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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/6/26 has been entered.
Response to Arguments
101 Rejection
The examiner disagrees that the claims are not directed towards an abstract idea. The claims here are merely an abstract idea implemented on a computer and are not directed to an improvement in the way computers operate or monitoring an optical fiber health, as there is no active monitoring step using the result of the abstract idea. While the claimed invention aids in determining an impact factor, the calculations for that determination are performed by a general-purpose computer. Thus here, as in Electric Power, “the focus of the claims is not on … an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools.” Elec. Power, 830 F.3d 1350, 2016 WL 4073318, at *4 [119 USPQ2d 1739].
Further, the claims as an ordered combination do not provide significantly more to the abstract idea, as the structure recited merely links the abstract idea to a field of use while the outputted alarm and suggestions merely read as instructions to apply the excepts; as the claim only recites the idea of a solution or outcome and fails to recite details of how a solution to a problem is accomplished.
Further, the claims do not provide details as to any non-conventional software for enhancing the calculation process based on machine learning and training. In fact, the machine learning model is recited as merely a tool performing a calculative process, as its not positively recited as part of the method steps, while the result is purely data, i.e. an impact factor, which has no impact to the fiber optical system itself. Therefore, the machine learning model fails to providing significantly more or integrate the abstract idea into a practical application.
Applicant alleges that the claims are an improvement on the technology of monitoring a fiber optic system, however this argument is not persuasive. Analyzing and performing the determination using and based on the ML model are some of the abstract concepts identified by the examiner in Step 2A. In Step 2B, the identified abstract ideas cannot be the concepts themselves that improve a technology or technical field, such that the claims themselves amount to significantly more than the abstract concept. As states about, the result of the identified abstract idea, i.e. impact factor, provides no improvement in the monitoring of optical fiber health, as there is no step of monitoring the fiber optic system using the impact factor.
Lastly, the reason why the machine learning model was evaluated at a high level of generality is because the claim lacks any active steps of training or how using the machine learning model improves the monitoring of a fiber optical system. How does this impact factor aid in the monitoring of the fiber optic system? As currently, the result as no impact on any of the additional elements recited. The claim merely implements mathematical operations based on the machine learning model, which under the broadest and most reasonable interpretation, purely involves math. Therefore, the examiner is not persuaded.
112 Rejection
Based on applicant’s filed remarks, specifically those seen in pages 8-9, the previously set for 112 Rejection(s) has been withdrawn.
102 Rejection
Based on applicant’s filed amendments, the previously set for 102 Rejection has been withdrawn.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites responsive to changes between the plurality of OTDR traces being above a threshold, analyzing the changes between the plurality of OTDR traces with a trained machine learning model; and determining an impact factor based on the machine learning model, wherein the impact factor is a classification of the changes between the plurality of OTDR traces, wherein the trained machine learning model is trained to distinguished between changes in the OTDR traces caused by variation in Raman amplification performance and changes caused by degradation or loss with the optical fiber itself, to enable monitoring of optical fiber health over time independently from Raman amplification effects wherein the impact factor classifies the changes between the plurality of OTDR traces as at least one of (i) a change of fiber loss, (ii) an unexpected change of Raman amplification. or (iii) a change of channel loading condition which falls into the abstract idea grouping of mathematical concepts; as utilizing machine learning models and the training thereof heavily rely on mathematical concepts, as evidenced by applicant disclosure, see para. [0051-0070]. The limitations directed towards the determining an impact factor further involves mathematical operations, as a differential between traces and baseline traces is what is used to classify the collected data; see para. [0064-0068].
