DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 8, 9, 11-15, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (US Patent No. 9,059,797 B2) in view of Jhaldiyal et al (US Pub. No. 2022/0376782 A1).
Regarding claim 1, Wang et al teaches a method comprising steps of:
collecting data associated with an optical fiber network, wherein the data includes optical fiber performance data, and data associated with one or more environmental factors (col. 5, lines 16-37; “Once ambient humidity changes, a refractive index of the optical fiber changes, and parameters, such as optical power and wavelength, of the optical detection signal that passes through the span of the optical fiber path also change. In the way, the optical parameter of the optical detection signal transmitted to the input optical detection signal side is different from the optical parameter existent at the time of receiving the optical detection signal. In the embodiment, the optical detection signal is sent from the onshore device, and is returned to the onshore device along the preset loop described in step 101. The onshore device judges whether the optical parameter of the returned optical detection signal changes, for example, whether optical power or optical wavelength changes. If any optical parameter changes, it indicates that humidity in a corresponding submarine communication device is huge, and the water penetration fault may exist. If water penetration is detected in the submarine communication device when a fault occurs, it indicates that the fault of the corresponding device is probably caused by the water penetration, and the water penetration needs to be handled in time.”).
Wang et al teaches data analysis and determining one or more issues relating to the optical fiber network based on results (col. 9, lines 22-26; “If an analysis result is that the optical power of the optical detection signal exceeds the normal loss, it indicates that the fault in the BU is caused by water penetration. With increase of humidity, the refractive index decreases, and the optical power also decreases.”) and differs from the claimed invention in that Wang et al does not specifically teach that the analysis is performed by a first linear regression analysis on the data; performing a second linear regression analysis on results of the first linear regression analysis. Jhaldiyal et al teaches detecting faults in optical communication system using multiple linear regression analysis (para [0028]; “Regression algorithms, a supervised machine learning approach are used in the present disclosure. Following is the brief introduction of the algorithm. Regression algorithms belong to family of Supervised Machine Learning algorithms. Purpose of supervised learning algorithms is to model the dependencies and relationships between the output and input features or dependent and independent variables, to predict the value for new data. The algorithm builds a model on the features of training data and using the model to predict value for new data. The simple linear regression attempts to establish a linear relationship between one independent variables and a dependent variable. In multiple linear regression model there are two or more independent variables and a dependent variable. Whereas in multivariate multiple linear regression both independent variables and dependent variable are two or more.”; para [0032]; “The first step in multiple linear regression analysis is to determine,…”). Therefore, it would have been obvious to an artisan of ordinary skill before the effective filling date of the claimed invention to modify the data analysis of Wang et al by performing first linear regression analysis on the data and second linear regression analysis on results of the first linear regression analysis, as taught by Jhaldiyal et al, in order to model system performance and predict signal quality using multiple operating parameters simultaneously.
Regarding claims 2 and 12, as discussed above, the combination of Wang et al as modified by Jhaldiyal et al, teaches multiple linear regression analysis and differs from the claimed invention in that the combination does not teach that the multiple regression analysis are performed over a period of time. However, since the combination teaches multiple linear regression analysis, therefore, it would have been obvious to an artisan of ordinary skill before the effective filling date of the claimed invention to performed the multiple regression analysis over a period of time in order to predict changing condition over time.
Regarding claims 3 and 13, as discussed above, the combination of Wang et al as modified by Jhaldiyal et al, teaches multiple linear regression analysis and differs from the claimed invention in that the combination does not teach collecting data is performed continuously, or at predetermined intervals based on a received configuration. However, since the combination teaches collecting data, therefore, it would have been obvious to an artisan of ordinary skill before the effective filling date of the claimed invention to performed collecting data continuously in order to detect changes instantaneously.
Regarding claims 4 and 14, as discussed above, the combination of Wang et al as modified by Jhaldiyal et al, teaches the optical fiber performance data includes optical power loss (Wang et al: col. 9, lines 19-24; “A analyzes whether the change of the optical power of the optical detection signal exceeds the optical power loss of normal working. If an analysis result is that the optical power of the optical detection signal exceeds the normal loss, it indicates that the fault in the BU is caused by water penetration.”).
