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 .
Information Disclosure Statement
2. The Information Disclosure Statement filed on 11/06/2024 has been considered.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,3-6 and 8 are rejected under 35 USC 103 as being unpatentable over Morette (WO 2022/248053 A1) in view of Tanimura et al; (US 11228367) and further in view of Al Sayeed (US 11990933).
Regarding claim 1, Morette discloses a controller ;(control plane 302, see figure 3) comprising: at least one memory storing instructions; a first processor configured to execute the instructions and collect performance monitoring (PM) data representing an optical power level of an optical signal of each of a plurality of optical transmission apparatuses provided in an optical transmission network;(the control plane 302 may be the optical performance monitoring device. The control plane 302 may be one or more processors in the optical performance monitoring device or a controller or a management plane, see page 12, lines 20-23 and figure 3) a database configured to hold an of the optical transmission apparatus for each of the plurality of optical transmission apparatuses;( the optical performance monitoring device 100 is further configured to request a set of monitored inputs from a physical layer database, including the monitored output power for each channel through the OMS, and the monitored SNR for each channel through the OMS, see page 11, lines 26-29).
However, Morette does not explicitly disclose a plurality of artificial intelligence (AI) models, being provided for each of the plurality of optical transmission apparatuses provided in the optical transmission network, configured to learn a variation in time series of the PM data of the optical transmission apparatus by using the PM data of the optical transmission apparatus as learning data; average value and an allowable error of the PM data, a second processor configured to execute the instructions and detect an abnormality within the optical transmission network by using the plurality of AI models; and a third processor configured to execute the instructions and, in a case where there is an optical transmission apparatus in which the PM data after restoration from the abnormality are outside a threshold range, which is derived from the average value and the allowable error, determine that an AI model of the relevant optical transmission apparatus needs retraining.
In a related field of endeavor, Tanimura discloses a plurality of artificial intelligence (AI) models, being provided for each of the plurality of optical transmission apparatuses provided in the optical transmission network;(optical transmission apparatuses transmitter 10T and receiver 10R in an optical network1, see figure 1A and prediction model 31 in the 31 in the corresponding receiver, see figure 2) configured to learn by using the PM data of the optical transmission apparatus as learning data, average value and an allowable error of the PM data;( the anomaly detector 30 performs machine learning on the normal state of the network including a drift within an allowable range that does not cause an operational problem, and sets appropriate parameters in the prediction model 31, see column 4, lines 66,67 and column lines 1-3 and figure 2) a second processor configured to execute the instructions and detect an abnormality within the optical transmission network by using the plurality of AI models;( In the monitoring mode after learning, the prediction model 31 is used to predict and detect the network anomaly based on the prediction value predicted from the digital sampling data obtained from the ADCs 12a to 12d and the monitor value output from the DSP 20. “Anomaly detection” is a collective term for detecting a failure, a malfunction, and the like that occur in a transmission path or a device inserted in the transmission path, and a sign of the failure, see column 5, lines 16-24 and figure 2) and a third processor configured to execute the instructions and, in a case where there is an optical transmission apparatus in which the PM data after restoration from the abnormality are outside a threshold range, which is derived from the average value and the allowable error;(by calculating the anomaly score continuously during operation and comparing the anomaly score with the threshold value set appropriately, it is possible to detect an anomaly or its sign in the transmission path, see column 5, lines 30-34) determine that an AI model of the relevant optical transmission apparatus needs retraining; (When the current prediction error exceeds the reference value L (set in anomaly detector) (“NO” in S25), the learning is repeated (retraining) while updating the parameter of the prediction model 31 so that the prediction error is smaller than the prediction error reference value “L”, see column 7, lines 50-53).
Thus, it would be obvious for one the ordinary skilled in the art before the effective filling date of the invention to combine the anomaly detector and AI model of Tanimura with Morette to provide to predict and detect the network anomaly based on the prediction value and the motivation is to detect an anomaly or its sign in the transmission path.
However, the combination of Morette and Tanimura does not explicitly disclose a variation in time series of the PM data of the optical transmission apparatus.
In a related field of endeavor, Al Sayeed discloses a variation in time series of the PM data of the optical transmission apparatus; (the power level repeatedly exceeds an upper fluctuation threshold 78 (e.g., 1.0 dB above the target level 74) and falls below a lower fluctuation threshold 80 (e.g., 1.0 dB below the target level 74) within a predetermined time period, see column 10, lines 31-35).
Thus, it would be obvious for one the ordinary skilled in the art before the effective filling date of the invention to combine the time variation of measure power of Al Sayeed with Morette and Tanimura for detecting detect whether a channel fluctuates in a problematic manner and motivation is fluctuation nonconformity.
