Chatbot Orchestration via Segmentation and Intermediary Routing
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Solution Overview
Problem
As chatbots handle increasingly diverse topics, their source code becomes complex and resource-intensive, leading to high CPU time and memory usage, making it inefficient to execute a single chatbot for all topics.
Innovation Solution
A system utilizing a master chatbot and modular chatbots, where the master chatbot receives messages, determines intent, and ranks itself and other modular chatbots to forward messages to the best-suited chatbot for response, optimizing resource utilization by leveraging specialized chatbots for specific topics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single chatbot handles all diverse topics, then it can respond to any message, but its source code becomes complex and resource-intensive
Solution Approach 1:
The system divides the single chatbot into multiple specialized chatbots, each handling specific topics. The master chatbot coordinates these specialized chatbots to respond to user messages, thereby reducing the complexity of individual chatbots while maintaining comprehensive topic coverage.
2Adaptability or versatility
If a single chatbot handles all diverse topics, then it can respond to any message, but CPU time and memory usage increase
Solution Approach 1:
The system segments the workload across multiple specialized chatbots, each optimized for specific topics. This distribution reduces the computational burden on any single chatbot, thereby lowering overall CPU time and memory usage while maintaining comprehensive topic coverage.
Solution Approach 2:
The master chatbot acts as an intermediary that receives user messages, determines the appropriate topic, and routes the message to the corresponding specialized chatbot. This intermediary approach optimizes resource utilization by ensuring that only the necessary specialized chatbot processes each message.
3Adaptability or versatility
If a single chatbot handles all diverse topics, then it can respond to any message, but execution efficiency decreases
Solution Approach 1:
The system segments the chatbot functionality into multiple specialized chatbots, each optimized for specific topics. This segmentation improves execution efficiency by allowing each chatbot to focus on its specialized area, reducing processing time and improving response quality.
Solution Approach 2:
The master chatbot serves as an intermediary that efficiently routes messages to the appropriate specialized chatbot based on topic analysis. This intermediary mechanism improves execution efficiency by avoiding unnecessary processing and ensuring that messages are handled by the most suitable chatbot.
4Adaptability or versatility
If a single chatbot handles all diverse topics, then it can respond to any message, but maintenance becomes time-consuming
Solution Approach 1:
The system segments the chatbot into multiple specialized chatbots, each handling specific topics. This segmentation simplifies maintenance by allowing developers to update and maintain individual chatbots independently, reducing the time and effort required for maintenance while preserving comprehensive topic coverage.
Data Source
AI summary
Utilizing a computing device executing a master chatbot and one or more modular chatbots to respond to one or more chat messages. A computing device executing the master chatbot receives one or more chat messages. The computing device parses the received one or more chat messages to discover an intent and entities contained within the chat messages. A ranking algorithm is employed to rank the master chatbot and a plurality of modular chatbots, the ranking algorithm scoring the master chatbot and the plurality of modular chatbots based upon the intent and entities contained within the one or more chat messages. The master chatbot responds to the chat message if the ranking algorithm rates the master chatbot highest or forwards automatically the one or more chat messages to a ranked modular chatbot for response if the ranking algorithm rates the ranked modular chatbot highest.