Semantic Document Retrieval From Conversational Request Context
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Solution Overview
Problem
Document management systems face challenges in efficiently retrieving relevant documents when users lack knowledge of document indicators, especially in large document repositories, leading to time-consuming and frustrating interactions for both users and representatives.
Innovation Solution
Implementing natural language processing (NLP) over a document repository to derive contextual meaning from conversations, using embeddings and semantic matching to identify relevant documents and prepopulate form fields, while querying third-party data sources when necessary, and providing prompts to gather missing information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional document retrieval methods using categories, keywords, or indices are used, then document storage and organization are maintained, but retrieval efficiency deteriorates when users lack knowledge of document indicators
Solution Approach 1:
The patent introduces an intermediary system that includes a conversation analyzer and semantic processor. This intermediary translates natural language user inputs into meaningful queries by analyzing conversation context, identifying user intent, and matching semantic concepts against document metadata and content, thereby bridging the gap between user intent and document retrieval without requiring users to know specific document indicators
Solution Approach 2:
The patent replaces traditional mechanical keyword-matching retrieval systems with an intelligent semantic analysis system. Instead of relying on users to input precise keywords or navigate categorical structures, the system uses natural language processing, semantic concept extraction, and contextual understanding to automatically interpret user needs and retrieve relevant documents, substituting manual query formulation with automated semantic interpretation
2Loss of information
If users manually provide information for document completion, then data accuracy is maintained, but user burden and interaction time increase
Solution Approach 1:
The patent applies preliminary action by having the system automatically extract relevant information from the conversation context and pre-populate document fields before the user needs to complete them. The conversation analyzer continuously processes user inputs during the interaction, identifies extractable information, and prepares document content in advance, so that when document completion is needed, much of the information is already populated, reducing user burden and preventing information repetition
Solution Approach 2:
The system implements feedback by continuously analyzing the conversation stream, comparing extracted information against required document fields, and dynamically updating document content based on user responses. This real-time feedback loop allows the system to adapt to user input patterns, verify information accuracy, and automatically complete document fields as conversation progresses, reducing the need for users to manually repeat information
3Reliability
If representatives manually track conversation details, then information accuracy is maintained, but representative workload and potential errors increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform functions that would otherwise require representative intervention. The conversation analyzer autonomously tracks conversation details, extracts relevant information, populates document fields, and manages data flow between the user, conversation context, and document repository without requiring representative manual tracking or memory, thereby maintaining information accuracy while eliminating cognitive load from representatives
Data Source
AI summary
System and techniques to use a document repository to enhance natural language processing are described herein. Text can be obtained from a conversation between two entities (e.g., a person, chatbot, etc.) in which one entity is making a request that may not be clear. The nature of the request is determined by semantically matching a part of the text to a document in a document repository. The nature of the document reveals the nature of the request in the text. The fields of the document can be used to provide prompts to continue the conversation to gather information used to fulfill the now identified request.