DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-16, 19-24 and 26-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bickel (Bickel; US 2020/0011908).
As per claim 1, Bickel discloses a method for reducing alarm nuisance behaviors in an electrical system (see e.g. para. [0167], voltage events can be considered as nuisance), comprising:
processing electrical measurement data from or derived from energy-related signals captured or derived by at least one intelligent electronic device (IED) (one or more IED devices measuring electrical parameters and at least temporarily processed for storage, see e.g. para. [122-124] and FIG. 1A, and further analyzed to detect an event; see e.g. para. [0215]) in the electrical system to identify events in the electrical system (processing data or measurements of the IEDs to detect voltage event alarms, see e.g. para. [0215] [0246] and 249]), and alarms triggered in response to the identified events and/or other events in or related to the electrical system (one or more thresholds to trigger a voltage alarm event; see e.g. para. [0246]);
taking or performing at least one action based on or in response to the at least one identified alarm nuisance behavior (null, silence or deprioritize the voltage event alarms for voltage events or any other type of event, see e.g. para. [0249], based on the one or more measure parameters by the IEDs);
Bickel does not explicitly teach, in the same embodiment, aggregating information related to at least the identified events and identified alarms and analyzing the aggregated information to identify at least one alarm nuisance behavior, related to at least the identified events and the identified alarms.
However, Bickel teaches aggregating information related to at least the identified events and identified alarms (multitude of voltage event data and alarms across a system by multiple IEDs can be aggregated into a single event for easier to analyze multiple events and alarms from multiple IED devices across the system; see e.g. para. [290-291] and [293-294]) and analyzing the aggregated information (information is analyzed in bundles or aggregation; see e.g. para. [0290]) to identify at least one alarm nuisance behavior, related to at least the identified events and the identified alarms (determination of nuisance behavior, i.e. an increased number of alarms or events a consumer has to review, see e.g. para. [0290] and [0167], wherein it would have been obvious that an occurrence of a same voltage event or alarm event for an increased number of times and the consumer reviewing/acknowledging it would be a nuisance behavior related to that [alarm or voltage] event).
Therefore, it would have been obvious, to a person having ordinary skills in the art before the effective filing date of the claimed invention, to recognize that the aggregate information feature is known in the art and would improve the alarm determination accuracy, because all the relevant data (e.g. voltage events and alarms) are available in one place for analyzing.
As per claim 2, Bickel made obvious above, Bickel further teaches the IEDs devices aggregated information further includes information from at least one of: an Electrical Power Monitoring System (EPMS) [Fig. 1A, para. 0002], a SCADA system, a building management system (BMS), 1/O devices, Programmable Logic Controller (PLC) data, and system users.
As per claim 3, Bickel made obvious above, Bickel further teaches the EPMS includes the at least one IED (121-124) responsible for capturing or deriving the energy-related signals [Abstract and para. 6].
As per claim 4, Bickel made obvious above, Bickel further teaches “non-periodic power quality events such as transients, short-duration rms variations (e.g. momentary interruptions and temporary interruptions), and some long-duration rms variations [para. 9 and 96-103] (e.g. short or long-duration interruption consider as lost data or incomplete data). With that, the aggregated information is further analyzed to identify lost or incomplete data, and impact of the lost or incomplete data on the at least one identified alarm nuisance behavior. As per claim 5. Bickel made obvious above, Bickel further teaches the non-significant disturbance alarm indicative of a not impacting the system, sub-system, process and/or load [step 4840 Fig. 48 and para. 345]. Thus, the non-disturbance means the system operation is normal or healthy and it is obvious that the identified alarm nuisance behavior is an indication of suboptimal alarm health.
As per claim 6, Bickel made obvious above, Bickel further teaches the baseline tolerance curve is modified, changed and/or customized based on the characteristics associated with specific recorded disturbance (e.g. not significant disturbance issue/alarm) [para. 345], the modified baseline would enhance the sensitivity level of event alarms, which may reduce any unnecessary alarms (e.g. nuisance alarm). Thus, one having ordinary skills in the art before the effective filing date of the claimed invention, to recognize that the action of modified baseline to alarm determination would obviously can improve the alarm health, because number of alarms can be suppressed by the modified baseline.
