Abstract: Embodiments of the present disclosure may include a method of training an electro-mechanical system that includes local trainable AI, the method including training a local AI for a controller of the electro-mechanical surgical device or system with simulated data. Embodiments may also include performing a task or set of tasks or operations with the device or system as controlled, instructed, influenced, or the like by the controller having the local AI system. Embodiments may also include collecting real-life data from the step of performing. Embodiments may also include further training the local AI system and improving the AI simulated data generator with real-life data. Another embodiment relates to a system for providing a user with suggestions during social interactions. Embodiments of the invention may extend across many types of devices and systems, control systems, sensors, types of AI, and so forth, and combinations of the foregoing.

Abstract: A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device.

Abstract: A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device.

Abstract: A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device.

Abstract: The present disclosure may include a method of training an electro-mechanical system that includes local trainable AI, including training a local AI for a controller of the electro-mechanical system with simulated data. A task or set of tasks or operations with the device or system as controlled, instructed, influenced, or the like by the controller having the local AI system may be performed. Real-life data may be collected from the step of performing. Further training of the local AI system is done and improving the AI simulated data generator with real-life data. A chat function may provide a human ability to ask questions. In another aspect, a system for providing a user with suggestions during social interactions. The invention may extend across many combinations of devices and systems, control systems, sensors, types of AI, and so forth.

Abstract: The invention presents a method for optimizing the design of structures or engineering designs utilizing an Artificial Intelligence (AI) system, specifically Generative Design AI. The AI system proposes an initial design, which is then iteratively optimized by a structural or design optimization system to maximize or minimize a property under certain constraints. The optimal design is used to construct a real-world structure or object, from which real-world data is gathered under actual use conditions. This data is used to retrain the AI system, facilitating ongoing improvement of the design process. The invention can be applied to a variety of fields, including but not limited to, biomedical devices, civil structures, mechanical systems, and electronic devices, among others.