Graph Database Conversation Assistance System
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Solution Overview
Problem
Conversations between participants from different domains often lead to misinterpretations due to differing meanings of words and phrases, and lack of relevant information, resulting in misunderstandings and frustration.
Innovation Solution
The system uses a graph database to automatically assist conversations by extracting entities from phrases using natural language processing, retrieving relevant tags and links to documents, and providing them to participants based on their domain context and access permissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If participants from different domains exchange ideas through conversations, then communication frequency and idea exchange increase, but misinterpretations and misunderstandings occur due to differing meanings of words and phrases
Solution Approach 1:
The patent introduces an intermediary system that includes a graph database and natural language processing components. This intermediary automatically analyzes conversation phrases, extracts entities, retrieves relevant domain context and definitions from the graph database, and provides clarification information to participants in real-time, thereby resolving misunderstandings without disrupting the natural flow of communication
Solution Approach 2:
The system implements feedback by monitoring conversation phrases and automatically providing contextual information, definitions, and clarifications back to participants. This continuous feedback loop helps correct misinterpretations as they occur, improving the reliability of communication while maintaining high productivity
2Measurement precision
If relevant information and resources are provided to participants in real-time during conversations, then accuracy and productivity of conversations are enhanced, but system complexity and processing requirements increase
Solution Approach 1:
The graph database is pre-populated with extensive domain knowledge, entities, relationships, and definitions before conversations occur. This preliminary preparation allows the system to quickly retrieve and provide accurate information during conversations without performing complex real-time analysis, thereby reducing system complexity while maintaining high accuracy
Solution Approach 2:
The system extracts only the specific entities and phrases that are actually used in the conversation from the larger graph database, rather than processing or presenting all available information. This selective extraction reduces the complexity of real-time processing while ensuring that the most relevant and accurate information is provided to participants
Data Source
AI summary
Examples of the present disclosure describe systems and methods for automatically assisting conversations using a graph database. In order to minimize misunderstanding of words and phrases used by participants during a conversation, phrases from the conversation may be received by conversation assistance application as the conversation takes place. Entities may be extracted from the phrase based on natural language recognition according to a domain context of the participant being assisted. One or more tags may be looked up from a graph database, and may be provided to the participant as a list of hashtags related to the conversation. Links to documents may be extracted based on the tags for the participant for viewing during the conversation.