rim 110. As the detection unit 4, for example, an acceleration sensor 41 for detecting acceleration and a force sensor 42 for detecting force are used)
the tire-road contact acceleration data comprises acceleration data captured by the accelerometer over a duration of time while a tread extending along an outer periphery of the sector of the tire contacts a surface (see at least [0034] The acceleration sensor 41 is provided, for example, on the outer surface of the tread portion 101 or the outer surface of the sidewall portion 102 of the tire 100. As the acceleration sensor 41, for example, a small-sized piezoelectric acceleration pickup of type 4517 manufactured by Brüel & Kjær Company can be used), and [0034] The acceleration sensor 41 detects the vibration transmitted to the tread part 101 and the sidewall part 102 by measuring the acceleration of the tread part 101 and the sidewall part 102.
receiving, by the computing device, normal load data for the tire over the duration of time; (see at least [0035] The force sensor 42 is provided on the tire shaft 120 to which the rim 110 is coupled, and detects a vibration transmitted to the rim 110 by detecting a force generated on the tire shaft 120, and [0059] By including the pressing step S20 of pressing the tread portion 101 against the contact road surface 6, the vibration characteristics of the tire 100 deformed by the load are measured. Further, the vibration characteristics of the tire 100 in a state where a part (the tread surface) of the tread portion 101 is restrained are measured).
Ueda does not disclose
the tire-road contact acceleration data comprises acceleration data captured by the accelerometer over a duration of time while a tread extending along an outer periphery of the sector of the tire contacts a surface
receiving, by the computing device, velocity data for the tire over the duration of time
receiving, by the computing device, normal load data for the tire over the duration of time
generating, by the computing device, a mean vibration characteristic for the tire based on the tire-road contact acceleration data, the velocity data, and the normal load data
Lee teaches,
the tire-road contact acceleration data comprises acceleration data captured by the accelerometer over a duration of time while a tread extending along an outer periphery of the sector of the tire contacts a surface; (see at least [0020] The sensor module includes: an acceleration sensor provided to measure circumferential and radial accelerations of the tire, [0022] The processing module is provided to extract a contact area of the tire from the acceleration waveform graph and to extract the parameter through frequency analysis of the contact area, [0076] Referring to FIGS. 3A and 3B, the processing module 130 may extract a contact area of the tire from the acceleration waveform graph and extract the parameter through frequency analysis of the contact area, and [112] The estimation module 140 which estimates the road surface condition by using such a neural network model has performed a test by using the number of about 5,000 data).
receiving, by the computing device, velocity data for the tire over the duration of time (see at least [0111] The travel speed of the vehicle can be obtained through the CAN/Bus connection of the vehicle, and the speed may be collected by installing an additional GPS sensor, and [0112] The estimation module 140 which estimates the road surface condition by using such a neural network model has performed a test by using the number of about 5,000 data)
receiving, by the computing device, normal load data for the tire over the duration of time (see at least [0111] The load applied to the tire can be estimated from the load and contact length estimated from the acceleration signal, and the value measured by the pressure sensor 112 is used as the pressure, and [112] The estimation module 140 which estimates the road surface condition by using such a neural network model has performed a test by using the number of about 5,000 data).
generating, by the computing device, a mean vibration characteristic for the tire based on the tire-road contact acceleration data, the velocity data, and the normal load data (see at least [0018] a processing module which extracts a parameter for estimating a road surface condition by analyzing the sensing information received by the receiver module, [0021] The processing module extracts the parameter by analyzing acceleration vibration characteristics through an acceleration waveform graph, [0023] The processing module extracts, from the acceleration waveform graph, between a minimum value and a maximum value of a differential value of a radial acceleration graph as the contact area and [0031] The estimation module is provided to estimate the road surface condition by further including a tire pressure, a tire bearing load, and a travel speed in addition to a plurality of the parameters extracted by the processing module).
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire and Lee teaches a method and apparatus for road condition estimation in changing environments using machine learning and taking as input a total of 11 input parameters consisting of test such as a tire pressure, a tire bearing load, and a travel speed and eight acceleration specific parameters (0110).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with the use of velocity and load data to help calculate the vibration characteristics of the tire as taught Lee, with a reasonable expectation of success, to accurately estimate various road surface types by using only the measured values of the acceleration sensor attached inside tire (0043).
