DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the amendments, filed on 10/23/2025, in which claims 1 and 13 are amended and claims 2, 4, 5, 9, 15, 16, and 20 are cancelled. Claims 1, 3, 6-8, 10-14, 17-19, and 21-22 are rejected.
Response to Arguments
Applicant's arguments, see REMARKS, filed 10/23/2025, with respect to the rejections under 35 USC §112(b), have been fully considered and are persuasive. Therefore, the previous rejections under 35 USC §112(b) have been withdrawn.
Applicant’s arguments with respect to the rejection of claims 1, 3, 6-8, 10-14, 17-19, and 21-22, under 35 USC §103, have been fully considered but are unpersuasive. Therefore, the previous rejections have been maintained.
With respect to the rejection of claims 1, 3, 6-8, 10-14, 17-19, and 21-22, under 35 USC §103, the Applicant argues:
Stanek fails to disclose "prior to determining whether a component anomaly exists, determine whether the current vibration pattern is reoccurring by comparing the current vibration pattern to stored prior vibration patterns and requiring that the current vibration pattern meets or exceeds a recurrence threshold" and "classifying the vibration pattern using a machine learning algorithm trained on historical component anomaly data in response to determining that the current vibration pattern is reoccurring," as recited in amended claim 1. The Office Action asserts that Stanek discloses the step of determining whether a vibration pattern is reoccurring by reference to "first order," "second order," and "third order" vibrations. Applicant respectfully disagrees that this discloses the claimed subject matter. Stanek discusses identification of vibration "orders" which refer to frequency content relative to component rotation-e.g., first, second, or third order vibrations-but does not teach determining whether a detected vibration pattern temporally recurs over distinct events or time periods. As explained in Stanek ( [0082]- [0083]), these terms are harmonic descriptors indicating the frequency of vibration relative to the rotation of a component (e.g., once, twice, or three times per revolution), not indicators of temporal recurrence of a specific vibration pattern across multiple operational events.
Amended claim 1 expressly recites that, before anomaly detection, the system must determine whether a current vibration pattern is reoccurring-i.e., that the same pattern has manifested over multiple, distinct events or time periods. Stanek, by contrast, analyzes instantaneous frequency content during a single diagnostic session and does not store or compare patterns across time to detect recurrence. There is no disclosure or suggestion in Stanek of maintaining a library of stored detected patterns or of evaluating whether a currently detected pattern has occurred previously, as recited in the amended claims.
Accordingly, the cited passages in Stanek do not teach or render obvious the limitation of determining temporal reoccurrence of a vibration pattern prior to anomaly detection, as recited in claim 1.
Stanek, as described by the Applicant above, discloses identification of vibration “orders” which refer to frequency content relative to a component rotation. The Applicant further states that Stanek does not teach indicators of temporal recurrence of a specific vibration pattern across multiple operational events. While the claim language requires “determine whether the current vibration pattern is reoccurring by comparing the current vibration pattern to stored prior vibration patterns and requiring that the current vibration pattern meets or exceeds a recurrence threshold.”
Stanek, according to the Applicant’s own interpretation, discloses both the Applicant’s description of their claim and the claim language itself. The “orders” discussed by the Applicant are a “temporal recurrence of a specific pattern”. Stanek is determining how many times a specific vibration frequency is occurring over a time period. For example, Stanek discloses: “Vibration frequencies, used in the diagnostic of a rotating system, are therefore described in terms of their order. In terms of a wheel system, for example, a first order wheel vibration is a vibration with a frequency that corresponds to once per revolution of a wheel, a second order wheel vibration is a vibration with a frequency that corresponds to two vibrations per revolution of the wheel, and a third order wheel vibration is a vibration that corresponds to two vibrations per revolution of the wheel.” (¶ [0082]) Here, the vibration frequencies are the “specific pattern” and the per revolution of the wheel is the “temporal recurrence”. In other words, a second order vibration would have a repeating vibration frequency, i.e., pattern, that reoccurs two times in one revolution, i.e. the temporal recurrence.
