Messaging Data Tracking via Decision Documenting Service
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Solution Overview
Problem
Current online messaging services lack the ability to effectively document key elements of chats and preserve data for easy reference by larger groups, as the data is typically managed exclusively by the messaging tool and not easily accessible or indexable for tracking specific topics or decisions.
Innovation Solution
Integration of online messaging services with document management systems using a Decision Documenting Service (DDS) that analyzes messaging data to identify seminal messages, extracts related messages, performs sentiment analysis, and generates a tracking document with a formatting protocol for viewing on collaboration platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If online messaging services manage data exclusively within their own platform, then data is easily stored and accessed by messaging users, but data is not accessible or indexable by larger groups outside the messaging tool
Solution Approach 1:
The patent introduces an intermediary service that connects the online messaging service with the document management system. This intermediary extracts data from messaging sessions, processes it, and stores it in the document management system, enabling cross-platform accessibility while maintaining the original messaging platform's data storage capabilities
Solution Approach 2:
The system segments data management into two distinct parts: the messaging service handles real-time communication data storage, while the document management system handles structured documentation and indexing. This segmentation allows each system to optimize for its specific function while improving overall data accessibility across platforms
2Quantity of substance
If all messaging data is stored and managed in a single messaging platform, then data volume is comprehensive, but key data is sparsely dispersed and difficult to locate
Solution Approach 1:
The system extracts key elements and decisions from voluminous messaging data using natural language processing and machine learning algorithms. These extracted key data points are then stored separately in the document management system with proper indexing, making them easily locatable while preserving the complete data volume in the original messaging platform
Solution Approach 2:
The patent adds a new dimension to data organization by creating a structured hierarchical view of messaging data. Instead of flat chronological storage, the system organizes data by topics, decisions, and key themes, enabling efficient retrieval through multiple access dimensions
3Device complexity
If messaging data is not indexed by topic or decision, then data storage is simple, but tracking and documenting specific topics or decisions is ineffective
Solution Approach 1:
The system performs preliminary indexing and categorization of messaging data as it is extracted from the messaging service. By pre-organizing data into topics, decisions, and key themes with metadata tags, the system enables efficient tracking and documentation without adding complexity to the original storage structure
Data Source
AI summary
Described herein is a method that includes obtaining a first set of messages from the online chat and analyzing the first set of messages to identify a decision flag portion based on content extracted from the first set of messages. The method includes determining, based on the decision flag portion, a decision topic and a decision author, and identifying a subset of messages from the first set of messages having content corresponding to the decision topic. The method includes analyzing the subset of messages to determine a sentiment of one or more messages included in the subset of messages. The method includes generating a table content item including the decision topic or the decision author, and at least an indicium related to the sentiment, and importing the table content item into a content page for displaying in response to a request to view the content page.