Communication Issue Identification Using Embedding Reference Vectors
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Solution Overview
Problem
Existing communication problem identification methods in mobile devices, particularly those using AI/ML models, struggle with generalizing to new or evolving issue patterns, leading to inefficiencies in detection and resolution due to the need for retraining, which consumes resources and affects performance.
Innovation Solution
A communication problem identification device and method that encodes communication-related information into an embedding vector, compares it with reference vectors in a database, and determines the issue type without requiring a classification model, allowing for updates to the database instead of retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI/ML models are used to detect communication problems, then detection accuracy and scalability are improved, but the system requires retraining when encountering new issue patterns, consuming CPU and memory resources and draining battery life
Solution Approach 1:
Instead of retraining the entire AI/ML model when new issue patterns are detected, the system creates a simplified copy or representation of the problem pattern and stores it in a database. This allows the system to recognize and respond to new issues without the computational overhead of model retraining, thus preserving battery life while maintaining detection accuracy.
Solution Approach 2:
The system extracts only the essential features and patterns from communication data and stores them as reference vectors in a database, rather than maintaining a complex AI/ML model. This extraction approach reduces the computational burden on the device, allowing it to operate more efficiently with lower energy consumption while still achieving accurate problem identification.
2Measurement precision
If AI/ML models are used to detect communication problems, then detection accuracy and scalability are improved, but the system requires significant CPU and memory usage for retraining, impacting overall user experience
Solution Approach 1:
The system creates a simplified database structure with reference vectors that represents the communication problems, avoiding the need to maintain and retrain complex AI/ML models. This copying approach stores problem patterns in a lightweight format that can be quickly queried without requiring significant CPU or memory resources.
Solution Approach 2:
The system uses a simple database structure with reference vectors that can be easily stored and queried, replacing the need for expensive and complex AI/ML models. This disposable approach allows the system to handle new issues by adding new reference vectors rather than retraining, significantly reducing computational requirements.
3Device complexity
If traditional rule-based systems are used to identify communication problems, then system complexity is reduced, but the ability to handle increasing complexity and scale of modern mobile networks is limited
Solution Approach 1:
The system creates a database of reference vectors that captures communication problem patterns, allowing it to handle diverse and evolving issues without requiring complex rule-based logic. This database approach provides adaptability to new patterns while maintaining relatively simple system architecture.
Solution Approach 2:
The database structure serves multiple functions: storing reference vectors, enabling quick lookup of problem patterns, and adapting to new issues without requiring system redesign. This universal approach allows the same simple structure to handle both known and emerging communication problems effectively.
Data Source
AI summary
A communication problem identification device is provided. The communication problem identification device includes a feature extraction module, a database module and an identification module. The feature extraction module is configured to encode communication-related information into an embedding vector. The database module is configured to store a plurality of reference vectors. Each reference vector corresponds to a respective issue type. The identification module is configured to determine the issue type of the communication-related information based on the embedding vector and the reference vectors.