Vision-based generation of navigation workflow for automatically filling application forms using large language models

Abstract

Robotic Process Automation (RPA) systems face challenges in handling complex processes and diverse screen layouts that require advanced human-like decision-making capabilities. These systems typically rely on pixel-level encoding through drag-and-drop or automation frameworks such as Selenium to create navigation workflows, rather than visual understanding of screen elements. Present disclosure provides systems and methods that implement large language models (LLMs) coupled with deep learning based image understanding which adapt to new scenarios, including changes in user interface and variations in input data, without the need for human intervention. System of the present disclosure uses computer vision and natural language processing to perceive visible elements on graphical user interface (GUI) and convert them into a textual representation. This information is then utilized by LLMs to generate one or more navigation workflows that include a sequence of actions that are executed by a scripting engine/code to complete an assigned task from a task-request.