DETAILED ACTION
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more (See 2019 Update: Eligibility Guidance).
Independent Claim(s) 1 recites
A machining tool life prediction method,
comprising:
establishing a prediction model;
inputting an instant vibration signal of a tested machining tool into the prediction model to obtain a health index of the tested machining tool;
the prediction model is established by:
obtaining a plurality of vibration signals of a plurality of machining tools when each of the machining tools is utilized to perform a machining operation,
each of the vibration signals corresponds to a wear degree of each of the machining tools;
utilizing an empirical mode decomposition to obtain a plurality of intrinsic mode functions included in each of the vibration signals;
analyzing a correlation between each of the intrinsic mode functions and the corresponding wear degree to obtain a plurality of sampling signals of the machining tools,
each of the sampling signals is correlated with a life of each of the machining tools;
obtaining a plurality of characteristic factors by calculating the sampling signals;
utilizing an anomaly score analysis to group the characteristic factors according to the wear degree,
thereby
grouping the characteristic factors into a plurality of clusters;
utilizing a variation comparison algorithm to verify a result of the anomaly score analysis and obtaining a health index of each of the machining tools, thereby establishing the prediction model
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
In combination with Independent Claim(s) 1, Claim(s) 2-12 recite(s)
each of the sampling signals is a sum of a first intrinsic mode function and a second intrinsic mode function of each of the machining tools,
the first intrinsic mode function is one of the intrinsic mode functions which has a smallest characteristic time scale,
the second intrinsic mode function is one of the intrinsic mode functions which has a second smallest characteristic time scale.
the first intrinsic mode function and the second intrinsic mode function respectively correspond to a dust collection frequency and a tool cutting frequency when each of the machining tools is utilized to perform the machining operation.
the clusters include
an initial wear stage,
a stable wear stage,
a rapid wear stage.
the anomaly score analysis is used to
analyze the characteristic factors
through
an isolation forest (IF) algorithm, a density-based spatial clustering of applications with noise (DBSCAN) algorithm, or a local outlier factor (LOF) algorithm,
thereby
determining one of the characteristic factors belongs to which one of the clusters.
the variation comparison algorithm is
a normalized Mahalanobis distance algorithm or a Euclidean distance algorithm.
the variation comparison algorithm utilizes a window function to analyze N of the characteristic factors to obtain the health index corresponding to N of the characteristic factors,
N of the characteristic factors belong to a same one of the clusters,
N is a window length of the window function,
N is a natural number,
N usage times corresponding to N of the characteristic factors are continuous.
the health index of each of the machining tools is a percentage of an average value of N Mahalanobis distances corresponding to N of the characteristic factors.
performing an importance filtering on the characteristic factors to exclude a plurality of low-importance characteristic factors from the characteristic factors.
the importance filtering is based on a sum of a monotonicity and a trendability of each of the characteristic factors,
the sum of the monotonicity and the trendability of each of the low-importance characteristic factors is less than a threshold.
the characteristic factors are obtained by:
performing a time domain feature extraction and a frequency domain feature extraction on each of the sampling signals to obtain the characteristic factors.
adjusting at least one parameter of a machining apparatus according to a corresponding one of the clusters and the health index of the tested machining tool
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f));
Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. generic data acquisition/output); or
Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e. a tested machining tool; a machining apparatus; the machining apparatus holds the tested machining tool to perform the machining operation).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure)).
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, 4, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over TSAI ET AL. (US 20110307093 A1) (hereinafter “TSAI”) in view of KUMMARI ET AL. (US 20190152011 A1) (hereinafter “KUMMARI”).
