Media Program Metadata Enrichment Using Embedded Vectors
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Solution Overview
Problem
Existing electronic program guides (EPGs) face challenges in maintaining consistent and complete metadata for media programs due to formatting limitations, data availability issues, and the need for substantial manual research and human error in data entry, which is costly and cumbersome.
Innovation Solution
An automated system using artificial intelligence (AI) with embedded vectors populates and updates metadata by querying large language models (LLM) to fill gaps and validate information, integrating with existing databases to ensure 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 validation, but the process becomes costly, cumbersome, and time-consuming
Solution Approach 1:
The system enables automated self-service by using AI models to automatically populate metadata fields, generate summaries, and extract information from various sources without requiring manual human intervention for each record, thereby reducing time loss while maintaining accuracy through automated validation
Solution Approach 2:
The patent replaces the mechanical process of manual data entry and research with an automated AI-based system that uses large language models and embedding vectors to automatically create and update database records, eliminating the need for human operators to manually input data while preserving data quality
2Loss of information
If commercial databases are used to obtain program information, then data availability is improved, but formatting limitations and incomplete information persist
Solution Approach 1:
The system introduces an AI-based intermediary layer that sits between commercial database sources and the final metadata output, automatically transforming and enriching the data by filling in missing fields, standardizing formatting, and generating additional information such as summaries and descriptions that were previously unavailable
Solution Approach 2:
The patent applies parameter changes by transforming raw data from commercial databases into enriched metadata records with additional attributes and standardized formatting, using AI models to modify and enhance the data parameters beyond what the original sources provide
3Productivity
If automated processes are implemented to create database records, then productivity increases, but reliability may decrease due to potential AI errors
Solution Approach 1:
The system implements feedback mechanisms where AI-generated metadata is validated against multiple sources, cross-checked for consistency, and subjected to quality assurance processes that detect and correct errors, ensuring that automated high-speed record creation maintains high reliability and accuracy standards
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, that represents information about the media programs as embedded vectors that can be compared to query data to identify additional information about the media programs.