Qualcomm incorporated (20240098496). MANAGING UNTRUSTED USER EQUIPMENT (UES) FOR DATA COLLECTION simplified abstract
Contents
- 1 MANAGING UNTRUSTED USER EQUIPMENT (UES) FOR DATA COLLECTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MANAGING UNTRUSTED USER EQUIPMENT (UES) FOR DATA COLLECTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential applications
- 1.6 Problems solved
- 1.7 Benefits
- 1.8 Potential commercial applications
- 1.9 Possible prior art
- 1.10 How does this technology impact the efficiency of wireless communication systems?
- 1.11 What are the potential challenges in implementing this technology on a large scale in existing wireless networks?
- 1.12 Original Abstract Submitted
MANAGING UNTRUSTED USER EQUIPMENT (UES) FOR DATA COLLECTION
Organization Name
Inventor(s)
Mohamed Fouad Ahmed Marzban of San Diego CA (US)
Wooseok Nam of San Diego CA (US)
MANAGING UNTRUSTED USER EQUIPMENT (UES) FOR DATA COLLECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240098496 titled 'MANAGING UNTRUSTED USER EQUIPMENT (UES) FOR DATA COLLECTION
Simplified Explanation
The abstract describes methods, systems, and devices for wireless communications where a network entity determines the trustworthiness of information obtained from a user equipment (UE) based on a machine learning model. If the information is deemed untrusted, the network entity may indicate this to another network entity, which can then train the machine learning model using only trusted information.
- Explanation of the patent/innovation:
- A network entity evaluates the trustworthiness of information from a UE based on a machine learning model. - If the information is considered untrusted, it is flagged for further action by another network entity. - The other network entity can then train the machine learning model using only trusted information. - If a UE is deemed untrusted, the network entity may configure it to stop data collection processes.
Potential applications
This technology can be applied in wireless communication systems to enhance security and reliability by ensuring that only trusted information is used for training machine learning models.
Problems solved
- Ensures that only reliable information is used for training machine learning models. - Helps in identifying and mitigating potential security risks in wireless communication systems.
Benefits
- Improves the accuracy and effectiveness of machine learning models in wireless communication systems. - Enhances the overall security and trustworthiness of data used in network operations.
Potential commercial applications
"Enhancing Wireless Communication Security Through Trust Evaluation and Machine Learning Model Training"
Possible prior art
There may be prior art related to trust evaluation in wireless communication systems and the use of machine learning models for enhancing security measures. Further research is needed to identify specific examples.
Unanswered questions
How does this technology impact the efficiency of wireless communication systems?
This technology can potentially improve the efficiency of wireless communication systems by ensuring that only reliable information is used for training machine learning models, leading to more accurate predictions and better network performance.
What are the potential challenges in implementing this technology on a large scale in existing wireless networks?
One potential challenge could be the integration of this trust evaluation mechanism into existing wireless networks without disrupting current operations. Additionally, ensuring the scalability and compatibility of the system with different network configurations may also pose challenges.
Original Abstract Submitted
methods, systems, and devices for wireless communications are described. in some systems, a network entity may obtain information (e.g., a data set, a model update) corresponding to a user equipment (ue), the information associated with a machine learning model. the network entity may determine whether the information or the ue providing the information is trusted or untrusted based on the information. the network entity may output, to another network entity, an indication that the information corresponding to the ue is considered untrusted or trusted based on a predicted output of the machine learning model (e.g., if the model is trained using the information). the other network entity may further train the machine learning model using trusted information and may refrain from using untrusted information. additionally, or alternatively, if a ue is determined to be untrusted, a network entity may configure the ue to refrain from further data collection processes.
