ORBITA Framework: Task Mining and Large Language Model-Driven Intelligent RPA Recommendation System

This article introduces the innovative ORBITA framework, which integrates task mining technology and large language models to enable intelligent RPA recommendation and code generation. The framework aims to address the pain point of low efficiency in traditional RPA development. Empirical studies show that it can reduce development time by 40% and achieve a verification accuracy of 97.3%, driving automation technology toward a more intelligent and accessible direction.

Robotic Process Automation (RPA) is an important tool for enterprise digital transformation, but traditional development relies on professionals to manually analyze processes and write scripts, which is time-consuming and error-prone. The emergence of the ORBITA framework is precisely to address this core challenge: by combining task mining and large language models, it enables intelligent recommendation and automatic generation of RPA.

ORBITA is an intelligent automation recommendation framework with a six-layer architecture, adopting a closed-loop process of "Discovery-Recommendation-Generation". The six layers include: 1. Data Collection Layer (collects heterogeneous user interaction data); 2. Task Mining Layer (extracts business process models); 3. Pattern Recognition Layer (classifies and clusters tasks); 4. Recommendation Engine Layer (generates personalized automation suggestions); 5. Code Generation Layer (LLM generates RPA scripts); 6. Verification and Governance Layer (ensures quality and compliance).

ORBITA deeply integrates large language models and uses a Retrieval-Augmented Generation (RAG) architecture to reduce code "hallucinations". The LLM plays three roles: 1. Natural Language Understanding (converts business requirements into structured requirements); 2. Code Generation (generates RPA scripts based on task mining results); 3. Document Generation (automatically creates instruction documents and maintenance manuals). RAG combines domain knowledge bases to improve code accuracy.

Empirical evaluations of ORBITA in four domains including invoice processing and data entry show: an average reduction of 40% in development time (a one-week project delivered in three days), a verification accuracy of 97.3% for generated code; non-technical users can independently complete 60% of simple automation tasks, enabling the "citizen developer" model.

Challenges faced by ORBITA and their solutions: 1. Data Privacy: Uses federated learning and differential privacy to protect user data; 2. Complex Processes: Introduces a human-machine collaboration mechanism to intervene in key links; 3. Platform Compatibility: Supports mainstream RPA platforms such as UiPath through an abstraction layer and code converter.

ORBITA promotes RPA from manual development to intelligent generation, democratizing automation technology. For enterprises, it accelerates digital transformation; for vendors, it emphasizes AI-native functions; for practitioners, it requires skill upgrading. In the future, it will integrate multimodal LLMs and reinforcement learning, evolving toward Intelligent Process Automation (IPA).