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 .
DETAILED OFFICE ACTION
Priority
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non- English application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2024-12-02 and 2025-10-12 in compliance with the provisions of 37 CFR 1.97 has been considered by the examiner and made of record in the application file.
Claim Status
Claims 1-20 are pending in this application and are under examination in this Office Action. No claims have been allowed.
Claim Rejections – 35 U.S.C. § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for the 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.
As reiterated by the Supreme Court in KSR, and as set forth in MPEP 2141 (R-01.2024), II, the factual inquiries of Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), applied for establishing a background for determining obviousness under 35 U.S.C. §103, are summarized as follows:
Determining the scope and content of the prior art;
Ascertaining the differences between the prior art and the claims at issue;
Resolving the level of ordinary skill in the pertinent art; and
Considering objective evidence indicative of obviousness or non-obviousness, if present.
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 C.F.R. § 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.
Claims 1,12,13,16,17,18,19,20 are rejected under 35 U.S.C. 103 as being unpatentable over Magri et al (US 11,901,938 B2) in view of Zhang et al (CN 110838872 B, EN translation) and further in view of Vall-Llosera et al (US 9,577,748 B2).
Claim 1
Magri teaches the device-side logging that a management entity can obtain: [Magri, p. 4, ll. 14–35] describes a monitoring circuit and buffer memory storing optical-signal samples over a time window (i.e., a retrievable sample set). [Magri, p. 4, ll. 52–54] further indicates the stored data can be accessed externally by a host/other entity (short snippet: “accessed externally”).
Therefore, it would have been obvious that a “network management device” obtains (collects) the stored optical-power sample history from an “optical network device,” because Zhang explicitly contemplates platform-side analytics with device-side data collection/uploading, and Magri provides the concrete logging mechanism in an optical module.
The first sample set comprises a plurality of optical powers obtained by sequentially sampling a first optical signal a plurality of times in a first fault locating time period
Magri teaches sequential sampling and storage over a time window: [Magri, p. 4, ll. 20–35] explains that optical power is sampled at a sampling rate and stored in a buffer within a sliding time window (i.e., multiple samples across a defined period). (short snippet: “sliding time window”).
Magri expressly uses a threshold on received optical power for event/fault detection: “At least one optical power in the first sample set is ≤ an optical power threshold” [Magri, p. 4, ll. 58–63] explains the fault/event detection behavior when the received optical power is “below a threshold value.” (short snippet: “below a threshold value”).
Magri teaches fault/event classification from the shape/trend of the stored power samples. [Magri, p. 6, ll. 4–12] describes trend features such as steep slope/negative gradient and sample-to-sample behavior, and [Magri, p. 6, ll. 6–10] explains using a classification model to identify the event using the stored log.
Magri does not expressly teach Obtaining, by a network management device, a first sample set from a first optical network device. However, in an analogue’s art, Zhang teaches a centralized/upper platform (or an OLT/ONU) obtaining performance data from an optical-link network device and using it for fault identification “Obtaining, by a network management device, a first sample set from a first optical network device”. [Zhang, p. 7, ll. 19–23] explains the method is applicable to “big data analysis online platform” and includes “obtaining … performance data … at least comprising the received optical power.” (short snippet: “received optical power”).
Zhang also frames the analysis over a defined time interval: [Zhang, p. 7, ll. 21–23] teaches extracting characteristic parameters “in the preset time window,” which corresponds to evaluating power behavior during a defined fault-locating period.
Zhang does not expressly teach a fault locating method for an optical network. However, in an analogue’s art, Vall-Llosera teach monitoring an optical network (PON/ODN) and locating faults in the ODN, including triggering fault-location operations when received optical power violates a defined threshold “a fault locating method for an optical network”. [Vall-Llosera, p. 10, ll. 17–29; ll. 31–33] “…violates a defined threshold…”; “…locating a fault in the ODN…”).
Vall-Llosera also teaches defined optical-power thresholds in a PON context: [Vall-Llosera, p. 6, ll. 7–12] requires monitoring optical power at ONT/OLT and using violation of a “defined threshold” for received optical power. (short snippet: “violating a defined threshold”).
Determining, by the network management device, a fault type based on a change trend of the plurality of optical powers
One of ordinary skill would have been motivated to combine Magri’s concrete optical-power logging + event classification with Zhang’s trend-feature extraction / model matching because both address optical-link fault identification using received power behavior over time, and centralized processing (a “network management device” / platform) improves diagnostic accuracy and scalability across many links. Therefore, claim 1 would have been obvious.
Claim 12
ZHANG teaches trend parameters including fluctuation (“rebounding times”) and descending trend (“degradation degree”) used for fault mode identification. [Zhang, p. 3, ll. 13–18; ll. 23–26] “change trend… degradation degree… determine the fault mode.”
