View source for 18154269. SELECTING LEARNING MODEL simplified abstract (Telefonaktiebolaget LM Ericsson (publ))

=SELECTING LEARNING MODEL=


==Organization Name==

[[:Category:Telefonaktiebolaget LM Ericsson (publ)|Telefonaktiebolaget LM Ericsson (publ)]]


[[Category:Telefonaktiebolaget LM Ericsson (publ)]]

==Inventor(s)==

[[:Category:Aitor Hernandez Herranz of STOCKHOLM (SE)|Aitor Hernandez Herranz of STOCKHOLM (SE)]][[Category:Aitor Hernandez Herranz of STOCKHOLM (SE)]]

[[:Category:Lars Andersson of SOLNA (SE)|Lars Andersson of SOLNA (SE)]][[Category:Lars Andersson of SOLNA (SE)]]

[[:Category:José Ara�jo of STOCKHOLM (SE)|José Ara�jo of STOCKHOLM (SE)]][[Category:José Ara�jo of STOCKHOLM (SE)]]

[[:Category:Soma Tayamon of STOCKHOLM (SE)|Soma Tayamon of STOCKHOLM (SE)]][[Category:Soma Tayamon of STOCKHOLM (SE)]]

==SELECTING LEARNING MODEL - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18154269 titled 'SELECTING LEARNING MODEL

==Simplified Explanation==

The abstract describes a method for dynamically selecting a learning model for a sensor device. The learning model is used to determine output data based on sensor input. The method involves the following steps:

* Detecting the need for a new learning model based on the performance of the currently loaded learning model in the sensor device.
* Determining feature candidates based on sensor data from different sources.
* Selecting a new learning model from a set of candidate learning models based on the feature candidates and input features of each candidate.
* Triggering the new learning model to be loaded on the sensor device, replacing the currently loaded learning model.

Potential applications of this technology:

* Internet of Things (IoT) devices that rely on sensor data for decision-making.
* Autonomous vehicles that need to adapt their learning models based on changing sensor inputs.
* Smart home devices that optimize their performance based on sensor data.

Problems solved by this technology:

* Outdated or underperforming learning models can be replaced with more suitable models.
* The method allows for continuous improvement and adaptation of the learning model based on changing sensor data.
* It enables efficient utilization of sensor data from different sources to select the most appropriate learning model.

Benefits of this technology:

* Improved accuracy and performance of the sensor device by using the most suitable learning model.
* Adaptability to changing sensor inputs and environmental conditions.
* Efficient utilization of sensor data from different sources to optimize the learning model selection process.


==Original Abstract Submitted==

According to a first aspect, it is presented a method for dynamically selecting a learning model for a sensor device. The learning model is configured for determining output data based on sensor. The method includes the steps of: detecting a need for a new learning model for the sensor device based on performance of a currently loaded learning model in the sensor device; determining at least one feature candidate based on sensor data from the at least one sensor, wherein each one of the at least one feature candidate is associated with a different source of sensor data; selecting a new learning model, from a set of candidate learning models, based on the at least one feature candidate and input features of each one of the candidate learning models; and triggering the new learning model to be loaded on the sensor device, replacing the currently loaded learning model.



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