The video talks about refuelling. So does this patent:
US2025147515A1 SYSTEMS AND METHODS FOR CONTROLLING AIRCRAFT DURING IN-FLIGHT REFUELING 20231106
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[0048] I
n at least one example, all or part of the systems and methods described herein may be or otherwise include an artificial intelligence (AI) or machine-learning system that can automatically perform the operations of the methods also described herein. For example, the control unit 112 can be an artificial intelligence or machine learning system. These types of systems may be trained from outside information and/or self-trained to repeatedly improve the accuracy with how data is analyzed to automatically determine economy speeds. Over time, these systems can improve by determining such information with increasing accuracy and speed, thereby significantly reducing the likelihood of any potential errors. For example, the AI or machine-learning systems can learn and determine features of aircraft, fuel ports, and/or the like to automatically determine locations within scanned data, and automatically generate 3D models. The AI or machine-learning systems described herein may include technologies enabled by adaptive predictive power and that exhibit at least some degree of autonomous learning to automate and/or enhance pattern detection (for example, recognizing irregularities or regularities in data), customization (for example, generating or modifying rules to optimize record matching), and/or the like. The systems may be trained and re-trained using feedback from one or more prior analyses of the data, ensemble data, and/or other such data. Based on this feedback, the systems may be trained by adjusting one or more parameters, weights, rules, criteria, or the like, used in the analysis of the same. This process can be performed using the data and ensemble data instead of training data, and may be repeated many times to repeatedly improve the determination and location of various structures within scan data. The training minimizes conflicts and interference by performing an iterative training algorithm, in which the systems are retrained with an updated set of data (for example, data received before, during, and/or after each flight of aircraft) and based on the feedback examined prior to the most recent training of the systems. This provides a robust analysis model that can better determine locations, features, structures, and/or the like in a cost effective and efficient manner.
This patent post-dates TENNs by many months. It uses lidar to monitor the distance between the planes and controls the petrol hose accordingly. It also uses ML, so it is an ideal application for Akida TENNs.
This one monitors turbulence to predictively correct flight path, very useful in refueling (think spitting the dummy).
US2025138194A1 Aircraft Acceleration Prediction Using Lidar Trained Artificial Intelligence 20231027
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A
n air vehicle management system comprising a controller for an air vehicle. The controller is configured to determine a prediction of an acceleration using an air vehicle model system and backscatter data. The air vehicle model system is trained to predict the acceleration using the backscatter data and is generated using backscatter light detected from emitting a laser beam in an atmosphere in a direction ahead of the air vehicle. The controller is configured to determine an adjustment to a number of flight control settings for the air vehicle that reduces a stress on the air vehicle using the prediction of the acceleration. The controller is configured to adjust the number of flight control settings using the adjustment.