Intelligent Conversational Messaging with Expert Agent Injection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional digital assistants are limited to responding to user commands and lack the ability to provide contextual assistance in conversations, failing to account for the context and content of communication.
Innovation Solution
An intelligent conversational messaging system that generates and injects customized expert systems into conversations, allowing for data exchange and interaction with participants without notification, using conversation analysis and machine learning to determine conversation types and activate relevant expert agents to execute specific goal processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional digital assistants respond only to direct user commands, then the system operation is simple and reliable, but the conversation efficiency and contextual assistance are limited
Solution Approach 1:
An expert system intermediary is introduced between the conversation participants and the digital assistant. This intermediary analyzes conversation context, identifies relevant expertise domains, and selectively activates expert agents to provide contextual assistance without requiring direct user activation, thereby improving conversation efficiency while managing complexity through modular architecture
Solution Approach 2:
The digital assistant performs self-service by automatically analyzing conversation context and activating relevant expert agents without requiring direct user commands. The system monitors conversation flow, determines when expert intervention is needed, and autonomously engages appropriate expertise, reducing the need for complex user-initiated commands while maintaining simple operation interfaces
2Adaptability or versatility
If expert systems are activated based on conversation context analysis, then contextual assistance is improved, but the measurement precision of conversation type determination is challenged
Solution Approach 1:
The conversation analysis function is segmented into multiple independent modules: conversation monitoring, context extraction, expertise domain identification, and expert agent selection. Each module handles a specific aspect of conversation type determination, improving overall adaptability while maintaining measurement precision through specialized processing in each segment rather than relying on a single complex determination system
Solution Approach 2:
The system performs partial analysis by focusing only on relevant conversation aspects that indicate expert intervention is needed, rather than analyzing the entire conversation in detail. This selective approach improves adaptability by quickly identifying when contextual assistance is appropriate while maintaining determination accuracy by concentrating analytical resources on key indicators
3Productivity
If expert agents execute actions within conversations without notification, then conversation flow is maintained, but the reliability of user awareness is reduced
Solution Approach 1:
The expert system creates a parallel copy of the conversation context that it analyzes independently. This copy allows the expert agent to execute actions based on contextual understanding without interrupting the original conversation flow, while the system maintains reliability by ensuring the copied analysis accurately reflects the actual conversation state through continuous synchronization
Data Source
AI summary
A system for implementing a computer-based assistance in a conversation can comprise determining an identification indication associated with a first participant in a conversation. The system can also comprise accessing, within a database, user information associated with the identification indication. The user information can comprise data relating to the first participant. Based upon the user information and one or more words communicated in the conversation, the system can include calculating one or more probabilities that the conversation is associated with one or more respective conversation types. Based upon a determined conversation type, the system can activate an expert agent in the conversation. The expert agent can comprise a virtual conversation participant that is associated with a goal. The system can also comprise executing a sequential lists of actions that are associated with the goal, wherein the list of actions comprises interactions with the first participant.