Dynamic Non-Sequential Video Fragment Assembly
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Solution Overview
Problem
Current online video streaming platforms fail to provide personalized video assemblies to users, as they do not dynamically adapt to changing user interests and do not utilize dynamic meta-tagging based on the context and ontology of search queries, resulting in inefficient video content delivery.
Innovation Solution
A method for real-time, dynamic, adaptive, and non-sequential assembly of tagged video fragments, which involves receiving user preference data, fragmenting and segregating videos, mining semantic context information, clustering fragments, and assembling them in a preferred order, while incorporating interactive elements and adaptive bitrate streaming based on device and network parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional video platforms assemble videos based on complete set of tags without dynamic adaptation, then video assembly process is simple, but user personalization and adaptability to changing interests are poor
Solution Approach 1:
The patent segments videos into smaller fragments or clips with individual tags, allowing selective assembly based on real-time user preferences. This segmentation enables the system to dynamically reconfigure video content without reprocessing entire videos, thus improving adaptability while managing complexity through modular processing.
Solution Approach 2:
The system implements dynamic video assembly where the sequence and selection of video fragments are determined in real-time based on user preferences, search queries, and contextual information. This dynamic approach allows the system to adapt to changing user interests during the viewing session, resolving the contradiction between adaptability and complexity through algorithmic optimization.
2Loss of information
If videos are assembled without dynamic meta-tagging based on search query context, then processing speed is faster, but relevance to user interests and semantic understanding are reduced
Solution Approach 1:
The patent applies preliminary tagging to video fragments during the upload and indexing phase, where metadata, tags, and semantic information are extracted and stored in advance. This preliminary action ensures that semantic context information is preserved without adding processing delays during real-time assembly, as the system directly queries pre-processed metadata rather than analyzing video content on-the-fly.
3Ease of operation
If video platforms provide personalized assembled videos to individual users, then user experience is improved, but system complexity and computational resources increase
Solution Approach 1:
The system implements self-service personalization where user preferences, viewing history, and search query patterns are automatically analyzed to generate personalized video assemblies without requiring manual user configuration. The system learns and adapts to user preferences autonomously, improving user experience while managing complexity through automated preference extraction and profile management.
4Adaptability or versatility
If non-sequential stitching of video clips is implemented in real-time, then adaptability to user preferences is improved, but processing complexity and computational load increase
Solution Approach 1:
The patent changes the parameter of video assembly from sequential to non-sequential stitching, allowing clips to be arranged based on their semantic content and user preferences rather than their original temporal order. This parameter change enables dynamic reconfiguration of video content while maintaining real-time processing efficiency through optimized data structures and indexing mechanisms that facilitate rapid clip selection and assembly.
Data Source
AI summary
The present disclosure provides a method and system for real time, dynamic, adaptive and non-sequential assembling of one or more mapped fragments of one or more tagged videos. The method includes a step of receiving a set of preference data from pre-defined selection criteria and set of user authentication data. The method includes another step of fetching the one or more tagged videos from the digitally processed repository of videos. The method includes yet another step of fragmenting each tagged video of the one or more tagged videos into the one or more tagged fragments and clustering one or more logical sets of mapped fragments into one or more logical clusters of mapped fragments. The method includes yet another step of assembling at least one of the one or more logical clusters of mapped fragments in a pre-defined order of preference to obtain an assembled video.