As per Claim 2,
Ueda discloses,
method of claim 1, further comprising generating a transfer function or a frequency response function for the tire based on the mean vibration characteristic for the tire (see at least [0037] The vibration characteristics of the tire 100 are evaluated based on, for example, a resonance frequency or a transfer function. The “transfer function” represents a relationship between an input and an output of a vibration transfer system or the like, and is a function represented by a ratio of an input Fourier spectrum to an output Fourier spectrum, and [0039] The arithmetic processing unit performs arithmetic processing such as Fourier transform on the basis of the electric signal (input waveform) input to the electric shaker 2 and the vibration (output waveform) detected by the detection unit 4 to obtain a transfer function. calculate. By examining the transfer function calculated by the arithmetic processing unit in detail, the vibration characteristics of the tire 100 are evaluated).
Lee also teaches,
method of claim 1, further comprising generating a transfer function or a frequency response function for the tire based on the mean vibration characteristic for the tire (see at least [0031] The estimation module is provided to estimate the road surface condition by further including a tire pressure, a tire bearing load, and a travel speed in addition to a plurality of the parameters extracted by the processing module, [0049] FIGS. 5A and 5B are views showing an example of a contact area of a radial acceleration and its time derivative graph according to the embodiment of the present disclosure;
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[0050] FIGS. 6A and 6B are graphs showing a frequency analysis method according to the embodiment of the present disclosure;
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and [0093] Specifically, a method which is mainly used for the frequency analysis of the signal is to use FFT analysis. However, the acceleration measured inside the tire includes various noises as shown in FIG. 6A. Accordingly, the processing module 130 may be provided to perform frequency analysis by using Welchi's method which is shown in FIG. 6B and has relatively low noise).
Thus, Ueda discloses generating a transfer function or a frequency response function for the tire based on the mean vibration characteristic for the tire and Lee also teaches generating a transfer function or a frequency response function for the tire based on the mean vibration characteristic for the tire.
As per Claim 3,
Ueda does not disclose,
method of claim 1, wherein:
the normal load data, the velocity data, and the tire-road contact acceleration data comprise dynamic tire data; and
the method further comprises chopping the dynamic tire data into a plurality of per- revolution data subsets, a per-revolution data subset among the plurality of per-revolution data subsets comprising dynamic tire data for one revolution of the tire.
Lee teaches,
method of claim 1, wherein:
the normal load data, the velocity data, and the tire-road contact acceleration data comprise dynamic tire data (see at least [0110] there are a total of 11 input parameters consisting of test such as a tire pressure, a tire bearing load, and a travel speed and eight acceleration specific parameters. There are outputs of four estimated road conditions, [0022] The processing module is provided to extract a contact area of the tire from the acceleration waveform graph and to extract the parameter through frequency analysis of the contact area), and [0041] the dynamic characteristics of a running tire is measured by a sensor attached inside the tire, and then the road surface condition is estimated through characteristic analysis of the measured waveform).
the method further comprises chopping the dynamic tire data into a plurality of per- revolution data subsets, a per-revolution data subset among the plurality of per-revolution data subsets comprising dynamic tire data for one revolution of the tire (see at least [0112] The estimation module 140 which estimates the road surface condition by using such a neural network model has performed a test by using the number of about 5,000 data. In other words, during wheel rotations 5,000 times, 80% of the data was used for the machine learning and 20% of the data was used for the road surface condition estimation test).
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire and Lee teaches a method and apparatus for road condition estimation in changing environments using machine learning and taking as input a total of 11 input parameters consisting of test such as a tire pressure, a tire bearing load, and a travel speed and eight acceleration specific parameters (0110).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with the calculation of individual rotation data points using acceleration, velocity and load data as taught Lee, with a reasonable expectation of success, to determine road condition (vibration characteristics of a tire) based upon load and velocity in addition to acceleration data, and to estimate the road surface condition accordingly(0116).
