Messaging System Semantic Tagging for Information Retrieval
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Solution Overview
Problem
Existing messaging systems lack effective tools for organizing and retrieving valuable information from voluminous conversations, particularly in business communication, where credibility of sources and subconscious judgment processes are crucial, leading to miscommunication and suboptimal outcomes.
Innovation Solution
A computer-implemented messaging system that uses user-post tag-based categorization and rule-based filtering, incorporating machine learning algorithms to generate semantic tags associated with posts and users, enabling precise information retrieval and collaboration by analyzing user interactions and content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional messaging systems organize conversations into channels and threads, then conversations are logically structured, but voluminous conversations become difficult to organize and retrieve valuable information rapidly
Solution Approach 1:
The patent segments information organization into multiple dimensions: traditional channel/thread hierarchy plus semantic tags, user profiles, and interaction metadata. This multi-level segmentation allows simultaneous organization of voluminous conversations while enabling rapid retrieval through various filter combinations.
Solution Approach 2:
The patent adds semantic tagging as an additional organizational dimension beyond the traditional channel/thread structure. Users can tag messages with semantic categories, and the system creates management views that combine channel, thread, and tag dimensions, enabling rapid information retrieval from large conversation volumes.
2Extent of automation
If pre-imposed tags are used to classify messages, then classification is automated, but tags are limited in nature and only address the surface of the problem
Solution Approach 1:
The patent implements a dynamic tagging system where tags evolve based on user interactions and machine learning. The system automatically generates tags from user profiles and conversation content, and tags can be refined over time, making the system both automated and highly adaptable to diverse classification needs.
Solution Approach 2:
The system performs self-service tagging by automatically analyzing user profiles, message content, and interaction patterns to generate relevant tags without requiring manual pre-definition. This self-service capability provides both automation and versatility, as the system adapts tags based on actual usage patterns.
3Productivity
If machine learning algorithms automatically classify conversations, then manual tagging is reduced, but credibility of sources and subconscious judgment processes are not adequately considered
Solution Approach 1:
The patent introduces user profiles as an intermediary layer between machine learning classification and final information assessment. User profiles capture credibility indicators and judgment patterns, serving as a mediator that enhances ML classification with human-like assessment criteria, thereby maintaining both efficiency and reliability.
Solution Approach 2:
The system incorporates feedback loops where user interactions with classified content refine the machine learning models. User credibility assessments and judgment patterns are fed back into the system, continuously improving classification accuracy while maintaining consideration of source credibility and subconscious judgment factors.
4Quantity of substance
If the system stores all user posts and interactions, then complete information is available, but finding high value information in growing repository becomes challenging
Solution Approach 1:
The patent applies preliminary action by pre-tagging and pre-organizing information as it is stored, rather than requiring post-retrieval filtering. Management views are pre-computed based on various tag combinations and user profiles, so when information is stored, it is immediately organized with multiple classification layers, enabling rapid retrieval without searching through the entire repository.
Data Source
AI summary
A computer implemented messaging and management system for organizing and extracting information sent between users, and for automatically generating and updating management data based on information extracted from messages. One or more processors are configured to receive posts and post relational data and configured to generate a set of P-tags from the received posts and post relational data, wherein each P-tag has a value associated therewith, wherein some of the values are derived from users' input and wherein some of the values are generated by a tag management engine and a statistics engine. A logic engine having logic gates groups the P-tags or P-tags and keywords into a plurality of logic expressions; and, a compilation engine for creates and displays one or more management views in dependence upon at least some of the plurality of logic expressions.