With respect to the amended claimed language Stanek discloses identifying the number of times a specific vibration pattern is occurring over a period of time. To perform this function there has to be some sort of comparison to the previous vibration patterns detected in order to identify a repeating pattern. Thus the vibration patterns must be stored in order to perform the functions of the system. However, Stanek explicitly discloses storing the patterns in ¶ [0046] and ¶ [0115]. Further, Stanek discloses that the vibration patterns have specific orders, these are orders are equivalent to a “threshold”. For example, a second order equation would have a threshold of two and a third order equation would have a threshold of three.
For the reasons above Stanek discloses both the Applicant’s description of the claim and the claimed language itself. Therefore, the Examiner finds this argument unpersuasive.
The Office further relies on Cella as disclosing the limitation of classifying a vibration pattern using a machine learning algorithm trained on historical component anomaly data. However, Cella's disclosure is limited to generalized references to "neural net expert systems" and "machine learning" in the context of industrial IoT applications (see, e.g., Cella [0205]- [0208]). While Cella mentions vibration data as one of many possible sensor inputs (see [0063]), it does not describe or suggest applying a machine learning classifier specifically to vehicle vibration patterns, nor does it disclose any process in which such classification is gated by a prior determination of temporal recurrence.
Cella states in ¶ [0675]: “In embodiments, such methods and systems for intelligent management of smart bands include an expert system and supporting technology components, services. processes, modules, applications and interfaces, for managing the smart bands (collectively referred to in some cases as a smart band platform 10722), which may include a model-based expert system, a rule-based expert system, an expert system using artificial intelligence (such as a machine learning system, which may include a neural net expert system, a self-organizing map system, a human-supervised machine learning system, a state determination system, a classification system, or other artificial intelligence system), or various hybrids or combinations of any of the above. References to an expert system should be understood to encompass utilization of any one of the foregoing or suitable combinations, except where context indicates otherwise. Intelligent management may be of data collection of various types of data (e.g., vibration data, noise data and other sensor data of the types described throughout this disclosure) for event detection, state detection, and the like.” (Emphasis added.) Here, Cella discloses the use of a machine learning system, which includes a classification system, to manage types of data. The data included in this intelligent management is vibration data.
Therefore, the Examiner does not find the above argument persuasive.
Moreover, the "collection parameter" referenced by the Examiner (Cella [0690]-[0691]) merely defines a frequency range for data collection and does not assess whether a given vibration pattern has manifested over multiple operational episodes. There is no teaching or suggestion in Cella of a sequential process in which a machine learning classifier is applied only after a vibration pattern has been determined to be reoccurring, as expressly required by the claims.
Therefore, even in combination, Stanek and Cella do not teach or suggest the conjunctive process recited in claim 1, namely. (1) determining temporal reoccurrence of a vibration pattern, and (2) classifying the pattern using a machine learning algorithm trained on historical anomaly data, only after such recurrence is established.
By definition a frequency1 is a pattern manifested over operational episodes. When that frequency is caused by a vibration in the system, then it is a vibration pattern. Frequency is the number of complete cycles of waves (patterns) passing a unit in time (operational episodes). These waves contain characteristics, e.g., time periods, wavelengths, amplitude, etc. which define the “pattern” of the frequency.
Therefore, the Examiner does not find this argument persuasive.
For the above reasons the Examiner finds the Applicant’s arguments unpersuasive and maintains the previous rejections under 35 USC §103.
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, 3, 8, 10-14 19, and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Stanek et al. (US 2018/0082492 A1, “Stanek”) in view of Cella et al. (US 2020/0110401 A1, “Cella”).