ZHANG expressly states the method can be applied to OLT/ONU as well as a platform. [Zhang, p. 7, ll. 18–23] “applied to OLT and ONU…”ZHANG teaches obtaining “received optical power” performance data and that the performance data is a “time sequence” in a “preset time window.” [Zhang, p. 2, ll. 39–43]“received optical power… time sequence… preset time window.”
ZHANG does not expressly teach communications and monitoring of optical power received. However, in an analogue’s art, Vall-Llosera teaches OLT↔ONT communications and monitoring of optical power received at each end, which inherently includes receiving an optical signal at a first device from a second device. [Vall-Llosera, p. 10, ll. 31–33] “optical power received by… ONT… from the OLT… and… by the OLT…”
obtaining, by the first optical network device, a first sample set… optical powers obtained by sequentially sampling the first optical signal… in a first fault locating time period… at least one ≤ threshold
Vall-Llosera teaches a threshold comparison where the received power “violates a threshold value,” satisfying the ≤ threshold condition. [Vall-Llosera, p. 11, ll. 38–40]“violates a threshold value…”
Determining, by the first optical network device, a fault type… based on a change trend of the plurality of optical powers
Therefore, it would have been obvious for the first optical network device itself to perform the same sampling-and-trend fault identification. Claim 12 would have been obvious.
Claim 13
With respect to claim 13, all limitations of claim 12 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera, as set forth above for claim 12. Claim 13 differs from claim 12 by additionally requiring determining that the first sample set meets the first condition (optical powers decrease sequentially within a time period ≤ a first time threshold) and determining that the fault type is a fault on a power supply module configured to supply power to the second optical network device.
MAGRI explicitly lists “power supply failure” as a root cause of an optical link fault scenario and teaches using recorded optical power variation during the fault process to identify the type of fault. [Magri, p. 20, ll. 58–60] “power supply failure… identify the type of fault…”
MAGRI also teaches that optical power is sampled fast enough “to monitor the dynamics of the typical power fault,” supporting a rapid sequential decrease within a bounded time window. [Magri, p. 20, ll. 44–45] “monitor the dynamics of the typical power fault.”
A rapid monotonic optical-power collapse is a predictable signature of loss of drive/bias power at an endpoint; using the time-windowed samples (MAGRI) and trend-based fault identification [ZHANG, p.3, ll.17–18: “descending trend…”).
It would have been obvious to implement the rule-based classification to a “power supply module” fault. Therefore, claim 13 would have been obvious.
Claim 16
With respect to claim 16, all limitations of claim 12 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera, as set forth above for claim 12. Claim 16 differs from claim 12 by additionally requiring receiving a third sample set from the second optical network device, where the third sample set comprises, optical powers obtained by sampling a third optical signal from the first optical network device during a third fault locating time period, and determining the transmission optical path fault based on both the first sample set and the third sample set. Receiving a third sample set from the second device (sampling third optical signal from the first device), then determining fault type based on first + third sample sets as a transmission optical path fault for first and third signals.
Same mapping as Claim 5, but executed by the first optical network device instead of a separate network manager.
ZHANG supports endpoint execution (OLT/ONU) and receiving KPI data / performing extraction and fault identification. [Zhang, p. 7, ll. 18–23; p. 7, ll. 36–44] “applied to OLT and ONU…” / “OLT obtains the KPI data…”
Vall-Llosera supports two-ended monitoring (power at ONT and at OLT). (Vall, p.10, ll.31–33)“monitor optical power… ONT… and… OLT…”
Same as Claim 5; endpoint execution is expressly contemplated by ZHANG and is a routine design to reduce backhaul/latency. Therefore, claim 16 would have been obvious.
Claim 17
With respect to claim 17, all limitations of claim 16 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera, as set forth above for claim 16. Claim 17 differs from claim 16 by additionally requiring that the first fault locating time period and the third fault locating time period at least partially overlap, or that a time interval between them is ≤ a second time threshold. First and third fault locating periods overlap or interval ≤ second time threshold
ZHANG supports preset time windows for time-sequence data. [Zhang,, p. 2, ll. 39–43] “preset time window…”
MAGRI supports sliding time windows around detection. [Magri, p. 20, ll. 45–54] “sliding time window…”
Aligning sample windows across endpoints is a standard diagnostic practice to correlate the same fault event. Therefore, claim 17 would have been obvious.
Claim 18
With respect to claim 18, all limitations of claim 16 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera, as set forth above for claim 16. Claim 18 differs from claim 16 by additionally requiring determining that the first sample set and the third sample set meet the fourth condition (both show downward trends over time periods > the first-time threshold) and determining that the fault occurred on an optical cable connected between the first optical network device and the second optical network device. Fourth condition first and third sample sets both downward trend; both time periods > first time threshold; determine fault on optical cable connected between first and second devices.
