Systems and methods for providing an artificial intelligence-based planning assistant

A method for determining a travel itinerary comprising:

• receiving, from a user interface, a travel request from a user;
• deriving, using a first machine learning engine configured with a convolutional neural network trained to identify travel patterns from structured and unstructured data, the first machine learning engine trained and validated from customer profiles, travel preferences, commerce benefits, loyalty rewards, payment options, dining preferences, and historical itineraries, input travel data from a purchase history and a travel history;
• providing, by the first machine learning engine, the input travel data to a second machine learning engine implemented as a recurrent neural network optimized for sequential itinerary prediction;
• determining, by the second learning engine, travel services based on the input travel data;
• receiving, as output from the second machine learning engine, the determined travel services;
• formatting, by an integration module of a backend associated with a server comprising a distributed processing architecture that dynamically allocates computational resources based on itinerary complexity, a digital itinerary including the determined travel services as line-item recommendations for the user;
• verifying the digital itinerary using a check machine learning engine that compares the determined travel services to real world data and the travel request;
• formatting, by the integration module of the backend associated with a server, the digital itinerary including the determined travel services as line-item recommendations for a user;
• generating, by an image generation machine learning engine comprising a generative adversarial network (GAN) configured to synthesize user images at identified locations with reduced artifact generation and improved facial alignment, an image of a user associated with the user preference at each identified location, wherein each identified location is based on each of the determined travel services, the determined travel services comprising a hotel room and an event, wherein an adjustment comprises applying a spatial transformer network to align facial features with the destination background;
• determining a pose of the user based on a context of an activity associated with the destination, wherein the adjustment comprises generating the pose of the user based on the user's face and features in the image of the user with each identified location and reducing variance of the user images with each identified location;
• adjusting, by the image generation machine learning engine, the image to reduce variance of the image of the user and a destination image based on a perspective of a user's face in the image of the user and a view of the identified location in the destination image;
• generating, by an integration module, each predicted travel service between travel service locations identified based on each of the determined travel services and the identified locations, the predicted travel services each comprising a mode of transportation and a time for transportation, the mode of transportation comprising one or more of a taxi, a rental car, and a train;
• determining, by the check machine learning engine, a feasibility of each predicted travel service and an alternative feasibility for an alternative travel service based on one or more of budget, a time for an activity, an additional time required for the activity, a user profile, and an average delay;
• replacing, by the check machine learning engine, the each predicted travel service in the digital itinerary with the alternative travel service if the alternative feasibility for the alternative travel service exceeds the feasibility of the each predicted travel service;
• and
• presenting, by the integration module, the digital itinerary to the user via the user interface in real time.