DETAILED ACTION
Claims 1-5, 8, 11, 13-14, 16-20, and 24-29 are pending.
Claims 16-20 and 26-29 are withdrawn.
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.
Election/Restrictions
Applicant's election with traverse of Group I (claims 1-5, 8, 11, 13-14, and 24-25) in the reply filed on 8/4/2026 is acknowledged. The traversal is on the ground(s) that Groups I and II have a common link. This is not found persuasive because the groups have different special technical features. The first group is characterized by the “special technical feature” that a noise threshold for a sensor is determined based on power measurements to a load. The second group is characterized by the “special technical feature” that an event is detected by comparing the power spectral density of an electrical power signal to a reference power spectral density.
The requirement is still deemed proper and is therefore made FINAL.
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.
Claims 1-5, 11, 13-14, and 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2022/0376501 (Pong) in view of
U.S. Patent Application Publication No. 2020/0293032 (Wang).
Claim 1:
The cited prior art describes a method of controlling a sensor apparatus for an electrical circuit, the electrical circuit comprising (Pong: “The present invention relates to anomaly detection in energy systems and, more particularly, to anomaly detection using both electric- and magnetic field sensors with data analysis by density-based spatial clustering.” Paragraph 0001)
one or more electrical loads and (Pong: see the household appliances as illustrated in figure 5 and as described in paragraph 0029)
a power line providing a supply of electrical power to the one or more electrical loads, (Pong: see the power lines as illustrated in figure 5 and as described in paragraph 0029)
the sensor apparatus comprising a sensor configured to monitor the power line, (Pong: see the sensors as illustrated in figures 1a, 5 and as described in paragraph 0029)
the method comprising:
applying a clustering algorithm to a database of sensor measurements acquired from the sensor, the database of sensor measurements comprising (Pong: see the DBSCAN algorithm for each data point P in database DB as illustrated in figure 3b)
sensor measurements indicative of the power supply during one or more events associated with the operation of one or more of the electrical loads and (Pong: see the conditions with line voltage and current as illustrated in Table II)
sensor measurements indicative of noise measurements during a baseline condition of the electrical circuit, (Pong: see the conditions with line voltage and current as illustrated in Table II)
the clustering algorithm being applied to the database of sensor measurements to determine a set of measurement clusters; (Pong: see the normalized data point P in database DB and the neighbor clustering process as illustrated in figure 3b and as described in paragraph 0022)
identifying a cluster, from the set of measurement clusters, indicative of noise measurements; (Pong: see the region determination and if neighbors are less than MnPts then labeled as noise as illustrated in figure 3b; “DBSCAN involves creating n-dimensional shapes around a particular data point and determining how many data points fall within that shape. A sufficient number of data points means that the shape is a cluster. Clusters of high density (many data points) are distinguished from clusters of low density (fewer data points).” Paragraph 0022)
determining a noise threshold for the sensor based on the cluster indicative of noise measurements; and (Pong: see the region query and expand cluster to associated data points together to fit or not fit data points into the noise threshold for the neighbors as illustrated in figure 3b; “By plotting the various data points and clustering the results based on the similarity of observation, points that are clearly outlying may be identified and investigated as outliers. For example, in FIG. 3(a), if cluster 1 is formed by data points collected in normal operation, then any data points which do not fall within cluster 1 are determined to be anomalies, such as data points in cluster 2. In other words, cluster 1 represents normal operation while cluster 2 represents one kind of anomaly which may be interruption.” Paragraph 0022)
Pong does not explicitly describe controlling as described below. However, Wang teaches the controlling as described below.
