On-Demand Interactive Video Snippet Matching for User Synchronization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video content for interactive activities lacks flexibility and synchronization with user performance, leading to mismatches in pace, speed, and actions, and provides ineffective static feedback, limiting customization and user experience.
Innovation Solution
A method and system that generate on-demand videos by creating video snippets from trainer performances, matching user preferences, and combining them to create synchronized content, using multimedia processing models and AI for dynamic feedback and synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-recorded videos are used for interactive activities, then video content is available on-demand, but the user cannot synchronize with the video pace and speed
Solution Approach 1:
The system dynamically adjusts video playback parameters (speed, pace) in real-time based on user performance detection, transforming the static pre-recorded video into an adaptive experience that synchronizes with user actions while maintaining on-demand availability
Solution Approach 2:
The system implements real-time feedback loops where user actions are detected, analyzed, and used to adjust video playback parameters, enabling synchronization between user performance and video content without requiring live interaction
2Loss of information
If pre-recorded videos provide static feedback at pre-decided intervals, then feedback is provided to the user, but the feedback is ineffective when user actions are not in synch with the video
Solution Approach 1:
The system replaces static, pre-scheduled feedback with dynamic, real-time feedback that is triggered by and synchronized to actual user actions, ensuring feedback accuracy and effectiveness by detecting user performance and providing appropriate responses at the right moments
Solution Approach 2:
Feedback timing and content are dynamically adjusted based on real-time user performance detection, transforming static feedback intervals into adaptive feedback that responds to user actions, thereby improving feedback precision and relevance
3Ease of manufacture
If the number of repetitions and variance in pre-recorded videos are fixed, then video production is simplified, but users cannot perform customized activities
Solution Approach 1:
The system dynamically generates customized video sequences by selecting and combining video snippets based on real-time user performance and preferences, enabling unlimited customization without requiring separate pre-recorded videos for each scenario
Solution Approach 2:
Videos are divided into modular snippets that can be independently selected, combined, and recombined based on user needs, allowing flexible customization of repetitions and variance while maintaining production efficiency through reuse of existing segments
4Adaptability or versatility
If live interactive video sessions are provided with trainer-user interaction, then user experience is enhanced, but video quality and lagging problems occur
Solution Approach 1:
The system uses pre-recorded video copies instead of live streams, eliminating real-time transmission issues while maintaining interactivity through AI-driven adaptation and synchronization, thereby ensuring video quality stability without sacrificing user experience
Data Source
AI summary
A method and system for generating on-demand video is disclosed. The method includes creating a plurality of video snippets from a plurality of videos comprising trainer performed activities. The method further includes generating a set of input vectors for at least one activity dimension based on a predetermined on-demand preferences. For each of the at least one activity dimension, the set of input vectors are compared with a set of activity vectors associated with each of the plurality of video snippets, and a distance between each of the set of input vectors relative to the set of activity vectors is determined. A set of video snippets is identified where the distance is below a predefined threshold. The identified set of video snippets are combined according to at least one of the predetermined on-demand preferences, and the on-demand video is generated.