BERT-Based Text Ranking for Trouble Report Analysis
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Solution Overview
Problem
The labor-intensive process of Trouble Report (TR) routing and analysis in telecommunication systems is inefficient, as existing automated solutions struggle with domain-specific and company-specific text, leading to high latency and computational complexity, and lack accuracy in identifying relevant solutions.
Innovation Solution
A multi-stage text ranking system using BERT-based models for initial retrieval and re-ranking, which preprocesses queries and answers to create dense vector representations, computes similarity metrics, and applies a re-ranking scheme to provide a ranked list of candidate answers, optimizing for accuracy and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of trouble reports is performed, then accuracy in identifying relevant solutions is high, but labor intensity and processing time are high
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated NLP-based system that uses BERT models to understand and match trouble report semantics, eliminating the need for human engineers to manually read and analyze each ticket while achieving comparable or superior accuracy through deep semantic understanding
Solution Approach 2:
The system enables self-service by automatically routing and analyzing trouble reports using pre-trained BERT models that can independently understand domain-specific language, extract key information, and match with relevant solutions without human intervention, allowing the system to serve itself in the analysis process
2Productivity
If automated solutions are used for trouble report analysis, then processing speed increases, but accuracy in identifying relevant solutions decreases due to inability to handle domain-specific text
Solution Approach 1:
The patent applies parameter changes by fine-tuning pre-trained BERT models on domain-specific trouble report data, adapting the model's parameters to understand telecommunications jargon, acronyms, and specific failure modes, thereby maintaining high accuracy while achieving automated processing speed
Solution Approach 2:
The system performs preliminary action by pre-processing and indexing trouble reports using BERT embeddings before actual analysis, creating a ready-to-query semantic database that enables fast accurate retrieval when new trouble reports arrive, avoiding the need for real-time manual analysis
3Productivity
If existing automated solutions are used, then processing speed increases, but computational complexity and latency increase
Solution Approach 1:
The patent segments the computational process into distinct stages: pre-processing and indexing of historical data using BERT models, storage of embeddings, and fast similarity search during actual trouble report analysis, separating computational heavy lifting from real-time processing to reduce latency
Solution Approach 2:
The system performs preliminary computation by pre-processing and embedding historical trouble reports and solutions using BERT models before actual queries arrive, storing these pre-computed representations for fast retrieval, thereby avoiding repeated complex computations during real-time analysis and reducing latency
4Ease of manufacture
If hard-coded rules are used for automation, then implementation is simple, but ability to handle complex domain-specific problems is limited
Solution Approach 1:
The patent uses parameter changes by fine-tuning pre-trained BERT models on domain-specific data, allowing the system to adapt to telecommunications jargon, acronyms, and failure modes without requiring complex hard-coded rules, achieving both ease of implementation and high adaptability
Solution Approach 2:
The system achieves universality by using a single BERT-based framework that can handle multiple tasks including trouble report classification, solution matching, and semantic search across different domains and companies, replacing the need for multiple specialized hard-coded systems
Data Source
AI summary
Systems and methods are disclosed herein efficient analysis of a (new) trouble report (TR) and providing a list of candidate answers. In one embodiment, a method performed by a computing device comprises obtaining a query from a trouble report, the query comprising text. The method further comprises pre-processing the query to provide a pre-processed query and applying the pre-processed query to a first representation-based model to provide a representation of the pre-processed query. The method further comprises computing similarity metrics between the representation of the pre-processed query and representations of pre-processed answers of existing, previously processed, trouble reports and creating an initial list of candidate answers based on the similarity metrics. The initial list of candidate answers comprises candidate answers selected from among answers of the existing trouble reports based on the similarity metrics.