Workflow Relationship Management via Contextual AI
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Solution Overview
Problem
Conventional productivity systems require users to store all data in a central electronic repository, leading to high costs and inefficiencies, and burden users with manually identifying relationships between data components, creating a negative user experience.
Innovation Solution
A workflow management system that periodically scans multiple data repositories to generate unique identifiers for files, uses artificial intelligence to identify related files based on context data, and displays these relationships in real-time, eliminating the need for central storage and manual relationship identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all files are stored on a central electronic data repository, then data management and relationship monitoring are centralized, but storage costs and processing requirements increase significantly
Solution Approach 1:
The system segments data storage across multiple electronic data repositories instead of using a single central repository. Each repository can be maintained by different users or organizations, reducing the storage burden and cost on any single entity while maintaining the ability to manage and monitor relationships between files across all repositories through the workflow management system.
2Productivity
If conventional productivity systems monitor files in real-time across multiple repositories, then relationship identification improves, but processing power requirements become prohibitive
Solution Approach 1:
The system performs preliminary actions by having users proactively notify the workflow management system when they create new files or establish relationships between files. This allows the system to monitor and identify relationships without continuously scanning all repositories, significantly reducing processing power requirements while maintaining real-time relationship identification capability.
3Measurement precision
If users manually identify and tag related Components of Work, then relationship accuracy improves, but user burden and time consumption increase
Solution Approach 1:
The workflow management system performs self-service by automatically monitoring file relationships, analyzing contextual data, and identifying connections between Components of Work without requiring manual user intervention. The system uses artificial intelligence to autonomously determine relationships based on file content, metadata, and usage patterns, eliminating the time burden on users while maintaining high relationship identification accuracy.
4Ease of operation
If the system uses artificial intelligence to automatically identify file relationships, then user effort is reduced, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer between users and the complex artificial intelligence algorithms. The workflow management system presents a simple user interface where users can interact with files normally, while the AI-based relationship identification operates in the background as an intermediary process. This shields users from the underlying system complexity while still providing the benefits of automated relationship identification.
Data Source
AI summary
Described herein are methods and system for electronic workflow management having a central server that periodically scans data accessible to multiple computers and data interacted with by different users to generate a nodal data structure comprising of interrelated nodes where each node corresponds to a workflow component, such as files, messages, tasks, and the like. The server then executes various analytical protocols to identify and link/merge nodes corresponding to related content. The server then allows a user to customize graphical user interfaces where the server arranges the content of each graphical user interface based on their corresponding relationships within the nodal data structure.