Regarding claims 5 and 15, as discussed above, the combination of Wang et al as modified by Jhaldiyal et al, teaches the optical fiber performance data is collected via one or more Optical Time-Domain Reflectometry (OTDR) devices (Jhaldiyal et al: para [0026]; “The switch controller (120) will take 16 PON inputs from OLT (110), and one input pulse from OTDR (121) embedded on the switch controller (120). WDM coupler (124) of switch controller (120) sends the multiplexed signal towards the PON side after coupling the signals from OLT (110) with OTDR pulse.”).
Regarding claims 8 and 18, as discussed above, the combination of Wang et al as modified by Jhaldiyal et al, teaches the data associated with environmental factors includes any of ambient temperature, humidity, atmospheric pressure, air/water salinity, wind speed and direction, visibility, precipitation, lightning frequency and distance, and dew point at one or more locations associated with the optical fiber network (Wang et al: col. 6, lines 32-38; “The transmissive optical component or the reflective optical component in the embodiment is an optical component that is sensitive to change of an environment state (such as humidity, temperature, or pressure). A refractive index of media changes once the humidity, temperature or pressure in an ambient environment changes.”).
Regarding claims 9 and 19, as discussed above, the combination of Wang et al as modified by Jhaldiyal et al, teaches the one or more locations associated with the optical fiber network include one or more locations having any of junctions, splices, connectors, and bends in optical fibers (Wang et al: col. 8, lines 29-38; “The two optical couplers are connected through an optical fiber 306 capable of transmitting an optical signal. An example that the fault is detected at landing station A is taken, one end of the optical fiber 306 is connected with the first optical cable through a remaining output port of the optical coupler 301, and another end is connected with the second optical cable through a remaining input port of the optical coupler 302, so that two output ports are formed at the optical coupler 301, and two input ports are formed at the optical coupler 302.”).
Regarding claim 11, Wang et al teaches at least one processor to perform steps of:
collecting data associated with an optical fiber network, wherein the data includes optical fiber performance data, and data associated with one or more environmental factors (col. 5, lines 16-37; “Once ambient humidity changes, a refractive index of the optical fiber changes, and parameters, such as optical power and wavelength, of the optical detection signal that passes through the span of the optical fiber path also change. In the way, the optical parameter of the optical detection signal transmitted to the input optical detection signal side is different from the optical parameter existent at the time of receiving the optical detection signal. In the embodiment, the optical detection signal is sent from the onshore device, and is returned to the onshore device along the preset loop described in step 101. The onshore device judges whether the optical parameter of the returned optical detection signal changes, for example, whether optical power or optical wavelength changes. If any optical parameter changes, it indicates that humidity in a corresponding submarine communication device is huge, and the water penetration fault may exist. If water penetration is detected in the submarine communication device when a fault occurs, it indicates that the fault of the corresponding device is probably caused by the water penetration, and the water penetration needs to be handled in time.”).
