AI Interaction System Using Deep Learning for Personalized User Data
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Solution Overview
Problem
Existing systems lack the capability to effectively generate personalized information for user interactions by leveraging interactions between multiple users, limiting the depth and relevance of user engagement.
Innovation Solution
An interactive electronic device system and method that registers and communicates with another user's device, filters data based on user relationships, and utilizes multiple AI learning models and databases to generate interactive information for a user by combining user-specific and contextual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rule-based smart systems are used, then system simplicity is maintained, but recognition rate and user preference understanding are limited
Solution Approach 1:
The patent replaces conventional rule-based smart systems with deep learning-based AI systems. This substitution transitions from mechanical rule-following to intelligent self-learning, enabling the system to automatically improve recognition rates and understand user preferences without manual rule configuration.
Solution Approach 2:
The AI system performs self-learning and self-determination, continuously improving its own performance through machine learning algorithms. The system automatically adjusts to better recognize user preferences and generate personalized interaction information without external intervention.
2Adaptability or versatility
If AI systems with self-learning capability are implemented, then recognition rate and user preference understanding improve, but system complexity increases
Solution Approach 1:
The AI system automatically learns from interaction data and improves its understanding of user preferences through self-service mechanisms. The deep learning models continuously train on new data, enabling the system to adapt to individual user preferences without manual programming.
Solution Approach 2:
The system segments user interaction data into multiple databases (first database, second database, third database) and processes different types of interaction information separately. This segmentation allows the complex AI system to manage and process diverse data types more effectively.
3Adaptability or versatility
If interaction information from multiple users is collected, then personalized interaction quality improves, but data processing complexity increases
Solution Approach 1:
The patent divides interaction information into multiple specialized databases: first database for first user interactions, second database for second user interactions, and third database for additional user interactions. This segmentation organizes complex multi-user data into manageable, purpose-specific categories.
Solution Approach 2:
The patent introduces an interaction information generation unit that acts as an intermediary between raw interaction data and personalized output. This unit applies deep learning models to process and synthesize interaction information from multiple users, transforming complex data into personalized interaction content.
4Measurement precision
If deep learning models are applied to generate interactive information, then interaction relevance and personalization improve, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by collecting and organizing interaction information from multiple users into structured databases before actual interaction occurs. The deep learning models are pre-trained on this organized data, enabling faster inference and reduced processing time during actual user interactions.
Data Source
AI summary
The present disclosure relates to an artificial intelligence (AI) system for simulating functions of the human brain, such as cognition and decision making, by using a machine learning algorithm such as deep learning or the like, and to an application thereof.A method for generating, by a first interactive electronic device of a first user, information for interaction with the first user includes: receiving, from a second interactive electronic device, information about an interaction between a second user and the second interactive electronic device; and generating interactive information to be provided to the first user, by applying the interactive information provided from the second interactive electronic device to a first AI learning model. At least a part of the method for generating interactive information may use a rule-based model, or an AI model trained according to at least one of a machine learning algorithm, a neural network algorithm, and a deep learning algorithm.