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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 28, 2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
The Amendment filed July 29, 2026, has been entered. Claims 1 & 4-8 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every 35 U.S.C. § 112(b) rejections previously set forth in the Non-Final Office Action mailed April 29, 2026, hereafter referred to as the Non-Final Office Action.
Response to Arguments
Applicant's arguments, see Applicant remarks pp. 6-9, filed July 29, 2026, with respect to the rejection of independent claim 1 under 35 U.S.C. § 103 have been fully considered but they are not persuasive. Further, in light of the amendments in claim 1, new grounds of rejection are made in view of Choi (KR 20200131492, hereinafter, Choi), in view of Xu (CN 109167667, hereinafter, Xu), and further in view of new prior art reference Wei (CN 110286302A, hereinafter, Wei).
In response to Applicant's argument, see pg. 7 of Applicant’s remarks, with respect to the rejection of independent claim 1 under 35 U.S.C. § 103, that prior art references, Choi, in view of Xu, as cited by the Applicant, fail to disclose, teach, and/or suggest individually or in combination, “a plurality of data acquisition apparatuses consisting of one master apparatus and a plurality of slave apparatuses, perform PTP-based time synchronization, and generated PRPD data by mapping processed sensing data receiving during a predefined time period with a time synchronization signal,” further stating that Choi calculates a time difference of arrival (TDOA) directly from raw signals, whereas the claim requires first identifying a PRPD dataset with matching times, finding a pattern, and then finding a location.
The Examiner respectfully disagrees and would like to break down the responses into four reasonings. First, Choi teaches that it is impossible to calculate a location if multiple discharges occur simultaneously, and therefore the system must first confirm the pattern/type of the partial discharge before searching for the location ([0066]). Choi solves the problem of simultaneous signals by checking the pattern/type first. The processor evaluates the PRPD data to confirm it is the same pattern, which is what the Broadest Reasonable Interpretation (BRI) of the claim recites as identifying the pattern corresponding to the PRPD dataset prior to identifying the location. See MPEP 2111 & 2111.01.
Second, in response to Choi lacking the integration of a time synchronization signal into the PRPD data generation and dataset grouping process. The Examiner respectfully disagrees, Choi maps the phase of the PRPD data format to a synchronization signal ([0093]-[0095]). Further, Choi teaches grouping/processing signals based on whether they were detected in the same time zone using a reference time signal ([0050]) therefore having “matching tagged times” by processing signals from the “same time zone” based on a reference time signal.
Third, in response to Choi not teaching “a plurality of data acquisition apparatuses consisting of one master apparatus and a plurality of slave apparatuses, perform PTP-based time synchronization, and generated PRPD data by mapping processed sensing data receiving during a predefined time period with a time synchronization signal,” one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. Applicant’s reply fails to address the combined teachings of the applied references and instead argues that each reference individually does not teach all of the claim limitations. Please refer to Non-Final OA pp. 3-10 for reasoning and See MPEP 2145(IV).
Fourth, in response to “Choi is directed to a location estimation technique based on the time difference of sensor signals, whereas the present claims integrate a PTP-based time synchronization signal into the PRPD data generation process, constructs a PRPD data set, performs pattern analysis based on the PRPD data set, and subsequently identifies the partial discharge occurrence location,” that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., ”data generation process,” “constructs a PRPD data set,” and “subsequently identifies the partial discharge occurrence location”) are not recited in the rejected claim 1. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). See MPEP 2145(VI) and 2111.01. While the BRI must be consistent with the specification, it is improper to import limitations from the specification into the claims.
In response to Applicant's argument, see pg. 8 of Applicant’s remarks, with respect to the rejection of independent claim 1 under 35 U.S.C. § 103, that prior art references, Choi (KR 20200131492, hereinafter, Choi), in view of Xu (CN 109167667, hereinafter, Xu), as cited by the Applicant, fail to disclose, teach, and/or suggest individually or in combination, “simultaneously acquire sensing data via time synchronization using a precision time-synchronization protocol (PTP),” further stating that Xu does not disclose nor suggests “generating PRPD data by mapping a time synchronization signal with processed sensing data, or identifying, as one PRPD data set, a plurality of pieces of PRPD data having the matching tagged times based on time synchronization signal.”