As per Claim 4,
Ueda does not disclose,
method of claim 3, further comprising removing outlier data from the plurality of per-revolution data subsets
Lee teaches,
method of claim 3, further comprising removing outlier data from the plurality of per-revolution data subsets (see at least [0025] The processing module analyzes a power spectrum density in a high frequency range in accordance with road surfaces to select a frequency range of interest, and determines signal energy calculated within the frequency range of interest as the parameter input to machine learning, [0090] it is preferable that the processing module 130 uses a signal from which a high frequency noise has been removed by using a low-pass filter for the purpose of distinguishing the acceleration contact area, [0093] the processing module 130 may be provided to perform frequency analysis by using Welchi's method which is shown in FIG. 6B and has relatively low noise, and [0136] the frequency range of interest may be referred to as a range determined to be capable of analyzing the power spectrum density in the high frequency range in accordance with road surfaces and of distinguishing differences between the road surfaces)
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire and Lee teaches removal of noise and outlier data and using the frequency range of interest.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with the calculation of individual rotation data points using acceleration, velocity and load data as taught Lee, with a reasonable expectation of success, to determine road condition (vibration characteristics of a tire) based upon load and velocity in addition to acceleration data, and to analyzes the power spectrum density in a high frequency range in accordance with road surfaces to select a frequency range of interest, and determines signal energy calculated within the frequency range of interest as the parameter input to machine learning (0095).
As per Claim 5,
Ueda does not disclose,
method of claim 4, further comprising feeding the plurality of per- revolution data subsets into a vibration modeler for generating the mean vibration characteristic for the tire
Lee teaches,
method of claim 4, further comprising feeding the plurality of per- revolution data subsets into a vibration modeler for generating the mean vibration characteristic for the tire (see at least [0073] parameter may be referred to as a variable which is used in a road surface estimation model on the basis of an analysis of acceleration vibration characteristics measured on a dry road surface, a wet road surface, an icy road, a snowy road, etc., [0085] As described above, since the acceleration vibration characteristics vary according to each road surface, it can be seen that an appropriate characteristic parameter for road surface estimation can be extracted from the acceleration waveform, [0107] FIG. 8 is a view showing an example of a neural network model structure of the estimation module according to the embodiment of the present disclosure, [0108] Referring further to FIG. 8, the estimation module 140 may be provided to estimate the road surface condition by using machine learning which has the extracted eight parameters and three test conditions as parameters, and [0112] The estimation module 140 which estimates the road surface condition by using such a neural network model has performed a test by using the number of about 5,000 data).
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire and Lee teaches a method and apparatus for road condition estimation in changing environments using machine learning and taking as input a total of 11 input parameters consisting of test such as a tire pressure, a tire bearing load, and a travel speed and eight acceleration specific parameters (0110).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with feeding a set of per revolution data to a machine learning model as taught Lee, with a reasonable expectation of success, to estimate the road surface condition accordingly (0116).
As per Claim 6,
Ueda discloses,
method of claim 1, wherein
the tire comprises a second accelerometer secured within the tire at a location closer to a center of the tire than to the accelerometer (see at least Fig. 2, [0033] the detection unit 4, for example, an acceleration sensor 41 for detecting acceleration and a force sensor 42 for detecting force are used, [0035] The force sensor 42 is provided on the tire shaft 120 to which the rim 110 is coupled, and detects a vibration transmitted to the rim 110 by detecting a force generated on the tire shaft 120 (near the center of the tire))
the second accelerometer being communicatively coupled to the computing device (see at least [0036] The evaluation step S5 is a step of evaluating the vibration characteristics of the tire 100 based on the vibration detected by the detection unit 4. The vibration characteristics of the tire 100 are evaluated by the evaluation unit 5, and [0038] The calculation unit is realized by, for example, an arithmetic processing unit including a CPU (Central Processing Unit) that executes various types of arithmetic processing and information processing, a program that controls the operation of the CPU, a memory that stores various types of information, and the like)
configured to transmit tire-center acceleration data to the computing device over the duration of time while the tread extending along the outer periphery of the sector contacts the surface (see at least Fig. 2, [0068] In the measuring device 1 having the basic structure shown in FIG. 2, the tread portion 101 of the tire 100 is radially vibrated by an electric vibrator (the FG-142 type inertial vibrator), and an acceleration sensor (the 4517 type acceleration pickup). 6), the vibration near the excitation point was measured, and the transfer function shown in FIG. 6 was calculated, and [0069] In the measuring device 1A having the basic structure shown in FIG. 5, the tread portion 101 of the tire 100 is loaded with a load of 4.0 kN in a radial direction by an electric vibrator (the above-described FG-142 type inertial vibrator). Then, the vibration at the tire shaft 120 was measured by the force sensor (1051V series described above), and the transfer function shown in FIG. 7 was calculated).
