Control Apparatus for Selective Learning Model Application
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Solution Overview
Problem
Existing communication systems face challenges in achieving high accuracy analysis when applying evaluation or analysis methods from one system to another, due to differing configurations, which reduces the possibility of obtaining appropriate analysis results.
Innovation Solution
A control apparatus and analysis apparatus that utilize machine learning to generate learning models from communication logs, with a determination unit that selects the appropriate analysis apparatus to apply the learning model based on statistical information, ensuring high accuracy analysis across multiple communication systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the analysis method performed in a certain communication system is applied to other communication systems, then the evaluation or analysis can be performed efficiently across multiple systems, but the analysis accuracy deteriorates due to configuration differences between systems
Solution Approach 1:
The patent applies local quality by transferring only specific learning model information (parameters, structure, training data characteristics) that are relevant to the target communication system's configuration, rather than applying the entire analysis method universally. This allows the analysis to be adapted to local characteristics of each communication system, maintaining accuracy while achieving efficiency through selective information transfer.
Solution Approach 2:
The patent utilizes parameter changes by modifying the learning model information based on the statistical characteristics of the target communication system. The learning model parameters are adjusted according to the specific configuration and data distribution of each communication system, enabling accurate analysis across different systems while maintaining efficiency through parameter optimization rather than complete retraining.
2Productivity
If machine learning is used to generate learning models from communication logs, then data processing speed increases compared to manual processing, but the complexity of the system increases due to the need for model generation and management
Solution Approach 1:
The patent applies copying by creating and transferring learning model information (parameters, structure, training data characteristics) from one communication system to another. Instead of manually processing data or completely retraining models in each system, the learned information is copied and adapted, maintaining high processing speed while reducing the complexity of model generation and management across multiple systems.
Data Source
AI summary
An object of the present disclosure is to provide a control apparatus that controls a plurality of communication systems so that the plurality of communication systems can perform analysis with high accuracy. The control apparatus (30) according to the present disclosure includes a communication unit (31) and a determination unit (32). The communication unit (31) receives, from an analysis apparatus (10) configured to perform machine learning using communication logs collected from a communication apparatus in order to generate a learning model, statistical information about each of the communication logs and information about the learning model. The determination unit (32) determines an analysis apparatus (20) to which the information about the learning model is applied based on the statistical information.