View source for 18527842. MACHINE LEARNING CONFIGURATION INFORMATION TRANSFER TO UE USING PROXIMITY SERVICES (PROSE)/SIDELINK WIRELESS COMMUNICATION simplified abstract (Nokia Technologies Oy)

=MACHINE LEARNING CONFIGURATION INFORMATION TRANSFER TO UE USING PROXIMITY SERVICES (PROSE)/SIDELINK WIRELESS COMMUNICATION=


==Organization Name==

[[:Category:Nokia Technologies Oy|Nokia Technologies Oy]]


[[Category:Nokia Technologies Oy]]

==Inventor(s)==

[[:Category:István Zsolt Kov�cs of Aalborg (DK)|István Zsolt Kov�cs of Aalborg (DK)]][[Category:István Zsolt Kov�cs of Aalborg (DK)]]

[[:Category:Nuno Manuel Kiilerich Pratas of Aalborg (DK)|Nuno Manuel Kiilerich Pratas of Aalborg (DK)]][[Category:Nuno Manuel Kiilerich Pratas of Aalborg (DK)]]

[[:Category:Oana-Elena Barbu of Aalborg (DK)|Oana-Elena Barbu of Aalborg (DK)]][[Category:Oana-Elena Barbu of Aalborg (DK)]]

[[:Category:Muhammad Majid Butt of Naperville IL (US)|Muhammad Majid Butt of Naperville IL (US)]][[Category:Muhammad Majid Butt of Naperville IL (US)]]

==MACHINE LEARNING CONFIGURATION INFORMATION TRANSFER TO UE USING PROXIMITY SERVICES (PROSE)/SIDELINK WIRELESS COMMUNICATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18527842 titled 'MACHINE LEARNING CONFIGURATION INFORMATION TRANSFER TO UE USING PROXIMITY SERVICES (PROSE)/SIDELINK WIRELESS COMMUNICATION

==Simplified Explanation==
The patent application describes a method where a user device acts as a relay for network communication, utilizing machine learning to determine other devices capable of communication. The user device then transmits machine learning configuration information to these devices for specific operations.

==Key Features and Innovation==
* User device acts as a relay for network communication.
* Discovery procedure to find other devices capable of communication.
* Machine learning utilized to determine suitable devices.
* Transmission of machine learning configuration information for specific operations.

==Potential Applications==
This technology can be applied in various scenarios such as:
* Improving network communication efficiency.
* Facilitating machine learning operations on multiple devices.
* Enhancing collaborative tasks among devices.

==Problems Solved==
The technology addresses the following issues:
* Efficient utilization of user devices for network communication.
* Seamless integration of machine learning capabilities across devices.
* Streamlining collaborative operations among multiple devices.

==Benefits==
The technology offers the following benefits:
* Enhanced network communication reliability.
* Optimized machine learning processes.
* Improved coordination among devices for specific tasks.

==Commercial Applications==
* **Network Communication Optimization Technology**: This innovation can be utilized in telecommunications, IoT devices, and smart home systems to enhance communication efficiency and reliability.

==Prior Art==
Further research can be conducted in the field of machine learning in network communication systems to explore similar technologies and advancements.

==Frequently Updated Research==
Stay updated on advancements in machine learning applications in network communication systems to leverage the latest technologies and methodologies.

==Questions about the Technology==
===What are the potential commercial applications of this technology?===
This technology can be applied in various industries such as telecommunications, IoT, and smart home systems to optimize network communication and enhance machine learning operations.

===How does this technology improve collaborative tasks among user devices?===
By utilizing machine learning to determine suitable devices for specific operations, this technology streamlines collaborative tasks and enhances overall efficiency.


==Original Abstract Submitted==

A method includes receiving, by a first user device from a network node, an indication for the first user device to act as a user device-network relay; and, in response to the receiving: carrying out a discovery procedure for determining at least one second user device that is capable of sidelink communications and utilizes machine learning; receiving, by the first user device from the network node, machine learning configuration information; and transmitting, by the first user device to the at least one second user device via sidelink communications, the machine learning configuration information for carrying out a machine learning related operation.



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