As per Claim 7,
Ueda discloses,
method of claim 6, further comprising comparing the mean vibration characteristic for the tire to the tire-center acceleration data (see at least Fig. 6, Fig. 7, [0020] In the measuring device according to the present invention, it is preferable that the evaluation unit includes a calculation unit that calculates a transfer function based on the electric signal input by the driving unit and the vibration detected by the detection unit, [0039] By examining the transfer function calculated by the arithmetic processing unit in detail, the vibration characteristics of the tire 100 are evaluated, and [0071] As shown in FIG. 6, it was confirmed that the transfer function in a state where no load was applied was appropriately measured by the measurement device 1. Further, as shown in FIG. 7, it was confirmed that the transfer function under a load was appropriately measured by the measuring device 1A, and that the vibration characteristics of the tire 100 could be measured)
As per Claim 8,
Ueda does not disclose,
method of claim 1, wherein
generating the mean vibration characteristic for the tire comprises applying a machine learning algorithm to the tire-road contact acceleration data, the velocity data, and the normal load data.
Lee teaches,
method of claim 1, wherein
generating the mean vibration characteristic for the tire comprises applying a machine learning algorithm to the tire-road contact acceleration data, the velocity data, and the normal load data (see at least [0043] the characteristics of the measured acceleration signal are extracted when the tire contacts and before and after the tire contacts, and the characteristics are used as an input parameter of the machine learning technique, and [0106] the estimation module 140 may be provided to estimate the road surface condition by further including a tire pressure, a tire bearing load, and a travel speed in addition to a plurality of the parameters extracted by the processing module).
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire and Lee teaches a method and apparatus for road condition estimation in changing environments using machine learning and taking as input a total of 11 input parameters consisting of test such as a tire pressure, a tire bearing load, and a travel speed and eight acceleration specific parameters (0110).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with feeding a set of per revolution data to a machine learning model as taught Lee, with a reasonable expectation of success, to estimate the road surface condition accordingly (0116).
As per Claim 9,
Ueda does not disclose,
method of claim 8, wherein
the machine learning algorithm comprises a decision tree and bootstrapped aggregation
Lee teaches,
method of claim 8, wherein
the machine learning algorithm comprises a decision tree and bootstrapped aggregation (see at least [0112] 80% of the data was used for the machine learning and 20% of the data was used for the road surface condition estimation test, [0114] the method for estimating the road surface condition by the estimation module 140 is not limited to the model structure using the neural network. Various machine learning algorithms such as decision trees and random forests can be used, and [0015] the estimation module 140 can include all the methods using a machine learning algorithm capable of estimating the road surface condition).
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire and Lee teaches a method and apparatus for road condition estimation in changing environments using machine learning and taking as input a total of 11 input parameters consisting of test such as a tire pressure, a tire bearing load, and a travel speed and eight acceleration specific parameters (0110).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with feeding a set of per revolution data to a machine learning model as taught Lee, with a reasonable expectation of success, to estimate the road surface condition accordingly (0116).
As per Claim 10,
Ueda does not disclose,
method of claim 8, wherein
the machine learning algorithm comprises a neural network.
Lee teaches,
method of claim 8, wherein
the machine learning algorithm comprises a neural network (see at least [0109] As one embodiment, the estimation module 140 may be formed to have a model structure using the neural network shown in FIG. 8, and [0112] The estimation module 140 which estimates the road surface condition by using such a neural network model has performed a test by using the number of about 5,000 data)
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire and Lee teaches a method and apparatus for road condition estimation in changing environments using machine learning and taking as input a total of 11 input parameters consisting of test such as a tire pressure, a tire bearing load, and a travel speed and eight acceleration specific parameters (0110).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with feeding a set of per revolution data to a machine learning model as taught Lee, with a reasonable expectation of success, to estimate the road surface condition accordingly (0116).