As per claim 7, Bickel made obvious above, Bickel further teaches alarms thresholds configuration [para. 231-238] and [steps 4825-4830; Fig. 48 and para. 342-343] the disturbance thresholds are pre-configured and stored on a memory for comparison with event data, that constitutes of the at least one identified alarm nuisance behavior includes behavior indicative of at least one of a predefined or prescribed alarm nuisance behavior, [user-defined alarm nuisance behavior, and learned alarm nuisance behavior.]
As per claim 8, Bickel made obvious above, Bickel further teaches the predefined or prescribed alarm nuisance behavior is defined based on some form of setpoint learning that necessitated a configuration “learning period” to determine what was normal and which alarms are important which are not [para. 231-238]. That the prescribed alarm nuisance behavior is defined by at least in part, on good engineering practices, [thresholds defined during one of the typical prescribed.]
As per claim 9, Bickel made obvious above, Bickel further teaches the learned alarm nuisance behavior is learned from analysis of data received from at least one of: system users, and I/O systems and devices [para. 231-238] the IED receives voltage even alarms from the system.
As per claim 10, Bickel made obvious above, Bickel further teaches “any or all captured events (including voltage events) may then be analyzed to automatically prioritize the alarms at a discrete, zone and/or system level based on any number of parameters including: alarm type…IED characteristics, load type, customer type” [para. 250], which means one of the parameters can be used as a recommendation as consumer’s preferences, such as priority events. That constitute of at least one predefined or prescribed alarm nuisance behavior, user-defined alarm nuisance behavior, or learned alarm nuisance behavior is based, at least in part, on customer segment type.
As per claim 11, Bickel made obvious above, Bickel further teaches the action taken to modified or customized the baseline of disturbance alarm threshold [para. 345] a non-perturbative, that not impacting the electrical system. That constitutes of the at least one action taken or performed based on or in response to the at least one identified alarm nuisance behavior includes characterizing and/or quantifying the at least one identified alarm nuisance behavior.
As per claim 12, Bickel made obvious above, Bickel further teaches a customized voltage tolerance curve [para. 141] and [Fig. 17, para. 167, 173 and 176] shows grouping of impacting voltage events, non-impacting events associated with voltage tolerance curve, that the voltage events is grouping with the identified event alarms (e.g. small voltage anomaly no significant impact or nuisance behaviors). Thus, the customized voltage tolerance curve would include the characterization includes grouping the at least one identified alarm nuisance behavior into one or more of a plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors. The customized voltage tolerance curve representative of predefined event alarms (e.g. non-impact and impact alarms and nuisance) as cited at [para. 176].
As per claim 13, Bickel made obvious above, Bickel further teaches [para. 289-290] “metering systems according to the disclosure may exhibit multiple alarms from different IEDs located across the facility. Source events generally impact the entire system, for example, resulting in every (or substantially every) capable IEDs indicating an event has occurred…aggregating/consolidating the multitude of voltage event data, alarms and impacts across a system is important for several reasons. First, many energy consumers have a tendency to ignore “alarm avalanches” from monitoring systems. Which means, a bundled of voltage events and alarms corresponding with a baseline tolerance curve (e.g. the interruption region and/or no damage region) are flood alarm nuisance behaviors, because all of the event alarms captured from multiple IEDs. That the plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors include at least one of: stale alarm nuisance behavior, chattering alarm nuisance behavior, fleeting alarm nuisance behavior, and flood alarm nuisance behavior.
As per claim 14, Bickel made obvious above, Bickel further teaches a customized voltage tolerance curve [para. 141] and [Fig. 17, para. 167, 173 and 176] shows grouping of impacting voltage events, non-impacting events associated with voltage tolerance curve, that the voltage events is grouping with the identified event alarms (e.g. small voltage anomaly no significant impact or nuisance behaviors), the customized tolerance curve includes at least one identified alarm nuisance behavior is grouped into the one or more of the plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors in response to determining the at least one identified alarm nuisance behavior meets predefined, prescribed, user-defined or learned thresholds associated with the one or more of the plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors.
As per claim 15, Bickel made obvious above, Bickel further teaches “apply filters to distinguish which alarms are important and which are not” [para. 233-238], to identify and filter the parameter of voltage for different alarms as cited, means that the alarm nuisance behavior has been pre-processed by the applied filters to distinguish which alarms. Thus, the filtered of voltage parameters is a pre-processing information related to the at least one identified alarm nuisance behavior prior to characterizing and/or quantifying the at least one identified alarm nuisance behavior.
