Content Metadata Generation with Multi-Source Quality Metrics
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Solution Overview
Problem
Existing systems face challenges in creating high-quality metadata from noisy sources, leading to inaccuracies and inconsistencies in metadata associated with content items.
Innovation Solution
A system and method that utilizes quality metrics to evaluate and combine metadata from multiple sources, employing embedding and logistic regression models to generate authoritative metadata and images, ensuring completeness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If metadata is collected from multiple noisy sources, then the quantity of metadata is increased, but the accuracy and consistency of metadata deteriorates
Solution Approach 1:
The patent introduces an intermediary processing system that receives metadata from multiple noisy sources, evaluates its quality using multiple metrics, and produces cleaned, consistent metadata. This intermediary layer filters out inaccuracies while preserving the benefits of multi-source data collection.
Solution Approach 2:
The system implements feedback mechanisms where quality metrics are continuously evaluated and used to improve metadata processing. The quality evaluation results feed back into the metadata generation process to refine accuracy over time.
2Measurement precision
If multiple quality metrics are evaluated for each metadata, then the accuracy of metadata is improved, but the complexity of processing increases
Solution Approach 1:
The patent segments the quality evaluation process into multiple independent metrics (completeness, accuracy, consistency, timeliness, source reliability). Each metric evaluates a specific aspect of metadata quality, making the complex evaluation task manageable and systematic.
Solution Approach 2:
The system changes parameters by evaluating different quality metrics at different stages of metadata processing. This allows flexible adjustment of evaluation depth and focus based on specific needs, managing complexity through parameter variation.
3Loss of information
If quality metrics are determined for each attribute of metadata, then the completeness of metadata is improved, but the time required for processing increases
Solution Approach 1:
The patent applies partial action by evaluating quality metrics selectively based on metadata type and importance. Not all metadata requires full evaluation of all five quality metrics, allowing the system to balance completeness with processing efficiency through targeted evaluation.
Data Source
AI summary
Disclosed herein are system, apparatus, device, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for creating high quality metadata and/or images for content. An example aspect operates by a computer-implemented method including receiving, from a plurality of sources, a set of metadata associated with an item of content. The method further includes determining a first quality metric for each metadata of the set of metadata, determining a set of quality metrics for attributes of each metadata of the set of metadata, and determining a second quality metric for each metadata of the set of metadata based on the set of quality metrics. The method further includes generating a final metadata based at least on the set of metadata, the first quality metric, and the second quality metric and providing the item of content and the final metadata associated with the item of content.