As per Claim 11,
Ueda does not disclose,
method of claim 1, wherein
generating the mean vibration characteristic for the tire comprises performing a frequency domain analysis on the tire- road contact acceleration data, the velocity data, and the normal load data
Lee teaches,
method of claim 1, wherein
generating the mean vibration characteristic for the tire comprises performing a frequency domain analysis on the tire- road contact acceleration data, the velocity data, and the normal load data (see at least [0022] The processing module is provided to extract a contact area of the tire from the acceleration waveform graph and to extract the parameter through frequency analysis of the contact area, [0031] The estimation module is provided to estimate the road surface condition by further including a tire pressure, a tire bearing load, and a travel speed in addition to a plurality of the parameters extracted by the processing module, [0093] Specifically, a method which is mainly used for the frequency analysis of the signal is to use FFT analysis. However, the acceleration measured inside the tire includes various noises as shown in FIG. 6A. Accordingly, the processing module 130 may be provided to perform frequency analysis by using Welchi's method which is shown in FIG. 6B and has relatively low noise, and [0110] there are a total of 11 input parameters consisting of test such as a tire pressure, a tire bearing load, and a travel speed and eight acceleration specific parameters. There are outputs of four estimated road conditions).
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire and Lee teaches a method and apparatus for road condition estimation in changing environments using machine learning and taking as input a total of 11 input parameters consisting of test such as a tire pressure, a tire bearing load, and a travel speed and eight acceleration specific parameters (0110).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with the calculation of individual rotation data points using acceleration, velocity and load data as taught Lee, with a reasonable expectation of success, to determine road condition (vibration characteristics of a tire) based upon load and velocity in addition to acceleration data, and to estimate the road surface condition accordingly(0116).
As per Claim 15,
Ueda discloses,
The method of claim 1, wherein
the sector comprises an area enclosed by a region of the tire defined between a contact patch angle having a vertex at a center of the tire, radii extending from the contact patch angle to a circular arc extending along the outer periphery of the tire between the radii, and the circular arc (see at least Circular arc (Fig. 2) – a meridian section with a vertex at the center shaft 120 has a thread portion 1-1 that is some radius from the center vertex and forms a circular arc at that radius, [0034] The acceleration sensor 41 detects the vibration transmitted to the tread part 101 and the sidewall part 102 by measuring the acceleration of the tread part 101 and the sidewall part 102, [0035] The force sensor 42 is provided on the tire shaft 120 to which the rim 110 is coupled, and detects a vibration transmitted to the rim 110 by detecting a force generated on the tire shaft 120, [0050] by bringing the first vibration surface 21 a that vibrates in the normal direction into contact with the outer surface of the tread portion 101, the tread portion 101 can vibrate in the radial direction of the tire 100. Thus, the radial vibration characteristics of the tire 100 can be measured) and [0066] In the present embodiment, the acceleration sensor 41 is also provided on the sidewall portion 102 of the tire 100. The acceleration sensor 41 is disposed on a meridian cross section including the vibration surface 21. Accordingly, in the tire 100 in a state where the tire 100 is deformed by the load, the vibration transmitted from the tread surface of the tread portion 101 to the sidewall portion 102 is measured.
As per Claim 16,
Ueda discloses,
method of claim 15, wherein
the accelerometer is secured to the spoke at position closer to the circular arc than to the center of the tire (see at least [0028] When the tire 100 is an airless tire, since the rim 110 corresponds to a hub connected to the spokes, the rim assembling step S1 may be included in a part of the manufacturing process of the tire 100, and [0034] When the tire 100 is an airless tire, the acceleration sensor 41 may be provided on a spoke portion or a hub portion of the tire 100)
As per Claim 17,
Ueda discloses,
method of claim 15, wherein
the mean vibration characteristic for the tire comprises an average vibration characteristic estimated at the center of the tire (see at least [0037] The vibration characteristics of the tire 100 are evaluated based on, for example, a resonance frequency or a transfer function. The “transfer function” represents a relationship between an input and an output of a vibration transfer system or the like, and is a function represented by a ratio of an input Fourier spectrum to an output Fourier spectrum, and [0071] As shown in FIG. 6, it was confirmed that the transfer function in a state where no load was applied was appropriately measured by the measurement device 1. Further, as shown in FIG. 7, it was confirmed that the transfer function under a load was appropriately measured by the measuring device 1A, and that the vibration characteristics of the tire 100 could be measured
the tire-road contact acceleration data comprises vibration characteristics measured while the tread along the outer periphery of the sector of the tire contacts the surface (see at least [0041] the vibration characteristics of the tread portion 101 can be mainly measured. The vibration surface 21 may be in contact with the outer surface of the sidewall portion 102 of the tire 100. In this case, the vibration characteristics of the tread portion 101 can be mainly measured, and [0050] by bringing the first vibration surface 21 a that vibrates in the normal direction into contact with the outer surface of the tread portion 101, the tread portion 101 can vibrate in the radial direction of the tire 100. Thus, the radial vibration characteristics of the tire 100 can be measured).
