Multimedia Error Probability Annotation via User Distraction Monitoring
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Solution Overview
Problem
Office workers often introduce errors into multimedia content due to distractions, which existing technologies fail to effectively address, as they are unable to reliably identify and annotate potential error locations in real-time based on user distraction levels.
Innovation Solution
A system and method that automatically determine the probability of errors in multimedia content by monitoring user states, sensitivity of content, and user profiles, using a function F(U,S,P) that incorporates machine learning algorithms to annotate distractions through visual, auditory, or haptic feedback, such as color changes, audio, or textures, indicating areas prone to errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If real-time monitoring of user state is implemented to identify error locations, then manufacturing precision of content creation is improved, but device complexity increases
Solution Approach 1:
The system segments the content creation process into monitorable units by tracking user interactions with specific elements (text, images, videos) and annotating individual locations with error probabilities. This allows precision monitoring without requiring complete system overhaul.
Solution Approach 2:
The patent introduces an intermediary annotation layer that sits between the user interface and the content. This layer captures user state information and distraction indicators without fundamentally changing the core application architecture, thus improving precision while limiting complexity increase.
2Reliability
If multiple monitoring parameters (user state, sensitivity, profile) are integrated to calculate error probability, then reliability of error identification is improved, but device complexity increases
Solution Approach 1:
The system merges multiple data sources (user state monitoring, content sensitivity analysis, user profile information) into a unified error probability calculation. This integration improves reliability by considering multiple factors simultaneously while managing complexity through consolidated processing.
Solution Approach 2:
The patent transforms qualitative user state information and content sensitivity into quantitative parameters that can be processed by the function F(U,S,P). This parameter transformation enables reliable error probability calculation while maintaining system manageability through standardized inputs.
3Manufacturing precision
If annotations are provided to indicate distraction levels and error probabilities, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The system applies annotations selectively rather than universally. It focuses computational resources on identifying and annotating high-risk locations where errors are most likely to occur, rather than annotating every element. This partial action approach maintains precision while reducing time loss.
Solution Approach 2:
The system provides self-service by automatically generating and applying annotations without requiring manual intervention. The automated calculation of error probabilities and placement of annotations reduces the time burden on users while maintaining high precision in error identification.
4Reliability
If user distraction level is monitored and annotated in multimedia content, then reliability of error detection is improved, but ease of operation deteriorates
Solution Approach 1:
The system uses color-coded annotations to indicate distraction levels and error probabilities. Different colors represent different risk levels, allowing users to quickly grasp error likelihood without interpreting complex data. This visual encoding maintains reliability while preserving ease of operation.
Solution Approach 2:
The patent creates a simplified visual copy or representation of the underlying complex data (user state, distraction levels, error probabilities) through annotations. This visual copy conveys essential information in an easily digestible format, maintaining detection reliability while ensuring ease of operation.
Data Source
AI summary
Disclosed is a novel system and method for indicating a probability of errors in multimedia content. The system determines a user state or possible user distraction level. The user distraction level is indicated in the multimedia content. In one example, work is monitored being performed on the multimedia content. Distractions are identified while the work is being monitored. A probability of errors is calculated in at least one location of the multimedia content by on the distractions that have been identified. Annotations are used to indicate of the probability of errors. In another example, the calculating of probability includes using a function F(U,S,P) based on a combination of: i) a determination of user state (U), ii) a determination of sensitivity (S) of user input, and iii) a determination of user characteristics stored in a profile (P).