AI-Powered Process Discovery and Automation Identification System for RPA

Abstract

This invention relates to an AI-driven system for automated process discovery in robotic process automation (RPA). The system passively monitors user interactions, including keystrokes, mouse clicks, and window activities, to detect repetitive workflows. An AI-based engine processes the collected data to identify patterns and rank automation feasibility. The system presents findings via a web portal, allowing business analysts to review recommended processes for automation. This approach enhances scalability, accuracy, and cost-effectiveness in automating business workflows.

Description

1 Description: 1. Introduction The invention provides an AI-driven method to analyze user interactions and recommend RPA-based automation solutions. Unlike traditional process mining tools that rely on log-based analysis, this system captures real-time behavior and applies machine learning to discover automation opportunities dynamically. The system does not generate RPA scripts but instead identifies highly automatable workflows. 2. System Architecture The system comprises the following modules: A. Data Capture Module ● Tracks user clicks, keystrokes, form submissions, and active window content. ● Captures timestamps to create a chronological sequence of actions. ● Collects screenshots or HTML metadata to provide contextual data. ● Supports data collection from multiple sources, such as enterprise applications, CRM systems, ERP tools, and email clients. ● Monitors user interactions not only in web browsers but also in any desktop application, ensuring comprehensive process discovery across all software environments. B. AI Process Discovery Engine ● Applies machine learning algorithms to detect repeated workflows. ● Uses unsupervised learning (e.g., clustering) to group similar actions. ● Filters noise and irrelevant interactions to refine process discovery. C. Automation Scoring System ● Assigns a feasibility score based on: ○ Repetition frequency (e.g., how often an action occurs). ○ Complexity (e.g., how many steps the process involves). ○ Variability (e.g., level of decision-making required). D. Recommendation Engine & Web Portal ● Suggests automation candidates ranked by feasibility score.  2 ● Provides an interactive web portal where business analysts can review automation opportunities. ● Displays AI-generated insights to help decision-makers prioritize automation. 3. Workflow Execution 1. The system records user actions in the background. 2. The AI engine analyzes interaction sequences to detect patterns. 3. The automation scoring system prioritizes processes based on feasibility. 4. The recommendation engine suggests workflows suitable for automation. 5. The system presents automation insights via the Web Portal.

Claims

1 Claims:

1. A system for automated process discovery, comprising: ○ A data capture module to collect user interaction data from multiple sources. ○ An AI-based process discovery engine to analyze user behavior. ○ An automation feasibility scoring system to rank tasks for RPA suitability. ○ An output module that presents insights via an interactive web portal.

2. The method of claim 1, wherein: ○ AI models utilize supervised and unsupervised learning for process analysis. ○ Pattern recognition algorithms detect repetitive workflows. ○ Automated dashboards allow users to review suggested automation processes.

3. The system of claim 1, further comprising: ○ A dashboard for RPA consultants to visualize detected processes. ○ A security layer ensuring compliance with enterprise data policies. ○ Real-time monitoring capabilities for continuous process analysis.