ZHANG teaches downward trend characterization (“degradation degree”) [Zhang, p. 3, ll. 17–18] “descending trend…”
ZHANG does not expressly teach fiber/ODN fault or break. However, in an analogue’s art, VALL-LLOSERA teaches fiber/ODN fault or break as cause of the violation. [Vall-Llosera, p. 11, ll. 44–46] “fiber fault or break…”
Same as Claim 7, but first device executes the determination; ZHANG expressly supports OLT/ONU execution. Therefore, claim 18 would have been obvious.
Claim 19
With respect to claim 19, all limitations of claim 16 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera, as set forth above for claim 16. Claim 19 differs from claim 16 by additionally requiring determining that the first sample set and the third sample set meet the seventh condition (first sample set downward trend over a time period > the first-time threshold and third sample set differences ≤ preset threshold) and determining that a fault has occurred on at least one of the recited first and second branch optical paths in the branched transmission arrangement.
Seventh condition — first sample set downward trend (time > threshold) and third sample set stable (difference ≤ preset threshold); determine fault on at least one of first branch optical path and second branch optical path (with defined signal path order)
MAGRI supports bounded variation (stability) metrics. (Magri, p.18, ll.10–16) “amount of variation…”
MAGRI does not expressly teach downward trend. However, in an analogue’s art, ZHANG teaches downward trend parameterization. (Zhang, p.3, ll.17–18) “descending trend…”
Vall-Llosera supports branch topology via splitters and distribution branches. (Vall, p.11, ll.55–68) “splitter… connected… ONTs…”
Same as Claim 10; in a branched splitter topology, stable readings at one endpoint and degradation at the other support localizing the impairment to a branch segment rather than shared trunk, and two-ended monitoring enables that inference. Therefore, claim 19 would have been obvious.
Claim 20
MAGRI discloses an optical module with buffer memory storing sampled optical power and a controller/host access to retrieve and classify the stored series of samples to identify the type of fault. [Magri, p. 20, ll. 39–52] “buffer memory… samples… accessed… externally…”
MAGRI does not expressly disclose a device for optical link fault identification with functional units that obtain received optical power performance data. However, in an analogue’s art, ZHANG expressly discloses a device for optical link fault identification with functional units that (i) obtain received optical power performance data as a time sequence in a preset time window, (ii) extract characteristic parameters indicating change, and (iii) identify fault mode based on the characteristic parameter “A memory including program code; at least one processor… to cause the device to obtain a first sample set… and determine a fault type based on change trend”. [Zhang, p. 4, ll. 1–7] “obtaining unit… receiving optical power… time sequence… extracting… identifying unit…”
ZHANG further teaches matching extracted characteristic parameters with a fault mode identification model to determine fault mode. [Zhang, p. 3, ll. 23–26]“matching… to determine the fault mode.”
Implementing ZHANG’s obtaining/extracting/identifying units as software (program code) on a processor with memory is a routine computer implementation, and MAGRI provides the concrete hardware architecture (sampling, buffer memory, controller/host access) that makes the claimed network-management device implementation straightforward with a reasonable expectation of success. Therefore, claim 20 would have been obvious.
Claims 2,3,4,5,6,7,8,9,10,11,14, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Magri et al in view of Zhang et al and further in view of Vall-Llosera et al and Liu et al (CN 108667513 A, EN translation)
Claim 2
With respect to claim 2, all limitations of claim 1 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera, as set forth above for claim 1. Claim 2 differs from claim 1 by additionally requiring (i) determining that the first sample set meets a first condition in which the sampled optical powers decrease sequentially within the first fault locating time period that is ≤ a first-time threshold, and (ii) determining that the fault type is a fault on a power supply module.
Magri teaches the “rapid/short-window decreasing power” behavior associated with a power fault: [Magri, p. 6, ll. 4–7] describes a very steep downward slope with power falling close to zero around a certain number of samples, and [Magri, p. 6, ll. 15–17] explains that when recorded samples match that pattern, a determination can be made of a “power fault.” (short snippet: “power fault”). This teaches using the short-duration trend behavior as a discriminator (i.e., a time-threshold concept).
Liu further supports threshold/time-period logic at the network manager: [Liu, p. 7, ll. 4–12] describes the network manager comparing loss/received power against set threshold values over specified time periods (T1/T2/T3) to judge fault causes and send alarms (short snippet: “specified time period”).
A power-supply fault is a well-recognized root cause of rapid monotonic power collapse in optical equipment (loss of bias/drive). Given Magri’s explicit “power fault” classification based on a rapid downward trend within a limited sample window and Liu’s explicit management-side threshold/time-period decision logic, it would have been obvious to implement Claim-2’s rule-based decision (short time threshold + decreasing sequence) and label that class as a power-supply module fault in the optical device. Therefore, claim 2 would have been obvious.
Claim 3
With respect to claim 3, all limitations of claim 1 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera, as set forth above for claim 1. Claim 3 differs from claim 1 by additionally requiring fault, and (ii) determining that the fault type is a fault on a transmission optical path that transmits the first optical signal from the second optical network device to the first optical network device.
