Duplicate Travel Path Detection via Leg Segmentation
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Solution Overview
Problem
Existing systems for travel path information management struggle with efficiently identifying and resolving duplicate travel reservations, particularly inexact duplicates, which can lead to confusion and incorrect selection of travel plans.
Innovation Solution
A system utilizing a duplicate path application that includes a leg similarity module, construct leg graph module, and traverse graph module to compare and merge similar legs, construct a segment graph, and recursively traverse it to identify and sort candidate paths, allowing for manual intervention to select the correct path.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems are used to identify duplicate travel reservations, then the system can process travel path information, but it fails to efficiently identify inexact duplicates leading to confusion and incorrect selection
Solution Approach 1:
The patent segments travel paths into discrete legs (individual flight segments) and compares these segmented components rather than treating entire travel paths as monolithic units. This segmentation enables precise identification of inexact duplicates by comparing individual leg characteristics (airline, flight number, times, airports) independently, thereby improving duplicate detection accuracy without compromising selection reliability
Solution Approach 2:
The patent introduces an intermediary duplicate path application that acts as a mediator between the travel reservation system and the path selection process. This intermediary application systematically compares travel paths, identifies duplicates through leg-by-leg analysis, and presents resolved options to users, thereby simultaneously improving both detection accuracy and selection reliability by removing the source of confusion
2Measurement precision
If manual comparison of travel paths is performed to identify duplicates, then accuracy can be maintained, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent implements a self-service automated system that performs duplicate path identification and resolution without requiring manual intervention. The duplicate path application automatically segments paths into legs, compares characteristics, identifies duplicates, and presents resolved options, thereby maintaining high accuracy through systematic comparison while dramatically improving processing efficiency by eliminating time-consuming manual operations
Solution Approach 2:
The patent replaces the mechanical manual comparison process with an automated computational system. Instead of human operators manually examining and comparing travel paths, the system uses algorithmic processing to automatically segment paths, compare leg characteristics, and identify duplicates, thereby maintaining the precision of careful comparison while achieving the productivity of automated processing
3Productivity
If exact duplicate removal is implemented, then processing speed improves, but inexact duplicates remain undetected causing confusion
Solution Approach 1:
The patent segments travel paths into individual legs and compares these segments systematically. This segmentation enables the system to efficiently process paths by breaking down complex comparisons into manageable leg-by-leg analyses, maintaining high processing speed while simultaneously detecting inexact duplicates that differ in specific leg characteristics such as airline, flight number, or timing
Solution Approach 2:
The patent changes the comparison parameters from requiring exact matches to allowing flexible matching based on leg characteristics. By comparing individual leg parameters (airline, flight number, departure/arrival times, airports) rather than requiring complete path identity, the system maintains processing efficiency while detecting inexact duplicates that would be missed by exact matching alone
Data Source
AI summary
Methods and systems for detecting a likelihood that a travel path is a correct travel path of a user using possible duplicate travel path information. A set of travel paths comprising at least two travel paths is obtained. Any paths that break up more than one reservation are removed. Each travel path is broken into at least one leg. Each leg in each travel path is compared to each leg in every other travel path to determine whether any travel paths are duplicates. A likelihood that each of the at least two travel paths is the correct travel path is determined using information about whether any of the at least two travel paths are duplicates.