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 . The rejections from the Office Action of 6/18/2025 are hereby withdrawn. New grounds for rejection are presented below.
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, 2, 5, 7, 8, 12, 14, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable Wang et al., Condition Monitoring System Design with One-class and Imbalanced-Data Classifier, IEEE, 2009; Totani et al. (US 6332355 B1)[hereinafter “Totani”]; Liao (US 20130197854 A1); and Dodge et al., The Environmental-Data Automated Track Annotation (Env-DATA) System: Linking Animal Tracks with Environmental Data, Movement Ecology, 2013 [hereinafter “Dodge”].
Regarding Claims 1, 5, and 7, Wang discloses a machine tool (including a system and information processing device and corresponding method) comprising: a detector that detects sensed data; a processor; and a memory storing instructions that, when executed by the processor, cause the processor [Page 781, second column – “Based on the general design procedure shown in Fig. 2, architecture of machine condition monitoring system is designed in the LabVIEW environment (as shown in Fig. 3). LabVIEW has been selected as software platform due to its powerful performance in data acquisition, graphical user interface (GUI) design, and hardware connectivity. The developed monitoring system is capable to acquire, analyze and present the data simultaneously due to the utilization of multithread programming techniques [13]. The hardware selected is the NI compactRIO 9004 module (Reconfigurable I/O Software Development Kit), which includes two main tools for building an embedded, customized CompactRIO system: FPGA and LabVIEW Real-Time (RT). The former is for synthesizing custom hardware in the user-configurable reconfigurable I/O FPGA core and the latter is for building deterministic real-time applications. Meanwhile, compactRIO supports applications in Internet.”] to:
resolve the sensed data into a plurality of data points [Page 780, second column – “The basic idea of SVDD can be formally expressed as follows: Consider a target data set { xi }, i = 1,2,…, N , N is the total number of samples and xi is a p dimension real vector”Page 781, second column – “As shown in Fig.3, the architecture can be divided into three levels. The data (e.g., vibration) is captured through the connection between sensors and compactRIO in the FPGA level. Then the data can be transferred to the RT level using FIFO technique in real-time model.”] and compress dimensions of the plurality of data points [Page 780, between the columns – “If necessary, some feature dimension reduction methods (e.g., principal components analysis (PCA) and independent component analysis (ICA) or feature subset selection methods (e.g., Fisher’s criterion, information gain and distance evaluation technique, etc.) will be used to get the final key feature vector.”Page 781, second column – “Feature extraction and feature dimension reduction can be processed in this level using the Advanced Signal Processing Toolkit.”] to generate first and second feature amounts as a two-dimensional feature amount [See the data point entries in Fig. 5 corresponding to feature1 and feature 2.Page 782, first column – “A testing example with 2-D features from practical bearing vibration data for SVDD boundary is shown in Fig. 5.”]; and
display, as a two-dimensional map based on the two-dimensional feature amount [Fig. 5 is a two-dimensional feature map], a plurality of points plotting the sensed value [See the plurality of points charted in Fig. 5], and first and second boundaries laid out like contour lines [See the 8 SVDD boundary contours in Fig. 5. Examiner’s Note: The boundary lines are shown in rainbow colors when viewed online at https://ieeexplore.ieee.org/abstract/document/5344481] to represent a possibility of generation of an anomaly [Page 780, first column – “If a test data falls inside or on the boundary of the trained hypersphere specified with a and R , than it is accepted as target; otherwise, it is regarded as non target, or something happened.”], on a plane having a first axis defined by the first feature amount and a second axis defined by the second feature amount [See Fig. 5, the x-axis is “feature1” and the y-axis is “feature2”], wherein the second boundary surrounds the first boundary [See the 8 SVDD boundary contours in Fig. 5. Each boundary increases in size (“surrounds”) as distance from the center cluster grows.].
Wang fails to disclose using a neural network in machine learning to compress dimensions of the plurality of data points to generate first and second feature amounts as a two-dimensional feature amount. Wang separately contemplates the use of neural networks in machine learning [Page 779, second column – “In addition, artificial intelligence based machine learning techniques, especially neural network (NN) have been explored to conduct machine condition monitoring with many success.”] and that classic AI or data mining methods can be used to extend the trained reference model as enough data because available for doing so [Page 780, second column – “If more and more data are collected and they are enough for training of classic artificial intelligence (AI) or data mining methods, e.g., support vector machine (SVM), the reference model can be extended from one-class classifier or classifier for imbalanced data to general classifiers to further improve the monitoring performance.”]. It would have been obvious to use a neural network in performing ongoing machine learning as part of the feature amount analysis process in order to more effectively monitor for anomalies over time.
