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 .
This is a Non-final Office Action on the merits. Claims 1-20 are currently pending and are addressed below.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 04/15/2024 and 01/22/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Reference WO 2004067307A1 was included in the file but not cited in an IDS. As a courtesy to the applicant and in the interest of compact prosecution, the reference has been considered and listed in the PTO-892 included with this Office Action.
In the IDS filed 01/22/2025, foreign patent documents 1, 3, and 12 (DE 102014205168, DE 102013220882, and JP 2002131191) were not considered because neither a concise explanation of the relevance nor an English translation of the abstract were provided, as required by 37 CFR 1.98(a)(3)(i).
In the IDS filed 01/22/2025, foreign patent documents 6, 8, and 10 (EP 1587704, FR 2970210, and EP 3210799) and NPL document 1 (“A Smartphone-Operable Densely Connected Convolutional Neural Network for Tire Condition Assessment”) were not considered since legible copies were not provided, as required by 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-4, 10-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites “…the plurality of factors…”. It is unclear whether “the plurality of factors” refers to the same factors recited earlier in the claim: “…a respective plurality of tire wear factors…”.
Claim 4 recites “…a respective plurality of factors…”. It is unclear whether “a respective plurality of factors” refers to the same factors recited in parent claim 3: “…a respective plurality of tire wear factors…” and “…the plurality of factors…”.
Claim 5 recites “…for the tire…”. It is unclear whether this refers to the same tire recited earlier in the claim: “…the at least one tire…”.
Claim 10 recites the limitation "the given type of tire" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites “…for the tire…”. It is unclear which tire from parent claim 5 this refers to: “…the at least one tire…” or “…the tire…”.
Claim 13 recites “…for the tire…”. It is unclear whether this refers to the same tire recited in parent claim 1: “…at least one tire…”.
Claim 14 recites “…wherein the current wear rate is determined further based on a brush-type tire wear model…”, and claim 13 recites generating a current wear rate based on time-series inputs for a predictive tire wear model. It is unclear if the “predictive tire wear model” and “brush-type tire wear model” are referring to the same model, or if the brush-type tire wear model is an additional model for generating current wear rate.
Claim 14 recites “…the interface…”. It is unclear whether “the interface” refers to the same interface recited earlier in the claim: “…a contact interface…”.
Claim 16 recites “…for the tire…”. It is unclear whether this refers to the same tire recited in parent claim 1: “…at least one tire…”.
Claim 16 recites the limitation "the tire wear input values" in line 5. There is insufficient antecedent basis for this limitation in the claim.
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.
Claim(s) 1 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh of US 20160159367 A1, filed 12/03/2014, hereinafter “Singh ‘367”, in view of Wood of US 20170168495 A1, filed 12/12/2016, hereinafter “Wood”.
Regarding claim 1, Singh ‘367 teaches:
A method for automatically estimating and selectively applying vehicle tire traction characteristics, the method comprising: collecting vehicle data and/or tire data in association with a vehicle; (See at least [0049]: “Referring to FIG. 1, a vehicle 10 is shown with an exemplary tire 12…The sensor module 24 includes a pressure sensor for measuring the air pressure within cavity 20, a temperature sensor for measuring the tire temperature and a tire identification transponder programmed to provide a tire-specific identification…”)
generating a tire traction model based at least in part on a feedback loop (See at least [0060]: “…A model-based optimal slip ratio estimator (FIG. 8) generates a model-derived optimal tire slip ratio estimation from an assessment of sensor-derived tire-specific parameter information (temperature, load, tread depth, pressure) based upon the tire-specific identification. Coefficients for the model are determined by a processor identification of the tire by construction type, enabled by tire ID obtained from the tire-based ID transponder. The model-derived optimal tire slip ratio estimation is substantially continuously updated during an operation of the vehicle to reflect changes in the tire parameter measurements…”. See also [0058].)
for a particular vehicle: determining a tire wear status for at least one tire associated with the vehicle based on current vehicle data and/or tire data; (See at least [0057]: “…The indirect tire wear state estimation algorithm utilizes a hub acceleration signal which is accessible via the Vehicle CAN bus from vehicle based sensors. The hub acceleration signal is analyzed and an estimation is made as to tread depth or wear. The tread depth used may be the percentage tread wear left or a quantitative value of tread wear depth left on the tire.”)
