Natural Language Response Clustering for Rapid Retrieval
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Solution Overview
Problem
Conversational systems face challenges in achieving rapid and relevant responses that mimic human interaction, with retrieval-based chatbots struggling to balance response latency and context relevance due to large datasets, leading to generic or insincere responses.
Innovation Solution
The method involves clustering natural language query-response pairs based on semantic criteria, using metadata tags to select relevant clusters, and employing a neural network-based response scoring engine to recommend responses within an acceptable latency range, ensuring responses are contextually relevant and timely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If retrieval-based chatbots use a large repository of pre-defined responses, then response relevance and context understanding improve, but response latency increases and responses become generic
Solution Approach 1:
The patent segments the large repository of pre-defined responses into multiple clusters based on metadata tags. Instead of searching the entire repository, the system identifies relevant clusters first, then searches only within those clusters. This segmentation reduces the search space and latency while maintaining response relevance.
Solution Approach 2:
The patent performs preliminary clustering and metadata tagging of responses before the actual query processing. By pre-organizing the response repository into clusters with associated metadata, the system prepares the data structure in advance to enable faster retrieval during interaction, reducing response latency without sacrificing relevance.
2Reliability
If retrieval-based chatbots search through all pre-defined responses, then response relevance improves, but response timing and interactivity deteriorate
Solution Approach 1:
The patent applies local quality by making different parts of the response repository accessible at different speeds. Frequently accessed or highly relevant clusters are optimized for rapid retrieval, while less critical clusters can be searched more thoroughly. This allows the system to prioritize speed for relevant responses while maintaining comprehensive search capability when needed.
3Adaptability or versatility
If generative chatbots are used to create unique responses, then response variety improves, but grammatical errors and irrelevance increase
Solution Approach 1:
The patent merges the advantages of retrieval-based and generative approaches. It uses a structured repository of pre-defined responses with metadata tags for reliable retrieval, while also incorporating generative capabilities to adapt and customize responses. This combination maintains response accuracy through the structured repository while achieving variety through generative adaptation.
Data Source
AI summary
An interaction pace for a live natural language interaction is set. An acceptable response latency range is determined according to the interaction pace. By analyzing a live natural language query, a set of metadata tags corresponding to the query is determined. Using the set of metadata tags, a set of clusters of natural language query-response pairs is selected, a size of the set of clusters selected according to the acceptable response latency range. From the set of clusters, a query-response pairs is selected, wherein the query of the query-response pair has above a threshold relevance score with the first natural language query. From the selected query-response pair, a response recommendation is extracted, the response recommendation being a recommended response to the live natural language query, a latency between receipt of the live natural language query and extraction of the response recommendation being within the acceptable response latency range.