Autonomous Conversation Flow Discovery Using Generative Language Models

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Solution Overview

Problem

Existing Conversational AI systems require substantial human effort for configuration and ongoing maintenance, lacking the ability to autonomously learn from past conversations to improve their performance over time.

Innovation Solution

An autonomous Conversational AI system (ACAi) that analyzes historical conversation logs using generative language models to mimic human agents, automatically configuring itself without human intervention and improving over time through self-learning from user interactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If human agents manually configure and maintain Conversational AI systems, then the system can be initially set up and monitored, but substantial human effort and time are required for configuration and ongoing maintenance

Engineering Contradiction:

Improvesystem configuration accuracyVSAvoidhuman effort for configuration and maintenance

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system automatically inspects historical conversations and configures itself without human intervention. The automated discovery process analyzes past conversation logs, identifies patterns, and generates configuration settings independently, eliminating the need for manual configuration while maintaining system reliability

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors performance metrics and automatically adjusts configuration based on observed patterns in historical conversations. This feedback loop enables the system to self-optimize its configuration without human intervention, reducing maintenance time while improving reliability

Inventive Principle:

Principle #23Feedback

2Extent of automation

If the system is designed to be fully autonomous without human configuration, then configuration time and human effort are eliminated, but the system must learn and adapt from historical data automatically

Engineering Contradiction:

Improveautonomous configuration capabilityVSAvoidautomatic learning and adaptation mechanisms

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The system copies and analyzes patterns from historical conversations to automatically generate configuration settings. By examining past conversation logs and identifying successful interaction patterns, the system replicates effective behaviors without manual programming, achieving autonomy while managing complexity through data-driven pattern recognition

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system performs preliminary analysis of historical conversations during the configuration phase to pre-learn optimal interaction patterns. This preliminary action enables the system to be fully autonomous during operation, as the learning and adaptation mechanisms are established in advance through analysis of past data

Inventive Principle:

Principle #10Preliminary action

3Reliability

If the system continuously learns from past conversations to improve accuracy, then performance over time is enhanced, but the system requires ongoing data processing and model updates

Engineering Contradiction:

Improveconversation accuracyVSAvoidcomputational resources for continuous learning

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system performs periodic updates by analyzing historical conversations at scheduled intervals rather than continuously. This periodic action allows the system to improve accuracy through continuous learning while managing computational resources by processing data in batches rather than in real-time constant monitoring

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS20250259022A1Automated discovery of conversation flows using generative language models

Publication Date: 2025.08.14 GICRM AI LLC

AI summary

Techniques for analyzing conversation logs to identify system actions for an autonomous conversational AI system are disclosed. Historical conversation logs are analyzed using a generative language model to identify conversation topics and subtopics. Conversations within each topic and subtopic are ranked based on frequency of occurrence and representative conversations are selected using normalized mean conversation embeddings. The selected conversations are analyzed to identify opportunities for system actions, and user messages and human agent responses are converted into system actions using a transformer-based natural language processing model. An action configuration comprising the identified system actions and required parameters is generated and stored for training the autonomous conversational AI system. The system preprocesses conversation logs to normalize data from multiple communication channels, detects and anonymizes personally identifiable information, and automatically improves performance over time by analyzing conversations where poor performance was observed.