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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/05/2026 has been entered.
Claims 1, 3 and 5-20 have been examined.
Claims 2 and 4 have been canceled.
P = paragraph e.g. P[0001] = paragraph[0001]
Response to Arguments
Applicant’s arguments filed 05/05/2026 have been considered but are moot in view of the new ground(s) of rejection.
Claim Interpretation
Regarding the Claims 1, 15 and 20 amended limitations “wherein the suspension response comprises a pattern of axle behavior, and wherein the pattern of axle behavior includes a pattern of temperature changes of at least one of the two axles”, these limitations amount to an intended use that does not limit the claims. With respect to Claim 1, this amendment merely describes a situation occurring and does not require the “sensor data” to specifically capture “a pattern of temperature changes of at least one of the two axles”, and does not require any identification of the “pattern”, as stating that the “suspension response” merely “includes” the “pattern” merely describes a situation that does not further limit and component of Claim 1, as the “sensor data” received from the “infrared camera” may be of some other characteristic of the “suspension response” other than the “pattern” under broadest reasonable interpretation. Regarding Claim 15, this amendment does not require any method step of specifically capturing “a pattern of temperature changes of at least one of the two axles”, and does not require any identification of the “pattern”, similar to the reasoning explained above. Regarding Claim 20, the claim recites “an infrared camera positioned to capture a view of at least one of two axles; or an accelerometer”, and when selecting the “accelerometer” and not the “infrared camera”, not only does this amendment does not require any method step of specifically capturing “a pattern of temperature changes of at least one of the two axles” and not require any identification of the “pattern” similar to the reasoning applied to Claims 1 and 15, it is clear that an accelerometer cannot capture data of a “pattern of temperature changes”, which further emphasizes that the amended limitation is directed to an intended use or intended situation that does not further limit Claim 20.
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 12 and 13 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 12 recites the limitation "the camera" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "the camera" in line 2. There is insufficient antecedent basis for this limitation in the claim.
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.
Claims 1, 3, 5, 6, 10-12 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Balboni et al. (2020/0309641) in view of Frank et al. (2014/0168433).
Regarding Claim 1, Balboni et al. teaches the claimed vehicle, comprising:
a chassis frame (see FIG. 1);
a wheel and axle assembly comprising at least two axles and at least two sets of wheels (see FIG. 1 and P[0042]);
a suspension system connected to the chassis frame and the wheel and axle assembly (“…suspensions…”, see P[0115], also see FIG. 1, where an axle such as either semi-axle of FIG. 1 is equivalent to the claimed “suspension system”, where the Examiner notes that the broadest reasonable interpretation of the word “connected” in terms of the present claim encompasses any direct or indirect connection between vehicle components, where all components of a single vehicle may be considered to be connected to another component either by physically contacting another component or by physically contacting an intermediate component(s) that is connected to another component);
…; and
a suspension health monitor, comprising:
at least one processing unit (“…data processing unit 2…”, see P[0116]); and
a memory including instructions, which when executed by the at least one processing unit (see P[0026]-P[0027]), cause the suspension health monitor to:
receive sensor data…, where the sensor data captures a suspension response to a driving event (“…the recorded vibrations may be caused by one or more of the following factors: vibrations of the vehicle engine (which may excite the vehicle frame), vibrations of the transmission, patches of the tire, compliance of the tire, roughness of the road, movement of the suspensions, dynamics of the vehicle, or wear of the rotating components of the driveline (i.e. transmission and axles)”, see P[0115]), wherein the suspension response comprises a pattern of axle behavior…(“Hence, the data processing unit 2 may be configured to detect that the rotating components of the axle 9 feature an anomaly known from LM”, see P[0121]);
determine, using a machine-learning model (“This adaptive model is referred to as a Learned Model (LM)”, see P[0120]), whether the suspension response correlates to a failing state of the suspension system (“Any classification algorithm can be used for this purpose, keeping in mind that the critical aspect is represented by the training of the classifier. For the sake of clarity and without loss of generality, a simple classification algorithm which may be run on the data processing unit 2 of the presently proposed system 1…”, see P[0116] and “…running a classification algorithm to classify the acquired axle vibrational data…”, see Claim 23 and “The classification algorithm run by the data processing unit 2 may be configured to detect that the feature included in the spectrum associated with D2 is also present in the measured spectrum f(tk). Hence, the data processing unit 2 may be configured to detect that the rotating components of the axle 9 feature an anomaly known from LM”, see P[0121] and “…the previously described techniques may be combined in order to improve the overall accuracy of the LM (and so of the classification)”, see P[0127]); and
when the suspension response is correlated to the failing state, perform a mitigation action based on the correlated failing state (“For each flag the information provided is identified. In the example depicted in FIG. 13 this information includes: “shocks on the axle and the wheel hub”, and “coarse metallic debris dispersed in the lubrication oil”. The intersection or pairing of the information provided identifies the failure mode which is present in the axle or axle arrangement. For example, in the example depicted in FIG. 13 the axle failure mode includes the mode “breakage of rotating components”. The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]).