predicting one or more tire traction characteristics for the at least one tire, based at least on the current vehicle data and/or tire data and the determined tire wear status; and (See at least Fig. 8 & [0060]: “…A model-based optimal slip ratio estimator (FIG. 8) generates a model-derived optimal tire slip ratio estimation from an assessment of sensor-derived tire-specific parameter information (temperature, load, tread depth, pressure) based upon the tire-specific identification…”)
selectively modifying one or more vehicle operation settings based on at least the predicted one or more tire traction characteristics. (See at least [0060]: “…the updated model-derived optimal tire slip ratio estimation may then be utilized to assist control systems such as ABS to improve braking and reduce stopping distance…” & [0058]: “…Thus, the model is pre-calibrated through vehicle testing which enables a lag-free, continuously updated, estimation of optimal slip ratio. This updated slip ratio may then be used in real time to pre-condition the brake system and ABS activation point…”)
Singh ‘367 does not explicitly teach the underlined:
…collecting vehicle data and/or tire data in association with each of a plurality of vehicles…
generating a tire traction model based at least in part on a feedback loop including comparing estimated tire traction, via at least the collected vehicle data and/or tire data and an estimated tire wear status, with corresponding determined actual tire traction; and
Wood teaches:
…collecting vehicle data and/or tire data in association with each of a plurality of vehicles… (See at least [0055]: “…The traction information 217 communicated from the individual vehicles may be determined from, for example, tire sensor logic 112, ABS sensor logic 114, active reflection logic 116 (see FIG. 1) and passive reflection logic 116 (see FIG. 1)…”)
generating a tire traction model based at least in part on a feedback loop including comparing estimated tire traction, via at least the collected vehicle data and/or tire data and an estimated tire wear status, with corresponding determined actual tire traction; and (See at least claim 14: “…further comprising training the model based on comparing the estimated traction value to the traction value that is correlated from the measured one or more sensor values” & [0050]: “…when the vehicle traverses a location of the traction map 144, the update component 146 compares an expected traction value to an actual traction value measured through sensors which rely on interaction with the corresponding road segment. For example, the vehicle 10 may implement the traction determination system 100 to determine an expected traction value for an upcoming location of a road segment using a LiDar. As the vehicle passes over the location, the traction determination system 100 may make a direct traction determination, using, for example, sensor data 111 from tire sensors or ABS sensors. The difference between the expected and direct traction information can be used as feedback for training models used with active reflection logic 114 and/or passive reflection logic 116.” See also [0039] & [0041] regarding the sensor data for directly determining the traction values including tire sensor data 103, which includes the grip state/value.)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Singh ‘367’s method with Wood’s technique of collecting data from a plurality of vehicles and generating a tire traction model based on comparing estimated tire traction with corresponding determined actual tire traction. Doing so would be obvious to “enable accurate prediction of the amount of traction on the road surface” (See [0073] of Wood).
Regarding claim 17, Singh ‘367 teaches:
A system for automatically estimating and selectively applying vehicle tire traction characteristics, the system comprising: (See at least Abstract: “An optimal tire slip ratio estimation system and method affixes a tire-identification device to a vehicle tire to provide a tire-specific identification and one or more sensors affixed to the tire for measuring one or more tire-specific parameters. A model-based optimal slip ratio estimator generates a model-derived optimal tire slip ratio estimation based upon an assessment of sensor-derived tire-specific parameter information based on the tire-specific identification.”)
a computing device or network functionally linked to a vehicle via a communications network, (See at least [0056]: “…Solving for the optimal slip ratio is made by a processor receiving as inputs the tire-based sensor information (inflation pressure, temperature)…” & [0049]: “Referring to FIG. 1, a vehicle 10 is shown with an exemplary tire 12…The module 24 is further equipped with telemetric transmission capability by which the temperature, pressure and identification information can be sent wireles sly to a remote receiver (not shown) for processing….”)
wherein vehicle data and/or tire data collected via an onboard device and/or one or more sensors are transmitted from the vehicle to the computing device or network, and (See at least [0049]: “…The sensor module 24 includes a pressure sensor for measuring the air pressure within cavity 20, a temperature sensor for measuring the tire temperature and a tire identification transponder programmed to provide a tire-specific identification. The module 24 is further equipped with telemetric transmission capability by which the temperature, pressure and identification information can be sent wireles sly to a remote receiver (not shown) for processing….”)
wherein the computing device or network is configured to: generate a tire traction model based at least in part on a feedback loop (See at least [0060]: “…A model-based optimal slip ratio estimator (FIG. 8) generates a model-derived optimal tire slip ratio estimation from an assessment of sensor-derived tire-specific parameter information (temperature, load, tread depth, pressure) based upon the tire-specific identification. Coefficients for the model are determined by a processor identification of the tire by construction type, enabled by tire ID obtained from the tire-based ID transponder. The model-derived optimal tire slip ratio estimation is substantially continuously updated during an operation of the vehicle to reflect changes in the tire parameter measurements…”. See also [0058].)
