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 .
Claims 1-20 are pending in the application.
Examiner’s Note: The examiner has cited particular passages including column and line numbers, paragraphs as designated numerically and/or figures as designated numerically in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claims, other passages, paragraphs and figures of any and all cited prior art references may apply as well. It is respectfully requested from the applicant, in preparing an eventual response, to fully consider the context of the passages, paragraphs and figures as taught by the prior art and/or cited by the examiner while including in such consideration the cited prior art references in their entirety as potentially teaching all or part of the claimed invention. MPEP 2141.02 VI: “PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS."
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/05/2024, 05/21/2024, 08/11/2025 was filed after the mailing date of the first office action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
A system, comprising:
a data processing system comprising one or more processors, coupled with memory, to:
receive a time-series data set of voltage measured by a plurality of meters at a plurality of loads in an electricity distribution grid;
construct, based on the time-series data set, a matrix with values that indicate similarities between pairs of meters of the plurality of meters;
generate, via a plurality of clustering techniques applied to the matrix, a silhouette score for each of the plurality of meters in the matrix to group the plurality of meters into a plurality of transformer groups; and
provide, for output via a graphical user interface, a digital map with an indication of a subset of meters of the plurality of meters associated with a transformer of the plurality of transformer groups generated based on the plurality of clustering techniques applied to the matrix.
Step 1: The claim recites a system, which is an electrical device such as a general-purpose computer. Thus, the claim is to a manufacture or a machine, which are statutory categories of invention.
Step 2A Prong one: Limitation (a) and (b) in the claim recites that a data processing system to receive a time-series data set of voltage measured by a plurality of meters and construct, based on the time-series data set, a matrix with values that indicate similarities between pairs of meters of the plurality of meters. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other that reciting “a data processing system,” nothing in the claim element precludes the step from practically being performed in the mind with the help from a pen and paper. For example, but for “a data processing system”, the claim encompasses a user simply construct a table of the measured data from a plurality of meters based on the observe relationships between pairs of meters. Thus, the limitations recite a “Mental Process” group of abstract idea.
Limitation (c) in the claim recites that a data processing system to generate, via a plurality of clustering techniques applied to the matrix, a silhouette score for each of the plurality of meters in the matrix to group the plurality of meters into a plurality of transformer groups. This limitation is directed to a calculation using mathematical and statistical techniques to compute a silhouette score for each of the plurality of meters in the matrix and to group the plurality meters. Thus, limitation (c) recites a concept that falls into the “Mathematical concept” group of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Besides the abstract ideas, the claim recites a data processing system comprising one or more processors to (d) provide, for output via a graphical user interface, a digital map with an indication of a subset of meters of the plurality of meters associated with a transformer of the plurality of transformer groups generated based on the plurality of clustering techniques applied to the matrix. The one or more processor provides for output via a graphic user merely represent extra-solution activity because it is a mere nominal and tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The claim further recites to receive a time-series data set of voltage measured by a plurality of meters. The plurality of meters is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity (see MPEP 2106.05(g)).
The a data processing system comprising one or more processors, coupled with memory, is also additional elements which are configured to carry out limitation (a) to (d), i.e., it is merely a tool that used to obtain data measurement, perform analysis and the mathematical calculation. The data processing system comprising one or more processors, coupled with memory are recited to generically that it represents no more than mere instruction to apply the judicial exceptions on a computer (see MPEP 2106.05(f)). As such, they are nothing more than an attempt to generally link the use of the judicial exceptions to the technological environment of a computer system (see MPEP 2106.05(h)). The claim also recites voltage data from an electricity distribution grid and refers to transformers and meters, these elements are used solely as data sources and labels for the analytical results. The claim does not recites controlling or modifying operation of grid equipment, improving meter hardware, transformers, resolve a technical problem in computer technology or power distribution. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B: As discussed with respect to Step 2A Prong Two, the elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. The recitation of a data processing system comprising one or more processors, coupled with memory, provide, for output via a graphical user interface, a digital map and a time-series data set of voltage measured by a plurality of meters was considered to be extra-solution activity in Step 2A. The specification does not provide any indication that the processor, memory, and meters are anything other than a generic, off-the-shelf computer component. These components are generic computer elements performing well-understood, routing, and conventional function, such as receiving data, measure data, performing calculation, and display results (see MPEP 2105.05(d)) under Berkheimer memo.