This judicial exception is not integrated into a practical application because although the claim defines these mathematical operations occurring from a non-transitory computer readable medium that causes one or more processors to perform these calculative steps, these generically claimed computer elements are merely acting as tools for performing the abstract idea; as neither the performance or result of the abstract idea improves their operation, thereby failing to integrate the abstract idea into a practical application. MPEP 2106.05(a)
The claim recites an additional element step of obtaining data associated with a plurality of Optical Time Domain Reflectometer (OTDR) traces taken in-service with Raman amplification ON in an optical fiber system each performed at a different time. This additional element merely reads as a data gathering step, where the data is purely fed into the abstract idea. The additional element obtaining step thereby fails to integrate the abstract idea into a practical application, as neither the result or performance of the abstract idea betters the data collection steps. MPEP 2106.05(g)
The additional element of the optical fiber system and the defined changes being at least one of a change of fiber loss, an unexpected change of Raman amplification, or a change of channel loading condition merely links the abstract idea to a field of use, as neither the result or the performance of the abstract has any real-world effect on the system or what the changes reflect. MPEP 210605(h)
Lastly, the additional element steps of raising an alarm based on the impact factor and with suggested corrective actions responsive to the impact factor being the change of fiber loss or the unexpected change of Raman amplification reads a mere instruction to apply the excepts, as the claim gives no details of how the alarm betters the system and covers every corrective action, under the broadest and reasonable interpretations; i.e. the claim recites only the idea of a solution or outcome by failing to recite details of how a solution to a problem is accomplished. MPEP 2106.05(f)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the generically claimed computer elements are merely acting as tools for performing the abstract idea. The additional element data gathering steps merely gather the data needed to perform the abstract idea without amounting to significantly more; as neither in combination or alone, do these additional elements amount to significantly more than the abstract idea. Lastly, the language related to the optical fiber system merely links the abstract idea to a field of use while the alarm and suggested corrections read as mere “to apply” limitation without providing significantly more or integrating the abstract idea into a practical application. Note: Claims 10 and 19 are rejected similarly.
Claims 2 and 11 further define the variables used by the identified abstract idea falling into the abstract idea grouping of mathematic operations. The further defined variables, i.e. a plurality of change of lumped loss or reflection events at different locations of fiber, change of Raman amplification after change of Raman configuration, change of fiber loss, unexpected change of Raman amplification, and change of channel loading condition merely link the abstract idea to a field of use without providing significantly more or integrating the abstract idea into a practical application. MPEP 2106.05(f)
Claims 3 and 12 further define the mathematical approach to analyzing the data without providing significantly more or integrating the abstract idea into a practical application. Additionally, the recited step of storing merely defines a generic computer operation needed to perform the identified abstract idea in a computer environment. Therefore, the act of storing fails to provide significantly more or integrate the abstract idea into a practical application.
Claims 4 and 13 further define the abstract idea by reciting smoothing and down sampling the obtained traces. Such an operation further defines mathematical concepts without providing significantly more or integrating the abstract idea into a practical application. Additionally, such filtering is well-known and routine in the art of OTDR trace monitoring; as evidenced by the relied upon reference below.
Claims 5, 14 and 20 define an additional element step of raising an alarm based on the result of the abstract idea with suggested corrective actions. The examiner finds raising an alarm and suggestive corrective actions as merely displaying the result of the abstract idea, similar to Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016). Therefore, the limitation attempts to limit the use of the abstract idea to a particular technological environment without providing significantly more or integrating the abstract idea into a practical application. MPEP 2106.05(f)
Claims 6 and 15 further define wherein change of lumped loss or reflection events are detected based on comparing OTDR reported events between current OTDR trace of the plurality of OTDR traces and the baseline. The examiner finds the limitation further defines an abstract idea that falls into the abstract idea grouping of mental processes. As comparing data, with the aid of pen and paper, can occur in the human mind.
Claims 7 and 16 further define the additional element step of data gathering without providing significantly more or integrating the abstract idea into a practical application.
Claims 8, 9, 17 and 18 further define the abstract idea falling into the abstract idea grouping of mathematical concepts, as training a ML model using randomly selected sample data exhibiting given impact factors relies heavily on mathematical concepts. Therefore, claims fails to provide significantly more or integrate the abstract idea into a practical application.
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.
Claim(s) 1-3, 6-9, 10-12 and 15-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rapp (2020/0049587) in view of Nasle et al. (2009/0113049).
With respect to claim 10, Rapp teaches a method comprising steps of: obtaining data associated with a plurality of Optical Time Domain Reflectometer (OTDR) traces (i.e. measured OTDR traces) taken in-service with Raman amplification ON [0021] in an optical fiber system (16) each performed at a different time (as the obtained data is in response to a succession of emitted light pulses at different time; abstract); responsive to changes between the plurality of OTDR traces being above a threshold, analyzing the changes between the plurality of OTDR traces with a trained machine learning model (as depending on a detected change above a preset value, as indirectly taught, a machine learning technique is applied to determine the detected change is due to an event or a change in diameter; [0027]); and determining an impact factor
(i.e. determine if the change is due to the event or change in diameter) based on the machine learning model [0027], wherein the impact factor is a classification of the changes between the plurality of OTDR traces (as Rapp explicitly discloses the classification of a detected changes between OTDR changes using machine learning to determine a change in reflected power caused by an attenuation event or a change in the mode field diameter; [0027]), wherein the trained machine learning model trained (as disclosed in [0027]) to distinguished between changes in the OTDR traces caused by variation in Raman amplification performance and changes caused by degradation or loss with the optical fiber itself, to enable monitoring of optical fiber health over time independently from Raman amplification effects (as Raman teaches the machine learning model is specifically designed to distinguish between different types of events, such as attenuation events/fiber degradation and changes in mode field diameter, which can be induced by Raman amplification, by analyzing OTDR traces; therefore, the ML model is trained to isolate unexpected changes from expected changes in Raman gain, thereby allowing the monitoring of fiber health), wherein the impact factor classifies the changes between the plurality of OTDR traces as a change of fiber loss (i.e. as a change of attenuation is an indication of fiber degradation).
