Hyper-video Linked Placement via Saliency Analysis
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Solution Overview
Problem
Current technologies lack a mechanism to automatically place linked hyper-videos within existing hyper-videos in a way that captures user attention without obstructing the viewing experience.
Innovation Solution
A method that identifies points of interest and features in hyper-videos, extracts relevant information, and dynamically displays linked hyper-videos within the existing hyper-video, optimizing placement to avoid obstructing the user's view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If linked hyper-videos are placed within existing hyper-videos to provide additional information, then information richness is improved, but viewing experience is obstructed
Solution Approach 1:
The system analyzes the hyper-video content to identify specific regions with lower visual importance or lower user attention probability, and places linked hyper-videos in these localized areas. This ensures that the additional information is provided without obstructing the main viewing experience, as the placement is tailored to local characteristics of the video content.
Solution Approach 2:
The placement of linked hyper-videos is dynamic rather than static. The system adjusts the position, size, and visibility of linked hyper-videos based on real-time analysis of user attention, video content importance, and contextual relevance. This dynamic adjustment allows the system to optimize between providing information and maintaining viewing experience continuously.
2Productivity
If linked hyper-videos are placed to capture user attention, then engagement is improved, but viewing experience is obstructed
Solution Approach 1:
The system changes multiple parameters of the linked hyper-video placement including position coordinates, display size, opacity level, and timing of appearance. These parameters are adjusted based on calculated attention capture probability and viewing obstruction metrics, allowing the system to optimize engagement while minimizing obstruction by finding the right parameter combination.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor user interaction patterns, attention duration, and engagement metrics. This feedback is used to dynamically adjust the placement and presentation of linked hyper-videos, optimizing the balance between capturing user attention for engagement and avoiding excessive obstruction that would degrade the viewing experience.
3Manufacturing precision
If automatic placement mechanism is implemented, then placement precision is improved, but device complexity is increased
Solution Approach 1:
The system performs self-analysis by automatically processing thehyper-video content itself to identify placement opportunities. It extracts visual features, determines content importance, and calculates optimal placement positions without requiring external manual input or complex external systems. This self-service approach achieves high placement precision while managing system complexity through autonomous operation.
Solution Approach 2:
The patent replaces manual or mechanical placement methods with automated computer vision and machine learning algorithms. Instead of relying on manual annotation or simple rule-based systems, the system uses automated content analysis, attention probability calculation, and optimization algorithms to determine placement positions, achieving higher precision while the computational nature of the system manages complexity differently than mechanical approaches.
Data Source
AI summary
A method for placing a linked hyper-video within a hyper-video. The method includes identifying one or more points of interest in the hyper-video, detecting one or more features of the hyper-video, extracting at least one of the one or more features, and displaying the linked hyper-video, within the hyper-video, based on the one or more identified points of interest and based on the extracted at least one of the one or more features. The method further includes extracting one or more features from the hyper-video that depict one or more viewing characteristics of a user. The method includes analyzing the one or more features from the hyper-video to identify a low-saliency area of the hyper-video, a low-frequency region within the low-saliency area, and a homogenous sub-region within the low-frequency region. The method further includes placing a linked hyper-video at the homogenous sub-region within the low-frequency region of the hyper video.