Orchestrator-Based Multimodal Chatbot Intent Segmentation
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Solution Overview
Problem
Current chatbot systems are limited in understanding complex natural language and require manual input, making them inefficient in processing multiple intents within a single statement and simulating natural conversations.
Innovation Solution
A speech analysis system that translates verbal statements into text, detects pauses to divide them into utterances, identifies intents using an orchestrator model, and selects appropriate bots to generate responses, enabling more conversational and efficient processing of complex statements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single chatbot application is used to understand user input, then the chatbot can be simple to implement, but it can only understand a limited scope of subject matter and requires manual input to access
Solution Approach 1:
The system segments the chatbot functionality into multiple specialized chatbot applications, each designed to understand specific subject matter or intents. An orchestrator component divides user input into multiple utterances and routes them to appropriate chatbots, enabling the system to handle diverse topics without requiring a single overly complex chatbot.
Solution Approach 2:
The orchestrator serves as a universal component that manages multiple specialized chatbots, allowing the system to handle various types of user input across different subject matters. The orchestrator automatically routes different intents to appropriate chatbots, eliminating the need for manual selection and providing multi-functional capability.
2Ease of operation
If a chatbot is designed to understand complex natural language with colloquialisms and slang, then it can simulate natural conversation, but it becomes difficult to interpret such language accurately
Solution Approach 1:
The system segments complex user input into multiple smaller utterances based on detected pauses and structural cues. Each utterance is then independently analyzed by the orchestrator to identify specific intents, making it easier to accurately interpret complex natural language by breaking it down into manageable units with distinct meanings.
3Extent of automation
If manual input is required to access a particular chatbot application, then the chatbot system can be simple to control, but the effectiveness in simulating natural conversation is reduced
Solution Approach 1:
The orchestrator performs preliminary analysis of user input to identify intents and determine which chatbot should handle each utterance before the actual chatbot processing occurs. This preliminary routing action automates the chatbot selection process, eliminating the need for manual input while maintaining system organization.
4Productivity
If a single chatbot processes all user input, then the system can be simple to manage, but it cannot efficiently process multiple intents within a single statement
Solution Approach 1:
The system segments user input into multiple utterances and the orchestrator segments these utterances into different intent categories, routing each to the appropriate specialized chatbot. This segmentation enables parallel processing of multiple intents within a single user statement, significantly improving processing efficiency and multi-intent handling capability.
Data Source
AI summary
A computer system for analyzing voice bots. The computer system is programmed to: (1) store a plurality of completed conversations, wherein each conversation of the plurality of completed conversations includes a plurality of interactions between a user and a voice bot; (2) analyze the plurality of completed conversations; (3) determine a score for each completed conversation based upon the analysis, the score indicating a quality metric for the corresponding conversation; and/or (4) generate a report based upon the plurality of scores for the plurality of completed conversations.