View source for Nec corporation (20240281712). LEARNING METHOD simplified abstract

=LEARNING METHOD=


==Organization Name==

[[:Category:nec corporation|nec corporation]]


[[Category:nec corporation]]

==Inventor(s)==

[[:Category:Yuji Kobayashi of Tokyo (JP)|Yuji Kobayashi of Tokyo (JP)]][[Category:Yuji Kobayashi of Tokyo (JP)]]

[[:Category:Yoshiaki Sakae of Tokyo (JP)|Yoshiaki Sakae of Tokyo (JP)]][[Category:Yoshiaki Sakae of Tokyo (JP)]]

[[:Category:Hiroki Tagato of Tokyo (JP)|Hiroki Tagato of Tokyo (JP)]][[Category:Hiroki Tagato of Tokyo (JP)]]

[[:Category:Takashi Konashi of Tokyo (JP)|Takashi Konashi of Tokyo (JP)]][[Category:Takashi Konashi of Tokyo (JP)]]

[[:Category:Jun Nishioka of Tokyo (JP)|Jun Nishioka of Tokyo (JP)]][[Category:Jun Nishioka of Tokyo (JP)]]

[[:Category:Jun Kodama of Tokyo (JP)|Jun Kodama of Tokyo (JP)]][[Category:Jun Kodama of Tokyo (JP)]]

[[:Category:Etsuko Ichihara of Tokyo (JP)|Etsuko Ichihara of Tokyo (JP)]][[Category:Etsuko Ichihara of Tokyo (JP)]]

==LEARNING METHOD - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240281712 titled 'LEARNING METHOD

'''Simplified Explanation''': 

This patent application describes a learning device that classifies measurement value data based on the situation of an object, selects the data based on the number of measurements, and uses machine learning on the selected data.

* The device includes a classification unit to categorize measurement value data according to the situation data of the object.
* A selection unit then picks the measurement value data based on the quantity of measurements for each classification.
* Finally, a learning unit conducts machine learning using the chosen measurement value data.

'''Key Features and Innovation''':

* Classification unit for organizing measurement value data.
* Selection unit based on the number of measurements.
* Machine learning capabilities for data analysis.

'''Potential Applications''':

This technology could be used in various fields such as:
* Predictive maintenance in industrial settings.
* Healthcare for patient monitoring and diagnosis.
* Environmental monitoring for data analysis.

'''Problems Solved''':

* Efficient organization and selection of measurement data.
* Improved accuracy in machine learning processes.
* Enhanced performance monitoring of objects.

'''Benefits''':

* Increased efficiency in data analysis.
* Better decision-making based on organized data.
* Enhanced predictive capabilities.

'''Commercial Applications''''':

Potential commercial applications include:
* Industrial automation systems.
* Healthcare analytics platforms.
* Environmental monitoring solutions.

'''Questions about the Technology''':

1. How does this technology improve the accuracy of machine learning processes?
2. What are the specific industries that can benefit most from this innovation?


==Original Abstract Submitted==

a learning device  according to this invention includes: a classification unit  configured to classify measurement value data measuring performance of an object on the basis of situation data each representing a situation of the object when the measurement value data are measured; a selection unit  configured to select the measurement value data from each of the classifications according to the number of the measurement value data for each of the classifications; and a learning unit  configured to perform machine learning on the basis of the selected measurement value data.



[[Category:G06N20/00]]
[[Category:CPC_G06N20/00]]