Although Wang teaches that the analysis method can be used in conjunction with devices including bearings (a ball screw being such a type of device)[Page 782, first column – “A testing example with 2-D features from practical bearing vibration data for SVDD boundary is shown in Fig. 5.”], Wang fails to disclose (only the underlined parts):
a detector that detects, as sensed data, a current value applied to drive a ball screw during a warming-up mode in which the ball screw is operated; and
that the processor is caused to:
display, as a two-dimensional map based on the two-dimensional feature amount, a plurality of points plotting the sensed value, and first and second boundaries laid out like contour lines to represent a possibility of generation of an anomaly in the ball screw, on a plane having a first axis defined by the first feature amount and a second axis defined by the second feature amount, wherein the second boundary surrounds the first boundary.
However, Totani discloses monitoring a ball screw by taking current measurements of electrical current used to drive it in ascertaining the health of the ball screw [See Column 3 lines 40-53]. It would have been obvious to evaluate a ball screw as the monitoring target in order to ensure a desirable machine condition is maintained. It would have been obvious to measure drive current as a feature because drive current is an operating parameter for a ball screw.
Liao discloses performing an analysis regarding the operation of a device comprising a ball screw during the warm-up period [See Figs. 2, 7, and 8 and associated text.Paragraph [0071] – “The lower part 720 of FIG. 7 illustrates that there is a short transient period at the beginning of each day in which the absolute value of the temperature difference increases over time. That period is considered the warm-up time of the feed axis. The transition can be also seen in FIG. 4 where there are preceding `tails` among different health conditions. It is difficult to diagnose the issues during the warm-up time.”Paragraph [0073] – “Although the same component (bearings, ball nut and ball screws) models were used in the trials, the system was actually different for each new installation of the same ball screw. An experiment was conducted to compare different baselines for different installations of the same ball screw, to its normal, reference condition. Nine sets of data, shown in the plot 800 of FIG. 8, were collected under the normal condition (baseline) for different new installations of the same feed axis components. The nine data sets provided slightly different MQE levels.”Paragraph [0074] – “The data collected from the original ball screw installation was used as baseline and the rest of the data was tested against the adopted baseline using the anomaly detection method mentioned above.”]. It would have been obvious to gather feature data from a ball screw during the warm-up period because Liao teaches that such data is useful for diagnostic purposes [Paragraph [0071] – “Another conclusion of the temperature-related findings is that additional attention must paid when using data collected during warm-up time for diagnosis purposes, since the non-uniform thermal expansion may lead to unreliable results.”].
Wang fails to disclose causing the processor to display a line connecting the plurality of points and an arrow indicating a direction of a state of the ball screw in order to represent whether or not the ball screw is becoming abnormal.
However, Dodge discloses such a manner of tracking how measurements change over time [See Fig. 4(a) and corresponding text]. It would have been obvious to track the data points over time in such a manner in order to be able to monitor change in health of the system over time.
Regarding Claims 2 and 8, Wang discloses that the memory further stores instructions that, when executed by the processor, cause the processor to correct a shape of the boundary [Fig. 2, the step “Updata/For Retrain” that flows back to SVDD modelling], in accordance with an instruction from a user [Page 782, first and second columns – “The main VI functions of SVDD and MDT are developed in LabVIEW environment. They are realized fully following the mathematic formulation described in section III and used as the reference model for training and testing, their key parameters setting can be adjusted from the UI.”].
Regarding Claim 12, Wang discloses the use of boundary lines [See the 8 SVDD boundary contours in Fig. 5. Each boundary increases in size (“surrounds”) as distance from the center cluster grows.] to indicate anomalies [Page 780, first column – “If a test data falls inside or on the boundary of the trained hypersphere specified with a and R , than it is accepted as target; otherwise, it is regarded as non target, or something happened.”]. Totani discloses the montoring of the health of a balls screw [See Column 3 lines 40-53]. As such, the combination of the multiple anomaly boundaries of Wang in the context of monitoring the health of a ball screw per Totani would naturally disclose that the points displayed within the first boundary represent that the ball screw operates normally [Ordinary and expected operation of the ball screw], the points displayed between the first and second boundaries represent that a small anomaly not affecting processing may occur [Mild anomaly as indicated by points crossing first boundaries], and the points displayed outside the second boundary represent that an anomaly affecting processing accuracy may occur [Serious anomaly as indicated by points crossing further boundaries].
Regarding Claim 14, Wang discloses that the memory stores instructions that, when executed by the processor, cause the processor to compress the dimensions of the plurality of data points to generate the first and second feature amounts as the two-dimensional feature amount by using Fourier transform [Fig. 3 – “FFT”] and Principal Component Analysis [Fig. 3 – “PCA”].
The combination would fail to explicitly disclose that the detector detects the current value as time-series data of more than 200 points. However, it would have been obvious to measure such an amount of data as a design choice in order to ensure a sufficient amount of data is available for performing the analysis.
Regarding Claim 15, Wang discloses the process is for determining system maintenance needs [Page 780, first column – “The Engineering Asset Management (EAM) or Computer Maintenance Management System (CMMS) is responsible for maintenance plan and optimal policy for machines, condition monitoring system (highlighted in grey) is responsible for status monitoring and diagnosis of machines, and prognostics system is for machine performance prediction before failure really occurs.”], but fails to disclose that the memory further stores instructions that, when executed by the processor, cause the processor to indicate whether the ball screw should be replaced.