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Ueda in view of Lee as applied to Claim 1 and further in view of Gustavo Antonio Navarro et. al. US US20170084817A1 (“Navarro”)
As per Claim 12,
Ueda discloses,
method of claim 1, wherein the tire comprises a plurality of spokes (see at least [0034] When the tire 100 is an airless tire, the acceleration sensor 41 may be provided on a spoke portion or a hub portion of the tire 100.
Ueda does not disclose,
the plurality of spokes comprising smart material, the smart material configured to change in stiffness based on an external stimuli.
Navarro teaches,
the plurality of spokes comprising smart material, the smart material configured to change in stiffness based on an external stimuli. (see at least [Abstract] facilitating the use of current within the circuit to harden or soften the piezoelectric cable, and therefore stiffen or soften the overall ride of the tires equipped with the apparatus, [0019] it should be understood that the present invention may be outfitted for use on airless tires, which employ semi-rigid spokes which are partially contorted as the tires rotate. In such instances, the at least one piezoelectric cable (20) of the present invention may be disposed within the semi-rigid spokes to efficiently generate and capture power for use within the electrical systems of the vehicle and [0021] a cabin control panel in communication with the at least one piezoelectric cable (20) of the present invention can supply additional current to the circuit of the system of the present invention, effectively causing the outer surface of the tire (30) to harden).
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire with spokes and Navarro teaches a system and method to harden or soften the piezoelectric cable, and therefore stiffen or soften the overall ride of the tires equipped with the apparatus (Abstract).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with the piezoelectric cable disposed within the semi-rigid spokes on airless tires as taught by Navarro, with a reasonable expectation of success, to enable the tire to change stiffness based on the estimated vibration characteristic of the tire to yielding better fuel mileage on solid, smoothly paved roads(0021) or facilitating a smoother drive on bumpy roads, while also sacrificing fuel mileage (0021).
As per Claim 13,
Ueda does not disclose,
wherein, the smart material comprises piezoelectric material
Navarro teaches,
wherein, the smart material comprises piezoelectric material (see at least [0019] it should be understood that the present invention may be outfitted for use on airless tires, which employ semi-rigid spokes which are partially contorted as the tires rotate. In such instances, the at least one piezoelectric cable (20) of the present invention may be disposed within the semi-rigid spokes to efficiently generate and capture power for use within the electrical systems of the vehicle).
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire with spokes and Navarro teaches a system and method to harden or soften the piezoelectric cable, and therefore stiffen or soften the overall ride of the tires equipped with the apparatus (Abstract).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with the piezoelectric cable disposed within the semi-rigid spokes on airless tires as taught by Navarro, with a reasonable expectation of success, to enable the tire to change stiffness based on the estimated vibration characteristic of the tire to yielding better fuel mileage on solid, smoothly paved roads(0021) or facilitating a smoother drive on bumpy roads, while also sacrificing fuel mileage (0021).
As per Claim 14,
Ueda does not disclose,
wherein, the method further comprises changing a stiffness of the plurality of spokes
Navarro teaches,
wherein, the method further comprises changing a stiffness of the plurality of spokes (see at least [0021] It should be known that the tension or rigidity of conventional piezoelectric cabling is variable when a current is supplied to the circuit. As such, a tire (30) equipped with the system of the present invention can be customized by the user with respect to the road surface type or condition in use, and [0021] For example, a cabin control panel in communication with the at least one piezoelectric cable (20) of the present invention can supply additional current to the circuit of the system of the present invention, effectively causing the outer surface of the tire (30) to harden, yielding better fuel mileage on solid, smoothly paved roads. Conversely, the driver may opt to decrease the current flowing through the circuit of the present invention, causing the outer surface of the tire (30) to soften, facilitating a smoother drive on bumpy roads, while also sacrificing fuel mileage).
