Media Program Metadata Automation with AI Source Validation
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Solution Overview
Problem
Existing systems for maintaining and updating database records for media programs, such as those used in electronic program guides, require substantial manual research and data entry, leading to inefficiencies and potential human errors.
Innovation Solution
An automated system utilizing a mapping service and an artificial intelligence (AI) engine to populate and update database records. The AI engine identifies missing information, formats natural language queries, and retrieves additional data from secondary sources, ensuring accuracy and completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual research and data entry are used to create and update database records, then data accuracy can be maintained through human verification, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system enables automated self-service by using AI agents to automatically search for, extract, and populate metadata from multiple data sources without requiring manual human intervention for each record, thereby reducing time consumption while maintaining data quality through automated verification processes
Solution Approach 2:
The patent replaces the mechanical process of manual data entry with an automated electronic system that uses AI agents, natural language processing, and automated querying to extract and populate metadata, significantly reducing the time required while maintaining or improving data accuracy through systematic validation
2Adaptability or versatility
If manual data entry is used, then flexibility in handling diverse data formats is possible, but the process becomes cumbersome and expensive
Solution Approach 1:
The system implements a universal automated framework that can handle multiple data sources and diverse metadata types through a single integrated platform, using AI agents that can adapt to different data formats and sources without requiring separate manual processes for each type of data
Solution Approach 2:
The system automatically adapts to different data formats and sources by dynamically adjusting its querying and extraction parameters based on the specific data source being accessed, allowing flexible handling of diverse metadata requirements without increasing operational complexity
3Loss of information
If comprehensive metadata is collected from multiple sources, then data completeness is improved, but the risk of errors and discrepancies increases
Solution Approach 1:
The system implements automated feedback loops where AI agents validate extracted metadata against multiple criteria, cross-check information across different data sources, and automatically resolve or flag discrepancies, thereby maintaining data consistency while collecting comprehensive metadata from multiple sources
Solution Approach 2:
The patent introduces AI agents as intermediary components that mediate between multiple data sources and the database, automatically verifying and reconciling information from different sources before insertion, which maintains data reliability while achieving comprehensive metadata collection
4Productivity
If automated systems are implemented, then productivity is increased, but the initial system complexity and development cost increase
Solution Approach 1:
The system segments the automated metadata creation process into distinct functional modules including AI agents for data extraction, validation components for error checking, and integration layers for different data sources, which manages system complexity through modular design while maintaining high productivity
Data Source
AI summary
Systems, devices and automated processes are described for automated enhancement of metadata in a database of information about movies, television shows or other media programs. Gaps or errors in metadata describing the different programs in the database can be corrected using a digital architecture in which one or more sources are queried for missing information. Queries may be directed toward a large language model (LLM) or other artificial intelligence (AI) engine, if desired.