determine a current tire wear status for at least one tire associated with the vehicle based at least in part on current vehicle data and/or tire data; (See at least [0057]: “…The indirect tire wear state estimation algorithm utilizes a hub acceleration signal which is accessible via the Vehicle CAN bus from vehicle based sensors. The hub acceleration signal is analyzed and an estimation is made as to tread depth or wear. The tread depth used may be the percentage tread wear left or a quantitative value of tread wear depth left on the tire.”)
predict one or more tire traction characteristics for the at least one tire, based at least on the current vehicle data and/or tire data, and the determined tire wear status, and by applying the generated tire traction model; and (See at least Fig. 8 & [0060]: “…A model-based optimal slip ratio estimator (FIG. 8) generates a model-derived optimal tire slip ratio estimation from an assessment of sensor-derived tire-specific parameter information (temperature, load, tread depth, pressure) based upon the tire-specific identification…”)
provide the one or more predicted tire traction characteristics to an active safety unit associated with the vehicle, wherein the active safety unit is configured to selectively modify one or more vehicle operation settings based on at least the predicted one or more tire traction characteristics. (See at least [0060]: “…the updated model-derived optimal tire slip ratio estimation may then be utilized to assist control systems such as ABS to improve braking and reduce stopping distance…” & [0058]: “…Thus, the model is pre-calibrated through vehicle testing which enables a lag-free, continuously updated, estimation of optimal slip ratio. This updated slip ratio may then be used in real time to pre-condition the brake system and ABS activation point…”)
Singh ‘367 does not explicitly teach the underlined:
wherein the computing device or network is configured to: generate a tire traction model based at least in part on a feedback loop from comparing estimated tire traction, via the transmitted vehicle data and/or tire data, and an estimated tire wear, with corresponding determined actual tire traction;
Wood teaches:
wherein the computing device or network is configured to: generate a tire traction model based at least in part on a feedback loop from comparing estimated tire traction, via the transmitted vehicle data and/or tire data, and an estimated tire wear, with corresponding determined actual tire traction; (See at least claim 14: “…further comprising training the model based on comparing the estimated traction value to the traction value that is correlated from the measured one or more sensor values” & [0050]: “…when the vehicle traverses a location of the traction map 144, the update component 146 compares an expected traction value to an actual traction value measured through sensors which rely on interaction with the corresponding road segment. For example, the vehicle 10 may implement the traction determination system 100 to determine an expected traction value for an upcoming location of a road segment using a LiDar. As the vehicle passes over the location, the traction determination system 100 may make a direct traction determination, using, for example, sensor data 111 from tire sensors or ABS sensors. The difference between the expected and direct traction information can be used as feedback for training models used with active reflection logic 114 and/or passive reflection logic 116.” See also [0039] & [0041] regarding the sensor data for directly determining the traction values including tire sensor data 103, which includes the grip state/value.)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Singh ‘367’s system with Wood’s technique of generating a tire traction model based on comparing estimated tire traction with corresponding determined actual tire traction. Doing so would be obvious to “enable accurate prediction of the amount of traction on the road surface” (See [0073] of Wood).
Regarding claim 18, Singh ’367 and Wood in combination teach all the limitations of claim 17 as discussed above.
Singh ‘367 additionally teaches:
wherein: the active safety unit comprises an automated braking system associated with the vehicle, and (See at least [0055]: “…The ABS is switched on when the current tire slip value exceeds a predefined threshold which has been set to λ=0.15…”)
the computing device or network is configured to provide one or more parameters of a predicted mu-slip curve associated with a respective tire to the automated braking system. (See at least [0052]: “…In FIG. 3A, a tire force slip curve showing peak grip and optimal slip ratio points on the curve is shown…” & [0054]: “In current ABS systems, a fixed value is assumed for the location of optimal slip ratio for use in their algorithms. However, as demonstrated in FIGS. 4A through 4D, optimal slip ratio for a given tire changes with operating conditions such as load, inflation pressure, temperature, and tread depth (wear state). Accurate knowledge of the optimal slip point based on tire ID and tire-sensed information (temperature, pressure, and tread wear) is used in the subject optimization of slip ratio point system and method to improve the performance of vehicle control systems such as the ABS.”)
Regarding claim 19, Singh ‘367 teaches:
A system for automatically estimating and selectively applying vehicle tire traction characteristics, the system comprising: (See at least Abstract: “An optimal tire slip ratio estimation system and method affixes a tire-identification device to a vehicle tire to provide a tire-specific identification and one or more sensors affixed to the tire for measuring one or more tire-specific parameters. A model-based optimal slip ratio estimator generates a model-derived optimal tire slip ratio estimation based upon an assessment of sensor-derived tire-specific parameter information based on the tire-specific identification.”)
a first computing device or network functionally linked to a vehicle via a communications network; (See at least [0056]: “…Solving for the optimal slip ratio is made by a processor receiving as inputs the tire-based sensor information (inflation pressure, temperature)…” & [0049]: “Referring to FIG. 1, a vehicle 10 is shown with an exemplary tire 12…The module 24 is further equipped with telemetric transmission capability by which the temperature, pressure and identification information can be sent wireles sly to a remote receiver (not shown) for processing….”)