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire with spokes and Navarro teaches a system and method to harden or soften the piezoelectric cable, and therefore stiffen or soften the overall ride of the tires equipped with the apparatus (Abstract).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with the piezoelectric cable disposed within the semi-rigid spokes on airless tires as taught by Navarro, with a reasonable expectation of success, to enable the tire to change stiffness based on the estimated vibration characteristic of the tire to yielding better fuel mileage on solid, smoothly paved roads(0021) or facilitating a smoother drive on bumpy roads, while also sacrificing fuel mileage (0021).
Claims 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ueda in view of Lee and Navarro
As per Claim 18,
Ueda discloses,
smart non-pneumatic tire (see at least [0033] an acceleration sensor 41 for detecting acceleration and a force sensor 42 for detecting force are used, [0034] When the tire 100 is an airless tire, the acceleration sensor 41 may be provided on a spoke portion or a hub portion of the tire 100, and [0035] The force sensor 42 is provided on the tire shaft 120 to which the rim 110 is coupled, and detects a vibration transmitted to the rim 110 by detecting a force generated on the tire shaft 120)
Navarro also teaches,
smart non-pneumatic tire (see at least [0019] it should be understood that the present invention may be outfitted for use on airless tires, which employ semi-rigid spokes which are partially contorted as the tires rotate, and [0022] Other alternate embodiments of the present invention include the implementation and positioning of the piezoelectric bank (10) on an Airless tire, disposed along the compressible spokes of such a tire).
Thus, Ueda discloses a smart non-pneumatic tire using a second accelerometer and Navarro teaches a smart non-pneumatic tire using a piezoelectric material.
Ueda further discloses,
a computing device communicatively coupled to the accelerometer; (see at least [0072] 5: Evaluation unit (calculation unit))
the computing device configured to: receive tire-road contact acceleration data from the accelerometer, wherein (see at least [0033] The detection step S4 is a step in which the detection unit 4 detects the vibration transmitted to the tire 100 or the rim 110. As the detection unit 4, for example, an acceleration sensor 41 for detecting acceleration and a force sensor 42 for detecting force are used)
the tire-road contact acceleration data comprises acceleration data captured by the accelerometer over a duration of time while a tread extending along an outer periphery of the sector of the tire contacts a surface (see at least [0034] The acceleration sensor 41 is provided, for example, on the outer surface of the tread portion 101 or the outer surface of the sidewall portion 102 of the tire 100. As the acceleration sensor 41, for example, a small-sized piezoelectric acceleration pickup of type 4517 manufactured by Brüel & Kjær Company can be used, and [0034] The acceleration sensor 41 detects the vibration transmitted to the tread part 101 and the sidewall part 102 by measuring the acceleration of the tread part 101 and the sidewall part 102.
Ueda does not disclose,
receive velocity data for the tire over the duration of time;
receive normal load data for the tire over the duration of time; and
generate a mean vibration characteristic for the tire based on the tire-road contact acceleration data, the velocity data, and the normal load data.