Regarding claims 1 and 13, Stanek discloses arrangements for collecting diagnostic information regarding vibrations of wheel-tire assembly and drive-line components of a wheeled vehicle and teaches:
A system for detecting component anomalies for a vehicle, the system comprising: (In accordance with one aspect of the present disclosure, a method, system and/or non - transient computer readable medium-embedded programming, are configured to identify signal signatures indicative of driveline system vibration and/or tire/wheel imbalance, i.e., an anomaly, and correlate the identified vibration signals and/or the tire/wheel imbalance to a vehicle speed – See at least ¶ [0032])
a first sensor positioned at a first position on the vehicle and configured to sense vibrations of the vehicle; and (the present disclosure provides systems that may receive real-time data from several existing sensors, for example, the wheel speed sensor measurements from all wheels or the suspension height sensors for all the suspensions, during the normal operation of the vehicle – See at least ¶ [0030])
an electronic processor communicatively coupled to the first sensor and configured to receive, from the first sensor, sensor information produced by a sensed vibration of the vehicle; (systems comprising a controller, such as, for example, a controller 26, i.e., an electronic processor, that is operatively associated with a plurality of vehicle sensors, i.e., at least a first and second sensor, that may produce signals indicative of a vibration of the vehicle 10, wherein the controller 26 is configured to determine whether or not a sensed vibration falls into a target frequency range (i.e., a target frequency band) that is indicative of a vehicle component (e.g., tire/wheel, driveline, or engine) or location – See at least ¶ [0080])
compare the sensor information to a vibration noise floor to extract one or more vibrations that exceed the vibration floor; (If ΔM is determined to be close to zero, the vibration signals are determined to be due to signal noise. If, however, ΔM is determined to be greater than a threshold value, i.e., a noise floor, such as, for example, 100 g, the vibration signals are determined to be due to a real physical source and not merely due to signal noise, thereby confirming the suspected fault – See at least ¶ [0095] Examiner further notes that the sensed frequencies are also associated with specific frequency bands which contain both a frequency floor and frequency ceiling – See at least ¶ [0085])
generate a current vibration pattern based on the one or more vibrations that exceed the vibration noise floor; (if the controller 26, however, determines that the source of the sensed vibration is one of the wheels 12a, 12b, 13a and 13b, the location of the vibration can then be determined by looking at the patterns of the vibration signals coming from the four wheels 12a, 12b, 13a and 13b. The errant wheel causing the vibration will have the strongest signal, and cross-coupling of vibrations through the chassis tend to occur in recognizable patterns – See at least ¶ [0091])
prior to determining whether a component anomaly exists, determine whether the current vibration pattern is reoccurring; (because Stanek determines the order of the vehicle vibration it is determining if the vibration pattern is reoccurring, i.e., first, second, third order – See at least ¶ [0083]) by comparing the current vibration pattern to stored prior vibration patterns (the disclosed systems and methods may also compile a history (e.g., a table) of occurrences experienced during the drive-time of the wheeled vehicle by storing, for each vibration episode or occurrence detected, i.e., for each vibration episode, compile vibration information stored in association with an identifier of the drive line component and with at least select ones of the differing types of the on-board signals regarding plural operational conditions of the wheeled vehicle – See at least ¶ [0115]) and requiring that the current vibration pattern meets or exceeds a recurrence threshold; (Stanek discloses identifying different orders of vibration data. These orders act as thresholds for identification of the vibration data, e.g., a second order vibration has a threshold of two – See at least ¶ [0083])
determine whether a component anomaly exists []; and (if the controller 26, however , determines that the source of the sensed vibration is one of the wheels 12a, 12b, 13a and 13b, the location of the vibration can then be determined by looking at the patterns of the vibration signals coming from the four wheels 12a, 12b, 13a and 13b. The errant wheel causing the vibration, i.e., an anomaly, will have the strongest signal, and cross-coupling of vibrations through the chassis tend to occur in recognizable patterns – See at least ¶ [0091])
