Chatbot Switching System Using Trust Scores for Seamless Handover
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Solution Overview
Problem
Existing chatbot systems struggle to seamlessly switch and handover between multiple intelligent conversational agents, leading to difficulties in providing unified and effective interactions with users across different queries and contexts.
Innovation Solution
A computer-implemented method and system that utilizes real-time data analysis, machine learning algorithms, and a trust score system to select and switch between intelligent conversational agents based on user behavioral data, agent performance metrics, and query aspects such as context and linguistic style.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple chatbots from different vendors are deployed to handle diverse user queries, then the coverage and versatility of the system is improved, but the complexity of managing and switching between these chatbots increases
Solution Approach 1:
A switching system is introduced as an intermediary component that manages communication between the user interface and multiple chatbots from different vendors. This mediator receives user queries, selects appropriate chatbots based on predefined criteria, and coordinates the handover process, thereby reducing the complexity of directly managing multiple chatbots while maintaining broad coverage.
Solution Approach 2:
The system segments the chatbot management function by separating the selection and coordination logic from the individual chatbots themselves. Each chatbot remains independent and specialized, while the switching system handles the orchestration, allowing the system to maintain versatility across multiple domains without increasing the complexity of individual chatbot implementations.
2Measurement precision
If real-time data analysis and machine learning algorithms are used to select suitable agents, then the response accuracy and user satisfaction are improved, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary actions by pre-defining selection criteria and pre-processing user query data before the actual chatbot selection is needed. User profiles, query histories, and chatbot performance metrics are maintained in advance, allowing the switching system to make rapid selections without requiring intensive real-time computational analysis, thus reducing the energy and resource consumption during active user interactions.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor user interactions and chatbot performance, using this information to refine future selections. By learning from past performance data and user responses, the system can make more accurate predictions about which chatbots will perform best, reducing the need for extensive real-time analysis and computational resources while maintaining high response accuracy.
3Ease of operation
If seamless switching and handover between chatbots is implemented, then the user interaction quality is improved, but the system complexity and data management requirements increase
Solution Approach 1:
The switching system is designed with universal functionality to handle multiple scenarios including query routing, context maintenance, and coordination between different chatbots. By implementing a unified multi-functional architecture, the system can provide seamless user experiences across various chatbot transitions without requiring separate complex mechanisms for each specific interaction type, thereby managing system complexity while improving ease of operation.
Solution Approach 2:
The system implements a nested structure where the switching system encapsulates the complexity of chatbot management within its own boundaries. The switching system maintains an internal representation of user context and chatbot states, allowing it to coordinate handovers smoothly while hiding the underlying complexity from both the user interface and the individual chatbots. This nesting approach enables seamless user interaction quality while containing system complexity within a manageable framework.
Data Source
AI summary
The present disclosure provides a method and system to perform switching and handover between one or more intelligent conversational agents. The system receives a first set of data in real-time. The system collects a second set of data in real-time. The system fetches one or more queries from a plurality of users for a mega bot. The system analyses the first set of data, the second set of data and the one or more queries using one or more machine learning algorithms. The system selects a suitable intelligent conversational agent from the one or more intelligent conversational agents having a trust score above a threshold level. The system switches between the one or more intelligent conversational agents in the mega bot interacting with the plurality of users based on a plurality of aspects of corresponding query of the one or more queries and a plurality of factors.