Navarro teaches,
receive velocity data for the tire over the duration of time; (see at least [0111] The travel speed of the vehicle can be obtained through the CAN/Bus connection of the vehicle, and the speed may be collected by installing an additional GPS sensor, and [0112] The estimation module 140 which estimates the road surface condition by using such a neural network model has performed a test by using the number of about 5,000 data)
receive normal load data for the tire over the duration of time; (see at least [0111] The load applied to the tire can be estimated from the load and contact length estimated from the acceleration signal, and the value measured by the pressure sensor 112 is used as the pressure, and [0112] The estimation module 140 which estimates the road surface condition by using such a neural network model has performed a test by using the number of about 5,000 data)
generate a mean vibration characteristic for the tire based on the tire-road contact acceleration data, the velocity data, and the normal load data (see at least [0018] a processing module which extracts a parameter for estimating a road surface condition by analyzing the sensing information received by the receiver module, [0021] The processing module extracts the parameter by analyzing acceleration vibration characteristics through an acceleration waveform graph, [0023] The processing module extracts, from the acceleration waveform graph, between a minimum value and a maximum value of a differential value of a radial acceleration graph as the contact area, and [0031] The estimation module is provided to estimate the road surface condition by further including a tire pressure, a tire bearing load, and a travel speed in addition to a plurality of the parameters extracted by the processing module)
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire and Lee teaches a method and apparatus for road condition estimation in changing environments using machine learning and taking as input a total of 11 input parameters consisting of test such as a tire pressure, a tire bearing load, and a travel speed and eight acceleration specific parameters (0110).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with the use of velocity and load data to help calculate the vibration characteristics of the tire as taught Lee, with a reasonable expectation of success, to accurately estimate various road surface types by using only the measured values of the acceleration sensor attached inside tire (0043).
As per Claim 19,
Ueda discloses,
The method of claim 1, wherein
the sector comprises an area enclosed by a region of the tire defined between a contact patch angle having a vertex at a center of the tire, radii extending from the contact patch angle to a circular arc extending along the outer periphery of the tire between the radii, and the circular arc (see at least Circular arc (Fig. 2) – a meridian section with a vertex at the center shaft 120 has a thread portion 1-1 that is some radius from the center vertex and forms a circular arc at that radius, [0034] The acceleration sensor 41 detects the vibration transmitted to the tread part 101 and the sidewall part 102 by measuring the acceleration of the tread part 101 and the sidewall part 102, [0035] The force sensor 42 is provided on the tire shaft 120 to which the rim 110 is coupled, and detects a vibration transmitted to the rim 110 by detecting a force generated on the tire shaft 120, [0050] by bringing the first vibration surface 21 a that vibrates in the normal direction into contact with the outer surface of the tread portion 101, the tread portion 101 can vibrate in the radial direction of the tire 100. Thus, the radial vibration characteristics of the tire 100 can be measured) and [0066] In the present embodiment, the acceleration sensor 41 is also provided on the sidewall portion 102 of the tire 100. The acceleration sensor 41 is disposed on a meridian cross section including the vibration surface 21. Accordingly, in the tire 100 in a state where the tire 100 is deformed by the load, the vibration transmitted from the tread surface of the tread portion 101 to the sidewall portion 102 is measured.
As per Claim 20,
Ueda does not disclose,
smart non-pneumatic tire of claim 18, wherein
the tire comprises a plurality of spokes, the plurality of spokes comprising smart material, the smart material configured to change in stiffness based on an external stimuli.
Navarro teaches,
smart non-pneumatic tire of claim 18, wherein
the tire comprises a plurality of spokes, the plurality of spokes comprising smart material, the smart material configured to change in stiffness based on an external stimuli (see at least [Abstract] facilitating the use of current within the circuit to harden or soften the piezoelectric cable, and therefore stiffen or soften the overall ride of the tires equipped with the apparatus, [0019] it should be understood that the present invention may be outfitted for use on airless tires, which employ semi-rigid spokes which are partially contorted as the tires rotate. In such instances, the at least one piezoelectric cable (20) of the present invention may be disposed within the semi-rigid spokes to efficiently generate and capture power for use within the electrical systems of the vehicle and [0021] a cabin control panel in communication with the at least one piezoelectric cable (20) of the present invention can supply additional current to the circuit of the system of the present invention, effectively causing the outer surface of the tire (30) to harden).
Thus, Ueda discloses a method for measuring a vibration characteristics of a non-pneumatic tire with spokes and Navarro teaches a system and method to harden or soften the piezoelectric cable, and therefore stiffen or soften the overall ride of the tires equipped with the apparatus (Abstract).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Ueda with the piezoelectric cable disposed within the semi-rigid spokes on airless tires as taught by Navarro, with a reasonable expectation of success, to enable the tire to change stiffness based on the estimated vibration characteristic of the tire to yielding better fuel mileage on solid, smoothly paved roads(0021) or facilitating a smoother drive on bumpy roads, while also sacrificing fuel mileage (0021).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicants should take note of the prior art in the PTO-892.
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/A.P./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668