RPA Flow Generation With Conditional Branch Extraction
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Solution Overview
Problem
Current robotic process automation (RPA) technologies struggle to efficiently generate and optimize flows of operations for software robots, particularly in handling variations and conditional branches, leading to inefficiencies in task automation.
Innovation Solution
An information processing apparatus and method that identifies operation items from user interactions, generates operation components, compares flows to detect differences, and adds conditional branches to optimize the flow of operations for software robots, using a recording function to automate the generation of task sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration of operation flows is used, then flexibility and customization are improved, but time consumption and labor effort increase
Solution Approach 1:
The system performs automatic flow generation and optimization without requiring manual configuration. The operation flow generation unit automatically creates operation flows from recorded operations, and the flow optimization unit autonomously compares and optimizes multiple generated flows, eliminating the need for manual intervention while maintaining flexibility through automated adaptation to different operation patterns.
2Productivity
If simple operation flow generation is used, then speed of automation is improved, but ability to handle variations and conditional branches deteriorates
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate operation flows in advance using different generation methods. The flow optimization unit then compares these pre-generated flows and selects or combines the most appropriate ones, allowing the system to handle variations and conditional branches effectively while maintaining fast execution speed during actual automation operations.
3Reliability
If multiple flow generation methods are used, then optimization capability is improved, but system complexity increases
Solution Approach 1:
The system segments the flow generation process into distinct functional units: an operation flow generation unit that creates multiple candidate flows using different generation methods, and a flow optimization unit that compares and selects the optimal flow. This segmentation allows the system to achieve high optimization capability through multiple methods while managing complexity through modular architecture, where each unit has a specific responsibility.
Data Source
AI summary
An information processing apparatus includes circuitry. The circuitry displays one or more screens that receive an operation by a user. The circuitry identifies an operation item corresponding to the operation based on display information of the one or more screens, according to the operation by on the one or more screens. The circuitry generates an operation component associated with a condition corresponding to the operation item. The circuitry generates a flow of operations based on the operation component according to an order of operations by the user. The circuitry compares multiple flows with one another, to extract a difference between multiple operation components included in one of the multiple flows and multiple operation components included in another one of the multiple flows. The circuitry adds an operation component of a conditional branch before one of the multiple operation components for which the difference is extracted.