18559728. Systems and Methods for Multidevice Learning and Inference in an Ambient Computing Environment simplified abstract (GOOGLE LLC)
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 18559728 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. This technology enables devices to learn from each other and make inferences across multiple devices in the environment.
- Devices can be trained based on supervision signals from existing devices.
- Multi-device inference is possible across two or more devices.
- Models can be trained to be robust to the addition or removal of devices in the ambient computing environment.
Key Features and Innovation:
- Cross-device learning in ambient computing environments.
- Multi-device inference capabilities.
- Robust training of models to accommodate changes in the device ecosystem.
Potential Applications: This technology can be applied in smart homes, industrial IoT systems, healthcare monitoring, and personalized user experiences across multiple devices.
Problems Solved: The technology addresses the challenge of enabling devices to learn from each other and make inferences in a multi-device environment seamlessly.
Benefits:
- Improved device collaboration and learning.
- Enhanced user experience through seamless device interactions.
- Adaptability to changes in the device ecosystem.
Commercial Applications: Title: Multi-Device Learning Technology for Ambient Computing Environments This technology can be commercialized in smart home automation systems, industrial IoT solutions, healthcare monitoring devices, and personalized marketing platforms.
Prior Art: Researchers can explore prior art related to multi-device learning, ambient computing, and collaborative inference systems in IoT environments.
Frequently Updated Research: Stay updated on the latest advancements in multi-device learning algorithms, ambient computing technologies, and collaborative inference methods for IoT ecosystems.
Questions about Multi-Device Learning in Ambient Computing Environments: 1. How does this technology improve the efficiency of device interactions in ambient computing environments? 2. What are the key challenges in implementing multi-device learning systems in IoT ecosystems?
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.
