Conversational Agent Semantic Clustering for Complex Query Handling
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Solution Overview
Problem
Traditional search technologies on company websites become ineffective as queries become complex, often requiring personalized information, leading to user frustration and increased reliance on human customer service, negatively impacting user experience and company perception.
Innovation Solution
Implementing semantic clustering techniques in conversational agents that perform deep linguistic analysis on natural language inputs to group user utterances by topics, allowing for improved intent identification and resource allocation, enabling automated assistance and enhancing user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional search technologies are used on company websites, then simple queries can be handled, but complex queries requiring personalized information cannot be effectively resolved
Solution Approach 1:
The patent introduces a conversational agent as an intermediary between users and company information systems. This agent performs deep linguistic analysis of user utterances, extracts semantic meaning, and routes queries appropriately, thereby resolving complex personalized queries that traditional search cannot handle while maintaining reliable user experience
2Ease of manufacture
If traditional search technologies are used, then implementation is simple, but users become frustrated and rely on human customer service
Solution Approach 1:
The conversational agent enables users to obtain personalized information and resolve complex queries through automated interaction without human intervention. The system performs self-service by analyzing user intent, retrieving relevant information, and providing accurate responses, thereby reducing reliance on human customer service representatives
Solution Approach 2:
The patent replaces manual customer service mechanisms with an automated conversational system that uses deep linguistic analysis and semantic processing. This substitution maintains ease of implementation while dramatically improving productivity by handling complex queries that previously required human agents
3Extent of automation
If semantic clustering techniques are implemented, then automated assistance is improved, but system complexity increases
Solution Approach 1:
The patent segments the linguistic analysis process into distinct modular components: utterance processing, semantic graph generation, clustering algorithms, and response generation. Each module handles a specific aspect of the analysis, making the overall complex system manageable and maintainable while achieving high levels of automation
Data Source
AI summary
Semantic clustering techniques are described. In various implementations, a conversational agent is configured to perform semantic clustering of a corpus of user utterances. Semantic clustering may be used to provide a variety of functionality, such as to group a corpus of utterances into semantic clusters in which each cluster pertains to a similar topic. These clusters may then be leveraged to identify topics and assess their relative importance, as for example to prioritize topics whose handling by the conversation agent should be improved. A variety of utterances may be processed using these techniques, such as spoken words, textual descriptions entered via live chat, instant messaging, a website interface, email, SMS, a social network, a blogging or micro-blogging interface, and so on.