For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
Regarding claims 2-10, they dependent on claim 1 and recite the same abstract idea and additional elements set forth above.
Claim 2 recites the time-series data set comprises a second matrix having a first dimension corresponding to timestamps and a second dimension corresponding to root mean square voltage values recorded by the plurality of meters at a fixed sampling interval. The additional element merely specifies how data matrix is organized before analysis and a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind.
Claim 3 recites generate the matrix with values that indicate the similarities using a correlation metric. The additional element is directed to a mathematical concept.
Claim 4 recites the matrix comprises a covariance matrix which is a canonical mathematical construction.
Claim 5 recites generate the matrix with a Monte Carlo of combinations of at least two of: a plurality of window sizes, filters, similarity metrics, or aggregation techniques to combine correlations between the plurality of meters. The additional element is directed to a mathematical techniques to optimize data which covers performance of the limitation in the mind.
Claim 6 recites generate the silhouette score for each meter via the plurality of clustering techniques with a Monte Carlo of a plurality of initial configurations of meters of the plurality of meters. The additional element is merely direct to a mathematical process to explores different starting point.
Claim 7 recites execute a Monte Carlo of combinations of parameters used to generate the matrix and initial configurations used to generate the silhouette score; and determine a confidence score for a meter of the plurality of meters placed in a transformer group of the plurality of transformer groups based on a count of the combinations of the Monte Carlo of combinations that place the meter into the transformer group. The additional element is also directed to a mathematical process that determine a confidence score based on count of Monte Carlo combinations placing a meter into a transformer group.
Claim 8 recites apply a distance-based clustering techniques of the plurality of clustering techniques to partition the plurality of meters to generate a plurality of partitions of the plurality of meters; and generate, via the plurality of clustering techniques applied to each of the plurality of partitions, the silhouette score for each of the plurality of meters. The additional element is directed to a mathematical process that apply distance-based clustering to generate partition and to generate silhouette score per partition.
Claim 9 recites detect that a meter is erroneously grouped into a first transformer group of the plurality of transformer groups based on a comparison of a first silhouette score of the meter and a first median silhouette score of the first transformer group; remove the meter from the first transformer group and insert the meter into a second transformer group of the plurality of transformer groups; and determine a second silhouette score of the meter inserted into the second transformer group is greater than the first silhouette score. The additional elements are merely detecting erroneous grouping based on silhouette score comparison, move meter to another group if score improve and a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind.
Claim 10 recites wherein the plurality of clustering techniques comprise at least one of a distance-based partition, a max rand index, a maximum entropy, or a recursive binary optimization applied to the matrix, and the data processing system is further configured to: adjust a first one or more parameters used to create the matrix to determine an updated matrix; adjust a second one or more parameters of at least one of the plurality of clustering techniques to determine, based on the updated matrix, an updated silhouette score for one or more of the plurality of meters in the matrix to generate an updated subset of meters of the plurality of meters that are associated with the transformer. The additional elements are merely a process of adjusting parameters to update matrix and silhouette scores and directed to mathematical statistic.
In summary, the additional elements recited in claims 2-10 are either directed to calculation using mathematical and statistical techniques or process that can be practically performed in human’s mind. They do not include additional elements that would integrate the judicial exception into a practical application or would amount to significantly more than the abstract idea. Accordingly, these claims fail under step 2A prong two and step 2B. Thus, the claims are not patent eligible.
Regarding claim 11, the claim recites receive an indication of an event associated with a transformer of the plurality of transformer groups; identify, via a lookup, a second plurality of meters grouped in a transformer group of the plurality of transformer groups that correspond to the transformer associated with the event; and perform, responsive to the event, an action on the second plurality of meters, the action comprising at least one of disabling the plurality of meters, restarting the plurality of meters, or updating an application executed by the plurality of meters. Claim 11, by extending the functionality of claim 1, also falls into the “Human activity” or “Mental process” group of abstract ideas. Receiving an event, identifying grouped meters via a lookup, and performing responsive actions are steps that can be conceptually performed by a human, even if a computer executes them. The claim does not introduce a specific technological improvement beyond the mere automation of existing practices. Thus, the claims are not patent eligible.
