Chat Response Clustering via Context Embeddings
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Solution Overview
Problem
In cloud-based chat systems, agents often waste resources by repeatedly providing similar responses to customers, as existing systems rely on static lists of responses that do not account for the nuances of customer inquiries and require manual input of canned responses.
Innovation Solution
A system that analyzes historical chat data using context and response word embedding functions to generate context and response vectors, processing these to identify coherence scores and cluster responses, thereby automatically recommending relevant candidate responses during live chats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static lists of canned responses are used, then agents can quickly respond to customers, but the responses do not account for nuances of customer inquiries and require manual input
Solution Approach 1:
The patent transforms static canned responses into dynamic, context-aware suggestions by implementing a system that continuously learns from historical chat data and adapts to current conversation contexts. The model dynamically generates response suggestions based on real-time analysis of customer inquiries, agent responses, and conversation flow, rather than relying on fixed pre-defined responses.
Solution Approach 2:
The system enables self-service by automatically analyzing historical chat data and generating response suggestions without requiring manual input of canned responses. The model autonomously learns from the data corpus, identifies patterns, and provides tailored response recommendations, freeing agents from manually maintaining response lists while improving response relevance.
2Adaptability or versatility
If agents manually input canned responses, then response lists can be customized, but this consumes time and resources
Solution Approach 1:
The system performs self-service by automatically processing historical chat data to generate customized response lists without human intervention. The model autonomously extracts meaningful patterns, clusters similar responses, and identifies high-quality response candidates based on coherence scores, eliminating the need for agents to manually create or maintain canned response lists.
Solution Approach 2:
The system performs preliminary action by pre-processing and analyzing historical chat data offline to build a trained model before deployment. This preliminary training phase automatically generates response suggestions that are ready for immediate use during live chats, eliminating the need for time-consuming manual response creation while maintaining high customization and relevance.
3Stability of the object's composition
If agents repeatedly provide similar responses, then consistency is maintained, but resources are wasted on repetitive tasks
Solution Approach 1:
The system implements feedback by continuously monitoring conversation contexts and comparing suggested responses against historical data patterns. The model provides real-time feedback to agents through contextual response suggestions that maintain consistency with organizational practices while reducing repetitive responses. The system learns from ongoing interactions and refines its recommendations, ensuring consistent yet efficient communication.
Data Source
AI summary
A system is configured to analyze a corpus of historical chat data to identify the list of “best” responses. As such, the user is not required to identify a list of canned responses for input into the system. The described system uses a context word embedding function and response word embedding function to generate context vectors and response vectors corresponding to the corpus of conversation data, and the vectors are represented by a respective context matrix and a response matrix. The system processes these matrices to generate scores for responses, clusters the responses, and identifies the responses corresponding to the best scores for each cluster.