However, Totani discloses such an indication [Column 1 lines 62-65 – “provides a warning when the total number of revolutions of the rotary member approaches or arrives at the fatigue life to prompt the operator to replace the rotary member with a new one.”]. It would have been obvious to provide such an indication in order to prevent an unexpected machine breakdown.
Claim(s) 3 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable Wang et al., Condition Monitoring System Design with One-class and Imbalanced-Data Classifier, IEEE, 2009; Totani et al. (US 6332355 B1)[hereinafter “Totani”]; Liao (US 20130197854 A1); Dodge et al., The Environmental-Data Automated Track Annotation (Env-DATA) System: Linking Animal Tracks with Environmental Data, Movement Ecology, 2013 [hereinafter “Dodge”]; and Koyata (US 6097880 A).
Regarding Claims 3 and 9, the combination fails to disclose that the memory further stores instructions that, when executed by the processor, cause the processor to resolve a frequency of the sensed value, normalize data after frequency resolution, and compress dimensions of the normalized data. However, Koyata discloses perform such steps as a digital signal processing technique to compress data [Column 2 lines 58 to Column 3 line 4 – “an input digital signal is split into a plurality of frequency band components thereby to obtain signal components in a plurality of time-frequency two-dimensional blocks. Data are normalized based on the signal components in the two-dimensional block for each of the time-frequency two-dimensional blocks thereby to obtain normalized data. A quantization coefficient representing the feature of the signal components in the two-dimensional block for each of the time-frequency two-dimensional blocks is obtained. A bit allocation amount is determined based on the quantization coefficient thus determined. The signal components in each of the time-frequency two-dimensional blocks are quantized by the bit allocation amount and the normalized data thereby to compress information”]. It would have been obvious to use such a technique with regards to the feature data in order to conserve computational resources during the analysis process.
Claim(s) 4 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable Wang et al., Condition Monitoring System Design with One-class and Imbalanced-Data Classifier, IEEE, 2009; Totani et al. (US 6332355 B1)[hereinafter “Totani”]; Liao (US 20130197854 A1); Dodge et al., The Environmental-Data Automated Track Annotation (Env-DATA) System: Linking Animal Tracks with Environmental Data, Movement Ecology, 2013 [hereinafter “Dodge”]; and Wang et al. (US 20100081891 A1)[hereinafter “Wang II”].
Regarding Claims 4 and 10, the combination fails to disclose that, when the plotted point is selected, said display displays information associated with the plotted point. However, Wang II discloses such functionality [See steps 130 and 132 of Fig. 5 and Paragraph [0032]]. It would have been obvious to incorporate such functionality into the display in order to allow for a user to obtain additional information regarding plotted points.
Response to Arguments
Applicant argues:
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Examiner’s Response:
The corresponding objection is hereby withdrawn.
Applicant argues:
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Examiner’s Response:
The Examiner agrees. However, Dodge discloses such a manner of tracking how measurements change over time [See Fig. 4(a) and corresponding text]. It would have been obvious to track the data points over time in such a manner in order to be able to monitor change in health of the system over time.
Applicant argues:
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Examiner’s Response:
The Examiner agrees. The corresponding rejections under 35 USC 101 are hereby withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Feuillard et al., Calibration of One-Class SVM for MV set estimation, ResearchGate, 2015 – See Fig. 1
Jin et al., METHODOLOGY FOR BALL SCREW COMPONENT HEALTH ASSESSMENT AND FAILURE ANALYSIS, ASME, 2013
Lazzaretti et al., An Adaptive Radial Basis Function Kernel for Support Vector Data Description, ResearchGate, 2015 – See Fig. 2
US 20210304521 A1 – ROTATING MACHINERY DIAGNOSIS AND MONITORING DEVICE AND METHOD
US 20120315151 A1 – CURRENT CONTROL VIA SPEED CONTROL FOR DRIVING SCREW COMPRESSOR UNDER COLD CONDITIONS
US 20080319590 A1 – Systems For Announcing The Health Of Ball Screw Actuators And Ball Recirculation
US 20160001410 A1 – CONTROLLER FOR MACHINE TOOL
US 4437163 A – Method And Apparatus For Symptom Diagnosis By Monitoring Vibration Of Shaft Of Rotary Machine
US 5469038 A – Method For Compensating For Efficient Variations In An Electric Motor
US 6097880 A – Digital Signal Processing Method, Digital Signal Processing Apparatus, Digital Signal Recording Method, Digital Signal Recording Apparatus, Recording Medium, Digital Signal Transmission Method And Digital Signal Transmission Apparatus
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE ROBERT QUIGLEY whose telephone number is (313)446-4879. The examiner can normally be reached 11AM-9PM EST.
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/KYLE R QUIGLEY/Primary Examiner, Art Unit 2857
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