Email Tagging System for Institutional Knowledge Sharing
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Solution Overview
Problem
Existing email communication systems fail to efficiently categorize and share institutional knowledge due to their private nature, making it difficult for individuals to locate information and align on common terminology, and existing solutions like wikis and mailing lists require significant effort to maintain and are not widely adopted.
Innovation Solution
A system that allows users to tag emails with a hash symbol followed by an alphanumeric string, enabling automatic suggestion and storage of tags in a database, allowing for easy categorization and sharing of emails within an institution without altering the user's workflow, using an email client plug-in with components like tag extractor, suggester, and manager.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If email communication remains private to maintain confidentiality, then security is improved, but knowledge sharing and accessibility deteriorate
Solution Approach 1:
The patent segments email content into tagged portions that can be independently categorized and shared. Tags extract specific topics, people, and entities from emails, allowing selective sharing of knowledge without exposing entire private communications. This enables partial information sharing while maintaining overall email confidentiality.
Solution Approach 2:
The patent introduces tags as an intermediary layer between private email content and public knowledge bases. Tags serve as metadata that bridge confidential communications and institutional knowledge repositories, allowing emails to be referenced and searched without revealing sensitive content to unauthorized users.
2Ease of operation
If manual email categorization is implemented, then information organization is improved, but user effort and time consumption increase
Solution Approach 1:
The system performs self-service by automatically extracting tags from email content using natural language processing and pattern recognition. The tag extraction algorithm autonomously identifies topics, entities, and categorization keywords without requiring manual user intervention, thereby eliminating the time users would otherwise spend on categorization tasks.
Solution Approach 2:
The patent replaces manual mechanical categorization efforts with automated computational processes. The system uses text analysis algorithms and machine learning models to automatically categorize emails, substituting human cognitive effort with automated information processing systems.
3Adaptability or versatility
If multiple individual categorization schemes are allowed, then user flexibility is improved, but system consistency and common terminology deteriorate
Solution Approach 1:
The patent creates a universal tag system that serves multiple functions simultaneously. The same tag framework accommodates diverse categorization needs of different users while maintaining consistent terminology across the organization. Tags can represent various dimensions (topic, person, project, client) using a unified vocabulary that works for all users.
Solution Approach 2:
The system allows parameter changes in categorization by enabling tags to represent different attributes (topic, entity, project phase, client type) while maintaining consistent tag naming conventions. This enables flexible categorization approaches for different users without compromising system-wide terminology consistency.
4Loss of information
If wikis are used for knowledge sharing, then information centralization is improved, but ease of creation and notification deteriorate
Solution Approach 1:
The patent uses email tags to automatically create and maintain knowledge base entries by copying relevant information from emails. When emails are tagged, the system automatically generates wiki pages or knowledge base articles from the tagged content, eliminating the need for manual wiki creation and reducing maintenance effort.
5Loss of information
If mailing lists are created for topic sharing, then information distribution is improved, but complexity of creation and management increases
Solution Approach 1:
The patent transforms static mailing list structures into dynamic, on-demand information distribution. Instead of pre-configuring mailing lists for every topic, the system dynamically routes tagged emails to relevant users based on their profiles and interests, automatically creating distribution channels as needed without manual mailing list management.
Data Source
AI summary
Suggesting email tags. A non-transitory machine-readable storage device includes executable instructions that, when executed, cause one or more processors to provide a suggestion for at least one suggested tag based on content of an email, receive a selection of a selected tag, store the email in a computer database, thus creating a stored email, and associate the selected tag with the stored email in the computer database.