Contextual Query Enhancement for Real-Time Customer Care Retrieval
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Solution Overview
Problem
Current information retrieval systems for real-time customer conversations lack the ability to effectively capture context from previous interactions, leading to suboptimal query modification and document retrieval, especially in chatbot applications where previous elements of the interaction are not considered.
Innovation Solution
A method that analyzes customer utterances to classify informational versus non-informational phrases, extracts content phrases using keyword extraction, intent/action extraction, and topic labeling, and filters out emotional content, then combines contextual information from prior conversations to create a contextually-enhanced content-based query for improved document retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If original user queries are used directly in information retrieval systems, then the system operates with simple processing, but the retrieval accuracy deteriorates because queries are too short or contain complex information that cannot be effectively handled
Solution Approach 1:
The system performs preliminary query modification by paraphrasing and expanding user queries before submitting them to the information retrieval system. This advance processing transforms short or complex queries into more effective search queries, improving retrieval accuracy without requiring complex processing during the actual search operation
Solution Approach 2:
The patent introduces an intermediary query modification component that acts as a mediator between the user's original query and the information retrieval system. This intermediary layer performs paraphrasing and expansion operations, translating user intent into optimized search queries that bridge the gap between simple input and accurate retrieval
2Reliability
If contextual information from prior conversations is integrated into query modification, then document retrieval relevance improves, but the system complexity increases due to additional processing requirements
Solution Approach 1:
The system merges contextual information from prior conversations with the current user query to create an enhanced query. By combining historical conversation data with present intent, the system generates more relevant search queries that leverage accumulated context, improving document retrieval relevance through integration of multiple information sources
Solution Approach 2:
The system performs preliminary analysis and extraction of contextual information from prior conversations before query submission. This advance preparation involves identifying relevant entities, topics, and relationships from historical data, so that when the current query is processed, the contextual enrichment can be efficiently applied without adding significant complexity during real-time operation
3Measurement precision
If emotional content and non-informational phrases are removed from queries, then retrieval precision improves, but information loss may occur during the filtering process
Solution Approach 1:
The system segments the user query into distinct components: emotional content, non-informational phrases, and core informational elements. By dividing the query structure, the system can selectively filter out emotional and non-essential portions while preserving the critical informational content needed for accurate retrieval, thereby improving precision without significant information loss
Data Source
AI summary
An enhanced information retrieval system takes a customer utterance and constructs a contextually-enriched content-based query allowing the system to retrieve the most relevant documents to assist an agent in a real-time conversation with the customer. Phrases in the utterance are classified as informational or non-informational using a machine learning system trained with phrases from prior conversations of multiple users. Content phrases are extracted from the informational phrases using keyword extraction (ranking noun phrases), intent/action extraction (semantic role labeling), and topic label extraction (clustering of historical logs). Emotional content is identified using a sequence tagging model and removed. Contextual information from prior conversations with this user is combined with the updated content phrases to create the contextually-enhanced content-based query, which can then be submitted to the information retrieval system.