Dynamic Workflow Engine for Conditional Branching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing workflow modeling approaches focus on unidirectional step serialization, lacking an interactive tool for defining and managing complex workflows effectively.
Innovation Solution
A graphical workflow definition and management tool allows administrators to create and manage complex workflows with defined steps, inputs, outputs, actions, and conditional criteria, using a digital computer system for workflow generation and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional unidirectional workflow modeling is used, then implementation is simple, but the system cannot effectively manage complex workflows with conditional branching and dynamic paths
Solution Approach 1:
The workflow model dynamically adapts its structure and paths based on runtime conditions and data. The system transitions from static unidirectional workflows to dynamic workflows that can branch, converge, and reconfigure based on conditional criteria, enabling effective management of complex workflows while maintaining manageable tool complexity through event-driven architecture
Solution Approach 2:
The system changes the fundamental parameters of workflow modeling by introducing conditional branching logic, multiple entry/exit points, and dynamic path selection based on event triggers. This transforms the workflow from a fixed linear sequence to a flexible parameter-driven process that can adapt to varying complexity requirements
2Productivity
If manual workflow management is used, then system simplicity is maintained, but processing efficiency and productivity decrease
Solution Approach 1:
The workflow system executes automatically based on defined triggers and conditions without requiring manual intervention at each step. The event-driven architecture enables the workflow to self-manage its progression through stages, automatically routing submissions, triggering actions, and transitioning between states based on predefined criteria, thereby significantly improving processing efficiency
Solution Approach 2:
The system incorporates feedback mechanisms where workflow execution status, event triggers, and condition outcomes are continuously monitored and fed back into the workflow engine. This enables automatic adjustment of workflow paths, dynamic form adjustments, and real-time status updates, enhancing both productivity and the extent of automation
3Reliability
If comprehensive workflow controls are implemented, then process reliability is improved, but system complexity increases
Solution Approach 1:
The workflow control system is segmented into discrete, manageable components including event triggers, conditional criteria, action definitions, and status transitions. Each component is independently configurable and can be combined to create reliable complex workflows. This modular segmentation maintains reliability while preventing overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary workflow engine that mediates between the user-defined workflow rules and the actual execution process. This intermediary layer handles the complexity of conditional logic, event routing, and state management, thereby improving execution reliability while shielding users from the underlying system complexity
Data Source
AI summary
A graphical workflow definition and management tool enables administrators and other authorized users to implement a workflow process that can be used to evaluate project submissions or other applications that require step-by-step process completion. The steps required to navigate through the workflow are first defined. Inputs, outputs, and actions, including conditional criteria, can be specified for the steps. The flow of control between the individual steps in the workflow is mapped out; changes to the status of a project submission can cause a submission to migrate to a succeeding step in the workflow. A “sandbox” testing environment allows changes to any aspect of the workflow to be safely evaluated without affecting live data. Conflicts between production and test workflows are identified and intelligently resolved.