Workflow Relationship Mapping Across Distributed Repositories
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Solution Overview
Problem
Conventional productivity systems require users to store all data in a central electronic data repository, leading to high costs and inefficiencies due to the need for significant processing power, and burden users with manually identifying relationships between components of work.
Innovation Solution
A workflow management system that automatically identifies related components of work by scanning multiple electronic data repositories, generating unique identifiers, and using artificial intelligence to determine relevance, allowing for real-time display of related files and context data without the need for a single central repository.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If all data is stored on a central electronic data repository, then data management is centralized and consistent, but processing power requirements and costs increase significantly
Solution Approach 1:
The patent divides the centralized data repository into multiple distributed electronic data repositories. Each repository stores a portion of the data independently, eliminating the need for a single central storage system. The system periodically scans these distributed repositories to identify and relationship-map files across them, achieving data consistency without requiring centralized processing power.
2Measurement precision
If users manually identify relationships between components of work, then relationship accuracy can be controlled, but user burden and time consumption increase
Solution Approach 1:
The system automatically performs relationship identification between components of work without requiring user intervention. It periodically scans electronic data repositories, generates unique identifiers for files, and uses artificial intelligence models to analyze context data (timestamps, access history, edit history) and determine relationships between files. This self-service approach eliminates user burden while maintaining relationship identification accuracy through AI-driven analysis.
3Reliability
If conventional productivity systems monitor files in a central repository, then file relationships can be tracked, but processing costs and system complexity increase
Solution Approach 1:
The system segments the monitoring function by periodically scanning distributed electronic data repositories rather than continuously monitoring a central repository. This reduces system complexity while maintaining reliable file relationship tracking through the generated unique identifiers and AI-based context analysis.
Solution Approach 2:
The patent introduces an intermediary relationship mapping system that connects files across distributed repositories. Instead of directly monitoring all files in a central repository, the system uses generated unique identifiers and AI models as intermediaries to track relationships between files in distributed storage locations, reducing complexity while maintaining tracking reliability.
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. Upon receiving a set of notifications for a user, the server augments the notifications with data retrieved/derived from the nodal data structure. The server then prioritizes outputting the notifications based on their respective contextualized data and/or based on attributes received from the user.