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 .
DETAILED ACTION
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.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
“A vibration measurement error determination method comprising: a vibration data acquisition step for acquiring vibration data by measuring a vibration generated in a structure; a first determination step for determining, on the basis of a preset error data selection rule, whether the vibration data is data generated by a measurement error; a second determination step for using a machine-learning algorithm to determine whether the vibration data is data generated by a measurement error; and a final determination step for determining, on the basis of the results determined in the first determination step and the second determination step, whether the vibration data is data generated by a measurement error.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “using a machine-learning algorithm to determine whether the vibration data is data generated by a measurement error” are treated as belonging to the mathematical concepts grouping while the steps of “determining, on the basis of a preset error data selection rule, whether the vibration data is data generated by a measurement error” and “determining, on the basis of the results determined in the first determination step and the second determination step, whether the vibration data is data generated by a measurement error” are treated as belonging to mental process grouping. These mental steps represent a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. In the context of this claim, the former mental step encompasses a user comparing the error with a known rule/threshold (“observation/evaluation”) to develop a judgement whether the vibration data is data generated by a measurement error and the latter mental step corresponds to a judgement of an error based on two determination results. The latter step, under the BRI, alternatively/additionally is treated as mathematical relationship step (MPEP 2106.04.II: “construing the claims in accordance with their broadest reasonable interpretation”).
Similar limitations comprise the abstract ideas of Claims 8.
Next, under the Step 2A, Prong Two, we consider whether the above claims that recites a judicial exception are integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 1: A vibration measurement error determination method comprising: a vibration data acquisition step for acquiring vibration data by measuring a vibration generated in a structure;
In Claim 8: A vibration measurement error discernment system comprising: a data acquisition unit configured to acquire vibration data by measuring a vibration generated in a structure.
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements in the claims such as a vibration data acquisition step/acquisition unit for acquiring vibration data by measuring a vibration generated in a structure generically recite collecting sample vibration data including erroneous data represent insignificant extra-solution activity of mere data gathering. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record including Su and Shibuya.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-7 and 8-14 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or additional elements that qualify for significantly more for substantially similar reasons as discussed with regards to Claim 1.
For example, additional elements in Claims 3-4 and 10-11 (using specific thresholds in abstract idea decision making) are all recited in generality and not meaningful to indicate a practical application and/or qualify for significantly more.
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 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.
Claims 1, 5-8, and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over KIM SANG SU et al. (KR 102226687), hereinafter ‘Su’, in view of Hisae Shibuya et al. (US 20120290879), hereinafter ‘Shibuya’.
With regards to Claim 1, Su discloses
A vibration measurement error determination method (The deep learning model learning unit 130 may generate error information based on the generated predicted value and the actual value of the vibration data. In addition, the deep learning model learning unit 130 may generate error information for each of the extracted feature variables (eg, shape, amplitude, phase, etc. of vibration data), p.9), comprising:
a vibration data acquisition step for acquiring vibration data by measuring a vibration generated in a structure (the data collection unit 110 may receive (collect) vibration data of the machine (equipment) from a vibration sensor installed for the machine (equipment), p.4;
a (second) determination step for using a machine-learning algorithm to determine whether the vibration data is data generated by a measurement error (the outlier detection unit 121 may detect the outlier using an artificial intelligence algorithm based on unsupervised learning, p.7; the deep learning model learning unit 130 may generate a defect prediction model that learns the presence or absence of a defect in a machine by using a machine learning algorithm, p.9).
Su also discloses a first determination step for determining, on the basis of a preset error data selection rule, whether the data is data generated by a measurement error (the abnormal value detection unit 121 may detect an abnormal value of a plurality of maintenance data, p.6).
However, Su does not specifically disclose a first determination step for determining, on the basis of a preset error data selection rule, whether the vibration data is data generated by a measurement error and, correspondingly, a final determination step for determining, on the basis of the results determined in the first determination step and the second determination step, whether the vibration data is data generated by a measurement error.
Shibuya discloses a first determination step for determining, on the basis of a preset error data selection rule, whether the vibration data is data generated by a measurement error (identifying the anomaly by comparing the threshold with the anomaly measurement [0020]; judging an anomaly by comparing the computed anomaly measurement with a predetermined threshold [0021]; quantized sensor signals having anomaly measures over the threshold are inputted as cause events, Abstract; Fig.1C).
Shibuya also discloses a (final) determination step for determining, on the basis of the results determined in a first determination step and a second determination step, whether the vibration data is data generated by a measurement error (anomaly identification unit 1207 identifying an anomaly based on the anomaly measurement computed from data of the normal model checked by the learning-data check unit 1206 and the feature vector acquired from the sensor signal 102 through the feature amount extraction unit 1201 and the feature-selection unit 1202 by using the anomaly-measurement computation unit 1205 [0129]; Fig. 12A).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Su in view of Shibuya to use more than one determination step, for example, two determination steps, in determining a final determination of whether the vibration data is data generated by a measurement error to increase a reliability of the determination (when the anomaly is mixed into the learning data, a divergence degree from the observational data showing the anomaly is reduced to be overlooked. As a result, sufficient checks are required to prevent the anomaly from being included in the learning data (Shibuya [0012]).
With regards to Claim 5, Su discloses wherein the machine-learning algorithm extracts a plurality of sample data from the vibration data (new sample, p. 7), extracts feature information for determining the measurement error from the extracted plurality of sample data (one characteristic representing the degree of wear of the vehicle with a dimension reduction algorithm, which is called feature extraction, p.7) and determines whether the vibration data is the data generated by the measurement error based on the extracted feature information (Outlier detection is a task that automatically removes strange values from a data set before injecting them into a learning algorithm. It is trained with a normal sample and can determine whether a new sample is normal or not, p.7; In addition, the deep learning model learning unit 130 may generate a defect prediction model that learns the presence or absence of a defect in a machine by using a machine learning algorithm that receives a feature variable extracted from the data preprocessor 120 as an input. The defect of the machine may include a plurality of types such as misalignment, unbalance, crack, and abrasion, p.9).
