Highlight Detection via User Purchasing Actions
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Solution Overview
Problem
Current push notifications are not personalized and often contain inappropriate content at the wrong time, leading to user annoyance and dissatisfaction. Additionally, existing communication protocols do not adapt to different scenarios, affecting user experience in real-time interactions and live streaming. Furthermore, manually clipping highlights from live streams is time-consuming and inefficient.
Innovation Solution
A message distribution system that collects feedback from user terminals to evaluate user preferences for messages, optimizing message delivery for personalization. Additionally, a data communication system that determines the appropriate communication protocol based on user actions, ensuring optimal user experience in different scenarios. Moreover, a highlight detection system that automatically detects highlights from live streams based on user purchasing information, eliminating the need for manual clipping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If push notifications are sent automatically or manually to bring back users to the platform, then user engagement is improved, but user annoyance increases when notifications contain inappropriate content at the wrong time
Solution Approach 1:
The notification system dynamically adjusts content and timing based on real-time user state detection. The system monitors user behavior patterns, context, and preferences to automatically adapt notification delivery, transforming static notifications into dynamic, context-aware communications that reduce annoyance while maintaining engagement.
Solution Approach 2:
The system implements feedback loops by monitoring user responses to notifications and adjusting future notification strategies accordingly. User interaction data, engagement metrics, and preference information are fed back into the notification system to continuously optimize content relevance and timing, reducing harmful factors while preserving productivity.
2Ease of operation
If different communication protocols are used for real-time interaction and live streaming, then user experience is improved, but system complexity increases
Solution Approach 1:
The communication system is segmented into different protocol pathways for distinct use cases. The system routes different types of communication requests through specialized protocols (e.g., WebRTC for real-time interaction, HLS for live streaming), allowing each protocol to be optimized for its specific function while presenting a unified interface to users.
Solution Approach 2:
The system implements a universal communication framework that can handle multiple protocols and scenarios through a single interface. The framework abstracts protocol complexity while providing multi-functional capabilities for real-time interaction, live streaming, and on-demand content delivery, improving ease of operation without proportionally increasing user-facing complexity.
3Manufacturing precision
If streamers manually clip highlights from live streams, then content quality is improved, but time consumption increases
Solution Approach 1:
The system implements self-service highlight detection by automatically analyzing live stream content and identifying highlight moments without human intervention. The system uses automated algorithms to detect engaging content patterns, user interaction spikes, and significant events, generating highlight clips autonomously to eliminate time consumption while maintaining content quality.
Solution Approach 2:
The manual mechanical process of highlight clipping is replaced with automated computational algorithms. The system uses computer vision, audio analysis, and data processing to automatically detect and extract highlight moments from live streams, substituting human manual labor with automated mechanical processes that are both time-efficient and quality-consistent.
Data Source
AI summary
The present application relates to a system and method for highlight detection, and includes a highlight detection method for detecting highlights of a stream. The method comprises: collecting purchasing information from a user terminal; and detecting the highlight of the stream according to the purchasing information. The purchasing information includes purchasing actions of the user terminal, and the purchasing actions are actions related to purchase of the user terminal. According to the subject application, the highlights may be detected, captured and generated automatically, and the time and efforts for manual work may be saved.