a vehicle control system associated with a vehicle, (See at least [0060]: “…the updated model-derived optimal tire slip ratio estimation may then be utilized to assist control systems such as ABS to improve braking and reduce stopping distance.”)
wherein, for each vehicle: vehicle data and/or tire data collected via an onboard device and/or one or more sensors is transmitted from the respective vehicle to the first computing device or network; (See at least [0049]: “…The sensor module 24 includes a pressure sensor for measuring the air pressure within cavity 20, a temperature sensor for measuring the tire temperature and a tire identification transponder programmed to provide a tire-specific identification. The module 24 is further equipped with telemetric transmission capability by which the temperature, pressure and identification information can be sent wireles sly to a remote receiver (not shown) for processing….”)
the first computing device or network is configured to generate a tire traction model based at least in part on a feedback loop (See at least [0060]: “…A model-based optimal slip ratio estimator (FIG. 8) generates a model-derived optimal tire slip ratio estimation from an assessment of sensor-derived tire-specific parameter information (temperature, load, tread depth, pressure) based upon the tire-specific identification. Coefficients for the model are determined by a processor identification of the tire by construction type, enabled by tire ID obtained from the tire-based ID transponder. The model-derived optimal tire slip ratio estimation is substantially continuously updated during an operation of the vehicle to reflect changes in the tire parameter measurements…”. See also [0058].)
determine a tire wear status for at least one tire associated with the vehicle based on current vehicle data and/or tire data; (See at least [0057]: “…The indirect tire wear state estimation algorithm utilizes a hub acceleration signal which is accessible via the Vehicle CAN bus from vehicle based sensors. The hub acceleration signal is analyzed and an estimation is made as to tread depth or wear. The tread depth used may be the percentage tread wear left or a quantitative value of tread wear depth left on the tire.”)
predict one or more tire traction characteristics for the at least one tire, based at least on the transmitted vehicle data and the determined tire wear status and by applying the generated tire traction model; and (See at least Fig. 8 & [0060]: “…A model-based optimal slip ratio estimator (FIG. 8) generates a model-derived optimal tire slip ratio estimation from an assessment of sensor-derived tire-specific parameter information (temperature, load, tread depth, pressure) based upon the tire-specific identification…”)
the processor is configured to interact with the respective vehicle control system for modifying the one or more vehicle operation settings based on at least the predicted one or more tire traction characteristics. (See at least [0060]: “…the updated model-derived optimal tire slip ratio estimation may then be utilized to assist control systems such as ABS to improve braking and reduce stopping distance…” & [0058]: “…Thus, the model is pre-calibrated through vehicle testing which enables a lag-free, continuously updated, estimation of optimal slip ratio. This updated slip ratio may then be used in real time to pre-condition the brake system and ABS activation point…”)
Singh ‘367 does not explicitly teach the underlined:
a first computing device or network functionally linked to each of a plurality of vehicles via a communications network;
a fleet management computing device or network functionally linked to the first computing device or network; and
the first computing device or network is configured to generate a tire traction model based at least in part on a feedback loop from comparing estimated tire traction, via the transmitted vehicle data and/or tire data, and an estimated tire wear, with corresponding determined actual tire traction;
provide the one or more predicted tire traction characteristics to the fleet management computing device or network; and
the fleet management computing device or network is configured to interact with the respective vehicle control system for modifying the one or more vehicle operation settings based on at least the predicted one or more tire traction characteristics.
Wood teaches:
a first computing device or network functionally linked to each of a plurality of vehicles via a communications network; (See at least [0036]: “The traction determination system 100 may be implemented as a component or module of a central control system for a vehicle. In variations, the traction determination system 100 may be implemented as part of a distributed control system of the vehicle 10. Still further, the traction determination system 100 may be implemented in part on a network service, using information provided from the vehicle 10.”)
a fleet management computing device or network functionally linked to the first computing device or network; and (See at least [0048]: “…the traction determination component includes an update component 146 which receives traction information 115 from a network service 200 (see FIG. 2). The network service 200 may receive and process traction information from other vehicles, and then communicate updates to the vehicle 10 and other vehicles of the fleet…”)
the first computing device or network is configured to generate a tire traction model based at least in part on a feedback loop from comparing estimated tire traction, via the transmitted vehicle data and/or tire data, and an estimated tire wear, with corresponding determined actual tire traction; (See at least claim 14: “…further comprising training the model based on comparing the estimated traction value to the traction value that is correlated from the measured one or more sensor values” & [0050]: “…when the vehicle traverses a location of the traction map 144, the update component 146 compares an expected traction value to an actual traction value measured through sensors which rely on interaction with the corresponding road segment. For example, the vehicle 10 may implement the traction determination system 100 to determine an expected traction value for an upcoming location of a road segment using a LiDar. As the vehicle passes over the location, the traction determination system 100 may make a direct traction determination, using, for example, sensor data 111 from tire sensors or ABS sensors. The difference between the expected and direct traction information can be used as feedback for training models used with active reflection logic 114 and/or passive reflection logic 116.” See also [0039] & [0041] regarding the sensor data for directly determining the traction values including tire sensor data 103, which includes the grip state/value.)
