Google llc (20240338234). Machine Learning for Automated Navigation of User Interfaces simplified abstract
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This abstract first appeared for US patent application 20240338234 titled 'Machine Learning for Automated Navigation of User Interfaces
- Simplified Explanation:
The framework described in the patent application enables the creation of agents capable of user interface (UI) navigation using neural networks that learn from human demonstrations.
- Key Features and Innovation:
- Creation of UI navigation agents with the power of neural networks - Agents learn from human demonstrations to navigate UI effectively - Framework provides a reliable method to build agents for UI navigation
- Potential Applications:
- Enhancing user experience in software applications - Improving accessibility for individuals with disabilities - Streamlining user interactions with complex interfaces
- Problems Solved:
- Difficulty in navigating complex user interfaces - Lack of personalized UI navigation assistance - Inefficient user interactions with software applications
- Benefits:
- Enhanced user experience - Improved accessibility for all users - Efficient and personalized UI navigation assistance
- Commercial Applications:
Optimizing user experience in software applications for various industries such as e-commerce, healthcare, and entertainment.
- Prior Art:
Readers can explore existing research on neural networks, human-computer interaction, and UI navigation to understand the background of this technology.
- Frequently Updated Research:
Stay updated on advancements in neural network technology, human-computer interaction studies, and UI navigation methods to enhance the effectiveness of the framework.
Questions about UI Navigation Agents: 1. How do UI navigation agents differ from traditional user interface navigation methods? UI navigation agents utilize neural networks to learn from human demonstrations, providing personalized and efficient navigation assistance.
2. What are the potential challenges in implementing UI navigation agents in different software applications? The challenges may include adapting the framework to diverse interfaces, ensuring seamless integration with existing systems, and addressing privacy concerns related to user data.
Original Abstract Submitted
provided is a framework to reliably build agents capable of user interface (ui) navigation. for example, example implementations create ui navigation agents with the power of neural networks that learn from human demonstrations.
