Interactive Generative AI Chat for Personalized Travel Recommendations
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Solution Overview
Problem
Existing reservation management systems struggle to provide personalized travel itinerary recommendations to new or sporadic users, requiring numerous search queries and leading to increased compute-intensive database calls, reduced CPU performance, and increased data latency.
Innovation Solution
Implement a computer-implemented method using generative artificial intelligence to classify user intents from chat messages, generate travel itinerary recommendations, and reduce the need for individual search queries by leveraging machine-learning models and natural language processing to provide contextual responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based search engines are used to provide travel itinerary recommendations, then users who already know specific destinations can find their desired travel items, but new or sporadic users cannot receive personalized recommendations and the system requires numerous search queries
Solution Approach 1:
The system uses generative AI to automatically analyze user profiles, travel history, and preferences to generate personalized itinerary recommendations without requiring users to manually search or specify detailed criteria. The AI autonomously creates customized travel plans based on inferred user intent.
Solution Approach 2:
The system pre-generates travel itinerary recommendations by analyzing user data and predicting potential travel interests before users perform any search queries. This allows new users to receive personalized suggestions immediately upon first use, eliminating the need for multiple exploratory searches.
2Reliability
If rule-based search engines require users to perform multiple individual search queries to find suitable travel itineraries, then comprehensive search coverage is achieved, but CPU performance decreases and data latency increases
Solution Approach 1:
The system merges multiple search queries and analysis steps into a single generative AI processing operation. Instead of executing separate database queries for each search term or filter combination, the AI model processes user intent comprehensively in one operation, generating multiple potential itineraries simultaneously.
Solution Approach 2:
The patent replaces traditional rule-based mechanical search engine operations with generative AI processing. The AI model substitutes multiple sequential database queries and rule evaluations with a single neural network inference operation, dramatically reducing computational overhead and data latency.
Data Source
AI summary
A computer-implemented method comprises receiving a first chat message at a server computer from a chat interface of a reservation application executing on a mobile computing device; determining, by the server computer, from the first chat message, whether the first chat message specifies an intent for a recommendation of a travel item by programmatically classifying the first chat message to output the intent; in response to determining that the first chat message specifies the intent, generating a first plurality of travel items and a second chat message comprising one or more descriptors of the intent, outputting the first plurality of travel items and the second chat message in the chat interface, and including, in each travel item of the first plurality of travel items, a graphical user interface (GUI) widget that is programmed when selected to initiate a reservation dialog based on the travel item; in response to determining that the first chat message does not specify the intent, outputting in the chat interface a third chat message comprising a prompt for contextual information, receiving, in the chat interface, a fourth chat message specifying the contextual information, executing an inference stage of one or more trained machine-learning models over the contextual information to output one or more named entities, deriving the intent from the first chat message and the one or more named entities, generating, using the server computer, the intent, and the one or more named entities, a second plurality of travel items and a fifth chat message comprising one or more descriptors of the intent, outputting in the chat interface the second plurality of travel items and the fifth chat message, and including, in each of the second travel items, a second GUI widget that is programmed when selected to initiate the reservation dialog based on one or more of the second travel items.