Automated Digital Media Title Validation via Scene and Character Matching
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Solution Overview
Problem
Misattributed title information in digital media libraries impairs users' ability to access digital media content, leading to customer support issues due to manual validation methods being inefficient and prone to errors.
Innovation Solution
Automated validation techniques using audio, language, and visual understanding to detect misattributed title information by identifying scenes and characters in media files and matching them with metadata synopsis, allowing for automatic correction of title attributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual validation methods are used to verify media file titles, then validation can be performed with simple tools, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual mechanical validation processes with automated computational systems that use audio analysis, language processing, and visual understanding to verify title attributions. This substitution eliminates human error and significantly reduces validation time while maintaining or improving accuracy.
Solution Approach 2:
The validation system performs self-validation by automatically analyzing its own media content and verifying title attributions without requiring external manual intervention. The system uses its internal capabilities to detect misattributions and correct them autonomously.
2Productivity
If manual validation methods are used to verify media file titles, then implementation is simple, but productivity is low and customer support issues increase
Solution Approach 1:
The validation system is designed to perform multiple functions: audio analysis, visual content understanding, language processing, and title verification. This multi-functionality allows a single system to handle diverse media types and validation requirements, increasing productivity without proportionally increasing complexity.
Solution Approach 2:
The validation process is divided into distinct modular components: audio scene detection, visual character identification, language processing, and title matching. Each module handles a specific aspect of validation, making the overall complex system manageable and scalable while maintaining high throughput.
3Reliability
If automated validation using audio, language, and visual understanding is implemented, then validation accuracy and productivity improve, but system complexity increases
Solution Approach 1:
The system uses intermediate processing stages that bridge raw media content and final validation decisions. Audio features, visual features, and language representations serve as intermediaries that transform complex raw data into structured information that can be reliably compared against title attributions.
Solution Approach 2:
The validation system incorporates feedback mechanisms where validation results are used to improve future validations. The system learns from misattributed cases and adjusts its analysis parameters, increasing reliability over time while the core system architecture remains manageable.
Data Source
AI summary
Techniques for automated content validation are provided. In some examples, a media file and a metadata file associated with a title of the media file may be received. One or more scene paragraphs may be identified based at least in part on information in the metadata file. Scenes may be identified at least in part by using a transformer model implemented in a neural network. One or more scene files may be generated from the media file. One or more characters in a scene file of the one or more scene files may be identified. A match score may be determined, based at least in part on an association of the scene file to a scene paragraph of the plurality of scene paragraphs. A validity criterion may be determined for the title associated with the media file based at least in part on the match score.