provide the one or more predicted tire traction characteristics to the fleet management computing device or network; and (See at least [0052]: “…When the vehicle traverses a given location, and the difference between the expected and direct traction value at that location is above a threshold, the traction map 144 may be updated on the vehicle 10. In some variations, the update component 146 may also determine when traction measurements of the vehicle are different (e.g., above a comparative threshold) from the traction values provided from the network service 200. In such cases, the update component 146 may selectively update the network service 200 when the traction information is determined to be different than those expected from the network service.”)
the fleet management computing device or network is configured to interact with the respective vehicle control system for modifying the one or more vehicle operation settings based on at least the predicted one or more tire traction characteristics. (See at least [01128-0129]: “With reference to an example of FIG. 8, the control system 300 of the vehicle 10 operates to determine an expected traction value for a region of a road segment on which the vehicle is approaching during a trip (810). In one implementation, the expected traction value is determined by receiving a traction map (or portions thereof) from the network service 200 (812)…Based on the expected traction value, the control system 300 determines a set of motion parameters (820). The set of motion parameters can affect the vehicle's immediate, upcoming, or contingent or planned trajectory. This may include, for example, which lane on a road the vehicle 10 travels on, or the position the vehicle takes in-lane on the road segment…”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Singh ‘367’s system with the teachings of Wood. Doing so would be obvious to “enable accurate prediction of the amount of traction on the road surface” (See [0073] of Wood).
Regarding claim 20, Singh ‘367 and Wood in combination teach all the limitations of claim 19 as discussed above.
Singh ‘367 additionally teaches:
wherein the predicted one or more tire traction characteristics comprise one or more parameters of a predicted mu-slip curve associated with a respective tire. (See at least Fig. 3A, [0052]: “…In FIG. 3A, a tire force slip curve showing peak grip and optimal slip ratio points on the curve is shown. Experimental data, a regression model fit and a tire model fit are shown for comparison purposes. As seem from the table 30B braking stiffness, peak grip, optimal slip ratio and shape factor are affected to varying extents by the identified parameters: load on the tire, tire inflation pressure, vehicle speed, tire wear state and tire temperature…” & [0055]: “…The variation of optimal slip ratio with various operating conditions is obtained from force slip curves generated from experiments…”)
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh ‘367 in view of Wood and further in view of Singh of US 9610810 B1, filed 10/21/2015, hereinafter “Singh ‘810”.
Regarding claim 2, Singh ‘367 and Wood in combination teach all the limitations of claim 1 as discussed above.
Singh ‘367 additionally teaches:
wherein the one or more tire traction characteristics comprise one or more parameters of a predicted mu-slip curve associated with a respective tire, and (See at least Fig. 3A, [0052]: “…In FIG. 3A, a tire force slip curve showing peak grip and optimal slip ratio points on the curve is shown. Experimental data, a regression model fit and a tire model fit are shown for comparison purposes. As seem from the table 30B braking stiffness, peak grip, optimal slip ratio and shape factor are affected to varying extents by the identified parameters: load on the tire, tire inflation pressure, vehicle speed, tire wear state and tire temperature…” & [0055]: “…The variation of optimal slip ratio with various operating conditions is obtained from force slip curves generated from experiments…”)
Singh ‘367 and Wood in combination do not explicitly teach:
a corresponding maximum speed for the vehicle.
Singh ‘810 teaches:
a corresponding maximum speed for the vehicle. (See at least Figs. 5A-5B, Figs. 6A-6B & col. 5, lines 50-59: “In FIG. 5A, curve 40 represents test results for a new tire mounted to a mid-tier sedan. The curve 40 shows braking force and vehicle speed. An amplitude spectrogram 42 shows frequency over time and magnitude dB. FIG. 5B shows the corresponding results in curve 44 for a worn tire. An amplitude spectrogram 46 shows frequency over time and magnitude dB. In FIG. 6A, testing results in curve 46 show brake pressure, slip-ratio and optimal slip-ratio for a new tire mounted to a mid-tier sedan. FIG. 6B show the corresponding results in curve 50 for a worn tire…”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Singh ‘367 and Wood’s method with Singh ‘810’s corresponding maximum vehicle speed. Doing so would be obvious to “accurately and reliably measures a tire state for use by vehicle operating systems such as braking and stability control systems” (See col. 1, lines 24-27 of Singh ‘810).