Magri teaches power-fault patterns from other link-related events by trend shape and gradient over time: [Magri, p. 6, ll. 27–30] explains a “less steep” falling slope compared to a power drop, supporting the concept that longer/slower trend behavior indicates a different fault class than rapid power-fault collapse.
Magri does not expressly teach locating a fault in the optical distribution network. However, in an analogue’s art, Vall-Llosera teaches locating a fault in the optical distribution network when received optical power violates a defined threshold: [Vall-Llosera, p. 3, ll. 1–6] teaches optical-power threshold violation and reflectometry for locating an ODN fault (i.e., an optical-path fault between network devices).
Vall-Llosera does not expressly teach that a network manager uses power-vs-time behavior However, in an analogue’s art, Liu teaches that a network manager uses power-vs-time behavior + thresholds over time periods to judge optical fiber breakage or other optical-path issues: [Liu, p. 7, ll. 4–6] teaches comparing loss power to a threshold over a specified time period to judge whether the optical fiber is broken, and [Liu, p. 7, ll. 6–12] further teaches using thresholds over a time period to classify fiber mechanical damage/aging, etc. This maps directly to identifying an optical transmission path fault from the power-trend behavior over time.
It would have been obvious to classify a longer-duration downward trend as an optical-path fault (rather than a power module fault) because Liu explicitly ties threshold-over-time analysis to fiber/link faults and Vall-Llosera explicitly uses optical-power threshold violations to trigger optical-distribution-network fault location. Combining that with Magri’s trend-based classification framework yields Claim 3’s time-threshold differentiation between power faults and optical-path faults. Therefore, claim 3 would have been obvious.
Claim 4
With respect to claim 4, all limitations of claim 3 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera and Liu, as set forth above for claim 3. Claim 4 differs from claim 3 by additionally requiring (i) obtaining a second sample set by sequentially sampling a second optical signal between a third optical network device and a fourth optical network device over a second fault locating time period, (ii) determining that the second sample set meets a third condition including a downward change trend over a time period that is > the first time threshold, and (iii) determining that the transmission optical path fault is a fault on an optical cable configured to transmit both the first optical signal and the second optical signal.
MAGRI teaches that received optical power is stored as time-series samples collected in a preset time window, each sample including a collection time and a corresponding light-power value “performance data includes time sequence data… collected in a preset time window.”. [Magri, p. 20, ll. 10–18] “performance data … time sequence … collecting time … light power value.”
A “second sample set” is simply another instance of the same taught acquisition process applied to another monitored optical signal/link (here, the “second optical signal”)—i.e., repeatedly sampling optical power values over a defined time window to form a dataset.
Magri does not expressly teach obtaining performance data from a network device. However, in an analogue’s art, ZHANG teaches obtaining performance data from a network device and extracting characteristic parameters from that data, where the performance data includes time-sequence data collected within a preset time window “Obtaining… a second sample set including a plurality of optical powers obtained by sequentially sampling a second optical signal… for a plurality of times in a second fault locating time period…”. [Zhang, p. 2, ll. 38–45]
ZHANG is directed to optical-link fault identification involving “devices in the optical link” and obtaining performance data of network devices participating in the optical link “the second optical signal being an optical signal transmitted between a third optical network device and a fourth optical network device”. [Zhang, p. 1, ll. 27–35]
ZHANG expressly ties optical-link early warning / fault identification to “performance data degradation,” and defines a degradation degree based on (i) a descending trend (via a trend coefficient) and (ii) a minimum optical-power value over a time window “Determining… that the second sample set meets a third condition providing that the plurality of optical powers… exhibit a downward change trend in the second fault locating time period, the second fault locating time period being greater than the first time threshold”. [Zhang, p. 3, ll. 29–35]
ZHANG also makes clear that the characteristic parameter(s) extracted from the time-sequence performance data are used to identify fault mode in the optical link “descending trend coefficient… minimum value of the optical power in a period…” (including degradation discovery/early warning). [Zhang, p. 1, ll. 27–35]“extracting the characteristic parameter… identifying the fault mode…”
A “downward change trend” is encompassed by Zhang’s extraction of characteristic parameters that indicate the change/trend of received optical power performance data collected as a time sequence within a preset time window for subsequent fault-mode identification. [Zhang, p. 4, ll. 1–14] “performance data … time sequence … extracting … characteristic parameter indicating the change … identify the fault mode.”
ZHANG does not expressly teach compare monitored optical power against a threshold. However, in an analogue’s art, Vall-Llosera explicitly teaches comparing monitored optical power against a threshold and determining the optical power violates (falls below) that threshold” wherein at least one optical power included in the second sample set is less than or equal to the optical power threshold”. [Vall-Llosera, p. 13, ll. 21–33] “monitor… optical power… compare against a threshold… determines… violates the threshold.” If a monitored optical power “violates” a threshold, then at least one sampled optical power value is at or below the claimed threshold value.
