Media Search Navigation Using Metadata and Entity Recognition
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Solution Overview
Problem
Conventional mechanisms for navigating media content, such as fast forwarding, rewinding, and skip operations, are inefficient and imprecise, and existing search methods fail to provide easy access to specific media segments featuring desired content.
Innovation Solution
Analyzing metadata like closed captions, social media content, and tags, combined with image and audio recognition algorithms, to identify and highlight relevant media segments, allowing precise navigation through media content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fast forward and rewind operations are used to navigate media content, then users can move through content, but navigation is highly inefficient and imprecise
Solution Approach 1:
The system performs preliminary analysis of media content during ingestion or preprocessing, extracting metadata, transcribing audio, and generating searchable indexes before the user needs to navigate. This preliminary action enables instant search-based navigation without requiring users to manually fast-forward or rewind through content, directly resolving the contradiction between navigation efficiency and time loss.
Solution Approach 2:
The patent introduces an intermediary search system that sits between the user and the media content. Instead of directly manipulating the media stream through fast-forward/rewind, users interact with a search interface that queries pre-processed metadata and returns precise segment locations. This intermediary layer transforms inefficient direct navigation into efficient indirect navigation through search results.
2Ease of operation
If skip forward and skip backward capabilities are used, then navigation through media content is possible, but the mechanism remains inefficient and imprecise
Solution Approach 1:
The patent replaces the mechanical skip-based navigation system with an information-based search system. Instead of relying on fixed-time skip operations that blindly move through content, the system substitutes mechanical navigation with intelligent query-based navigation that uses metadata, transcripts, and content analysis to precisely locate desired segments, thereby achieving both ease of operation and measurement precision.
Solution Approach 2:
The system changes the navigation parameter from fixed time increments (skip forward/backward by X seconds) to semantic search parameters (find segments containing specific keywords, topics, or entities). This parameter change allows users to navigate to precisely located segments based on content meaning rather than arbitrary time positions, resolving the precision problem while maintaining ease of operation.
3Productivity
If bookmarks are provided by content provider, then navigation efficiency improves, but the mechanism has significant drawbacks including limited coverage and lack of user customization
Solution Approach 1:
The patent creates a universal navigation system that combines the benefits of provider-generated bookmarks with user-created bookmarks and search-based navigation. The system handles multiple navigation needs through a single unified interface: predefined bookmarks for common segments, user bookmarks for personal favorites, and search capabilities for discovering new segments. This multi-functional approach maintains high navigation efficiency while dramatically improving adaptability and versatility.
Solution Approach 2:
The navigation system transitions from static bookmarks (fixed positions set by providers) to a dynamic system where bookmark positions and availability are determined by both provider input and user interaction patterns. The system adapts to different user needs, content types, and viewing contexts, allowing navigation methods to flexibly change based on the situation, thereby resolving the contradiction between efficiency and adaptability.
Data Source
AI summary
Mechanisms are provided to allow for improved media content navigation. Metadata such as closed captioning, social media content, and tags associated with various media segments are analyzed to allow identification of particular entities depicted in the various media segments. Image recognition and audio recognition algorithms can also be performed to further identify entities or validate results from the analysis of metadata.