DETAILED ACTIONS
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 .
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 07/07/2026.
Status of the Claims
Claims 1-3, 5-10, and 12-24 are pending.
Claims 1, 3, 5-6,17, and 20 are amended.
Claims 4, and 11 are cancelled.
Response to Arguments
Rejections Under 35 U.S.C. 103
Applicants’ Argument:
Applicant argued in remarks pages 9-10, filed on 07/07/2026 in regard to the independent claims that “
“Respectfully, Applicant submits that classifying different features of a measured
waveform such as whether the waveform is periodic, has a voltage imbalance/fluctuation, long duration variation, etc. does not teach "performing a point-by-point comparison between data points in at least one first cycle of the at least one captured energy-related waveform, and corresponding data points in at least one second cycle of the previously captured energy-related waveform " as now recited in claim 1. See Figure 4. For instance, para. [0032] of Bicke/'114 states "[a]t act 212, the controller 110 compares the evaluated symptoms [of event data extracted at act 206] (e.g., represented by a partial classification, additional event data, metadata and IED information) to a lookup table including previously-stored electrical event symptoms and a diagnosed cause of the electrical event." Para. [0032] further states "the controller 110 may compare the evaluated symptoms to a waveform library in lieu of, or in addition to, the lookup table to diagnose a cause of an electrical event." At most, Bicke/'114 teaches comparing symptoms derived from an electrical event (e.g., represented by a partial classification, additional event data, metadata and IED information) to a waveform library. However, there is no suggestion in Bicke/'114 that this comparison would include "performing a point-by-point comparison between data points in at least one first cycle" of a waveform to "corresponding data points in at least one second cycle" of a previously captured waveform. Because Capute does not cure these defects in Bicke/'114, Applicant submits the combination of references does not render claim 1 obvious”
Examiners Response:
Applicant's amendments/arguments, see remarks pages 9-10, filed on 07/07/2026 in regard to the independent claim 1 and claim 20 rejections under 35 U.S.C. §103 have been considered, and are not persuasive.
Bickel”114 teaches analyzing capture waveform data automatically using the system 100 figure 1. System 100 consists of IEDs and each of the one or more IEDs 102 is capable of measuring waveform data (e.g., voltages, currents, power, etc.) and automatically generating graphical waveform captures of the waveform data when an electrical event is detected (see [0022]). (Bickel,”114, Figure 2, step 212, [0032]), the controller 110 compares the evaluated symptoms (e.g., represented by a partial classification, additional event data, metadata and IED information) to a lookup table including previously-stored electrical event symptoms and a diagnosed cause of the electrical event and historical event data. The controller 110 evaluates a time-series data with a timestamp and date at which the waveform capture was acquired. Therefore, the captured waveform data includes data points for each time interval or point. IED information includes an IED's sampling rate, IED system time synchronization information, IED system clock accuracy, and other characteristics inherent to the IED, Therefore, controller comparing corresponding timestamp points of waveform captures and evaluating symptoms based on the point data reads on point by point comparison. See [0032], [0040]-[0043]) . The controller 110 may further evaluate the time and date at which the waveform capture was acquired, the synchronicity and initial polarity of the waveform capture, and so forth. At act 212, controller 110 compares the evaluated symptoms the controller 110 may compare the evaluated symptoms to a waveform library in lieu of, or in addition to, the lookup table to diagnose a cause of an electrical event. Controller uses software specialized to analyze waveform capture see ([0022]). “the waveform capture data is utilized by an external entity (e.g., a head-end system
[e.g., software]”). Comparing time series data at different time period is a design/algorithm choice, not an inventive step.
Therefore, applicant arguments are not persuasive. The rejections are maintained below.
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 1-3, 5-8, 12-25 are rejected under 35 U.S.C. 103 as being unpatentable over Bickel et al. (US 2017/0285114 A1, hereinafter Bickel”114) and in view of Capote et al. (US 2021/0055839 A1, hereinafter Capote) and in further view of
Marshall, Jr. et al. (US 2014/0055461 A1, hereinafter Marshal).
Regarding Claim 1, Bickel”114 teaches,
A method for automatically identifying, analyzing and reducing extraneous waveform captures (WFCs) (Bickel”114, Figure 2, [0003] “At least one aspect of the disclosure is directed to a method for analyzing waveform capture data”) comprising:
capturing at least one energy-related waveform (Bickel”114, Figure 2, Step 204, Receive waveform data) in an electrical system using at least one waveform capture device (Bickel”114, Figure 2, “the method comprising receiving, by a controller from an intelligent electronic device. [0019] FIG. 1 illustrates a power monitoring system 100 for monitoring voltage, current, frequency, power, energy and/or other related values measured by one or more IEDs”).