Balboni et al. does not expressly recite the bolded portions of the claimed
an infrared camera positioned to capture a view of at least one of the two axles
and
receive sensor data from the infrared camera, where the sensor data captures a suspension response to a driving event, wherein the suspension response comprises a pattern of axle behavior, and wherein the pattern of axle behavior includes a pattern of temperature changes of at least one of the two axles.
However, Frank et al. (2014/0168433) teaches an infrared camera positioned to capture a view of at least one axle, and receiving sensor data from the infrared camera, where the sensor data captures a suspension response to a driving event, wherein the suspension response comprises a pattern of axle behavior, and wherein the pattern of axle behavior includes a pattern of temperature changes of at least one of the two axles (Frank et al.; “…one of infrared imaging modules 1402b may be mounted to a component of power system 1430 (e.g., motor 1540) in FIG. 16 so that…axle 1575…is within FOV 1406b, as is also illustrated in FIG. 14”, see P[0248], also see P[0081] and “Processor 1410 may be configured to receive thermal images of at least a portion of a component of power system 1430 captured by an infrared imaging module, perform thermal image processing operations as further described herein, and extract data from thermal image to, for example, determine a condition of the portion of the component, of the component, or of other components of power system 1430”, see P[0210], also see P[0243]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Balboni et al. with the teachings of Frank et al., and the vehicle comprising an infrared camera positioned to capture a view of at least one of the two axles, and receive sensor data from the infrared camera, where the sensor data captures a suspension response to a driving event, wherein the suspension response comprises a pattern of axle behavior, and wherein the pattern of axle behavior includes a pattern of temperature changes of at least one of the two axles, as rendered obvious by Frank et al., in order to, for a component such as an axle, “determine a condition of the portion of the component, of the component, or of other components of power system 1430” (Frank et al.; see P[0210]) and in order to provide an indication of “developing operational problems” (Frank et al.; see P[0243]).
Regarding Claim 3, Balboni et al. teaches the claimed vehicle of claim [[2]] 1, wherein the pattern of axle behavior includes a pattern of movement of at least one of the two axles (“…“shocks on the axle and the wheel hub”…”, see P[0164]).
Regarding Claim 5, Balboni et al. teaches the claimed vehicle of claim [[2]] 1, wherein using the machine-learning model to determine whether the suspension response correlates to the failing state of the suspension system comprises using the machine-learning model to determine whether the pattern of axle behavior correlates to a pattern of a failing state of at least one component of the suspension system (“For each flag the information provided is identified. In the example depicted in FIG. 13 this information includes: “shocks on the axle and the wheel hub”, and “coarse metallic debris dispersed in the lubrication oil”. The intersection or pairing of the information provided identifies the failure mode which is present in the axle or axle arrangement. For example, in the example depicted in FIG. 13 the axle failure mode includes the mode “breakage of rotating components”. The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]).
