An AI-powered automation system for intelligent computer control

Abstract

AI-supported automation system (100) for intelligent computer control, comprising the following: a speech-to-text module configured to receive a user's speech input and convert the input into a text command using an offline speech recognition engine; a real-time screen analyzer configured to capture and analyze the current screen state of a computer device using deep learning-based computer vision models to identify user interface (UI) elements; an agent module configured to: receives the text command and screen data; establishes a task plan based on user intent and UI context; the resolution of ambiguities by querying additional data or clarification by the user; an action command generator configured to dynamically generate executable action modules based on the system state and the plan specified by the agent; an automated action executor configured to execute the aforementioned action modules by interacting with the UI elements or the system backend; an execution confirmation engine configured to verify the successful completion of the executed task by re-evaluating the screen state after execution; an error handling engine configured to analyze failed executions, retry modified action plans, or prompt the user for additional information; an enhancement learning engine configured to improve system performance over time by adjusting future actions based on the success or failure of previous tasks; The entire system operates locally on the user's device without transferring user data to cloud-based services.

Description

[0001] The present invention relates to intelligent system automation, in particular a dynamic and secure offline automation system based on artificial intelligence, which is capable of interpreting user commands, recognizing screen elements in real time and autonomously executing tasks using computer vision, natural language processing and enhanced learning.

[0002] Currently, automation systems rely heavily on predefined workflows, which significantly limits their adaptability to real-time changes in the user interface and evolving user needs. Many popular voice assistants, such as Apple Siri, Google Assistant, and Microsoft Cortana, are restricted to executing predefined commands and cannot dynamically interact with the system's graphical user interface. These assistants can execute simple commands like opening applications or retrieving information, but they cannot interpret complex UI elements or adaptively respond to changing screen states. Existing automation solutions, such as AutoHotkey and Robotic Process Automation (RPA) frameworks, require manual scripting and predefined workflows.This approach reduces flexibility because automation is limited to fixed sequences of actions that do not dynamically adapt to real-time system feedback. If the user interface layout changes or an unexpected element appears, these systems cannot adapt without manual reprogramming. Therefore, there remains a need in engineering for an intelligent automation system that can interpret dynamic user interfaces in real time, adapt to changing screen environments, and execute complex tasks based on natural language commands—without relying on predefined scripts or cloud-based processing.

[0003] To solve this problem, the present invention offers an AI-supported automation system for intelligent computer control.

[0004] The system is configured to operate completely offline, using only a single local API for speech-to-text conversion. This ensures that no user data is transferred to external servers or cloud platforms, guaranteeing complete data privacy and autonomy.

[0005] The system eliminates the risks associated with cloud-based AI solutions – such as data leaks, unauthorized access and data breaches – by running all functions locally.

[0006] The system protects sensitive information, including login credentials, financial documents, and private communications.

[0007] The system includes a secure and self-contained automation framework that gives users complete control over their data and the automation process, without requiring an internet connection or third-party infrastructure.

[0008] The system is capable of executing dynamic and interactive user commands in real time. It interprets the user's intent and adjusts the execution accordingly.

[0009] The system integrates real-time screen reading and recognition of graphical user interface (GUI) elements, enabling it to dynamically understand and respond to the user's operating environment.

[0010] The system performs a context-based UI analysis and an intelligent screen analysis to identify actionable interface elements and appropriate responses.

[0011] The system dynamically generates action modules that can be executed in real time and are contextually tailored to the current screen state and the user's intent.

[0012] In one embodiment, the present invention provides an AI-powered automation system designed to dynamically execute complex user commands, leveraging real-time screen parsing, adaptive learning, and natural language understanding. Unlike conventional automation tools that rely on rigid, predefined workflows, the disclosed system uses deep learning-based computer vision models to interpret UI elements and reinforcement learning algorithms to adapt its behavior over time.It accepts natural language voice input, interprets system state and visual context, and generates executable action modules dynamically-enabling it to respond to changes in user interfaces and carry out multi-step tasks without predefined scripts.

[0013] Another embodiment of the present invention involves providing an AI-powered automation system for intelligent computer control that operates entirely offline, ensuring that all processing, decision-making, and execution operations take place locally on the user's device. The system enhances user data security and privacy by eliminating reliance on cloud-based processing. Furthermore, the system provides real-time visual feedback via screen overlays, increasing user transparency and trust. By combining real-time adaptability, enhanced learning, dynamic action generation, NLP-based command interpretation, and secure offline processing, the system represents a significant advancement in intelligent system control and user-centric automation.

[0014] The invention is explained again below with reference to the figures. These show: Fig. : the block diagram of an AI-supported automation system for intelligent computer control. Fig. : the flowchart of an AI-supported automation system for intelligent computer control.