comparing the at least one captured energy-related waveform to a previously captured energy-related waveform by performing a point-by-point comparison between data points in at least one first cycle of the at least one captured energy-related waveform, and corresponding data points in at least one second cycle of the previously captured energy-related waveform (Bickel”114,Figure 2, step 212,[0032] “ the controller 110 may evaluate the sampling rate of the IED that provided the waveform capture to the controller 110 to ensure that the IED is capable of accurately representing the original event signal. The controller 110 may further evaluate a time and date at which the waveform capture was acquired, the synchronicity and initial polarity of the waveform capture, and so forth. At act 212, the controller 110 compares the evaluated symptoms the controller 110 may compare the evaluated symptoms to a waveform library in lieu of, or in addition to, the lookup table to diagnose a cause of an electrical event”. NOTE: The controller 110 evaluates a time and date at which the waveform capture was acquired. IED information includes include an IED's sampling rate, IED system time synchronization information, IED system clock accuracy, and other characteristics inherent to the IED, Therefore, controller comparing corresponding timestamp points of waveform captures and evaluating symptoms reads on point by point comparison) (Bickel”114, Figure 2, Step 212”) (Bickel, Figure 2, At act 214, the controller 110 diagnoses at least one potential electrical event responsible for causing the observed waveform symptoms (Bickel, Figure 4, [0041] A waveform capture data can be classified into at least one of the categories ( e.g., one of the categories from the categories section 402) during the partial classification stage. For example, after waveform capture data is received, the controller 110 evaluates an electrical event illustrated by the waveform capture data. NOTE: electrical events are categorized based on standard classified events. However, additional or alternate possible categories of electrical events determination is possible. see [0042], [0049] (fig 5, step 510) [0049]). WFC (Bickel Figure 2, 114, The selected power quality classification standard can provide information that assists in determining actions that may be taken to reduce or eliminate the possibility of the identified electrical event from occurring again. Step 216 Correct or Mitigate and store information step220”);
Bickel”114 is silent on determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional WFC.
And performing one or more actions to reduce WFC data to be stored or analyzed, in response to determining the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional.
However, Capote teaches on determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional WFC. (Capote, [0027] “Identifying events that are determined to be associated with one another allows excluding other events from further processing. In an example, processing of received indications of events is able to determine which events are associated with a particular event and create a presentation that only includes indications of events that are determined to be related to an that particular event. By including only events that are related to a particular event, unrelated events, which may be considered as extraneous information or "noise" in evaluating and analyzing events that affect the operations of an electrical distribution system, are able to be specially marked or excluded from further processing in some examples” NOTE: identifies unrelated events or extraneous events).
And performing one or more actions to reduce WFC data to be stored or analyzed, in response to determining the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional (Capote,[0028] Allowing personnel to quickly and automatically identify related events in order to focus their analysis on those related events is able to greatly reduce the time required to address underlying problems in a distribution system and improve the efficiency of the operations of an electrical system distribution system by reducing the effort and time required to properly analyze, identify, mitigate, dispatch repair or service crews, otherwise address indicated problems, or combinations of these”)
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bickel”114 event identification method in view of Capote method to identify unrelated extraneous events and further process to reduce WFC data with the benefits of providing accurate event identification for a power distribution network.(Capote, [0019]-[0020]) Moreover, the aggregating groups of data and using a processor to categorize and analyze data is well-known technique applied within the art and would yield the expected results yet with higher accuracy (KSR).
Regarding Claim 2, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 further teaches wherein the one or more actions that are performed (Bickel”114, Figure 2, step 216-step 220) in response to determining the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional WFC, include at least one of:
deleting or otherwise removing the at least one captured energy-related waveform, tagging (Bickel”114, 0047] For example, the encoded information can include an electrical event identification tag (e.g., El, E2, E3, etc.) that uniquely identifies an electrical event, a selected electrical event category (e.g., Category 1, CAT2, Category 8, etc.) or otherwise indicating the defined status of the at least one captured energy- related waveform, storing the at least one captured energy-related waveform in specific location(s) (Bickel”114,[0044] By analyzing the characteristics of a waveform produced by an electrical event with known causes, the controller 110 is operable to store the analysis information in a data storage format (e.g., in a lookup table format) that associates a cause of an electrical event with the characteristic(s) of the electrical event. [0047],
recommending or updating waveform capture setting(s) or configuration(s) in the at least one waveform capture device capturing the at least one captured energy- related waveform (Bickel, Figure 2, step 216, “correct and mitigate causes [0035] At act 216, the controller 110 automatically initiates corrective or mitigative steps to fix the diagnosed cause of the electrical event”).
lowering or reducing the priority or importance of the at least one captured energy-related waveform, compressing the at least one captured energy-related waveform, or reducing the at least one captured energy-related waveform by one or more cycles to minimize its memory requirements (Bickel, Figures 1-2, 216-220, “[0039], The selected power quality classification standard can provide information that assists in determining actions that may be taken to reduce or eliminate the possibility of the identified electrical event from occurring again”.” In some examples, the controller 110 may be operable to automatically execute the prescribed actions, while in other examples, the controller 110 may display the recommendation(s) to an operator in a textual, graphical or other descriptive format”).