Vall-Llosera further teaches an optical network where optical signals are transmitted between optical-network devices (e.g., OLT/ONT) and optical power is monitored on those links for fault analysis “obtaining the performance data of the network device… identifying the fault mode in the optical link…”. [Vall-Llosera, p. 13, ll. 21–33]“optical line terminal (OLT)… optical network terminal (ONT)… monitor… optical power…”
The claimed “third” and “fourth” optical network devices read on the communicating network devices in the optical link (e.g., OLT/ONT, or any other paired devices whose performance data is monitored). The “second optical signal” reads on the signal exchanged over that link whose optical power is being sampled. Vall-Llosera teaches that once optical power violates the threshold, an OFDR analysis is performed to determine the location of a fault in an optical fiber of the optical distribution network “determining… that a fault has occurred on an optical cable configured to transmit the first optical signal and the second optical signal”. [Vall-Llosera, p. 13, ll. 21–33].Vall-Llosera further teaches performing OFDR analysis using optical signals at at least two different wavelengths, consistent with multiple optical signals being carried/used in the same fiber/cable for diagnosing the fault location “perform… OFDR analysis to determine a location of a fault in an optical fiber…”. [Vall-Llosera, p. 10, ll. 17–29]
Vall-Llosera`s OFDR-based pinpointing identifies that the fault is physically on the optical fiber (i.e., the optical cable/fiber segment) “OFDR analysis… at least two different wavelengths.”. Where the first and second optical signals traverse the same fiber segment (e.g., shared cable section, shared ODN fiber, or multi-wavelength signaling), locating the fault in that fiber corresponds to determining a fault on the optical cable configured to transmit both signals.
One of ordinary skill in the art would have been motivated to combine ZHANG’s fast fault-mode identification from time-sequence optical performance data with Vall-Llosera threshold-triggered OFDR fault-location technique in order to (i) not only identify that a degradation/fault mode exists from a descending optical-power trend, but also (ii) pinpoint the physical cable/fiber location responsible for the degradation when threshold violations occur—thereby improving troubleshooting speed and repair efficiency, exactly the type of operational benefit ZHANG emphasizes (reducing manual investigation and improving fault removing efficiency). [Zhang, p. 1, ll. 27–35]
Further, one of ordinary skill would have had a reasonable expectation of success because both references operate on the same observable: received/monitored optical power behavior on optical links, and Vall-Llosera`s OFDR procedure is explicitly invoked when optical power crosses a threshold—an event consistent with Zhang’s degradation/trend-based detection and early-warning framework. [Vall-Llosera, p. 13, ll. 21–33; Zhang, p. 3, ll. 29–35]
Accordingly, claim 4 would have been obvious to one of ordinary skill in the art at the time of the invention.
Claim 5
With respect to claim 5, all limitations of claim 1 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera, as set forth above for claim 1. Claim 5 differs from claim 1 by additionally requiring (i) obtaining a third sample set at the second optical network device by sequentially sampling a third optical signal from the first optical network device over a third fault locating time period, and (ii) determining that the fault type is a transmission optical path fault based on the first sample set and the third sample set.
MAGRI teaches obtaining a stored “series of samples” of optical power (a sample set) over a time period, which is retrievable for later analysis. [Magri, p. 7, ll. 15–23]“Store… samples… predetermined time… retrieve… series of samples.”
MAGRI does not expressly teach the architecture where an ONU transmits KPI/performance data. However, in an analogue’s art, ZHANG teaches the architecture where an ONU transmits KPI/performance data to an OLT or platform, and the receiving entity “obtains the KPI data” for analysis in a time window. [Zhang, p. 7, ll. 36–44] “OLT obtains the KPI data… perform feature extraction…”
ZHANG teaches identifying fault mode from characteristic parameters extracted from time-sequence performance data. [Zhang, p. 2, ll. 39–43] “performance data… time sequence… preset time window… identify the fault mode.”
ZHANG does not expressly teach bidirectional monitoring between an OLT and ONT. However, in an analogue’s art, Vall-Llosera teaches bidirectional monitoring between an OLT and ONT, which inherently yields multiple optical-power datasets at different endpoints (e.g., one from the ONT side and another from the OLT side). [Vall-Llosera, p. 10, ll. 31–33] “monitor optical power received… by ONT… and… by the OLT…”
Therefore: It would have been obvious that a management entity obtains a “third sample set” from the second device (e.g., ONT/ONU) consisting of sequential optical-power samples for the “third optical signal” over a fault-locating time period, because Vall-Llosera and ZHANG explicitly contemplate endpoints reporting/using power KPI data, and MAGRI teaches storing and retrieving a time series sample set.
