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 .
Response to Amendment
Claims 1-20 are currently pending.
Claims 1-2, 4, 6-13, and 15 are currently amended.
Claims 16-20 are newly added.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gurke et al. (US 20210144917 A1), herein after will be referred to as Gurke, in view of Mackin et al. (US 7739861 B2), and herein after will be referred to as Mackin.
Regarding Claim 1, Gurke teaches A knife drivetrain associated with a header, the knife drivetrain comprising (A cutting device for a combine harvester; Gurke [0042]):
a rotatable drive shaft (A drive mechanically coupled to the drive motor of the combine harvester connected to the cutting device; Gurke [0044]);
a mechanical drive having a housing and a rotatable input axle, the mechanical drive transforming rotation of the input axle into a reciprocating motion of an outlet member of the mechanical drive (The drive includes a gearbox (housing) containing toothed gear, a traction drive or similar transmission to perform the function of transforming rotation into back and forth (reciprocating) motion of the knife; Gurke [0044]);
a support member with a set of knives attached thereto, the support member coupled to the outlet member of the mechanical drive so that the knives undergo a reciprocating cutting movement (Knife blades (support member) that interact with counter cutting edges when the mowing knife is moving back and forth; Gurke [0043]);
and, wherein the at least one sensor monitors a condition of the knives based on signals received from the at least one sensor (Sensor signals are sent to a processing unit for evaluation to determine the wear condition and defect of the cutting device; Gurke [0046]).
Gurke does not explicitly teach at least one sensor mounted on the support member that measures a vibration of one or more components of the drivetrain in a direction of the reciprocating cutting movement of the knives.
However, Mackin discloses a vibration monitoring system for a harvester header utilizing accelerometers to detect the mechanical health and issues in a drivetrain. Mackin teaches applying one or more vibration sensors to the toolbar or mainframe of the harvester header to detect slippage in clutches by measuring the vibrations from drive components (Mackin Col 4 lines 1-5, Col 4 lines 21-27). Mackin further teaches that vibration sensors can be located at or near locations 58 and 66 on the corn head “where drive shaft is supported for rotation on the frame 18.” (Mackin Col 4 lines 14-17). These teachings are equivalent to the claimed limitation of at least one sensor mounted on the support member that measures a vibration of one or more components of the drivetrain because the vibration sensor is mounted at a location where the drive shaft is supported by the frame. It is well known that a vibration sensor is a type of accelerometer that measures acceleration as a force vector along one or more axes to determine an orientation (direction) of mechanical vibrations.
Gurke and Mackin are considered to be analogous to the claim invention because they are in the same field of monitoring the conditions of agriculture headers. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system of Gurke by substituting the force sensor with the vibration sensor mounted on the support member as taught by Mackin based on a reasonable expectation of success and a motivation to improve the detection of mechanical faults in the drivetrain and condition of the knives based on the signals from the vibration sensor.
Regarding Claim 2, Gurke and Mackin remains as applied above in claim 1. Gurke does not explicitly teaches one sensor comprises an accelerometer or a strain gauge mounted on the support member, and wherein the knife drivetrain further comprises an accelerometer mounted on the housing of the mechanical drive that measures the vibration of one or more components of the drivetrain in a direction of the reciprocating cutting movement of the knives. However, Mackin discloses a vibration monitoring system that utilizes multiple accelerometers placed at various structural and drivetrain support locations. Mackin teaches each vibration sensor may be implemented as an accelerometer or knock sensor and generating signals of frequency and amplitude of vibrations where the sensors are located (Mackin, Col 4 lines 21-26). Mackin further teaches that vibration sensors can be located at or near locations 58 and 66 on the corn head “where drive shaft is supported for rotation on the frame 18.” (Mackin Col 4 lines 14-17) and that the drive shaft is driven by gearboxes located near location 58 of the vibration sensors (Mackin Col 3 lines 47-52; FIG 1). These teachings are equivalent to the claimed limitation because the vibration sensor is implemented as an accelerometer and is mounted in a location that is a support member of the drive shaft on the frame. Furthermore, the accelerometer being located at or near the location of the gearbox provides the housing of the mechanical drive to measure the vibrations of the drive shaft. It would have been obvious to modify Gurke to include the teachings of mounting accelerometers at various locations on the header including the gearboxes as taught by Mackin based on the motivation to improve the accuracy of detecting damage obtaining vibration signals from the gearbox housing and frame. This provides the benefit of allowing the system to distinguish vibrations originating from the drive unit and vibrations caused by knife damage.
