Insight Enterprises has patented a method for automatically generating baggage driver staffing recommendations using historical flight data and machine learning models. The system simulates missed bag quantities to predict optimal driver quantities for each flight, improving efficiency in driver scheduling. GlobalData’s report on Insight Enterprises gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Insight Enterprises, Holographic HUDs was a key innovation area identified from patents. Insight Enterprises's grant share as of April 2024 was 40%. Grant share is based on the ratio of number of grants to total number of patents.
Automated baggage driver staffing recommendations based on flight data
A recently granted patent (Publication Number: US11961024B1) outlines a method for automatically generating baggage driver staffing recommendations. The method involves receiving historical flight data, generating training and test data, and iteratively training a machine learning model to predict missed bag quantities based on driver quantities and flight parameters. By adjusting model parameters based on training data, the model's fit to test data is optimized. Flight parameters for a flight set are received, and predictive staffing models are generated to recommend driver quantities that result in a desired quantity of missed bags. These recommendations are used to modify driver schedules stored in an electronic system during the departure window, ensuring efficient staffing levels.
Furthermore, the patent describes a system that implements this method, comprising a flight database, an electronic driver scheduling system, a processor, a user interface, and memory with encoded instructions. The system receives historical flight data, generates a trained machine learning model, and simulates missed bag quantities to recommend driver quantities for each flight in a set. It can query the scheduling system to determine available drivers and adjust recommendations accordingly. The system also accounts for historical missed transfer bag quantities, flight parameters like ramp events and aircraft type, and a threshold quantity of missed bags to optimize staffing levels. By automating the process of generating staffing recommendations based on flight data and predictive models, this system aims to streamline baggage driver allocation and improve operational efficiency within the airline industry.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
