Dynamic Closed-Captioning System for User Comprehension
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Solution Overview
Problem
Current closed-captioning systems do not account for the individual hearing and reading abilities of users, leading to difficulties in comprehension for users who may struggle with fast dialogue or non-native language captions.
Innovation Solution
A system that includes a user monitoring device to collect data on user characteristics, such as gaze, facial expressions, and physiological responses, and uses this data to determine a user's comprehension state, allowing for dynamic adjustments to the closed-captioning state, such as speed or format, to improve understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard closed-captioning is displayed at normal speed, then the system maintains simple operation and uniform content delivery, but users with hearing or reading difficulties cannot comprehend the dialogue
Solution Approach 1:
The closed-captioning system dynamically adjusts caption display parameters (such as display duration, font size, or pacing) based on real-time monitoring of user comprehension indicators. This allows the system to adapt to individual user needs without requiring manual configuration, thereby improving comprehension accuracy while maintaining ease of operation.
Solution Approach 2:
The system monitors user responses or comprehension indicators and uses this feedback to automatically adjust captioning parameters. This closed-loop feedback mechanism ensures that captions are optimized for each user's comprehension ability, resolving the contradiction between reliable comprehension and simple operation.
2Productivity
If closed-captioning text is displayed for a short duration to match fast dialogue, then the system maintains faithful representation of original content timing, but slow readers cannot understand the text before it is replaced
Solution Approach 1:
The system dynamically adjusts the display duration of closed-captioning text based on monitored user reading speed and comprehension indicators. When a user is identified as a slow reader, the system extends the display time of captions without altering the original dialogue timing, thereby maintaining both dialogue transmission fidelity and reading comprehension reliability.
Solution Approach 2:
The system changes the temporal parameters of caption display (such as duration, spacing, or refresh rate) based on user characteristics detected through monitoring. This parameter adjustment allows the system to optimize caption visibility for slow readers while preserving the original content's timing structure.
3Reliability
If closed-captioning is customized for individual user needs, then comprehension ability is improved, but the system requires complex monitoring and adjustment mechanisms
Solution Approach 1:
The system automatically monitors user comprehension indicators and adjusts captioning parameters without requiring external intervention or complex configuration. The self-service mechanism uses simple monitoring of user responses to trigger pre-defined adjustment rules, improving comprehension while avoiding the need for complex system architecture.
Solution Approach 2:
The system implements personalization through controlled changes to captioning parameters (such as speed, size, or display timing) based on user characteristics. By limiting adjustments to a predefined set of parameters with simple adjustment logic, the system achieves individualized comprehension support without requiring complex system architecture.
Data Source
AI summary
Systems and methods for displaying dynamic closed-captioning content are disclosed. In one embodiment, a system includes a user monitoring device for monitoring one or more characteristics of at least one user viewing display content produced by a display device, one or more processors, and a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the one or more instructions to receive user data from the user monitoring device that corresponds to the one or more characteristics of the at least one user, determine a comprehension state of the at least one user based at least in part on the user data, and adjust a closed-captioning state of closed-captioning of the display content based on the comprehension state.