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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/13/2026 has been entered.
Status of Claims
This action is in response to the amendments, filed on 05/13/2026, 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 05/13/2026 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:
Claim 1, as amended, recites in relevant part: "prior to determining whether a component anomaly exists, determine whether the current vibration pattern is reoccurring by comparing the current vibration pattern to a library of stored prior vibration patterns detected during prior vibration detection events and requiring that the current vibration pattern meets or exceeds a recurrence threshold."The Office has alleged that Stanek's vibration "orders" (first, second, third order) teach the claimed reoccurrence determination. Applicant respectfully disagrees. Stanek explicitly defines these terms as harmonic descriptors, not indicators of temporal recurrence. As Stanek explains, "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." Stanek, paragraph [0082]. This describes frequency content within a single measurement (i.e., how many vibration cycles occur per wheel revolution) not whether the same vibration pattern has appeared across multiple, temporally distinct operational events.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “whether the same vibration pattern has appeared across multiple, temporally distinct operational events”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
The claims do not require that a vibration pattern “has appeared across multiple, temporally distinct operational events.” The relevant limitations instead require “…determine whether the current vibration pattern is reoccurring by comparing the current vibration pattern to a stored vibration patterns in a library of stored prior vibration patters detected during prior vibration detection events and requiring that the current vibration pattern meets or exceeds a recurrence threshold…”
As cited previously by the Examiner and presented above by the Applicant, 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 order is the number of times the pattern is “reoccurring” per revolution of the wheel. To determine if a pattern is reoccurring, there must necessarily be a comparison between the patterns. If there were no comparison, then the system would not be able to identify if the specific pattern was reoccurring.
Further, Stanek provides “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.” (¶ [0115]) Additionally, Stanek discloses “The controller 26 may be programmed to perform various functions and control various outputs. Controller 26 may also have a memory 27 associated therewith. Memory 27 may be a stand-alone memory or may be incorporated within the controller 26. Memory 27 may store various algorithms, parameters, thresholds, patterns, tables or maps, which may be calibrated during vehicle development and/or upgradeable in the field.” (¶ [0046] emphasis added)
In any case Stanek provides a system that meets the newly amended claim limitation which requires “a library of stored prior vibration patterns detected during prior vibration detection events”. In other words Stanek must at least store a pattern to determine if the pattern is reoccurring, thus creating a library of prior vibration patters, and Stanek explicitly provides for memory and storage that keeps patterns and historical vibration events for use within its method and process.
Therefore, the Examiner finds the above arguments unpersuasive.
Examiner additionally notes that the claims do not require a “temporally distinct operational events”. However, the revolution of the tire produces temporally distinct operational events.
Applicant further argues:
In contrast, claim 1 as amended requires comparing a current vibration pattern to "a library of stored prior vibration patterns detected during prior vibration detection events." This is a fundamentally different operation: determining whether the same pattern has manifested over multiple distinct events or time periods versus analyzing harmonic frequency content within a single diagnostic session.
Applicant acknowledges that Stanek does disclose storing vibration history: "compile a history (e.g., a table) of occurrences experienced during the drive-time of the wheeled vehicle." Stanek, paragraph [0115]. However, this storage is for diagnostic reporting to service personnel. It is not for gating whether machine learning classification occurs. There is no disclosure in Stanek of comparing a current vibration pattern to this stored history to determine reoccurrence as a prerequisite to anomaly classification.
This argument is addressed above and the Examiner finds the argument unpersuasive for the reasons presented above.
Applicant further argues:
Neither Stanek nor Cella teaches applying machine learning classification "in response to determining that the current vibration pattern is reoccurring," as expressly required by the claims. Cella's disclosure of machine learning is generalized to industrial IoT applications and does not teach a sequential process where ML classification is gated by a prior determination of temporal recurrence. Even in combination, the references fail to teach or suggest this conditional relationship.