Regarding Claim 6, Balboni et al. teaches the claimed vehicle of claim 5, wherein:
the failing state is associated with one of a plurality of stages ranging from an early stage of failure of the at least one component (“…1. wear of rotating components that evolves with a slow dynamic (such as compared to the vehicle dynamics) and may include at least one of adhesive wear, abrasive wear, surface fatigue, fretting, erosion, corrosion…”, see P[0073]) to a later stage of failure of the at least one component (“…2. breakage of rotating components, typically evolving rapidly…”, see P[0072]); and
the mitigation action is determined based on the stage associated with the failing state (“…discriminate between the different failure modes…”, see P[0087] and “The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]).
Regarding Claim 10, Balboni et al. teaches the claimed vehicle of claim 1, wherein:
the driving event is a discrete event comprising at least one of:
acceleration (“…the recorded vibrations may be caused by one or more of the following factors: vibrations of the vehicle engine (which may excite the vehicle frame), vibrations of the transmission, patches of the tire, compliance of the tire, roughness of the road, movement of the suspensions, dynamics of the vehicle, or wear of the rotating components of the driveline (i.e. transmission and axles)”, see P[0115]);
deceleration;
turning; or
encountering a driving surface condition; and
the sensor data includes data about the driving event (“…the recorded vibrations may be caused by one or more of the following factors: vibrations of the vehicle engine (which may excite the vehicle frame), vibrations of the transmission, patches of the tire, compliance of the tire, roughness of the road, movement of the suspensions, dynamics of the vehicle, or wear of the rotating components of the driveline (i.e. transmission and axles)”, see P[0115]).
Regarding Claim 11, Balboni et al. teaches the claimed vehicle of claim 1, wherein the driving event is a non-discrete event including a time period of operating the vehicle (“…the recorded vibrations may be caused by one or more of the following factors: vibrations of the vehicle engine (which may excite the vehicle frame), vibrations of the transmission, patches of the tire, compliance of the tire, roughness of the road, movement of the suspensions, dynamics of the vehicle, or wear of the rotating components of the driveline (i.e. transmission and axles)”, see P[0115]).
Regarding Claim 12, Balboni et al. does not expressly recite the claimed vehicle of claim 1, wherein
the camera is one of a plurality of cameras; and at least one of the plurality of cameras is located on at least one of the two axles; or
at least one of the plurality of cameras is located on the chassis frame.
However, Frank et al. (2014/0168433) teaches wherein the camera is one of a plurality of cameras; and at least one of the plurality of cameras is located on at least one of the two axles; or at least one of the plurality of cameras is located on the chassis frame (Frank et al.; “…infrared imaging modules 1402a, 1402b, 1402c may be positioned/mounted to view any component of power system 1430”, see P[0206] and “…axles 1581…”, see P[0249] and FIG. 16, and “…one of infrared imaging modules 1402a in FIG. 16 may be positioned so that a portion of fuel tank 1570 and/or exhaust system 1568 is within FOV 1406a, as illustrated in FIG. 14. In another example, one of infrared imaging modules 1402b may be mounted to a component of power system 1430…”, see P[0248] and FIG. 16).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Balboni et al. with the teachings of Frank et al., and wherein the camera is one of a plurality of cameras, and at least one of the plurality of cameras is located on at least one of the two axles; or at least one of the plurality of cameras is located on the chassis frame, as rendered obvious by Frank et al., in order to, for a component such as an axle, “determine a condition of the portion of the component, of the component, or of other components of power system 1430” (Frank et al.; see P[0210]) and in order to provide an indication of “developing operational problems” (Frank et al.; see P[0243]).
