AI-Driven Travel Comparison With Dynamic Preference Profiles
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Solution Overview
Problem
Travel planning is arduous due to the variety of options and the evolving nature of user preferences, which existing systems fail to accurately capture and adapt to.
Innovation Solution
A dynamic traveler profile is developed using AI to analyze both active and passive interactions across multiple platforms, updating preferences over time and providing personalized travel recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static user profile is used to store travel preferences, then the system structure is simple, but the system cannot adapt to evolving user preferences and provides outdated recommendations
Solution Approach 1:
The patent implements a dynamic user profile system that automatically updates travel preferences based on real-time interactions across multiple platforms. The profile transitions from a static structure to a dynamic one that evolves with user behavior, enabling the system to adapt to changing preferences without requiring manual intervention or complex reconfiguration.
Solution Approach 2:
The system performs self-updating of user profiles by automatically analyzing interactions from messaging apps, social media, and other platforms. The AI-driven mechanism independently infers preference changes and updates the profile without requiring user input or system reconfiguration, reducing operational complexity while maintaining adaptability.
2Ease of operation
If the system requires explicit user input to update preferences, then the accuracy of preferences is high, but the ease of operation decreases and users must manually update their profiles
Solution Approach 1:
The system continuously monitors user interactions across multiple platforms and uses AI analysis to infer preference changes. This feedback loop automatically updates the user profile based on observed behavior patterns, eliminating the need for manual preference updates while maintaining high accuracy through intelligent inference from interaction data.
Solution Approach 2:
The patent replaces the mechanical process of manual preference entry with an AI-driven automated inference system. Instead of requiring users to explicitly input preference changes, the system uses machine learning models to analyze interaction patterns and automatically determine preference updates, significantly improving ease of operation while maintaining precision through intelligent data analysis.
3Loss of information
If the system only analyzes active user interactions, then the data processing is simple, but it misses passive interactions that also indicate preference changes
Solution Approach 1:
The system implements a unified interaction analysis framework that handles both active and passive interactions through a single AI-driven processing pipeline. This multi-functional approach consolidates data from various interaction types (direct feedback, implicit behavior patterns, engagement metrics) into a comprehensive preference update mechanism, reducing overall system complexity while capturing all relevant information.
Solution Approach 2:
The patent merges the analysis of active and passive interactions into a single integrated processing system. By combining multiple interaction data sources and analysis methods into one unified AI model, the system eliminates the need for separate processing pipelines and reduces overall complexity while ensuring no information is lost from any interaction type.
Data Source
AI summary
One embodiment relates to a method for travel comparison. The method includes obtaining, by one or more processing circuits, interaction data indicating a set of interactions of a first user across a plurality of platforms. The method includes generating a dynamic user profile indicating a plurality of travel preferences for the first user using the interaction data. The method includes providing, to the first user, a first set of travel search results based on the dynamic user profile. The method includes generating a comparison among two or more travel items included in the first set of travel search results, where the comparison relates to one or more features of the two or more travel items, and where generating the comparison includes selecting the one or more features based at least in part on one or more characteristics of the dynamic user profile.