Dynamic Chatbot Network Adaptation for Missed User Paths
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Solution Overview
Problem
Conventional chatbots with fixed network structures often fail to handle all user requests correctly, leading to the need for human intervention and inefficiencies due to user paths not reaching target nodes, despite reducing inaccuracies like hallucinations.
Innovation Solution
A computer-implemented method for adapting a network by modifying identifiers, associations, adding or removing nodes, and connections based on user input data and path accuracy to ensure paths reach target nodes, using a trained model for node selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed network structure is used in a conventional chatbot, then hallucinations and inaccuracies are avoided, but the chatbot cannot handle all user requests correctly and requires human intervention
Solution Approach 1:
The patent implements dynamic adaptation of the network structure by automatically adding nodes and connections based on user inputs that do not reach target nodes. The system transitions from a static fixed network to a dynamic evolving network that adapts its topology to improve handling capability while maintaining accuracy through controlled adaptation mechanisms.
Solution Approach 2:
The system changes the structural parameters of the network (number of nodes, connections, and their relationships) in response to performance metrics. When accuracy drops below a threshold or specific failure patterns are detected, the network parameters are automatically adjusted by adding new nodes and connections to improve handling capability.
2Productivity
If the network structure is fixed, then the chatbot operates efficiently within set boundaries, but user paths may not reach target nodes and human operator intervention is required
Solution Approach 1:
The system implements feedback loops where the performance of the fixed network is continuously monitored. When user paths fail to reach target nodes, this feedback triggers automatic network adaptation by adding new nodes and connections, thereby improving the request completion rate while maintaining operational efficiency through automated responses.
Solution Approach 2:
The network performs self-adaptation without human intervention. When performance degradation is detected, the system automatically modifies its own structure by adding nodes and connections, enabling it to serve itself and improve its own reliability without external assistance.
3Reliability
If a fixed network is used, then the chatbot avoids hallucinations, but some user requests cannot be handled and efficiency gains are reduced
Solution Approach 1:
The system maintains the benefits of a fixed network structure while introducing dynamic adaptation capabilities. The network evolves over time by adding nodes and connections based on performance feedback, allowing it to handle more requests accurately without sacrificing the stability and accuracy control that characterized the original fixed structure.
Data Source
AI summary
There is provided a computer-implemented method for adapting a network. The network may comprise a plurality of network nodes, at least one network connection, and at least one identifier. Each network connection connects two network nodes. At least one of the network nodes may be a target node. Each identifier may be associated with at least one of the network nodes. A path taken by a user through the network comprises at least one of the network nodes. The network nodes comprised in the path may be selected base don comparing at least one user input with data pertaining to the at least one identifier. The computer-implemented method may comprise receiving data representative of network nodes comprised in the path. The method may comprise determining, based on the data, whether the path comprises at least one target node. If the path does not comprise at least one target node, the method may comprise adapting the network.