Hierarchical Fare Graph and Tree Structure for Travel Planning
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Solution Overview
Problem
Current computerized travel planning systems lack the flexibility to efficiently extract actual travel fares, making it difficult to propose multiple bookable options to customers with loose destination or date requirements, and require significant computational resources to handle numerous combinations of origins and destinations.
Innovation Solution
A method using a graph data structure with nodes representing travel destinations and edges referencing the lowest travel fares, combined with a tree structure for each edge to store context-specific fares, allows for efficient extraction of less expensive fare paths, enabling the system to handle many destinations in a single request and offer thematic travel options without specifying a destination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system precomputes all combinations of origins and destinations fares, then complete fare information is available, but enormous computational resources and storage are required
Solution Approach 1:
The patent segments the complete fare computation problem into smaller sub-problems by organizing fares hierarchically: base fares are stored first, then supplements and modifications are added separately. This allows the system to build complete fare information incrementally without computing all combinations at once, reducing memory requirements while maintaining completeness.
Solution Approach 2:
The system performs preliminary actions by pre-storing base fares and common supplements in a structured database format. When a specific fare is needed, the system quickly assembles it from pre-prepared components rather than computing everything from scratch, significantly reducing computational burden while ensuring accuracy.
2Adaptability or versatility
If the system handles a large number of destinations and date combinations, then more travel options are available to customers, but the query processing time increases significantly
Solution Approach 1:
The patent segments the search space by organizing destinations into regions and dates into ranges, allowing the system to process queries in manageable chunks. The hierarchical fare structure enables the system to evaluate multiple destinations quickly by reusing common base fare calculations across similar routes.
Solution Approach 2:
The system changes parameters efficiently by using a hierarchical fare structure where base fares, supplements, and modifications can be independently adjusted. This allows rapid recalculation of fares when dates or destinations change, enabling the system to present multiple travel options with different parameters without excessive computation time.
3Measurement precision
If the system stores detailed fare information for all destinations and dates, then accurate pricing is available, but memory requirements become prohibitive
Solution Approach 1:
The patent segments fare information into hierarchical components (base fares, supplements, modifications) that can be stored separately. Instead of storing complete fare records for every destination-date combination, the system stores reusable components that can be assembled as needed, maintaining pricing accuracy while dramatically reducing memory requirements.
Solution Approach 2:
The system uses copying by storing base fare structures that can be replicated and modified for similar routes. Common fare components are stored once and then copied or referenced multiple times across different destinations and dates, ensuring consistent pricing accuracy without redundant storage.
Data Source
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AI summary
A method of building actual travel fares in a computer, from fare databases, is disclosed. A graph of nodes representing travel destinations is built which comprises edges connecting pairs of nodes. Each edge references a lowest travel fare. Also, a tree of fares is built for each graph edge. Trees comprise at least a root node holding the lowest travel fare of the corresponding graph edge. They possibly include more nodes comprising a context key and an associated travel fare. Trees are organized to have children nodes holding a travel fare equal to or larger than travel fare of a parent node. Thus, less expensive fare paths can efficiently be extracted since graph edges, included in the fare paths, reference the associated trees of fares and are gone through in ascending order of their lowest fare values. A learning entity is used to build and update the trees of fares. It gathers its data from processes aimed at building travel solutions for the end-users. A system implementing the method of extracting fare paths according to the invention is made capable of handling many destinations in the elapsed time of a computer transaction thus, can propose thematic travel options to end-users who are not required to choose a particular destination to start planning a trip.