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 1 is objected to because of the following informalities:
Antecedent basis –
applicant uses the term data in various manners. “transforming data” “the data”, “the data acquired”, “data associated” , then “fitting the data”, “using at least the transformed data” “at least one of the data acquired and the transformed data” “fitted data”, “the lower representation of data”
Here “fitting the data” could refer to either “the data acquired” or “data associated” or “the data acquired” Which data is fitting the data directed to the acquired or associated data for example?
FFT – fast fourier transform should be fully written out to avoid confusion.
Claims 2, 12, 13 contains – “extract significant features” and “the significant features” respectively. “the significant features” lacks antecedent basis to significant features because claim 12 is not dependent on claim 2. Thus the examiner does not know what significant features are referred to. Furthermore for all three claims “significant features” is a point of interpretation as one of ordinary skill in the art might find differences in what is significant. Applicant should define significant features.
Claims 8, 12 “sub spectrogram” (antecedent basis) because while “spectrogram” is introduced in claim 1, sub spectrogram is introduced in claim 7 but, claim 7 which is not in the chain of dependency.
Claims 4, 6, 8, 8, 12, 13, and 16 contain the finger print data which is introduced in claim 2 but not claim 1. However, each of the above claims is dependent on claim 1 so “a fingerprint data” has not been introduced. Claims 16, and 4 introduce “a spectrogram” when one has already be introduced”
Claims 3, 8, 12, discussed a trained unsupervised learning module when such module is in claim 2 and not claim 1.
All claims will be interpreted as if they were correct though correction might change the meaning. It appears that claim 2 was at one point combined with claim 1 and separated.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
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) 1-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication to Yan 20200292608
As per claim 1 Yan discloses;
transforming data acquired from the grid based on at least one of
a Fast Fourier Transformation and Yan(0076)
a spectrogram of the data, Yan(0088)
wherein the data acquired comprises data associated with at least one of
(really just a choice of one)
grid voltage, (0106)
grid current, (0106)
grid frequency, (0106)
and phase; (0053)
fitting the data using a fitting function initialized using at least the transformed data, (0037)
wherein the fitting function includes
at least one of a (a choice of only one from here on out)
sinusoidal function;
or generating a lower representation of at least one of the data acquired
and the transformed data; and detecting the anomaly in the grid based on at least one outlier detected in the fitted data or the lower representation of data using at least one of a parameter deviation and a similarity index. Yan (0104, or is a choice of options)
As per claim 2 Yan discloses;
compressing the data acquired from the grid using a trained autoencoder, Yan(0071, irrelevant or redundant information may be reduced)
wherein the autoencoder is trained based on a minimal loss function used to extract significant features from a fingerprint data associated with the grid, wherein the fingerprint data comprises data associated with at least one of historical operation of the grid and standard operating parameters of the grid;
Yan (0074 loss)
generating encoded data from the compressed data as an output of the trained autoencoder; and classifying datapoints in the encoded data as the outlier using a trained unsupervised learning module, wherein the unsupervised learning module is trained using trained encoded data generated from the fingerprint data.
(0067, 0094, 0047)
Note Fingerprint=signature as interpreted
As per claim 3 Yan discloses: compressing the spectrogram using the trained autoencoder; generating the encoded data from the compressed data as the output of the trained autoencoder; and classifying the datapoints in the encoded data as the outlier using the trained unsupervised learning module.
Yan(0092)
As per claim 4 Yan discloses; The method according to claim 1, further comprising: initializing the fitting function based on at least one transformation function performed on the fingerprint data, wherein the transformation function results in at least one of a spectrogram of the fingerprint data and a FFT of the fingerprint data. (0060, FFT)
As per claim 5, Yan discloses;
The method according to claim 1, wherein fitting the data using a fitting function initialized using at least the transformed data comprises: fitting at least one parameter of the sinusoidal functions onto the transformed data, wherein the parameter includes at least one of
amplitude, offset, frequency and phase of the sinusoidal function.
Yan (0062, frequency, phase 0073, fig. 8)
As per claim 6 Yan discloses; The method according to claim 1, wherein detecting the anomaly in the grid based the outlier detected comprises: determining if the parameter deviation falls outside a confidence interval generated for the fingerprint data, wherein the confidence interval is generated based on a risk aversion parameter associated with the grid; and determining if a mean square error estimated for the fitted data is greater than a mean square error estimated for the fingerprint data.