With regards to Claim 6, Su additionally discloses wherein the feature information is extracted through principal component analysis from candidate feature information extracted from the plurality of sample data (variable extraction algorithm may be a Principal Component Analysis (PCA) algorithm, p.6; a principal component analysis (eg, PCA) technique can be applied, and three features can be extracted … learning model training unit 130 may extract an influence degree for each feature for each word vector with respect to three features extracted by applying principal component analysis to a word vector, p.11).
With regrds to Claim 7, Su additionally discloses wherein in the machine-learning algorithm, the feature information is extracted for each sample data (as discussed above) and discloses also using clustering techniques (unsupervised learning may include K-Means divided into clustering, Hierarchical Cluster Analysis (HCA), p.7).
However, Su does not specifically disclose that the sample data is clustered based on the feature information, and whether the vibration data is the data generated by a measurement error is determined based on a clustering result.
Shibuya discloses the sample data is clustered based on the feature information, and whether the vibration data is the data generated by a measurement error is determined based on a clustering result (An event occurrence period from a first time to a final time of the event array and an interevent period inserted between the event arrays are sequentially extracted from the sensor signal 102. The aforementioned cluster is acquired by the processing [0079]; as a result, subsequent creation of the model in the normal state may be performed with high accuracy [0080]; Specifically, a time when a warning or a failure occurs is examined from the event signal 103 and all signal data of the cluster (periods sequentially extracted in the aforementioned mode dividing) including the time are removed [0091]; A feature vector at the time of judging anomaly is picked up (S1013) and vector-quantized by adopting an unsupervised clustering technique such as a k-means method or an EM algorithm (S1014) [0114]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Su in view of Shibuya to determine that the vibration data is generated by error using clustering technique known in the art of machine learning to ensure accuracy of the determination.
With regards to Claim 8, Su in view of Shibuya discloses the claim limitations as discussed above with regards to Claim 1.
With regards to Claims 12-14, Su in view of Shibuya discloses the claim limitations as discussed above with regards to Claims 8 and Claims 5-7, respectfully.
Claims 2-4 and 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over SANG SU in view of Shibuya, and further in view of John Adrian Morey et al. (US 10168248), hereinafter ‘Morey’.
With regards to Claim 2, Su discloses analyzing abnormality based on amplitude value (the data preprocessor 120 may identify an amplitude having a low abnormal peak. In addition, the data preprocessor 120 may identify a case in which the amplitude of the abnormal peak gradually increases. The data preprocessor 120 may identify whether or not there is an abnormality in the rotating equipment from the characteristic of the amplitude of the frequency of the vibration data collected based on a time series, p.5). Su also discloses analyzing data in low-frequency region extracted from the vibration data (applying a preset low-pass filter or a band-pass filter to the received vibration data, p.5).
However, Su does not explicitly disclose wherein the error data selection rule determines the error data selection rule determines whether the vibration data is the data generated by the measurement error based on a difference between an amplitude value of a low-frequency region extracted from the vibration data and a threshold value.
More discloses determining whether the vibration data is the data generated by the measurement error based on a difference between an amplitude value of a low-frequency region extracted from the vibration data and a threshold value (this spectral line has amplitude exceeding a minimum spectral amplitude threshold, Col.7, Lines 23-24; check velocity spectrum for low frequency noise in a predetermined range, i.e. up to 3 Hz, Col.18, Lines 57-58).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Su in view of Shibuya and Morey that the error data selection rule determines whether the vibration data is the data generated by the measurement error based on a difference between an amplitude value of a low-frequency region extracted from the vibration data and a threshold value to determine outliers in low frequency region corresponding to noise to improve signal to noise ratio for more accurate detection of erroneous data (there is excessive noise at the low frequency end of the spectrum, Morey, Col.32, Lines 59-60).
With regards to Claim 3, Su in view of Shibuya and Morey discloses the claim limitations as discussed above with regards to Claim 2.
With regards to Claim 4, Su in view of Shibuya and Morey discloses the claim limitations as discussed above with regards to Claim 2.
However, Su does not disclose wherein the threshold value is 0.6 mm/s.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Su in view of Shibuya and Morey to use a threshold value of 0.6 mm/s as an arbitrary value selected by the inventors as a matter of a design preference, since the applicant has not disclosed that such value solves any stated problem or are for any particular purposes, and it appears that the invention would perform equally well with just another set value of the threshold as discussed in the instant application (“Based on FIG. 5, in order to set the threshold value of the amplitude, when the threshold values respectively are 0.4 mm/s (=0.0157 in/s), 0.5 mm/s (=0.0197 in/s), 0.6 mm/s (=0.2362 in/s), 0.7 mm/s (=0.0276 in/s), and 0.8 mm/s (=0.0315 in/s), the number of cases where the vibration data including the measurement error was determined to be the normal vibration data was compared with the number of cases where the normal vibration data was determined to be the vibration data including the measurement error” [0063]), as published.
With regards to Claims 9-11, Su in view of Shibuya, and Morey discloses the claim limitations as discussed above with regards to Claims 8 and Claims 2-4, respectfully.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Stewart V. Bowers et al. (US 20180217109) discloses detecting faulty collection of vibration data including detecting and discarding undesirable vibration data prior to analysis of the data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2857
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