Regarding Claim 3, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 further teaches comprising: taking one or more additional actions subsequent to or in parallel to performing the at least one of the actions in response to determining the at least one captured energy- related waveform meets the criteria of being deemed an extraneous WFC, a redundant WFC (Bickel, “[0026] Event data includes data descriptive of the electrical event. Event data includes data descriptive of the electrical event. For example, the event data can include instantaneous time-series waveform data recurrence and repetition information (e.g., whether or not the event is repetitive, how often the event recurs, etc.), date and time data, onset rate characteristics”) or a provisional WFC, the one or more additional actions including at least one of:
extracting associated alarm data, (Bickel, Figure 2, (“[0036] At act 218, the controller 110 notifies a user(s) of the occurrence of the electrical event and the steps taken to correct the cause of the electrical event. The notification can be sent graphically, textually, or by any other means to convey information to the user”) using data or information associated with the at least one captured energy-related waveform for other purposes (Bickel, “[0026] Event data includes data descriptive of the electrical event). such as a sample of the electrical system's post-event response ([0026] post-event data), changing other settings in association with alarm settings for more efficient alarms and alarm prioritization and using data or information associated with the at least one captured energy-related waveform to enhance segment- related analytics in cloud-based applications or to simplify what is presented to users in reports or on displays. (Bickel, [0037] “The notification can further include recommended steps to be taken to replace or repair the disconnected component accordingly. Furthermore, the notification can include recommended steps suggesting, for example, that a user install additional components to help mitigate an electrical event. At act 220, the analysis information is stored for use in subsequent diagnoses. For example, analysis information stored from previous diagnoses is at least partially used by the controller 110 to determine an electrical event diagnosis at act 212 ”. Figure 8, [0058] “The disclosure is not limited to a particular memory system 810 or a storage system 812.”).
Regarding Claim 5, combination of Bickel”114 and Capote teaches the method of claim 4,
Bickel”114 further teaches wherein at least one of: the data points in the at least one first cycle of the at least one captured energy-related waveform, or the corresponding data points in the at least one second cycle of the captured energy-related waveform (Bickel, Figure 4, Column 404, a typical duration section 404), are at least one of empirically determined and derived by interpolating to ensure the data points are correctly positioned based on their occurrence within the at least one captured energy-related waveform. (Bickel, Figure 4,[0043], “Additional examples of determinations to be executed can include (…) whether the duration of the electrical event is shorter or longer than a selected threshold, whether, if the electrical event is aperiodic, the aperiodic electrical event is a short-duration event,(…) the long-duration electrical event is an interruption, whether, if the electrical event is a long-duration electrical event”. [0046] The received waveform capture information is encoded according to a consistent file format by one or more entities including, for example, software-based entities, hardware-based entities, and so forth. Once a waveform capture has been partially classified as discussed above, information describing the categorization is encoded by the controller 110 for subsequent reference by either the controller 110 or by alternate power quality analysis tool”).
Regarding Claim 6, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 further teaches wherein sensitivity of the algorithm used to perform the point-by-point comparison can be configured or determined based on at least one of (Bickel, [0046], The received waveform capture information is encoded according to a consistent file format by one or more entities including, for example, software-based entities, hardware-based entities, and so forth. Once Once a waveform capture has been partially classified as discussed above, information describing the categorization is encoded by the controller 110 for subsequent reference by either the controller 110 or by alternate power quality analysis tools”. [0050] FIG. 6 illustrates an analysis view 600 of a software implementation of a graphical user interface in accordance with one embodiment of the invention NOTE: algorithm set up is a design choice programed based on data type, duration, categories or classification):
the data points or cycles being compared the number of data points or cycles used in the comparison, comparison tolerance of the date points or cycle phase angles comparison tolerance of the data point or cycle magnitude, number of consecutive data points being compared, and specific phases being compared. (Figure 4, duration 404), [0040] FIG. 4 illustrates a table 400 of power quality event phenomena categories proposed by a first power quality classification standard. The table 400 includes a categories section 402, a typical duration section 404 and a types
section 406. also see [0026] for different data)
Regarding Claim 7, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 is silent on wherein the at least one captured energy- related waveform is compared to at least one other WFC to determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous a redundant WFC, or a provisional WFC.
However, Capote teaches wherein the at least one captured energy- related waveform is compared to at least one other WFC to determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous a redundant WFC, or a provisional WFC (Capote, [0027] “Identifying events that are determined to be associated with one another allows excluding other events from further processing. In an example, processing of received indications of events is able to determine which events are associated with a particular event and create a presentation that only includes indications of events that are determined to be related to an that particular event. By including only events that are related to a particular event, unrelated events, which may be considered as extraneous information or "noise" in evaluating and analyzing events that affect the operations of an electrical distribution system, are able to be specially marked or excluded from further processing in some examples” NOTE: identifies unrelated events or extraneous events).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bickel”114 event identification method in view of Capote method to identify unrelated extraneous events and further process to reduce WFC data with the benefits of providing accurate event identification for a power distribution network.(Capote, [0019]-[0020]) Moreover, the aggregating groups of data and using a processor to categorize and analyze data is well-known technique applied within the art and would yield the expected results yet with higher accuracy (KSR).
