Natural-Language Travel Itinerary Interface With Policy Constraints
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Solution Overview
Problem
Optimization problems with unknown or unspecified objective functions, particularly in scenarios like travel itinerary booking, are challenging due to conflicting user and corporate constraints, requiring iterative and cumbersome processes that prolong booking times and may result in sub-optimal itineraries.
Innovation Solution
A self-adjusting profile system, such as Vox2i, uses historical travel data to infer user preferences and corporate policies, reducing the need for explicit input, and integrates compliance checks to generate itineraries that align with both user and corporate requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative solution provision is used to balance conflicting constraints, then solution reliability is improved, but booking time increases significantly
Solution Approach 1:
The system pre-processes and stores travel policy rules, constraints, and user preferences in structured formats before actual itinerary generation. By preparing constraint models, policy frameworks, and preference profiles in advance, the system eliminates the need for iterative checking during booking, achieving both high reliability and fast processing times.
Solution Approach 2:
The patent introduces an intermediary constraint satisfaction layer that mediates between user preferences and corporate policies. This intermediary model translates natural language requests into structured constraints and automatically resolves conflicts without requiring iterative user-system interactions, thus reducing booking time while maintaining solution reliability.
2Manufacturing precision
If detailed constraint information is collected to ensure policy compliance, then itinerary quality is improved, but user input burden increases
Solution Approach 1:
The system automatically retrieves and processes user travel preferences, corporate policy constraints, and historical travel data without requiring explicit user input for each booking. The constraint satisfaction model self-adjusts to balance user preferences with policy requirements, eliminating the burden of detailed user input while maintaining high itinerary quality.
Solution Approach 2:
The patent replaces manual user input mechanisms with automated information retrieval and processing systems. Natural language processing and machine learning models automatically extract constraints from user requests and historical data, substituting the mechanical process of detailed user input with intelligent automated systems that achieve the same or better results.
3Measurement precision
If multiple constraints are enforced to balance user and corporate motivations, then solution accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the constraint satisfaction problem into distinct modular components: user preference models, corporate policy rules, budget constraints, and availability checks. Each constraint type is processed independently through dedicated algorithms, reducing overall processing complexity while maintaining high solution accuracy through systematic combination of results.
Data Source
AI summary
Provided is a process, comprising receiving, with a computer system, a natural language request that relates to a travel itinerary; obtaining, with the computer system, a user profile and one or more sets of constraints corresponding to the natural language request; determining, with the computer system, a travel itinerary template, based on the user profile and the one or more sets of constraints; populating, with the computer system, the travel itinerary template with a plurality of travel components based on the user profile and the one or more set of constraints; and presenting, with the computer system, at least some candidate results, the candidate results stored in memory.