As per claim 16, Bickel made obvious above, Bickel further teaches [para. 236] “Capture Everything” approach that requires the energy consumer to apply filters to distinguish which alarms are important and which are not.” And [para. 247] It is possible to use other parameters to customize the alarm templates. For example, the energy consumer may only be interested in voltage events with a recovery time greater than 5 minutes. Voltage event characteristics that typically produce recovery times shorter than 5 minutes could be filtered by using historical event data to configure the alarm templates accordingly.” [para. 247]. To identify any remediation event alarm, the captured data needed to be filtered for target event alarm, such as (less than 5 minutes) the event alarm is not a significant disturbance alarm. That, constitutes the pre-processing the alarm nuisance information includes filtering the information related at least one identified alarm nuisance behavior.
As per claim 19, Bickel made obvious above, Bickel further teaches “In this third voltage event, the voltage sags to 30% of the nominal voltage and lasts for 2 milliseconds in duration. This time the pre/during/post-event analysis of the third event indicates 25% of the load was impacted. Subsequently, the alarms setpoint thresholds are left unchanged because of the 25% impact to the load (i.e., a load impact occurred where it was expected to occur) … The energy consumer may be notified of the third event occurrence, and the voltage event data, calculations, derivation and any analyses may be stored for future reference/benefits.” [Fig. 39 and para. 253] the [para. 266-267]. Thus, the third voltage event contains a minimum quantity of occurrences which used for future reference.
As per claim 20, Bickel made obvious in claim 19 above, Bickel further teaches “The energy consumer may be notified of the third event occurrence, and the voltage event data, calculations, derivation and any analyses may be stored for future reference/benefits.” [para. 253 ]. The alarm nuisance behavior not meeting the prescribed conditions is filtered or removing from the final setting of the voltage event alarm threshold, and notified the energy consumer and stored for future reference. That the constitutes of in response to the at least one identified alarm nuisance behavior not meeting the prescribed conditions, filtering or removing the at least one identified alarm nuisance behavior from a data set.
As per claim 21, Bickel made obvious above, Bickel further teaches [Fig. 17 and para. 173 which shows the parameters characteristics information related to voltage events of series of time and [Fig. 25, para. 187] shows events on the graph relative with time series (e.g. different type of dots). That constitutes the operational parameters information related to the characterized at least one identified alarm nuisance behavior to time series information associated with the identified alarms, the displayed dots indicated the different voltage event alarms with time.
As per claim 22, Bickel made obvious above, Bickel performed at least one action as described above (e.g. modified the baseline tolerance curve), Bickel further teaches at [para. 198] “[0198] The dynamic voltage-impact tolerance curve provides a baseline of voltage events at each discretely metered point that captures impacted or potentially impacted processes, operations or facilities. Post-installation evaluations may be performed using data taken from the areas predicted to experience the benefits. In embodiments, these post-installation evaluations compare “before vs. after” to quantify the real benefits of installing the mitigative equipment. Determined quantities may include reduced event impact, recovery time, operational costs, maintenance costs, or any other operational or economic variable.” The modified baseline tolerance curve would include the potential avoided events [see Fig. 25, para. 187]. With the teachings above, constitutes of identifying at least one potential nuisance remediation to address the at least one identified alarm nuisance behavior.
As per claim 23, Bickel made obvious above, Bickel cited at [para. 249] which teaches customer alarm prioritization and voltage event alarm setpoint thresholds may be evaluated and modified and [para. 327] cited “by evaluating pre-event/post-event power characteristics of power quality events, it is possible to quantify the susceptibility of the electrical system at metered points to power quality disturbances. This information could be used to identify product offerings for mitigative solutions and provide better qualified leads to organizations marketing those solutions”. That, constitutes of selecting and recommending one or more of the at least one potential nuisance remediation based on the particular user(s) and/or customer segment type(s) associated with the electrical system.
As per claim 24, Bickel made obvious above, Bickel further teaches, wherein the aggregated information is automatically or dynamically analyzed [para. 141].
As per claim 26, The apparatus limitations are similar to those in method steps of claim 1 above, that the rejection would be in the same manner.
As per claim 27, The apparatus limitations are similar to those in method steps of claim 2 above, that the rejection would be in the same manner.
