AI Video Title Matching System
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Solution Overview
Problem
Content providers face challenges in accurately and efficiently publishing video content online due to varying formats and error-prone manual metadata input, leading to difficulties in scaling up human staff to meet demand and maintaining timely publication of fresh content.
Innovation Solution
An artificial intelligence model comprising sub-models for image recognition and title matching is used to automatically match video content with video titles, utilizing neural networks to infer categories and probabilities for accurate and scalable video content cataloging and publishing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human staff are used to process and catalog incoming videos, then video content can be cataloged with some level of accuracy, but it is difficult to scale up to meet increasing demand and human errors occur in cataloging
Solution Approach 1:
The patent replaces the mechanical system of human staff manually cataloging videos with an automated AI-based system. The system uses machine learning models to automatically extract metadata from video content, including object recognition, scene understanding, and automatic title generation, eliminating the need for human manual intervention while maintaining or improving cataloging accuracy and enabling unlimited scaling.
Solution Approach 2:
The patent implements a self-service system where the video cataloging process performs automatically without human intervention. The AI system independently processes incoming videos, extracts relevant information, generates metadata, and catalogs content autonomously, allowing the system to handle increasing volumes of video content without requiring additional human resources.
2Productivity
If human staff are added to process videos faster, then more video content can be published timely, but errors in cataloging increase under time pressure
Solution Approach 1:
The patent replaces human manual cataloging with an automated AI system that processes videos at high speed without compromising accuracy. The system uses sophisticated machine learning algorithms including object detection, image recognition, and natural language generation to accurately catalog videos while maintaining publication speed, eliminating the trade-off between speed and accuracy that plagues human-operated systems.
3Ease of manufacture
If manual metadata input is used from partner providers, then video content can be cataloged, but the metadata is error-prone and not reliable
Solution Approach 1:
The patent replaces manual metadata input from partner providers with an automated AI-based metadata extraction system. The system directly analyzes video content using machine learning models to generate accurate metadata, eliminating reliance on potentially erroneous manual inputs while maintaining process simplicity through automation.
Solution Approach 2:
The patent creates an independent copy of metadata generation by having the AI system extract metadata directly from video content rather than relying on partner-provided metadata. This creates a verification mechanism where the system can generate its own metadata independently, improving reliability by not depending on potentially erroneous external inputs.
Data Source
AI summary
Computer systems of a multimedia service provider may utilize an artificial intelligence model to automatically match a video content with an existing video title of a catalog, allowing the provider to efficiently and accurately process a high volume of video content being received. The artificial intelligence model may be proceed by analyzing video frames from the video content to extract features and then determining the relevance of a set of features to a particular video title of the catalog. Based on the relevance determination, the computer system may associate the video content with the particular video title. In cases where no match with an existing title is determined, the artificial intelligence model may create a new video title based in part on the extracted features.