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 .
Election/Restrictions
Applicant's election with traverse of Group I in the reply filed on 08/19/2026 is acknowledged. The traversal is on the ground(s) that the restriction requirement does not sufficiently establish distinctness between Groups I and II or a serious search and/or examination burden that would result from examination of the claims together (i.e., Groups I and II logically overlap; Both groups recite limitations concerning the same first DNN (DNN-TI) and second DNN (DNN-DSSE); examination of the unrestricted independent claims already involves the first and second DNNs that form the subject matter relied upon by the Office in defining Groups I and II; Applicant also traverses the restriction with respect to Group III, does not concede that Group III is properly restrictable from the elected claims). This is not found persuasive because Groups I and II are restricted for the differences in “details” – one to the types and operations of the first DNN and the second DNN and the other to the training the first DNN and the second DNN. Even though the groups concern the same DNNs, the “details” clearly belong to distinct technical fields and require different search strategies and considerations. As to Group III, Applicant has not provided adequate arguments why the restriction is improper.
The requirement is still deemed proper and is therefore made FINAL.
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.
MPEP 2106 outlines a two-part analysis for Subject Matter Eligibility as shown in the chart below.
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930
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Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter.
Step 2, the claimed invention also must qualify as patent-eligible subject matter, i.e., the claim must not be directed to a judicial exception unless the claim as a whole includes additional limitations amounting to significantly more than the exception.
Step 2A is a two-prong inquiry, as shown in the chart below.
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881
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Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. If the claim recites a judicial exception (i.e., an abstract idea enumerated in MPEP § 2106.04(a), a law of nature, or a natural phenomenon), the claim requires further analysis in Prong Two. If the claim does not recite a judicial exception (a law of nature, natural phenomenon, or abstract idea), then the claim cannot be directed to a judicial exception (Step 2A: NO), and thus the claim is eligible at Pathway B without further analysis. Abstract ideas can be grouped as, e.g., mathematical concepts, certain methods of organizing human activity, and mental processes.
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B.
Claims 1-5, 14-16, and 20 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.
Regarding claim 1, Step 1: Is the claim to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea (judicially recognized exceptions)? Yes (see analysis below).
Prong one: Whether the claim recites a judicial exception? (Yes). The claim is directed to an abstract idea because it recites the limitations beginning from “accessing a set of synchrophasor measurement device (SMD) observation data for a distribution network” to the end of the claim. These limitations are directed to mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations; and/or mental processes – concepts performed in the human mind (or with a pen and paper).
Prong two: Whether the claim recites additional elements that integrate the exception into a practical application of that exception? (No). The claim recites no additional elements. Accordingly, no additional elements are sufficient to integrate the abstract idea into a practical application of the abstract idea.
Step 2B: Does the claim recite additional elements (other than the judicial exception) that amount to significantly more than the judicial exception? No (see analysis below).
The claim does not include additional elements that are sufficient to make the claim significantly more than the judicial exception. Considered as a whole, the claim does not amount to significantly more than the abstract idea.
Claims 14 and 20 are similarly rejected by analogy to claim 1. Note that the processor, memory, and non-transitory computer-readable medium are generic computer components, invoked to facilitate the application of the abstract idea. See MPEP 2106.05(f).
Dependent claims 2-5, 15, and 16 when analyzed as a whole respectively are held to be patent ineligible under 35 U.S.C. 101 because they either extend (or add more details to) the abstract idea or the additional recited limitation(s) (if any) fail(s) to establish that the claim(s) is/are not directed to an abstract idea, as discussed below: there is no additional element(s) in the dependent claims that sufficiently integrates the abstract idea into a practical application of, or makes the claims significantly more than, the judicial exception (abstract idea). The additional element(s) (if any) are mere instructions to apply an except, field of use, and/or insignificant extra-solution activities (applied to Step 2A_Prong Two and Step 2B; see MPEP 2016.05(f)-(h)) and/or well-understood, routine, or conventional (applied to Step 2B; see MPEP 2106.05(d)) to facilitate the application of the abstract idea.
Claim Rejections - 35 USC § 102
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 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 1-5, 14-16, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Azimian et al. ("State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks" arXiv:2104.07208, 2022, available at https://web.archive.org/web/20220329062304/https://arxiv.org/ftp/arxiv/papers/2104/2104.07208.pdf 03/29/2022; hereinafter “Azimian”).