Regarding Claim 3, Gurke and Mackin remains as applied above in claim 2. Gurke does not explicitly the accelerometer mounted on the support member is a knock sensor.
However, a teaching from Mackin discloses the vibration sensor maybe implemented as an accelerometer or a knock sensor (Mackin, Col 4 lines 21-24). It would have been obvious to modify Gurke by choosing the specific type of accelerometer as a knock sensor as taught by Mackin. Choosing a knock sensor as the accelerometer would have been an obvious design choice for its known characteristics, such as robustness suitable for agricultural environments and sensitivity to vibrations associated with knife damage. This selection from known types of accelerometers would achieve the intended purpose with a reasonable expectation of success.
Regarding Claim 4, Gurke and Mackin remains as applied above in claim 1. Gurke further teaches a position sensor suitable at determining a position of the knives during each cycle of the reciprocating cutting movement of the knives, wherein the position sensor is an encoder mounted on the input axle of the mechanical drive (A position sensor encoded to determine stroke position of a rotary position of a rotating drive element; Gurke [0011]) .
Regarding Claim 5, Gurke and Mackin remains as applied above in claim 1. Gurke further teaches a agricultural harvester comprising a header comprising the knife drivetrain (The cutting device (header) is part of an agricultural harvester; Gurke [0042]).
Regarding Claim 18, Gurke and Mackin remains as applied above in claim 1. Gurke does not explicitly teach at least one sensor is disposed at an incline relative to the direction of the reciprocating cutting movement of the knives.
However, Mackin discloses a vibration monitoring system where sensors, specifically accelerometers are placed in various locations anywhere on the corn head to detect vibrations (Mackin, Col 4 lines 1-20). This teaching is equivalent to the claimed limitation of a sensor disposed at an incline because the accelerometer detects vibration as a force vector and providing mounting flexibility anywhere encompasses mounting the sensor at an angle that best captures the intended vibration vector. Furthermore, the Applicant’s specification at [0007] of the published application states that the measurement of vibration in the direction of reciprocating movement “…is not limited to sensors mounted strictly parallel to the direction of said movement, but includes sensors mounted at a slight inclination relative to said movement, which still allow to measure a signal that is representative of the vibrations in the direction of the reciprocating movement.” The Applicant confirms that the sensor mounted at an incline is functionally equivalent to one mounted parallel, as both orientations capture the same representative vibration signal. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify Gurke to incorporate the teachings of mounting vibration sensors in various orientations and locations as taught by Mackin based on a reasonable expectation of success and the motivation to install the sensor in limited or inclined surface areas on the support member while maintaining the sensor’s ability to capture the vibration signal generated by the reciprocating knives. This provides the benefit of improving the mounting options to monitor the mechanical condition of the header.
Regarding Claim 19, Gurke and Mackin remains as applied above in claim 1. Gurke does not explicitly teach at least one sensor measures the vibration of the knives.
However, Mackin discloses vibration sensors that are configured to detect the vibrations of the drivetrain components such as slip clutches and generate signals indicating frequency and amplitude of the frame (Mackin, Col 4 lines 1-27). This teaching is equivalent to the claimed limitation because the vibration sensor captures the mechanical vibration signals and detects specific signatures of the drivetrain components. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify Gurke to incorporate the teachings of using vibration sensors to monitor specific components as taught by Mackin based on the motivation to detect mechanical damage on the header, drivetrain, or knives by capturing the vibration signatures from the sensors. This provides the benefit of allowing remote detection of components in the monitoring system and improving the operational efficiency without having to visually inspect the harvester before each operational use.
