Livestream AI Assistance for Real-Time Viewer Interaction Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Models engaging in livestreaming face challenges in managing dynamically changing data, such as audience comments and rewards, due to their active performance, leading to missed valuable information and complications with application settings.
Innovation Solution
An interaction system and method utilizing agentic AI to monitor operational data, determine trigger conditions, speculate user demands, and execute user-perceptible operations through modules like monitoring, judgment, speculation, and execution, enhancing data management during livestreams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If models actively perform in livestream broadcasts, then engagement with viewers is improved, but ability to monitor and manage real-time data deteriorates
Solution Approach 1:
The patent introduces an AI agent as an intermediary between the model and the livestream data. The AI agent continuously monitors operational data (viewer counts, rewards, comments) and automatically executes actions on behalf of the model, such as responding to comments or managing rewards, thereby eliminating the need for the model to directly monitor all data while maintaining full engagement capability
Solution Approach 2:
The AI agent performs self-service by autonomously monitoring operational data and executing appropriate actions without requiring model intervention. The system automatically detects trigger conditions (e.g., viewer count thresholds, reward amounts) and executes pre-defined or learned actions, allowing the model to focus entirely on performance
2Productivity
If models focus on performing actions for viewers, then broadcast quality is improved, but ability to attend to system messaging deteriorates
Solution Approach 1:
The AI agent serves as a dedicated intermediary that handles all system messaging and configuration tasks. It monitors operational data, detects anomalies or important messages, and executes appropriate responses, ensuring that the model never needs to divert attention from performance to system management
3Ease of operation
If models manually manage application settings, then control over broadcast is improved, but time consumption and errors increase
Solution Approach 1:
The AI agent performs self-service by automatically managing application settings and configurations. It monitors operational data, detects when settings need adjustment (e.g., based on viewer engagement patterns or platform changes), and executes modifications without model intervention, thereby eliminating time consumption and human error
Solution Approach 2:
The system performs preliminary actions by pre-configuring the AI agent with appropriate settings and behaviors. The agent has pre-loaded knowledge of the livestreaming platform and can execute appropriate actions based on detected conditions, eliminating the need for models to manually configure settings during broadcasts
Data Source
AI summary
An interaction method is disclosed. The interaction method includes invoking a monitoring module to monitor, via an agentic AI, operational data associated with a livestream of a model user, wherein the operational data includes passive data not acted on by the model user without prompting, and invoking a judgment module to determine, via the agentic AI, whether the operational data satisfies a trigger condition. The interaction method also includes invoking a speculation module to speculate, via the agentic AI, at least one demand of the model user based on the operational data and the trigger condition, in response to a determination that the operational data satisfies the trigger condition, and invoking a determination module to determine, via the agentic AI, at least one execution module and an execution instruction corresponding to the at least one demand of the model user.