Advanced Visual System Characterization and Optimization

A next-generation system for characterizing and optimizing the visual system of a subject, leveraging machine learning and neural networks to provide personalized visual feedback and training, enabling improved visual performance in various environments.

The original patent described a method for characterizing the visual system of a subject using measures of sensitivity to contrast. However, this method had limitations, such as relying on manual visual tests and not providing personalized feedback. The new inventive concept addresses these limitations by introducing a neural network-based visual signal processing module, enabling real-time characterization and optimization of the visual system.

The new inventive concept comprises a system for characterizing the visual system of a subject, featuring a neural network-based visual signal processing module. This module is trained to predict the sensitivity to contrast of the visual system based on a set of visual test patterns and internal noise sources. The system can generate personalized visual test patterns for a subject based on their individual visual system characteristics, and use these patterns to measure the sensitivity to contrast of the visual system. Additionally, the system can be integrated into a wearable device, providing real-time visual feedback to the subject. The inventive concept also enables predicting visual performance in various environments, and generating customized visual training programs to improve the sensitivity to contrast of the visual system.

The new claims introduce a paradigm shift in visual system characterization by leveraging machine learning and neural networks. The use of a neural network-based visual signal processing module, personalized visual test patterns, and real-time feedback enables a more accurate and efficient characterization of the visual system, making the original patent's method obsolete.

Alternative embodiments of the inventive concept could include the use of different machine learning algorithms, such as deep learning or reinforcement learning, to improve the accuracy of the visual signal processing module. Additionally, the system could be integrated into various devices, such as virtual reality headsets or mobile devices, to provide a more comprehensive visual experience.

The inventive concept has significant commercial potential in various industries, including healthcare, education, and entertainment. The system could be used to improve visual performance in individuals with visual impairments, or to enhance visual experience in virtual reality applications. The market for visual system characterization and optimization is expected to grow significantly in the coming years, driven by advancements in machine learning and neural networks.