Rapp remains silent regarding steps further include raising an alarm based on the impact factor and with suggested corrective actions responsive to the impact factor being the change of fiber loss or the unexpected change of Raman amplification.
Archambault et al. teaches a similar method that includes a step of raising an alarm based on the impact factor and with suggested corrective actions responsive to the impact factor being the change of fiber loss or the unexpected change of Raman amplification.
Nasle et al. teaches a similar method that teaches a step of raising an alarm (as Nasle et al. teaches sending alarms; [0059]) based on an impact factor (as Nasle et al. teaches the alarms are based on the real-time data deviation determined by a machine learning engine; [0057] and Fig. 19) and with suggested corrective actions responsive to the impact factor being the change of fiber loss or the unexpected change of Raman amplification (Nasle et al. teaches displaying a alarm; [0066], where the alarm can indicated a needed repair).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the method of Rapp to include the step of producing an alarm, suggesting repairs based on detected conditions. Nasle et al. teaches such a modification aids in improving alarm conditions, thereby preventing false conclusions; [0012].
With respect to claim 1, Rapp as modified teaches a non-transitory computer-readable medium (as indirectly taught through the disclosed use of a machine trained model) comprising instructions that, when executed, cause one or more processors (as indirectly taught and modified) to perform the rejected steps of claim 10.
With respect to claims 2 and 11, Rapp teaches the method wherein the impact factors include a plurality of change of lumped loss or reflection events at different locations of fiber (as seen in Fig. 2), change of Raman amplification after change of Raman configuration (as an event is capable of being a change of Raman configurations; as Rapp teaches using Raman amplifiers), change of fiber loss (i.e. as a change in diameter can indicate fiber loss), unexpected change of Raman amplification (as an event is capable of being a loss of Raman configurations; as Rapp teaches using Raman amplifiers), and change of channel loading condition (as an event is capable of being a change of channel loading conditions; as Rapp teaches using Raman scattering, which indirectly teaches loaded channels).
With respect to claims 3 and 12, Rapp teaches the method wherein the steps further include storing a specific OTDR trace of the plurality of OTDR traces as a baseline (as information is derived from at least the first trace is used to compare, thereby reading as a specific OTDR trace is used as a baseline; [0028]); and performing the analyzing based on a current OTDR trace of the plurality of OTDR traces and the baseline (i.e. as Rapp teaches using cases in which detected changes is due to one root cause and then compare those detected changes from that current trace to the first OTDR trace set as the baseline; [0028]).
With respect to claims 6 and 15, Rapp teaches the method wherein change of lumped loss or reflection events are (capable of being) detected based on comparing OTDR reported events (as seen in Fig. 2) between current OTDR trace (as sensed) of the plurality of OTDR traces (i.e. of the traces subsequently sensed during monitoring after the first trace has been set as the baseline) and the baseline (i.e. first trace).
With respect to claims 7 and 16, Rapp teaches the method wherein the plurality of OTDR traces are performed in-service (i.e. as Rapp teach during operation of the fiber optic).
With respect to claims 8 and 17, Rapp teaches the method wherein the steps further include training the machine learning model prior to the analyzing (as Rapp teaches using a trained machine learning model for the analysis and determination; [0027]).
With respect to claims 9 and 18, Rapp teaches the method wherein the training utilizes randomly selected sample that exhibit a given impact factor (as Rapp teaches in [0027] a machine learning tool is trained using aforementioned first, and optionally, second, and/or third OTDR traces for training and detecting events).
