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 1 is objected to because of the following informalities: In claim 1 , line 1, change “Method” to -A method-. Appropriate correction is required. In claim 1, line 16, change “ centre ” to -center-. Appropriate correction is required. Claim 1 5 is objected to because of the following informalities: In claim 1 5 , line 1, change “ System ” to -A system -. Appropriate correction is required. Claim s 1 1 , 25 and 2 6 is objected to because of the following informalities: In claim 11, line 3, change “ synchronised ” to -synchronized-. Appropriate correction is required. In claim 25, line 4, change “ synchronised ” to -synchronized-. Appropriate correction is required. In claim 26, line 6, change “analyser” to -analyzer-. Appropriate correction is required. 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 and 1 5 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more. Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a process (claim 1, a method) or a machine (claim 1 5 , a) or a system which are statutory categories. However, evaluating claim 1, under Step 2A, Prong One , the claim is directed to the judicial exception of an abstract idea using the grouping of a mathematical relationship/mental process. The limitations include: transforming the time series data to feature vector format data where the time series data is grouped into a plurality of datasets, each dataset representing a subset of the first set of data points; carrying out a statistical data clustering scheme to generate distinct cluster patterns as clustered data from the feature vector format data, the clustered data comprising a first cluster relating to a first electrical trend and a second cluster relating to a second electrical trend which is different from the first electrical trend, wherein the clustered data comprises an outlier data pattern that is part of either the first or second cluster, and the outlier data pattern is far from its respective cluster cent er ; and detecting an anomalous event based at least in part on the outlier data. Next, Step 2A, Prong Two evaluates whether additional elements of the claim “ integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The claim does not recite additional elements that integrate the judicial exception into a practical application. This judicial exception is not integrated into a practical application . Therefore, the claims are directed to an abstract idea. At Step 2B , consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B, there are no additional elements that make the claim significantly more than the abstract idea. The additional elements of “ obtaining high resolution electrical measurement data related to time series data of an electrical or other parameter measured from an electrical power grid system or other electrical apparatus, wherein the time series data comprises a first set of data points ” are considered insignificant extra-solution activity of collecting data that is not sufficient to integrate the claim into a particular practical application. The act of data gathering by the sensors is considered insufficient to elevate the claim to a practical application. The recitation of “high resolution electrical measurement data” and an “electrical power grid system” merely provide a generic data source and generic technological environment, which does not meaningfully limit the abstract idea or integrate it into a practical application. The limitations have been considered individually and as a whole and do not amount to significantly more than the abstract idea itself. Dependent claim 2-14 do not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above. The examiner notes that the element “ unsupervised machine learning technique ” (claim 2) is considered performing mathematical calculation which falls within the “mathematical concept” grouping of abstract ideas (see Example 47, in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence). The examiner also that the additional element of claim 10, “ micro- synchrophasor unit ” is merely a generic data-gathering device and does not add significantly more since the claim is centered on abstract mathematical processing of measurement data, the micro- synchrophasor unit acting only as generic data source. The examiner further notes that the additional element of claim 14, because it merely identifies generic environments for data collection without providing a technological improvement or inventive concept and limits the abstract idea to a particular filed of use, which does not integrate the judicial exception in a practical application . Claim 1 5 is rejected 35 USC § 101 for the same rational e as in claim 1. This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas. As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ ing ] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application Therefore, the claims are directed to an abstract idea. Dependent claim 16-29 do not add anything which