Content Classification Model Generation via Interactive GUI
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Solution Overview
Problem
Existing content classification methods are inefficient and accuracy-dependent on user experience, requiring large amounts of learning data and varying in accuracy due to individual knowledge and skills, especially when classifying large datasets like patents with multiple metadata.
Innovation Solution
A content classification system using machine learning that generates classification models interactively through a graphical user interface, where metadata is used to create feature vectors and classification models, reducing user burden and improving accuracy by averaging model outputs and evaluating criteria like precision and sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification by user knowledge and experience is used, then classification can be performed, but accuracy varies depending on individual skills and efficiency is low when handling large datasets
Solution Approach 1:
The patent replaces manual mechanical classification processes with machine learning-based automated classification systems. The system uses algorithms to analyze content metadata and perform classification without human intervention, thereby eliminating variability in human skills while maintaining high accuracy through trained models.
Solution Approach 2:
The classification system performs self-service by automatically generating classification results using machine learning models trained on historical data. The system independently processes content, applies learned patterns, and generates classifications without requiring continuous human oversight or manual rule application.
2Measurement precision
If large amounts of learning data are used for machine learning, then classification model accuracy improves, but user burden increases excessively
Solution Approach 1:
The system performs preliminary action by pre-processing and organizing learning data before model training. It automatically prepares datasets, performs feature extraction, and structures information in advance, reducing the manual effort users would otherwise need to invest in data preparation while maintaining model accuracy.
Solution Approach 2:
The patent introduces an intermediary layer between raw data and the classification model. This intermediary automatically manages data preprocessing, feature selection, and model training processes, shielding users from complex technical operations while enabling the system to utilize large datasets effectively for accurate classification.
3Measurement precision
If variation in the number of classified contents in learning data is reduced, then classification model accuracy improves, but data selection complexity increases
Solution Approach 1:
The system applies parameter changes by dynamically adjusting data selection criteria based on the specific classification task. It modifies parameters such as data sampling rates, feature weighting, and model complexity to match the characteristics of the input data, thereby maintaining accuracy without requiring manual tuning of data selection parameters.
Data Source
AI summary
A classification model which classifies contents is provided. Learning contents and contents are included. The learning contents are provided with a first feature and a learning label, and the contents are provided with a second feature. The content classification method includes a step of generating a plurality of first classification models by machine learning using the plurality of learning contents, a step of generating a second classification model with the use of the plurality of first classification models, and a step of providing judgment data for the plurality of contents with the use of the second classification model and performing display on a GUI. The judgment data includes a classification label or a score. The GUI designates a particular numerical range of the score and displays a corresponding content in a list form. Note that features provided for the contents are management parameters (metadata).