With respect to Claim(s) 1, TSAI teaches ‘An early-warning apparatus for health detection of a servo motor and a method for operating the same are applied to estimate vibration phenomenon of a CNC tool machine. First, a vibration signal is produced through a vibration detecting unit. Afterward, the vibration signal is sequentially sent to a data buffer. Afterward, the vibration signal is transformed in time and frequency domains through a time-frequency transforming unit. Finally, a deterioration index is calculated through a deterioration index calculating unit to obtain a health index which is calculated through a health index calculating unit. Therefore, the build-in vibration detecting unit is provided to dispense with additional external sensors. Furthermore, the vibration phenomenon of the servo motor of the CNC tool machine is estimated according to the health index, thus analyzing non-linear and non-stationary characteristics of the estimated vibration phenomenon’ and the BRI of:
A machining tool life prediction method (See, e.g., ¶ ABSTRACT; See also, e.g., Fig(s). 2-4),
comprising:
establishing a prediction model (See, e.g., ¶ ABSTRACT; See also, e.g., Fig(s). 2-4);
inputting an instant vibration signal of a tested machining tool into the prediction model to obtain a health index of the tested machining tool (See, e.g., Fig(s). 4);
the prediction model is established by:
obtaining a plurality of vibration signals of a plurality of machining tools when each of the machining tools is utilized to perform a machining operation, each of the vibration signals corresponds to a wear degree of each of the machining tools (See, e.g., Fig(s). 4);
utilizing an empirical mode decomposition to obtain a plurality of intrinsic mode functions included in each of the vibration signals (See, e.g., Fig(s). 4);
analyzing a correlation between each of the intrinsic mode functions and the corresponding wear degree to obtain a plurality of sampling signals of the machining tools, each of the sampling signals is correlated with a life of each of the machining tools (See, e.g., ¶ ABSTRACT; See also, e.g., Fig(s). 2-4);
obtaining a plurality of characteristic factors by calculating the sampling signals (See, e.g., ¶ ABSTRACT; See also, e.g., Fig(s). 2-4);
utilizing an anomaly score analysis (See, e.g., ¶ ABSTRACT; See also, e.g., Fig(s). 2-4);
a result of the anomaly score analysis (See, e.g., ¶ ABSTRACT; See also, e.g., Fig(s). 2-4);
obtaining a health index of each of the machining tools, thereby establishing the prediction model (See, e.g., ¶ ABSTRACT; See also, e.g., Fig(s). 2-4).
However, TSAI is lacking the explicit language of:
group the characteristic factors according to the wear degree, thereby grouping the characteristic factors into a plurality of clusters;
utilizing a variation comparison algorithm to verify a result of the analysis.
KUMMARI teaches ‘a system and method for determining the health of a cutting tool used in milling operations or the like. In one example, the method may include receiving operating characteristics of a cutting machine which are captured during an iteration of a cutting operation, generating a signature pattern associated with the cutting machine based on the operating characteristics, the signature pattern representing a unique pattern of the operating characteristics of the cutting machine during the cutting operation, determining health information of a cutting tool of the cutting machine based on the signature pattern and a benchmark signature pattern, and outputting the determined health information of the cutting tool for display on a display device. Accordingly, a cutting tool can be replaced at the optimum time thereby improving productivity and conserving cost’ and the BRI of:
group the characteristic factors according to the wear degree, thereby grouping the characteristic factors into a plurality of clusters (See, e.g., ¶ 0038; See also, e.g., Fig(s). 5-7);
utilizing a variation comparison algorithm to verify a result of the analysis (See, e.g., ¶ 0044; See also, e.g., Fig(s). 5-7).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify TSAI to include group the characteristic factors according to the wear degree, thereby grouping the characteristic factors into a plurality of clusters; utilizing a variation comparison algorithm to verify a result of the analysis.
One of ordinary skill in the art would have been motivated to modify TSAI because it would be beneficial to improve health determination of a machining component. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
With respect to Claim(s) 4, the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s).
KUMMARI further teaches the BRI of:
the clusters include
an initial wear stage (See, e.g., ¶ 0038; See also, e.g., Fig(s). 6),
a stable wear stage (See, e.g., ¶ 0038; See also, e.g., Fig(s). 6),
a rapid wear stage (See, e.g., ¶ 0038; See also, e.g., Fig(s). 6).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify TSAI to include the clusters include an initial wear stage, a stable wear stage, a rapid wear stage.
One of ordinary skill in the art would have been motivated to modify TSAI because it would be beneficial to improve health determination of a machining component. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
With respect to Claim(s) 11, the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s).
TSAI further teaches the BRI of:
the characteristic factors are obtained by:
performing a time domain feature extraction and a frequency domain feature extraction on each of the sampling signals to obtain the characteristic factors (See, e.g., ¶ ABSTRACT; See also, e.g., Fig(s). 2-4).
Claim(s) 5, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited reference(s) of the parent claim(s) in view of CHEON ET AL. (US 20230259112 A1) (hereinafter “CHEON”).
With respect to Claim(s) 5, the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s).
TSAI the BRI of:
the anomaly score analysis.
However, TSAI is lacking the explicit language of:
analyze the characteristic factors through an isolation forest (IF) algorithm, a density-based spatial clustering of applications with noise (DBSCAN) algorithm, or a local outlier factor (LOF) algorithm, thereby determining one of the characteristic factors belongs to which one of the clusters.
