CAE has been granted a patent for a federated machine learning system designed to train students. The system includes adaptive training modules, data property extraction, a data simulator, and a federation computing device to generate or refine a federated model based on individual learning performance metrics. GlobalData’s report on CAE 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 CAE, AI assisted CAD was a key innovation area identified from patents. CAE's grant share as of February 2024 was 52%. Grant share is based on the ratio of number of grants to total number of patents.
Federated machine learning system for training students
A federated machine learning system for training students has been granted a patent (Publication Number: US11915111B2). The system includes adaptive training systems at different training centers to provide individualized training to groups of students. These adaptive training systems utilize artificial intelligence modules to develop learning models based on learning performance metrics. A federation computing device is employed to generate or refine a federated model using model weights from different training centers. The system aims to enhance student training by combining and refining learning models from multiple sources.
Furthermore, the federated machine learning system involves determining maturity coefficients for learning models and generating federated model weights based on these coefficients. The system utilizes various metrics such as F1 scores, accuracy classification scores, logarithmic loss function, area under a curve, mean absolute error, or mean squared error to assess model maturity. Additionally, the system incorporates simulation performance results obtained from user input via tangible instruments in a simulation system. These tangible instruments replicate control elements of machines like aircraft, with examples including control yokes, rudder pedals, and throttle switches. Overall, the patented system offers a comprehensive approach to student training by leveraging federated machine learning techniques and simulation-based performance metrics to enhance individualized learning experiences.
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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.