controlling one or more feedback actions of the sensor apparatus in dependence on the determined noise threshold. (Wang: “Processor 1220 may also classify power system related data from field devices to generate state of substation system, and component, and an unclassified state, for example. Transmitter 1215 may transmit one or more messages, such as one or more alerts, based on calculations by processor 1205. For example, if processor 1205 identifies an anomaly such as an asset or sensor which has failed or is about to fail, an alert, such as a message, may be transmitted to computing device tasked with managing operation of that asset or sensor.” Paragraph 0119) (Pong: see the clustering results as illustrated in Table III)
One of ordinary skill in the art would have recognized that applying the known technique of Pong, namely, anomaly detection in energy systems, with the known techniques of Wang, namely, asset monitoring system for power systems, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Pong to analyze data to detect anomalies in power systems with the teachings of Wang to monitor data to detect states of power systems would have been recognized by those of ordinary skill in the art as resulting in an improved power monitoring system. In other words, the combination of references provides for a power monitoring system analyzing data and controlling the system based on the teachings of analyzing power data in Pong and the teachings of controlling a power system based on analyzed power data in Wang.
Claim 2:
The cited prior art describes a method according to claim 1, wherein identifying the cluster indicative of noise measurements comprises:
determining a representative value for each measurement cluster; and (Pong: see the distance between neighbors and check is neighbors should be expanded or changed to noise as illustrated in figure 3b)
identifying the cluster indicative of noise measurements based on a comparison of the representative values. (Pong: see the neighbor query regarding distance and subsequent determined if a neighbor is labeled as noise as illustrated in figure 3b)
Claim 3:
The cited prior art describes a method according to claim 2, wherein each representative value is determined as a centroid value for the respective measurement cluster or a boundary value for the respective measurement cluster. (Pong: see the distance between neighbors and check is neighbors should be expanded or changed to noise as illustrated in figure 3b)
Claim 4:
The cited prior art describes a method according to claim 2, wherein the representative value associated with the cluster indicative of noise measurements is less than, or greater than, the representative values associated with the other measurement clusters. (Pong: see the distance between neighbors and check with surrounding neighbors as illustrated in figure 3b)
Claim 5:
The cited prior art describes a method according to claim 1, wherein the clustering algorithm is configured to determine the set of measurement clusters by:
partitioning the database of sensor measurements into a plurality of sets of measurement clusters, each set having a different number of measurements clusters; and (Pong: see the normalized data point P in database DB and the neighbor clustering process as illustrated in figure 3b and as described in paragraph 0022)
determining a natural number of measurement clusters for the database of sensor measurements. (Pong: see the number of neighbor clusters joined and expanded as illustrated in figure 3b and as described in paragraph 0022; see the normalized data point P in database DB and the neighbor clustering process as illustrated in figure 3b and as described in paragraph 0022)
Claim 11:
The cited prior art describes a method according to claim 1, further comprising:
acquiring one or more further sensor measurements from the sensor; and (Pong: see the sensors as illustrated in figures 1a, 5 and as described in paragraph 0029)
updating the database of sensor measurements to include the one or more further sensor measurements. (Pong: see the DBSCAN algorithm for each data point P in database DB as illustrated in figure 3b)
Claim 13:
13. (Currently Amended) A method according to claim 1, wherein the one or more feedback actions include:
transmitting, via a communications module of the sensor apparatus, a warning signal to the one or more electrical loads and/or to an external server in dependence on the determined noise threshold; (Wang: “Processor 1220 may also classify power system related data from field devices to generate state of substation system, and component, and an unclassified state, for example. Transmitter 1215 may transmit one or more messages, such as one or more alerts, based on calculations by processor 1205. For example, if processor 1205 identifies an anomaly such as an asset or sensor which has failed or is about to fail, an alert, such as a message, may be transmitted to computing device tasked with managing operation of that asset or sensor.” Paragraph 0119)
filtering noise measurements from the database of sensor measurements by applying the noise threshold; and/or
selectively interrupting, via a circuit breaker mechanism of the sensor apparatus, the power supply to the one or more electrical loads.
