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 .
Status of Claims
Claims 1-8 are pending for examination.
Claims 1 and 8 are independent Claims.
Claims 1-8 are rejected under 35 U.S.C. §112(b).
Claims 1-8 are rejected under 35 U.S.C. §103.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 and/or 8 recites the limitation "the", “the ADC-acquired stream”, “the regularized cost function”, “the reciprocal” and “the computed frequency or RMS” in the Claims. There is insufficient antecedent basis for this limitation in the claim.
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) 1-4, 6 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oda (U.S. 20190245337 hereinafter Oda) in view of Veeramsetty et al. ("Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks ", hereinafter Veeramsetty) in view of Kant et al. (U.S. 2009/0093892 hereinafter Kant).
As Claim 1, Oda teaches A method of measuring electrical parameters in an Intelligent Electronic Device (IED), the method comprising:
acquiring, by at least one analog-to digital-converter (ADC) of the IED, a stream of digital samples of a power-line signal (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), MU converts current signal and voltage signal from analog to digital. The processing is performed by IED);
detecting zero-crossing candidates in the stream by selecting adjacent digital
samples disposed on opposite sides of a zero-crossing and performing an interpolation of
the adjacent digital samples to compute a zero-crossing position in time (Oda (¶0079 last 3 lines), “The zero-cross point can be obtained by interpolating between sample points before and after the zero-cross point by linear approximation”), the interpolation reducing noise-induced error (this limitation is intended use);
forming, in the IED and storing in non-volatile memory (Oda (¶0143 last 4 lines, ¶0144 line 1-4), IDE includes data storage. Data storage includes current and voltage data), a training dataset that includes, wherein the training dataset is created dynamically inside the IED from the ADC-acquired stream (Oda (¶0160, fig. 14, ¶0161), digital time-series data is stored in data storage);
obtaining, by processing circuitry of the IED (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), MU converts current signal and voltage signal from analog to digital. The processing is performed by IED),
computing a fundamental frequency as the reciprocal of a time difference between a first estimated zero-crossing position and a next estimated zero-crossing position (Oda (¶0079 line 1-4), system frequency can be calculated from the interval between adjacent zero-cross points),
initiating, by the IED, an output action comprising at least one of: (i) generating a digital control pulse that alters an operational state of the IED (Oda (¶0145 line 1-7), data storage (part of IED) latches data stored when receiving failure detection signal), or (ii) transmitting a notification e-mail including the computed fundamental frequency or RMS value via a communication interface.
Oda may not explicitly disclose:
for each training example, an input variable set derived from the interpolated zero-crossing position and a corresponding zero-crossing position output value,
estimating zero-crossing positions of the power-line signal using the hypothesis function and an input variable set derived from the currently acquired digital samples;
and computing a root-mean-square (RMS) value using a positive integer number N of digital samples that are bounded by the first and next estimated zero-crossing positions, wherein N is stored in a configuration memory of the IED; and
Veeramsetty teaches:
for each training example, an input variable set derived from the interpolated zero-crossing position (Veeramsetty (2.3 Feature Exatraction, first paragraph), signal data points over 5 cycles are split into multiple sets based on the sliding window approach. Four input features called slope, intercept, correlation coefficient and root mean square error are input data) and a corresponding zero-crossing position output value (Veeramsetty (2.3 Feature Exatraction, second paragraph), data sample is stored with class label such as ZCP or NZCP),
estimating zero-crossing positions of the power-line signal using the hypothesis function and an input variable set derived from the currently acquired digital samples (Veeramsetty (page 16, “4.Discussion” first 3 lines), the proposed DNN model with 3 hidden layers and 64 hidden neurons is used to predict zero-crossing point class in real time);
and computing a root-mean-square (RMS) value using a positive integer number N of digital samples that are bounded by the first and next estimated zero-crossing positions, wherein N is stored in a configuration memory of the IED (Veeramsetty (2.3 Feature Exatraction, first paragraph), signal data points over 5 cycles are split into multiple sets based on the sliding window approach. Four input features called slope, intercept, correlation coefficient and root mean square error are input data); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify zero-cross point approximation method of Oda instead be a zero-crossing machine learning method taught by Veeramsetty, with a reasonable expectation of success. The motivation would be to “provide valuable contributions to the ZCP
problem. However, to improve the accuracy in predicting the true ZCPs, a new deep neural
network (DNN) based machine learning model is developed in this paper” (Veeramsetty (fifth paragraph of page 2).