determining… based on the first sample set and the third sample set… that the fault type is a fault on a transmission optical path configured to transmit the first optical signal and the third optical signal
VALL-LLOSERA teaches that threshold violations can be caused by “a faulty ODN… a fiber fault or break,” which is a transmission optical-path fault between endpoints. [Vall-Llosera, p. 11, ll. 38–47] “faulty ODN… a fiber fault or break…”
VALL-LLOSERA does not expressly teach a network manager forming received-power-time. However, in an analogue’s art, LIU teaches a network manager forming received-power-time / loss-power-time curves from recorded power and using “change rule” of the curve for judgment, which supports using two ends’ time behaviors jointly. [Liu, p. 3, ll. 12–15] “receiving power-time curve… loss power-time curve… change rule…”
One of ordinary skill would have been motivated to use two-ended sample sets (first and third) to improve confidence that the fault lies on the shared transmission path rather than a single endpoint, because Vall-Llosera explicitly distinguishes fiber/ODN faults from endpoint faults and ZHANG/LIU teach time-sequence trend-based fault identification using such KPI data. Therefore, claim 5 would have been obvious.
Claim 6
With respect to claim 6, all limitations of claim 5 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera and Liu, as set forth above for claim 5. Claim 6 differs from claim 5 by additionally requiring that the first fault locating time period and the third fault locating time period at least partially overlap, or that a time interval between the first fault locating time period and the third fault locating time period is ≤ a second time threshold.
MAGRI teaches storing samples in a time window around fault detection (pre-fault and post-fault) and using a “sliding time window,” which supports aligning time periods between endpoints (overlap or short gap). [Magri, p. 20, ll. 45–54] “sliding time window… around the time of detection…”
MAGRI does not expressly teach performance data is the time sequence in the preset time window. However, in an analogue’s art, ZHANG teaches that the “performance data is the time sequence in the preset time window,” which supports defining comparable time windows at different devices (overlapping or closely spaced). [Zhang, p. 2, ll. 39–43] “time sequence… preset time window.”
ZHANG does not expressly teach the network manager records power over time. However, in an analogue’s art, LIU teaches the network manager records power over time and forms power-time curves, which necessarily uses defined time intervals and supports comparing intervals between devices. [Liu, p. 3, ll. 12–14] “recorded… receiving power-time curve…”
Aligning the time windows (overlap or small separation) is a standard way to ensure that two sample sets correspond to the same fault episode and improves diagnostic reliability; MAGRI explicitly uses time windows around fault events, and ZHANG/LIU depend on time-window KPI sequences. Therefore, claim 6 would have been obvious.
Claim 7
With respect to claim 7, all limitations of claim 5 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera and Liu, as set forth above for claim 5. Claim 7 differs from claim 5 by additionally requiring determining that the first sample set and the third sample set satisfy a fourth condition in which (i) the first sample set exhibits a downward change trend in the first fault locating time period, (ii) the third sample set exhibits a downward change trend in the third fault locating time period, and (iii) both time periods are > the first time threshold, and then determining that the fault occurred on an optical cable connected between the first optical network device and the second optical network device.
ZHANG defines a parameter set for “characterizing the performance data change trend,” including “degradation degree… characterizing the descending trend.” [Zhang, p. 3, ll. 13–18]“degradation degree… descending trend…”
ZHANG does not expressly teach that when received optical power violates a threshold. However, in an analogue’s art, Vall-Llosera teaches that when received optical power violates a threshold, the violation may be caused by “a faulty ODN… a fiber fault or break,” i.e., a cable/fiber between endpoints. “Determining a fault has occurred on an optical cable connected between the first optical network device and the second optical network device” [Vall-Llosera, p. 11, ll. 38–47] “fiber fault or break…”
Vall-Llosera does not expressly teach using power-time / loss-time curve. However, in an analogue’s art, LIU teaches using power-time / loss-time curve “change rule” and calculating time to reach a set threshold, supporting a “time threshold” decision boundary. [Liu, p. 3, ll. 21–26] “calculates… reaches… set… threshold value… of time.”
If both ends observe a sustained descending trend (both time windows exceeding the first-time threshold), a person of ordinary skill would be motivated to localize the fault to the interconnecting cable/fiber rather than a single endpoint because two-ended concordant degradation is classic evidence of a line impairment; Vall-Llosera explicitly identifies fiber/ODN faults as a cause of threshold/power abnormalities. Therefore, claim 7 would have been obvious.
Claim 8
With respect to claim 8, all limitations of claim 5 are taught by the combination of Magri in view of Zhang and further in view of Liu and Vall-Llosera, as set forth above for claim 5. Claim 8 differs from claim 5 by additionally requiring determining that the first sample set and the third sample set satisfy a fifth condition in which (i) the first sample set exhibits a downward change trend in the first fault locating time period, (ii) a change trend of the third sample set fluctuates in the third fault locating time period, and (iii) both time periods are > the first time threshold, and then determining that the fault occurred on an optical cable connected between the first optical network device and the second optical network device.
