Audiovisual Transcript Search Engine with Real-Time Entity Indexing
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Solution Overview
Problem
Current television systems are inefficient in identifying meaningful entities and topics in real-time, requiring users to manually search through program guides for specific content, and lack context-sensitive advertising and real-time indexing of video content.
Innovation Solution
A self-learning entity network (SLEN) system that processes data in real-time to identify and categorize entities, build relationships between them, and provide trending information, while also recognizing advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual indexing methods are used for video content, then implementation simplicity is maintained, but indexing speed and real-time capability deteriorate
Solution Approach 1:
The patent replaces manual mechanical indexing processes with automated optical character recognition (OCR) technology. The system captures video frames, converts them to text through OCR, and automatically indexes content without human intervention. This substitution of manual mechanical work with automated optical and computational processes resolves the contradiction by dramatically increasing indexing speed while maintaining system accessibility.
Solution Approach 2:
The system performs self-service indexing by automatically capturing video content, transcribing it through OCR, extracting keywords, and building search indexes without requiring manual human operation. The automated pipeline processes video content independently, generating searchable indexes in real-time, thereby achieving high productivity while keeping the implementation relatively simple.
2Measurement precision
If comprehensive video content is indexed, then search accuracy improves, but processing time and system complexity increase
Solution Approach 1:
The system extracts only the essential textual information from video content through OCR and keyword extraction, rather than processing and storing all video data. By extracting and indexing only relevant text elements and keywords, the system achieves high search accuracy for content retrieval while minimizing processing time and computational resources required.
Solution Approach 2:
The system performs partial indexing by focusing on extracting and indexing only the most relevant keywords and text elements from video content, rather than comprehensively processing every detail. This selective approach maintains search accuracy for typical queries while significantly reducing processing time and system complexity.
3Speed
If real-time processing is implemented, then responsiveness improves, but system complexity and computational resources increase
Solution Approach 1:
The real-time processing system is segmented into distinct modular components: video frame capture, OCR text recognition, keyword extraction, and index updating. Each component handles a specific task independently, allowing the system to achieve real-time responsiveness through coordinated modular operations while keeping individual component complexity manageable.
Solution Approach 2:
The system performs preliminary actions by pre-processing video frames through OCR and extracting keywords before actual search queries are submitted. This preliminary indexing prepares the data in advance, enabling rapid real-time search responsiveness when users submit queries, as the heavy processing work has already been completed.
Data Source
AI summary
Self-learning systems process data in real-time and output the processed data to client applications in an effective manner. They comprise a capture platform that captures data and generates a stream of text, a text decoding server that extracts individual words from the stream of text, an entity extractor that identifies entities, a trending engine that outputs trending results, and a live queue broker that filters the trending results. The self-learning systems provide more efficient realization of Boxfish technologies, and provide or work in conjunction with real-time processing, storage, indexing, and delivery of segmented video. Furthermore, the self-learning systems efficiently perform entity relationing by creating entity network graphs, and are operable to identify advertisements from the data.