Automated Task Graph Merging for Robotic Process Automation
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Solution Overview
Problem
Manual merging of variants of automatable tasks in robotic process automation (RPA) is a time-consuming and cumbersome process, hindering efficiency and productivity.
Innovation Solution
The development of systems and methods for automatically merging task graphs representing variants of automatable tasks into a single merged task graph using machine learning-based models to identify similarities and decision points, thereby accelerating RPA development and covering nearly all scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual merging of task graph variants is performed, then accuracy and completeness of task representation is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-service by automatically merging task graph variants using machine learning algorithms. The automated merging mechanism analyzes similarities between task graphs and consolidates them without requiring manual user intervention, thereby reducing time consumption while maintaining representation accuracy through algorithmic comparison and integration of task sequences, decisions, and loops
Solution Approach 2:
The patent replaces the mechanical manual merging process with an automated machine learning-based system. The machine learning model substitutes human cognitive processing with computational algorithms that automatically identify similarities, merge task graphs, and generate unified task representations, eliminating the time-consuming manual operation while preserving merging accuracy
2Adaptability or versatility
If manual merging of task graph variants is performed, then completeness of automatable task coverage is improved, but ease of operation deteriorates
Solution Approach 1:
The automated merging system performs self-service by autonomously analyzing and consolidating multiple task graph variants. The machine learning algorithm automatically identifies common patterns, decisions, and loops across different task graphs, generating comprehensive task representations without requiring users to manually compare and integrate multiple variants, thereby maintaining completeness while significantly improving ease of operation
Solution Approach 2:
The patent replaces the complex manual operation of comparing and merging multiple task graph variants with an automated machine learning system. The computational algorithms automatically analyze task graph structures, identify similarities and differences, and generate unified representations, eliminating the cumbersome manual process while preserving comprehensive task coverage across all variants
Data Source
AI summary
Systems and methods are provided for merging task graphs representing variants of an automatable task into a merged task graph. A plurality of task graphs each representing a variant of an automatable task is received. Similarities between the plurality of task graphs are identified. The plurality of task graphs is merged into a merged task graph of the automatable task based on the identified similarities. The merged task graph is output.