US-12620318-B2 - Artificial intelligence co-pilot for manned and unmanned aircraft

US12620318B2US 12620318 B2US12620318 B2US 12620318B2US-12620318-B2

Abstract

An Artificial Intelligence Co-pilot method and systems that encompasses a range of advanced technologies and methodologies, all focused on enhancing flight safety and efficiency. By integrating AI into the cockpit, we can significantly reduce the potential for human error and empower pilots to make better, more informed decisions in real-time. The AI Co-Pilot System is a sophisticated solution designed to support human pilots to Aviate, Navigate, and Communicate and in managing aircraft operations by processing voice commands and inquiries. It employs advanced (CHATGPT) Natural Language Processing (NLP) and Human-Machine Interface (HMI) technologies to establish seamless interaction between the pilot and the aircraft system, ultimately enhancing navigation, safety, and overall flight performance.

Inventors

Ricardo Alejandro Arteaga

Assignees

Ricardo Alejandro Arteaga

Dates

Publication Date: 20260505
Application Date: 20230409

Claims (7)

1 . An Artificial Intelligence (AI) Co-Pilot system designed to assist human pilots to aviate, navigate, communicate, and manage aircraft operations, comprising: a. a speech recognition module configured to receive and process voice commands from a pilot; b. a speech synthesis module configured to generate audible feedback and responses for the pilot; c. a natural language processing module, comprising an augmented fine-tuned large language transformer model (LLM) trained to contextually understand, process, and generate responses to aviation-specific language, communication methods, and safety issues by utilizing contextual information derived from ongoing pilot conversations and stored aviation safety datasets; d. a processor configured to manage speech recognition, text-to-speech conversion, text prompt generation, graphics processing, and command execution; e. an interface configured for integrating the AI Co-Pilot system with the aircraft's avionics and other systems; f. a training module configured to preprocess aviation specific-training data and fine-tune the LLM; and g. a deployment flight hardware kit configured to integrate the fine-tuned LLM transformer model into the AI Co-Pilot system.
2 . The AI Co-Pilot system of claim 1 , wherein the speech recognition module and speech synthesis module are integrated into the aircraft's cockpit to enable seamless and efficient interaction between the pilot and aircraft systems.
3 . The AI Co-Pilot system of claim 1 , wherein the natural language processing module comprises an augmented fine-tuned LLM transformer model, configured to interpret commands and/or inquiries from the pilot and to provide real-time decision support by leveraging data from the sensor suite to reduce the potential for human error.
4 . The AI Co-Pilot system of claim 1 , wherein the processor comprises CPU(s) and GPU(s) for efficient processing of speech recognition, text-to-speech conversion, text prompts, graphics processing, and command execution.
5 . The AI Co-Pilot system of claim 1 , wherein the training module is implemented in a cloud computing infrastructure and utilizes Aviation Safety Reporting System (ASRS) and Federal Aviation Administration (FAA) datasets for preprocessing, training, and fine-tuning the LLM transformer model.
6 . The AI Co-Pilot system of claim 1 , wherein the deployment flight hardware kit is configured to seamlessly integrate the fine-tuned LLM transformer model into the AI Co-Pilot system, thereby enhancing the system's capacity to comprehend and process aviation-specific information.
7 . A method for deploying an AI Co-Pilot system for an aircraft, comprising: a. integrating a speech recognition module and a speech synthesis module into the aircraft's cockpit; b. preprocessing an aviation-specific dataset and fine-tuning an LLM transformer model using a cloud computing infrastructure; c. deploying the fine-tuned LLM transformer model into the AI Co-Pilot system using onboard flight hardware to enable real-time onboard processing independent from external networks; d. interpreting voice commands from a pilot and generating audible feedback and operational responses using the onboard AI Co-Pilot system in real-time during flight operations; and e. providing real-time decision support to the pilot through the natural language processing module.