in response to determining that a component anomaly exists, execute a mitigation action based on the component anomaly. (A warning device 112 may also be coupled to controller 26. The warning device 112 may warn of various conditions, such as, for example, a tire imbalance, vibration, impending rollover, understeer, oversteer, or an approach of an in-path object. The warnings may be provided in time for the driver to take corrective or evasive action, or as an indicator to the driver that repair at a service shop is recommended. The warning device 112 may be a visual display 114 such as warning lights or an alpha-numeric display such an LCD screen. The display 114 may be integrated with the display 68. The warning device 112 may also be an audible display 116 such as a warning buzzer, chime or bell. The warning device 112 may also be a haptic warning such as a vibrating steering wheel. Of course, a combination of audible, visual, and haptic display may be implemented as would be understood by those of ordinary skill in the art – See at least ¶ [0072])
Stanek does not explicitly teach determine whether a component anomaly exists by classifying the vibration pattern using a machine learning algorithm trained on historical component anomaly data in response to determining that the current vibration pattern is reoccurring. However, Cella discloses systems and methods for data collection and frequency evaluation for a vehicle steering system and teaches:
determine whether a component anomaly exists by classifying the vibration pattern (A further embodiment of any of the foregoing embodiments of the present disclosure may include situations wherein the pattern recognition operation is performed on vibration data of the plurality of detection values – See at least ¶ [0063]) using a machine learning algorithm trained on historical component anomaly data in response to determining that the vibration pattern is reoccurring (FIG. 4 also shows on-device sensor fusion 80, such as for storing on a device data from multiple analog sensors 82, which may be analyzed locally or in the cloud, such as by machine learning 84, including by training a machine based on initial models created by humans that are augmented by providing feedback (such as based on measures of success) when operating the methods and systems disclosed herein – See at least ¶ [0167])
In summary, Stanek discloses determining a fault with a vehicle component based on vibration data from a sensor. Stanek further teaches that this determination may be based on a model of the vehicle. Stanek does not explicitly teach that his model or the analysis is performed with the aid of a machine learning algorithm. However, Cella discloses systems and methods for data collection and frequency evaluation for a vehicle steering system and teaches identifying and classifying vibration patterns using machine learning.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the arrangements for collecting diagnostic information regarding vibrations of wheel-tire assembly and drive-line components of a wheeled vehicle of Stanek to provide for the systems and methods for data collection and frequency evaluation for a vehicle steering system, as taught in Cella, to provide improved monitoring, control, intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments, i.e., automobiles. (At Cella ¶ [0012])
Regarding claims 3 and 14, Stanek further teaches:
wherein the electronic processor is further configured to: determine a vehicle attribute; and (if the controller 26, however , determines that the source of the sensed vibration is one of the wheels 12a, 12b, 13a and 13b, i.e., a vehicle attribute, the location of the vibration can then be determined by looking at the patterns of the vibration signals coming from the four wheels 12a, 12b, 13a and 13b – See at least ¶ [0091])
determine whether a component anomaly exists based on the vibration pattern and the vehicle attribute. (The errant wheel causing the vibration will have the strongest signal, and cross-coupling of vibrations through the chassis tend to occur in recognizable patterns – See at least ¶ [0091])
Regarding claims 8 and 19, Stanek further teaches:
