Online Document Comment Feedback Push System

The system performs preliminary actions by automatically generating comment quality assessments and pushing feedback information to users before they need to evaluate comments manually. The server pre-processes comments using AI models to determine quality metrics, so when users receive push notifications, the evaluation work has already been done in advance, eliminating the need for users to read and evaluate each comment individually.

The system introduces an intermediary mechanism (AI-based comment quality assessment system) that acts as a mediator between comment submission and user evaluation. This intermediary automatically analyzes comment quality, generates feedback information, and pushes it to users, thereby eliminating the need for users to directly evaluate all comments while maintaining high identification accuracy.

The system extracts and separates high-quality comment information from the overall comment set using AI-based quality assessment. Instead of displaying all comments equally, the system extracts key quality indicators (such as helpfulness, relevance, author credibility) and pushes only the most valuable feedback information to users through targeted notifications, allowing users to focus on high-quality comments without being overwhelmed by low-quality ones.

The system applies local quality enhancement by providing different information presentations to different users based on their needs and the specific comment context. High-quality comments receive prominent push notifications with quality indicators, while lower-quality comments are either suppressed or presented with less prominence, allowing users to quickly identify valuable feedback without losing access to complete information.

The system performs preliminary actions by pre-calculating comment quality metrics and preparing push notification content before users need the information. AI models pre-assess comment quality and generate feedback information in advance, so when a comment is posted, the system can immediately push ready-to-display feedback to all opened document pages without requiring complex real-time processing during user interaction.

The system implements self-service mechanisms where the AI-based quality assessment automatically evaluates and prioritizes comments without human intervention. The system autonomously determines which comments warrant push notifications, manages the distribution to multiple document pages, and handles the complexity of real-time updates, freeing users from needing to manually manage or filter comments while reducing the perceived system complexity for end users.