View source for Apple inc. (20240188167). SYSTEMS, METHODS, AND DEVICES FOR UE-ASSISTED PRIVACY-AWARE DATA-DRIVEN NW CONTROL simplified abstract

=SYSTEMS, METHODS, AND DEVICES FOR UE-ASSISTED PRIVACY-AWARE DATA-DRIVEN NW CONTROL=


==Organization Name==

[[:Category:apple inc.|apple inc.]]


[[Category:apple inc.]]

==Inventor(s)==

[[:Category:Said Medjkouh of San Diego CA (US)|Said Medjkouh of San Diego CA (US)]][[Category:Said Medjkouh of San Diego CA (US)]]

[[:Category:Tarik Tabet of Carlsbad CA (US)|Tarik Tabet of Carlsbad CA (US)]][[Category:Tarik Tabet of Carlsbad CA (US)]]

[[:Category:Kenza Hamidouche of Cupertino CA (US)|Kenza Hamidouche of Cupertino CA (US)]][[Category:Kenza Hamidouche of Cupertino CA (US)]]

[[:Category:Ayman F. Naguib of Cupertino CA (US)|Ayman F. Naguib of Cupertino CA (US)]][[Category:Ayman F. Naguib of Cupertino CA (US)]]

[[:Category:Sreevalsan Vallath of Dublin CA (US)|Sreevalsan Vallath of Dublin CA (US)]][[Category:Sreevalsan Vallath of Dublin CA (US)]]

==SYSTEMS, METHODS, AND DEVICES FOR UE-ASSISTED PRIVACY-AWARE DATA-DRIVEN NW CONTROL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240188167 titled 'SYSTEMS, METHODS, AND DEVICES FOR UE-ASSISTED PRIVACY-AWARE DATA-DRIVEN NW CONTROL

==Simplified Explanation==

The patent application focuses on user equipment (UE) assisted privacy-aware data-driven network control in wireless communication networks. The system involves UEs, base stations, an aggregation and analysis system, and a data-driven network control unit working together to enhance network efficiency through machine learning tools and information exchange while maintaining user privacy.

* UEs, base stations, aggregation and analysis system, and data-driven network control unit collaborate to improve wireless communication network efficiency.
* Machine learning tools are utilized for training, developing, and using models to optimize network control.
* Information of varying privacy levels is exchanged, protected, and anonymized to enable privacy-aware data-driven network control.

== Potential Applications ==
This technology can be applied in various industries such as telecommunications, IoT, and smart cities to enhance network performance while ensuring user privacy.

== Problems Solved ==
1. Enhances network efficiency through data-driven control.
2. Maintains user privacy while exchanging information for network optimization.

== Benefits ==
1. Improved network performance.
2. Enhanced user privacy protection.
3. Efficient data-driven network control.

== Potential Commercial Applications ==
"Privacy-Aware Data-Driven Network Control in Wireless Communication Networks" can be utilized by telecommunications companies, IoT providers, and smart city developers to optimize network operations and protect user privacy.

== Possible Prior Art ==
There may be prior art related to machine learning tools in network optimization and privacy-aware data exchange in wireless communication networks.

=== Unanswered Questions ===

=== How does this technology impact network security? ===
This article does not delve into the specific security measures implemented to protect the exchanged information and ensure network security.

=== What are the potential limitations of this technology in real-world implementations? ===
The article does not address any potential challenges or limitations that may arise when implementing this technology in practical scenarios.


==Original Abstract Submitted==

solutions for user equipment (ue) assisted privacy-aware data-driven network (nw) control. ues, base stations, an aggregation and analysis system, and a data-driven nw control unit may coordinate and communicate with one another to enhance the efficiency and effectiveness of a wireless communication network by implementing machine learning tools and exchanging information in a manner that enables the training, development, and use of models. information of various levels of privacy may be exchanged, protected, anonymized, etc., to enable ue-assisted privacy-aware data-driven nw control while still protecting the privacy of individual users.



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