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh ‘367 in view of Wood and further in view of Alghooneh of US 20220324266 A1, filed 04/09/2021, hereinafter “Alghooneh”.
Regarding claim 3, Singh ‘367 and Wood in combination teach all the limitations of claim 1 as discussed above.
Singh ‘367 and Wood in combination do not explicitly teach:
wherein the tire wear status is determined by: accumulating in data storage information regarding probability distributions corresponding to each of a respective plurality of tire wear factors;
generating at least one observation corresponding to one or more of the plurality of factors based on the current vehicle data and/or tire data; and
providing a Bayesian estimation of the tire wear status at a given time for the at least one tire, based at least on the at least one generated observation and the stored information regarding probability distributions.
Alghooneh teaches:
wherein the tire wear status is determined by: accumulating in data storage information regarding probability distributions corresponding to each of a respective plurality of tire wear factors; (See at least [0027]: “FIG. 3 is a graphical illustration of a fusion of distributions of direct tire tread depth measurements with an indirect tire tread depth estimation…” & [0051]: “The indirect tire tread wear distribution (where μ.sub.tire tread wear is the mean value of the tire tread wear distribution and σ.sub.T.sub.tire tread wear is the covariance of the tire tread wear distribution) uses available vehicle data (e.g., velocity, wheel speed, steering angle, yaw rate, tire pressure, etc.) obtained from the sensors 22 and/or the vehicle systems 24…”. See also Eqn. 1 below and [0040-0050] regarding the equation for indirect tire tread wear distribution for each tire, which includes the vehicle data (e.g., velocity).)
Eqn. 1:
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generating at least one observation corresponding to one or more of the plurality of factors based on the current vehicle data and/or tire data; and (See at least [0037]: “…The direct tire tread wear measurement data 23 is obtained, in some embodiments, by a direct, physical measurement, image data, and/or determined by other direct, real-time measurement means…”)
providing a Bayesian estimation of the tire wear status at a given time for the at least one tire, based at least on the at least one generated observation and the stored information regarding probability distributions. (See at least [0053]: “…Fusing the direct tire tread wear measurement with the indirect tire tread wear estimate is accomplished using Bayesian filtering and weighted averaging, for example and without limitation, to reduce accumulated inaccuracies in the indirect tire tread wear estimation” & [0058]: “With reference to FIG. 3, the indirect tire tread wear estimation, bel(x.sub.t), is illustrated as line 302. The direct tire tread wear measurement, p(z.sub.t|x.sub.t), is illustrated as line 304. Bayesian filtering uses the distribution of the estimation (mean value and standard distribution) and that of the direct measurement and fuses them together to the line 30 that represents the fused direct tire tread wear measurement data with the lower uncertainty bel(x.sub.t). η is the normalization factor. In various embodiments, the indirect tire tread wear estimation is fused with the minimum of distribution of the direct tire tread depth measurement.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Singh ‘367 and Wood’s method with the teachings of Alghooneh. Doing so would be obvious “to reduce accumulated inaccuracies in the indirect tire tread wear estimation” (See [0053] of Alghooneh).
Regarding claim 4, Singh ‘367, Wood, and Alghooneh in combination teach all the limitations of claim 3 as discussed above.
Alghooneh additionally teaches:
further comprising storing information regarding updated probability distributions corresponding to a respective plurality of factors contributing to tire wear for the at least one tire, based at least on the generated at least one observation. (See at least [0067]: “At 416, the controller 26 compares the direct tire tread wear measurement data to the indirect tire tread wear estimation to adaptively adjust or refine the tire tread wear calibration coefficients used to perform future indirect tire tread wear estimations.” See also Eqn. 1 below and [0040-0050] regarding the equation for indirect tire tread wear distribution for each tire, which includes the calibration coefficient
γ
.)
Eqn. 1:
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Claim(s) 5 and 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh ‘367 in view of Wood and further in view of Kurz of DE 102021205522 A1, filed 05/31/2021, hereinafter “Kurz”.
Regarding claim 5, Singh ‘367 and Wood in combination teach all the limitations of claim 1 as discussed above.
Singh ‘367 additionally teaches:
wherein the tire wear status is determined by: storing a tread depth at a first stage for the at least one tire; (See at least [0060]: “…A model-based optimal slip ratio estimator (FIG. 8) generates a model-derived optimal tire slip ratio estimation from an assessment of sensor-derived tire-specific parameter information (temperature, load, tread depth, pressure) based upon the tire-specific identification…”)
Singh ‘367 and Wood in combination do not explicitly teach:
responsive to a first modal analysis for the tire, sensing and storing a first set of one or more modal frequencies for the at least one tire at the first stage;
responsive to a second modal analysis for the tire, at a subsequent second stage, sensing a second set of a corresponding one or more modal frequencies for the at least one tire; and
estimating the tire wear status of the at least one tire at the second stage based on a calculated frequency shift between at least one corresponding modal frequency from each of the first and second sets.
