Multimodal Chatbot Orchestrator for Complex Intent Routing
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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 failing to simulate natural conversations effectively.
Innovation Solution
A speech analysis system that receives verbal statements, translates them into text, detects pauses to divide into utterances, identifies intents using an orchestrator model, and selects appropriate bots to process each utterance, generating responses while prioritizing and routing data efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a chatbot is designed to understand only simple commands or sentences, then the chatbot can be easily implemented and maintained, but it cannot interpret complex natural language statements with multiple intents
Solution Approach 1:
The system segments complex natural language statements into multiple utterances by detecting pauses in speech. Each utterance is then independently analyzed by the orchestrator model to identify specific intents. This segmentation allows the system to handle complex multi-intent statements by breaking them down into manageable pieces that can be processed by specialized chatbots.
Solution Approach 2:
The orchestrator model serves as a universal component that can identify multiple types of intents across different domains. It acts as a gateway that routes different types of utterances to appropriate specialized chatbots, making the overall system capable of handling diverse complex statements while maintaining modularity.
2Adaptability or versatility
If a single chatbot application is designed to cover a broad scope of subject matter, then it can handle diverse queries, but it becomes difficult to accurately interpret statements with multiple different intents
Solution Approach 1:
The system divides broad subject matter coverage into specialized domains, with each chatbot application focusing on a specific intent type or domain. The orchestrator model segments the analysis task by identifying which part of the statement corresponds to which intent, then routes to the appropriate specialized chatbot, thereby maintaining both broad coverage and high precision.
Solution Approach 2:
The orchestrator model acts as an intermediary between the user's complex multi-intent statement and the specialized chatbots. It identifies multiple intents within the statement and routes each intent to the appropriate chatbot application, enabling accurate interpretation of diverse queries without requiring each chatbot to understand all subject matters.
3Reliability
If manual input is required to access a particular chatbot application, then the system can ensure accurate routing, but the effectiveness in simulating natural conversation is reduced
Solution Approach 1:
The system performs self-service routing by automatically analyzing the user's statement through the orchestrator model, which identifies intents and determines the appropriate chatbot to handle each intent. This automatic routing eliminates the need for manual input to select chatbots, allowing the system to maintain reliable routing while naturally simulating conversation by seamlessly directing queries without user intervention.
4Productivity
If a chatbot processes each utterance independently without identifying pauses, then the processing is simpler, but it cannot accurately separate multiple intents within a single statement
Solution Approach 1:
The system performs preliminary action by detecting pauses in the user's speech statement before processing the content. This pause detection pre-segments the statement into potential utterances, allowing the orchestrator model to more accurately identify multiple intents and route them appropriately, thereby improving both intent separation accuracy and processing efficiency.
Data Source
AI summary
A computer system includes a multimodal server and an audio handler. The audio handler is programmed to: (1) receive, from the user computer device via the multimodal server, a verbal statement of a user including a plurality of words; (2) translate the verbal statement into text; (3) select a bot to analyze the translated text; (4) generate an audio response from a text response provided by executing the bot selected for the translated text to generate the text response, wherein the audio response is a response to the user; and (5) transmit the audio response to the multimodal server. The multimodal server is programmed to: (1) receive the audio response to the user's verbal statement from the audio handler; (2) enhance the audio response; and (3) cause the enhanced audio response to be communicated to the enhanced response to the user via the user computer device.