The Examiner respectfully disagrees that Xu does not teach nor suggest PRPD generation of location calculation and that Xu is cited merely “collecting or transmitting synchronized data.” The Examiner would like to highlight that Xu teaches generating and using timestamps (tagged times) to achieve precise PTP synchronization across nodes ([0062]). The rejection relies on the combination of references, where Choi provides the PRPD generation, pattern identification, and location calculations. Xu provides the PTP master-slave network and the exact mechanism for generating timestamps (“tagged times”). When a POSITA applies Xu’s PTP timestamping to Choi’s system, the resulting apparatus inherently groups the PRPD data utilizing the precise PTP timestamps (“matching tagged times”) to confirm the signals belong to the same event dataset before calculating the location. The predictable result of this combination meets the limitations claimed “mapping PRPD data using precise PTP timestamps (“tagged times”) to identify a matched dataset and its corresponding pattern before the chronological location calculation. See MPEP 2145(I), 2145(II), & 2145(IV).
Therefore, Applicant’s arguments are unconvincing and the rejections of amended independent claim 1, and dependent claims 4-8, which depend from and incorporate the limitations of independent claim 1, are respectively maintained. Updated rejections based on the new prior art reference(s) follow below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 & 4-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "receive the plurality of pieces of PRPD data from the plurality of data acquisition apparatuses through the communicator" in ll. 24-25, without previous disclosure resulting in a lack of antecedent basis for this limitation in the claim, and “identify a plurality of pieces of PRPD data…” in line 26. For examination purposes, the examiner interprets the first limitation in ll. 24-25 to read as “receive a plurality of pieces of PRPD data from the plurality of data acquisition apparatuses through the communicator” and “identify the plurality of pieces of PRPD data…” in line 26 in claim 1. Claims 4-8, which do not correct the defect, are rejected by virtue of dependency to claim 1.
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 & 4-8 are rejected under 35 U.S.C. 103 as being unpatentable over Choi (KR 20200131492 A, Pub. Date Nov. 24, 2020, hereinafter, Choi), in view of Xu (CN 109167667 A, Pub. Date Jan. 8, 2019, hereinafter, Xu), and further in view of Wei (CN 110286302A, Pub. Date Sep. 27, 2019, hereinafter, Wei).
Regarding independent claim 1, Choi, teaches:
A system for estimating a partial discharge occurrence location, comprising ([Summary], [0011], & [Claim 1]: teaches a system designed for estimating/detecting the location of partial discharges):
a determination apparatus that determines a partial discharge occurrence location of the measurement target by using a plurality of pieces of sensing data received from the plurality of data acquisition apparatuses ([Summary], [0011], & [Claim 1]: utilizes a partial discharge location detection apparatus (determination apparatus) that processes the sensing data from the plurality of sensors to calculate time differences and determine the target location);
a PRPD generator configured to generate PRPD data by mapping the plurality of pieces of processed sensing data received from the sensor data processor for a predefined time and a time synchronization signal received from the precision time synchronizer ([0093]-[0095]: teaches a PRPD generator (calculation unit 245) that generates PRPD data by mapping the processing sensing data (phase, magnitude, frequency), and further teaches that this data mapping is synchronized with a time synchronization signal. In combination with Xu, synchronization relies on the precise PTP synchronization signal received from the time synchronizer).