Regarding claims 12-18, they are directed to the method to implement the system as set forth in claims 1-11 and are substantially similar to claims 1-11. Thus, they do not correct the issues set forth above. The claims are not patent eligible.
Regarding claims 19-20, they are directed to non-transitory computer readable medium storing processor executable instructions to implement the system as set forth in claims 1 and 7 and are substantially similar to claims 1 and 7. Thus, they do not correct the issues set forth above. The claims are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-10, 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuloor et al. US Pub. No. 2019/0041445 (“Kuloor”) in view of Sheoran et al. US Pub. No. 2022/0129316 (“Sheoran”).
Regarding claim 1, Kuloor teaches a system, comprising:
a data processing system comprising one or more processors, coupled with memory, to:
receive a time-series data set of voltage measured by a plurality of meters [meter 140-158] at a plurality of loads in an electricity distribution grid [SEE fig. 1];
[0003] Aspects and examples are provided for maintaining connectivity information for meters and transformers located in a power distribution network. One exemplary method includes selecting a meter, obtaining voltage data for the selected meter and voltage data for other meters, where the voltage data may include measured voltages for a number of interval periods within a time range.
construct, based on the time-series data set, a matrix with values that indicate similarities between pairs of meters of the plurality of meters;
[0081] The method calculates correlation coefficients between the meters by comparing the profile of one meter with the profiles of the other meters associated with the group of transformers. Once the correlation coefficients are calculated, the method builds a block sparse matrix of correlation coefficients with rows and columns corresponding to the meters associated with the group of transformers. The process of building the block sparse matrix is iterative. After the matrix is populated with the correlation coefficients for the meters associated with the group of transformers, a next transformer that has not already been evaluated is selected and the process is repeated. The selection of the next transformer may be random or may be based on its location relative to the previously selected transformer.
generate, via a clustering techniques applied to the matrix, a confidence factors
[0045] When a transformer experiences an outage, but a meter that is connected to the transformer according to the GIS connectivity information still reports receiving power, the meter is flagged as being associated with the incorrect transformer. An exemplary method for identifying incorrect connectivity information based on an outage analysis is shown in FIG. 6. In step 610, the method analyzes voltage data from a number of meters to determine outage patterns. In step 620, the method clusters together meters with identical outage patterns. In step 630, the method selects a transformer associated with a meter in one of the clusters and compares the meters associated with the selected transformer based on the GIS connectivity information. In step 640, the method identifies incorrect GIS connectivity information when the voltage information for meters associated with the selected transformer is inconsistent. For example, if voltage data for one meter indicates that the meter remained powered when the voltage data for the remaining meters indicates that the meters experienced an outage, then the meter is flagged as having incorrect connectivity information.
[0048] Voltage Correlation Analysis: The system may also use voltage data with statistical methods to evaluate GIS connectivity information. Voltage data among sibling meters (i.e., meters connected to the same transformer) are generally highly correlated so voltage data may be used to flag any meters that are not correctly associated with a transformer.
[0082] Once the matrix is built, confidence factors that provide a probability of a meter being connected to each of the transformers are calculated. In this method, two factors are used to calculate a confidence factors for a given meter. The first factor relates to identifying the meter that has the highest correlation coefficient (i.e., closest to 1) with respect to the given meter and considering the transformer to which the meter with the highest correlation coefficient is connected. This factor is referred to herein as ρ.sub.ik.sup.m. The value of ρ.sub.ik.sup.m for the meter with the highest correlation coefficient is the value of the correlation coefficient. The value of ρ.sub.ij.sup.m for the other meters (i.e., the ones that do not have the highest correlation coefficient with the given meter) is zero.
[0086] For a meter i the transformer with the highest value of P.sub.ik is determined to be the most likely transformer to which the meter is connected. A head-end system may use this information to correct or confirm its GIS connectivity information. In some systems when there is a high confidence factor that a meter is connected to a transformer, the suggested correction may be automatically applied, but when there is a lower confidence factor, the connectivity recommendation may be subject to additional analysis or verification (e.g., field verification) prior to correction. Machine learning techniques may be used to adjust the weighting factors based on the additional analysis or verification to further improve the method.
provide, for output via a graphical user interface, a digital map with an indication of a subset of meters of the plurality of meters associated with a transformer of the plurality of transformer groups generated based on the plurality of clustering techniques applied to the matrix [SEE par. 0089-0090].
