Advanced Multi-Modal Sensing for Next-Generation Automated Surgical Robots

A novel approach to multi-modal sensing in automated surgical robots, enabling real-time, high-resolution surface reconstruction and enhanced spatial resolution through the integration of advanced imaging modalities and machine learning algorithms.

The original patent disclosed systems and methods for multi-modal sensing of depth in vision systems for automated surgical robots. However, the existing approach has limitations in terms of spatial resolution, accuracy, and adaptability to varying surgical environments. The new inventive concept addresses these limitations by introducing a neural network-based depth estimation module, hybrid sensing approaches, and GPU-accelerated computational frameworks to provide more accurate and reliable positional information in real-time.

The new inventive concept comprises a system for multi-modal sensing of depth in vision systems for automated surgical robots, featuring a neural network-based depth estimation module that leverages real-time data from multiple imaging modalities to generate high-resolution, three-dimensional surface maps of an object in a surgical scene. The system also incorporates a hybrid sensing approach that combines data from fiducial markers, stereo vision, and structured light scanning to generate a comprehensive, high-fidelity representation of an object's surface topography. Furthermore, the system utilizes a GPU-accelerated computational framework that leverages parallel processing to rapidly generate detailed, three-dimensional surface models of an object from multi-modal sensing data. The system's adaptability is ensured through a method for adaptive, real-time depth sensing, which dynamically adjusts the weighting of depth measurements from multiple imaging modalities based on real-time feedback from the surgical scene.

The new claims introduce a paradigm shift in multi-modal sensing for automated surgical robots by integrating advanced machine learning algorithms, hybrid sensing approaches, and GPU-accelerated computational frameworks. The inventive concept's novelty lies in its ability to provide real-time, high-resolution surface reconstruction and enhanced spatial resolution, while its inventive step is the combination of these advanced technologies to overcome the limitations of the original patent.

Alternative embodiments of the inventive concept could include the use of other machine learning algorithms, such as convolutional neural networks or generative adversarial networks, to enhance the system's adaptability and accuracy. Additionally, the system could be modified to accommodate different imaging modalities, such as optical coherence tomography or hyperspectral imaging, to further expand its capabilities.

The new inventive concept has significant commercial potential in the field of automated surgical robotics, enabling more accurate and efficient surgical procedures. The target market includes medical device manufacturers, hospitals, and surgical centers, with potential applications in various surgical specialties, such as neurosurgery, orthopedic surgery, and laparoscopic surgery.