Claim 14:
The cited prior art describes a method according to claim 1,
wherein the sensor apparatus includes N sensors for monitoring the power line, where N is a positive integer, and (Pong: see the sensors as illustrated in figures 1a, 5 and as described in paragraph 0029)
wherein the database of sensor measurements is a database of N-dimensional data points, each data point comprising sensor measurements acquired from the N sensors at a respective time; and (Pong: see the normalized data point P in database DB as illustrated in figure 3b and as described in paragraph 0022)
wherein a respective noise threshold is determined for each of the N sensors based on the respective sensor measurements acquired from that sensor in the cluster indicative of noise measurements. (Pong: see the region query and expand cluster to associated data points together to fit or not fit data points into the noise threshold for the neighbors as illustrated in figure 3b; “By plotting the various data points and clustering the results based on the similarity of observation, points that are clearly outlying may be identified and investigated as outliers. For example, in FIG. 3(a), if cluster 1 is formed by data points collected in normal operation, then any data points which do not fall within cluster 1 are determined to be anomalies, such as data points in cluster 2. In other words, cluster 1 represents normal operation while cluster 2 represents one kind of anomaly which may be interruption.” Paragraph 0022)
Claim 24:
The cited prior art describes a sensor apparatus for an electrical circuit, the electrical circuit comprising (Pong: “The present invention relates to anomaly detection in energy systems and, more particularly, to anomaly detection using both electric- and magnetic field sensors with data analysis by density-based spatial clustering.” Paragraph 0001)
one or more electrical loads and (Pong: see the household appliances as illustrated in figure 5 and as described in paragraph 0029)
a power line providing a supply of electrical power to the one or more electrical loads, (Pong: see the power lines as illustrated in figure 5 and as described in paragraph 0029)
the sensor apparatus comprising: (Pong: see the sensors and data processing unit as illustrated in figures 1a, 5 and as described in paragraph 0029)
a sensor configured to monitor the power line; and (Pong: see the sensors as illustrated in figures 1a, 5 and as described in paragraph 0029)
a control module configured to execute the method of claim 1; (Pong: see data processing unit as illustrated in figures 1a, 5 and as described in paragraph 0029; “Analysis may be performed in the micro-controller unit 45; alternatively, analysis may be performed in a host system 60.” Paragraph 0017)
wherein the sensor is configured to acquire the database of sensor measurements. (Pong: “The analog signals from sensors 20 and 30 are converted to digital signals in the ADC module 40. Analysis may be performed in the micro-controller unit 45; alternatively, analysis may be performed in a host system 60.” Paragraph 0017; see the DBSCAN algorithm for each data point P in database DB as illustrated in figure 3b)
Claim 25:
Pong does not explicitly describe a circuit breaker mechanism as described below. However, Wang teaches the circuit breaker mechanism as described below.
The cited prior art describes a sensor apparatus according to claim 24,
wherein the sensor apparatus further comprises a circuit breaker mechanism, and (Wang: “SCADA component 1110 may provide functions such as data acquisition, control of power plants, and alarm display. SCADA component 1110 may also allow operators at a central control center to perform or facilitate management of energy flow in the power grid system. For example, operators may use a SCADA component (e.g., using a computer such as a laptop or desktop) to facilitate performance of certain tasks such opening or closing circuit breakers, or other switching operations which might divert the flow of electricity.” Paragraph 0104; “The switching components may be circuit breakers that are used to connect (or disconnect) any power system component (e.g., unit, line, transformer, etc.) to or from the rest of the power system network. Typical ways of determining topology may be by monitoring of the circuit breaker status, which may be done using measurement devices and components associated with those devices (e.g., RTUs, SCADA, PMUs). It may be determined as to which equipment has gone out of service, and actually, which circuit breaker has been opened or closed because of that equipment going out of service.” Paragraph 0116)
wherein the one or more feedback actions of the sensor apparatus comprise controlling the circuit breaker mechanism to selectively interrupt the power supply to the one or more electrical loads. (Wang: “SCADA component 1110 may provide functions such as data acquisition, control of power plants, and alarm display. SCADA component 1110 may also allow operators at a central control center to perform or facilitate management of energy flow in the power grid system. For example, operators may use a SCADA component (e.g., using a computer such as a laptop or desktop) to facilitate performance of certain tasks such opening or closing circuit breakers, or other switching operations which might divert the flow of electricity.” Paragraph 0104)
Pong and Wang are combinable for the same rationale as set forth above with respect to claim 1.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over
U.S. Patent Application Publication No. 2022/0376501 (Pong) in view of
U.S. Patent Application Publication No. 2020/0293032 (Wang) and further in view of
U.S. Patent Application Publication No. 2022/0278527 (Knezovic).