Yan(0092 mean square error is considered, “if” might not occur, if it does not broadly it’s not required)
As per claim 7 Yan discloses;
the method according to claim 1, wherein transforming the data based on the spectrogram comprises: transforming the acquired data into the spectrogram representing time-varying frequencies in the acquired data; and splitting the spectrogram into sub-spectrograms, wherein the sub-spectrograms comprise snippets of the spectrogram for varying time intervals.
Yan(time series, 0092, “snippets” could be samples in Yan)
As per claim 8 Yan discloses; The method according to claim 1, wherein detecting the anomaly in the grid comprises: calculating the similarity index for the sub-spectrograms based on structural parameters of the sub-spectrograms; and classifying the datapoints in the fitted data
as the outlier based on the calculated similarity index using the trained unsupervised learning module, wherein the trained unsupervised learning module is trained based on similarity index generated for sub- spectrums associated with the fingerprint data.
Yan(0037 similarity metrics)
As per claim 9 Yan discloses; The method according to claim 1, further comprising: determining a certainty measure for the outlier based on at least one of a distance threshold and a probability threshold, wherein the distance threshold is a distance that determines whether a datapoint is classified as the outlier, wherein the probability threshold is a cut-off probability that determines whether the datapoint is classified as the outlier, and wherein the distance threshold and the probability threshold are generated based on the fingerprint data; and detecting the anomaly of the outlier based on parameters associated with the outlier, wherein the outlier parameters include the certainty measure.
Yan(0060 statistics- 0062, lists a few different ones, anomaly is essentially an outlier, 0123 for the literal classification of events)
As per claim 10 Yan discloses; The method according to claim 1, further comprising: generating at least one control signal to control one or more operating conditions of the grid; and generating a notification comprising at least the detected anomaly, wherein an external control signal to control the operating conditions of the grid is triggerable upon receipt of the notification.
Yan(0038)
Claim 11 is essentially a computer medium version of claim 1.
As per claim 12 Yan discloses; A method of generating the trained unsupervised learning model according to claim 1, the method comprising at least one of: training at least one of a support vector machine and a Gaussian mixture model based on the similarity indexes generated for the sub-spectrums associated with the fingerprint data, wherein the trained support vector machine is configured to identify patterns in the similarity indexes of the sub-spectrums associated with the fingerprint data; and training the support vector machine and/or the Gaussian mixture model based on trained encoded data generated from the fingerprint data, wherein the trained encoded data comprises the significant features extracted from the fingerprint data and wherein the trained encoded data is generated using the trained autoencoder comprising at least one hidden encoder layer and at least one decoder layer.
Yan(0030 feature extraction, here significant features is one of interpretation, what’s significant)
As per claim 13 Yan discloses; A method of generating the trained autoencoder according to claim 1, the method comprising: fitting the fingerprint data associated with the grid to an autoencoder, wherein the fingerprint data comprises data associated with at least one of historical operation of the grid and standard operating parameters of the grid; extracting significant features from the fitted fingerprint data using a minimum loss function; and generating a hidden encoder layer and a decoder layer of the autoencoder as linear layers. Yan(0045 history, 0092 hidden layer)
Claim 14 is an apparatus claim of claim 1.
Claim 15 is a system claim for claim 1.
As per claim 16 Yan discloses; The system according to claim 15, wherein the server is further configured to: initialize a fitting function for the fingerprint data based on at least one transformation function performed on the fingerprint data, wherein the transformation function results in at least one of a spectrogram of the fingerprint data and a FFT of the fingerprint data; generate a trained unsupervised learning module; and generate a trained autoencoder, wherein the server is configured to store the fitting function, the transformation function, the trained autoencoder and the trained unsupervised learning module.
Yan(0057)
As per claim 17 Yan discloses; The apparatus according to claim 14, configured to detect at least one anomaly in the grid using at least one of the fitting function, the transformation function, the trained autoencoder and the trained unsupervised learning module generated by the server .
Yan(0067 autoencoder)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Crowdsourced Wireless Spectrum Anomaly Detection, IEEE 2020
A review of data-driven and probabilistic algorithms for detection purposes in local power systems, IEEE 2020
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/BRUCE I EBERSMAN/Primary Examiner, Art Unit 3693