AI Classification Apparatus for Unknown Stimuli Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI classification systems are limited in recognizing and classifying unknown external stimuli, as they require pre-defined information and manual design of features and groups, making them ineffective for unpredictable environments.
Innovation Solution
A classification apparatus and method that includes an encoding module for extracting features using an element classification model, an integration module for converting classification information into a collation vector based on an element estimation model, and a determination module for determining the group affiliation by collating the collation vector with representative vectors stored as a semantic model, allowing autonomous recognition and classification of unknown stimuli.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-defined information and manual feature design are used for classification, then classification accuracy for known stimuli is improved, but the system cannot recognize or classify unknown stimuli
Solution Approach 1:
The system performs self-learning by autonomously acquiring recognition and classification groups from input data without requiring manual design or pre-definition. The classification apparatus automatically builds its own feature extraction capabilities and classification rules through the learning process, enabling it to handle both known and unknown stimuli effectively
Solution Approach 2:
The system transitions from a static, pre-defined classification approach to a dynamic, adaptive system that continuously learns and updates its recognition and classification groups. The classification model evolves over time by incorporating new information from input data, allowing the system to adapt to new types of stimuli while maintaining accuracy for known categories
2Reliability
If manual design of features and classification groups is performed, then system performance on predefined tasks is improved, but the system lacks autonomy in acquiring recognition capabilities
Solution Approach 1:
The classification apparatus autonomously acquires recognition and classification groups by processing input data through the learning process without human intervention. The system automatically extracts features, identifies patterns, and builds classification models, eliminating the need for manual feature design and group definition while maintaining reliable performance
3Productivity
If pre-defined classification groups are used, then classification speed for known categories is improved, but the system cannot adapt to unpredictable environments
Solution Approach 1:
The system maintains fast classification for known categories through learned models while simultaneously adapting to new and unpredictable stimuli through continuous learning. The dynamic nature of the classification groups allows the system to quickly incorporate new patterns without sacrificing the speed achieved on established categories
Solution Approach 2:
The classification apparatus achieves multi-functionality by being able to handle both pre-defined classification tasks with high speed and adapt to new, unpredictable environments. The same learning mechanism serves dual purposes: maintaining efficient classification for known categories and enabling adaptation to novel situations
Data Source
AI summary
A classification apparatus includes: an encoding module that includes an element classification part that extracts a feature of input data and outputs classification information based on an element classification model stored in a first storage unit; an integration module that includes an element estimation part that receives the classification information and converts the classification information to a collation vector based on an element estimation model stored in a second storage unit; and a determination module that includes a determination part that determines a group to which the collation vector belongs by collating the collation vector with a representative vector of an individual group stored as a semantic model in a third storage unit and outputs a group ID of the group as a classification result.