Kurz teaches:
responsive to a first modal analysis for the tire, sensing and storing a first set of one or more modal frequencies for the at least one tire at the first stage; (See at least Fig. 4, [0004]: “…determining a first natural frequency (13) of the vehicle tire (1) with at least one first sensor…” & [0045]: “The maximum 13 of the measurement curve 11 shows the first natural frequency of the vehicle tire for the new vehicle tire. This first natural frequency of the new tire lies in a frequency spectrum between 80 and 120 Hz, for example it is at approximately... 80 Hz…”. See also [0020] regarding the natural frequencies being stored on a data storage device.)
responsive to a second modal analysis for the tire, at a subsequent second stage, sensing a second set of a corresponding one or more modal frequencies for the at least one tire; and (See at least Fig. 4 & [0045]: “…After the vehicle tire is worn down and has a lower tread depth, a natural frequency shift
estimating the tire wear status of the at least one tire at the second stage based on a calculated frequency shift between at least one corresponding modal frequency from each of the first and second sets. (See at least Fig. 4 & [0045]: “…The remaining profile depth can thus be determined via the natural frequency shift 15. The figure also shows that the amplitude value of the first natural frequency increases in a worn vehicle tire. This change in the amplitude value can also be used to determine the remaining profile depth.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Singh ‘367 and Wood’s method with Kurz’s technique of sensing/storing multiple sets of modal frequencies and estimating the tire wear status based on a calculated frequency shift between a corresponding modal frequency from each of the first and second sets. Doing so would be obvious to improve “the accuracy in determining the remaining tread depth” (See [0008] of Kurz).
Regarding claim 11, Singh ‘367, Wood, and Kurz in combination teach all the limitations of claim 5 as discussed above.
Kurz additionally teaches:
wherein the first and second sets of corresponding modal frequencies are sensed via one or more accelerometers, (See at least [0011]: “…it is provided that the first sensor (4) and/or the second sensor detects and analyzes the vertical vibrations of the vehicle tire (1) in the frequency range between 80 and 120 Hz, whereby the first natural frequency (13) is determined during the data analysis” & [0015]: “…it is provided that the first and/or second sensor is an accelerometer…”)
Regarding claim 12, Singh ‘367, Wood, and Kurz in combination teach all the limitations of claim 11 as discussed above.
Kurz additionally teaches:
wherein the tire structural modes for a given tire are: randomly excited during operation of the tire and associated output signals generated by the one or more accelerometers are captured; excited by controlled impacting of the tire with an external object; and/or excited by directing movement of the vehicle with respect to one or more predetermined obstacles. (See at least [0036-0037]: “Fig. 2 schematically shows the vibrations occurring of the vehicle tire 5 in the direction of travel 6 when the vehicle tire rolls, for example, on a road surface. Fig. 3 schematically shows the vehicle tire 7 as it oscillates in a vertical direction. With this type of vibration, the natural frequency is usually determined within a frequency spectrum between 80 and 120 Hz…”. See also [0015] regarding the first/second sensors being an accelerometer.)
Claim(s) 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh ‘367 in view of Wood and Kurz and further in view of Oldenettel of DE 19716586 C1, filed 04/21/1997, hereinafter “Oldenettel”.
Regarding claim 6, Singh ‘367, Wood, and Kurz in combination teach all the limitations of claim 5 as discussed above.
Singh ‘367, Wood, and Kurz in combination do not explicitly teach:
further comprising storing a mass of the at least one tire at the first stage, wherein the step of estimating the tire wear status at the second stage comprises determining a change in mass of the at least one tire between the first and second stages based on the calculated frequency shift.
Oldenettel teaches:
further comprising storing a mass of the at least one tire at the first stage, wherein the step of estimating the tire wear status at the second stage comprises determining a change in mass of the at least one tire between the first and second stages based on the calculated frequency shift. (See at least [0028]: “If one wants to be able to measure the decreasing tread depth of the tire or tire wear with a resolution of 1 mm, the following estimate can be given: A 10 kg heavy brand new tire loses approximately 200 g of mass when the tread depth decreases from 9 mm to 8 mm. The mass is therefore reduced by 1/20. The natural frequency given by equation 9 therefore changes by 1/40…” & [0007]: “…- Calculating the moment of inertia of the tire belt about the axis of rotation from the torsional stiffness of the tire wall and the determined natural frequency, - Calculating the current mass and the change in diameter of the tire from the determined moment of inertia and the mass function of the tire as a function of the diameter, - Calculating the profile loss from the diameter change.”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Singh ‘367, Wood, and Kurz’s method with Oldenettel’s technique of estimating the tire wear status by determining a change in mass of the tire based on the calculated frequency shift. Doing so would be obvious “to measure the decreasing tread depth of the tire or tire wear with a resolution of 1 mm” (See [0028] of Oldenettel).
