Caption Mining Engine for Keyword Extraction
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Solution Overview
Problem
Current technologies do not effectively utilize caption data to provide relevant information to users during media consumption, missing opportunities for enhanced user experience, accessibility, and targeted advertising.
Innovation Solution
A system that uses a caption mining engine to identify and tag keywords from media content metadata, including caption data, and provides this information to native and third-party applications for delivering relevant content, advertisements, and interactive features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If caption data is collected and processed through a data mining engine to identify keywords, then relevant information and advertisements can be provided to users, but system complexity and processing time increase
Solution Approach 1:
The system segments the caption data processing by separating keyword identification (data mining engine) from information delivery (native and third-party applications). This modular approach allows the system to handle complex tasks through specialized components, reducing overall system complexity while maintaining adaptability.
Solution Approach 2:
The system performs preliminary action by pre-identifying keywords from caption data before user requests. The data mining engine processes and tags keywords in advance, so when users need information, the relevant data is already prepared and ready for rapid delivery, reducing real-time processing time.
2Loss of information
If caption data is mined for keywords to provide targeted information, then user relevance and engagement improve, but data processing time and computational resources increase
Solution Approach 1:
The system performs keyword identification and tagging in advance through the data mining engine, preparing relevant information before users need it. This preliminary processing ensures high information relevance is available when requested, minimizing the time users wait for relevant content during actual media consumption.
Solution Approach 2:
The system creates keyword tags as simplified copies of the complex caption data. Instead of processing entire caption transcripts in real-time, the system uses pre-generated keyword copies that capture essential information, reducing processing time while maintaining information relevance.
3Adaptability or versatility
If multiple native and third-party applications are integrated to deliver information, then service versatility improves, but system integration complexity increases
Solution Approach 1:
The system implements a universal data mining engine that can serve multiple native and third-party applications through a common interface. This multi-functional approach allows different applications to access processed caption data without requiring separate integration for each, reducing integration complexity while maintaining service versatility.
Solution Approach 2:
The data mining engine acts as an intermediary layer between caption data sources and multiple applications. It standardizes the data format and interface, allowing diverse applications to receive processed information without direct complex integration between each application and the data source, thereby reducing overall system integration complexity.
Data Source
AI summary
Identification of keywords from media content metadata including caption data is provided. When a piece of media content is received by a user, media content metadata and caption data may be provided to a data mining engine operable to identify and tag keywords. Identified keyword data may be provided to one or more native or third party applications for providing information to the user relevant to what he is watching.