[0089] The display of flagged meters can also be done by coding meter to transformer relationships using color, shape, animation, or other display characteristics. An exemplary display output is provided in FIG. 11 and FIG. 12. In this these figures, transformers are shown as color coded diamonds surrounded by a white circle. Meters are shown as triangles, wherein the triangles are colored to match the associated transformer based on the GIS connectivity information. Suspect or flagged meters may be displayed with a different colored border. This allows the display of suspect meters while still being able to visually see the original meter to transformer connectivity information.
Kuloor does not teach expressly teach generate, via a plurality of clustering techniques applied to the matrix, a silhouette score for each of the plurality of meters. However, such feature is old and well known in the art of cluster evaluation. The Silhouette Score is a metric used to evaluate the quality of clustering results. It measures how similar each data point is to its own cluster compared to other cluster, helping assess how well the data has been grouped. This score is widely used to evaluate clustering algorism like K-means. For example, Sheoran teaches a system configured to group similar workloads into a small number of equivalence classes. Specifically, Sheoran teaches generate, via a plurality of clustering techniques applied to the matrix, a silhouette score for each of the plurality of workload to group the plurality of workload into a plurality of groups.
[0060] Generally, different distance metric generation systems 302 use different features or characteristics of workloads to determine a representation of the workload (e.g., a vector representation of the workload) based on the resource usage of the workload. For each distance metric generation system 302, the clustering module 304 determines, based on the workload representation generated by the distance metric generation systems 302, a number of clusters into which a set of workload resource usage histories 320 are grouped. This number of clusters is also the number of equivalence classes into which the equivalence class prediction module 310 corresponding to the distance metric generation systems 302 is trained to classify workload requests (e.g., each cluster corresponds to one of the equivalence classes).
[0062] Each distance metric generation system 302 includes a workload representation generation module 322 that generates workload representations based on the workload resource usage histories 320, the workload representations 324 being measurements or characterizations of the resource usage of the workloads. Different workload representation generation modules 322 generate workload representations 324 in different manners. Distance metric generation systems 302 each include a distance determination module 326 that determines the distance between two workloads in any of a variety of different manners, also referred to as distance metric. The distance metrics allow the workloads to be compared to one another, allowing the distance between workloads to be determined and allowing the workloads to be grouped together based on their similarities.
[0067] Each distance metric generation system 302 provides, to the clustering module 304, the distances 328 determined by the distance metric generation system 302. The clustering module 304 implements functionality to cluster, for each distance metric generation systems 302, the workload resource usage histories 320 into multiple different clusters using any of a variety of different clustering techniques, such as k-means clustering techniques (e.g., using k-means++ initialization), k-medoid clustering techniques (e.g., using random medoid initialization), and so forth. The clustering module 304 uses the distances 328 between the workloads as determined by the distance metric generation systems 302 to perform the clustering. Additional information regarding the k-means clustering techniques can be found in “Some methods for classification and analysis of multivariate observations,” by J. B. MacQueen, Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, pages 281-297, University of California Press (1967), which is hereby incorporated by reference herein in its entirety. Additional information regarding the k-medoid clustering techniques can be found in “Clustering by means of medoids,” by L. Kaufmann and P. Rousseeuw, Data Analysis based on the L1-Norm and Related Methods, pages 405-416 (1987), which is hereby incorporated by reference herein in its entirety.
[0069] For each distance metric generation system 302, the clustering module 304 performs clustering for each of multiple different numbers of clusters (also referred to herein as k). As an example, the clustering module 304 performs clustering for each integer value of k from 3 to 18. For each distance metric generation system 302, the distances between the clusters are provided to the clustering module 304 by the distance determination module 326. The clustering module 304 evaluates the clusters generated for each of these different values of k and determines an appropriate value of k for the particular workload resource usage histories 320 (e.g., a value of k that best separates the workload resource usage histories 320 into different clusters). Accordingly, different distance metric generation systems 302 oftentimes generate different values of k.