wherein the determination apparatus comprises ([Summary], [0047]-[0049], [0051], [0096], & [Claim 1]):
a communicator ([0016] & [0047]-[0048]: teaches the determination apparatus (partial discharge location device 200) comprising a communicator (communication unit 210)); and
a processor configured to ([0035], [0047]-[0048], [0051], [0067], & [0096]: teaches the determination apparatus (partial discharge location device 200) comprising a communicator (communication unit 210) and a processor (e.g., control unit 280, broadband analysis unit 240, etc.)):
receive the plurality of pieces of PRPD data from the plurality of data acquisition apparatuses through the communicator ([0011]-[0012], [0016], [0040]-[0041], [0043], [0048]-[0049],[0093], [0095]-[0096], [0105], [0108], [Claim 1] & [Claim 2]: teaches that PRPD is the standard format for this packaged partial discharge data and is obvious to send the data in this packaged PRPD format;
identify a pattern corresponding to the PRPD data set (Fig. 3; [0012]-[0013], [0016], [0020], [0025], [0040]-[0041], [0043], [0050], [0058]-[0059], [0072], [0077], [0091]-[0095], [0107], [0141], [Claim 2], [Claim 3], [Claim 6], [Claim 10] & [Claim 15]: teaches the processor (e.g., calculation unit 245) identifying and analyzing patterns corresponding to the PRPD data to diagnose the partial discharge);
arrange the plurality of pieces of PRPD data in chronological order based on time synchronization signal ([Summary], [0011]-[0013], [0015]-[0016], [0018]-[0019], [0022]-[0023], [0025], [0027], [0029], [0040], [0047], [0049]-[0057], [0064], [0066]-[0068], [0071]-[0072], [0074], [0077]-[0078], [0094], [0107], [0123]-[0124], [0135], [0141]-[0142], [0144], [Claim 1], [Claim 2], [Claim 3], [Claim 5], [Claim 6], [Claim 8], [Claim 9], [Claim 12], [Claim 13] & [Claim 15]: arranges data chronologically using the synchronized time variables to calculate the time difference of arrival (∆t) of signals from multiple sensors); and
identify the partial discharge occurrence location based on installation locations of the plurality of data acquisition apparatuses and the plurality of pieces of arranged PRPD data ([0050]-[0057] & [0118]-[0121]: teaches calculating and identifying the exact location by utilizing/combining the known installation locations/distance (L) between the sensors and the chronologically arranged time difference (∆t) extracted from the data).
Choi, is silent in regard to:
a plurality of data acquisition apparatuses that are installed in one area of a measurement target; and simultaneously acquire sensing data via time synchronization using a precision time-synchronization protocol (PTP); and
wherein the plurality of data acquisition apparatuses consist of one master apparatus and a plurality of slave apparatuses and each of the plurality of data acquisition apparatuses comprises:
a precision time synchronizer configured to transmit a time synchronization signal based on PTP based on the master apparatus;
a sensor data processor configured to collect the sensing data based on a time synchronization signal received from the precision time synchronizer and process the collected sensing data; and
However, Xu, further teaches:
The Examiner is combining Choi in view of Xu, by implementing Choi’s plurality of partial discharge sensors installed in power equipment to detect signals and simultaneously acquiring/processing data using a time synchronization module ([Summary], [0011], [0050], & [Claim 1].
a plurality of data acquisition apparatuses that are installed in one area of a measurement target; and simultaneously acquire sensing data via time synchronization using a precision time-synchronization protocol (PTP) ([Abstract] & [0022]: teaches applying the PTP synchronization protocol to a plurality of distributed acquisition nodes/sensors to achieve high-precision time synchronization); and
wherein the plurality of data acquisition apparatuses consist of one master apparatus and a plurality of slave apparatuses ([0007], [0022], [0034], [0053], [0056], [0066], [0074], [Claim 1], & [Claim 6]: teaches that the data acquisition system consists of one master apparatus (master clock node) and multiple slave apparatuses (slave clock nodes/acquisition nodes)) and each of the plurality of data acquisition apparatuses comprises ([Abstract], [0005]-[0008], [0022]-[0024], [0035], [0039], [0053]-[0055], [0066], [0068]-[0069], [0075], [0077]-[0079], [0084]-[0085], [Claim 1], & [Claim 6]: introduces the internal components of each data acquisition apparatus (acquisition node)):
a precision time synchronizer configured to transmit a time synchronization signal based on PTP based on the master apparatus ([0007]-[0008], [0022], [0053], [0056], [0066], [0074], [0077], [Claim 1] & [Claim 6]: the PTP-tamed crystal oscillator and protocol handler act as the precision time synchronizer, receiving/exchanging PTP time synchronization signals based on the master apparatus (master clock node));
a sensor data processor configured to collect the sensing data based on a time synchronization signal received from the precision time synchronizer and process the collected sensing data ([0024], [0055], [0068], [0075], [0077] & [Claim 6]: teaches a sensor data processor (A/D conversion chip and node controller) that collects sensing data based precisely on the synchronized local time (the time synchronization signal) achieved via PTP); and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the specific master-slave PTP synchronization architecture and data collection mechanism taught by Xu into the partial discharge and PRPD generation system taught by Choi, according to known methods. Choi teaches a partial discharge location system featuring PRPD mapping to a synchronization signal but relies on a generic time synchronizer without a master-slave PTP architecture. Xu teaches a high-precision data acquisition network consisting of one master apparatus and a plurality of slave apparatuses, wherein each node comprises a precision time synchronizer that exchanges PTP-based time synchronization signals. Xu further teaches sensor data processors configured to collect sensing data based on the precise local time matched to the received PTP synchronization signal. The motivation to do so would improve the synchronization accuracy of the distributed partial discharge sensors (data acquisition apparatuses) to the nanosecond level, eliminating location calculation errors caused by clock drift. Precise synchronization across multiple distributed nodes ensures that the generated PRPD data maps the time-domain characteristics of the partial discharge, reducing calculation errors. This modification represents a substitution of a known high-precision time-synchronization technique to improve a similar networked measurement device, yielding the predictable variation of an accurate synchronized sensing data collection and reliable location estimation (KSR).
