Traveler Classification Model Using Statistical Inference
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Solution Overview
Problem
In the travel sector, identifying the most suitable products for prospective travelers is challenging due to the vast number of options and subjective or misleading preferences, leading to poor choices and increased effort for consumers.
Innovation Solution
A system using statistical inference to classify prospective travelers based on their preferences and goals, assigning levels to these preferences and goals, and matching them with traveler profiles to offer targeted consumer choices, utilizing natural language processing and machine learning techniques to analyze free text data and provide prioritized product options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vendors ask prospective buyers to provide preferences, then product matching accuracy is improved, but consumer effort increases
Solution Approach 1:
The system performs automatic traveler classification and preference extraction without requiring consumers to manually provide detailed preferences. The processor automatically analyzes input data, extracts encoded representations of preferences and goals, and classifies travelers into profiles, allowing the system to serve itself in gathering necessary information rather than requiring active consumer participation.
Solution Approach 2:
The patent replaces manual preference provision (mechanical human action) with automated natural language processing and statistical inference systems. The processor uses machine learning algorithms to automatically extract and interpret preferences from unstructured input data, substituting the mechanical process of manual preference entry with an automated computational system that achieves higher accuracy without increasing consumer effort.
2Measurement precision
If vendors ask prospective buyers to provide preferences, then better fitting products may be presented, but information quality deteriorates due to subjective or misleading input
Solution Approach 1:
The system introduces an intermediary processing layer between the consumer input and the product matching process. The processor acts as a mediator that transforms subjective or potentially misleading raw input into objective, structured preference data through statistical inference and natural language processing. This intermediary layer filters and validates information, converting unreliable consumer self-reporting into reliable structured data that can be accurately matched with products.
Solution Approach 2:
The patent replaces direct reliance on consumer-provided preference information with an automated computational system that objectively extracts and interprets preferences from input data. The machine learning-based processor substitutes the unreliable human judgment and self-reporting mechanism with an automated system that applies consistent analytical rules, thereby improving information quality and reliability.
3Quantity of substance
If all consumer choices are presented, then completeness is improved, but decision difficulty increases
Solution Approach 1:
The system extracts and presents only the most relevant product choices based on the classified traveler profile. Instead of presenting all available options, the processor identifies and extracts the subset of products that best match the inferred preferences and goals, thereby maintaining completeness of relevant options while reducing the overall number of choices to a manageable level that facilitates easier decision-making.
Solution Approach 2:
The patent applies local quality by providing different levels of detail and variety to different segments of consumers based on their classified profiles. Each traveler receives a customized presentation of product choices tailored to their specific preferences and goals, rather than a uniform presentation of all options. This localized approach ensures that each consumer sees the most relevant options in appropriate quantities, optimizing both completeness and decision ease for each individual.
Data Source
AI summary
A method for classifying a prospective traveler based on statistical inference is described herein. The method comprises receiving an input associated with the prospective traveler. Encoded representation of preferences and goals may be extracted from the input and levels and may be assigned to the preferences and goals. Based on the levels assigned to the preferences and goals, the prospective traveler may be classified according to one or more traveler profiles. Based on the classification, one or more consumer choices may be offered to the prospective traveler.