Regarding Claim 14, Balboni et al. teaches the claimed vehicle of claim 1, wherein:
…
the machine-learning model is a first machine-learning model (“…the classification algorithm may possibly be enhanced by sharing learned models and classification results between different axles of the same vehicle or of different vehicles”, see P[0127] and “…utilizing simplified models derived from theory or from laboratory tests. If such models are available, their results can be fused to the estimations made by the learned model in order to improve the overall accuracy of the extrapolation”, see P[0160]);
the failing state of the suspension system is a first failing state (“…discriminate which failure mode is present…”, see P[0162] and “For each flag the information provided is identified. In the example depicted in FIG. 13 this information includes: “shocks on the axle and the wheel hub”, and “coarse metallic debris dispersed in the lubrication oil”. The intersection or pairing of the information provided identifies the failure mode which is present in the axle or axle arrangement. For example, in the example depicted in FIG. 13 the axle failure mode includes the mode “breakage of rotating components”. The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]);
…and
the instructions further cause the suspension health monitor to:
determine, using a second machine-learning model (“…the classification algorithm may possibly be enhanced by sharing learned models and classification results between different axles of the same vehicle or of different vehicles”, see P[0127] and “…utilizing simplified models derived from theory or from laboratory tests. If such models are available, their results can be fused to the estimations made by the learned model in order to improve the overall accuracy of the extrapolation”, see P[0160]), whether the sensor data correlates to a second failing state of the at least one of:
the suspension system (“For each flag the information provided is identified. In the example depicted in FIG. 13 this information includes: “shocks on the axle and the wheel hub”, and “coarse metallic debris dispersed in the lubrication oil”. The intersection or pairing of the information provided identifies the failure mode which is present in the axle or axle arrangement. For example, in the example depicted in FIG. 13 the axle failure mode includes the mode “breakage of rotating components”. The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]); or
[[or]] at least one of the at least two sets of wheels (“For each flag the information provided is identified. In the example depicted in FIG. 13 this information includes: “shocks on the axle and the wheel hub”, and “coarse metallic debris dispersed in the lubrication oil”. The intersection or pairing of the information provided identifies the failure mode which is present in the axle or axle arrangement. For example, in the example depicted in FIG. 13 the axle failure mode includes the mode “breakage of rotating components”. The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]); and
when the suspension response is correlated to the second failing state, performing a mitigation action based on the correlated second failing state (“…discriminate which failure mode is present…”, see P[0162] and “For each flag the information provided is identified. In the example depicted in FIG. 13 this information includes: “shocks on the axle and the wheel hub”, and “coarse metallic debris dispersed in the lubrication oil”. The intersection or pairing of the information provided identifies the failure mode which is present in the axle or axle arrangement. For example, in the example depicted in FIG. 13 the axle failure mode includes the mode “breakage of rotating components”. The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]).
Balboni et al. does not expressly recite the claimed
the camera is a first camera
and
the vehicle comprises a second camera positioned to capture a view of at least one of:
the suspension system; or
at least one of the at least two sets of wheels;
receiving the sensor data comprises receiving data from the second camera.
However, Frank et al. (2014/0168433) teaches the camera is a first camera, and the vehicle comprises a second camera positioned to capture a view of at least one of: the suspension system; or at least one of the at least two sets of wheels; receiving the sensor data comprises receiving data from the second camera (Frank et al.; “…infrared imaging modules 1402a, 1402b, 1402c may be positioned/mounted to view any component of power system 1430”, see P[0206] and “…axles 1581…”, see P[0249] and FIG. 16, and “…one of infrared imaging modules 1402a in FIG. 16 may be positioned so that a portion of fuel tank 1570 and/or exhaust system 1568 is within FOV 1406a, as illustrated in FIG. 14. In another example, one of infrared imaging modules 1402b may be mounted to a component of power system 1430…”, see P[0248] and FIG. 16).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Balboni et al. with the teachings of Frank et al., and the camera is a first camera, and the vehicle comprises a second camera positioned to capture a view of at least one of: the suspension system; or at least one of the at least two sets of wheels; receiving the sensor data comprises receiving data from the second camera, as rendered obvious by Frank et al., in order to, for a component such as an axle, “determine a condition of the portion of the component, of the component, or of other components of power system 1430” (Frank et al.; see P[0210]) and in order to provide an indication of “developing operational problems” (Frank et al.; see P[0243]).
