Dynamic Chat Message Template Clustering and Ranking
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Solution Overview
Problem
Conventional chatbot systems face challenges in providing flexibility and reducing manual configuration and curation of chat messages, often resulting in suboptimal performance due to rigid tree-based methodologies or complex generative models that lack control over message selection.
Innovation Solution
A machine learning-based approach that clusters semantically related chat messages and uses a feedback-based pipeline to dynamically select and modify message templates, optimizing message selection through a template ranking model trained on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If tree-based methodologies are used for chat message selection, then control over message selection is improved, but flexibility and adaptability deteriorate
Solution Approach 1:
The patent implements a hybrid architecture that dynamically switches between rule-based message selection (for stability and control) and machine learning-based selection (for flexibility and adaptability). The system adapts its message selection strategy based on conversation context, user preferences, and feedback, resolving the contradiction between rigid control and flexible adaptation.
Solution Approach 2:
The patent introduces an intermediate layer that bridges the rule-based tree methodology and the flexible chatbot responses. This intermediary component manages the transition between structured message templates and dynamically generated responses, allowing the system to maintain control while incorporating adaptive elements.
2Manufacturing precision
If manual configuration and curation of chat messages is performed, then message quality and relevance are improved, but time consumption and operational complexity deteriorate
Solution Approach 1:
The patent pre-configures message templates and conversation trees in advance, preparing structured message sets that can be quickly deployed. This preliminary preparation reduces the need for extensive manual configuration during operation, maintaining message quality while reducing time consumption.
Solution Approach 2:
The system incorporates automated feedback mechanisms that allow it to self-optimize message selection based on user interactions. The chatbot learns from user responses and automatically refines its message selection, reducing the need for continuous manual curation while maintaining or improving message quality over time.
3Adaptability or versatility
If complex generative models are used for message generation, then flexibility is improved, but control over message selection deteriorates
Solution Approach 1:
The patent applies different quality controls to different parts of the message generation process. Rule-based constraints and templates are applied to specific conversation contexts where control is critical, while more flexible generative approaches are used in contexts where adaptability is more important. This localized application of control mechanisms resolves the contradiction between flexibility and control.
4Adaptability or versatility
If a large number of chat message templates are created, then message coverage and relevance are improved, but device complexity and maintenance effort deteriorate
Solution Approach 1:
The patent merges multiple message templates into clustered groups based on semantic similarity and conversation context. Instead of managing thousands of individual templates, the system clusters related templates together and selects appropriate clusters based on the conversation state, significantly reducing management complexity while maintaining comprehensive message coverage.
Solution Approach 2:
The patent creates universal message templates that can serve multiple functions across different conversation contexts. A single template can be adapted through parameter substitution to address various user needs, reducing the total number of templates required while maintaining broad message coverage and relevance.
Data Source
AI summary
A pipeline is provided for management of a pool of chat message templates for an automated dialog system. The pool of chat messages may be managed using machine learning-based clustering and feedback-based modifications. A set of chat messages may be analyzed using a machine learning model to generate different clusters of messages that are semantically related. Representative messages may be selected from each cluster and used in chat sessions according to the semantic context of the chat sessions. Based on feedback obtained during the chat sessions, metrics generated based on the feedback, and/or other data, modifications may be made to the clusters and/or the representative messages to improve the performance of the automated dialog system.