View source for Tyco Fire & Security GmbH (20240305689). INTELLIGENT EDGE COMPUTING PLATFORM WITH MACHINE LEARNING CAPABILITY simplified abstract

=INTELLIGENT EDGE COMPUTING PLATFORM WITH MACHINE LEARNING CAPABILITY=


==Organization Name==

[[:Category:Tyco Fire & Security GmbH|Tyco Fire & Security GmbH]]


[[Category:Tyco Fire & Security GmbH]]

==Inventor(s)==

[[:Category:Abhishek Sharma of Mountain View CA (US)|Abhishek Sharma of Mountain View CA (US)]][[Category:Abhishek Sharma of Mountain View CA (US)]]

[[:Category:Sastry KM Malladi of Fremont CA (US)|Sastry KM Malladi of Fremont CA (US)]][[Category:Sastry KM Malladi of Fremont CA (US)]]

==INTELLIGENT EDGE COMPUTING PLATFORM WITH MACHINE LEARNING CAPABILITY - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240305689 titled 'INTELLIGENT EDGE COMPUTING PLATFORM WITH MACHINE LEARNING CAPABILITY

==Simplified Explanation==
An edge computing platform with machine learning capability is used to process sensor data locally without the need for constant communication with a remote network.

* The platform creates and trains a machine learning model in the remote network using aggregated sensor data.
* The model is optimized for the edge device's limited resources before deployment.
* The model operates on real-time sensor data to make decisions locally without relying on the remote network.
* The platform periodically updates the model based on feedback from the remote network.

==Key Features and Innovation==
* Edge computing platform with machine learning capability.
* Training machine learning models remotely and deploying them to the edge device.
* Optimizing models for constrained edge device resources.
* Real-time processing of sensor data for local decision-making.
* Closed-loop system for iterative model updates.

==Potential Applications==
This technology can be used in various industries such as:
* Industrial automation
* Smart cities
* Healthcare monitoring
* Environmental monitoring
* Autonomous vehicles

==Problems Solved==
* Reduce latency by processing data locally.
* Improve efficiency by making decisions without constant communication.
* Enhance privacy and security by minimizing data transfer.

==Benefits==
* Faster decision-making.
* Lower bandwidth usage.
* Improved data privacy and security.
* Enhanced reliability in remote areas.

==Commercial Applications==
* Edge computing platforms for IoT devices.
* Data analytics solutions for real-time processing.
* Remote monitoring and control systems.
* Predictive maintenance applications.

==Questions about Edge Computing with Machine Learning==
===How does edge computing with machine learning improve data processing efficiency?===
Edge computing with machine learning allows for real-time processing of data locally, reducing the need for constant communication with a remote network. This improves efficiency by enabling faster decision-making and reducing latency.

===What are the potential security implications of deploying machine learning models on edge devices?===
Deploying machine learning models on edge devices raises concerns about data privacy and security. It is essential to ensure that sensitive information is protected and that the models are secure from potential cyber threats.


==Original Abstract Submitted==

an edge computing platform with machine learning capability is provided between a local network with a plurality of sensors and a remote network. a machine learning model is created and trained in the remote network using aggregated sensor data and deployed to the edge platform. before being deployed, the model is edge-converted (“edge-ified”) to run optimally with the constrained resources of the edge device and with the same or better level of accuracy. the “edge-ified” model is adapted to operate on continuous streams of sensor data in real-time and produce inferences. the inferences can be used to determine actions to take in the local network without communication to the remote network. a closed-loop arrangement between the edge platform and remote network provides for periodically evaluating and iteratively updating the edge-based model.



[[Category:H04L67/12]]
[[Category:G06F18/214]]
[[Category:G06F18/25]]
[[Category:G06N5/04]]
[[Category:G06N20/00]]
[[Category:G16Y40/20]]
[[Category:H04L67/00]]
[[Category:CPC_H04L67/12]]