Media Context Analysis for Automated Information Display
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users often consume media without knowing relevant contextual information, such as the name or location of a stadium shown in a video, making it inconvenient to search for this information.
Innovation Solution
A method and system using machine learning to determine media context by extracting features from collected data, applying models to identify and display contextual information, and utilizing feedback loops to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users manually search for contextual information about media content, then they can obtain relevant details, but it is inconvenient and tedious
Solution Approach 1:
The system automatically performs contextual analysis and information retrieval without requiring user intervention. The media context analyzer autonomously extracts features from media content, queries relevant information from data sources, and presents contextual information to users, eliminating the need for manual searching.
Solution Approach 2:
The media context analyzer acts as an intermediary between the user and the vast amount of available information. It automatically processes media content, identifies relevant contextual elements, and retrieves appropriate information from various data sources, serving as a bridge that translates user media consumption into relevant contextual information delivery.
2Extent of automation
If automated media context analysis is implemented, then contextual information is provided conveniently, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a media context analyzer that extracts features from media content, a data source interface that queries external information, and a presentation layer that displays contextual information. This segmentation allows each component to be optimized independently and simplifies the overall system architecture.
Solution Approach 2:
The media context analyzer is designed as a universal system that can handle multiple types of media content (images, videos, audio) and extract various contextual information (location, entities, events, descriptions). This multi-functionality reduces the need for separate specialized systems for different media types.
Data Source
AI summary
The exemplary embodiments disclose a method, a computer program product, and a computer system for determining the context of media. The exemplary embodiments may include a user consuming media, collecting data of the media, extracting one or more features from the collected data, and determining a media context based on the extracted one or more features and one or more models.