As per claim 28, The apparatus limitations are similar to those in method steps of claim 7 above, that the rejection would be in the same manner.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bickel in view of Lee et al. (Lee; US 2021/0058307]
As per claim 17, Bickel made obvious above, Bickel further teaches the errors are based on incorrect or incomplete data. Lee teaches [para. 70] an indicator for identifying a phenomenon where a time of data generated by a sensor is incorrectly set or where data arriving late occurs. It would have been obvious to one having ordinary skills in the art before the effective filing date of the claimed invention, to employ the incorrect data as taught by Lee to the system of Bickel above, for the benefit of optimizing the accuracy of detection and analyzing the event alarms, because incorrect time indication would render to bigger problem to the system, such as delaying maintenance indication or performance.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 1-25 is/are rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1-24 of prior U.S. Patent No. 12,131,625. This is a statutory double patenting rejection.
Instant Application
US Patent No. 12,131,625
1. A method for reducing alarm nuisance behaviors in an electrical system, comprising: processing electrical measurement data from or derived from energy-related signals captured or derived by at least one intelligent electronic device (IED) in the electrical system to identify events in the electrical system, and alarms triggered in response to the identified events and/or other events in or related to the electrical system; aggregating information related to at least the identified events and the identified alarms; analyzing the aggregated information to identify at least one alarm nuisance behavior; and taking or performing at least one action based on or in response to the at least one identified alarm nuisance behavior.
25. The method of 1, further comprising: correcting event timestamps associated with the identified alarms to the entire intervals/periods of the identified events and/or other events in or related to the electrical system responsible for triggering the identified alarms.
1. A method for reducing alarm nuisance behaviors in an electrical system, comprising: processing electrical measurement data from or derived from energy-related signals captured or derived by at least one intelligent electronic device (IED) in the electrical system to identify events in the electrical system, and alarms triggered in response to the identified events and/or other events in or related to the electrical system; aggregating information related to at least the identified events and the identified alarms; analyzing the aggregated information to identify at least one alarm nuisance behavior; taking or performing at least one action based on or in response to the at least one identified alarm nuisance behavior; and correcting event timestamps associated with the identified alarms to the entire intervals/periods of the identified events and/or other events in or related to the electrical system responsible for triggering the identified alarms.
2. The method of claim 1, wherein the aggregated information further includes information from at least one of: an Electrical Power Monitoring System (EPMS), a SCADA system, a building management system (BMS), I/O devices, Programmable Logic Controller (PLC) data, and system users.
2. The method of claim 1, wherein the aggregated information further includes information from at least one of: an Electrical Power Monitoring System (EPMS), a SCADA system, a building management system (BMS), I/O devices, Programmable Logic Controller (PLC) data, and system users.
3. The method of claim 2, wherein the EPMS includes the at least one IED responsible for capturing or deriving the energy-related signals.
3. The method of claim 2, wherein the EPMS includes the at least one IED responsible for capturing or deriving the energy-related signals.
4. The method of claim 1, wherein the aggregated information is further analyzed to identify lost or incomplete data, and impact of the lost or incomplete data on the at least one identified alarm nuisance behavior.
4. The method of claim 1, wherein the aggregated information is further analyzed to identify lost or incomplete data, and impact of the lost or incomplete data on the at least one identified alarm nuisance behavior.
5. The method of claim 1, wherein the at least one identified alarm nuisance behavior is an indication of suboptimal alarm health.
5. The method of claim 1, wherein the at least one identified alarm nuisance behavior is an indication of suboptimal alarm health.
6. The method of claim 5, wherein the at least one action taken or performed based on or in response to the at least one identified alarm nuisance behavior includes at least one action to improve the alarm health.
6. The method of claim 5, wherein the at least one action taken or performed based on or in response to the at least one identified alarm nuisance behavior includes at least one action to improve the alarm health.
7. The method of claim 1, wherein the at least one identified alarm nuisance behavior includes behavior indicative of at least one of a predefined or prescribed alarm nuisance behavior, user-defined alarm nuisance behavior, and learned alarm nuisance behavior.
7. The method of claim 1, wherein the at least one identified alarm nuisance behavior includes behavior indicative of at least one of a predefined or prescribed alarm nuisance behavior, user-defined alarm nuisance behavior, and learned alarm nuisance behavior.