Regarding claim 7, Singh ‘367, Wood, Kurz, and Oldenettel in combination teach all the limitations of claim 6 as discussed above.
Oldenettel additionally teaches:
wherein an estimated loss in tire tread is determined in relation to the change in mass of the tire between the first and second stages based on the calculated frequency shift. (See at least [0028]: “If one wants to be able to measure the decreasing tread depth of the tire or tire wear with a resolution of 1 mm, the following estimate can be given: A 10 kg heavy brand new tire loses approximately 200 g of mass when the tread depth decreases from 9 mm to 8 mm. The mass is therefore reduced by 1/20. The natural frequency given by equation 9 therefore changes by 1/40…”)
Regarding claim 8, Singh ‘367, Wood, Kurz, and Oldenettel in combination teach all the limitations of claim 6 as discussed above.
Oldenettel additionally teaches:
wherein an estimated loss in tire tread is determined via a retrievable correlation between an observed frequency shift and a change in tire tread for a given tire. (See at least [0028]: “If one wants to be able to measure the decreasing tread depth of the tire or tire wear with a resolution of 1 mm, the following estimate can be given: A 10 kg heavy brand new tire loses approximately 200 g of mass when the tread depth decreases from 9 mm to 8 mm. The mass is therefore reduced by 1/20. The natural frequency given by equation 9 therefore changes by 1/40…”)
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh ‘367 in view of Wood, Kurz, and Oldenettel, and further in view of Singh of US 20180154707 A1, filed 10/18/2017, hereinafter “Singh ‘707".
Regarding claim 9, Singh ‘367, Wood, Kurz, and Oldenettel in combination teach all the limitations of claim 8 as discussed above.
Singh’ 367, Wood, Kurz, and Oldenettel in combination do not explicitly teach:
wherein the correlation is retrieved from data storage with respect to a given type of tire.
Singh’ 707 teaches:
wherein the correlation is retrieved from data storage with respect to a given type of tire. (See at least [0052-0054]: “…FIG. 9 also illustrates a three dimensional plot of the correlation model of tire vertical mode vs pressure and tread depth…The correlation model as described above, is unique to a model and size of a tire. The steps are repeated for each tire as desired, and the coefficients are stored in a data matrix that correspond to the tire ID…” & [0048]: “…The dependence of tire wear (reduction in mass of the tread) and tire vertical mode of a rotating tire forms the basis for a correlation model between the tire wear state and the tire vertical mode frequency.” See also [0046], wherein the tire ID includes the tire size and tire model.)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Singh ‘367, Wood, Kurz, and Oldenettel’s method with Singh ‘707’s technique of retrieving the correlation from data storage with respect to the given type of tire. Doing so would be obvious “to determine a real time estimate of tire inflation pressure and tire tread depth” (See [0047] of Singh ‘707).
Regarding claim 10, Singh ‘367, Wood, Kurz, and Oldenettel in combination teach all the limitations of claim 8 as discussed above.
Singh ‘367, Wood, Kurz, and Oldenettel in combination do not explicitly teach:
wherein the correlation is developed over time based on historical measurements of changes in tire tread and shifts between corresponding modal frequencies associated with the given type of tire.
Singh ‘707 teaches:
wherein the correlation is developed over time based on historical measurements of changes in tire tread and shifts between corresponding modal frequencies associated with the given type of tire. (See at least [0051]: “…In order to generate a correlation model for a specific tire, a series of experiments is conducted as shown in FIGS. 6 and 7. As shown in FIG. 6, a series of vertical acceleration measurements Az are made of the specific tire over a range of inflation pressures, 18, 24, 30, 36, and 42 psi, step 40. These measurements are conducted at a known tread depth, in this case 2/32 in. The measurements are repeated at additional tread depths, step 50. The peak of the signal curve is then selected as representative of the tire vertical mode for each pressure and tread depth measured, (step 60)…”)
One having ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine Singh ‘707’s technique of developing the correlation over time based on historical measurements of changes in tire tread and shifts between corresponding modal frequencies associated with the given type of tire. Doing so would be obvious “to determine a real time estimate of tire inflation pressure and tire tread depth” (See [0047] of Singh ‘707).
Claim(s) 13 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh ‘367 in view of Wood and further in view of Shirato of JP 2006327368 A, filed 05/25/2005, hereinafter “Shirato”, and Johnston of US 20200334922