Real-time Automated Classification System for Broadcast Content
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Solution Overview
Problem
Traditional manual methods for analyzing and editing broadcast content are labor-intensive, time-consuming, and inefficient, as they rely on human analysts to identify significant elements and generate metadata, making it difficult to adapt live broadcasts into digital formats and maintain high-quality content production.
Innovation Solution
A real-time automated classification system using machine learning to recognize important moments in broadcast content based on log data from various devices, generating metadata and control inputs to automate the addition of supplemental elements such as graphics, clips, and reports, thereby reducing human subjectivity and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used to identify significant elements in broadcast content, then human analysts can characterize content with subjectivity and quality control, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated computer-based system that uses machine learning algorithms to analyze broadcast content. The system automatically processes video and audio data, generates metadata, and identifies significant elements without human intervention, thereby increasing processing speed while maintaining characterization accuracy through sophisticated algorithms.
Solution Approach 2:
The system enables self-service automation where the broadcast content analysis process serves itself through automated metadata generation and significant element identification. The machine learning model continuously learns from data and improves its own performance, reducing the need for human analysts while maintaining high-quality content characterization.
2Reliability
If manual metadata entry is performed into predetermined fields, then content can be properly categorized and enhanced, but the process becomes inconsistent and inefficient
Solution Approach 1:
The patent replaces manual metadata entry with automated computer-based metadata generation. The system automatically extracts relevant information from broadcast content, structures it according to predetermined schemas, and populates metadata fields consistently without human intervention, eliminating both the time loss and inconsistency issues associated with manual entry.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from generated metadata and improves its accuracy over time. The automated system refines its metadata generation processes based on performance feedback, ensuring consistent and reliable metadata output while minimizing time loss through optimized algorithms.
3Manufacturing precision
If traditional manual approaches are used to produce edited broadcast content, then quality control can be maintained through human judgment, but the process becomes labor-intensive and slow
Solution Approach 1:
The patent replaces manual content editing processes with automated machine learning-based editing systems. The system automatically analyzes broadcast content, identifies significant moments, and produces edited versions with appropriate metadata and enhancements. This substitution maintains editing quality through sophisticated algorithms while reducing the labor-intensive nature of manual editing, despite the increased system complexity.
Data Source
AI summary
The current embodiments relate to a real-time automated classification system that uses machine learning system to recognize important moments in broadcast content based on log data and/or other data received from various classification systems. The real-time automated classification system may be trained to recognize correlations between the various log data to determine key moments in the broadcast content. The real-time automated logging system may determine and generate metadata that describe or give information about what is happening or appearing in the broadcast content. The real-time automated logging system may automatically generate control inputs, suggestions, recommendations, and/or edits relating to broadcast content based upon the metadata, during broadcasting of the broadcast content.