8. The method of claim 7, wherein the predefined or prescribed alarm nuisance behavior is defined based, at least in part, on good engineering practices, thresholds defined during one of the typical steps of alarms ISA 18.2 "audit and philosophy loop" (A, B, C, D, H, I, J) or at commissioning of a site or prescribed.
8. The method of claim 7, wherein the predefined or prescribed alarm nuisance behavior is defined based, at least in part, on good engineering practices, thresholds defined during one of the typical steps of alarms ISA 18.2 “audit and philosophy loop” (A, B, C, D, H, I, J) or at commissioning of a site or prescribed.
9. The method of claim 7, wherein the learned alarm nuisance behavior is learned from analysis of data received from at least one of: system users, and I/O systems and devices.
9. The method of claim 7, wherein the learned alarm nuisance behavior is learned from analysis of data received from at least one of: system users, and I/O systems and devices.
10. The method of claim 7, wherein the at least one predefined or prescribed alarm nuisance behavior, user-defined alarm nuisance behavior, or learned alarm nuisance behavior is based, at least in part, on customer segment type.
10. The method of claim 7, wherein the at least one predefined or prescribed alarm nuisance behavior, user-defined alarm nuisance behavior, or learned alarm nuisance behavior is based, at least in part, on customer segment type.
11. The method of claim 1, wherein the at least one action taken or performed based on or in response to the at least one identified alarm nuisance behavior includes characterizing and/or quantifying the at least one identified alarm nuisance behavior.
11. The method of claim 1, wherein the at least one action taken or performed based on or in response to the at least one identified alarm nuisance behavior includes characterizing and/or quantifying the at least one identified alarm nuisance behavior.
12. The method of claim 11, wherein the characterization includes grouping the at least one identified alarm nuisance behavior into one or more of a plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors.
12. The method of claim 11, wherein the characterization includes grouping the at least one identified alarm nuisance behavior into one or more of a plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors.
13. The method of claim 12, wherein the plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors include at least one of: stale alarm nuisance behavior, chattering alarm nuisance behavior, fleeting alarm nuisance behavior, and flood alarm nuisance behavior.
13. The method of claim 12, wherein the plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors include at least one of: stale alarm nuisance behavior, chattering alarm nuisance behavior, fleeting alarm nuisance behavior, and flood alarm nuisance behavior.
14. The method of claim 12, wherein the at least one identified alarm nuisance behavior is grouped into the one or more of the plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors in response to determining the at least one identified alarm nuisance behavior meets predefined, prescribed, user-defined or learned thresholds associated with the one or more of the plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors.
14. The method of claim 12, wherein the at least one identified alarm nuisance behavior is grouped into the one or more of the plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors in response to determining the at least one identified alarm nuisance behavior meets predefined, prescribed, user-defined or learned thresholds associated with the one or more of the plurality of: predefined or prescribed alarm nuisance behaviors, user-defined alarm nuisance behaviors, or learned alarm nuisance behaviors.
15. The method of claim 11, further comprising: pre-processing information related to the at least one identified alarm nuisance behavior prior to characterizing and/or quantifying the at least one identified alarm nuisance behavior.
15. The method of claim 11, further comprising: pre-processing information related to the at least one identified alarm nuisance behavior prior to characterizing and/or quantifying the at least one identified alarm nuisance behavior.
16. The method of claim 15, wherein the pre-processing includes identifying, filtering and/or correcting errors in the information related at least one identified alarm nuisance behavior.
16. The method of claim 15, wherein the pre-processing includes identifying, filtering and/or correcting errors in the information related to the at least one identified alarm nuisance behavior.
17. The method of claim 16, wherein the errors are based on incorrect or incomplete data.
17. The method of claim 16, wherein the errors are based on incorrect or incomplete data.
18. The method of claim 16, wherein the errors include missing or incomplete timestamps, and the pre-processing includes correcting, creating and/or appending new or corrected timestamps.
18. The method of claim 16, wherein the errors include missing or incomplete timestamps, and the pre-processing includes correcting, creating and/or appending new or corrected timestamps.
19. The method of claim 11, further comprising: determining if the at least one identified alarm nuisance behavior meets prescribed conditions, the prescribed conditions including a minimal quantity of occurrences of the at least one identified alarm nuisance behavior over a given analysis period for the at least one identified alarm nuisance behavior to be considered a nuisance behavior for purposes of future analysis.
