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 .
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Boggio (U.S. 2019/0130669) and further in view of Hernandez et al (U.S. 4,931,949).
1. As per claims 1,9,17 Boggio disclosed a computer-implemented method that, when executed by data processing hardware of a vehicle having a drive unit, causes the data processing hardware to perform operations comprising:
receiving sensor data associated with the drive unit; receiving one or more vehicle operating parameters comprising a motor signal [The vehicle control module 110 is configured to receive the at least one time series of numerical sensor data 112TA-112Tn from the at least one sensor 101, such as over the wired or wireless connection so that the at least one time series of numerical sensor data 112TA-112Tn is stored in the memory 111 in any suitable manner.] [Paragraph. 0031];
transforming the sensor data into a phase domain based on the motor speed signal [the vehicle control module being configured to transform the at least one time series of numerical sensor data for the respective system parameter into an analysis image of at least one system parameter and detect] [Paragraph. 0081];
after transforming the sensor data, filtering the transformed sensor data with a time synchronous averaging process to isolate a gear fault signature from one or more operational signatures associated with the drive unit [Paragraph. 0031] extracting one or more health indicators from each of the gear fault signature segments;
estimating, for each of the one or more health indicators, a respective fault level estimate; fusing the respective fault level estimate to generate a fused health indicator;
determining whether the fused health indicator exceeds a threshold health indicator [The vehicle control module 110 may be configured to cause the user interface 125 to present/display the output of the convolutional neural network deep learning model 122MA on the user interface 125 as an indication of anomalous behavior 189 in the component 102C for the prediction 189P of the fault/failure to the operator (FIG. 11, Block 1140), where when degraded performance of the component 102C is illustrated in the deep learning model output the operator may perform preventative maintenance on the component 102C (FIG. 11, Block 1150) prior to a component 102C fault/failure occurring] [Paragraph. 0042]; and
when the fused health indicator exceeds the threshold health indicator, generating a gear health notification indicating a fault condition of the drive unit [Paragraph. 0041].
However, Baggio did not disclose in detail, “separating the gear fault signature into a plurality of gear fault signature segments”.
In the same field of endeavor Hernandez disclosed step 6B illustrated in FIG. 4A-B is an individual tooth based analysis of gear conditions. By computing one or more measures (e.g. individual amplitude) of the individual tooth vibration pattern, or the vibration pattern for an individual tooth pair (containing a hunting tooth pattern), and tracing the variation of this measure (variance, moving average based changes, transient behavior, etc.) over time, reliable indications of fault development can be obtained (col. 9, lines 25-33). Examiner interpreted the gear fault signature as tooth defects/vibration pattern.
It would have been obvious to one having ordinary skill in the art before the effective filing date was made to have incorporated step 6B illustrated in FIG. 4A-B is an individual tooth based analysis of gear conditions. By computing one or more measures (e.g. individual amplitude) of the individual tooth vibration pattern, or the vibration pattern for an individual tooth pair (containing a hunting tooth pattern), and tracing the variation of this measure (variance, moving average based changes, transient behavior, etc.) over time, reliable indications of fault development can be obtained as taught by Hernandez in the method and system of Boggio to optimize the gear fault detection system.
2. As per claims 2,10,18 Boggio-Hernandez disclosed further comprising: determining whether one or more of the vehicle operating parameters exceeds a vehicle operating parameter threshold; and when one or more of the vehicle operating parameters exceeds the vehicle operating parameter threshold, generating enabling instructions to initiate the operation of transforming the sensor data (Boggio, Paragraph. 0039).
3. As per claims 3,11,19 Boggio-Hernandez disclosed further comprising enhancing the gear fault signature (Hernandez, col. 8, lines 29-54). Claims 3,11,19 has the same motivation as to claim 1.
4. As per claims 4,12,20 Boggio-Hernandez disclosed wherein enhancing the gear fault signature includes applying at least one of a wavelet filter or an envelope filter to the gear fault signature (Hernandez, col. 5, lines 55-63). Claims 4,12,20 has the same limitation as to claim 1.
5. As per claims 5,13 Boggio-Hernandez disclosed further comprising applying a Fast Fourier transformation to each of the gear fault signature segments (Hernandez, col. 8, lines 17-23). Claims 5,13 has the same motivation as to claim 1.
6. As per claims 6,14 Boggio-Hernandez disclosed further comprising normalizing each of the extracted health indicators (Hernandez, col. 6, lines 6-11). Claims 6,14 has the same motivation as to claim 1.
7. As per claims 7,15 Boggio-Hernandez disclosed wherein fusing the respective fault level estimates to generate a fused indicator comprises aggregating each of the extracted health indicators to determine the fused health indicator (Boogio, Paragraph. 0042).
8. As per claims 8,16 Boggio-Hernandez disclosed wherein determining the fused health indicator includes applying a respective health indicator weight value to each of the health indicators (Hernandez, col. 6, lines 6-11). Claims 8,16 has the same motivation as to claim 1.