would render the claimed invention a patent eligible application of the abstract idea. The claim merely extends (or narrow) the abstract idea which do not amount for "significant more" because it merely adds details to the algorithm which forms the abstract idea as discussed above. The examiner notes that the element “ unsupervised machine learning technique ” (claim 16 ) is considered performing mathematical calculation which falls within the “mathematical concept” grouping of abstract ideas (see Example 47, in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence). The examiner also that the additional element of claim 24, “ micro- synchrophasor unit ” is merely a generic data-gathering device and does not add significantly more since the claim is centered on abstract mathematical processing of measurement data, the micro- synchrophasor unit acting only as generic data source. The examiner further notes that the additional element of claim 29 , because it merely identifies generic environments for data collection without providing a technological improvement or inventive concept and limits the abstract idea to a particular filed of use, which does not integrate the judicial exception in a practical application. 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. Claims 1 , 2, 6 , 15 , 1 6 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa et al. (Pub. No. US 2019/0 056436 ) (hereinafter Nishikawa ) in view of Harale et al. (Pub. No. US 2021/0286780) (hereinafter Harale ) . As per claims 1 and 15 , Nishikawa teaches obtaining high resolution electrical measurement data related to time series data of an electrical or other parameter measured from an electrical power grid system or other electrical apparatus , wherein the time series data comprises a first set of data points (see Abstract and ¶¶ [0002] , [0009] , [0038]-[0039], i.e., frequency, voltage, power ) , and detecting an anomalous event based at least in part on the outlier data (see ¶¶ [00 40 ] -[0042], i.e. detecting anomalous events from time -series by identifying outliers relative to threshold conditions ) . Nishikawa fails to teach transforming the time series data to feature vector format data where the time series data is grouped into a plurality of datasets, each dataset representing a subset of the first set of data points; carrying out a statistical data clustering scheme to generate distinct cluster patterns as clustered data from the feature vector format data, the clustered data comprising a first cluster relating to a first electrical trend and a second cluster relating to a second electrical trend which is different from the first electrical trend, wherein the clustered data comprises an outlier data pattern that is part of either the first or second cluster, and the outlier data pattern is far from its respective cluster cent er . However, Harale teaches transforming time-series data into feature vector ( i.e. “data wedges” and “data pattern models” (see ¶¶ [0017]-[0020] and [004 7 ]-[0048]), performing statistical clustering and profiling (see ¶¶ [00 38 ]-[00 39 ] and [00 55 ]-[00 5 8]) , and identifying outlier patterns far from their cluster centers as “data anomaly cluster” (see ¶¶ [0038]-[0039] and [0059]-[0061]). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate Harale’s clustering-based anomaly detection framework into the PMU-based anomaly detection system of Nishikawa because it would improve accuracy, scalability, and identification of different operating trends, thereby enhancing anomaly detection in high volume time-series data. As per claim s 2 and 16 , the combination of Nishikawa and Harale teaches the system as stated above. Harale further teaches that the statistical clustering scheme is an unsupervised machine learning technique (see ¶ [0026]) . As per claims 6 and 20 , the combination of Nishikawa and Harale teaches the system as stated above. Nishikawa further teaches identifying the outlier data, wherein the outlier data is identified automatically by comparing a value of the outlier data with a threshold (see ¶ [00 46 ] , i.e., “ Column 620 is representative of a base value that is used in combination with the percentages of Columns 610 and 620 to determine the thresholds used to identify outliers in data. For example, an upper threshold value in the first row may correspond to 110% of the base value (e.g. 550 volts) and the lower threshold value in the first row may correspond to 90% of the base value (e.g. 450 volts ) ” and ¶ [0040], i.e., “…detection of these outliers (16a-16f) may be used to identify similar events in historical PMU data automatically ” ) . Claims 3 , 4, 17 and 1 8 are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa and Harale and further in view of Yan et al. (Pub. No. US 2020/0292608) (hereinafter Yan) . As per claims 3 and 17 , the combination of Nishikawa and Harale teaches the system as stated above. While Harale teaches generating feature vectors from electrical time-series measurem e nts and classifying operational states using decision boundaries or supervised learning m odels (see ¶¶ [0031], [0055] -[ 0057], [0062]-[0063] and [0083]), Harale fails to explicitly specify which clustering algorithm is used. However, Yan explicitly identifies k-means clustering as suitable unsupervised machine-learning method for processing time-series features of power-system data (see ¶ ¶ [0089] -[ 0090] ), k-means is an established partitioning-based clustering method in which data points are assigned to clusters by minimizing distance to cluster centroids ). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to implement the generic “statistical data clustering scheme” of Harale using a partitioning-based clustering algorithm such as k-means as taught by Yan, because k-means is well known to be computationally efficient, widely used for feature-vector clustering, and suitable for anomaly/outlier detection in high-resolution time-series data. Selecting such a known clustering algorithm from among common choices represents noting more than routine optimization of known technique (KSR) . As per claims 4 and 18 , the combination of Nishikawa and Harale teaches the system as stated above. As also stated above Yan explicitly identifies k-means clustering as suitable unsupervised machine-learning method for processing time-series features of power-system data (see ¶¶ [0089] -[ 0090]), however, Yan does not explicitly teach that the statistical clustering scheme is Clustering Large Applications based on Randomized Search, CLARANS. CLARANS is well-known partitioning-based clustering algorithm that constitutes a scalable randomized improvement over k-medoids, intended to handle larger datasets efficiently by applying randomized search to the medoid-update step . CLARANS produces clusters based on minimizing within, cluster dissimilarity, the same objective function optimized by k-means and k-medoids. Because Yan teaches using unsupervised, partitioning-based clustering(k-means) for organizing feature-vector data derived from power-system measurements, a skilled artisan would have readily appreciated that any known partition-based algorithm, including CLARANS, could be substituted to achieve predictable benefits, such as improved scalability or runtime efficiency, especially given the large PMU/SCADA datasets processed by Nishikawa and Harale . Under K S R v. Teleflex, 550 U.S. 398, 417 (2007), applying a known improvement technique (CLARANS) to an analogous method (k-means clustering of feature vectors for anomaly detection) is merely the use of known variant to obtain predictable results. Moreover, under MPEP § 2144.04 (design choice), selecting CLARANS rather than k-means represents a predictable substitution of one known equivalent for another, where all algorithms perform the same function (partitioning data into cluster), and achieve the same result (cluster feature vectors distinguishing normal from abnormal behavior). Ac c ordingly, incorporating CLARANS in place of k-means is considered a design choice. Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa and Harale and further in view of Urmanov et al. (Pub. No. US 2020/0336500) (hereinafter Urmanov ) . As per claims 5 and 19 , the combination of Nishikawa and Harale teaches the system as stated above except that clustered data is generated as a first graphical representation. However, Urmanov teaches visualizing clustered data in a graphical plot, where Fig. 12 shows two clusters in a plotted representation and ¶ [0069] describes that “ the plot shows two clusters w ith light grey and dark shading ” and uses symbols to distinguish normal vs, anomalous points . It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate the graphical cluster-visualization techniques of Urmanov into the clustering and anomaly-detection systems of Nishikawa and Harale because it would help interpret cluster separation and detect outliers, thereby, enhancing operator interpretability and enabling quicker recognition of abnormal trends. Claims 7 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa and Harale and further in view of Bures et al. (Pub. No. US 2020/0322703) (hereinafter Bures) . As per claims 7 and 21 , the combination of Nishikawa and Harale teaches the system as stated above except for compressing the time series data of the electrical parameter measured in an electrical power grid prior to obtaining. However, Bures teaches processing and compressing or reducing high-rate time-series sensor measurements , including electrical measurements, to lower sampling rate or data size in order to satisfy bandwidth and transmission constraints (see ¶¶ [0019] and [0052] -[ 0054]). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate the known compression techniques of Bures to the high-rate PMU time-series data of the combination of Nishikawa and Harale because PMU streams inherently generate large volumes of data and would benefit from reduced transmission load, reduced storage requirements, thereby real time responsiveness would be improved, which is consistent with KSR, applying a known data-compression technique to known high-rate grid measurements constitute a predictable, routine optimization . Claims 8 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa and Harale and further in view of Bures and Arye et al. (Pub. No. US 2021/0034598) (hereinafter Arye ) . As per claims 8 and 22 , the combination of Nishikawa and Harale and Bures teaches the system as stated above except that the compressing comprises lossless data compression in a column - based storage format. However, Arye teaches storing data in columnar from to improve compressibility (see ¶ [0049]: “column-style storage”), (see ¶ [0052]: “mini-sets stored in columnar from”), (see ¶ [0053]: “compressed array of 100 metric1 values…metric2 values…metric3 values”) and applying lossless compression schemes, including LZ, DEFLATE, LZW, Huffman, Snappy, delta-encoding, and Gorilla (see ¶ [0019]). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate the column-based, lossless-compression techniques of Arye into the measurement-compression framework of Bures in combination with Nishikawa and Harale’s high-volume PMU data because high-resolution grid measurements are naturally column-oriented time-series (voltage, current, frequency, etc.), which would benefit from column-wise lossless compression to reduce bandwidth, storage load, and latency while preserving analytical accuracy. Under KSR, applying a known, predictable data-compression architecture (column-based, lossless compression) from Arye to the already-compressed measurement pipeline of Bures in the context of the high-rate power-system data of Nishikawa and Harale constitutes a routine optimization that one of ordinary skill in the art would implement to improve system efficiency. Claims 9 and 2 3 are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa and Harale and further in view of Bures, Arye and NIEDRITE et al. (NPL: “A comparison of HDFS compact data format: Avro versus Parquet”) (hereinafter NIEDRITE ). As per claims 9 and 23 , the combination of Nishikawa, Harale , Bures and Arye teaches the system as stated above. While Arye teaches performing lossless data compression using well-known algorithms such as LZ, DEFLATE/ Gzip , LZW , Burrows-Wheeler, Huffman, Snappy, and run-length encoding in the context of column-based storage formats (see ¶ [0019]). The combination of Nishikawa, Harale , Bures and Arye fails to explicitly teach that the lossless data compression is in the Apache Parquet format . However, NIEDRITE expressly identifies Parquet as one of several standard binary data storage formats (“ RCFiles , ORC, Avro, Parquet”) designed for MapReduce-type analytical systems, and explains that a storage “structure is a systematic combination of multiple components including data storage format, data compression, and optimization techniques for data reading ” (see page 268, col.1, third paragraph) . NIEDRITE further teaches that selecting an “ appropriate data format” is crucial for efficient storage utilization in distributed systems such as HDFS, where both storage cost and analytical performance are affected by the combination of format and compression. Because Parquet is well-known column-based storage format whose purpose is to apply standard lossless compression algorithms to multi-column or time-series datasets to reduce I/O and improve query performance (see page 268, col.1, third paragraph and page 269, col. 1, fourth paragraph) , and because Arye already teaches the same lossless codecs used by Parquet, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to implement the claimed lossless compression using the Apache Parquet format as predictable design choice . Under KSR, selecting Parquet, one of the “binary data storage formats” specifically recommended by NIEDRITE for efficient compressed analytical processing, would merely constitute using known, optimized storage-compression structure to obtain the expected benefits of efficient compressed analytical processing. Claims 10 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa and Harale and further in view of Yan and McEachern (Patent No. US 9,383,397). As per claims 10 and 24 , the combination of Nishikawa, Harale and Yan teaches the system as stated above. While Nishikawa teaches obtaining time-synchronized voltage/frequency/phasor measurements from P M Us deployed through a power grid and using such measurem e nts for anomaly/ou t lier detection (see Abstract ¶¶ [00 09 ] , [0040] and [00 53 ]), while Harale teaches generating feature vectors from high-resolution electrical time-series measurements and detecting anomalies using statistical or machine-learning models (see ¶¶ [0018] and [0026]), and while Yan teaches phasor-domain measurement and feature extraction from grid-connected PMUs and processing synchrophasor -based frequency-domain signals (see ¶¶ [0002]-[0004], [0027]-[0035] , [0077], and [0089]). None of these references explicitly teaches that the high-resolution data is obtained from a micro-Phasor Unit (µPMUs) . However, McEachern teaches micro-Phasor Measurement Units (µPMUs) used in the electrical power grid to provide highly accurate, timing-corrected, frequency-domain phase-angle measurements (see col. 1, lines 1-65, discussing PMUs and µPMUs measuring grid parameters relative to a GPS signal ). