AI User Recognition Confidence Feedback Mechanism
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Solution Overview
Problem
Current devices that perform control based on video or sound input data struggle with accurately recognizing users or actions, failing to determine the factors affecting recognition confidence and lacking controls to enhance recognition performance.
Innovation Solution
An artificial intelligence apparatus that analyzes image data to recognize users or actions, calculates confidence levels, and provides feedback on factors influencing recognition, allowing for improved recognition performance by adjusting controls based on confidence thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current devices perform control based on video or sound input, then device functionality is enhanced, but recognition accuracy and confidence level remain insufficient
Solution Approach 1:
The patent implements a feedback mechanism that provides users with specific information about recognition failures. Instead of merely indicating failure, the system analyzes and communicates the cause (e.g., lighting conditions, distance, angle) and offers actionable suggestions to improve recognition, thereby enhancing reliability through iterative user correction
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes recognition results and identifies failure causes. This intermediary component bridges the gap between raw recognition output and user understanding, providing structured feedback without requiring complete system redesign
2Ease of operation
If devices provide simple recognition failure feedback, then system complexity is reduced, but user convenience and recognition performance improvement are limited
Solution Approach 1:
The patent performs preliminary analysis of recognition failure causes immediately when recognition fails, identifying issues such as lighting, distance, or angle problems before the user attempts re-recognition. This preliminary diagnostic action guides the user's next steps, reducing unnecessary retry attempts and saving time
Solution Approach 2:
The system provides detailed feedback including the specific cause of failure and actionable suggestions for improvement. This informative feedback loop enables users to make targeted adjustments, improving ease of operation by reducing frustration and time for re-recognition
3Reliability
If no analysis of recognition failure causes is performed, then device complexity is minimized, but recognition performance cannot be improved
Solution Approach 1:
The patent introduces an intermediary analysis module that specifically examines recognition failure causes without requiring complete redesign of the recognition system. This modular intermediary layer analyzes factors like lighting, distance, and angle, providing actionable insights while maintaining manageable system complexity
Solution Approach 2:
The patent extracts and analyzes specific failure cause factors (lighting conditions, distance, angle) separately from the main recognition process. By isolating and examining these individual factors, the system can identify problems and provide targeted suggestions without requiring complex overhaul of the entire recognition system
Data Source
AI summary
An artificial intelligence apparatus for recognizing a user includes a camera, and a process configured to receive, via the camera, image data including a recognition target object, generate recognition information corresponding to the recognition target object from the received image data, calculate a confidence level of the generated recognition information, determine whether the calculated confidence level is greater than a reference value, if the calculated confidence level is greater than the reference value, perform a control corresponding to the generated recognition information, and if the calculated confidence level is not greater than the reference value, provide a feedback for the object recognition.