Video Point Detection for Timed Deliverable Content
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Solution Overview
Problem
Users frequently stop watching content before completion, indicating a negative viewing experience, which can be attributed to suboptimal engagement or enjoyment, necessitating a system to improve user experience by identifying key points of interest within content.
Innovation Solution
A system analyzes user experience information to identify points of interest in content, retrieves metadata, and determines deliverable content to be overlaid or delivered alongside the main content, tailored to enhance user engagement and maintain viewer interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the delivery of main content starts to the client device, then the user can begin viewing the content, but the user may stop playback before finishing, resulting in negative user experience
Solution Approach 1:
The system performs preliminary analysis of user experience information from historical data to identify points of interest before the user reaches them. Deliverable content is prepared and queued for delivery at these predetermined points, allowing the system to proactively engage users at critical moments rather than reactively responding to dropout behavior.
Solution Approach 2:
The system utilizes feedback from aggregated user experience information to continuously improve point detection accuracy. By analyzing patterns from multiple users' playback behavior, the system refines its ability to predict where users may lose interest and adjusts deliverable content strategies accordingly, creating a closed-loop system that improves user engagement over time.
2Reliability
If the system delivers additional deliverable content at points of interest, then user engagement is improved, but the system complexity increases due to analysis and detection requirements
Solution Approach 1:
The system processes and analyzes user experience information automatically without requiring manual intervention. The point detection algorithm autonomously identifies points of interest by analyzing playback patterns, and the system automatically selects and delivers appropriate deliverable content, reducing the need for complex manual configuration and management.
Solution Approach 2:
The system performs multiple functions using a unified architecture: it collects user experience data, analyzes playback patterns, detects points of interest, selects deliverable content, and delivers it all through an integrated system. This multi-functional approach reduces overall system complexity compared to having separate specialized systems for each function.
3Loss of information
If the system analyzes user experience information to identify points of interest, then relevant deliverable content can be provided, but processing time and computational resources increase
Solution Approach 1:
The system pre-processes user experience information during off-peak times to build models and identify points of interest before they are needed. By performing analysis in advance and caching results, the system minimizes real-time processing requirements when delivering content to users, reducing latency and computational overhead during critical delivery moments.
Data Source
AI summary
In some embodiments, a method determines user experience information for an instance of main content. The user experience information is based on user experience of users while displaying the instance of main content. The user experience information is analyzed to determine a point of interest in the instance of main content. A type for the point of interest is determined based on a deviation in the user experience information. The method retrieves metadata for the point of interest. The metadata is based on content in the instance of main content that is associated with the point of interest. Deliverable content for the point of interest is determined based on the type and the metadata for the point of interest. The method causes a delivery of deliverable content based on when the point of interest is displayed in the instance of main content. Feedback is used to adjust the deliverable content.