Regarding Claim 20, Gurke and Mackin remains as applied above in claim 1. Gurke does not explicitly teach at least one sensor measures the vibration of the knives.
However, Mackin discloses vibration sensors that are configured to detect the vibrations of the drivetrain components such as slip clutches and generate signals indicating frequency and amplitude of the frame (Mackin, Col 4 lines 1-27). This teaching is equivalent to the claimed limitation because the vibration sensor captures the mechanical vibration signals and detects specific signatures of the drivetrain components. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify Gurke to incorporate the teachings of using vibration sensors to monitor specific components as taught by Mackin based on the motivation to detect mechanical damage on the header, drivetrain, or knives by capturing the vibration signatures from the sensors. This provides the benefit of allowing remote detection of components in the monitoring system and improving the operational efficiency without having to visually inspect the harvester before each operational use.
Claim(s) 6-17 are rejected under 35 U.S.C. 103 as being unpatentable over Gurke in view of Mackin, as applied to claim 1 above, and in further view of Morey et al. (US 10168248 B1), and herein after will be referred to as Morey.
Regarding Claim 6, Gurke teaches a method of monitoring a condition of a knife drivetrain for a header (A method for analyzing the condition of a cutting device; Gurke [0005]), the method comprising (The detection of a signal representing stoke position and knife force from different cutting positions ranges of several strokes; Gurke [0013]) and deriving…information on the condition of the knives, the information including potential damage to one or more of the knives (Determining the wear condition of the cutting device and the defect of the cutting device; Gurke [0014]);
wherein the knife drivetrain comprises: a rotatable drive shaft , a mechanical drive, a support member, and at least one sensor mounted on the support member (A drivetrain structure including the drive shaft, mechanical drive/gearbox, and cutter bar as a support member. Mounting a sensor on the reciprocating knife bar/support member; [0043-0044] [0050] [0011]).
Gurke does not explicitly teach a vibration signal; deriving one or more features from the vibration signal; comparing the one or more features to one or more thresholds; deriving from the comparison information on the condition of the knives…; and communicating the information regarding the condition of the knives to an operator.
However, Mackin, in the same field of endeavor teaches using a vibration sensor to acquire a vibration signal for the purpose of fault detection on a harvester head (Mackin Col 4 lines 21-27). Furthermore, Mackin discloses that when a magnitude from a sensor exceeds a predetermined threshold, an audio and visual indicator is provided to the annunciator notifying an operator (Mackin Col 4 lines 59-64). It would have been obvious to modify the base method of Gurke by incorporating the teachings of Mackin by using a vibration sensor to acquire a vibration signal to monitor the cutting device condition and communicating the condition of the cutting device when the signal value exceeds a predetermined threshold to notify the operator providing the benefit of improved accuracy for monitoring the condition of the cutting device and notifying an operator when a fault is detected.
The combination of Gurke and Mackin does not explicitly teach deriving one or more features from the vibration signal; comparing the one or more features to one or more thresholds; deriving from the comparison information on the condition…, the information including potential damage …when a feature has been found to exceed one or more of the thresholds.
However, Morey, in the same field of endeavor teaches the analysis of signals from a range of sensors including vibration and displacement sensors (Morey Col 10 lines 51-55) where one or more analysis metrics are generated from the signals and used to identify the presence of gear or bearing faults (Morey Col 8 lines 48-56). Furthermore, Morey discloses that gear or bearing faults are identified by comparing one or more of the analysis metric values to one or more threshold values (Morey Col 8 lines 28-31). It would have been obvious to modify the combination of Gurke and Mackin to incorporate the teachings of Morey to derive analysis metrics from vibration signals and comparing the metrics to one or more threshold values to identify the presence of mechanical faults in the system. This provides the benefit of an improved monitoring and detection system to include vibration signal analysis from the vibration sensors.