ZHANG teaches downward trend via “degradation degree… descending trend.” (Zhang, p.3, ll.17–18) “descending trend…”
ZHANG also teaches fluctuation characterization via “rebounding times… characterizing the fluctuation times.” [Zhang, p. 3, ll. 15–16] “rebounding times… fluctuation…”
ZHANG does not expressly teach the abnormal power could be caused by break. However, in an analogue’s art, Vall-Llosera teaches the abnormal power could be caused by “a faulty ODN… a fiber fault or break.” ,“Determining fault on optical cable connected between first and second devices”
[Vall-Llosera, p. 11, ll. 44–47] “faulty ODN… fiber fault or break…”
A person of ordinary skill would still be motivated to assign the fault to the interconnecting cable/fiber when one end shows sustained degradation and the other shows instability (fluctuation) during the same episode, because branch/connector intermittency and fiber stress can manifest as fluctuations at one end while appearing as net degradation at another; ZHANG explicitly models both “descending” and “fluctuation” trends, and VALL-LLOSERA identifies fiber faults as a root cause. Therefore, claim 8 would have been obvious.
Claim 9
With respect to claim 9, all limitations of claim 5 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera and Liu, as set forth above for claim 5. Claim 9 differs from claim 5 by additionally requiring determining that the first sample set and the third sample set satisfy a sixth condition in which (i) a change trend of the first sample set fluctuates in the first fault locating time period, (ii) the third sample set exhibits a downward change trend in the third fault locating time period, and (iii) both time periods are > the first time threshold, and then determining that the fault occurred on an optical cable connected between the first optical network device and the second optical network device.
ZHANG teaches fluctuation via “rebounding times.” [Zhang, p. 3, ll. 15–16] “fluctuation times…”
ZHANG teaches downward trend via “degradation degree.” (Zhang, p.3, ll.17–18): “descending trend…” Determining fault on optical cable connected between first and second devices.
ZHANG does not expressly teach the same fiber/ODN fault. However, in an analogue’s art, Vall-Llosera teaches the same fiber/ODN fault cause for power threshold violations. (Vall, p.11, ll.44–47) “fiber fault or break…”
Same rationale as Claim 8, with endpoints swapped. Using ZHANG’s modeled trend parameters and VALL-LLOSERA’s fiber-fault localization rationale, it would have been obvious to attribute the impairment to the connecting cable/fiber when the correlated episode produces downward trend at one end and fluctuation at the other. Therefore, claim 9 would have been obvious.
Claim 10
With respect to claim 10, all limitations of claim 5 are taught by the combination of Magri in view of Zhang and further in view of Liu and Vall-Llosera, as set forth above for claim 5. Claim 10 differs from claim 5 by additionally requiring determining that the first sample set and the third sample set satisfy a seventh condition in which (i) the first sample set exhibits a downward change trend in the first fault locating time period where the first fault locating time period is > the first time threshold, and (ii) a difference between any two of the optical powers in the third sample set is ≤ a preset threshold, and then determining that a fault has occurred on at least one of a first branch optical path and a second branch optical path in the recited branched transmission arrangement.
MAGRI teaches evaluating “amount of variation of the power” and “amount of noise” in the optical power series, which supports a “difference between any two samples” being bounded by a threshold (i.e., low variation). [Magri, p. 18, ll. 10–16] “amount of variation… amount of noise…”
Determining that a fault occurred on at least one of a first branch optical path and a second branch optical path… (branch path + optical cable topology)
MAGRI does not expressly teach downward trend characterization. However, in an analogue’s art, ZHANG teaches downward trend characterization. [Zhang, p. 3, ll. 17–18] “descending trend…”.
ZHANG does not expressly teach a point-to-multipoint PON architecture. However, in an analogue’s art, Vall-Llosera teaches a point-to-multipoint PON architecture with splitters and branches (distribution fibers) and monitoring of OLT/ONT optical power. [Vall-Llosera, p. 11, ll. 55–68] “splitter… split ratio… connected… to… ONTs…”
Vall-Llosera also teaches monitoring power at both ends (OLT/ONT), which enables distinguishing which portion/branch is affected. [Vall-Llosera, p. 10, ll. 31–33] “monitor optical power… ONT… and… OLT…”
In a branched PON, if one endpoint’s samples show sustained degradation while the other endpoint’s samples show little variation (bounded differences), one of ordinary skill would be motivated to localize the fault to a branch segment (one of the branch optical paths) rather than the shared trunk, because branch-specific attenuation/intermittency can affect one direction/endpoint differently depending on topology and monitoring point; Vall-Llosera explicitly describes a splitter-based branched topology and two-ended monitoring, and MAGRI provides the concrete “low variation” metric for sample stability. Therefore, claim 10 would have been obvious.
Claim 11
With respect to claim 11, all limitations of claim 5 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera and Liu, as set forth above for claim 5. Claim 11 differs from claim 5 by additionally requiring determining that the first sample set and the third sample set satisfy an eighth condition in which (i) a difference between any two of the optical powers in the first sample set is ≤ a preset threshold, (ii) the third sample set exhibits a downward change trend in the third fault locating time period, and (iii) the third fault locating time period is > the first time threshold, and then determining that a fault has occurred on at least one of a third branch optical path and a fourth branch optical path in the recited branched transmission arrangement.