Oda in view of Veeramsetty does not explicitly disclose:
a hypothesis function that is a third-order polynomial in the input variable set, polynomial parameters being obtained by minimizing a cost function that includes a regularization term, wherein minimizing the regularized cost function improves robustness against noise;
Kant teaches:
a hypothesis function that is a third-order polynomial in the input variable set (Kant (¶0072 last 5 lines), 3rd order polynomial is considered and selected), polynomial parameters being obtained by minimizing a cost function that includes a regularization term (Kant (¶0075, ¶0076), error function is minimized), wherein minimizing the regularized cost function improves robustness against noise (Kant (¶0074 last 7 lines), error function is selected so that the model is not affected by noise);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify zero-cross point approximation method of Oda in view of Veeramsetty instead be a polynomial taught by Kant, with a reasonable expectation of success. The motivation would be to “by prudent selection of the value of E, it is possible to fashion a regression model that fits the data but is not affected by the noise.” (Kant (¶0074 last 3 lines)).
As Claim 2, besides Claim 1, Oda in view of Veeramsetty in further view of Kant further comprising deriving the input variable set from detected zero-crossing positions of an electrical signal using a digital signal processor (DSP) within an Intelligent Electronic Device (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), the MU and the IED are not separate. The processing is performed by IED).
As Claim 3, besides Claim 1, Oda in view of Veeramsetty in further view of Kant teaches wherein the hypothesis function is deduced using a multivariate polynomial regression approach (Kant (¶0072 last 5 lines), 3rd order polynomial is considered and selected) executed by the processing circuitry of the Intelligent Electronic Device (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), the processing is performed by IED).
As Claim 4, besides Claim 3, Oda in view of Veeramsetty in further view of Kant wherein the hypothesis function is defined as a function of the input variable set X = [X0 X1 X2 … Xn] and the theta parameters θ = [θ0 θ1 θ2 θ3] given by hθ(X) = θ0+ θ1X + θ2X2 + θ3X3, wherein the theta parameters θ = [θ0 θ1 θ2 θ3] are real numbers determined from a cost function by iteratively adjusting the theta parameters to minimize the cost function using an optimization algorithm (Kant (¶0072 last 5 lines), 3rd order polynomial is considered and selected), the optimization being performed in the processing circuitry of the Intelligent Electronic Device based on the ADC-acquired digital samples stored in memory (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), the processing is performed by IED).
As Claim 6, besides Claim 2, Oda in view of Veeramsetty in further view of Kant teaches wherein a detected zero-crossing position is determined by interpolating a pair of digital samples, each sample being disposed on a different side of the detected zero-crossing (Oda (¶0079 last 3 lines), “The zero-cross point can be obtained by interpolating between sample points before and after the zero-cross point by linear approximation”) the interpolation being performed by the DSP of the Intelligent Electronic Device (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), the processing is performed by IED) to improve noise resilience in frequency calculation (this is intended use).
As Claim 8, Claim is rejected for the same reasons of Claims 1. The rest of the limitation(s) are taught by Oda in view of Veeramsetty in further view of Kant:
at least one sensor configured to sense at least one electrical parameter of a power-line
signal (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), MU senses and converts current signal and voltage signal from analog to digital);
at least one analog-to-digital converter (ADC) coupled to the at least one sensor
and configured to convert an analog signal output from the at least one sensor to digital
samples (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), MU senses and converts current signal and voltage signal from analog to digital);
programmable logic configured to transfer the digital samples from the ADC (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), MU senses and converts current signal and voltage signal from analog to digital. Signal is transfer to IDE.);
processing circuitry comprising at least one of a digital signal processor (DSP) and
a central processing unit (CPU), the processing circuitry being coupled to the ADC and
the programmable logic (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), the processing is performed by IED); and
a non-volatile memory storing (Oda (¶0143 last 4 lines, ¶0144 line 1-4), IDE includes data storage. Data storage includes current and voltage data) (i) a training dataset formed dynamically within the IED, the dataset including, for each training example, an input variable set derived from interpolation of adjacent ADC samples straddling a zero-crossing (Veeramsetty (2.3 Feature Exatraction, first paragraph), signal data points over 5 cycles are split into multiple sets based on the sliding window approach. Four input features called slope, intercept, correlation coefficient and root mean square error are input data) and a corresponding zero-crossing position output value (Veeramsetty (2.3 Feature Exatraction, second paragraph), data sample is stored with class label such as ZCP or NZCP), (ii) at least one threshold value defining a threshold condition. and (iii) a positive integer N specifying a sample count for RMS computation (Oda (¶0109 line 13-16), m is the number of data point, m is greater than 1);
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oda and Veeramsetty in view of Kant in further view of Olabiyi et al. (U.S. 10,510,003 hereinafter Olabiyi).
As Claim 5, besides Claim 4, Oda in view Veeramsetty in further view of Kant does not explicitly disclose:
wherein the optimization algorithm is a gradient descent algorithm the gradient descent being implemented by the processing circuitry of the Intelligent Electronic Device to iteratively adjust the theta parameters for real-time estimation under noisy power-line conditions
Olabiyi teaches:
wherein the optimization algorithm is a gradient descent algorithm the gradient descent being implemented by the processing circuitry of the Intelligent Electronic Device to iteratively adjust the theta parameters for real-time estimation under noisy power-line conditions (Olabiyi (col. 6 line 49-52, col. 7 line 5-9), theta parameter represents the noise. System estimates theta in order minimize objective function with linearization in order to eliminate over-confidence problem.).