Regarding Claim 7, Gurke, Mackin, and Morey remains as applied above in claim 6. The prior art combination of Gurke and Mackin does not explicitly teach the that obtains a same number of samples in each cycle of a series of consecutive cycles of the plurality of cycles of the reciprocating cutting movement performed during the monitoring interval, each sample having a value that is representative of vibration measured by the at least one sensor when the support member is in well-defined consecutive positions during each cycle of the series of consecutive cycles, the the one or more features comprises deriving the one or more features for each cycle of the series of consecutive cycles; and the the one or more features comprises comparing the one or more features to the one or more thresholds.
However, Morey teaches a method of vibration analysis where the accuracy of the fault detection is improved by converting the acquired signal and resampling the signal from a time-based signal into angle-based data signal with a fixed number of data points per revolution of the shaft (Morey, Col 6 lines 7-10), where a revolution is equivalent to a cycle. Furthermore, Morey discloses obtaining vibration data corresponding to a rotating component from resampling and obtaining a vibration signature at predetermined angular increments (Morey, Col 10-11 lines 67-6) that is equivalent to deriving features from the consecutive cycles. The last limitation of comparing the features to one or more thresholds is taught by Morey where gear or bearing faults are identified by comparing one or more analysis metric values to one or more threshold values (Morey Col 8 lines 28-31). It would have been obvious to one of ordinary skill in the art to modify the combination of Gurke and Mackin to incorporate the teachings of Morey to incorporate the signal analysis methods of resampling a signal with a fixed number of samples to obtain a rotational position and compare the analysis to a threshold to detect any faults based on the motivation to improve the monitoring of the conditions in the cutting device. This provides the benefit of an improved and precise fault detection for the cutting system.
Regarding Claim 8, Gurke, Mackin, and Morey remains as applied above in claim 7. Gurke further teaches the rotatable drive shaft is coupled to a power shaft of an agricultural harvester (A drive mechanically coupled to the drive motor of the combine harvester connected to the cutting device; [0044]);
wherein the mechanical drive comprises a housing and a rotatable input axle, the mechanical drive transforming rotation of the input axle into a reciprocating motion of an outlet member of the mechanical drive (The drive includes a gearbox (housing) containing toothed gear, a traction drive or similar transmission to perform the function of transforming rotation into back and forth (reciprocating) motion of the knife; [0044]);
wherein the support member is operably attached to the knives thereto, the support member being coupled to the outlet member of the mechanical drive that enables the knives in undergoing a reciprocating cutting movement (Knife blades (support member) that interact with counter cutting edges when the mowing knife is moving back and forth; [0043]).
Gurke does not explicitly teach at least one sensor is mounted on the support member and measures a vibration of one or more components of the knife drivetrain in a direction of the reciprocating cutting movement of the knives.
However, Mackin discloses a vibration monitoring system for a harvester header utilizing accelerometers to detect the mechanical health and issues in a drivetrain. Mackin teaches applying one or more vibration sensors to the toolbar or mainframe of the harvester header to detect slippage in clutches by measuring the vibrations from drive components (Mackin Col 4 lines 1-5, Col 4 lines 21-27). Mackin further teaches that vibration sensors can be located at or near locations 58 and 66 on the corn head “where drive shaft is supported for rotation on the frame 18.” (Mackin Col 4 lines 14-17). These teachings are equivalent to the claimed limitation of at least one sensor mounted on the support member that measures a vibration of one or more components of the drivetrain because the vibration sensor is mounted at a location where the drive shaft is supported by the frame. It is well known that a vibration sensor is a type of accelerometer that measures acceleration as a force vector along one or more axes to determine an orientation (direction) of mechanical vibrations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the system of Gurke by substituting the force sensor with the vibration sensor mounted on the support member as taught by Mackin based on a reasonable expectation of success and a motivation to improve the detection of mechanical faults in the drivetrain and condition of the knives based on the signals from the vibration sensor.