Claim 8:
Pong and Wang do not explicitly describe a silhouette value as described below. However, Wang teaches the silhouette value as described below.
The cited prior art describes a method according to claim 1, wherein the clustering algorithm is configured to validate the determined set of measurement clusters by:
determining a silhouette value for the set of measurement clusters; and (Knezovic: “Calculate the internal validity score based on the obtained clusters.” Paragraph 0084; “In an embodiment of the disclosure, the determining/tuning of at least one parameter of the clustering, e.g. an optimal hyperparameter, is performed using an internal cluster validity index. According to an embodiment, a Calinski-Harabasz index, is used which relies on the between-cluster and within-cluster variations. However, in general, other internal validity indices can also be used, e.g. Silhouette index, Davies-Bouldin index, etc.” paragraph 0079)
comparing the determined silhouette value to a threshold value; and (Knezovic: “Choose the optimal hyperparameters based on the extreme value of the internal validity score. Depending on which index was used, either the maximum or minimum value is the optimum, e.g. the maximum value is used for the Calinski-Harabasz index.” Paragraph 0085; “In an embodiment of the disclosure, the determining/tuning of at least one parameter of the clustering, e.g. an optimal hyperparameter, is performed using an internal cluster validity index. According to an embodiment, a Calinski-Harabasz index, is used which relies on the between-cluster and within-cluster variations. However, in general, other internal validity indices can also be used, e.g. Silhouette index, Davies-Bouldin index, etc.” paragraph 0079)
redetermining the set of measurement clusters using different partitioning parameters if the silhouette value is less than the threshold value. (Knezovic: ; “In an embodiment of the disclosure, the determining/tuning of at least one parameter of the clustering, e.g. an optimal hyperparameter, is performed using an internal cluster validity index. According to an embodiment, a Calinski-Harabasz index, is used which relies on the between-cluster and within-cluster variations. However, in general, other internal validity indices can also be used, e.g. Silhouette index, Davies-Bouldin index, etc.” paragraph 0079) (Pong: see the region determination and if neighbors are less than MnPts then labeled as noise as illustrated in figure 3b; “DBSCAN involves creating n-dimensional shapes around a particular data point and determining how many data points fall within that shape. A sufficient number of data points means that the shape is a cluster. Clusters of high density (many data points) are distinguished from clusters of low density (fewer data points).” Paragraph 0022)
One of ordinary skill in the art would have recognized that applying the known technique of Pong, namely, anomaly detection in energy systems, with the known techniques of Wang, namely, asset monitoring system for power systems, and the known techniques of Knezovic, namely, power grid data analysis, would have yielded predictable results and resulted in an improved system. Accordingly, applying the teachings of Pong to analyze data to detect anomalies in power systems with the teachings of Wang to monitor data to detect states of power systems and teachings of Knezovic to analyze data for a power grid would have been recognized by those of ordinary skill in the art as resulting in an improved power monitoring system. In other words, the combination of references provides for a power monitoring system analyzing data and controlling the system based on the teachings of analyzing power data in Pong and the teachings of controlling a power system based on analyzed power data in Wang and the teachings of analyzing data for a power system in Knezovic.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2005/0182581 describes verifying the performance and health of wire systems.
U.S. Patent Application Publication No. 2005/0210027 describes data stream clustering for abnormality monitoring.
U.S. Patent Application Publication No. 2012/0041575 describes anomaly detection.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER E EVERETT whose telephone number is (571)272-2851. The examiner can normally be reached Monday-Friday 8:00 am to 5:00 pm (Pacific).
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/Christopher E. Everett/Primary Examiner, Art Unit 2117
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