Auto Quoting Chat System Using NLP to Reduce Search Time
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Solution Overview
Problem
Current chat services lack the ability to automatically suggest relevant chat messages or threads to quote or fork into, disrupting the ongoing conversation and requiring manual effort from users to find the right information.
Innovation Solution
Implementing a non-intrusive auto quoting and forking feature using Natural Language Processing (NLP) and deep learning to monitor ongoing chat sessions, detect salient words or phrases, and suggest relevant content from previous conversations without human intervention, utilizing a cache to store key-value pairs and identify matching threads or messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual searching through chat history is used to find relevant messages, then users can locate information, but user efficiency decreases and time is lost
Solution Approach 1:
The chat system automatically monitors ongoing sessions, detects salient words and phrases, identifies related previous conversations, and generates quoting suggestions without user intervention. The system serves itself by autonomously performing the information retrieval and suggestion generation tasks that would otherwise require manual user effort to search through chat history.
Solution Approach 2:
The system pre-processes chat data by monitoring ongoing sessions, detecting key words and phrases, and identifying topically related conversations in advance. When a user needs information, the suggestions are already prepared and ready for immediate display, eliminating the need for users to manually search through historical chat data.
2Productivity
If automated chat monitoring and suggestion systems are implemented, then user efficiency improves, but system complexity increases
Solution Approach 1:
The chat system integrates multiple functions into a unified automated process: monitoring ongoing chat sessions, detecting salient words and phrases using NLP, identifying topically related previous conversations, and generating quoting suggestions. This multi-functional approach consolidates what would otherwise require separate manual operations into a single automated system.
Solution Approach 2:
The system introduces an automated suggestion mechanism as an intermediary between the user and chat history. Instead of users directly searching through complex chat data, the intermediary system processes the information and presents simplified suggestions, reducing the perceived complexity for users while maintaining sophisticated backend processing.
3Measurement precision
If deep learning and NLP processing are used to detect salient words and identify related sessions, then suggestion accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies NLP and deep learning selectively to detect only salient words and phrases rather than processing entire chat messages. By focusing computational resources on key detectable elements rather than comprehensive text analysis, the system achieves sufficient suggestion accuracy while reducing overall computational burden and energy consumption.
Data Source
AI summary
Presented herein are techniques for a non-intruding auto quoting feature on-the-fly for chat applications based on previous conversations and effectively forking/quoting such conversations to ease the burden of the user going through all the chat history. The techniques may involve monitoring an ongoing first text communication session between a first user and at least one second user; detecting one or more salient words or phrases in the first text communication session based on the monitoring; based on the one or more salient words or phrases, identifying a second text communication session that is topically related to the first text communication session; and displaying in a user interface presented to the first user a prompt to quote relevant content from the second text communication session into the first text communication session or to fork the first user to be a participant in the second text communication session.