Artificial Agent Generator Using Transcript Analysis
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Solution Overview
Problem
Creating artificial agents, such as chatbots, that can handle various scenarios and user interactions effectively is challenging due to the need for flexibility and natural conversation, often resulting in low user satisfaction and frequent transitions to human agents.
Innovation Solution
A system that generates artificial agents by analyzing transcripts of human-to-human communications to identify intents and elicit necessary information, using intent and slot clustering, and integrating with an application programming interface (API) to define configuration data for the agent, enabling it to recognize user intents and respond appropriately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If artificial agents are designed to handle various scenarios and user interactions with flexibility and natural conversation, then user satisfaction should improve, but the system complexity and difficulty of creation increase
Solution Approach 1:
The system enables automatic generation of artificial agents by analyzing existing human-to-human communication transcripts. The generator component autonomously extracts intents, responses, and slot definitions without requiring manual configuration, allowing the system to create adaptable agents while reducing creation complexity
Solution Approach 2:
The system pre-processes human-to-human communication transcripts to extract and structure communication patterns before agent deployment. By analyzing real conversation data in advance and generating configuration data automatically, the system prepares adaptable agents ahead of time, reducing the complexity of real-time adaptation
2Ease of operation
If artificial agents follow a heavily scripted approach to handle scenarios, then the ease of creation improves, but user satisfaction decreases due to awkward or stilted interactions
Solution Approach 1:
The system copies natural communication patterns from human-to-human transcripts and applies them to artificial agent behavior. By extracting real conversation structures, intents, and responses from actual human interactions, the agent learns natural communication styles while maintaining ease of creation through automated pattern replication
Solution Approach 2:
The system dynamically adjusts agent behavior parameters based on analyzed communication patterns. By extracting intent definitions, response templates, and slot structures from transcripts, the system configures agents with flexible parameters that enable natural conversation while maintaining straightforward creation through automated parameter extraction
3Adaptability or versatility
If artificial agents are designed to achieve high user satisfaction through natural conversation, then adaptability improves, but the time and resources required for creation increase
Solution Approach 1:
The generator component automatically analyzes human-to-human transcripts and creates agent configuration data without manual intervention. This self-service approach extracts intents, responses, and slot definitions autonomously, reducing creation time while maintaining high adaptability through learned communication patterns
Solution Approach 2:
The system performs preliminary analysis of communication transcripts to pre-extract and structure all necessary agent configuration data before deployment. By analyzing real conversation data in advance and generating complete agent definitions automatically, the system reduces creation time while ensuring high adaptability through pre-learned natural communication patterns
Data Source
AI summary
A system receives one or more transcripts of communications between entities. The system identifies a requested action in the communications based at least in part on a mapping between the requested action and an application programming interface. The system identifies one or more statements eliciting information, based on parameters to the application programming interface. The system generates a definition of an artificial agent based, at least in part, on the requested action and the one more statements eliciting information.