17955977. LEARNED FEATURE PRIORITIZATION TO REDUCE IMAGE DISPLAY NOISE simplified abstract (International Business Machines Corporation)

LEARNED FEATURE PRIORITIZATION TO REDUCE IMAGE DISPLAY NOISE

Organization Name

International Business Machines Corporation

Inventor(s)

Xiao Xia Mao of Shanghai (CN)

Meng Ran Chen of Shanghai (CN)

Ya Qing Chen of Shanghai (CN)

Yan An of Shanghai (CN)

Yin Hu of Ningbo (CN)

LEARNED FEATURE PRIORITIZATION TO REDUCE IMAGE DISPLAY NOISE - A simplified explanation of the abstract

This abstract first appeared for US patent application 17955977 titled 'LEARNED FEATURE PRIORITIZATION TO REDUCE IMAGE DISPLAY NOISE

Simplified Explanation

The patent application describes a method for reducing image noise in a virtual environment using machine learning feature prioritization. Here are the key points:

• Providing a device access to a virtual environment with images and a navigation tool.
• Monitoring user interaction data.
• Calculating priority values for areas of objects using an ML model trained with historic data.
• Processing image data of predefined areas using image processing based on display specifications and priority values.
• Pre-loading new data in a buffer for display to the user.

Potential Applications

This technology could be applied in virtual reality (VR) and augmented reality (AR) applications to enhance user experience by reducing image noise and improving image quality.

Problems Solved

1. Image noise reduction in virtual environments. 2. Enhancing user interaction and experience in VR/AR environments.

Benefits

1. Improved image quality. 2. Enhanced user experience. 3. Efficient use of machine learning for feature prioritization.

Potential Commercial Applications

"Machine Learning Feature Prioritization for Image Noise Reduction in Virtual Environments" could be used in industries such as gaming, training simulations, virtual tours, and remote collaboration tools.

Possible Prior Art

One possible prior art could be the use of image processing techniques in virtual environments to enhance image quality and reduce noise. Another could be the application of machine learning models for prioritizing features in interactive systems.

How does this technology impact user engagement in virtual environments?

This article does not delve into the specific effects of this technology on user engagement and interaction within virtual environments. Further research or user studies may be needed to understand the full extent of its impact.

What are the potential limitations or challenges of implementing this technology in real-world applications?

The article does not address any potential limitations or challenges that may arise when implementing this technology in practical settings. Factors such as computational resources, compatibility with different devices, and scalability could be important considerations to explore further.

Original Abstract Submitted

Systems and methods enable machine learning (ML) feature prioritization to reduce image noise in a virtual environment. In embodiments, a method includes providing a device access to a virtual environment via a graphical user interface (GUI), the environment including images of objects and a navigation tool enabling a user to navigate the environment and interact with the images; monitoring interaction data of the user; calculating priority values for predefined areas of a first object in the environment, using an ML model trained with historic user interaction data and object data; processing image data of one or more of the predefined areas of the first object using image processing to generate new data based on display specifications of the client device and the priority values; and pre-loading the new data in a buffer, such that the new data is available prior to display of the new data to the user.