Regarding Claim 8, combination of Bickel”114 and Capote teaches the method of claim 7,
Bickel”114 further teaches wherein the at least one other WFC is or includes at least one WFC or at least one model of a WFC from a WFC library or repository. (Bickel, [0027] In at least one example, the event data, metadata and IED information are each stored in one or more information libraries. The one or more information libraries may be stored in a memory element (e.g., memory element 114) internal to the controller 110 in some embodiments, while in other embodiments, the information libraries may be externally stored from the controller 110. The controller 110 is operable to access the one or more libraries and update the stored information according to a schedule (e.g., periodically, a-periodically, automatically, manually, etc.). For example, the controller 110 can poll one or more libraries containing information and data that is relevant to electrical event diagnoses, including, for example, libraries containing up-to-date power quality classification standards, up-to-date IED information, up-to-date metadata information, historical information describing previous electrical event diagnoses, and so forth”).
Regarding Claim 12, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 further teaches comprising in response to determining the at least one captured energy-related waveform does not meet the criteria of being considered ([0040]” FIG. 4 illustrates a table 400 of power quality event phenomena categories proposed by a first power quality classification standard, and types section 406”. A typical duration section 404 404, [0042] NOTE: electrical events are categorized based on standard classified events. However, additional or alternate possible categories of electrical events determination is possible. see [0042], [0049] (fig 5, step 510) [0049]). an extraneous WFC a redundant WFC, or a provisional WFC, determining whether the at least one captured energy-related waveform meets the criteria of being considered a redundant WFC or another WFC classification. (Bickel”114, Figure 2, [0026] “Event data includes data descriptive of the electrical event. For example, the event data can include recurrence and repetition information (e.g., whether or not the event is repetitive, how often the event recurs, etc.”. one of the detected events is a “redundant event” categorized based on repetitive event data).
Regarding Claim 13, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 further teaches comprising: determining whether each WFC of the at least one captured waveform to be analyzed was captured using same or similar WFC characteristics; and in response to determining each WFC of the at least one captured waveform to be analyzed was not captured using same or similar WFC characteristics, determining whether one or more of the WFCs need to be reconstructed to make the WFCs suitable for comparisons or other meaningful analysis.(Bickel”114, Figure 2, [0044] The controller 110 is further operable to refine the discussed diagnostic processes by executing a Wave Shape Learning procedure on waveform captures that have known electrical event causes. By analyzing the characteristics of a
waveform produced by an electrical event with known causes. [0045] Figure 3-4, For example, with reference to the electrical event 302, the controller 110 may identify a wave shape or wave shapes with characteristics that are substantially identical to previously-learned wave shapes, and can ascribe the stored, known cause of the previously-learned wave shape(s) to the electrical event 302”)
Regarding Claim 14, combination of Bickel”114 and Capote teaches the method of claim 13,
Bickel”114 further teaches, wherein the WFC characteristics include at least one of: sample rate, resampling algorithms, down sampling algorithms, and other waveform capture constraints. (Bickel”114, Figures 1- 2, [0032] At act 210, the controller 110 evaluates the partial classification determined at act 208 against metadata, IED
information and the additional event data to further refine the partial classification. For example, the controller 110 may evaluate the sampling rate of the IED that provided the waveform capture to the controller 110 to ensure that the IED is capable of accurately representing the original event signal”).
Regarding Claim 15, combination of Bickel”114 and Capote teaches the method of claim 13,
Bickel”114 further teaches, wherein in response to determining one or more of the WFCs need to be reconstructed to make the WFCs suitable for comparisons or other meaningful analysis, the one or more of the WFCs are reconstructed based on or using one or more techniques. (Bickel”114, [0006] the controller is further configured to request additional information from the at least one intelligent electronic device. In one embodiment, the controller is further configured to modify a diagnosis based in part on the additional information. [0033] “the controller 110 can diagnose a cause of an electrical event (e.g., a three-phase capacitor switching event) by comparing the classification information discussed above (i.e., the partial classification [ e.g., as an oscillatory transient], the additional event data [ e.g., voltage data, current data, event synchronicity information, event polarity information, etc.], the IED information [ e.g., IED sampling rate information l and the meta data re. g., date and time information, hierarchy information, etc.]) against a lookup table or other data storage entity”)
Regarding Claim 16, combination of Bickel”114 and Capote teaches the method of claim 15,
Bickel”114 further teaches wherein the one or more techniques include at least one of: resampling, up sampling, down sampling, decimating, normalizing, and adding a range of acceptability. (Bickel”114, Figures 1- 2, [0032] the controller 110 may evaluate the sampling rate of the IED that provided the waveform capture to the controller 110 to ensure that the IED is capable of accurately representing the original event signal”).
