Google llc (20240242122). Systems and Methods for Multidevice Learning and Inference in an Ambient Computing Environment simplified abstract
Contents
Systems and Methods for Multidevice Learning and Inference in an Ambient Computing Environment
Organization Name
Inventor(s)
Matthew Sharifi of Kilchberg (CH)
Systems and Methods for Multidevice Learning and Inference in an Ambient Computing Environment - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240242122 titled 'Systems and Methods for Multidevice Learning and Inference in an Ambient Computing Environment
Simplified Explanation: The patent application discusses systems and methods for multi-device learning and inference in an ambient computing environment. It focuses on cross-device learning, multi-device inference, and training models that are robust to changes in the number of devices present in the environment.
Key Features and Innovation:
- Cross-device learning based on supervision signals from existing devices
- Multi-device inference across two or more devices
- Training models that can adapt to the addition or removal of devices in the ambient computing environment
Potential Applications: The technology can be applied in smart homes, smart offices, industrial automation, and healthcare monitoring systems.
Problems Solved: The technology addresses the challenges of integrating multiple devices for seamless learning and inference in an ambient computing environment.
Benefits:
- Improved efficiency in learning and inference processes
- Enhanced adaptability to changes in the device ecosystem
- Seamless integration of new devices into existing systems
Commercial Applications: The technology could be used in developing smart home automation systems, industrial IoT solutions, and personalized healthcare monitoring devices.
Prior Art: Prior research in the field of ambient computing, multi-device learning, and inference systems can provide valuable insights into the development of this technology.
Frequently Updated Research: Stay updated on advancements in ambient computing, machine learning, and IoT technologies to enhance the capabilities of multi-device learning and inference systems.
Questions about Multi-Device Learning and Inference: 1. How does the technology ensure data privacy and security in a multi-device learning environment? 2. What are the potential challenges in scaling up multi-device learning and inference systems for large-scale applications?
Ensure the content is informative, engaging, and optimized for SEO to attract relevant traffic and provide valuable insights into the technology of multi-device learning and inference in ambient computing environments.
Original Abstract Submitted
systems and methods for multi device learning and inference in an ambient computing environment. in some aspects, the present technology discloses systems and methods for performing cross-device learning in which new devices may be trained based on supervision signals from existing devices in the ambient computing environment. in some aspects, the present technology discloses systems and methods for performing multi-device inference across two or more devices in the ambient computing environment. likewise, in some aspects, the present technology discloses systems and methods for training models that are robust to the addition or removal of one or more devices from an ambient computing environment.
