Dynamic Composition Reference Video for Personalized Activity Guidance
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Solution Overview
Problem
Users face challenges in following instructional videos as actual activities often differ from those presented, requiring adjustments such as cooking preferences that may not be accounted for in available videos.
Innovation Solution
A computer-implemented method dynamically creates a composition reference video by identifying personalized user parameters, predicting activity performance, selecting appropriate videos, normalizing content, and integrating AI to customize video and audio for seamless user guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users follow available instructional videos blindly, then they can get basic guidance for activities, but the video content does not account for user-specific preferences and variations in actual activity performance
Solution Approach 1:
The system changes parameters of the instructional video by dynamically adjusting content selection, timing, and presentation based on identified user preferences and activity variations. The video generation module modifies video parameters to align with user-specific requirements, transforming a generic instructional video into a personalized one that accounts for individual preferences and activity variations.
Solution Approach 2:
The system creates a virtual copy of the instructional video that can be dynamically customized. By generating a synthetic instructional video from templates and existing videos, the system can replicate the essential instructional content while adapting it to user preferences, effectively copying the structure of existing videos while adding user-specific customization.
2Adaptability or versatility
If the system dynamically creates personalized videos for each user, then user-specific preferences can be accommodated, but the complexity of video processing and content generation increases
Solution Approach 1:
The system segments the video generation process into distinct modules: user preference analysis module, activity prediction module, video selection module, and video generation module. Each module handles a specific aspect of the complex task independently, making the overall system more manageable and scalable. The segmentation allows each component to process information in isolation and combine results, reducing the computational burden on any single component.
Solution Approach 2:
The system introduces an intermediary video generation module that acts as a mediator between user preferences and final video output. This intermediary component processes and transforms raw video data into personalized instructional videos, simplifying the interaction between different system components and making the overall process more manageable.
3Ease of manufacture
If existing instructional videos are used as-is, then the content is ready and accessible, but the videos do not account for variations in user preferences and activity execution
Solution Approach 1:
The system transforms static, pre-recorded instructional videos into dynamic, adaptable content. The video generation module dynamically adjusts video content based on real-time user preference analysis and activity prediction, allowing the instructional video to adapt its content, timing, and presentation to match user-specific requirements and activity variations.
Solution Approach 2:
The system changes parameters of the instructional video by dynamically adjusting content selection, timing, and presentation based on identified user preferences and activity variations. The video generation module modifies video parameters to align with user-specific requirements, transforming a generic instructional video into a personalized one that accounts for individual preferences and activity variations.
Data Source
AI summary
A computer-implemented method, a computer program product, and a computer system for dynamically creating a composition reference video to support a user activity. In response to that a user selects a reference video for performing an activity, the computer system identifies a search query of a reference video. The computer system identifies personalized parameters of the user, based on a knowledge corpus user preferences of performing activities, and the search query. The computer system identifies appropriate videos and video transcripts in an online video repository and identifies textual contents through document and text search, based on a prediction about how the user is to perform the activity. The computer system draws series of images based on the textual contents. The computer system normalizes contents from the appropriate videos and the series of images and normalizes voices in the contents from the appropriate videos.