Regarding Claim 17, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 further teaches identifying events based is/are based, at least in part, on at least one of: load type(s), load mix, process(es), application(s) and customer type(s)). (Bickel”114, [0025] Metadata includes data indicative of the context in which the waveform capture data was acquired. For example, the metadata can include load type information, load characteristic information”)
Bickel”114 is silent on identifying events as an extraneous WFC, a redundant WFC, or a provisional WFC .
However, Capote teaches an extraneous WFC, a redundant WFC, or a provisional WFC (Capote, [0027] “Identifying events that are determined to be associated with one another allows excluding other events from further processing. In an example, processing of received indications of events is able to determine which events are associated with a particular event and create a presentation that only includes indications of events that are determined to be related to an that particular event. By including only events that are related to a particular event, unrelated events, which may be considered as extraneous information or "noise" in evaluating and analyzing events that affect the operations of an electrical distribution system, are able to be specially marked or excluded from further processing in some examples” NOTE: identifies unrelated events or extraneous events).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bickel”114 event identification method in view of Capote method to identify unrelated extraneous events and further process to reduce WFC data with the benefits of providing accurate event identification for a power distribution network.(Capote, [0019]-[0020]) Moreover, the aggregating groups of data and using a processor to categorize and analyze data is well-known technique applied within the art and would yield the expected results yet with higher accuracy (KSR).
Regarding Claim 18, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 further wherein the at least one waveform capture device includes at least one Intelligent Electronic Device (IED). (Bickel”114, Figure 1, [0019] FIG. 1 “The power monitoring system 100 includes one or more IEDs 102”).
Regarding Claim 19, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 further wherein the at least one waveform capture device is associated with an Electrical Power Monitor System (EPMS) responsible for monitoring or controlling one or more aspects of the electrical system. (Bickel”114, Figure 1, [0019] FIG. 1 “The power monitoring system 100 “)
Regarding Claim 20, Bickel”114 teaches,
A system (Bickel”114, Figure 1, [0019] “The power monitoring system 100 “)
for automatically identifying, analyzing and reducing extraneous waveform captures (WFCs), comprising:
at least one processor; (Bickel”114, Figure 8, 806-processor);
at least one memory device coupled to the at least one processor, the at least one processor and the at least one memory device (Bickel”114, Figure 8, 810-memory);
configured to: capture at least one energy-related waveform (Bickel”114, Figure 2, Step 204, Receive waveform data) in an electrical system using at least one waveform capture device (Bickel”114, Figure 2, “the method comprising receiving, by a controller from an intelligent electronic device. [0019] FIG. 1 illustrates a power monitoring system 100 for monitoring voltage, current, frequency, power, energy and/or other related values measured by one or more IEDs”).;
comparing the at least one captured energy-related waveform to a previously captured energy-related waveform (Bickel,”114, Figure 2, step 212, , [0032], At act 212, the controller 110 compares the evaluated symptoms (e.g., represented by a partial classification, additional event data, metadata and IED information) to a lookup table including previously-stored electrical event symptoms and a diagnosed cause of the electrical event”) by performing a point-by-point comparison between data points in at least one first cycle of the at least one captured energy-related waveform, and corresponding data points in at least one second cycle of the previously captured energy-related waveform (Bickel”114,Figure 2, step 212,[0032] “ the controller 110 may evaluate the sampling rate of the IED that provided the waveform capture to the controller 110 to ensure that the IED is capable of accurately representing the original event signal. The controller 110 may further evaluate a time and date at which the waveform capture was acquired, the synchronicity and initial polarity of the waveform capture, and so forth. At act 212, the controller 110 compares the evaluated symptoms the controller 110 may compare the evaluated symptoms to a waveform library in lieu of, or in addition to, the lookup table to diagnose a cause of an electrical event”. NOTE: The controller 110 evaluates a time and date at which the waveform capture was acquired. IED information includes include an IED's sampling rate, IED system time synchronization information, IED system clock accuracy, and other characteristics inherent to the IED, Therefore, controller comparing corresponding timestamp points of waveform captures and evaluating symptoms reads on point by point comparison)
(Bickel”114, Figure 2, Step 212”) (Bickel, Figure 2, At act 214, the controller 110 diagnoses at least one potential electrical event responsible for causing the observed waveform symptoms (Bickel, Figure 4, [0041] A waveform capture data can be classified into at least one of the categories ( e.g., one of the categories from the categories section 402) during the partial classification stage. For example, after waveform capture data is received, the controller 110 evaluates an electrical event illustrated by the waveform capture data. NOTE: electrical events are categorized based on standard classified events. However, additional or alternate possible categories of electrical events determination is possible. see [0042], [0049] (fig 5, step 510) [0049]). WFC (Bickel Figure 2, 114,‘The selected power quality classification standard can provide information that assists in determining actions that may be taken to reduce or eliminate the possibility of the identified electrical event from occurring again. Step 216 Correct or Mitigate and store information step220”);
Bickel”114 is silent on determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional WFC, and performing one or more actions to reduce WFC data to be stored or analyzed, in response to determining the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional.