further comprising: a second sensor positioned at a second position on the vehicle and configured to sense vibrations of the vehicle, (the present disclosure provides systems that may receive real-time data from several existing sensors, i.e., at least a first and second sensor, for example, the wheel speed sensor measurements from all wheels or the suspension height sensors for all the suspensions, during the normal operation of the vehicle – See at least ¶ [0030]) wherein the electronic processor is communicatively coupled to the second sensor and further configured to (systems comprising a controller, such as, for example, a controller 26, i.e., an electronic processor, that is operatively associated with a plurality of vehicle sensors, i.e., at least a first and second sensor, that may produce signals indicative of a vibration of the vehicle 10, wherein the controller 26 is configured to determine whether or not a sensed vibration falls into a target frequency range (i.e., a target frequency band) that is indicative of a vehicle component (e.g., tire/wheel, driveline, or engine) or location – See at least ¶ [0080])
receive, from the second sensor, additional sensor information produced by the sensed vibration of the vehicle; and (a suspension height sensor 60 may also be operationally coupled to the controller 26 – See at least ¶ [0062])
determine the vibration pattern based on the sensor information and the additional sensor information. (Within such equations: Zs, denotes a body vertical displacement, Zw, a wheel vertical displacement, w a vertical road profile, Fsusp a suspension force if a controllable suspension is used (Fsusp = 0 if the suspension is passive), M, a Susp sprung mass, Mw an unsprung mass, K, a passive suspension stiffness, Cs a passive suspension damping, K, a tire vertical stiffness, and Ct a tire vertical damping. FIG . 11, for example, demonstrates how the above equations can be used to improve the robustness of the disclosed algorithm. As illustrated in FIG . 11, the vibration signature (i.e., vibration signal) is first checked against the expected frequency ranges of a target fault (i.e., a target frequency band indicative of a vehicle vibration) to determine whether the vibration could potentially be an indication of the target fault – See at least ¶ [0095]; Here, the Equation (5) uses the suspension information to help determine if the vibrations patterns identified are related to a fault.)
Regarding claims 10 and 21, Stanek further teaches:
wherein the mitigation action is at least one selected from the group consisting of transmitting a notification to a vehicle owner, transmitting a notification to a fleet operator, transmitting a notification to a vehicle manufacturer, transmitting a notification to a public safety agency, controlling the vehicle to exit traffic, and producing an alert on a human machine interface of the vehicle. (A warning device 112 may also be coupled to controller 26. The warning device 112 may warn of various conditions, such as, for example, a tire imbalance, vibration, impending rollover, understeer, oversteer, or an approach of an in-path object. The warnings may be provided in time for the driver to take corrective or evasive action, or as an indicator to the driver that repair at a service shop is recommended. The warning device 112 may be a visual display 114 such as warning lights or an alpha-numeric display such an LCD screen. The display 114 may be integrated with the display 68. The warning device 112 may also be an audible display 116 such as a warning buzzer, chime or bell. The warning device 112 may also be a haptic warning such as a vibrating steering wheel. Of course, a combination of audible, visual, and haptic display may be implemented as would be understood by those of ordinary skill in the art – See at least ¶ [0072])
Regarding claims 11 and 22, Stanek further teaches:
wherein the first sensor is an accelerometer. (As above, in a vehicle equipped with a conventional on-board diagnostic system, which looks at the sensors (e.g., accelerometers and/or wheel speed) of each wheel separately, the system would perform an on-board diagnosis of each wheel separately (i.e., a separate diagnosis for each of the LR and RR wheels) and set a diagnostic flag indicating that each such wheel experienced a vibration – See at least ¶ [0099])
Regarding claims 12 and 23, Stanek further teaches:
wherein the vehicle attribute is at least one selected from the group consisting of a vehicle speed, a wheel speed, a steering angle, a throttle level, a braking level, a gear selection, and a temperature. (As above, in a vehicle equipped with a conventional on-board diagnostic system, which looks at the sensors (e.g., accelerometers and/or wheel speed) of each wheel separately, the system would perform an on-board diagnosis of each wheel separately (i.e., a separate diagnosis for each of the LR and RR wheels) and set a diagnostic flag indicating that each such wheel experienced a vibration – See at least ¶ [0099])
Claim(s) 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Stanek in view of Cella, as applied to claims 1 and 13, and in further view of Goyal et al. (US 2023/0377598 A1, “Goyal”).