Regarding Claim 15, Balboni et al. teaches the claimed method for providing suspension health monitoring in a vehicle, comprising:
receiving sensor data…(“…the recorded vibrations may be caused by one or more of the following factors: vibrations of the vehicle engine (which may excite the vehicle frame), vibrations of the transmission, patches of the tire, compliance of the tire, roughness of the road, movement of the suspensions, dynamics of the vehicle, or wear of the rotating components of the driveline (i.e. transmission and axles)”, see P[0115]); and
the sensor data captures a suspension response to a driving event (“…the recorded vibrations may be caused by one or more of the following factors: vibrations of the vehicle engine (which may excite the vehicle frame), vibrations of the transmission, patches of the tire, compliance of the tire, roughness of the road, movement of the suspensions, dynamics of the vehicle, or wear of the rotating components of the driveline (i.e. transmission and axles)”, see P[0115]), wherein the suspension response comprises a pattern of axle behavior…(“Hence, the data processing unit 2 may be configured to detect that the rotating components of the axle 9 feature an anomaly known from LM”, see P[0121]);
determining, using a machine-learning model (“This adaptive model is referred to as a Learned Model (LM)”, see P[0120]), whether the suspension response correlates to a failing state of a suspension system of the vehicle (“Any classification algorithm can be used for this purpose, keeping in mind that the critical aspect is represented by the training of the classifier. For the sake of clarity and without loss of generality, a simple classification algorithm which may be run on the data processing unit 2 of the presently proposed system 1…”, see P[0116] and “…running a classification algorithm to classify the acquired axle vibrational data…”, see Claim 23 and “The classification algorithm run by the data processing unit 2 may be configured to detect that the feature included in the spectrum associated with D2 is also present in the measured spectrum f(tk). Hence, the data processing unit 2 may be configured to detect that the rotating components of the axle 9 feature an anomaly known from LM”, see P[0121] and “…the previously described techniques may be combined in order to improve the overall accuracy of the LM (and so of the classification)”, see P[0127]); and
when the suspension response is correlated to the failing state, performing a mitigation action based on the correlated failing state (“For each flag the information provided is identified. In the example depicted in FIG. 13 this information includes: “shocks on the axle and the wheel hub”, and “coarse metallic debris dispersed in the lubrication oil”. The intersection or pairing of the information provided identifies the failure mode which is present in the axle or axle arrangement. For example, in the example depicted in FIG. 13 the axle failure mode includes the mode “breakage of rotating components”. The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]).
Balboni et al. does not expressly recite the bolded portions of the claimed
receiving sensor data from an infrared camera positioned to capture a view of at least one of two axles included in the vehicle
and
and wherein the pattern of axle behavior includes a pattern of temperature changes of at least one of the two axles.
However, Frank et al. (2014/0168433) teaches receiving sensor data from an infrared camera positioned to capture a view of at least one of two axles included in the vehicle, and wherein a pattern of axle behavior includes a pattern of temperature changes of at least one axle (Frank et al.; “…one of infrared imaging modules 1402b may be mounted to a component of power system 1430 (e.g., motor 1540) in FIG. 16 so that…axle 1575…is within FOV 1406b, as is also illustrated in FIG. 14”, see P[0248], also see P[0081] and “Processor 1410 may be configured to receive thermal images of at least a portion of a component of power system 1430 captured by an infrared imaging module, perform thermal image processing operations as further described herein, and extract data from thermal image to, for example, determine a condition of the portion of the component, of the component, or of other components of power system 1430”, see P[0210], also see P[0243]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Balboni et al. with the teachings of Frank et al., and receiving sensor data from an infrared camera positioned to capture a view of at least one of two axles included in the vehicle, and wherein the pattern of axle behavior includes a pattern of temperature changes of at least one of the two axles, as rendered obvious by Frank et al., in order to, for a component such as an axle, “determine a condition of the portion of the component, of the component, or of other components of power system 1430” (Frank et al.; see P[0210]) and in order to provide an indication of “developing operational problems” (Frank et al.; see P[0243]).