However, Capote teaches on determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional WFC. (Capote, [0027] “Identifying events that are determined to be associated with one another allows excluding other events from further processing. In an example, processing of received indications of events is able to determine which events are associated with a particular event and create a presentation that only includes indications of events that are determined to be related to an that particular event. By including only events that are related to a particular event, unrelated events, which may be considered as extraneous information or "noise" in evaluating and analyzing events that affect the operations of an electrical distribution system, are able to be specially marked or excluded from further processing in some examples” NOTE: identifies unrelated events or extraneous events).and performing one or more actions to reduce WFC data to be stored or analyzed, in response to determining the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional (Capote,[0028] Allowing personnel to quickly and automatically identify related events in order to focus their analysis on those related events is able to greatly reduce the time required to address underlying problems in a distribution system and improve the efficiency of the operations of an electrical system distribution system by reducing the effort and time required to properly analyze, identify, mitigate, dispatch repair or service crews, otherwise address indicated problems, or combinations of these”)
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bickel”114 event identification method in view of Capote method to identify unrelated extraneous events and further process to reduce WFC data with the benefits of providing accurate event identification for a power distribution network.(Capote, [0019]-[0020]) Moreover, the aggregating groups of data and using a processor to categorize and analyze data is well-known technique applied within the art and would yield the expected results yet with higher accuracy (KSR).
Regarding Claim 21, combination of Bickel”114 and Capote teaches the system of claim 20,
Bickel”114 further wherein the at least one waveform capture device is associated with an Electrical Power Monitor System (EPMS) responsible for monitoring or controlling one or more aspects of the electrical system. (Bickel”114, Figure 1, [0019] FIG. 1 “The power monitoring system 100 “)
Regarding Claim 22, combination of Bickel”114 and Capote teaches the system of claim 21,
Bickel”114 further teaches wherein the EPMS is responsible for monitoring electrical signals, data derived from electrical signals, or controlling one or more aspects of the electrical system. (Bickel”114, Figure 1, [0019] “FIG. 1 illustrates a power monitoring system 100 for monitoring voltage, current, frequency, power, energy and/or other related values measured by one or more IEDs”).
Regarding Claim 23, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 is silent on wherein the at least one captured energy- related waveform is compared to at least one other WFC to determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous a redundant WFC, or a provisional WFC.
However, Capote teaches wherein the at least one captured energy- related waveform is compared to at least one other WFC to determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous a redundant WFC, or a provisional WFC (Capote, [0027] “Identifying events that are determined to be associated with one another allows excluding other events from further processing. In an example, processing of received indications of events is able to determine which events are associated with a particular event and create a presentation that only includes indications of events that are determined to be related to an that particular event. By including only events that are related to a particular event, unrelated events, which may be considered as extraneous information or "noise" in evaluating and analyzing events that affect the operations of an electrical distribution system, are able to be specially marked or excluded from further processing in some examples” NOTE: identifies unrelated events or extraneous events).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bickel”114 event identification method in view of Capote method to identify unrelated extraneous events and further process to reduce WFC data with the benefits of providing accurate event identification for a power distribution network.(Capote, [0019]-[0020]) Moreover, the aggregating groups of data and using a processor to categorize and analyze data is well-known technique applied within the art and would yield the expected results yet with higher accuracy (KSR).
Regarding Claim 24, combination of Bickel”114 and Capote teaches the method of claim 1,
Bickel”114 is silent wherein the extraneous WFC does not contain relevant information for a real event that occurred on the electrical system
However, Capote teaches wherein the extraneous WFC does not contain relevant information for a real event that occurred on the electrical system (Capote, [0027] “Identifying events that are determined to be associated with one another allows excluding other events from further processing. In an example, processing of received indications of events is able to determine which events are associated with a particular event and create a presentation that only includes indications of events that are determined to be related to an that particular event. By including only events that are related to a particular event, unrelated events, which may be considered as extraneous information or "noise" in evaluating and analyzing events that affect the operations of an electrical distribution system, are able to be specially marked or excluded from further processing in some examples” NOTE: identifies unrelated events or extraneous events).
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bickel”114 event identification method in view of Capote method to identify unrelated extraneous events and further process to reduce WFC data with the benefits of providing accurate event identification for a power distribution network.(Capote, [0019]-[0020]) Moreover, the aggregating groups of data and using a processor to categorize and analyze data is well-known technique applied within the art and would yield the expected results yet with higher accuracy (KSR).
