Intelligent Assistant for Travel Booking Optimization
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Solution Overview
Problem
Existing travel booking systems overwhelm users with excessive options and resource burdens due to the exponential increase in possible combinations of travel items, leading to inefficiencies in identifying cost-effective or optimal travel packages.
Innovation Solution
A computer-implemented travel booking system featuring an intelligent assistant that determines user intent and suggests or optimizes trip elements based on selections, preferences, and availability, using machine learning to provide real-time guidance and alternative options, thereby simplifying the booking process and reducing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system provides all possible combinations of travel items to users, then the completeness of travel package options is improved, but the device complexity and user burden increase exponentially
Solution Approach 1:
The patent introduces an intelligent assistant as an intermediary component between the user and the travel booking system. This assistant automatically analyzes user preferences, determines intent, and filters the exponential combination space to present only relevant travel packages. The assistant acts as a mediator that translates user needs into optimized search queries and interprets results, thereby maintaining system completeness while reducing perceived complexity for users.
Solution Approach 2:
The system implements self-service through automated intent determination and package optimization. Rather than requiring users to manually explore all combinations, the intelligent assistant autonomously analyzes preferences, generates optimized travel packages, and presents recommendations. This self-service mechanism handles the complexity internally while providing simplified user interaction.
2Adaptability or versatility
If the system allows users to adjust multiple factors for travel items, then the adaptability of travel packages is improved, but the difficulty of detecting and measuring optimal combinations increases
Solution Approach 1:
The intelligent assistant implements feedback loops by continuously monitoring user preferences, analyzing selected travel items, and adjusting package recommendations accordingly. The system provides feedback to users about why certain packages are recommended and allows iterative refinement of preferences, making the complex multi-factor optimization process transparent and manageable through continuous interaction and adjustment.
Solution Approach 2:
The patent replaces manual mechanical exploration of travel combinations with automated computational intelligence. The intelligent assistant uses algorithms to automatically analyze multiple factors (dates, transportation, locations, pricing) and identify optimal combinations, substituting the manual trial-and-error process with automated computational optimization that handles complexity invisibly to the user.
3Loss of information
If existing systems transmit extensive search results and dynamic pages to users, then the information completeness is improved, but the network bandwidth and resource consumption increase
Solution Approach 1:
The intelligent assistant extracts only the most relevant information from the complete set of travel options based on user preferences and intent analysis. Rather than transmitting all possible combinations, the system extracts and presents a curated subset of optimized packages, maintaining information quality while significantly reducing data transmission volume and associated network resource consumption.
Solution Approach 2:
The system performs preliminary filtering and optimization of travel packages before presenting results to users. The intelligent assistant pre-processes the complete option set, identifies relevant packages based on analyzed preferences, and prepares optimized recommendations in advance. This preliminary action reduces the amount of data that needs to be transmitted and processed during user interaction, conserving network bandwidth and system resources.
Data Source
AI summary
In an embodiment, a method comprises: receiving, from a user computing device, digital data indicating one or more selected trip elements in association with a particular trip; adding the one or more selected trip elements to a stored data record representing the particular trip; analyzing the one or more selected trip elements to determine, based at least on analysis of the one or more selected trip elements, a trip intent associated with the particular trip; identifying, based on the trip intent, one or more suggested trip elements for the particular trip; and causing the one or more suggested trip elements to be displayed in a graphical user interface of the user computing device.