Regarding claim 3, the combination of Morette and Al Sayeed does not explicitly disclose the controller according to claim 1, wherein the third processor is further configured to execute the instructions and instruct retraining on an AI model of a relevant optical transmission apparatus that has been determined that retraining is necessary.
In a related field of endeavor, Tanimura discloses the controller according to claim 1, wherein the third processor is further configured to execute the instructions and instruct retraining on an AI model of a relevant optical transmission apparatus that has been determined that retraining is necessary; (When the current prediction error exceeds the reference value L (set in anomaly detector) (“NO” in S25), the learning is repeated (retraining) while updating the parameter of the prediction model 31 so that the prediction error is smaller than the prediction error reference value “L”, see column 7, lines 50-53). Motivation same as claim 1.
Regarding claim 4, the combination of Morette and Al Sayeed does not explicitly disclose the controller according to claim 1, wherein the third processor is further configured to execute the instructions and determine that retraining on an AI model of an optical transmission apparatus is unnecessary for the optical transmission apparatus in which the PM data after restoration from an abnormality is within the threshold range.
In a related field of endeavor, Tanimura discloses the controller according to claim 1, wherein the third processor is further configured to execute the instructions and determine that retraining on an AI model of an optical transmission apparatus is unnecessary for the optical transmission apparatus in which the PM data after restoration from an abnormality is within the threshold range;( The fact that the error between the measured value and the prediction value obtained by the prediction model 31 is equal to or less than the prediction error reference value “L” means that the normal state of the transmission path including the transmission path fluctuation within a range that does not hinder the operation, see column 7, lines 55-60). Motivation same as claim 1.
Regarding claim 5, the combination of Morette and Tanimura does not explicitly disclose the controller according to claim 1, wherein the threshold range is a range in which a value acquired by adding the allowable error to the average value is set as an upper limit, and a value acquired by subtracting the allowable error from the average value is set as a lower limit.
In a related field of endeavor, Al Sayeed discloses the controller according to claim 1, wherein the threshold range is a range; (the incoming channel powers are monitored over a given period of time to ensure that a median or moving average of the incoming channel power remains steady (e.g., within less than +/−0.3 dB), see column 9, lines 20-24) in which a value acquired by adding the allowable error to the average value is set as an upper limit, and a value acquired by subtracting the allowable error from the average value is set as a lower limit; (the power level repeatedly exceeds an upper fluctuation threshold 78 (e.g., 1.0 dB above the target level 74) and falls below a lower fluctuation threshold 80 (e.g., 1.0 dB below the target level 74) within a predetermined time period, see column 10, lines 31-35). Motivation same as claim 1.
Regarding claim 6, the combination of Morette and Tanimura does not explicitly disclose the controller according to claim 1, wherein the first processor is further configured to execute the instructions and periodically collect the PM data of each of the plurality of optical transmission apparatuses.
In a related field of endeavor, Al Sayeed discloses the controller according to claim 1, wherein the first processor is further configured to execute the instructions and periodically collect the PM data of each of the plurality of optical transmission apparatuses ; (the incoming channel powers are monitored over a given period of time to ensure that a median or moving average of the incoming channel power remains steady (e.g., within less than +/−0.3 dB), see column 9, lines 20-24). Motivation same as claim 1.
Regarding claim 8, the combination of Morette and Al Sayeed does not explicitly disclose the controller according to claim 1, wherein the abnormality in the optical transmission network is an abnormality in an optical fiber between the plurality of optical transmission apparatuses.
In a related field of endeavor, Tanimura discloses the controller according to claim 1, wherein the abnormality in the optical transmission network is an abnormality in an optical fiber between the plurality of optical transmission apparatuses. “Anomaly detection” is a collective term for detecting a failure, a malfunction, and the like that occur in a transmission path or a device inserted in the transmission path, and a sign of the failure, see column 5, lines 20-23). Motivation same as claim 1.
Claim 7 is rejected under 35 USC 103 as being unpatentable over Morette (WO 2022/248053 A1) in view of Tanimura et al; (US 11228367) and further in view of Al Sayeed (US 11990933) and further in view of Corredor et al ;(WO 2022/058935A1).
Regarding claim 7, the combination of Morette, Tanimura and Al Sayeed does not explicitly disclose the controller according to claim 1, wherein the abnormality in the optical transmission network is an abnormality in a physical port of any of the plurality of optical transmission apparatuses.
In a related field of endeavor, Corredor discloses the controller according to claim 1, wherein the abnormality in the optical transmission network is an abnormality in a physical port of any of the plurality of optical transmission apparatuses ;( sensors configured in step 1814 may be embedded in a network device (e.g., sensors can be inserted in network devices) and be configured to identify a network port failure, see paragraph 182).
