AI Algorithm Diversion for Personalized Voice Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI agent systems that respond to user input by voice struggle with adapting to user-specific preferences and learning data, as they often require manual correction and lack the ability to seamlessly divert or integrate algorithms from other users.
Innovation Solution
An information processing device and method that enables re-learning of algorithms based on both initial learning data and specific learning data from another user's algorithm, allowing for the diversion and integration of databases to tailor responses to individual user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If an AI agent system uses accumulated learning data to generate responses, then the system can provide automated voice responses to user input, but the system cannot easily adapt to user-specific preferences or allow users to correct accumulated learning data
Solution Approach 1:
The patent segments learning data into two distinct types: first learning data (accumulated by the current user) and second learning data (from other users). This segmentation allows the system to selectively apply different learning data to different users, enabling personalized adaptation while maintaining a shared knowledge base. The algorithm is divided into multiple versions corresponding to different learning data sets, making it easier to manage and switch between different learning configurations.
Solution Approach 2:
The patent creates a universal algorithm framework that can function with multiple different learning data sets. The same base algorithm can be applied to first learning data, second learning data, or combinations thereof, allowing the system to serve multiple users with different preferences using a single algorithmic structure. This multi-functionality enables the system to adapt to individual users while maintaining efficiency through code reusability.
2Adaptability or versatility
If another user wants to use an algorithm from a predetermined user, then knowledge can be shared, but there is no mechanism to divert or integrate algorithms between users
Solution Approach 1:
The patent implements a copying mechanism where learning data and corresponding algorithm versions can be transferred between users. When a user wants to use another user's algorithm, the system copies the relevant learning data and algorithm configuration to the target user's environment. This allows seamless knowledge sharing and algorithm diversion without requiring complex integration processes, as the entire algorithm package can be copied and applied to different learning data sets.
Solution Approach 2:
The patent makes the algorithm dynamic by allowing it to change based on which learning data set is being applied. The same base algorithm can dynamically adapt to different learning data configurations, enabling users to switch between using their own accumulated learning data or another user's learning data. This dynamic capability facilitates easy algorithm diversion and integration between users.
3Measurement precision
If the AI system accumulates learning data over time, then response accuracy improves, but the system cannot selectively apply learning data from different users or correct specific data accumulations
Solution Approach 1:
The patent applies local quality by allowing different learning data sets to be applied to different users or different contexts. Instead of a single uniform learning data accumulation, the system maintains multiple learning data sets with different qualities and characteristics. Each user can selectively apply learning data that is locally optimized for their specific needs and preferences, thereby maintaining high response accuracy while enabling selectivity in learning data application.
Data Source
AI summary
The present technology relates to an information processing device and an information processing method capable of diverting a database.For an algorithm that changes on the basis of accumulation of first learning data, re-learning is caused to be performed on the basis of the first learning data and specific learning data out of second learning data forming another algorithm that changes on the basis of accumulation of learning data. The first learning data includes data regarding output information from the algorithm based on input information to the algorithm. The present technology can be applied to, for example, artificial intelligence.