System and method to plan and book flights utilizing artificial intelligence

A computer-implemented method to plan and book flights utilizing artificial intelligence from unstructured user input with a large language model (LLM), the method comprising:

• receiving, at a computer system, an input flight travel query reflecting a user intention;
• wherein a machine learning model has been trained based on user data of a user's flight preferences, as well as flight preferences based on metadata of that user, such as location of the user, time of the flight travel query, income level, flight destination and flight departure location, flight schedules and many variables relating to flights;
• analyzing the input flight travel query using the trained machine learning model;
• generating possible flights in terms of dates, airports, nonstop or not, fastest time and number of stops based on the input flight travel query, external context, and additional structured and unstructured flight attributes as specified in appendix FLIGHT ATTRIBUTES, and the user's flight preferences;
• wherein the machine learning model utilizes a natural language understanding and probabilistic model;
• and
• wherein training data is from all available flights from an origin city of the flight travel query to the destination city of the flight travel query, and from all available searches of flights conducted by the user.

A computer-implemented system to plan and book flights utilizing artificial intelligence from unstructured user input with a small language model (“SLM”), the method comprising:

• receiving, at a computer system, an input flight travel query reflecting a user intention;
• wherein a machine learning model has been trained based on user data of a user's flight preferences, as well as flight preferences based on metadata of that user, such as location of the user, time of the flight travel query, income level, flight destination and flight departure location, flight schedules and many variables relating to flights;
• analyzing the input flight travel query using the trained machine learning model;
• generating possible flights in terms of dates, airports, nonstop or not, fastest time and number of stops based on the input flight travel query, external context, and additional structured and unstructured flight attributes as specified in appendix FLIGHT ATTRIBUTES,
• and the user's flight preferences;
• and
• wherein the machine learning model utilizes a natural language understanding and probabilistic model.