Regarding claim 1, Azimian teaches a method, comprising:
accessing a set of synchrophasor measurement device (SMD) observation data for a distribution network (i.e., “Real-time SMD measurement”; see FIG. 3);
identifying, at a first deep neural network (DNN-TI), a present topology of the distribution network based on the set of SMD observation data (i.e., “Perform DNN based TI”; see FIG. 3); and
estimating, at a second deep neural network (DNN-DSSE) in communication with the first deep neural network (DNN-TI), an estimated state vector for a plurality of nodes of the distribution network (i.e., “Perform DNN based DSSE”; see FIG. 3; see FIG. 2 showing a state vector in the output layer).
Regarding claim 2, Azimian further teaches: where identifying the present topology of the distribution network based on the set of SMD observation data includes:
comparing the present topology of the distribution network with a base topology that was used to train the second deep neural network (DNN-DSSE) (i.e., “Topology consistent with trained DNN for DSSE?”; see FIG. 3).
Regarding claim 3, Azimian further teaches:
fine-tuning one or more weights of the second deep neural network (DNN-DSSE) based on the present topology of the distribution network and based on a set of topology information stored at a transfer learning database that is associated with the present topology (i.e., “Do Transfer learning using stored dataset”; see FIG. 3; “fine-tuning is used in this paper to update the weights of the DNN for DSSE when topology changes”; see p. 2, col. 2, ¶ 4), the set of topology information including a set of three-phase power flow data and a set of error-modeled SMD data stored at a transfer learning database (i.e., “In order to replicate actual SMD measurements, appropriate measurement error must be added to the error-free voltages and currents obtained from the power flow solution”; see p. 5, col. 2, ¶ 2; “Store all voltage phasors and erroneous SMD measurements for topology
i
”; see FIG. 3; see FIG. 2 indicating three-phase power flow data).
Regarding claim 4, Azimian further teaches:
the second deep neural network (DNN-DSSE) being a regression-based deep neural network (i.e., “the regression DNN that was built for DSSE”; see p. 3, col. 1, ¶ 4).
Regarding claim 5, Azimian further teaches:
the first deep neural network (DNN-TI) being a classification-based deep neural network (i.e., “a classification DNN is built for DNN-based TI”; see p. 3, col. 1, ¶ 4).
Regarding claim 14, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings. Note that the DNNs require at least a computer (see, also, “All simulations were performed on a computer”; p. 7, col. 1, ¶ 2).
Regarding claim 15, the claim recites the same substantive further limitations as claim 2 and is rejected by applying the same teachings.
Regarding claim 16, the claim recites the same substantive further limitations as claim 3 and is rejected by applying the same teachings.
Regarding claim 20, the claim recites the same substantive limitations as claim 1 and is rejected by applying the same teachings. Note that the DNNs require at least a computer (see, also, “All simulations were performed on a computer”; p. 7, col. 1, ¶ 2).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Amoateng et al. ("Topology Detection in Power Distribution Networks: APMU Based Deep Learning Approach" IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 37, NO. 4, JULY 2022) teaches a method for detecting topology transitions in a distribution network using voltage and current phasors recorded by PMUs and an ensemble-based deep learning model.
Li et al. ("Artificial Intelligence for Real-Time Topology Identification in Power Distribution Systems" 2020 52nd North American Power Symposium (NAPS), Tempe, AZ, USA, 2021) teaches a Convolutional Neural Network (CNN) that uses the mu-PMU measurements as inputs and provides a full observation of the network topology in real-time.
SUN et al. ("A Classification Identification Method Based on Phasor Measurement for Distribution Line Parameter Identification Under Insufficient Measurements Conditions" IEEE Access, vol. 7, 2019) teaches a classification identification method for distribution line parameter identification (DLPI) under the condition of insufficient PMU measurements based on phasor measurement (CIMPM), involving extracting the main features of a large number of multitime measurements via a convolutional neural network (CNN).
Varghese et al. ("Time-Synchronized Full System State Estimation Considering Practical Implementation Challenges" arXiv:2212.01729, 2022) teaches a Deep Neural network-based State Estimator (DeNSE), involving employing a Bayesian framework to indirectly combine inferences drawn from slow timescale but widespread supervisory control and data acquisition (SCADA) data with fast timescale but select PMU data to attain sub-second situational awareness of the entire system; and using Transfer Learning to update the DNN of the DeNSE when topology changes.
YANG et al. (CN 114091816 A) teaches a distribution network state estimation method based on a fusion of PMU/SCADA mixed measurement and a distribution network fast state estimation method based on GGNN network structure.
Gu et al. (US 20210141029 A1) teaches a method of state estimation in an electric power system based on PMU measurement and a deep neural network (DNN).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN C KUAN whose telephone number is (571)270-7066. The examiner can normally be reached M-F: 9:00AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN C KUAN/Primary Examiner, Art Unit 2857
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