Telefonaktiebolaget lm ericsson (publ) (20240119369). CONTEXTUAL LEARNING AT THE EDGE simplified abstract
Contents
- 1 CONTEXTUAL LEARNING AT THE EDGE
CONTEXTUAL LEARNING AT THE EDGE
Organization Name
telefonaktiebolaget lm ericsson (publ)
Inventor(s)
Kristijonas Cyras of San Jose CA (US)
Athanasios Karapantelakis of Solna (SE)
[[:Category:Jörg Niem�ller of Sollentuna (SE)|Jörg Niem�ller of Sollentuna (SE)]][[Category:Jörg Niem�ller of Sollentuna (SE)]]
Leonid Mokrushin of Uppsala (SE)
Aneta Vulgarakis Feljan of Stockholm (SE)
Ramamurthy Badrinath of Bangalore Karnataka (IN)
CONTEXTUAL LEARNING AT THE EDGE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119369 titled 'CONTEXTUAL LEARNING AT THE EDGE
Simplified Explanation
The patent application describes a method where a central entity of a network selects a set of features for a machine learning model to analyze data, with the model to be deployed at an edge entity of the network. The selection is based on information about the available data, features, and contextual information associated with the network.
- The method involves selecting a specific set of features for a machine learning model to analyze data.
- The machine learning model is to be deployed at an edge entity of the network.
- The selection of features is based on information about available data, features, and contextual information associated with the network.
Potential Applications
This technology could be applied in various industries such as telecommunications, healthcare, finance, and manufacturing for optimizing data analysis processes at the edge of networks.
Problems Solved
This technology helps in efficiently selecting features for machine learning models based on available data and contextual information, improving the accuracy and performance of data analysis at the edge of networks.
Benefits
The benefits of this technology include enhanced data analysis capabilities, improved decision-making processes, and increased efficiency in deploying machine learning models at the edge of networks.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of edge computing solutions for real-time data analysis and decision-making in industries such as IoT, autonomous vehicles, and smart cities.
Possible Prior Art
One possible prior art for this technology could be the use of machine learning models for data analysis in edge computing environments, but the specific method of selecting features based on available data, features, and contextual information may be novel.
Unanswered Questions
== What is the specific criteria used to select the set of features for the machine learning model? The patent application does not provide detailed information on the specific criteria used to select the set of features for the machine learning model. Further clarification on this aspect would be beneficial.
== How does the contextual information associated with the network impact the selection of features for the machine learning model? The patent application mentions the use of contextual information associated with the network in the selection process, but it does not elaborate on how this information influences the selection of features. More insight into this relationship would be helpful for understanding the technology better.
Original Abstract Submitted
there is provided a method performed by a central entity of a network. a first set of features is selected for a machine learning model to take into account when analysing data. the machine learning model is to be deployed at an edge entity of the network. the selection is based on first information indicative of data that is available for the machine learning model to analyse, second information indicative of features that are available for the machine learning model to take into account when analysing data, and contextual information associated with the network.