Regarding Claim 16, Balboni et al. teaches the claimed method of claim 15, wherein the further comprises:
a pattern of movement of at least one of the two axles (“…“shocks on the axle and the wheel hub”…”, see P[0164])
.
Regarding Claim 17, Balboni et al. teaches the claimed method of claim 16, wherein determining whether the suspension response correlates to the failing state of the suspension system comprises using the machine-learning model to determine whether the pattern of axle behavior correlates to a pattern of the failing state of at least one component of the suspension system (“For each flag the information provided is identified. In the example depicted in FIG. 13 this information includes: “shocks on the axle and the wheel hub”, and “coarse metallic debris dispersed in the lubrication oil”. The intersection or pairing of the information provided identifies the failure mode which is present in the axle or axle arrangement. For example, in the example depicted in FIG. 13 the axle failure mode includes the mode “breakage of rotating components”. The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]).
Regarding Claim 18, Balboni et al. teaches the claimed method of claim 17, wherein determining whether the suspension response correlates to the failing state of the suspension system comprises:
determining the failing state is associated with one of a plurality of stages ranging from an early stage of failure of the at least one component (“…1. wear of rotating components that evolves with a slow dynamic (such as compared to the vehicle dynamics) and may include at least one of adhesive wear, abrasive wear, surface fatigue, fretting, erosion, corrosion…”, see P[0073]) to a later stage of failure of the at least one component of the suspension system (“…2. breakage of rotating components, typically evolving rapidly…”, see P[0072]); and
the mitigation action is determined based on the stage associated with the failing state (“…discriminate between the different failure modes…”, see P[0087] and “The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]).
Claims 7-9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Balboni et al. (2020/0309641) in view of Frank et al. (2014/0168433) further in view of Bruno et al. (2021/0291611).
Regarding Claim 7, Balboni et al. does not expressly recite the claimed vehicle of claim 5, wherein the at least one component of the suspension system comprises:
a leaf spring;
an air spring; or
a shock absorber.
However, Bruno et al. (2021/0291611) teaches using an axle sensor assembly including accelerometers (Bruno et al.; see P[0029]) and determining degradation of a shock absorber based on acceleration data (Bruno et al.; see P[0041]-P[0049]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Balboni et al. with the teachings of Bruno et al., and wherein the at least one component of the suspension system comprises a leaf spring, an air spring; or a shock absorber, as rendered obvious by Bruno et al., in order to provide “estimation of performance degradation of shock absorbers” (Bruno et al.; see P[0002]).
Regarding Claim 8, Balboni et al. teaches the claimed vehicle of claim 1, wherein the mitigation action comprises:
generating an alert about the failing state of the suspension system (“For each flag the information provided is identified. In the example depicted in FIG. 13 this information includes: “shocks on the axle and the wheel hub”, and “coarse metallic debris dispersed in the lubrication oil”. The intersection or pairing of the information provided identifies the failure mode which is present in the axle or axle arrangement. For example, in the example depicted in FIG. 13 the axle failure mode includes the mode “breakage of rotating components”. The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]).
Balboni et al. does not expressly recite the claimed
and communicating the alert to at least one of:
a driver of the vehicle;
a fleet management system;
a cloud analytics service;
maintenance personnel; or
a driver of another vehicle of a vehicle fleet comprising the vehicle.
However, Bruno et al. (2021/0291611) teaches alerting a driver based on a detected shock absorber degradation (Bruno et al.; see P[0055], also see P[0031]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Balboni et al. with the teachings of Bruno et al., and communicating the alert to at least one of a driver of the vehicle, a fleet management system, a cloud analytics service, maintenance personnel, or a driver of another vehicle of a vehicle fleet comprising the vehicle, as rendered obvious by Bruno et al., in order to provide “estimation of performance degradation of shock absorbers” (Bruno et al.; see P[0002]).
