Annotation Accuracy Monitoring via Operator Concentration Detection
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Solution Overview
Problem
Existing methods for determining a decrease in operator concentration during annotation addition are cumbersome and prone to errors, often requiring unnecessary equipment like electroencephalographs, which complicates the process.
Innovation Solution
An information processing apparatus that acquires and compares annotations, determines recognition accuracy decreases, and provides warnings when concentration levels drop, using a CPU-driven system with machine learning algorithms to manage and present addition targets effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physiological measurement equipment (electroencephalograph) is used to monitor operator concentration, then concentration detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the concentration detection function from complex physiological measurement equipment and implements it through simple annotation comparison logic. The system monitors only the annotation addition operation results rather than continuously monitoring physiological signals, thereby reducing device complexity while maintaining detection capability for the specific task context.
Solution Approach 2:
The system uses the operator's own annotation output as the measurement object. By comparing annotations against ground truth or previous annotations, the system self-evaluates concentration levels without requiring external physiological monitoring equipment. The annotation data itself serves as the measurement medium.
2Reliability
If continuous monitoring of operator concentration is performed, then operational safety is improved, but work interruption increases
Solution Approach 1:
The system performs concentration monitoring periodically at annotation completion events rather than continuously. The determination unit evaluates concentration levels whenever an annotation is added, which provides timely safety checks without requiring constant interruption of the workflow. This event-driven periodic monitoring balances safety with productivity.
Solution Approach 2:
The system provides feedback through warnings when concentration decrease is detected, allowing operators to self-correct without mandatory work interruption. The warning mechanism enables operators to maintain awareness of their concentration state and take appropriate action, balancing safety monitoring with continuous work capability.
3Quantity of substance
If manual annotation addition operation is performed repeatedly, then annotation completeness is improved, but operator concentration decreases
Solution Approach 1:
The system implements feedback by detecting annotation completion events and evaluating concentration levels at these natural workflow breakpoints. This allows the system to monitor concentration at meaningful moments in the annotation process without disrupting the rhythmic flow of work, maintaining both completeness and concentration awareness.
Solution Approach 2:
The patent replaces continuous mechanical monitoring (physiological sensors) with a computational approach that analyzes annotation data patterns. By substituting physical measurement with data-driven detection, the system maintains monitoring capability while allowing uninterrupted workflow, as the evaluation occurs through software analysis rather than physical intervention.
Data Source
AI summary
An information processing apparatus includes an acquisition unit configured to acquire a first annotation that is for an addition target and is based on input from an operator, a comparison unit configured to compare the first annotation acquired by the acquisition unit and a second annotation that is a comparison target, a determination unit configured to determine whether there is a decrease in recognition accuracy of the operator based on the comparison by the comparison unit, and a warning unit configured to provide a warning in a case where the determination unit determines that there is a decrease in recognition accuracy of the operator.