Regarding claims 6 and 17, the combination of Stanek and Cella does not explicitly teach determining, for each of the potential component anomalies, a confidence score; and selecting the component anomaly from the plurality of potential component anomaly based on the confidence scores. However, Goyal discloses system and method for processing audio data of aircraft cabin environment and teaches:
wherein the electronic processor is further configured to classify the vibration pattern using a machine learning algorithm by generating a plurality of potential component anomalies based on the vibration pattern; (Once the anomalous sound detection model 1212m is trained, the anomalous sound detection model 1212m may be used in real time for LRUs/Systems/Components identification and different working condition of normal and malfunctioning with different scenarios of failure conditions. The real time sound may be preprocessed and utilized for features extraction in terms of spectrogram. The spectrogram may be convoluted and applied to the trained anomalous sound detection model 1212m. The anomalous sound detection model 1212m may classifies the LRUs/Systems/Components from where sound is coming – See at least ¶ [0129])
determining, for each of the potential component anomalies, a confidence score; and (The anomalous sound detection model 1212m may also provide the working condition of the LRUs/Systems/Components in terms of normal functioning or malfunctioning with identification of failure conditions along with a confidence score – See at least ¶ [0129])
selecting the component anomaly from the plurality of potential component anomaly based on the confidence scores. (Thus, at least one audio processing device may be configured to analyze the processed audio data and the event report to label portions of the processed audio data as an aircraft system failure – See at least ¶ [0129])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the arrangements for collecting diagnostic information regarding vibrations of wheel-tire assembly and drive-line components of a wheeled vehicle of Stanek and Cella to provide for the system and method for processing audio data of aircraft cabin environment, as taught in Goyal, to provide augmented inputs additional sample and synthetic data to arrive at better accuracy. (At Goyal ¶ [0128])
Claim(s) 7 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Stanek in view of Cella and Goyal, as applied to claims 6 and 17, and in further view of Kovscek et al. (US 2021/0183227 A1, “Kovscek”).
Regarding claims 7 and 18, the combination of Stanek, Cella, and Goyal, does not explicitly teach the use of “meta-data”. However, Kovscek discloses sound monitoring system and teaches:
wherein the electronic processor is further configured to: assign a weight to each of the plurality of potential component anomalies based on metadata (A pre-training routine 405 ensures that the sensor level training data 403 collected meets the expectations for the target object 305 as defined by the Base Models 349 and the category of the target object. The pre-training routine 407 draws on basic data (e.g., meta-data 401) from the user about the target object 305 or environment – See at least ¶ [0154]) for the potential component anomaly; and (In creating Sensor Development Models 409, a step of frequency weighting 411 occurs. This examines the range of frequency data collected by the device 101 and determines the normalized frequencies that generate the strongest response to the sound that is emitted by the target object, but at this juncture, the system also has the Base Models 349, i.e., meta-data, against which to compare. As in the case of creating base models 300 frequency weighting 411 in the context of Sensor Development Models 409 are created to strengthen the performance of other models. The approach applies a custom weighting for each sensor that combines the learning from the Sensor Development Models with the sensor-level frequency response – See at least ¶ [0156])
select the component anomaly from the plurality of potential component anomalies based on the confidence score and the weight; (For example, data is transmitted based on the behavior of water usage in the property. However, if the sensor detects an anomalous use or catastrophic water event, the transmission management application 607 will automatically trigger a data transmission – See at least ¶ [0177]-[0178])
wherein the weights are dynamically adjusted based on feedback from confirmed component anomalies. (The data management system may support a sound prediction improvement engine, wherein the application periodically updates and changes the models or algorithms applied to the data after validation and remediation processes so as to increase the level of predict ability and confidence, while filtering outlying data – See at least ¶ [0043])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the arrangements for collecting diagnostic information regarding vibrations of wheel-tire assembly and drive-line components of a wheeled vehicle of Stanek, Cella, and Goyal to provide for the meta-data, as taught in Kovscek, to informs the system of the correct Base Models to select and apply from the library. (At Kovscek ¶ [0154])
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.L.C./Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
1 Frequency definition states that it is the number of complete cycles of waves passing a point in unit time. (https://byjus.com/physics/period-angular-frequency/)