19. The method of claim 11, further comprising: determining if the at least one identified alarm nuisance behavior meets prescribed conditions, the prescribed conditions including a minimal quantity of occurrences of the at least one identified alarm nuisance behavior over a given analysis period for the at least one identified alarm nuisance behavior to be considered a nuisance behavior for purposes of future analysis
20. The method of claim 19, further comprising: in response to the at least one identified alarm nuisance behavior not meeting the prescribed conditions, filtering or removing the at least one identified alarm nuisance behavior from a data set including the at least one identified alarm nuisance behavior.
20. The method of claim 19, further comprising: in response to the at least one identified alarm nuisance behavior not meeting the prescribed conditions, filtering or removing the at least one identified alarm nuisance behavior from a data set including the at least one identified alarm nuisance behavior.
21. The method of claim 11, further comprising: appending information related to the characterized and/or quantified at least one identified alarm nuisance behavior to time-series information associated with the identified alarms.
21. The method of claim 11, further comprising: appending information related to the characterized and/or quantified at least one identified alarm nuisance behavior to time-series information associated with the identified alarms.
22. The method of claim 1, wherein the at least one action taken or performed based on or in response to the at least one identified alarm nuisance behavior includes: identifying at least one potential nuisance remediation to address the at least one identified alarm nuisance behavior.
22. The method of claim 1, wherein the at least one action taken or performed based on or in response to the at least one identified alarm nuisance behavior includes: identifying at least one potential nuisance remediation to address the at least one identified alarm nuisance behavior.
23. The method of claim 22, further comprising: selecting and recommending one or more of the at least one potential nuisance remediation based on the particular user(s) and/or customer segment type(s) associated with the electrical system.
23. The method of claim 22, further comprising: selecting and recommending one or more of the at least one potential nuisance remediation based on the particular user(s) and/or customer segment type(s) associated with the electrical system.
24. The method of claim 1, wherein the aggregated information is automatically or dynamically analyzed.
24. The method of claim 1, wherein the aggregated information is automatically or dynamically analyzed.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 26-28 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 25-27 of U.S. Patent No. 12,131,625.
Instant Application
US Patent No. 12,131,625
26. A system for reducing alarm nuisance behaviors in an electrical system, comprising: at least one 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 configured to: process electrical measurement data from or derived from energy-related signals captured or derived by at least one intelligent electronic device (IED) in the electrical system to identify events in the electrical system, and alarms triggered in response to the identified events and/or other events in or related to the electrical system; aggregate information related to at least the identified events and the identified alarms; analyze the aggregated information to identify at least one alarm nuisance behavior; and take or perform at least one action based on or in response to the at least one identified alarm nuisance behavior.
25. A system for reducing alarm nuisance behaviors in an electrical system, comprising: at least one 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 configured to: process electrical measurement data from or derived from energy-related signals captured or derived by at least one intelligent electronic device (IED) in the electrical system to identify events in the electrical system, and alarms triggered in response to the identified events and/or other events in or related to the electrical system; aggregate information related to at least the identified events and the identified alarms; analyze the aggregated information to identify at least one alarm nuisance behavior; take or perform at least one action based on or in response to the at least one identified alarm nuisance behavior; and correct event timestamps associated with the identified alarms to the entire intervals/periods of the identified events and/or other events in or related to the electrical system responsible for triggering the identified alarms.
27. The system of claim 26, wherein the system corresponds to, includes, or is part of an Electrical Power Monitoring System (EPMS).
26. The system of claim 25, wherein the system corresponds to, includes, or is part of an Electrical Power Monitoring System (EPMS).
28. The system of claim 26, wherein the at least one identified alarm nuisance behavior includes behavior indicative of at least one predefined or prescribed alarm nuisance behavior, user-defined alarm nuisance behavior, or learned alarm nuisance behavior.
27. The system of claim 25, wherein the at least one identified alarm nuisance behavior includes behavior indicative of at least one predefined or prescribed alarm nuisance behavior, user-defined alarm nuisance behavior, or learned alarm nuisance behavior.
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the disclosed patent anticipate the claims of the instant application.
Conclusion
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/MUHAMMAD ADNAN/Primary Examiner, Art Unit 2688