Choi, and Xu, are silent in regard to:
identify a plurality of pieces of PRPD data having matching tagged times based on the time synchronization signal as one PRPD data set;
However, Wei, further teaches:
identify a plurality of pieces of PRPD data having matching tagged times based on the time synchronization signal as one PRPD data set ([0066]-[0068] & [0073]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the timestamp-based dataset grouping of Wei to the specific master-slave PTP synchronized PRPD mapping system of Choi and Xu. The combination of Choi and Xu teaches the system but lacks the step of identifying pieces of PRPD data having matching tagged times as one discrete PRPD data set prior to pattern analysis. Wei teaches a partial discharge detection system wherein a processor utilizes timestamp information (tagged times) to classify multiple pieces of sample data from different sensors into one grouped signal dataset. Wei specifies that the data detected by different sensors at the exact same time will inevitably have matching timestamp information, allowing the system to identify them as belonging to the same dataset for pattern analysis. The motivation for this modification is to filter out noise and separate simultaneous but unrelated discharge events by ensuring only data from the exact same temporal event is analyzed together. This represents the application of a known technique to a known device to yield predictable results, specifically ensuring the processor only identifies patterns from correlated PRPD datasets before calculating physical locations (KSR).
Regarding dependent claim 4, Choi, teaches:
The system of claim 1 ([Summary], [0011], & [Claim 1]),
wherein the processor is configured to ([0035], [0047], [0067], & [0093]: teaches a system with a processor/controller configured to perform the subsequent steps):
arrange the plurality of pieces of PRPD data in chronological order based on the identified feature information ([0004], [0011], [0016], [0023], [0039], [0066], [0074]-[0075],[0077]-[0078], [0118], [0121]-[0126], [Claim 1] & [Claim 13]: teaches using the extracted waveform features (e.g., the arrival times t1, t2 of specific signal modes, or the 1ns rapid pulse edges) to determine the time difference of arrival (∆t). Determining the time difference inherently requires the processor to chronologically arrange/order the data pieces based on the identified temporal waveform features).
Choi, is silent in regard to:
identify feature information corresponding to a characteristic waveform commonly appearing in the plurality of pieces of waveforms of each PRPD data according to the identified pattern, and
However, Wei, further teaches:
The Examiner is combining Choi in view of Wei, by implementing Choi’s teachings of analyzing the feature information (characteristics) of the PRPD pattern to identify the partial discharge ([0013]-[0018], [0020]-[0021], [0028], [0043], [0050], [0059]-[0061], [0065], [0074], [0087], [0091]-[0096], [0105]-[0106], [0114], [0133], [0135]-[0136], [Claim 3], [Claim 4], [Claim 5], [Claim 6], [Claim 7], [Claim 8], [Claim 10], & [Claim 11]: further discloses specific “waveform” feature information, such as the rapid rise and fall times of the pulse, as well as specific frequency components over time S(t, f), which define the partial discharge pattern).
