Boeing had three patents in big data during Q2 2024. Boeing has developed systems and methods for generating synthetic flight performance data using a generative adversarial network trained on actual aircraft performance data. This technology allows for the generation of synthetic aircraft performance data based on input parameters, providing valuable insights for improving aircraft performance and safety. GlobalData’s report on Boeing gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Boeing had no grants in big data as a theme in Q2 2024.
Recent Patents
Application: Synthetic flight performance data generation (Patent ID: US20240150034A1)
The patent filed by The Boeing Co. describes systems and methods for generating synthetic flight performance data using a generative adversarial network. The method involves obtaining training data from actual aircraft flights, training the network to generate synthetic data that mimics actual performance data, and then generating synthetic data based on input parameters. The synthetic data includes various flight parameters such as altitude, Mach number, air temperature, ground speed, weight, and fuel consumption. The network architecture includes recurrent cells, convolution layers, max-pooling layers, and densely-connected cells to improve the accuracy of the generated data.
The system described in the patent includes a memory storing instructions for obtaining training data, training the generative adversarial network, and generating synthetic flight performance data based on input parameters. The system architecture mirrors the method, with the network consisting of recurrent cells, convolution layers, max-pooling layers, and densely-connected cells. The system is designed to handle various input parameters such as number of flights, cruising altitude, weight, weather conditions, and airspace identifiers to generate synthetic data for use in data analytics and aircraft performance analyses. The instructions also include data processing steps such as filtering, smoothing, normalization, and resampling to ensure the quality and accuracy of the generated synthetic data.
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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.