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to substitute the standard PMUs of Nishikawa and Yan with the µPMUs of McEachern because µPMUs were a known , higher-resolution improvement over conventional PMUs used for precisely the same purpose of measuring voltage/current phasors in the frequency domain for grid monitoring. Under KSR, selecting a known improved sensor ( µPMU ) to enhance resolution, accuracy, and anomaly-detection fidelity constitutes a predictable design choice. Claims 11 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa in view of Harale and further in view of Piyasinghe et al. (Pub. No. US 2018/0292447) (hereinafter Piyasinghe ). As per claims 11 and 25 , the combination of Nishikawa, Harale teaches the system as stated above. While Nishikawa teaches obtaining a first set of data points comprising electrical parameters such as voltage, current, and frequency (see Abstract ¶¶ [0009], [0040] and [0053] ) the combination does not explicitly teach that a power quality monitor operates in the time-domain and generates a second set of data points of the electrical parameter measured from an electrical power grid system with synchronized time stamps relative to the first set of data points . However, Piyasinghe discloses phasor-based measurement systems that assign a common time reference and generate synchronized data streams across different points of an electrical grid (see ¶¶ [00 04 ] -[ 00 08 ] and [00 39 ]-[00 47 ] ). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention combine Piyasinghe’s synchroniz ation with the monitoring frameworks of Nishikawa and Harale to place multiple time-domain data streams on a unified synchronized time base , thereb y , improving correlation accuracy, multi-device diagnostics, and system-level event detection in electrical-grid monitoring . Claims 1 3 , 14, 28 and 2 9 are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa in view of Harale and further in view of Saarinen et al. (Pub. No. US 2012/0004869) (hereinafter Saarinen). As per claims 13 and 28 , the combination of Nishikawa, Harale teaches the system as stated above except that the detecting further comprises determining further information relating to the outlier data including whether there is a fault event in a particular window of time. Saarinen teaches detecting power-line events, isolating transient segments, determining event type, and classifying whether a disturbance corresponding to a fault with a defined time window (see ¶ ¶ [0093]-[0101], [0096]: “ As may be seen in FIG. 6, the residual fault current passed through the device (I RES ) 124 is estimated by extrapolating the pre-fault data 126 over a period of time corresponding to the measured fault current 122 to provide a reference current (I REF ) 128 that is subtracted from the measured current 122 to estimate the residual fault current (I RES ) 124. The estimated fault inception time 130 and fault clearance time 132 may be determined from the estimated residual fault current (I RES ) 124, such as by determining the times, or sample number, at which the residual fault current violates , or ceases to violate, certain thresholds ” ). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate Saarinen’s teaching into the combination of Nishikawa and Harale’s teaching because the fault-classification over those segments would be applied to the clustered outlier data of Harale to determine whether the anomalous window that corresponds to a fault event since Saarinen teaches identifying a fault and its time interval, from isolated transient windows, thereby improving anomaly detection system . As per claims 1 4 and 2 9 , the combination of Nishikawa, Harale teaches the system as stated above except that the electricity power grid system includes at least one of: solar farm, wind turbine, electrical load, transmission & distribution system, or energy storage plant, or other electrical facility. Saarinen teaches that signal analysis processes for detecting faults or abnormal operating conditions are implemented in the context of an electrical power system specifically within electrical power transmission and distribution systems (see ¶¶ [0003] and [0052] -[ 005 8 ] , i.e., Intelligent Electronic Devices (IEDs) measure grid signals and provide real-time, detailed, transient-level event data for grid operations ) . It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate Saarinen’s teaching into the combination of Nishikawa and Harale’s teaching because anomaly detection and analysis would be applied to electrical power grid environment, thereby improving anomaly detection system. Claim 26 are rejected