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 .
Claim Objections
Claim 15 recites: “The non-transitory computer-readable medium of claim 10”. However, the CRM claim starts at claim 11. It appears this is a typographical error – the “10” should be changed to “11”. For art purposes, claim 15 is analyzed as if it’s a dependent on claim 11. Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5, 8-9, 11-12, and 15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Peng et al. (“Sensor Fault Detection, Identification, and Recovery Techniques for Wireless Sensor Networks: A Full-scale Study”).
Regarding claim 1, Peng discloses a computer-implemented method, comprising:
accessing time series signals to be separated into clusters (see fig. 1b, 1c and 2; Section 1 in page 1 and section 2.1 in page 2 – acceleration time-series signals from a WSSN in which sensor nodes are grouped into clusters);
determining similarity of power spectral density among the time series signals (see abstract – “a fault sensitive feature is proposed based on spatial PSD similarity among multiple sensor nodes in WSSN”; see section 2.1 in page 2 and section 2.2 in page 3);
extracting a cluster of similar time series signals from the time series signals based on the similarity of power spectral density (section 2.2 in page 3 and equation 2 and section 3.2.3); and
training a machine learning model to detect anomalies based on the cluster (see abstract – “an artificial neural network model will be trained and applied to identify the types of sensor faults”; also see 2.1 and section 4).
Regarding claim 2, Peng discloses wherein determining similarity of power spectral density among the time series signals further comprises performing a comparison of the time series signals in a frequency domain (Section 2.1 and fig. 3 – PSD in frequency domain).
Regarding claim 3, Peng discloses wherein extracting a cluster of similar time series signals from the time series signals based on the similarity of power spectral density further comprises choosing those of the time series signals below a jump of dissimilarity to be the cluster of time series signals (see section 2.2 – “Therefore, an index, Ind, is proposed to indicate faults. And if it is smaller than the threshold, we consider the corresponding data has faults. The threshold is considered as 0.85/n, in which n is the number of sensor nodes in the cluster.”; see fig. 6).
Regarding claim 5, Peng discloses wherein extracting a cluster of similar time series signals from the time series signals based on the similarity of power spectral density further comprises:
sorting the time series signals based on the similarity of power spectral density of a time series signal with other time series signals (section 2.2);
detecting a change point in the similarities for the time series signals (section 2.2); and
selecting the time series signals below the change point to be in the cluster (section 2.2 – “And if it is smaller than the threshold, we consider the corresponding data has faults.”).
Regarding claim 8, Peng discloses further comprising:
detecting an anomaly with the trained machine learning model (see abstract – “an artificial neural network model will be trained and applied to identify the types of sensor faults”); and
generating an electronic alert that the anomaly was detected in the cluster of the time series signals (section 2.1 – “Once it find the faults, it will send message of warning to the corresponding leaf nodes.”).
Regarding claim 9, Peng discloses wherein noise on one or more of the time series signals is in excess of 50% (see for example fig. 10).
Regarding claim 11, see rejection of claim 1.
Regarding claim 12, see rejection of claim 2.
Regarding claim 15, see rejection of claim 8.
Allowable Subject Matter
Claims 16-20 are allowed. Claims 4, 6-7, 10 and 13-14 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the prior art of record does not disclose the combination of limitations specified in the independent claim 16 especially:
“determine measures of correlation among the time series signals based on analysis in the frequency domain;
transfer time series signals into a cluster based on the measures of correlation;
train a machine learning model to detect anomalies based on the training range of the cluster; and
in response to detecting an anomaly in a surveillance range of the cluster, transmit an electronic alert that the anomaly has occurred in the cluster”.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAFIZ E HOQUE whose telephone number is (571)270-1811. The examiner can normally be reached M-F 8-5.
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/NAFIZ E HOQUE/ Primary Examiner, Art Unit 2693
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