18274909. TRAVEL PLANNING ASSISTANCE SYSTEM, METHOD, AND PROGRAM simplified abstract (NEC Corporation)
Contents
- 1 TRAVEL PLANNING ASSISTANCE SYSTEM, METHOD, AND PROGRAM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAVEL PLANNING ASSISTANCE SYSTEM, METHOD, AND PROGRAM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Unanswered Questions
- 1.10 Original Abstract Submitted
TRAVEL PLANNING ASSISTANCE SYSTEM, METHOD, AND PROGRAM
Organization Name
Inventor(s)
TRAVEL PLANNING ASSISTANCE SYSTEM, METHOD, AND PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18274909 titled 'TRAVEL PLANNING ASSISTANCE SYSTEM, METHOD, AND PROGRAM
Simplified Explanation
The abstract describes a patent application for a system that learns a cost function for travel itineraries using inverse reinforcement learning based on training data that includes traveler information and actual travel results.
- Function input accepts cost function for itinerary
- Learning means learns cost function through inverse reinforcement learning
- Data extraction means extracts training data based on traveler attributes
- Learning process is based on attributes of travelers in training data
Potential Applications
This technology could be applied in the travel industry for optimizing travel planning and cost estimation for travelers.
Problems Solved
1. Efficient cost estimation for travel itineraries 2. Personalized travel planning based on traveler attributes
Benefits
1. Improved accuracy in cost estimation 2. Customized travel itineraries for individual travelers
Potential Commercial Applications
Optimized Travel Cost Estimation: Using AI for Personalized Itineraries
Unanswered Questions
How does the system handle outliers in the training data?
What is the computational complexity of the learning process?
Original Abstract Submitted
A function input means accepts input of a cost function that calculates a cost incurred by an itinerary, the cost function being expressed as a linear sum of terms weighted for each feature that a traveler is expected to intend in the itinerary. A learning means learns the cost function by inverse reinforcement learning using training data that includes scheduled information indicating travel planning of the traveler, attribute information indicating an attribute of the traveler, and actual information indicating an actual travel result of the traveler. A data extraction means extracts the training data whose specified attribute matches the attribute information. Then, the learning means learns the cost function according to the attributes by inverse reinforcement learning using the extracted training data.
