AI-powered system for predicting flight disruptions for airline mobile applications

Abstract

A system (100) for agent-based detection of semantic variations in the life cycle of documents, consisting of: a data acquisition module configured to collect real-time and historical data relating to flight operations, weather conditions, air traffic, airport operations and airline resources; a data processing module configured to normalize, validate, and preprocess the collected data; an artificial intelligence-based analysis engine configured to analyze the processed data and generate predictions of potential flight disruptions, including at least delays, cancellations, diversions, or flight schedule changes; and a communication and integration module configured to transmit the forecast results to a mobile application of the airline, the system (100) enables proactive notification and management of flight disruptions before they occur.

Description

[0001] The present invention relates to the field of artificial intelligence-based aviation support systems and mobile software applications. In particular, the invention relates to an AI-controlled system that is integrated into mobile applications of airlines and serves to predict potential flight disruptions such as delays, cancellations, diversions, and flight schedule changes.

[0002] Air travel is a vital part of modern transportation, yet disruptions such as delays, cancellations, gate changes, and diversions continue to affect passengers and flight operations daily. These disruptions can be caused by a variety of factors, including adverse weather conditions, airspace congestion, technical malfunctions, flight crew shortages, and operational limitations at airports. Existing airline systems and mobile applications largely follow a reactive approach, informing passengers only after a disruption has occurred, often resulting in inconvenience, uncertainty, missed connections, and reduced customer satisfaction.Given the increasing complexity of flight operations and the growing availability of real-time and historical aviation data, traditional rule-based and manual monitoring systems are no longer sufficient to effectively predict disruptions. While airline mobile applications provide real-time flight status updates, they typically lack the predictive intelligence to alert passengers and flight crew in advance. Therefore, there is a clear need for an AI-powered solution capable of analyzing diverse data sources to proactively predict potential flight disruptions and deliver timely, actionable insights via airline mobile applications, thereby improving operational efficiency and enhancing the overall passenger experience.

[0003] To solve this problem, the present invention offers an AI-controlled system for predicting flight disruptions for mobile airline applications.

[0004] The system aims to provide an AI-driven mechanism to predict potential flight disruptions, including delays, cancellations, diversions and flight schedule changes, before such disruptions occur.

[0005] The system is designed to analyze real-time and historical data on flight operations, weather conditions, air traffic and airport restrictions in order to make accurate and timely predictions of disruptions.

[0006] The system is designed to integrate seamlessly into airline mobile applications to deliver forecasts and notifications directly to passengers and flight crew.

[0007] The system is designed to enable airlines to make proactive decisions by identifying disruption risks early on, thereby improving operational planning and resource management.

[0008] The system aims to improve the passenger experience by reducing uncertainty and inconvenience through early communication of potential disruptions and suggested alternatives.

[0009] The system is configured to support personalized and context-related notifications based on passengers' travel routes, preferences, and travel conditions.

[0010] The system is intended to reduce operating and customer service costs associated with managing last-minute disruptions and manual interventions.

[0011] The system aims to improve the overall reliability, efficiency and responsiveness of air services by shifting disruption management from a reactive to a predictive approach.

[0012] The present invention provides a system for predicting flight disruptions using artificial intelligence and for integrating such predictions into airline mobile applications. The system is configured to collect and process real-time and historical data from multiple sources, including flight operations data, weather information, air traffic conditions, airport restrictions, and operational schedules. Using advanced AI and machine learning models, the system analyzes these data inputs to identify patterns and risk indicators associated with potential disruptions such as delays, cancellations, diversions, and schedule changes.

[0013] Furthermore, the system generates predictions that represent the probability, nature, and expected impact of a potential disruption and transmits these predictions to airline mobile applications via secure interfaces. Based on these predictions, the system enables proactive alerts, personalized notifications, and recommended actions for passengers and airlines. By directly embedding predictive intelligence into airline mobile platforms, the invention allows airlines to transition from reactive disruption handling to proactive management, thereby improving operational efficiency, reducing uncertainty, and enhancing the overall passenger travel experience. Fig. : shows an illustrative block diagram of an AI-driven system for predicting flight disruptions for mobile airline applications.

[0014] Fig.Figure 1 shows an illustrative block diagram of an AI-powered flight disruption prediction system (100) for airline mobile applications. The system (100) includes a data acquisition module configured to receive input from multiple data sources, including flight operations data, weather conditions, air traffic information, airport operations data, and airline resource data. The collected data is forwarded to a data processing module that cleans, normalizes, and structures the data to prepare it for analysis. The processed data is then analyzed by an AI-based analytics engine that applies machine learning models to predict potential flight disruptions.The forecast results are transmitted via a communication and integration module to a mobile application of the airline, enabling proactive warnings, notifications and actionable insights for passengers and airlines.

[0015] The present invention relates to an AI-controlled system (100) for predicting flight disruptions and integrating such predictions into mobile applications of airlines. The system (100) is configured to collect real-time and historical data from multiple sources, including flight plans, aircraft status, crew availability, air traffic conditions, airport operations, and weather information. A data processing module cleans, normalizes, and structures the collected data to ensure its accuracy and consistency before analysis.

[0016] The system (100) further includes an AI analytics engine trained on historical disruption data to identify patterns and risk indicators related to delays, cancellations, diversions, and schedule changes. Based on live operational data, the system (100) generates predictions such as disruption probabilities, estimated delay durations, and impact levels for individual flights. These predictions are dynamically updated and can be contextualized using passenger travel plans, connecting dates, and operational priorities to enable personalized and actionable insights.

[0017] The system (100) also includes a secure integration and communication layer that connects the prediction platform with airlines' mobile applications and backend systems. Through this interface, the system (100) delivers early warnings, notifications, and recommended actions to passengers and airlines in near real-time. Furthermore, the system (100) is capable of continuous learning by incorporating real-world results and feedback to refine its prediction models, thereby improving accuracy, operational efficiency, and the overall passenger experience over time.

Claims

[1] A system (100) for agent-based detection of semantic variations in the life cycle of documents, consisting of: a data acquisition module configured to collect real-time and historical data relating to flight operations, weather conditions, air traffic, airport operations and airline resources; a data processing module configured to normalize, validate, and preprocess the collected data; an artificial intelligence-based analysis engine configured to analyze the processed data and generate predictions of potential flight disruptions, including at least delays, cancellations, diversions, or flight schedule changes; and a communication and integration module configured to transmit the forecast results to a mobile application of the airline, the system (100) enables proactive notification and management of flight disruptions before they occur. [2] System (100) according to claim 1, wherein the artificial intelligence-based analysis engine uses machine learning models trained on historical disruption data to identify patterns and risk indicators related to flight disruptions. [3] System (100) according to claim 1, wherein the prediction results include disruption probability values, estimated delay durations and impact levels for individual flights or flight segments. [4] System (100) according to claim 1, wherein the communication and integration module provides personalized alerts and recommendations to passengers via the airline's mobile application based on the passengers' travel routes and travel context. [5] System (100) according to claim 1, wherein the system (100) is configured to continuously improve the prediction accuracy by incorporating actual disturbance results and feedback into the artificial intelligence-based analysis engine. [6] System (100) according to claim 1, wherein the system (100) supports proactive operational decisions of the airline staff by early detection of disruption risks.

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