Trip Group has been granted a patent for a computer-implemented method that reduces data storage requirements and infers available airfare classes. The method involves receiving live bookable prices without complete fare class information, storing the prices in a smaller data storage capacity, analyzing statistical patterns to determine estimated prices, and comparing them with prices from a distribution system to infer airfare class availability. The inferred availability is then provided to a computing device for storage. GlobalData’s report on Trip Group gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Trip Group, hypervisor management was a key innovation area identified from patents. Trip Group's grant share as of June 2023 was 1%. Grant share is based on the ratio of number of grants to total number of patents.
Inferring airfare class availability using historical price data
A recently granted patent (Publication Number: US11687842B2) describes a computer-implemented method for reducing data storage requirements and inferring available airfare classes. The method involves receiving live bookable prices for flights without complete fare class information and storing them in a computer data store. By analyzing statistical patterns in the stored data, estimated prices for requested airfares can be determined. These estimates are then compared with airfare prices received from a Distribution System to infer airfare class availability.
The method includes several steps to achieve its objectives. First, observable live bookable prices for flights are received and stored in a computer data store, creating an incomplete historical travel-related price dataset. This dataset uses less data storage capacity compared to a complete historical dataset. The method then involves receiving requests for airfare prices and parameters defining those airfares. Using the stored incomplete historical price dataset, estimated prices are calculated by analyzing statistical patterns and using stored classifiers. The request is then sent to a Distribution System to obtain actual airfare prices, which are compared with the calculated estimates to infer airfare class availability. Finally, the inferred airfare class availability is provided to a computing device for storage.
The patent also mentions additional features and variations of the method. For example, the parameters defining airfares can include date range, destination, origin, desired weather conditions, star ratings, and keywords. The method can also involve inferring, deriving, or predicting estimated prices. It may use rules or a naive Bayes classifier machine learning approach to analyze statistical patterns in the dataset. The method can be applied to train fares, car hire prices, and hotel prices as well.
The patent also describes a system that includes a server, a storage computer, a computing device, and a computer data store. The server is configured to receive and store live bookable prices, determine estimated prices, send requests to a Distribution System, compare prices, and provide inferred airfare class availability to the computing device.
Overall, this patent presents a computer-implemented method and system for reducing data storage requirements and inferring airfare class availability by analyzing statistical patterns in incomplete historical price datasets.
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