AI Context Metadata for Searchable Video and Audio Archives
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Solution Overview
Problem
The challenge of efficiently capturing and searching unstructured data formats such as audio, audiovisual, and video data is exacerbated by the difficulty in articulating search parameters and retrieving relevant information, especially in large archives.
Innovation Solution
A context-based data processing tool utilizing artificial intelligence to generate and associate relevant metadata with captured content data, facilitating improved data processing, storage, and retrieval within a computing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual searching is used to locate specific content in video archives, then search capability is maintained, but time consumption and operational efficiency deteriorate as archive size increases
Solution Approach 1:
The system performs preliminary actions by automatically generating metadata tags, captions, and summaries for video content during ingestion and processing phases. This pre-processing enables rapid retrieval operations without requiring manual searching later, directly resolving the time consumption issue while maintaining search capability.
Solution Approach 2:
The patent introduces metadata as an intermediary layer between the video archive and search queries. Instead of directly searching through raw video content, the system uses generated metadata tags, captions, and summaries as mediators to enable efficient retrieval, thereby reducing time consumption while preserving search functionality.
2Loss of information
If comprehensive video archives are maintained for future retrieval, then data availability is improved, but storage requirements and system complexity increase
Solution Approach 1:
The system extracts essential information from video content and stores it as separate metadata elements (tags, captions, summaries) rather than storing only the raw video files. This extraction approach maintains data availability for retrieval while reducing the complexity of managing comprehensive video archives, as the metadata can be searched and processed independently.
Solution Approach 2:
The patent segments video content information into distinct metadata components (tags, captions, summaries, transcripts) that can be independently generated, stored, and processed. This segmentation reduces system complexity by allowing each metadata type to be handled separately while collectively maintaining comprehensive data availability for various retrieval needs.
3Productivity
If AI-generated metadata is created for all captured content data, then data processing efficiency and retrieval capability are improved, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by generating metadata selectively based on content type, importance, and user needs rather than processing all captured data uniformly. This approach improves data processing efficiency for critical content while reducing computational resource consumption by applying less intensive processing to less important data, thereby resolving the contradiction between productivity and energy use.
Data Source
AI summary
A context-based data processing tool is provided to facilitate data processing within a computing environment, where executing the context-based data processing tool includes capturing, by at least one processor set, selected content data from an electronic media, and generating, via artificial intelligence, relevant context metadata for the captured content data, and associating the generated context metadata with the captured content data. Further, executing the data processing tool includes integrating, by the at least one processor set, the captured content data into a context-based data store using the generated context metadata, where the generated context metadata facilitates processing of the captured content data within the computing environment.