Regarding Claim 25, Bickel”114 teaches
A method for automatically identifying, analyzing and reducing extraneous waveform captures (WFCs), (Bickel”114, Figure 2, [0003] “At least one aspect of the disclosure is directed to a method for analyzing waveform capture data”) comprising:
capturing at least one energy-related waveform (Bickel”114, Figure 2, Step 204, Receive waveform data) in an electrical system using at least one waveform capture device (Bickel”114, Figure 2, “the method comprising receiving, by a controller from an intelligent electronic device. [0019] FIG. 1 illustrates a power monitoring system 100 for monitoring voltage, current, frequency, power, energy and/or other related values measured by one or more IEDs”).
comparing the at least one captured energy-related waveform to a previously captured energy-related waveform (Bickel,”114, Figure 2, step 212, , [0032], At act 212, the controller 110 compares the evaluated symptoms (e.g., represented by a partial classification, additional event data, metadata and IED information) to a lookup table including previously-stored electrical event symptoms and a diagnosed cause of the electrical event”) by performing either
(i) a point-by-point comparison between data points in at least one first cycle of the at least one captured energy- related waveform, and corresponding data points in at least one second cycle of the previously captured energy-related waveform, (Bickel”114,Figure 2, step 212,[0032] “ the controller 110 may evaluate the sampling rate of the IED that provided the waveform capture to the controller 110 to ensure that the IED is capable of accurately representing the original event signal. The controller 110 may further evaluate a time and date at which the waveform capture was acquired, the synchronicity and initial polarity of the waveform capture, and so forth. At act 212, the controller 110 compares the evaluated symptoms the controller 110 may compare the evaluated symptoms to a waveform library in lieu of, or in addition to, the lookup table to diagnose a cause of an electrical event”. NOTE: The controller 110 evaluates a time and date at which the waveform capture was acquired. IED information includes include an IED's sampling rate, IED system time synchronization information, IED system clock accuracy, and other characteristics inherent to the IED, Therefore, controller comparing corresponding timestamp points of waveform captures and evaluating symptoms reads on point by point comparison) (Bickel”114, Figure 2, Step 212”) (Bickel, Figure 2, At act 214, the controller 110 diagnoses at least one potential electrical event responsible for causing the observed waveform symptoms (Bickel, Figure 4, [0041] A waveform capture data can be classified into at least one of the categories ( e.g., one of the categories from the categories section 402) during the partial classification stage. For example, after waveform capture data is received, the controller 110 evaluates an electrical event illustrated by the waveform capture data. NOTE: electrical events are categorized based on standard classified events. However, additional or alternate possible categories of electrical events determination is possible. see [0042], [0049] (fig 5, step 510) [0049]). Figure 2, 114, ‘The selected power quality classification standard can provide information that assists in determining actions that may be taken to reduce or eliminate the possibility of the identified electrical event from occurring again. Step 216 Correct or Mitigate and store information step220”);
or
(ii) a comparison to at least one other WFC using at least one of: an expert-based algorithm, a rules-based algorithm, a statistics-based algorithm, a visual comparison, a curve fitting algorithm, a signal processing algorithm, or an unsupervised, semi-supervised or supervised learning technique and algorithm, to determine whether the at least one captured energy-related waveform meets a criteria of being considered an extraneous WFC, a redundant WFC, or a provisional WFC; and
Bickel”114 is silent on determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional WFC.
And performing one or more actions to reduce WFC data to be stored or analyzed, in response to determining the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional.
However, Capote teaches on determine whether the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional WFC. (Capote, [0027] “Identifying events that are determined to be associated with one another allows excluding other events from further processing. In an example, processing of received indications of events is able to determine which events are associated with a particular event and create a presentation that only includes indications of events that are determined to be related to an that particular event. By including only events that are related to a particular event, unrelated events, which may be considered as extraneous information or "noise" in evaluating and analyzing events that affect the operations of an electrical distribution system, are able to be specially marked or excluded from further processing in some examples” NOTE: identifies unrelated events or extraneous events).
And performing one or more actions to reduce WFC data to be stored or analyzed, in response to determining the at least one captured energy-related waveform meets the criteria of being considered an extraneous WFC, a redundant WFC, or a provisional (Capote,[0028] Allowing personnel to quickly and automatically identify related events in order to focus their analysis on those related events is able to greatly reduce the time required to address underlying problems in a distribution system and improve the efficiency of the operations of an electrical system distribution system by reducing the effort and time required to properly analyze, identify, mitigate, dispatch repair or service crews, otherwise address indicated problems, or combinations of these”)
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bickel”114 event identification method in view of Capote method to identify unrelated extraneous events and further process to reduce WFC data with the benefits of providing accurate event identification for a power distribution network.(Capote, [0019]-[0020]) Moreover, the aggregating groups of data and using a processor to categorize and analyze data is well-known technique applied within the art and would yield the expected results yet with higher accuracy (KSR).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Bickel”114 and in view of Capote and in further view of Menzel et al. (CA 3093991 A 1, hereinafter Menzel”991, previously cited).