Regarding Claim 9, Balboni et al. does not expressly recite the claimed vehicle of claim 1, wherein the mitigation action comprises automatically controlling a vehicle function.
However, Bruno et al. (2021/0291611) teaches determining degradation of a shock absorber based on acceleration data (Bruno et al.; see P[0041]-P[0049]), and teaches a mitigation action comprising automatically controlling a vehicle function (Bruno et al.; “…the data element Δ indicative of the estimated degradation of the performance of a shock absorber (or suspension system as a whole) may be used by the control module of the suspension system and possibly by other modules connected to the CAN network to make decisions and implement a corresponding change in the control current of the characteristics of the shock absorbers depending on the strategy used. This makes it possible to adapt the behavior of the suspension system to the degradation (as long as sustainable) of the shock absorbers, so that passengers do not perceive a change in absolute driving comfort, the comfort settings desired by the driver and the dynamic performance of the vehicle”, see P[0054]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Balboni et al. with the teachings of Bruno et al., and wherein the mitigation action comprises automatically controlling a vehicle function, as rendered obvious by Bruno et al., in order to provide “estimation of performance degradation of shock absorbers” (Bruno et al.; see P[0002]).
Regarding Claim 19, Balboni et al. does not expressly recite the claimed method of claim 17, wherein the at least one component of the suspension system comprises:
a leaf spring;
an air spring; or
a shock absorber.
However, Bruno et al. (2021/0291611) teaches using an axle sensor assembly including accelerometers (Bruno et al.; see P[0029]) and determining degradation of a shock absorber based on acceleration data (Bruno et al.; see P[0041]-P[0049]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Balboni et al. with the teachings of Bruno et al., and wherein the at least one component of the suspension system comprises a leaf spring, an air spring, or a shock absorber, as rendered obvious by Bruno et al., in order to provide “estimation of performance degradation of shock absorbers” (Bruno et al.; see P[0002]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Balboni et al. (2020/0309641) in view of Frank et al. (2014/0168433) further in view of Harrill et al. (7,877,884).
Regarding Claim 13, Balboni et al. does not expressly recite the claimed vehicle of claim 12, wherein:
at least one of the plurality of cameras captures movement of at least one of the two axles relative to the chassis frame; or
at least one of the plurality of cameras captures movement of a first axle of the two axles relative to movement of a second axles of the two axles.
However, Harrill et al. (7,877,884) wherein at least one of the plurality of cameras captures movement of at least one of the two axles relative to the chassis frame, or at least one of the plurality of cameras captures movement of a first axle of the two axles relative to movement of a second axles of the two axles (Harrill et al.; “As axle 58 moves under dynamic conditions (see FIG. 6b), baseline 28 simultaneously moves relative to sensor 40. When an axle misalignment conditions occur baseline 28 exceeds the predetermined threshold value 74”, see col.14, particularly lines 36-52 and “FIG. 14 shows an example of one embodiment of the present invention utilizing a time of flight sensor such as a camera 28b located at a first fixed control point 38 (see FIG. 18 box 140) on or in proximity to the vehicle's body or frame 62 and a target 28c located at a second control point 58a (see FIG. 18 box 142) on or in proximity to axle 58. A baseline 28 is used to determine a measurable relationship between the first and second control points (see FIG. 18 box 144). Obtained data may be sent to a computer 82 for collecting, storing, calculating, displaying, or comparing distance between camera 28b and target 28c”, see col.19, particularly lines 13-38).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Balboni et al. with the teachings of Harrill et al., and wherein at least one of the plurality of cameras captures movement of at least one of the two axles relative to the chassis frame, or at least one of the plurality of cameras captures movement of a first axle of the two axles relative to movement of a second axles of the two axles, as rendered obvious by Harrill et al., in order to provide for “determining an actual alignment condition of the vehicle's axle during either dynamic or static conditions” (Harrill et al.; see Abstract).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Balboni et al. (2020/0309641) in view of Brandon et al. (2024/0144743).