Claims 9 and 10 are rejected under 35 USC 103 as being unpatentable over Morette (WO 2022/248053 A1) in view of Tanimura et al; (US 11228367) and further in view of Al Sayeed (US 11990933).
Regarding claim 9, Morette discloses a training cost reduction method to be executed by a controller, the method comprising: collecting performance monitoring (PM) data representing an optical power level of an optical signal of each of a plurality of optical transmission apparatuses provided in an optical transmission network ;(the control plane 302 may be the optical performance monitoring device. The control plane 302 may be one or more processors in the optical performance monitoring device or a controller or a management plane, see page 12, lines 20-23 and figure 3) storing of the PM data of the optical transmission apparatus in a database for each of the plurality of optical transmission apparatuses ;( the optical performance monitoring device 100 is further configured to request a set of monitored inputs from a physical layer database, including the monitored output power for each channel through the OMS, and the monitored SNR for each channel through the OMS, see page 11, lines 26-29).
However, Morette does not explicitly disclose learning, with a plurality of artificial intelligence (AI) models provided for each of the plurality of optical transmission apparatuses, an average value and an allowable error, a variation in time series of the PM data of the optical transmission apparatus by using the PM data of the optical transmission apparatus as learning data; detecting an abnormality within the optical transmission network by using the plurality of AI models; and, in a case where there is an optical transmission apparatus in which the PM data after restoration from the abnormality are outside a threshold range, which is derived from the average value and the allowable error, determining that an AI model of the relevant optical transmission apparatus needs retraining.
In a related field of endeavor, Tanimura discloses learning, with a plurality of artificial intelligence (AI) models provided for each of the plurality of optical transmission apparatuses ;(optical transmission apparatuses transmitter 10T and receiver 10R in an optical network1, see figure 1A and prediction model 31 in the 31 in the corresponding receiver, see figure 2) an average value and an allowable error, a variation in time series of the PM data of the optical transmission apparatus by using the PM data of the optical transmission apparatus as learning data;( the anomaly detector 30 performs machine learning on the normal state of the network including a drift within an allowable range that does not cause an operational problem, and sets appropriate parameters in the prediction model 31, see column 4, lines 66,67 and column lines 1-3 and figure 2) detecting an abnormality within the optical transmission network by using the plurality of AI models;(in the monitoring mode after learning, the prediction model 31 is used to predict and detect the network anomaly based on the prediction value predicted from the digital sampling data obtained from the ADCs 12a to 12d and the monitor value output from the DSP 20. “Anomaly detection” is a collective term for detecting a failure, a malfunction, and the like that occur in a transmission path or a device inserted in the transmission path, and a sign of the failure, see column 5, lines 16-24 and figure 2) and, in a case where there is an optical transmission apparatus in which the PM data after restoration from the abnormality are outside a threshold range, which is derived from the average value and the allowable error ;(by calculating the anomaly score continuously during operation and comparing the anomaly score with the threshold value set appropriately, it is possible to detect an anomaly or its sign in the transmission path, see column 5, lines 30-34) determining that an AI model of the relevant optical transmission apparatus needs retraining (when the current prediction error exceeds the reference value L (set in anomaly detector) (“NO” in S25), the learning is repeated (retraining) while updating the parameter of the prediction model 31 so that the prediction error is smaller than the prediction error reference value “L”, see column 7, lines 50-53).
Thus, it would be obvious for one the ordinary skilled in the art before the effective filling date of the invention to combine the anomaly detector and AI model of Tanimura with Morette to provide to predict and detect the network anomaly based on the prediction value and the motivation is to detect an anomaly or its sign in the transmission path.
However, the combination of Morette and Tanimura does not explicitly disclose a variation in time series of the PM data of the optical transmission apparatus
In a related field of endeavor, Al Sayeed discloses a variation in time series of the PM data of the optical transmission apparatus; (the power level repeatedly exceeds an upper fluctuation threshold 78 (e.g., 1.0 dB above the target level 74) and falls below a lower fluctuation threshold 80 (e.g., 1.0 dB below the target level 74) within a predetermined time period, see column 10, lines 31-35).
Thus, it would be obvious for one the ordinary skilled in the art before the effective filling date of the invention to combine the time variation of measure power of Al Sayeed with Morette and Tanimura for detecting detect whether a channel fluctuates in a problematic manner and motivation is fluctuation nonconformity.
Regarding claim 10, Morette discloses a non-transitory computer-readable medium storing a program causing a computer to execute: a procedure of collecting performance monitoring (PM) data representing an optical power level of an optical signal of each of a plurality of optical transmission apparatuses provided in an optical transmission network ;(the control plane 302 may be the optical performance monitoring device. The control plane 302 may be one or more processors in the optical performance monitoring device or a controller or a management plane, see page 12, lines 20-23 and figure 3) a procedure of storing of the PM data of the optical transmission apparatus in a database for each of the plurality of optical transmission apparatuses ;(the optical performance monitoring device 100 is further configured to request a set of monitored inputs from a physical layer database, including the monitored output power for each channel through the OMS, and the monitored SNR for each channel through the OMS, see page 11, lines 26-29).