With respect to claim 19, Rapp teaches a network element in an optical network (seen in Fig. 10) comprising an Optical Time Domain Reflectometer (abstract) and circuitry (as indirectly taught) connected thereto, wherein the circuitry is configured to: obtain data associated with a plurality of Optical Time Domain Reflectometer (OTDR) traces (i.e. measured OTDR traces) taken in-service with Raman amplification ON [0021] in an optical fiber system (16) each performed at a different time (as the obtained data is in response to a succession of emitted light pulses at different time; abstract); responsive to changes between the plurality of OTDR traces being above a threshold, analyze the changes between the plurality of OTDR traces with a trained machine learning model (as depending on a detected change above a preset value, as indirectly taught, a machine learning technique is applied to determine the detected change is due to an event or a change in diameter; [0027]); and determine an impact factor( i.e. determine if the change is due to the event or change in diameter) based on the machine learning model [0027], wherein the impact factor is a classification of the changes between the plurality of OTDR traces (as Rapp explicitly discloses the classification of a detected changes between OTDR changes using machine learning to determine a change in reflected power caused by an attenuation event or a change in the mode field diameter; [0027]), wherein the trained machine learning model trained (as disclosed in [0027]) to distinguished between changes in the OTDR traces caused by variation in Raman amplification performance and changes caused by degradation or loss with the optical fiber itself, to enable monitoring of optical fiber health over time independently from Raman amplification effects (as Raman teaches the machine learning model is specifically designed to distinguish between different types of events, such as attenuation events/fiber degradation and changes in mode field diameter, which can be induced by Raman amplification, by analyzing OTDR traces; therefore, the ML model is trained to isolate unexpected changes from expected changes in Raman gain, thereby allowing the monitoring of fiber health).
Rapp remains silent regarding steps further include raising an alarm based on the impact factor and with suggested corrective actions responsive to the impact factor being the change of fiber loss or the unexpected change of Raman amplification.
Archambault et al. teaches a similar method that includes a step of raising an alarm based on the impact factor and with suggested corrective actions responsive to the impact factor being the change of fiber loss or the unexpected change of Raman amplification.
Nasle et al. teaches a similar method that teaches a step of raising an alarm (as Nasle et al. teaches sending alarms; [0059]) based on an impact factor (as Nasle et al. teaches the alarms are based on the real-time data deviation determined by a machine learning engine; [0057] and Fig. 19) and with suggested corrective actions responsive to the impact factor being the change of fiber loss or the unexpected change of Raman amplification (Nasle et al. teaches displaying a alarm; [0066], where the alarm can indicated a needed repair).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the method of Rapp to include the step of producing an alarm, suggesting repairs based on detected conditions. Nasle et al. teaches such a modification aids in improving alarm conditions, thereby preventing false conclusions; [0012].
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rapp (2020/0049587) in view of Nasle et al. (2009/0113049), as applied to claims 1 and 10, further in view of Vandewede et al. (EP 1772 979A2).
With respect to claims 4, and 13, Rapp teaches all that is claimed in the above rejection of claims 1 and 10 but remains silent regarding the method wherein the steps further include prior to the analyzing and the determining, one or more of smoothing and down sampling one of the plurality of OTDR traces.
Vandewede et al. teaches a similar method that incudes prior to analysis and determining steps, smoothing and down sampling a OTDR trace (as Vandewede et al. teaches in [0057-0059] using a digital decimation filter and mathematical operations to down sample and smooth the OTDR trace).
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to substitute the filter techniques taught in Rapp with the techniques taught in Vandewede et al. to achieve the predictable results of filtering a trace for analysis. Such a modification aids in providing localized information about a section of a fiber [0059], thereby improving the event detection in Rapp.
Claim(s) 5, 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rapp (2020/0049587) in view of Nasle et al. (2009/0113049), as applied to claims 1, 10 and 19 further in view of Archambault et al. (EP 2701248 A2).
With respect to claims 5, 14 and 20, Rapp teaches all that is claimed in the above rejection of claims 1, 10 and 19 but remains silent regarding the method wherein the steps further include subsequent to the determining, raising an alarm based thereon with the classification and with suggested corrective actions.
Archambault et al. teaches a similar method that includes subsequent to an event determination (for example fiber discontinuities), raising an alarm based thereon with the classification and with suggested corrective actions [0004].
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the method of Rapp to include the alarm and suggested corrective actions as taught by Archambault et al. because Archambault et al. teaches such a modification aids in reducing possible damage to the fiber cable itself, [0002].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Perron (11,125,648) which teaches a similar method for detecting OTDR traces in a fiber optic link.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW G MARINI whose telephone number is (571)272-2676. The examiner can normally be reached Monday-Friday 8am-5pm.
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/MATTHEW G MARINI/ Primary Examiner, Art Unit 2853