Regarding Claim 9, combination of Bickel”114 and Capote teaches the method of claim 8,
Bickel Bickel”114 is silent on wherein the WFC library or repository is a cloud- based WFC library or repository.
However, Manzel”991 teaches wherein the WFC library or repository is a cloud- based WFC library or repository. (Manzel”991, [0024] “to store the power event profiles in a digital repository (e.g., library) in accordance with an exemplary embodiment of the present disclosure”. [0040] For clarity, some or
all calculations may be performed within the software application, cloud-based
application, gateway and/or other location/device/system remote from the IED extracting said energy-related signal(s)”)
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bickel”114 external storage (library/ storage) of computing system with a cloud based application as taught by Manzel”991 with the benefit of real-time centralized better and improved event monitoring for a power distribution network.(Manzel”991,[0093]) Moreover, using a cloud-based computing system for data processing and storage and maintaining network systems is well-known technique applied within the art and would yield the expected results yet with higher accuracy and faster implication(KSR).
Regarding Claim 10, combination of Bickel”114 and Capote teaches the method of claim 7,
Bickel”114 teaches, wherein the at least one captured energy-related waveform is compared to at least one other WFC using one or more data analysis techniques (Bickel”114, [0046] The received waveform capture information is encoded according to a consistent file format by one or more entities including, for example, software-based entities, hardware-based entities, and so forth. Once a waveform
capture has been partially classified as discussed above, information describing the categorization is encoded by the controller 110 for subsequent reference by either the controller 110 or by alternate power quality analysis tools”).
Bickel”114 is silent on the one or more data analysis techniques including at least one of: expert- based algorithms, rules-based algorithms, statistics-based algorithms, visual comparison(s), curve fitting algorithms, signal processing algorithms, and unsupervised, semi-supervised and supervised learning techniques and algorithms.
However, Manzel”991 teaches the one or more data analysis techniques including at least one of: expert- based algorithms, rules-based algorithms, statistics-based algorithms, visual comparison(s), curve fitting algorithms, signal processing algorithms, and unsupervised, semi-supervised and supervised learning techniques and algorithms. (Menzel”991, [0051] “The analysis on each waveform is something that may optionally be localized. For example, analysis, interpretation, and/or models that are implemented at a system level may be communicated to the IED to implement locally”. NOTE: models are expert based algorithm)
It would have been obvious to a person of ordinary skill before the effective filing date to modify Bickel”114 analysis tools of computing system with statistical analysis tool/ model/algorithm application as taught by Manzel”991 with the benefit of analysis/ diagnosis of events better and improved event monitoring for a power distribution network.(Manzel”991,[0034]-[0036], and [0051]) Moreover, using a statistical analysis/ models/ algorithm software with computing system for data processing for maintaining network systems is well-known technique applied within the art and would yield the expected results yet with higher accuracy and faster implication(KSR).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Izadi et al. (US 2014/0055461 A1) recites “Synchronous Waveform Measurements to Locate Transient Events and Incipient Faults in Power Distribution Networks”, IEEE TRANSACTIONS ON SMART GRID, VOL. 12, NO. 5, SEPTEMBER 2021.
Abstract—A new method is proposed to identify the location of transient events, including incipient faults, in power distribution systems, by using synchronized measurements from an emerging class of sensors, called waveform measurement units
(WMUs). WMUs capture the voltage and current waveforms in time domain. The proposed method consists of three steps. The first step is to characterize the oscillatory modes of the transient components of all the captured synchronized voltage and current waveforms from all WMUs, by conducting a multi-signal modal analysis. The second step is to construct a circuit model for the underlying distribution feeder at the identified dominant mode(s) of the transient event. The final step is to identify
the location of the transient event with the means of a method that involves certain forward and backward analyses of the constructed circuit model. The proposed method requires installing as few as only two WMUs. It can also utilize several synchronized
waveform measurements when several WMUs are available. The performance of the proposed method is assessed on the IEEE 33-bus test system; for different cases of transient events, such as sub-cycle incipient faults, multi-cycle incipient faults, permanent faults, as well as benign yet informative events such as capacitor bank switching. Both the accuracy and the robustness of the proposed method are verified. The analysis and results in this paper provide new insights on possible applications of synchronized WMU measurements; while they also address a highly challenging problem in power distribution networks.”
Menzel et al. (CA 3093991 A 1) recites “In a method and system, voltage and/or current signals on an electrical/power system is monitored. A power event is identified from the monitored voltage and/or current signals. In response to event identification, waveforms of the monitored voltage and/or current signals are captured. Energy-related signals are calculated and extracted from pre-event measurements, event measurements and post-event measurements using the captured waveforms. Additional information associated with the event is identified and calculated by comparing (a) the calculated and used energy-related signals from pre-event measurements, with (b) the calculated and used energy-related signals from post-event measurements”(Abstract).
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/DILARA SULTANA/Examiner, Art Unit 2858
09/09/2026
/SON T LE/Primary Examiner, Art Unit 2858
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