MAGRI teaches classifying/logging using metrics including “amount of variation” and “noise,” supporting a bounded difference threshold across a sample set. [Magri, p. 18, ll. 10–16] “amount of variation…”
MAGRI does not expressly teach degradation degree. However, in an analogue’s art, ZHANG teaches downward trend characterization (degradation degree). [Zhang, p. 3, ll. 17–18]“descending trend…”
ZHANG does not expressly teach the same splitter-based branched distribution topology. However, in an analogue’s art, Vall-Llosera teaches the same splitter-based branched distribution topology and monitoring of both directions (OLT↔ONT) which supports identifying faults on branch segments in either direction. [Vall-Llosera, p. 10, ll. 31–33; p. 11, ll. 55–68] “monitor optical power… OLT… ONT…” / “splitter… connected… ONTs…”
Vall-Llosera does not expressly teach computing time to reach a receiving threshold. However, in an analogue’s art, LIU teaches computing time to reach a receiving threshold from a receiving power-time curve, supporting the “time period > threshold” gating logic. [Liu, p. 3, ll. 24–26]“receiving power… to… threshold value… of time.”
Determining fault on at least one of third branch optical path and fourth branch optical path… (reverse direction topology)
Same reasoning as Claim 10, but applied to the reverse signaling direction (first device sending the “third optical signal” toward the second device). With Vall-Llosera’s branched PON topology + two-ended monitoring and ZHANG/MAGRI/LIU’s time-sequence trend and variation thresholds, it would have been obvious to localize the impairment to one or more branch segments. Therefore, claim 11 would have been obvious.
Claim 14
With respect to claim 14, all limitations of claim 12 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera, as set forth above for claim 12. Claim 14 differs from claim 12 by additionally requiring determining that the first sample set meets the second condition (downward change trend over a time period > a first-time threshold) and determining that the fault type is a fault on a transmission optical path configured to transmit the first optical signal.
ZHANG teaches “degradation degree… descending trend” for received optical power and fault mode identification based on the extracted trend parameter. (Zhang, p.3, ll.17–18; ll.23–26)“descending trend… determine the fault mode.”
ZHANG does not expressly teach threshold violation could be caused by. However, in an analogue’s art, Vall-Llosera teaches that threshold violation could be caused by “a faulty ODN… a fiber fault or break,” i.e., transmission path fault. [Vall-Llosera, p. 11, ll. 44–46] “faulty ODN… fiber fault or break…”
Using a longer observation window (> time threshold) to distinguish path degradation from fast power-fault collapse is a predictable design choice supported by LIU’s time-to-threshold framework (Liu, p.3, ll.24–26) “to… threshold value… of time”). Therefore, claim 14 would have been obvious.
Claim 15
With respect to claim 15, all limitations of claim 14 are taught by the combination of Magri in view of Zhang and further in view of Vall-Llosera and Liu, as set forth above for claim 14. Claim 15 differs from claim 14 by additionally requiring receiving a second sample set from sampling a second optical signal between a third optical network device and a fourth optical network device, determining the second sample set meets the third condition (downward trend over a time period > the first-time threshold), and determining that the transmission optical path fault is a fault on an optical cable configured to transmit both the first and second optical signals. ZHANG teaches obtaining received optical power KPI time-sequence data in preset time windows and extracting characteristic parameters for fault mode identification. (Zhang, , p.2, ll.39–43) “time sequence… preset time window…”
ZHANG does not expressly teach that upon threshold violation, an OFDR analysis of the ODN is performed. However, in an analogue’s art, VALL-LLOSERA teaches that upon threshold violation, an OFDR analysis of the ODN is performed and the ODN may have “a fiber fault or break,” and the remote node distinguishes “two or more wavelengths” for OFDR, consistent with multiple optical signals on the same fiber/cable. (Vall-Llosera, , p.11, ll.38–47; p.11, ll.44–46) “receive… OFDR-signal… fiber fault or break…”
VALL-LLOSERA further teaches OFDR may use “at least two different wavelengths.” (Vall-Llosera, , p.10, ll.23–24) “at least two different wavelengths…”
When two monitored optical signals (or wavelengths) show abnormal power behavior and the OFDR analysis indicates a fiber fault, it would have been obvious to conclude the shared optical cable is faulty, because VALL-LLOSERA expressly uses OFDR to analyze the ODN fiber and explicitly supports multi-wavelength operation; ZHANG provides the trend-based detection over time windows. Therefore, claim 15 would have been obvious.
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
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/MOHAMMED ABDELRAHEEM/Examiner, Art Unit 2635
/DAVID C PAYNE/Supervisory Patent Examiner, Art Unit 2635