under 35 U.S.C. 103 as being unpatentable over Nishikawa in view of Harale and further in view of Piyasinghe and Guo et al. (Pub. No. US 2013/0154878) (hereinafter Guo). As per claim 26 , the combination of Nishikawa, Harale and Piyasinghe teaches the system as stated above except for a micro- synchrophasor or phasor measurement unit that is operable in the frequency domain, wherein high - resolution electrical phasor measurement data is measurable by the micro- synchrophasor measurement unit, and wherein the micro- synchrophasor measurement unit and the power quality monitor are integrated into grid data unit and operate as an operative pair of signal analy z ers. Guo teaches a phasor measurement unite (PMU) operable to compute phasor, frequency, and rate-of-change of frequency parameters in synchrophasor measurement (see Abstract, ¶¶ [0057] and [0059]) and further teaches a combined system in which power quality analysis and synchrophasor measurement share common resampling and signal-processing hardware (see ¶ [0057]), thereby constituting an integ ra ted arrange ment of a PMU and power quality analyzer within a unified device . It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to incorporate the PMU-based frequency-domain measurement capabilities of Guo into the power grid anomaly detection systems of Nishikawa and Harale because it would improve anomaly detection accuracy, provide higher-resolution phasor information for event classification, and consolidate PQM and PMU analysis into a single grid data unit for reduced cost, thereby, improving synchronization and simplifying deployment. Examiner’s Notes Claim 12 d istinguishes over the prior art of record because none of the prior art of record teaches or fairly suggests a method for processing high resolution electrical measurement data , the method including the step of: validating the clustered outlier data by mapping the outlier data with the second set of data points from the power quality monitor , in combination with the rest of the claim limitations as claimed and defined by the applicant. Claim 27 d istinguishes over the prior art of record because none of the prior art of record teaches or fairly suggests a s ystem for processing high resolution electrical measurement data, comprising a processing unit operable to: wherein the processing unit is operable to validate the clustered outlier data by mapping the outlier data with the second set of data points from the power quality monitor , in combination with the rest of the claim limitations as claimed and defined by the applicant. Prior art The prior art made record and not relied upon is considered pertinent to applicant’s disclosure: de Callafon et al. [‘709] discloses Systems, methods, and products are described herein for identifying deviations within a power system. Using time-synchronized measurement devices, a set of voltages and currents associated with a plurality of electrical components within the power system are continuously measured. For each electrical component of the plurality of electrical components, a representative set of parameters are recursively determined based on the measured set of voltages and currents. For each electrical component, an electrical characteristic value is determined based on the representative set of parameters. For each electrical component, a deviation of the electrical component is identified based on comparison of the determined electrical characteristic value with a reference value of the electrical characteristic of the electrical component or based on identifying the deviation by means of a filtered rate of change. An alert of the deviation is provided for further characterization of an abnormality in the power system. Davies et al. [‘103] discloses a device configured to monitor a power grid determines whether a configuration of a field-programmable gate array (FPGA) of the device requires updating, the configuration comprising one or more digital signal processor (DSP) applications (apps ), and updates the configuration when required. The device measures grid metrics of a power grid (e.g., voltage and current), and also determines GPS-based time for synchronizing zero crossing events of the grid metrics to the GPS-based time. According to the one or more DSP apps, the grid metrics can be processed by the device, and then the processed grid metrics and synchronized zero crossing events can be shared. In one specific embodiment, processing the grid metrics according to the one or more DSP apps comprises estimating an impedance of one or more individual circuit segments based on measured grid metric response to a transient event (e.g., active or passive events). Contact information Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED CHARIOUI whose telephone number is (571)272-2213 . The examiner can normally be reached Monday through Friday, from 9 am to 6 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Mohamed Charioui /MOHAMED CHARIOUI/ Primary Examiner, Art Unit 2857