AI Curation Models for Travel Review Classification
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Solution Overview
Problem
Conventional travel planning methods overwhelm individuals with multiple internet tabs and conflicting information, leading to task saturation and disillusionment, while also missing opportunities for travel service providers to enhance customer experiences.
Innovation Solution
A method utilizing curation content machine learning models to classify textual reviews into multiple classifications with assigned relative weights, allowing for the aggregation of a relative value score and the assignment of a unified point value, which can be used to determine resale prices and enhance travel planning collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional travel planning methods are used with multiple internet tabs and information sources, then comprehensive travel information can be gathered, but task saturation and information overload occur leading to planner disillusionment
Solution Approach 1:
The patent introduces an AI-powered intermediary system that acts as a mediator between multiple travel information sources and the planner. This system automatically aggregates, filters, and synthesizes information from various tabs and sources, presenting curated results to the planner. The intermediary handles the complexity of managing multiple information streams, reducing cognitive load while maintaining comprehensive information gathering.
Solution Approach 2:
The patent replaces the mechanical manual process of manually browsing and synthesizing information across multiple tabs with an automated AI system. The AI performs text analysis, sentiment detection, and information synthesis automatically, substituting the manual cognitive and physical effort of the planner with algorithmic processing.
2Adaptability or versatility
If manual travel planning coordination is performed among group members, then personalized preferences can be considered, but the planner becomes overwhelmed with competing information and schedules
Solution Approach 1:
The patent enables each group member to independently input their preferences, constraints, and requirements into the system. The AI then automatically processes these inputs, reconciles conflicts, and generates coordinated travel plans. Each member serves themselves by inputting data, while the system handles the complex coordination work, reducing the burden on any single planner.
Solution Approach 2:
The patent transforms qualitative group preferences and constraints into quantifiable parameters that the AI can process mathematically. By converting preferences into weighted parameters and constraints into definable criteria, the system can objectively evaluate and balance competing requirements, finding optimal solutions that accommodate multiple preferences without manual negotiation.
3Ease of manufacture
If conventional travel planning processes are used, then basic travel arrangements can be made, but opportunities to enhance customer experience through data insights are missed
Solution Approach 1:
The patent implements feedback loops where customer review data and travel outcome information are continuously collected, analyzed, and used to improve future travel recommendations and arrangements. The AI learns from past customer experiences and data insights, feeding this knowledge back into the planning process to enhance service quality and personalize future recommendations, thereby capturing value from customer data.
Solution Approach 2:
The patent performs preliminary analysis of customer data, preferences, and historical travel patterns before the actual travel planning begins. By pre-processing and pre-analyzing available customer information, the system prepares personalized recommendations and identifies potential enhancements in advance, ensuring that valuable data insights are utilized proactively rather than lost.
Data Source
AI summary
In some aspects, the techniques described herein relate to a method including: providing one or more curation content machine learning models, wherein the one or more curation content machine learning models are configured to classify a textual review into a plurality of classifications, and wherein the plurality of classifications have been assigned respective relative weights; receiving, at a collaboration service, a user review of a travel objective from a user of the collaboration service; providing the review as an input feature dataset to the one or more curation content machine learning models; receiving, as output of the one or more curation content machine learning models, a plurality of classifications of the user review; and aggregating, by the collaboration service, a relative value score based on the assigned relative weight of the plurality of classifications of the user review.