Automated Data Gathering Device Settings Adjustment via Confidence Scores
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Solution Overview
Problem
Machine learning algorithms in computer vision often fail to recognize objects due to insufficient input quality, which requires manual adjustment of data collection devices, leading to inefficiencies.
Innovation Solution
A computer-implemented method and system that automatically improves data gathering by processing input data to generate predictions and confidence scores, adjusting data gathering device settings using a pre-trained acquisition settings adjustment model, and applying reinforcement learning to refine these settings iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjustment of data collection device is used to ensure sufficient input quality, then the reliability of object recognition is improved, but the productivity and ease of operation deteriorate due to manual intervention requirements
Solution Approach 1:
The system enables self-service by having the machine learning model automatically assess input quality and trigger recapture operations without human intervention. The model evaluates confidence scores, determines when recapture is needed, and coordinates with the data collection device to obtain improved inputs automatically, eliminating the need for manual monitoring and adjustment.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously monitors output quality (confidence scores) and uses this information to feed back into the data collection process. When low confidence indicates poor input quality, the system automatically initiates recapture operations, creating a closed-loop feedback system that maintains high recognition reliability while improving operational efficiency.
2Measurement precision
If manual adjustment of capture device settings is performed, then the measurement precision of input quality is improved, but the ease of operation deteriorates due to requiring human intervention
Solution Approach 1:
The system replaces manual mechanical adjustment operations with automated computational processes. Instead of humans visually assessing and manually adjusting camera settings, the machine learning model automatically evaluates input quality through confidence scores and triggers appropriate recapture operations, substituting human cognitive and manual processes with automated AI-based systems.
Solution Approach 2:
The system enables self-service by having the machine learning model automatically assess input quality and trigger recapture operations without human intervention. The model evaluates confidence scores, determines when recapture is needed, and coordinates with the data collection device to obtain improved inputs automatically, eliminating the need for manual monitoring and adjustment.
3Productivity
If automated system is implemented to improve data gathering, then the ease of operation and productivity are improved, but the device complexity increases due to integration of machine learning models and reinforcement learning mechanisms
Solution Approach 1:
The system applies multi-functionality by integrating multiple capabilities into a unified platform: the machine learning model performs object recognition, quality assessment, and recapture triggering functions; the reinforcement learning component handles optimization and adaptation; and the data collection device manages capture operations. This consolidation of multiple functions into a single integrated system improves productivity while managing complexity through functional unification.
Data Source
AI summary
A computer-implemented system and method for automatically improving gathering of data includes processing first input data acquired by a data gathering device to generate a first prediction and a corresponding confidence score. If the confidence score is below a threshold, then generating and applying a set of at least one action to the data gathering device, such as changing acquisition settings of the device. Second input data acquired using the data gathering device having the action applied thereto is further received and processed to generate a second prediction and corresponding confidence score. The set of action to-be-applied to the device is further modified based on the difference.The generation of the at least one action, such as changing the acquisition settings, can be based on an acquisition settings adjustment model and the modification of the action can include updating the model by machine learning (ex: reinforcement learning). The updated model can be applied to subsequent iterations of data gathering.