Stanek provides for determining whether a component anomaly exists in response to determining that the current vibration pattern is reoccurring. Specifically Stanek states “Accordingly, the controller 26 may determine that the indicated (sensed) vehicle vibration is associated with one or more wheels 12a, 12b, 13a and 13b of the vehicle 10 if the sensed vehicle vibration falls within one of the pre-calculated frequency bands that are indicative of a wheel vibration (i.e., a first or second order wheel vibration ). Furthermore, as described below with reference to FIG. 11, the wheel rotational velocity must also be consistent with the appropriate first or second order frequency. If one or more of the parameters are out of range, the controller 26 may determine that the source of the sensed vibration is not one of the wheels 12a , 12b , 13a and 13b, and a wheel vibration/imbalance issue can be ruled out” (¶ [0090] emphasis added) As previously cited, Stanek additionally provides “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.” (¶ [0091] emphasis added.)
In both of the above paragraphs Stanek is teaching determining whether a component anomaly exists in response to the current vibration pattern is reoccurring.
Stanek does not explicitly teach that the process of determining whether a component anomaly exists includes using machine learning classification. However, Cella discloses systems and methods for data collection and frequency evaluation for a vehicle steering system and teaches utilizing machine learning techniques to identify patterns within data sets associated with industrial machines and vehicles. The data includes vibration sensor data from a vehicle steering system. (¶ [0021]) Thus, combining the machine learning techniques of Cella with the systems and methods of Stanek discloses the entirety of the independent claims.
Therefore, the Examiner finds the above arguments unpersuasive.
Applicant further argues:
Even if, purely for the sake of argument, Stanek's vibration orders could somehow be characterized as a form of "recurrence," Stanek still fundamentally fails to disclose comparing a current pattern to "a library of stored prior vibration patterns detected during prior vibration detection events," as expressly required by the amended claims. Stanek's order analysis is performed entirely within a single measurement session by analyzing frequency content relative to component rotation speed at that moment in time. Critically, this analysis does not involve maintaining a library of previously detected vibration patterns, nor does it involve comparing a currently detected pattern against such stored patterns from temporally distinct prior detection events to establish whether the pattern has reoccurred over time. The claims require a fundamentally different architecture: one that accumulates vibration patterns across multiple detection events, stores them in a library, and then performs a comparison operation to determine whether a current pattern matches or corresponds to patterns that were previously detected and stored. Stanek is entirely silent on this temporal comparison functionality.
This argument is substantially similar to those addressed above and is unpersuasive for the reasons provided above.
Applicant further argues:
Goyal and Kovscek were not cited for the purpose of curing these deficiencies and therefore cannot cure the deficiencies of Stanek and Cella.
For the reasons provided above, there are no deficiencies for Goyal and Kovscek to cure. Therefore, this argument is moot.
For the above reasons the Examiner maintains the previous rejections under 35 USC § 103.
Claim Objections
Claims 1 and 13 are objected to because of the following informalities:
Claim 1 recites: “…determine whether the current vibration pattern is reoccurring by comparing the current vibration pattern to stored prior vibration patternsa library of stored prior vibration patterns detected during prior vibration detection events and…” The Examiner believes this is a typo and should read – determine whether the current vibration pattern is reoccurring by comparing the current vibration pattern to stored prior vibration patterns in a library of stored prior vibration patterns detected during prior vibration detection events and –
Claim 13 recites: “…determining whether a component anomaly exists by comparing the current vibration pattern to stored prior vibration patternsa library of stored prior vibration patterns detected during prior vibration detection events…” The Examiner believes this is a typo and should read – a determining whether a component anomaly exists by comparing the current vibration pattern to stored prior vibration patterns in a library of stored prior vibration patterns detected during prior vibration detection events –
For the purposes of this action the limitations will be interpreted as presented above. Appropriate correction is required.
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 in a library of stored prior vibration patterns detected during prior vibration detection events (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]; See also ¶ [0046]; Further, the determination of “orders” necessarily requires the storage of patterns in order to determine if the pattern is repeating over the revolution of the wheel. Without storing the pattern the system could not identify repeating patterns.) 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 [] in response to determining that the current vibration pattern is reoccurring; 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]; See also ¶ [0090])
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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHASE L COOLEY whose telephone number is (303)297-4355. The examiner can normally be reached Monday-Thursday 7-5MT.
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/CHASE L COOLEY/Examiner, Art Unit 3662
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