Fiber Testing With Machine-Learned Signatures for Report Matching
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Solution Overview
Problem
Fiber testing systems face challenges in accurately identifying and managing large quantities of fiber reports, which can lead to errors, repeated testing, and difficulty in distinguishing between different fibers due to inconsistent identifiers and high variability in test data.
Innovation Solution
Implementing a machine learning model, such as an autoencoder or variational autoencoder, to generate invariant signatures for fibers based on metadata and trace data, allowing for unique identification and reducing redundant reports through similarity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fiber testing methods are used with manual identification, then fiber testing can be performed, but errors increase and identification accuracy decreases when managing large quantities of fiber reports
Solution Approach 1:
The patent replaces manual mechanical identification processes with an automated machine learning system. The ML model automatically generates invariant signatures from fiber test data, eliminating human error in fiber identification and matching processes. This substitution of mechanical/manual operations with intelligent automation directly improves identification accuracy and reduces errors in managing large quantities of fiber reports.
Solution Approach 2:
The patent creates digital copies of fiber characteristics through invariant signatures generated by the machine learning model. These signatures serve as unique identifiers that accurately represent each fiber's properties without requiring manual inspection. By copying essential fiber characteristics into standardized signature formats, the system enables precise automated matching and identification.
2Productivity
If manual analysis of fiber test data is performed, then detailed evaluation is possible, but productivity decreases and time consumption increases when managing large quantities of fibers
Solution Approach 1:
The patent replaces time-consuming manual analysis with automated machine learning processing. The ML model rapidly generates invariant signatures and performs fiber matching operations that would be impractical to execute manually at scale. This automation dramatically increases productivity in processing large volumes of fiber test reports while reducing the time required for analysis.
Solution Approach 2:
The patent transforms complex fiber test data into simplified invariant signatures through parameter transformation. The machine learning model extracts essential characteristics and represents them in a standardized format that enables rapid comparison and matching. This parameter transformation allows the system to process large datasets efficiently without losing critical identification information.
3Adaptability or versatility
If inconsistent identifiers are used for fiber identification, then flexibility in data collection is maintained, but the ability to distinguish between different fibers deteriorates
Solution Approach 1:
The patent creates a universal identification system through invariant signatures that work across different data formats and collection methods. The machine learning model processes various input formats (OTDR traces, loss measurements, metadata) and generates standardized signatures that serve as universal identifiers. This universal approach maintains data collection flexibility from multiple sources while ensuring consistent and accurate fiber distinction through standardized output.
Solution Approach 2:
The patent transforms inconsistent identifier formats into standardized invariant signatures through parameter transformation. The machine learning model normalizes various input parameters and outputs consistent signature representations that enable reliable fiber identification regardless of the original data format. This parameter standardization resolves the conflict between collection flexibility and identification precision.
Data Source
AI summary
In some implementations, a fiber testing system may obtain data associated with a fiber testing operation performed by a fiber testing device. The data may include at least one of metadata or trace data associated with the fiber testing operation. The metadata may include at least one of a context, a description, a date, a timestamp, or an identifier associated with the fiber testing operation, and the trace data may include at least one of a loss measurement or an optical time domain reflectometer measurement associated with the fiber testing operation. The fiber testing system may generate, based on the data, a signature that identifies a fiber associated with the fiber testing operation.