[0070] In one or more implementations, the clustering module 304 evaluates the clusters by generating, for each value of k, a silhouette score for the k clusters. The silhouette score measures how similar elements of a cluster are to their own cluster compared to other clusters, e.g., taking into account both the intra-cluster (point-to-mean) and the inter-cluster (point-to-neighboring-cluster) distances. The silhouette score ranges, for example, from −1 to +1 with higher silhouette score values indicating an element is more similar to its own cluster and lower silhouette score values indicating an element is less similar to its own cluster. The silhouette score for a cluster value of k is generated by combining the silhouette scores for the elements in the clusters generated for the cluster value of k, such as by averaging the silhouette scores for the elements in the clusters generated for the cluster value of k. The clustering module 304 selects the value of k having the highest silhouette score. Additional information regarding silhouette scores can be found in “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” by P. J. Rousseeuw, Journal of computational and applied mathematics, 20:53-65 (1987), which is hereby incorporated by reference herein in its entirety.
[0071] The clustering module 304 outputs an indication of the selected value of k as part of cluster data 330. The cluster data 330 indicates to the training module 308 to train the corresponding equivalence class prediction module 310 to classify workload requests 106 into one of multiple different equivalence classes (the number of different equivalence classes being equal to the selected value of k for the distance metric generation system 302).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the system of Kuloor with the well-known Silhouette method of Sheoran to generate, via a plurality of clustering techniques applied to the matrix, a silhouette score for each of the plurality of meters in the matrix to group the plurality of meters into a plurality of transformer groups. Using the Silhouette score for cluster evaluation offers several key advantages, primarily centered on its ability to provide clear and intuitive measure of clustering quality. It helps in selecting an optimal number of clusters by balancing compactness within clusters and separate between them. It provides a comprehensive evaluation of cluster quality by considering both cohesion and separation. Accordingly, Silhouette score provided the system the ability to identify poorly assigned meters, detect boundary or ambiguous meters, and diagnose problematic clusters rather than just the overall result.
Regarding claim 2, Kuloor teaches the time-series data set comprises a second matrix having a first dimension corresponding to timestamps and a second dimension corresponding to root mean square voltage values recorded by the plurality of meters at a fixed sampling interval [par. 0050-0052].
Regarding claim 3, Kuloor teaches generate the matrix with values that indicate the similarities using a correlation metric [See fig. 9].
Regarding claim 4, Kuloor teaches the matrix comprises a covariance matrix [See fig. 9A; par. 0081].
Regarding claim 5, Kuloor teaches a plurality of window sizes, filters, similarity metrics, or aggregation techniques to combine correlations between the plurality of meters [par. 0079-0087, 0089-0090].
Regarding claim 6, Kuloor in view of Sheoran teaches generate the silhouette score for each meter via the plurality of clustering techniques with a Monte Carlo of a plurality of initial configurations of meters of the plurality of meters [See par. 0079-0087, 0089-0090 of Kuloor and par. 0069-0070 of Sheoran].
Regarding claim 7, Kuloor in view of Sheoran teaches execute a Monte Carlo of combinations of parameters used to generate the matrix and initial configurations used to generate the silhouette score [See par. 0079-0087 of Kuloor and par. 0069-0070 of Sheoran]; and determine a confidence score for a meter of the plurality of meters placed in a transformer group of the plurality of transformer groups based on a count of the combinations of the Monte Carlo of combinations that place the meter into the transformer group [par. 0005, 0070-0073 of Kuloor].
Regarding claim 8, Kuloor in view of Sheoran teaches apply a distance-based clustering techniques of the plurality of clustering techniques to partition the plurality of meters to generate a plurality of partitions of the plurality of meters [par. 0061-0062 of Kuloor]; and generate, via the plurality of clustering techniques applied to each of the plurality of partitions, the silhouette score for each of the plurality of meters [See par. 0065-0070 of Sheoran].
Regarding claim 9, Kuloor in view of Sheoran teaches detect that a meter is erroneously grouped into a first transformer group of the plurality of transformer groups based on a comparison of a first silhouette score of the meter and a first median silhouette score of the first transformer group; remove the meter from the first transformer group and insert the meter into a second transformer group of the plurality of transformer groups; and determine a second silhouette score of the meter inserted into the second transformer group is greater than the first silhouette score [See par. 0060-0066, 0079-0087 of Kuloor].
