RPA Failure Evaluation Using Successful Execution Step Matching
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Solution Overview
Problem
Current robotic process automation systems fail to detect and address changes in processes or screen configurations, leading to operational failures without determining the nature of the failure.
Innovation Solution
A system and method that includes a task queue database, a robotic process automation unit, a failed tasks queue database, and a failure evaluation processor to collect, record, and evaluate successful execution steps to provide selected execution steps that fix failed tasks, thereby updating the process to prevent future failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robotic process automation systems operate without failure detection mechanisms, then operational simplicity is maintained, but system reliability deteriorates due to undetected failures
Solution Approach 1:
The patent implements a feedback mechanism where the system automatically detects task execution failures, analyzes the failure causes by comparing against historical data, and triggers re-execution with corrective actions. This closed-loop feedback system continuously monitors and self-corrects failures, improving reliability without requiring constant human intervention.
Solution Approach 2:
The system performs self-diagnosis and self-correction by automatically detecting failures, analyzing root causes through comparison with historical successful executions, and implementing fixes without external assistance. The robotic process automation system serves itself by maintaining and updating its own execution patterns based on learned experiences.
2Measurement precision
If the system collects and analyzes detailed failure data, then failure detection accuracy improves, but data processing complexity increases
Solution Approach 1:
The system creates simplified copies or representations of complex execution patterns by storing successful task execution sequences as reference models. When failures occur, the system compares actual executions against these copied successful patterns to quickly identify deviations and root causes, reducing the complexity of analyzing detailed failure data.
Solution Approach 2:
The system pre-processes and stores historical successful execution data in structured formats before failures occur. This preliminary organization of data includes categorizing execution steps, parameters, and outcomes, so that when failures happen, the analysis can quickly reference pre-organized information rather than processing raw data from scratch.
3Productivity
If the system implements automatic failure fixing mechanisms, then productivity is improved by reducing manual intervention, but system complexity increases
Solution Approach 1:
The system dynamically adapts its behavior based on failure patterns and historical data. Rather than following rigid predefined rules, the automation adjusts its correction strategies in real-time by learning from past successes and failures, allowing it to handle diverse failure scenarios with a flexible, evolving decision-making process that improves productivity without excessive complexity.
Data Source
AI summary
A system and method for detecting and fixing robotic process automation failures, including collecting tasks from at least one client computerized device, processing the tasks via robotic process automation, collecting tasks that failed to complete per task type, recording successful execution steps per each of the failed tasks, evaluating the recorded successful execution steps with respect to the failed task types, and providing selected execution steps that best fix the failed tasks, thereby fixing the robotic process automation failures.