Task Mining Engine for Cross-System RPA Process Automation
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Solution Overview
Problem
Conventional operating and software systems lack the capability to provide an automation experience for data and process mining across distinct and separate operating and software systems.
Innovation Solution
A task mining engine is implemented to integrate robotic process automation (RPA) into a computing environment, allowing for task and process mining by clustering user activities into steps and extracting sequences to mimic user interactions, thereby generating automation code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional operating and software systems are used for data and process mining, then basic data analysis can be performed, but automation experience across distinct and separate systems cannot be provided
Solution Approach 1:
The task mining engine is designed to operate across multiple distinct operating and software systems, providing universal automation capabilities. It can record, cluster, and extract tasks from various systems (Windows, macOS, web browsers, mobile devices) using a single unified platform, enabling cross-system automation without requiring system-specific implementations.
Solution Approach 2:
The task mining engine acts as an intermediary layer between users and multiple operating systems. It captures user interactions at the interface level rather than requiring deep integration into each specific system, allowing it to mine and automate tasks across different platforms without being constrained by individual system architectures.
2Productivity
If manual user activities are performed across multiple systems, then flexibility and adaptability are maintained, but efficiency and productivity are reduced
Solution Approach 1:
The system performs self-service by automatically recording user tasks, clustering them into meaningful steps, and generating automation code without requiring manual programming. The task mining engine observes user interactions, processes the data through clustering algorithms, and produces ready-to-execute automation scripts, eliminating the need for manual automation development while preserving operational flexibility.
Solution Approach 2:
The system performs preliminary analysis by recording and clustering user tasks before automation is implemented. This preliminary action captures the existing manual processes, identifies patterns and repetitions, and prepares automation code in advance, allowing the system to transition from manual to automated operation without disrupting workflow flexibility.
3Adaptability or versatility
If task mining is performed across distinct operating and software systems, then comprehensive automation coverage is achieved, but system complexity increases
Solution Approach 1:
The task mining engine segments the automation process into distinct modular components: task recording, task clustering, step extraction, and code generation. Each component handles specific aspects of the mining process independently, making the overall system manageable despite cross-platform complexity. The segmentation allows each module to be optimized for specific operating systems while maintaining a unified architecture.
Data Source
AI summary
Disclosed herein is a method implemented by a task mining engine. The task mining engine is stored as processor executable code on a memory. The processor executable code is executed by a processor that is communicatively coupled to the memory. The method includes receiving recorded tasks identifying user activity with respect to a computing environment and clustering the recorded user tasks into steps by processing and scoring each recorded user task. The method also includes extracting step sequences that identify similar combinations or repeated combinations of the steps to mimic the user activity.