The prior art combination of Gurke and Mackin does not explicitly teach the sampling or resampling is
However, Morey teaches the step of resampling a rotating component with a tachometer that is a dedicated sensor providing a signal that represents the measurement of a shaft’s rotation cycle (Morey Col 2 lines 54-57, Col 14 lines 38-40). A measurement of a shaft’s rotational cycle using a dedicated sensor such as the tachometer is equivalent to obtaining a position of a component during each cycle within the resampling period. It would have been obvious to one of ordinary skill in the art to modify the combination of Gurke and Mackin to incorporate the teachings of Morey to resample a signal from a dedicated tachometer sensor where the signal represents the position of the shaft’s rotational cycle during the resampling period based on the motivation to pinpoint an event to a specific position in the shaft’s rotational cycle. This provides the benefit of improving the system’s monitoring and detection of the cutting device by calculating a position in the cutting knives where a fault is detected.
Regarding Claim 9, Gurke, Mackin, and Morey remains as applied above in claim 8. The prior art combination of Gurke and Mackin does not explicitly teach the method according to claim 8, comprising: deriving a position of the support member during each cycle of the series of consecutive cycles from the vibration signal, and wherein the sampling or resampling is done based on the position of the support member as derived from the separate signal.
However, Morey teaches the method of deriving a position from the vibration signal through obtaining a vibration signature at predetermined angular increments based on a defined component of interest and the shaft (Morey Col 5 lines 39-47). The defined component would be equivalent to the claimed support member and would be dependent on the physical characteristics of the cutting knives at a given revolution of the shaft. Furthermore, Morey discloses the tachometer indicates each rotation cycle of the shaft as vibrational samples are recorded to correlate the rotational position of the shaft with the vibration data to establish the relationship between the two data signals (Morey Col 20 lines 2-11). It would have been obvious to one of ordinary skill in the art to modify the combination of Gurke and Mackin to incorporate the teachings of Morey to derive a position from the vibration signal at predetermined angular increments and deriving a position of the support member from the tachometer signal based on the motivation to establish the relationship between the vibration data and position of the cutting knives and determine the location of a mechanical fault. This provides the benefit of improving the reliability and monitoring of the knives condition in the cutting device.
Regarding Claim 10, Gurke, Mackin, and Morey remains as applied above in claim 7. The prior art combination of Gurke and Mackin does not explicitly teach at least one filter that removes at least a frequency of the support member.
However, Morey teaches the step of recording vibration data and performing an operation check of the sensors that comprises applying filters to remove waveforms (Morey Col 3 lines 29-42). Furthermore, Morey discloses isolating vibrations from rotating components by identifying and removing the harmonics of a gear in the shaft by performing a FFT operation on the waveform (Morey Col 3 lines 57-61). It would have been obvious to one of ordinary skill in the art to modify the combination of Gurke and Mackin to incorporate the signal processing techniques to remove vibration signals from the gears in a shaft as taught by Morey based on the motivation to rule out false error detections in the cutting device. The removal of vibration signals from the gears provides the benefit of further isolating the vibration signals to the knives of the cutting device and improving the detection of mechanical failures in the system.
Regarding Claim 11, Gurke, Mackin, and Morey remains as applied above in claim 10. The prior art combination of Gurke and Mackin does not explicitly teach the at least one filter is one of the four following filters: fixed synchronous average residual (FSAR) filter; for the FSAR filter, the average value is an average of a fixed number of cycles acquired at a beginning of the monitoring interval.