CHEON teaches ‘A method includes receiving first data associated with measurements taken by a sensor during a first manufacturing procedure of a manufacturing chamber. The method further includes receiving second data. The second data includes reference data associated with the first data. The method further includes providing the first and second data to a comparison model. The method further includes receiving a similarity score from the comparison model, associated with the first and second data. The method further includes performance of a corrective action in view of the similarity score’ and the BRI of:
analyze the characteristic factors through an isolation forest (IF) algorithm, a density-based spatial clustering of applications with noise (DBSCAN) algorithm, or a local outlier factor (LOF) algorithm, thereby determining one of the characteristic factors belongs to which one of the clusters (See, e.g., ¶ 0177, 0191, 0203)
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify TSAI to include analyze the characteristic factors through an isolation forest (IF) algorithm, a density-based spatial clustering of applications with noise (DBSCAN) algorithm, or a local outlier factor (LOF) algorithm, thereby determining one of the characteristic factors belongs to which one of the clusters.
One of ordinary skill in the art would have been motivated to modify TSAI because it would be beneficial to improve performance analysis. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
With respect to Claim(s) 12, the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s).
TSAI further teaches the BRI of:
the machining apparatus holds the tested machining tool to perform the machining operation (See, e.g., ¶ ABSTRACT; See also, e.g., Fig(s). 1-2).
CHEON further teaches the BRI of:
adjusting at least one parameter of a machining apparatus according to a corresponding one of the clusters and the health index of the tested machining tool (See, e.g., ¶ 0032, 0038, 0058, 0059, 0069, 0100).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify TSAI to include adjusting at least one parameter of a machining apparatus according to a corresponding one of the clusters and the health index of the tested machining tool.
One of ordinary skill in the art would have been motivated to modify TSAI because it would be beneficial to improve performance analysis. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited reference(s) of the parent claim(s) in view of KRISHNASWAMY ET AL. (US 20190384255 A1) (hereinafter “KRISHNASWAMY”).
With respect to Claim(s) 6, the cited reference(s) of the parent claim(s) teaches the BRI of the parent claim(s).
TSAI teaches the BRI of:
the variation comparison algorithm.
However, TSAI is lacking the explicit language of:
a normalized Mahalanobis distance algorithm or a Euclidean distance algorithm
KRISHNASWAMY teaches ‘A framework for autonomous predictive health monitoring includes online monitoring, offline training, and self-learning components. The monitoring component includes analyzing streaming incoming process data, which includes process variable and key performance indicators (KPIs), from multiple sources, in real time, to determine an overall health index, determine faults, diagnose and isolate faulty process variables that contribute to the health index, and predict a trend and a magnitude of the health index before failure. The self-learning component includes services linked to event management, to correct the health index from probabilities calculated based on operator feedback on true or false events after analyzing each of the detected events, self-tune limits and other model parameters, and trigger training of a model when a new normal pattern is detected. The offline training component creates models to classify each of the moving data window, cluster training windows, remove duplicate windows, and minimize training data storage size’ and the BRI of:
a normalized Mahalanobis distance algorithm or a Euclidean distance algorithm
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify TSAI to include a normalized Mahalanobis distance algorithm or a Euclidean distance algorithm (See, e.g., ¶ 0086).
One of ordinary skill in the art would have been motivated to modify TSAI because it would be beneficial to improve autonomous predictive health monitoring. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
Allowable Subject Matter (over prior art)
The following is a statement of reasons for the indication of allowable subject matter over prior art:
None of the cited prior art alone or in combination provides motivation to explicitly teach:
each of the sampling signals is a sum of a first intrinsic mode function and a second intrinsic mode function of each of the machining tools,
the first intrinsic mode function is one of the intrinsic mode functions which has a smallest characteristic time scale,
the second intrinsic mode function is one of the intrinsic mode functions which has a second smallest characteristic time scale
of claim(s) 2 (including dependent claim(s));
the variation comparison algorithm
utilizes
a window function
to
analyze N of the characteristic factors to obtain the health index corresponding to N of the characteristic factors, N of the characteristic factors belong to a same one of the clusters, N is a window length of the window function, N is a natural number, N usage times corresponding to N of the characteristic factors are continuous
of claim(s) 7 (including dependent claim(s))
performing an importance filtering on the characteristic factors to exclude a plurality of low-importance characteristic factors from the characteristic factors
of claim(s) 10 (including dependent claim(s)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYMOND NIMOX whose telephone number is (469)295-9226. The examiner can normally be reached Mon-Thu 10am-8pm CT.
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RAYMOND NIMOX
Primary Examiner
Art Unit 2857
/RAYMOND L NIMOX/Primary Examiner, Art Unit
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