Video Series Identification via Machine Learning Analysis
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Solution Overview
Problem
Users of content creation platforms face difficulties in finding videos that are part of a series, as they often need to spend significant time scrolling through content to locate related videos, without automated assistance.
Innovation Solution
A machine learning model is employed to automatically identify video series by analyzing metadata, text, images, and audio from user-created videos, using components like extraction, data cleaning, TF-IDF, and mining to determine video connections based on labels and create times, enabling the system to recommend related videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search for related videos by scrolling through content, then they can find video series, but it requires significant time and effort
Solution Approach 1:
The system automatically identifies and links video series without requiring user intervention. The machine learning model autonomously analyzes video metadata, text, images, and audio to determine series relationships, eliminating the need for users to manually search or creators to manually tag videos.
Solution Approach 2:
The patent replaces manual user scrolling and manual creator tagging with an automated machine learning system. The mechanical action of users scrolling through content is substituted by an intelligent system that automatically detects and organizes video series based on multi-modal analysis.
2Extent of automation
If automated identification systems are implemented, then video series can be automatically identified and recommended, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional components: extraction component for collecting data, data cleaning component for preprocessing, TF-IDF component for feature extraction, and mining component for pattern recognition. This segmentation allows each component to handle specific tasks independently, managing overall system complexity.
Solution Approach 2:
The patent introduces intermediate processing layers including data cleaning and TF-IDF feature extraction between the raw video data and the final series identification. These intermediary components transform complex multi-modal data into standardized features that the mining component can efficiently process.
3Reliability
If manual tagging or linking is required for video series, then video connections can be accurately identified, but it requires additional creator effort and time
Solution Approach 1:
The system performs self-service by automatically identifying video series relationships without requiring creator intervention. The machine learning model autonomously analyzes the content and metadata to determine series connections, eliminating the manual tagging process entirely.
Solution Approach 2:
The patent changes the approach from discrete manual tagging to continuous automated analysis using multiple parameters including metadata, text, images, and audio. By analyzing multiple parameters simultaneously, the system achieves reliable series identification without manual input.
Data Source
AI summary
The present disclosure describes techniques for automatically identifying video series. A first video may be input into a machine learning model. The machine learning model may be trained to identify content that is any part of a connected series. It may be determined whether there is at least a second video in a series with the first video using the machine learning model. The series of videos may comprise the first video and the at least a second video. The series of videos may be uploaded by a same creator. Information indicative of a connection among the series of videos comprising the first video and the at least a second video may be stored.