Oda in view of Veeramsetty in further view Kant teaches a neural network to estimate zero-crossing point using a mathematical function. Olabiyi teaches a neural network for using gradient descent function. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Oda in view of Veeramsetty in further view of Kant’s function with Olabiyi’s gradient descent, with a reasonable expectation of success. The motivation would be to allow “some aspects described herein may introduce a regularization that may disallow the model over confidence associated with equation (3) by removing loss contribution from correctly predicted examples” (Olabiyi (col. 7 line 18-23)).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oda and Veeramsetty in view of Kant in further view of Ivanenko et al. (“Zero-Crossing Technique Modification for the Frequency Measurements of Real Power Grids” hereinafter Ivanenko).
As Claim 7, besides Claim 6, Oda in view of Veeramsetty in further view of Kant teaches:
the computation being executed in the DSP of the Intelligent Electronic Device and used to trigger subsequent frequency and RMS calculations (Oda (¶0050, ¶0054 last 7 lines, ¶0055 line 1-4), the processing is performed by IED).
Oda in view of Veeramsetty in further view of Kant does not explicitly disclose:
wherein interpolating a pair of digital samples includes computing a first zero-crossing based upon the following equation:
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wherein ZC represents the first zero-crossing position in time; i represents an index number of the digital samples; v(i) represents a voltage of a digital sample disposed immediately after the first zero-crossing position; and v(i-1) represents a voltage of a digital sample disposed immediately before the first zero-crossing position
Ivanenko teaches:
wherein interpolating a pair of digital samples includes computing a first zero-crossing based upon the following equation:
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wherein ZC represents the first zero-crossing position in time; i represents an index number of the digital samples; v(i) represents a voltage of a digital sample disposed immediately after the first zero-crossing position; and v(i-1) represents a voltage of a digital sample disposed immediately before the first zero-crossing position (Ivanenko (II. Zero-Crossing Technique; B. Additional Dermination of the Zero-Crossing Moments), formula 5 and 6).
Oda in view of Veeramsetty in further view Kant teaches a neural network to estimate zero-crossing point using a mathematical function. Ivanenko teaches a formular for calculating zero-crossing point. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Oda in view of Veeramsetty in further view of Kant’s function with Ivanvenko’s formula, with a reasonable expectation of success. The motivation would be to allow “to improve the measurement accuracy” (Ivanenko (II. Zero-Crossing Technique; B. Additional Dermination of the Zero-Crossing Moments, first 5 lines)).
Response to Arguments
Claim Rejections under 35 U.S.C. §112:
Applicant amended the Claims to overcome “the estimated zero-crossing postions”. However, current Claims are being rejected due to new amended limitation(s).
Claim Rejections under 35 U.S.C. §101:
Applicant’s arguments are persuasive; therefore, 35 U.S.C. §101 rejections are respectfully withdrawn.
Claim Rejections under 35 U.S.C. §103:
As Claim 1 and similarly Claim 8, Applicant arguments that current Claims overcome cited references (first paragraph of page 9 in the remarks and last paragraph of page 10 in the remarks).
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Applicant’s arguments are moot because Veeramsetty and new reference Oda and Kant teach teaches the Claims’ limitation(s).
As Claim 2, Applicant arguments that current Claims overcome cited references (second paragraph of page 9 in the remarks).
Applicant’s arguments are moot because new reference Oda teach teaches the Claims’ limitation(s).
As Claim 3, Applicant arguments that current Claims overcome cited references (third paragraph of page 9 in the remarks).
Applicant’s arguments are moot because Veeramsetty and new reference Oda and Kant teach teaches the Claims’ limitation(s).
As Claim 4, Applicant arguments that current Claims overcome cited references (last paragraph of page 9 in the remarks).
Applicant’s arguments are moot because new reference Oda teach teaches the Claims’ limitation(s).
As Claim 5, Applicant arguments that current Claims overcome cited references (second paragraph of page 10 in the remarks).
Applicant’s arguments are moot because new reference Obaliyi teach teaches the Claims’ limitation(s).
As Claim 6, Applicant arguments that current Claims overcome cited references (third paragraph of page 10 in the remarks).
Applicant’s arguments are moot because new reference Oda teach teaches the Claims’ limitation(s).
As Claim 7, Applicant arguments that current Claims overcome cited references (fourth paragraph of page 9 in the remarks).
Applicant’s arguments are moot because Veeramsetty, Ivanenko and new reference Oda and Kant teach teaches the Claims’ limitation(s).
Conclusion
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.
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/NHAT HUY T NGUYEN/ Primary Examiner, Art Unit 2147