AI Workflow Extraction for Faster RPA Process Discovery
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Solution Overview
Problem
Current robotic process automation (RPA) techniques are inefficient in identifying and automating repetitive tasks, as they rely on costly and time-consuming manual logging and human review, which can be inaccurate, making it difficult to discover and improve suitable automation processes.
Innovation Solution
An AI-based system that deploys listener applications on user computing systems to generate logs of user interactions, which are then analyzed by AI layers to identify potential RPA processes, automatically generating workflows and robots to automate these processes without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual logging and human review are used to identify automation processes, then process identification can be performed, but it is costly and time-consuming
Solution Approach 1:
The patent replaces the mechanical system of manual logging and human review with an automated AI-based system that uses machine learning models to analyze user interactions and identify automation opportunities. This substitution eliminates the time-consuming human review process while maintaining or improving identification accuracy through automated pattern recognition.
Solution Approach 2:
The system enables self-service by allowing the AI model to autonomously analyze log data, identify repetitive tasks, and generate automation workflows without requiring human intervention. The system serves itself by automatically processing and interpreting the data to discover automation opportunities.
2Measurement precision
If manual logging and human review are used to identify automation processes, then process identification can be performed, but it is costly
Solution Approach 1:
The patent replaces expensive human review resources with automated AI-based analysis that processes log data through machine learning models. This substitution significantly reduces the cost of process identification while maintaining high accuracy through automated pattern recognition and classification algorithms.
Solution Approach 2:
The system uses computationally efficient AI models that can be rapidly trained and deployed on existing infrastructure, replacing the need for expensive human expertise. The automated system provides a cost-effective alternative by using standard computing resources rather than human labor for analysis.
3Ease of operation
If human reviewers analyze user actions, then process identification can be performed, but the reviewer's account may not be accurate
Solution Approach 1:
The patent replaces human reviewers with automated AI-based analysis that objectively processes log data without subjective interpretation errors. The machine learning models consistently identify patterns and processes with high accuracy, eliminating the inconsistency and potential inaccuracy of human review while maintaining ease of operation through automated execution.
Data Source
AI summary
Artificial intelligence (AI)-based process identification, extraction, and automation for robotic process automation (RPA) is disclosed. Listeners may be deployed to user computing systems to collect data pertaining to user actions. The data collected by the listeners may then be sent to one or more servers and be stored in a database. This data may be analyzed by AI layers to recognize patterns of user behavioral processes therein. These recognized processes may then be distilled into respective RPA workflows and deployed to automate the processes.