Machine Learning User Clustering for Representative Personality Analysis
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Solution Overview
Problem
Existing systems struggle to accurately and easily classify users into groups and analyze their representative personalities, relying heavily on human intuition and lacking precision.
Innovation Solution
An information processing system that utilizes machine learning models to classify users into clusters based on attributes, calculate contribution degrees, and generate representative personality information through large language and image generation models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user grouping and personality analysis rely on data scientist intuition, then the process is simple to implement, but the classification precision and analysis accuracy are limited
Solution Approach 1:
The patent introduces an AI model as an intermediary between raw user attribute data and personality analysis results. The model automatically performs classification and contribution degree calculation, eliminating the need for manual intuition-based analysis while maintaining high precision through learned patterns from training data
Solution Approach 2:
The patent replaces the mechanical process of manual data analysis by data scientists with an automated AI-based system. The machine learning model substitutes human intuition with algorithmic decision-making, achieving consistent and precise classification without manual intervention
2Measurement precision
If automated classification systems are introduced to improve precision, then analysis accuracy improves, but the system complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-training the AI model on comprehensive user attribute data before actual classification. The model learns optimal classification boundaries and attribute importance weights in advance, enabling fast and accurate online classification without complex real-time computations
Solution Approach 2:
The patent creates a simplified representation of complex user data through the trained model's classification output and contribution degrees. Instead of processing all raw attributes during classification, the system uses the pre-learned model to generate concise personality descriptions that capture essential patterns
3Loss of information
If contribution degree calculation is performed for each user to identify characterizing attributes, then the ability to find group-characterizing attributes improves, but the computational load increases
Solution Approach 1:
The patent extracts only the essential contribution degree information needed for personality characterization from the full set of user attributes. By focusing on the most significant attributes that differentiate clusters, the system avoids computing and storing all possible attribute combinations, reducing computational overhead while preserving key insights
Data Source
AI summary
An information processing system configured to: classify, based on an attribute value of each of a plurality of types of attributes stored in association with each of a plurality of users, the plurality of users into a plurality of clusters; calculate, for each of a plurality of users classified into a target cluster being any one of the plurality of clusters, a contribution degree of each of the plurality of types of attributes to classify into the target cluster; calculate, based on the attribute value of each of the plurality of types of attributes stored in association with each of the plurality of users classified into the target cluster, a representative attribute value that represents the target cluster for the type of attribute; and output, based on the representative attribute value and the contribution degree, information indicating a representative personality of a user belonging to the target cluster.