Travel Customization System Predicting Companion Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional vacation rental marketplaces are inadequate in guiding travelers to desired activities and providing sufficient information, lacking effective concierge-like services and utilizing limited data to enhance the travel experience due to sub-optimal technological solutions.
Innovation Solution
A travel customization system that includes a traveler profile generator and an adaptive advisory engine, which uses traveler attributes and data from various sources to predict preferred activities and provide real-time, customized recommendations for navigation and activity engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional vacation rental marketplaces are used, then property renting is enabled, but guidance to desired activities and concierge-like services are insufficient
Solution Approach 1:
The system automatically generates personalized activity recommendations and navigation instructions based on traveler profiles and location data, eliminating the need for manual concierge intervention. The adaptive advisory engine self-adjusts recommendations based on real-time traveler behavior and preferences.
Solution Approach 2:
The patent replaces manual concierge services with an automated computational system that uses machine learning algorithms to predict traveler preferences and generate personalized recommendations. The system substitutes human intervention with algorithmic processing of traveler data.
2Loss of information
If conventional computing devices are used in traditional vacation rental marketplaces, then basic renting functions are provided, but sufficient information about local activities is not provided
Solution Approach 1:
The system pre-generates personalized activity recommendations and information packages before the traveler arrives at the destination. Traveler profiles are built in advance using historical data, and the adaptive advisory engine prepares customized itineraries and local activity information ahead of time.
Solution Approach 2:
The system continuously monitors traveler behavior, location data, and interaction patterns to refine activity recommendations in real-time. The feedback loop adjusts recommendations based on actual traveler responses, ensuring information relevance and improving the travel experience dynamically.
3Adaptability or versatility
If limited data is used in conventional rental marketplaces, then system complexity is reduced, but the ability to enhance travel experience is limited
Solution Approach 1:
The system uses a single comprehensive traveler profile data structure to serve multiple functions: activity prediction, navigation optimization, recommendation generation, and personalization. This universal data approach maximizes the value of collected information across all system functions without requiring separate specialized data systems.
Solution Approach 2:
The system dynamically adjusts the depth and type of data processing based on traveler characteristics and situation. The adaptive advisory engine modifies recommendation granularity and data utilization intensity according to traveler profile attributes, optimizing the balance between data processing complexity and experience enhancement.
Data Source
AI summary
Various embodiments relate generally to electrical and electronic hardware, computer software, wired and wireless network communications, and wearable computing devices for identifying activities and/or destinations of relative importance. More specifically, a system, a device and a method are provided to predict a type of companion with whom a traveler collaborates to, among other things, predict activities (e.g., preferential activities) available at a geographic region association with lodging. In one or more embodiments, a method can include determining a location at which a user computing device associated with a user is disposed, and calculating that a computing device associated with a candidate companion is within a region coterminous with the location. Also, the method may include predicting data representing a companionship type for data representing the candidate companion based at least the location at which the user and the candidate companion are co-located.