Context-Driven Conversation Automation Pipeline
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Solution Overview
Problem
Conventional chat room software lacks an automated means to analyze messages and extract insights such as frequently discussed topics, user queries, and actionable items from conversation rooms, which are essential for firms interacting with the public.
Innovation Solution
A method and system that utilize an AI algorithm based on Natural Language Processing (NLP) and machine learning to download, analyze, and categorize messages from chat rooms, generating metrics and actionable items, and using Natural Language Generation (NLG) to create responses, while storing historical data for training and displaying results through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional chat room software is used, then basic communication functionality is provided, but automated analysis of messages to determine topics, entities, context, and actionable items is not available
Solution Approach 1:
The patent introduces an AI algorithm as an intermediary component between the chat room software and the analysis requirements. This AI algorithm acts as a mediator that processes messages automatically, extracting topics, entities, context, and actionable items without requiring complex manual analysis systems. The AI algorithm absorbs the complexity of automated analysis, allowing the core chat room functionality to remain simple while adding advanced analytical capabilities.
2Productivity
If manual analysis of chat room messages is performed, then insights can be obtained, but time consumption and labor requirements increase significantly
Solution Approach 1:
The patent replaces the mechanical manual analysis process with an automated AI-based system. Instead of human operators manually reading and analyzing messages to extract insights, the system uses an AI algorithm that automatically processes messages, determines topics, identifies entities, understands context, and extracts actionable items. This substitution dramatically increases productivity while reducing the time required for message analysis.
3Loss of information
If comprehensive message analysis is implemented, then valuable insights and actionable items are extracted, but processing time and computational resources increase
Solution Approach 1:
The patent segments the message analysis process into distinct functional components handled by the AI algorithm: topic determination, entity identification, context understanding, and actionable item extraction. By dividing the comprehensive analysis into these segments, the system can process messages more efficiently while maintaining completeness of information extraction. Each segment can be optimized independently, reducing overall processing time while preserving thoroughness.
Data Source
AI summary
A method and system for automating a process of downloading and analyzing messages from conversation rooms and chat rooms to determine topics, entities, context, and actionable items are provided. The method includes downloading a set of messages that have been communicated over a communication channel; analyzing each respective message in order to determine at least one respective topic that relates to each respective message; determining, based on a result of the analysis, metrics that relate to the set of messages; and storing historical data that relates to the downloaded set of messages and each of the metrics. The analysis may be performed by executing an artificial intelligence (AI) algorithm that is based on a Natural Language Processing (NLP) model and is trained by using the historical data.