View source for 18523099. Device and Method for Parameter Estimation in Micro-Electro-Mechanical System Testing simplified abstract (Robert Bosch GmbH)

=Device and Method for Parameter Estimation in Micro-Electro-Mechanical System Testing=


==Organization Name==

[[:Category:Robert Bosch GmbH|Robert Bosch GmbH]]


[[Category:Robert Bosch GmbH]]

==Inventor(s)==

[[:Category:Alexander Buhmann of Reutlingen (DE)|Alexander Buhmann of Reutlingen (DE)]][[Category:Alexander Buhmann of Reutlingen (DE)]]

[[:Category:Monika Heringhaus of Stuttgart (DE)|Monika Heringhaus of Stuttgart (DE)]][[Category:Monika Heringhaus of Stuttgart (DE)]]

==Device and Method for Parameter Estimation in Micro-Electro-Mechanical System Testing - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18523099 titled 'Device and Method for Parameter Estimation in Micro-Electro-Mechanical System Testing

'''Simplified Explanation''': 

This patent application describes a method for training a Graph Neural Network to predict second measurement results of produced products based on received first measurement results. 

* The method involves receiving first and second measurement results for multiple products, constructing graphs of the first measurements, and generating a training data set by assigning the corresponding second measurements to the graphs.
* The Graph Neural Network is then trained on this data set to predict the second measurements based on the graphs.

'''Key Features and Innovation''':

* Training a Graph Neural Network to predict second measurement results of products based on first measurement results.
* Constructing graphs of the first measurements and generating a training data set for the network.
* Utilizing the network to make predictions based on the graphs.

'''Potential Applications''':

This technology could be applied in industries where predicting product quality or performance based on initial measurements is crucial, such as manufacturing, healthcare, or environmental monitoring.

'''Problems Solved''':

This technology addresses the need for accurate and efficient prediction of product outcomes based on initial measurements, which can help improve quality control and decision-making processes.

'''Benefits''':

* Enhanced accuracy in predicting product outcomes.
* Improved efficiency in analyzing and interpreting measurement data.
* Potential cost savings by reducing the need for manual analysis and intervention.

'''Commercial Applications''''':

Predictive maintenance in manufacturing, quality control in healthcare diagnostics, and environmental monitoring for regulatory compliance are potential commercial applications of this technology.

'''Questions about Graph Neural Networks''':

1. How do Graph Neural Networks differ from traditional neural networks in terms of structure and function?
2. What are some common challenges in training Graph Neural Networks for predictive tasks?


==Original Abstract Submitted==

A computer-implemented method of training a Graph Neural Network for predicting second measurement results of produced products based on received first measurement results is disclosed. The method includes (i) receiving first measurement and second measurement results for a plurality of produced products, (ii) constructing graphs of the first measurements and generating a training data set by assigning the corresponding second measurement of the first measurement to the corresponding graphs, respectively, and (iii) training the Graph Neural Network on the training data set to predict the second measurements based on the graphs.



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