18532136. CLASSIFYING AN INSTANCE USING MACHINE LEARNING simplified abstract (Telefonaktiebolaget LM Ericsson (publ))
Contents
- 1 CLASSIFYING AN INSTANCE USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CLASSIFYING AN INSTANCE USING MACHINE LEARNING - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
CLASSIFYING AN INSTANCE USING MACHINE LEARNING
Organization Name
Telefonaktiebolaget LM Ericsson (publ)
Inventor(s)
Tommy Arngren of Södra Sunderby (SE)
Markus Andersson of Boden (SE)
[[:Category:Rickard C�ster of Hägersten (SE)|Rickard C�ster of Hägersten (SE)]][[Category:Rickard C�ster of Hägersten (SE)]]
CLASSIFYING AN INSTANCE USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18532136 titled 'CLASSIFYING AN INSTANCE USING MACHINE LEARNING
Simplified Explanation
The abstract describes a selection server that chooses other communication devices to classify an instance using Machine Learning based on various information provided in a selection request message.
- The selection server receives a selection request message from a communication device, containing information such as user identity, contact list, type of data in a feature vector, origin of the feature vector, classification using a local ML model, location of the device, location associated with the instance, and related classified instances.
- The selection server then selects one or more other communication devices to classify the instance using Machine Learning based on the information in the selection request message.
Potential Applications
This technology could be applied in various fields such as telecommunications, data analysis, and artificial intelligence.
Problems Solved
This technology helps in efficiently classifying instances using Machine Learning by selecting appropriate communication devices based on relevant information.
Benefits
The benefits of this technology include improved accuracy in classification, optimized resource allocation, and enhanced decision-making processes.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of smart communication systems for personalized services.
Possible Prior Art
One possible prior art could be the use of selection servers in telecommunications networks for routing and managing data traffic efficiently.
Unanswered Questions
How does the selection server prioritize the information in the selection request message to choose the most suitable communication devices for classification?
The article does not provide details on the specific algorithm or methodology used by the selection server to prioritize the information in the selection request message.
What security measures are in place to protect the sensitive information contained in the selection request message?
The article does not address the security protocols or encryption methods implemented to safeguard the user data and classified instances shared in the selection request message.
Original Abstract Submitted
A selection server for selecting one or more other communications devices for classifying an instance using Machine Learning, ML, is provided. The selection server is operative to receive, from a communications device for classifying an instance using ML, a selection request message for selecting one or more other communications devices for classifying an instance using ML, the selection request message comprising information pertaining to at least one of: an identity of a user of the communications device, a contact list of the user, a type of data comprised in a feature vector representing the instance, an origin of the feature vector, a classification of the instance using a local first ML model of the communications device, a location of the communications device, a location associated with the instance, and one or more classified instances which are related to the instance represented by the feature vector.