Regarding Claim 20, Balboni et al. teaches the claimed suspension health monitor, comprising:
at least one processing unit (“…data processing unit 2…”, see P[0116]); and
a memory including instructions, which when executed by the at least one processing unit (see P[0026]-P[0027]), cause the suspension health monitor to perform operations comprising:
receiving sensor data from at least one sensor (“…the recorded vibrations may be caused by one or more of the following factors: vibrations of the vehicle engine (which may excite the vehicle frame), vibrations of the transmission, patches of the tire, compliance of the tire, roughness of the road, movement of the suspensions, dynamics of the vehicle, or wear of the rotating components of the driveline (i.e. transmission and axles)”, see P[0115]), wherein:
the at least one sensor comprises one of:
an infrared camera positioned to capture a view of at least one of two axles; or
an accelerometer attached to one of the two axles (“…an accelerometer is placed on the OH axle…”, see P[0115]); and
the sensor data captures a suspension response to a driving event (“…the recorded vibrations may be caused by one or more of the following factors: vibrations of the vehicle engine (which may excite the vehicle frame), vibrations of the transmission, patches of the tire, compliance of the tire, roughness of the road, movement of the suspensions, dynamics of the vehicle, or wear of the rotating components of the driveline (i.e. transmission and axles)”, see P[0115]), wherein the suspension response comprises a pattern of axle behavior (“…“shocks on the axle and the wheel hub”…”, see P[0164]), and wherein the pattern of axle behavior includes a pattern of temperature changes of at least one of the two axles;
determining, using a machine-learning model (“This adaptive model is referred to as a Learned Model (LM)”, see P[0120]), whether the suspension response correlates to a failing state of a component of a suspension system…(“Any classification algorithm can be used for this purpose, keeping in mind that the critical aspect is represented by the training of the classifier. For the sake of clarity and without loss of generality, a simple classification algorithm which may be run on the data processing unit 2 of the presently proposed system 1…”, see P[0116] and “…running a classification algorithm to classify the acquired axle vibrational data…”, see Claim 23 and “The classification algorithm run by the data processing unit 2 may be configured to detect that the feature included in the spectrum associated with D2 is also present in the measured spectrum f(tk). Hence, the data processing unit 2 may be configured to detect that the rotating components of the axle 9 feature an anomaly known from LM”, see P[0121] and “…the previously described techniques may be combined in order to improve the overall accuracy of the LM (and so of the classification)”, see P[0127]); and
when the suspension response is correlated to the failing state, performing a mitigation action based on the correlated failing state (“For each flag the information provided is identified. In the example depicted in FIG. 13 this information includes: “shocks on the axle and the wheel hub”, and “coarse metallic debris dispersed in the lubrication oil”. The intersection or pairing of the information provided identifies the failure mode which is present in the axle or axle arrangement. For example, in the example depicted in FIG. 13 the axle failure mode includes the mode “breakage of rotating components”. The approach described herein may be extended to each damaging factor or axle failure mode, thus obtaining the correlations between flags and failure modes shown in FIG. 14”, see P[0164]).
Balboni et al. does not expressly recite the claimed
wherein the component comprises a leaf spring, an air spring, or a shock absorber.
However, Brandon et al. (2024/0144743) teaches using measurement by an accelerometer to identify a failed shock absorber (Brandon et al; see P[0037]-P[0038]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Balboni et al. with the teachings of Brandon et al., and wherein the component comprises a leaf spring, an air spring, or a shock absorber, as rendered obvious by Brandon et al., in order to provide for “improving the detection and reporting of degraded suspension components” (Brandon et al.; see P[0001]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ISAAC G SMITH whose telephone number is (571)272-9593. The examiner can normally be reached Monday-Thursday, 8AM-5PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANISS CHAD can be reached at 571-270-3832. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ISAAC G SMITH/ Primary Examiner, Art Unit 3662
Prosecution Insights