Description

ORIGIN OF THE INVENTION U.S. Non-Provisional patent application Ser. No. 18/132,417, filed 9 Apr. 2023. BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention discloses an Artificial Intelligence Co-pilot assistance to assist human pilots during complex and high-workload scenarios. More specifically, the invention is an Artificial Intelligence method and system for use by aircraft for manned and unmanned aircrafts. 2. Description of the Related Art Natural Language Processing (NLP): AI-based virtual co-pilots rely on advanced NLP techniques to understand and process pilot commands, facilitating seamless communication between pilots and the AI assistant. These systems can interpret spoken language, generate human-like responses, and even translate instructions into machine-readable formats for interaction with aircraft systems. Machine Learning (ML) and Deep Learning (DL): Virtual co-pilots employ ML and DL algorithms to continuously improve their performance, adapt to new situations, and make real-time decisions. These AI systems can learn from past experiences, identify patterns, and make recommendations based on the current context, ensuring that they provide relevant and timely decision support. Sensor Fusion and Data Analytics: AI assistant co-pilots integrate and process data from various sensors and aircraft systems to provide comprehensive situational awareness. By combining information from multiple sources, these systems can make more accurate and informed decisions, helping pilots navigate challenging scenarios and respond effectively to changing conditions. Human-Machine Interface (HMI): A key component of AI assistant co-pilot systems is the HMI, which facilitates interaction between pilots and the AI. Advanced HMI designs incorporate voice recognition, touch interfaces, and graphical displays to ensure that pilots can easily access and understand the AI's recommendations, enabling seamless collaboration and decision-making. SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to provide an AI assistant co-pilot method and systems encompasses a range of advanced technologies and methodologies, all focused on enhancing flight safety and efficiency. By integrating AI into the cockpit, we can significantly reduce the potential for human error and empower pilots to make better, more informed decisions in real-time. This breakthrough technology has the potential to revolutionize the aviation industry, paving the way for a new era of safer, smarter, and more reliable air travel. Another object of the present invention is to provide an AI Drone ChatGPT method and system for unmanned for autonomous swarm operations. BRIEF DESCRIPTION OF THE DRAWING(S) Other objects, features and advantages of the present invention will become apparent upon reference to the following description of the preferred embodiments and to the drawings, wherein corresponding reference characters indicate corresponding parts throughout the several views of the drawings and wherein: FIG. 1 is a conceptual view of an AI system that can understand, interpret, and process complex scenarios in real-time, providing valuable assistance to human pilots in accordance with an embodiment of the present invention; FIG. 2 is a use case diagram that represents a use case of a user (pilot) issuing a command or inquiry to the AI co-pilot in accordance with an embodiment of the present invention; FIG. 3 is a domain model for an NLP model interacting with an aircraft pilot could involve the following six entities in accordance with an embodiment of the present invention; FIG. 4 is an object diagram that depicts the instances of classes and their relationships at a specific point in time of a user (pilot) issuing a command or inquiry to the AI co-pilot in accordance with an embodiment of the present invention; FIG. 5 is a conditional activity diagram that illustrates the flow of control and data within a system of a user (pilot) issuing a command or inquiry to the AI co-pilot in accordance with an embodiment of the present invention; FIG. 6 is a sequence diagram that represents the interaction between objects in a time-ordered sequence of a user (pilot) issuing an inquiry to the AI co-pilot in accordance with an embodiment of the present invention; FIG. 7 is a concurrent state diagram that depicts the interactions of a user (pilot) issuing a command and/or inquiry to the AI co-pilot in accordance with an embodiment of the present invention. FIG. 8 is a recurrent activity diagram that models the behavior of the dynamic flow of the system from one task to another in response to events of a user (pilot) issuing a command or inquiry to the AI co-pilot in accordance with an embodiment of the present invention; FIG. 9 is a component diagram that illustrates the organization and dependencies of a system's components, including software modules, libraries, and subsystems of a user (pilot) making a command to th