identify feature information corresponding to a characteristic waveform commonly appearing in the plurality of pieces of waveforms of each PRPD data according to the identified pattern ([0065]-[0068 & [0070]-[0073]: teaches identifying feature information (waveform characteristics) alongside PRPD statistical parameters to classify signals into groups where multiple pieces of waveforms share the exact same type/characteristic waveform (commonly appearing)), and
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the waveform characteristic grouping and PRPD classification teachings of Wei into the partial discharge location system of Choi. Choi teaches a system that extracts waveform features to chronologically arrange partial discharge signals but lacks details on identifying feature information corresponding to a characteristic waveform commonly appearing in the plurality of pieces of waveforms of each PRPD data according to the identified pattern. Wei teaches a partial discharge detection method that utilizes waveform characteristics and PRPD statistical parameters to classify and group original waveform characteristics. The motivation to incorporate and combine these teachings is to improve signal processing efficiency and accuracy by filtering out noise and ensuring that only signals sharing the exact same characteristic waveform are grouped together for subsequent analysis. This combination represents the substitution of a known technique to improve similar devices, predictably resulting in a system capable of accurately identifying common characteristic waveforms in PRPD data sets before utilizing those features to chronologically arrange the data for precise location calculations (KSR).
Regarding dependent claim 5, Choi, teaches:
The system of claim 1 ([Summary], [0011], & [Claim 1]),
wherein the processor is configured to ([0035], [0047], [0067], & [0093]: teaches a system with a processor/controller configured to perform the subsequent steps):
identify a partial discharge propagation path based on the installation locations of the plurality of data acquisition apparatuses and the plurality of pieces of arranged PRPD data ([0118] & [0121]-[0126]: teaches modeling “propagation” of the partial discharge signal along a path, such as power lines or equipment surfaces. The processor utilizes the chronologically arranged data (to find arrival times t1, t2, yielding ∆t) and the characteristics of the signal’s propagation (group velocities vg1, vg2) to calculate the physical path distance (L) to the sensor installation locations), and
identify the partial discharge occurrence location through the partial discharge propagation path ([0024], [0043], [0118], [0121]-[0126], [0135]-[0136], & [Claim 14]: by calculating the exact distance/length (L) of the propagation path that the waves traveled, utilizing the chronological time differences and wave speeds, the system accurately identifies the final physical occurrence location of the partial discharge).
Regarding dependent claim 6, Choi, teaches:
The system of claim 5 ([Summary], [0011], & [Claim 1]),
wherein the processor is configured to ([0035], [0047], [0067], & [0093]: teaches a system with a processor/controller configured to perform the subsequent steps):
identify the partial discharge occurrence location based on a model trained to perform an operation on the partial discharge occurrence location ([0059], [0092], [0094]-[0095], & [0107]: teaches utilizing a trained model, specifically a “neural network” that undergoes a “repetitive learning process and fuzzy techniques” to identify patterns. A POSITA would find it obvious to configure the disclosed trained model to calculate/identify the location, extending it “automatic diagnosis” capabilities to the location coordinates) according to the installation locations of the plurality of data acquisition apparatuses and the plurality of pieces of arranged PRPD data ([Summary], [0004], [0008], [0011]-[0012], [0016], [0023]-[0025], [0043], [0047], [0052]-[0053], [0056], [0086]-[0087], [0093]-[0095], [0106]-[0108], [0123], [0135]-[0137], [0140]-[0142], [0144], [Claim 1], [Claim 2], [Claim 3], [Claim 5], [Claim 6], [Claim 8], [Claim 13], & [Claim 14]: teaches that the system’s calculations inherently rely on the PRPD data parameters (phase, magnitude, etc., chronologically arranged via time differences ∆t) and the sensor installation locations (sensor distance L). This is the exact data the trained model operates on).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to configure the iteratively trained neural network model taught by Choi to perform operations on the partial discharge occurrence location, according to known methods. The motivation to do so would be to fully automate the partial discharge analysis pipeline, using trained machine learning model to handle the complex PRPD data for both classification (diagnosing the type) and regression (calculating the precise physical location). Applying the neural network taught by Choi to the location-determination steps also taught by Choice is a simple application of a known technique to a known device to yield predictable results (KSR): a fully automated, Ai-driven location output. Xu provides the accurate PTP timing that makes the arranged PRPD data reliable for a trained model to process without being off by clock drift.