However, Morey teaches a method to find a repeating pattern that identifies faults in a gear by applying the Turns Synchronized Averaging (TSA) that averages data over a fixed number of cycles (Morey Col 20 lines 25-30, Col 20 lines 65-67). The TSA is functionally equivalent to the FSAR filter because both processes create an average signal value from a set number of cycles creating a baseline average to detect residual signals. It would have been obvious to one of ordinary skill in the art to modify the combination of Gurke and Mackin to incorporate the Turns Synchronized Averaging as taught by Morey based on the motivation to create a baseline signal average to find repeating patterns and identify faults in the signal. This provides the benefit of enhancing the signal processing by applying filters to create a baseline average to analyze the residual signal for improved detection of faults.
Regarding Claim 12, Gurke, Mackin, and Morey remains as applied above in claim 11. The prior art combination of Gurke and Mackin does not explicitly teach one of the MSAR, EWMSAR and EWMSNR filters is applied, and wherein, if when a potentially damaging event is detected in one of the plurality of cycles, the one of the plurality of cycles is excluded from the average value applied for filtering subsequent ones of the plurality of cycles.
However, Morey teaches using a moving or running average in a condition indicator function called NA4 where the denominator is the “square of average variance of all residual signals up to a current time (running average)” achieving the same end results as a Moving Synchronous Average Residual (MSAR), where the MSAR filter is a moving average applied to the signal data to adapt to changing signal conditions and drift (Morey, Table 1. NA4 Denominator, Col 25 lines 24-39). Furthermore, Morey teaches the selective process of updating the running average where the running average is tested via a hypothesis test to determine if it falls within the limit of a healthy dataset to be included in the denominator and the frequency harmonics contain the average profile error for all teeth on a gear to detect a potentially damaging event (Morey, Table 1. NA4* Denominator, Col 25 lines 24-39). It would have been obvious to one of ordinary skill in the art to modify the combination of Gurke and Mackin to incorporate the running average of the NA4 filters applied to the TSA waveform as taught by Morey based on the motivation to detect the average profile error information for all teeth in a gear or cutting device. This provides the benefit of improving the reliability for detecting a single tooth or knife fault in the system.
Regarding Claim 13, Gurke, Mackin, and Morey remains as applied above in claim 7. The prior art combination of Gurke and Mackin does not explicitly teach a mean of the samples of one of the plurality of cycles.
However, Morey teaches averaging sampled signal data values of a shaft over a fixed number of turns that is equivalent to a mean of samples in in one of the plurality of cycles (Morey Col 6 lines 49-51). It would have been obvious to one of ordinary skill in the art to modify the combination of Gurke and Mackin to incorporate Morey averaging the sampled signal data values for fault detection based on the motivation for identifying changes in the signal that could indicate a fault. This provides the benefit of improving the reliability of the detection in the system when a damaging event occurs.
Regarding Claim 14, Gurke, Mackin, and Morey remains as applied above in claim 7. The prior art combination of Gurke and Mackin does not explicitly teach the information on the condition of the knives comprises a classification of the condition into two or more classes, the classes being related to different degrees of damage to one or more of the knives.
However, Morey discloses setting alarm limits for spectral bands where the bands are defined in orders or frequency and a baseline is applied to the machine where measurements of a 4 dB change is the first alarm limit and 8 dB is the shutdown limit. Morey further teaches that the alarm limits of a 4 dB change is minor and allows the system to plan ahead for any necessary maintenance, 8 dB change is a significant change that needs serious investigation, and a possible 3rd limit of 12dB change can indicate urgent action required to the system (Morey Col 32 lines 32-46). It would have been obvious to one of ordinary skill in the art to modify the combination of Gurke and Mackin to incorporate the different classification of alarm limits as taught by Morey based on the motivation of measuring vibration changes in the cutting knives to determine a degree of damage. This provides the benefit of improving the reliability of detecting damage in the cutting device of the monitoring system.
Regarding Claim 15, Gurke, Mackin, and Morey remains as applied above in claim 14. The prior art combination of Gurke and Mackin does not explicitly teach the classification is made based on one of the plurality of cycles during which a potentially damaging event is detected and on at least a subsequent one of the plurality of cycles immediately following the one of the plurality of cycles; and wherein the classification is further made based on a number of representative samples within the plurality of cycles.