[0015] The present invention relates to an AI-powered automation system (100) capable of interpreting and executing dynamic, multi-stage user commands through real-time interaction with a computer interface. The invention overcomes the limitations of pre-scripted automation by integrating computer vision, natural language processing (NLP), and augmented learning to control the intelligent system (100) entirely offline. The system (100) comprises a speech-to-text module, a real-time screen analyzer, an agent module, an action command generator, an automated action executor, an execution confirmation engine, an error handling engine, and an augmented learning engine. The speech-to-text module is configured to receive a user's speech input and convert it into a text command using an offline speech recognition engine.The real-time screen analyzer is configured to capture and analyze the current screen state of a computer device using deep learning-based computer vision models to identify user interface (UI) elements. The agent module is configured to receive the text command and screen data, establish a task plan based on user intent and UI context, and resolve ambiguities by requesting additional data or clarification from the user. The action command generator is configured to dynamically generate executable action modules based on the system state and the plan established by the agent. The automated action executor is configured to execute these action modules by interacting with the UI elements or the system backend.The execution confirmation engine is configured to verify the successful completion of the executed task by re-evaluating the screen state after execution. The error handling engine is configured to analyze failed executions, retry modified action plans, or prompt the user for additional information. The reinforcement learning engine is configured to improve system performance over time by adjusting future actions based on the success or failure of previous tasks.

[0016] Fig.Figure 101 shows the flowchart of an AI-powered automation system for intelligent computer control. The system operates entirely offline and uses a local speech-to-text API to convert user voice commands into structured text without transferring data to external servers or cloud platforms, thus ensuring complete security and autonomy of user data. At its core, the system processes user input in real time, starting with a voice command (e.g., "Open my emails and search for invoices"), which is captured via a microphone and converted into text using natural language processing (NLP) models within the speech-to-text module.Once the command is transcribed, the real-time screen analyzer interprets the system's current visual context using deep learning-based computer vision to identify and classify visible GUI elements such as buttons, text fields, and icons. This contextual data, along with the user's prompt, is sent to the agent module—the system's AI brain. The agent processes both the voice command and the screen context to generate a coherent action response. It can dynamically request further context or ask for clarification in case of unclear input, ensuring an intelligent and adaptive response mechanism. The generated response is then passed to the action command generator, which dynamically creates executable action modules based on the real-time screen conditions and the analyzed user intent.These modules are executed by the automated action executor, which interacts with the system interface through simulated inputs such as mouse clicks, keystrokes, or direct backend calls. After the task is executed, the execution confirmation engine assesses its success by re-evaluating the current system state to verify task completion. If successful, the process terminates; otherwise, a command error handler is triggered to investigate issues such as missing UI elements or unclear instructions. The agent then revises its strategy or prompts the user for clarification.

[0017] The system (100) incorporates a robust feedback mechanism via a user feedback handler that informs the user of the outcome either visually (on the screen) or audibly via a text-to-voice module, thus completing the interaction loop. Reinforcement learning further enhances the system's adaptability by continuously improving the agent's performance based on past successes and failures. Over time, this learning process enables more efficient and accurate task execution. To improve transparency, the system also provides real-time visual overlays of the recognized UI elements on the screen, allowing users to see the interface components with which the AI ​​is interacting.By combining dynamic screen parsing, intelligent action planning, real-time execution, adaptive learning and privacy-friendly architecture, the system (100) redefines the landscape of intelligent automation and offers a transformative alternative to existing cloud-based automation and voice assistant technologies. Reference symbol list 100 System

Claims

[1] AI-supported automation system (100) for intelligent computer control, comprising the following: a speech-to-text module configured to receive a user's speech input and convert the input into a text command using an offline speech recognition engine; a real-time screen analyzer configured to capture and analyze the current screen state of a computer device using deep learning-based computer vision models to identify user interface (UI) elements; an agent module configured to: receives the text command and screen data; establishes a task plan based on user intent and UI context; the resolution of ambiguities by querying additional data or clarification by the user; an action command generator configured to dynamically generate executable action modules based on the system state and the plan specified by the agent; an automated action executor configured to execute the aforementioned action modules by interacting with the UI elements or the system backend; an execution confirmation engine configured to verify the successful completion of the executed task by re-evaluating the screen state after execution; an error handling engine configured to analyze failed executions, retry modified action plans, or prompt the user for additional information; an enhancement learning engine configured to improve system performance over time by adjusting future actions based on the success or failure of previous tasks; The entire system operates locally on the user's device without transferring user data to cloud-based services. [2] System (100) according to claim 1, wherein the speech-to-text module uses an offline voice recognition model that protects privacy. [3] System (100) according to claim 1, wherein the real-time screen analyzer uses convolutional neural networks (CNNs) or transformer-based imaging models to detect user interface components. [4] System (100) according to claim 1, wherein the agent module uses a natural language processing (NLP) model to analyze and interpret multi-level, context-dependent user commands. [5] System (100) according to claim 1, wherein the action command generator creates command sequences in real time, thereby eliminating the dependence on pre-written workflows. [6] System (100) according to claim 1, wherein the error handling system is configured to log error cases and suggest alternative task execution paths based on previous experience. [7] System (100) according to claim 1, wherein the reinforcement learning machine updates its internal strategy based on reward signals corresponding to execution success, execution time or user satisfaction.

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