Dependency Graph Controller for Multi-Actor Workflow Orchestration
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Solution Overview
Problem
In multi-actor workflows, productivity is hindered by communication gaps and independent operations among actors, leading to cascading delays when issues arise, as human workers and robots may not coordinate effectively, resulting in incomplete tasks due to lack of knowledge about subtask dependencies and resource utilization.
Innovation Solution
A controller dynamically generates dependency graphs to model state transformations, allocates actors and resources efficiently, and adjusts tasks in real-time based on current states and feedback loops to optimize task completion, ensuring balanced usage and minimizing wait times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If actors operate independently without centralized coordination, then each actor has autonomy in performing subtasks, but productivity decreases due to communication gaps and cascading delays when issues arise
Solution Approach 1:
The patent introduces a centralized controller as an intermediary that coordinates actors without eliminating their autonomy. The controller generates dependency graphs that model subtask relationships and allocates actors to subtasks dynamically, enabling autonomous actors to work efficiently together by understanding their dependencies through the controller's coordination mechanism
2Productivity
If a centralized controller coordinates all actors, then productivity improves through optimized resource allocation and conflict minimization, but system complexity increases due to the need for dynamic dependency graph generation and real-time adjustments
Solution Approach 1:
The patent segments the control function into modular components: dependency graph generation, actor allocation, state transformation monitoring, and dynamic adjustment mechanisms. This segmentation allows the complex control system to be broken down into manageable, independent modules that can be implemented and maintained separately, reducing overall system complexity while maintaining coordination capabilities
3Reliability
If actors wait for issue resolution before proceeding, then task accuracy is maintained by avoiding errors, but time loss increases due to cascading delays affecting the entire workflow
Solution Approach 1:
The patent implements dynamic workflow adjustment where the controller continuously monitors actor states and dependency graph conditions. When issues are detected, the system dynamically reallocates actors to alternative subtasks or adjusts dependency relationships in real-time, allowing the workflow to adapt and continue rather than static waiting, thus reducing time loss while maintaining reliability through controlled error handling
Data Source
AI summary
A controller is provided to orchestrate operations of two or more different actors or resources in completing a task. The controller orchestrates the operations based on a dependency graph that the controller generates for each received task. The dependency graph has a linked list of nodes. Each node includes a different set of operations for completing a different part of a task represented by that dependency graph. Each set of operations associated with a node may be dependent on the successful completion of a prior set of operations associated with a prior linked node in the same dependency graph. The controller may allocate different sets of operations from different nodes of the dependency graph to different human or robotic resources, and may track the status of multiple tasks in parallel based on the progression through different nodes of the corresponding dependency graphs.