However, Morey discloses identifying a rotating component fault by the trending condition indicator FM4 peak to peak value and triggering an alarm once the trended value exceeds a predetermined level (Morey Col 4 lines 18-22). The trending value inherently requires the analysis of data over a series of cycles. Furthermore, Morey teaches that the analysis is based on a number of data sets within a defined plurality of cycles (Morey Col 21 lines 11-15). It would have been obvious to one of ordinary skill in the art to modify the combination of Gurke and Mackin to incorporate the classification of conditions based on a trending value through the analysis of data sets over a series of cycles as taught in Morey. The motivation to base the classification on the subsequent plurality of cycles is to avoid any false positives that may trigger the alarm and provides a trend for analysis to improve the reliability of the classification to the alert system.
Regarding Claim 16, Gurke, Mackin, and Morey remains as applied above in claim 10. The prior art combination of Gurke and Mackin does not explicitly teach at least one filter comprises: a fixed synchronous average residual (FSAR) filter.
However, Morey teaches a method to find a repeating pattern that identifies faults in a gear by applying the Turns Synchronized Averaging (TSA) that averages data over a fixed number of cycles (Morey Col 20 lines 25-30, Col 20 lines 65-67). The TSA is functionally equivalent to the FSAR filter because both processes create an average signal value from a set number of cycles creating a baseline average to detect residual signals. It would have been obvious to one of ordinary skill in the art to modify the combination of Gurke and Mackin to incorporate the Turns Synchronized Averaging as taught by Morey based on the motivation to create a baseline signal average to find repeating patterns and identify faults in the signal. This provides the benefit of enhancing the signal processing by applying filters to create a baseline average to analyze the residual signal for improved detection of faults.
Regarding Claim 17, Gurke and Mackin remains as applied above in claim 1. Gurke further teaches the control unit identifies damage to the knives (The processing unit receives and evaluates the sensor signals to determine the operating state or condition of the cutting device; [0027] [0014]).
The prior art combination of Gurke and Mackin does not explicitly teach the signals are outside of a range between a lower knife vibration control limit value and an upper knife vibration threshold control limit value.
However, Morey discloses a method for identifying faults by comparing the vibration metrics to thresholds and parameter bands. Morey teaches defining the frequency or order bands that represent reasonable vibration levels for a mechanical system and settings alarm limits for each defined band by applying a base line ratio (Morey Col 31 21-67, Col 32 13-46). These teachings are equivalent to the claimed limitation because the band in the spectral context provides the upper and lower boundaries for signal validation and the ratios applied to each band gives a limit when a signal is not considered nominal to catch minor, significant, or urgent events for an alarm limit and shutdown limit. It would have been obvious to one having ordinary skill in the art at the time the invention was made to modify Gurke and Mackin to incorporate the teachings of the vibration parameter bands with an upper and lower limit as taught by Morey based on the motivation to improve the fault detection accuracy by identifying excessive vibrations indicating damage, imbalance, or poor lubrication. This provides the benefit of enhancing the monitoring system by specifying an operable range and identifying specific faults.
Response to Arguments
Applicant’s arguments, see Page 9 and 10, filed 11/18/2025, with respect to the rejection(s) of claim(s) 1 and 6 under 35 USC § 103 have been fully considered.
Applicant argues that “Gurke does not describe a vibration sensor, as was acknowledged on Page 4 of the Office Action. Gurke's sensor 5 is not a vibration sensor. Sensor 5 is not mounted to the support for the knives 2; instead, it is mounted to an input side of a drive unit 4. Sensor 5 does not monitor vibration of the knives 2… Gurke sensor 5 is configured for determining "knife force" which bears no relation to knife vibration.” and that “Mackin may disclose a vibration sensor, but the vibration sensor is configured for measuring vibration chatter of a slip clutch. See column 4, lines 30-60.1 Mackin's slip clutch vibration sensor is not mounted to a support for knives. Moreover, Mackin does not disclose any reciprocating knives. Thus, Mackin's sensor does not monitor vibration of any knives…". The Examiner respectfully disagrees.
