Telefonaktiebolaget lm ericsson (publ) (20240184272). MACHINE LEARNING IN A NON-PUBLIC COMMUNICATION NETWORK simplified abstract
Contents
- 1 MACHINE LEARNING IN A NON-PUBLIC COMMUNICATION NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MACHINE LEARNING IN A NON-PUBLIC COMMUNICATION NETWORK - 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 Original Abstract Submitted
MACHINE LEARNING IN A NON-PUBLIC COMMUNICATION NETWORK
Organization Name
telefonaktiebolaget lm ericsson (publ)
Inventor(s)
Peter Vaderna of Budapest (HU)
Zsófia Kallus of Budapest (HU)
Maxime Bouton of Stockholm (SE)
Carmen Lee Altmann of Täby (SE)
MACHINE LEARNING IN A NON-PUBLIC COMMUNICATION NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240184272 titled 'MACHINE LEARNING IN A NON-PUBLIC COMMUNICATION NETWORK
Simplified Explanation
The patent application describes equipment that supports a non-public communication network and trains a machine learning model to make predictions or decisions within the network. The equipment determines the validity of the trained model based on its performance with a validation dataset, and adds additional training data as needed. It also configures autonomous or automated mobile devices to help collect the additional training data.
- Equipment supports non-public communication network
- Trains machine learning model for predictions or decisions
- Determines validity of trained model based on performance with validation dataset
- Adds additional training data as needed
- Configures mobile devices to collect additional training data
Potential Applications
This technology could be applied in various industries such as telecommunications, healthcare, finance, and transportation for improving decision-making processes and predictions within private communication networks.
Problems Solved
This technology solves the problem of ensuring the accuracy and reliability of machine learning models within non-public communication networks by continuously analyzing and updating training data.
Benefits
The benefits of this technology include improved performance and efficiency of machine learning models, enhanced decision-making capabilities, and increased accuracy of predictions within private communication networks.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of secure and reliable predictive analytics solutions for private communication networks in industries such as cybersecurity and financial services.
Possible Prior Art
One possible prior art for this technology could be the use of machine learning models in public communication networks for predictive analytics and decision-making processes. However, the specific application within non-public communication networks with continuous training and validation processes may be a novel aspect of this innovation.
What are the potential security implications of using autonomous or automated mobile devices to collect training data in private communication networks?
Using autonomous or automated mobile devices to collect training data in private communication networks may raise security concerns related to data privacy, unauthorized access, and potential breaches. It is essential to implement robust security measures to protect sensitive information and ensure the integrity of the training data.
How can the equipment ensure the accuracy and relevance of the additional training data collected by the configured mobile devices?
The equipment can ensure the accuracy and relevance of the additional training data by implementing data validation processes, quality control measures, and data cleansing techniques. It is crucial to establish strict criteria for collecting, storing, and analyzing training data to maintain the effectiveness of the machine learning model.
Original Abstract Submitted
equipment that supports a non-public communication network trains a machine learning model with a training dataset to make a prediction or decision in the network. the equipment determines whether the trained model is valid or invalid based on whether predictions or decisions that the trained model makes from a validation dataset satisfy performance requirements. based on the trained model being invalid, the equipment analyzes the training dataset and/or the trained model to determine what additional training data to add to the training dataset. the equipment transmits signaling for configuring one or more autonomous or automated mobile devices served by the network to help collect the additional training data. the equipment then re-trains the model with the training dataset as supplemented with the additional training data.
