Robotic Script Generation Using Process Variation Detection
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Solution Overview
Problem
Current software applications require repetitive and error-prone manual GUI interactions, which are time-consuming and heavily reliant on human decision-making.
Innovation Solution
A robotic script generation system using Artificial Neural Networks (ANNs), specifically Long Short-Term Memory (LSTM) networks, to capture and analyze user interactions, determine process variations, and generate automated scripts to perform activities efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual GUI interactions are used for repetitive tasks, then human decision-making abilities can handle complex processes, but time consumption and error rates increase significantly
Solution Approach 1:
The system enables self-service automation by capturing user interactions and automatically generating robotic scripts that can independently execute repetitive tasks without continuous human intervention, thereby reducing both time consumption and error rates while maintaining the ability to handle complex decision-making processes
Solution Approach 2:
The system creates copies of user interaction patterns by capturing and analyzing manual GUI interactions, then uses these captured patterns to generate automated robotic scripts that replicate the same tasks with higher precision and speed, eliminating human errors while reducing time consumption
2Productivity
If robotic automation is implemented, then time and resource consumption decrease, but the system requires complex training processes using Artificial Neural Networks
Solution Approach 1:
The system performs preliminary actions by capturing and storing user interaction patterns during the training phase, allowing the Artificial Neural Network to learn and prepare automated responses in advance, which simplifies the deployment process and reduces the apparent complexity during actual automation execution
Solution Approach 2:
The system implements feedback mechanisms where the captured user interactions are continuously analyzed and used to refine the robotic scripts through Neural Network training, creating a self-improving system that manages complexity by learning from actual usage patterns rather than requiring manual programming of every scenario
3Measurement precision
If automated robotic scripts are generated, then repetitive tasks can be executed with high accuracy, but the initial setup and training process becomes more complex
Solution Approach 1:
The system uses self-service automation by automatically capturing user interactions and generating training data without requiring manual annotation or programming, which reduces training complexity while maintaining high execution accuracy through learned patterns from actual user behavior
Solution Approach 2:
The system copies actual user interaction patterns to create training datasets and automated scripts, ensuring high execution accuracy by replicating proven human decision-making patterns while reducing training complexity by eliminating the need for manual script writing or pattern definition
Data Source
AI summary
Techniques for generating Robotic Scripts via Process Variation Detection are described. In one example, captured process steps related to an activity performed in an application may be received. Variations of the process steps are then determined by training a first Artificial Neural Network (ANN) with the captured process steps. A set of the process steps for performing the activity, may then be determined based on the determined variations of the process steps. Robotic scripts may be generated using the determined set of process steps to perform the activity.