Regarding claim 10, Kuloor in view of Sheoran teaches the plurality of clustering techniques comprise at least one of a distance-based partition, a max rand index, a maximum entropy, or a recursive binary optimization applied to the matrix [par. 0038, 0062, 0075-0075 of Kuloor], and the data processing system is further configured to: adjust a first one or more parameters used to create the matrix to determine an updated matrix [par. 0038, 0062, 0075-0075 of Kuloor]; adjust a second one or more parameters of at least one of the plurality of clustering techniques to determine, based on the updated matrix, an updated silhouette score for one or more of the plurality of meters in the matrix to generate an updated subset of meters of the plurality of meters that are associated with the transformer [par. 0038, 0062, 0075-0075 of Kuloor; Sheoran teaches silhouette score].
Regarding claims 12-18, they are directed to the method of steps to implement the system as set forth in claims 1-10. Therefore, they are rejected on the same basis as set forth hereinabove.
Regarding claims 19-20, they are directed to a non-transitory computer readable medium storing processor executable instructions to implement the system as set forth in claims 1 and 7. Therefore, they are rejected on the same basis as set forth hereinabove.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuloor/Sheoran as applied to claim 1 above, and further in view of Charpentier et al. US 20200134761 (“Charpentier”).
Regarding claim 1, Kuloor teaches receive an indication of an event associated with a transformer of the plurality of transformer groups; identify, via a lookup, a second plurality of meters grouped in a transformer group of the plurality of transformer groups that correspond to the transformer associated with the event [0045 - When a transformer experiences an outage…the method analyzes voltage data from a number of meters to determine outage patterns. In step 620, the method clusters together meters with identical outage patterns. In step 630, the method selects a transformer associated with a meter in one of the clusters and compares the meters associated with the selected transformer based on the GIS connectivity information]. Kuloor/Sheoran does not teach perform, responsive to the event, an action on the second plurality of meters, the action comprising at least one of disabling the plurality of meters, restarting the plurality of meters, or updating an application executed by the plurality of meters.
Charpentier teaches an invention relates to ensuring the safety of people needing to service a low-voltage network of an electric power distribution system. Specifically, Charpentier teaches perform, responsive to the event [power outage], an action on the second plurality of meters, the action comprising at least one of disabling the plurality of meters, restarting the plurality of meters, or updating an application executed by the plurality of meters [0084 - in the case of scheduled servicing in the network, or in portions having power in the case of an outage, it is possible to shut off the meters remotely by lowering the cut-off power of the meters. Thus, the meters of dwellings having a probability above a certain threshold p of being an undeclared self-consumer can be cut off in advance. During the shutoff it is still possible for there to be a non-zero measured voltage, and it is possible to decide to restore power to the substation in order to cut off additional meters].
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify the system of Kuloor/Sheoran with perform, responsive to the event [power outage], an action on the second plurality of meters, the action comprising at least one of disabling the plurality of meters of Charpentier. The motivation for doing so would has been to minimize danger and ensure safety to the technicians, as suggested by Charpentier in par. 0002 and 0005.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0278527 to Knezovic et al teach a method for determining phase connections of grid components in a power grid, the method comprising assessing a relative similarity of time series of measured voltage data of the grid components by clustering the time series of measured voltage data of the grid components; grouping the grid components into a plurality of clusters based on the assessing a relative similarity; and assessing a phase connection of the grid components in each cluster of the plurality of clusters. The present disclosure also relates to a respective device and computer program.
US 20150052088 to Arya et al. teach a systems, and articles of manufacture for voltage-based clustering to infer connectivity information in smart grids. A method includes clustering multiple voltage time series measurements into one or more groups, wherein said multiple voltage time series measurements are derived from one or more sensors; determining a connectivity model based on the one or more groups; comparing the determined connectivity model to an existing connectivity model to detect one or more inconsistencies between the determined connectivity model and the existing connectivity model; and updating the existing connectivity model based on said one or more detected inconsistencies.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VINCENT HUY TRAN whose telephone number is (571)272-7210. The examiner can normally be reached M-F 7:00-4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini S Shah can be reached at 571-272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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VINCENT H TRAN
Primary Examiner
Art Unit 2115
/VINCENT H TRAN/Primary Examiner, Art Unit 2115