18554337. Facilitating Ambient Computing Using a Radar System simplified abstract (GOOGLE LLC)
Contents
Facilitating Ambient Computing Using a Radar System
Organization Name
Inventor(s)
Eiji Hayashi of Cupertino CA (US)
Jaime Lien of Mountain View CA (US)
Nicholas Edward Gillian of Palo Alto CA (US)
Andrew C. Felch of Palo Alto CA (US)
Jin Yamanaka of Mountain View CA (US)
Blake Charles Jacquot of San Carlos CA (US)
Facilitating Ambient Computing Using a Radar System - A simplified explanation of the abstract
This abstract first appeared for US patent application 18554337 titled 'Facilitating Ambient Computing Using a Radar System
Simplified Explanation: The patent application describes techniques and apparatuses for ambient computing using a radar system, allowing for eye-free interaction and gesture-based user interface.
Key Features and Innovation:
- Radar system for ambient computing
- Eye-free interaction
- Gesture-based user interface
- Machine-learned module for gesture recognition
- Addressing challenges like power consumption, environmental variations, background noise, size, and user privacy
Potential Applications: The technology can be applied in smart devices, home automation, healthcare monitoring, and security systems.
Problems Solved: The technology addresses challenges associated with ambient computing, such as power consumption, environmental variations, background noise, and user privacy.
Benefits:
- Enhanced user experience
- Efficient gesture recognition
- Reduced cognitive load on users
- Improved privacy protection
Commercial Applications: The technology can be used in smart home devices, wearable technology, healthcare monitoring systems, and security applications.
Prior Art: Readers can explore prior research on radar systems, ambient computing, gesture recognition, and machine learning in human-computer interaction.
Frequently Updated Research: Stay updated on advancements in radar technology, ambient computing, gesture recognition algorithms, and machine learning models for user interaction.
Questions about Ambient Computing with Radar Systems: 1. How does the radar system in ambient computing improve user interaction compared to traditional physical interfaces? 2. What are the potential privacy implications of using radar systems for ambient computing?
1. A relevant generic question not answered by the article, with a detailed answer. How does the machine-learned module in the radar system enhance gesture recognition accuracy in ambient computing applications? The machine-learned module in the radar system utilizes advanced algorithms to quickly and accurately recognize gestures performed by users up to at least two meters away. By filtering out background noise and minimizing false positives, the module ensures a seamless and efficient user experience in ambient computing environments.
2. Another relevant generic question, with a detailed answer. What are the key challenges in implementing radar systems for ambient computing, and how does this technology address them? Implementing radar systems for ambient computing poses challenges such as power consumption, environmental variations, background noise interference, and user privacy concerns. This technology addresses these challenges by optimizing power efficiency, adapting to environmental changes, filtering out noise, and prioritizing user privacy through advanced design and machine learning algorithms.
Original Abstract Submitted
Techniques and apparatuses are described that facilitate ambient computing using a radar system. Compared to other smart devices that rely on a physical user interface, a smart device with a radar system can support ambient computing by providing an eye-free interaction and less cognitively demanding gesture-based user interface. The radar system can be designed to address a variety of challenges associated with ambient computing, including power consumption, environmental variations, background noise, size, and user privacy. The radar system uses an ambient-computing machine-learned module to quickly recognize gestures performed by a user up to at least two meters away. The ambient-computing machine-learned module is trained to filter background noise and have a sufficiently low false positive rate to enhance the user experience.