Wang et al teaches data analysis and determining one or more issues relating to the optical fiber network based on results (col. 9, lines 22-26; “If an analysis result is that the optical power of the optical detection signal exceeds the normal loss, it indicates that the fault in the BU is caused by water penetration. With increase of humidity, the refractive index decreases, and the optical power also decreases.”) and differs from the claimed invention in that Wang et al does not specifically teach that the analysis is performed by a first linear regression analysis on the data; performing a second linear regression analysis on results of the first linear regression analysis. Jhaldiyal et al teaches detecting faults in optical communication system using multiple linear regression analysis (para [0025]; “The paths (111, 125, 126) by which the OLT (110) connected with the switch controller (120) and the processor (123) connected with the OTDR (121) and optical switch (122) are control paths.”; it is inherent there is computer-readable code stored thereon for programming the processor); para [0028]; “Regression algorithms, a supervised machine learning approach are used in the present disclosure. Following is the brief introduction of the algorithm. Regression algorithms belong to family of Supervised Machine Learning algorithms. Purpose of supervised learning algorithms is to model the dependencies and relationships between the output and input features or dependent and independent variables, to predict the value for new data. The algorithm builds a model on the features of training data and using the model to predict value for new data. The simple linear regression attempts to establish a linear relationship between one independent variables and a dependent variable. In multiple linear regression model there are two or more independent variables and a dependent variable. Whereas in multivariate multiple linear regression both independent variables and dependent variable are two or more.”; para [0032]; “The first step in multiple linear regression analysis is to determine,…”). Therefore, it would have been obvious to an artisan of ordinary skill before the effective filling date of the claimed invention to modify the data analysis of Wang et al by performing first linear regression analysis on the data and second linear regression analysis on results of the first linear regression analysis, as taught by Jhaldiyal et al, in order to model system performance and predict signal quality using multiple operating parameters simultaneously.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al (US Patent No. 9,059,797 B2) in view of Jhaldiyal et al (US Pub. No. 2022/0376782 A1) and further in view of Janulewics et al (US Patent No. 12,149,284 B2).
Regarding claims 10 and 20, as discussed above, the combination of Wang et al as modified by Jhaldiyal et al, teaches multiple linear regression analysis and differs from the claimed invention in that the combination does not specifically teach providing one or more visual representations of the data and the results of the first linear regression analysis and second linear regression analysis via a Graphical User Interface (GUI). Janulewics et al teaches results or reports may be displayed via a Graphical User Interface (GUI) (col. 5, line 52 to col. 6, line 8; “Thus, the present disclosure provides a comprehensive fiber connection monitoring and analysis system for the entire optical network, including both the inter-NE and intra-NE fiber connections. According to the implementations of data analysis procedures described in the present disclosure, the performance of fibers and fiber connections can be evaluated. From the evaluations detected over time, vulnerable fibers and fiber connections can be identified, classified, and rated. The embodiments of the present disclosure are configured to report the results of the analysis of the health of fibers and fiber connections for both immediate issues (e.g., fiber pinch condition, etc.) and long-term issues (e.g., accumulation of bad splices over time, etc.). Users (e.g., network operators at Network Operations Centers (NOCs), data centers, etc.) will be able to review the summary of health of fibers and fiber connections of the entire network to understand the quality of each of the fiber components in their network. For example, the reports may be displayed on an interactive user interface (e.g., graphical user interface) in order to enable the user to sort or group different criteria as he or she wishes. This may allow the user to rank and evaluate the risk of the vulnerable connections on their own terms and prioritize potential issues and remedial actions.”). Therefore, it would have been obvious to an artisan of ordinary skill before the effective filling date of the claimed invention to provide a GUI to the system of the combination, as taught by Janulewics et al, in order to visually represent data to users which will enable the user to sort or group different criteria as he or she wishes, rank and evaluate the risk of the vulnerable connections on their own terms and prioritize potential issues and remedial actions.
Allowable Subject Matter
Claims 6, 7, 16 and 17 are 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Leblanc et al (US Patent No. 9,709,460 B2) is cited to show method and system for measuring an optical loss value of an optical fiber link.
Swanson et al (US Patent No. 9,680,567 B2) is cited to show fault localization and fiber security in optical transponders.
Oron (US Pub. No. 2009/0028553 A1) is cited to show method and apparatus of detecting abnormal behavior in a Passive Optical Network (PON).
Archambault et al (US Pub. No. 2017/0294959 A1) is cited to show dual wavelength optical time domain reflectometer systems and methods embedded in a WDM system.
Xia et al (US Pub. No. 2024/0133735 A1) is cited to show systems and methods for utilizing machine learning to minimize a potential of damage to fiber optic cables.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DALZID E SINGH whose telephone number is (571)272-3029. The examiner can normally be reached Monday-Friday 9-5 ET.
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DALZID E. SINGH
Primary Examiner
Art Unit 2635
/DALZID E SINGH/Primary Examiner, Art Unit 2635