In response to the arguments filed 11/18/2025, it appears that the applicant is arguing the references individually. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). ). As was specifically stated in the nonfinal office action mailed 09/26/2025, Gurke was used to teach the main structural base of a knife drivetrain for a header and monitoring the condition of the knives based on signals received from the sensor. Gurke acknowledges that “natural vibrations of the mowing knife” are present in the system ([0010]) and further teaches mounting a sensor on the mowing knife or a component moving in a reciprocating matter with the mowing knife ([0011]). Mackin was used to teach utilization of vibration sensors on a header to detect mechanical fault signals generated by moving drivetrain components. Mackin teaches using the vibration sensors to identify mechanical events based on frequency and amplitude characteristics (Col 5 lines 21-24), where a person of ordinary skill in the art would find it obvious to apply this method to detect knife damage in Gurke, as a damaged blade generates an abnormal vibration signature in the same periodic manner as the slipping clutch as taught in Mackin.
Applicant argues that Gurke, Mackin, and Morey fails to disclose measuring a vibration “in a direction of the reciprocating cutting movement of the knives" The Examiner respectfully disagrees.
In the system of Gurke, the knives are driven in a reciprocating motion along a single axis ([0042]) where the primary vibration forces are concentrated in the reciprocating motion and is generated by the acceleration and deceleration of the knife mass at each cyclical stroke. A vibration sensor, often using accelerometers as taught in Mackin (Col 4 lines 21-24), is a directional transducer that measures acceleration, velocity, and displacement as a force vector along a specific axis and detects the vibration of a component that aligns with its internal orientation. A person of ordinary skill in the art applying the vibration sensor of Mackin to the reciprocating drivetrain of Gurke would find it obvious to align the sensor’s axis with the drivetrain’s reciprocating movement based on the motivation to capture the concentrated acceleration data needed to detect knife damage. If the sensor were oriented perpendicular to the reciprocating movement, it would fail to capture the primary impact of the reciprocating movement.
Applicant argues that “Morey may disclose a vibration sensor, but the vibration sensor is configured for measuring vibration of gears and bearings. See Abstract. Morey's vibration sensor is not mounted to a support for knives. Moreover, Morey does not disclose any reciprocating knives. Thus, Morey's sensor does not monitor vibration of any knives. Morey's sensor also does not measure a vibration ‘in a direction of the reciprocating cutting movement of the knives.’”. The Examiner respectfully disagrees.
In response to the arguments filed 11/18/2025, it appears that the applicant is arguing the references individually. One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). ). As was specifically stated in the nonfinal office action mailed 09/26/2025, Morey was not used to teach the structural components of a knife drivetrain but rather to teach the signal processing method and analysis for isolating damage related vibrations from background noise in a mechanical drivetrain. Morey teaches a method of “measuring and analyzing vibrations of a mechanical system” by synchronizing vibration data with the rotation of a reference shaft. A knife drivetrain in a harvester as taught by Gurke is a mechanical system driven by a rotatable shaft. One of ordinary skill would recognize that the vibration analysis applies to any mechanical component coupled to the drivetrain and that the mathematical methods of thresholds and filters for detecting a chipped gear tooth as taught by Morey is equally applied and pertinent to the detection of a damaged reciprocating knife blade of Gurke. The rejection of claims 1 and 6 are maintained.
Prior Art
The prior art made of record and not relied upon is considered pertinent, most relevant, to applicant's disclosure.
Pirro (US 20040182061 A1)
McClure (US 5018342 A)
Weaver (US 4799625 A)
Bischoff (US 20140208851 A1)
Priepke (US 7401458 B2)
Dunn (US 20170127609 A1)
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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/EDWARD ANDREW IZON DIZON/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663