However, Morette does not explicitly disclose a procedure of learning, with a plurality of artificial intelligence (AI) models provided for each of the plurality of optical transmission apparatuses, a variation in time series of the PM data of the optical transmission apparatus by using the PM data of the optical transmission apparatus as learning data; an average value and an allowable error, a procedure of detecting an abnormality within the optical transmission network by using the plurality of AI models; and a procedure of, in a case where there is an optical transmission apparatus in which the PM data after restoration from the abnormality are outside a threshold range, which is derived from the average value and the allowable error, determining that an AI model of the relevant optical transmission apparatus needs retraining.
In a related field of endeavor, Tanimura discloses a procedure of learning, with a plurality of artificial intelligence (AI) models provided for each of the plurality of optical transmission apparatuses ;(optical transmission apparatuses transmitter 10T and receiver 10R in an optical network1, see figure 1A and prediction model 31 in the 31 in the corresponding receiver, see figure 2) by using the PM data of the optical transmission apparatus as learning data; an average value and an allowable error;( the anomaly detector 30 performs machine learning on the normal state of the network including a drift within an allowable range that does not cause an operational problem, and sets appropriate parameters in the prediction model 31, see column 4, lines 66,67 and column lines 1-3 and figure 2) a procedure of detecting an abnormality within the optical transmission network by using the plurality of AI models ;(in the monitoring mode after learning, the prediction model 31 is used to predict and detect the network anomaly based on the prediction value predicted from the digital sampling data obtained from the ADCs 12a to 12d and the monitor value output from the DSP 20. “Anomaly detection” is a collective term for detecting a failure, a malfunction, and the like that occur in a transmission path or a device inserted in the transmission path, and a sign of the failure, see column 5, lines 16-24 and figure 2) and a procedure of, in a case where there is an optical transmission apparatus in which the PM data after restoration from the abnormality are outside a threshold range, which is derived from the average value and the allowable error, ;(by calculating the anomaly score continuously during operation and comparing the anomaly score with the threshold value set appropriately, it is possible to detect an anomaly or its sign in the transmission path, see column 5, lines 30-34) determining that an AI model of the relevant optical transmission apparatus needs retraining (when the current prediction error exceeds the reference value L (set in anomaly detector) (“NO” in S25), the learning is repeated (retraining) while updating the parameter of the prediction model 31 so that the prediction error is smaller than the prediction error reference value “L”, see column 7, lines 50-53).
Thus, it would be obvious for one the ordinary skilled in the art before the effective filling date of the invention to combine the anomaly detector and AI model of Tanimura with Morette to provide to predict and detect the network anomaly based on the prediction value and the motivation is to detect an anomaly or its sign in the transmission path.
However, the combination of Morette and Tanimura does not explicitly disclose a variation in time series of the PM data of the optical transmission apparatus
In a related field of endeavor, Al Sayeed discloses a variation in time series of the PM data of the optical transmission apparatus; (the power level repeatedly exceeds an upper fluctuation threshold 78 (e.g., 1.0 dB above the target level 74) and falls below a lower fluctuation threshold 80 (e.g., 1.0 dB below the target level 74) within a predetermined time period, see column 10, lines 31-35).
Thus, it would be obvious for one the ordinary skilled in the art before the effective filling date of the invention to combine the time variation of measure power of Al Sayeed with Morette and Tanimura for detecting detect whether a channel fluctuates in a problematic manner and motivation is fluctuation nonconformity.
Allowable Subject Matter
3. Claim 2 is objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is reproduced below.
a. Tanaka et al; (US 2025/0365068) discloses an abnormal section estimation method in a system in which an optical transmitter and an optical receiver are connected by an optical transmission line, the optical transmission line being divided into a plurality of sections at one or more monitoring points from the optical transmitter to the optical receiver, section 10.
b. Takashi et al; (WO 2022/024248 A1) discloses an optical transmission system (100), with a loss generator that generates a loss in an optical signal passing through an optical path is provided in each segment of the optical path, and a failure point specifying device, see figure 1.
c. Zhang et al; (Adaptive alarm prediction in optical network based on model
generalization in cross-layer AI- September 2022 attached) discloses a method to optimize the model generalization ability in alarm prediction, i.e., the training of a small sample model is solved through parameter transfer in the training process, see Abstract and figure 1.
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/AMRITBIR K SANDHU/ Primary Examiner, Art Unit 2634
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