Response to Arguments
9. Applicant's arguments filed 12/02/2025 have been fully considered but they are not persuasive. Response to applicant’s argument as follows.
Applicant argued that prior art did not disclose, “estimating, for each of the one or more health indicators, a respective fault level estimate; fusing the respective fault level estimate to generate a fused health indicator”.
As to applicant’s argument Boggio disclosed, “[The vehicle control module 110 may be configured to cause the user interface 125 to present/display the output of the convolutional neural network deep learning model 122MA on the user interface 125 as an indication of anomalous behavior 189 in the component 102C for the prediction 189P of the fault/failure to the operator (FIG. 11, Block 1140), where when degraded performance of the component 102C is illustrated in the deep learning model output the operator may perform preventative maintenance on the component 102C (FIG. 11, Block 1150) prior to a component 102C fault/failure occurring] (Paragraph. 0042). Examiner interpreted the “fused health indicator” as convolutional neural network deep learning model 122MA on the user interface 125 as an indication of anomalous behavior 189 in the component 102C for the prediction 189P of the fault/failure to the operator.
Applicant argued that prior art did not disclose, “when the fused health indicator exceeds the threshold health indicator, generating a gear health notification indicating a fault condition of the drive unit”.
As to applicant’s argument Boggio disclosed, “The vehicle control module 110 employs the at least one deep learning model 122M of the deep learning module 122 to detect anomalous behavior of the respective system parameter 112A-112n based on the at least one analysis image 180 of at least one system parameter 112A-112n (FIG. 11, Block 1130). For example, the convolutional neural network deep learning model 122MA (see FIG. 7A) compares the at least one analysis image 180 with the knowledge learned from the anomalous 161 and ordinary 162 at least one training image 160 to determine if the at least one analysis image 180 is indicative of an impending fault/failure in the component 102C of the vehicle system 102 being monitored. As another example, the stacked auto-encoder deep learning model 122MB (see FIG. 7B) deconstructs the at least one analysis image 180 and then reconstructs the deconstructed at least one analysis image 180 to determine a reconstructed input error between the original and reconstructed version of the at least one analysis image 180. If the reconstructed input error is over, for example, a predetermined threshold (e.g., about 50% error or more or less than about 50% error) then the at least one analysis image 180 is determined by the vehicle control module 110 to be indicative of an impending fault/failure in the component 102C of the vehicle system 102 being monitored” (Paragraph. 0041).
Applicant argued that prior art did not disclose, “filtering the transformed sensor data with a time-synchronous averaging process to isolate a gear fault signature from one or more operational signatures associated with the drive unit”.
As to applicant’s argument Boggio disclosed, “The at least one sensor 101 is configured to generate at least one time series of numerical sensor data 112TA-112Tn for a respective system parameter 112A-112n of a vehicle system 102 (or component 102C thereof) being monitored. The vehicle control module 110 is configured to receive the at least one time series of numerical sensor data 112TA-112Tn from the at least one sensor 101, such as over the wired or wireless connection so that the at least one time series of numerical sensor data 112TA-112Tn is stored in the memory 111 in any suitable manner. For example, the memory 111 may be configured so that, when the at least one time series of numerical sensor data 112TA-112Tn is received, the at least one time series of numerical sensor data 112TA-112Tn is categorized within the memory. The at least one time series of numerical sensor data 112TA-112Tn may be categorized by one or more of an excursion 170, by a component 102CA-102Cn and a respective system parameter 112A-112n. Where the at least one time series of numerical sensor data 112TA-112Tn is categorized by the excursion 170, the at least one time series of numerical sensor data 112TA-112Tn is categorized according to the excursion 170 in which the at least one time series of numerical sensor data 112TA-112Tn was obtained. Where the at least one time series of numerical sensor data 112TA-112Tn is categorized by a component 102CA-102Cn, at least one time series of numerical sensor data 112TA-112Tn is categorized by the component 102CA-102Cn from which the at least one time series of numerical sensor data 112TA-112Tn was obtained. Where the at least one time series of numerical sensor data 112TA-112Tn is categorized by the respective system parameter 112A-112n, the at least one time series of numerical sensor data 112TA-112Tn is categorized by the respective system parameter 112A-112n to which the at least one time series of numerical sensor data 112TA-112Tn corresponds (e.g., at least one of (or each of) the at least one time series of numerical sensor data 112TA-112Tn corresponds to a respective system parameter 112A-112n of the vehicle system 102 being monitored) (Paragraph. 0031).
Conclusion
10. 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.
11. Any inquiry concerning this communication or earlier communication from the
examiner should be directed to Adnan Mirza whose telephone number is (571)-272-3885.
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examiner’s supervisor, Faris Almatrahi can be reached on (313)-446-4821.
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/ADNAN M MIRZA/Primary Examiner, Art Unit 3667