Regarding dependent claim 7, Choi, teaches:
The system of claim 1 ([Summary], [0011], & [Claim 1]),
wherein the processor is configured to ([0035], [0047], [0067], & [0093]: teaches a system with a processor/controller configured to perform the subsequent steps):
identify the partial discharge occurrence location when the patterns corresponding to the plurality of pieces of PRPD data are all identified as the same pattern ([Summary], [0005], [0008], [0011]-[0013], [0015]-[0018], [0020], [0022]-[0023], [0025], [0039]-[0041], [0043], [0050], [0058]-[0061], [0064], [0065]-[0067], [0083], [0091]-[0095], [0106]-[0107], [0135], [0141]-[0142], [0144], [Claim 1], [Claim 2], [Claim 3], [Claim 5], [Claim 6], [Claim 7], [Claim 10], [Claim 13] & [Claim 15]: identifies the problem of “simultaneous” but different types of discharges. To correctly calculate the location, teaches that the processor must first analyze the PRPD data to confirm the “type” (pattern), then searches for the location after confirming the type, correlating data pieces that are identified as the same pattern (same type of discharge event) to calculate a valid physical location).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Choi’s pattern-matching prerequisite for location calculation with Xu’s PTP synchronization architecture, according to known methods. The motivation to do so would be to ensure that when the central processor filters and matches the PRPD data to confirm they represent the same pattern (e.g., discharge event) across multiple sensors, the temporal alignment of the matched signals is synchronized. Combining Choi’s pattern-verification step with Xu’s precise PTP network prevents the system from erroneously calculating a location using mismatched signals from unrelated discharge events, yielding predictable and accurate location tracking (KSR).
Regarding dependent claim 8, Choi, teaches:
The system of claim 1 ([Summary], [0011], & [Claim 1]),
Choi, is silent in regard to:
wherein the sensor data processor is configured to:
receive the time synchronization signal when periodically updated from the precision time synchronizer.
However, Xu, further teaches:
The Examiner is combining Choi in view of Xu by implementing Choi’s analysis units (processor) receiving a synchronization signal that updates on a “constant period”/regular cycle (periodically) from the time synchronizer ([0019], [0029], [0035], [0047], [0050], [0059], [0061], [0065], [0067]-[0068], [0075], [0088], [0090], [0093], [0102]-[0104], [0116], & [Claim 9])
receive the time synchronization signal when periodically updated from the precision time synchronizer ([0005]-[0006], [0008], [0022], [0053], [0056], [0066], [0074], [0077], [Claim 1] & [Claim 6]: teaches the sensor data processor (A/D conversion chip) receiving its working clock directly from the precision time synchronizers (PTP-based crystal oscillator), which is dynamically and continuously updated via message exchanges (periodic updates) to maintain exact time synchronization).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Choi’s regularly cycling reference signal with Xu’s dynamic PTP synchronization architecture, according to known methods, to teach the sensor data processor receiving the time synchronization signal when periodically updated from the precision time synchronizer. Choi teaches a partial discharge location system comprising a time synchronization unit (270) that generates a reference time signal updating at a regular, periodic cycle for its processors. Choi lacks the disclosure of a sensor data processor receiving this periodically updated signal from a Precision Time Protocol (PTP) precision time synchronizer. Xu teaches an acquisition node comprising an A/D conversion chip acting as a sensor data processor that derives its working clock directly from a PTP-tamed crystal oscillator, which acts as the precision time synchronizer. Xu further teaches that this precision time synchronizer continuously exchanges messages to maintain dynamic, periodically updated time synchronization for the sensor data processor. The motivation for this substitution of a known technique to improve similar devices is to prevent clock drift over continuous monitoring periods. The calculation of the partial discharge location requires nanosecond-level accuracy across distributed sensors. Therefore, a POSITA would recognize that a static synchronization event is insufficient. By configuring the processor to receive periodically updated time synchronization signal (via continuous PTP message exchanges as taught by Xu, mapping to the regular cycle taught by Choi), the system ensures long-term timing stability (timestamps) across distributed sensor nodes and predictable, improved fault location precision (KSR).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HUGO NAVARRO/ Examiner, Art Unit 2858 September 28, 2026
/SON T LE/Primary Examiner, Art Unit 2858
