17968085. SENSOR TRANSFORMATION ATTENTION NETWORK (STAN) MODEL simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

SENSOR TRANSFORMATION ATTENTION NETWORK (STAN) MODEL

Organization Name

SAMSUNG ELECTRONICS CO., LTD.

Inventor(s)

Stefan Braun of Saint-Louis (FR)

Daniel Neil of Zurich (CH)

Enea Ceolini of Zurich (CH)

Jithendar Anumula of Zurich (CH)

Shih-Chii Lui of Zuerich (CH)

SENSOR TRANSFORMATION ATTENTION NETWORK (STAN) MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 17968085 titled 'SENSOR TRANSFORMATION ATTENTION NETWORK (STAN) MODEL

Simplified Explanation

The abstract describes a patent application for a model called Sensor Transformation Attention Network (STAN). The model includes sensors that collect input signals, attention modules that calculate attention scores for feature vectors corresponding to the input signals, a merge module that calculates attention values and generates a merged transformation vector based on the attention values and feature vectors, and a task-specific module that classifies the merged transformation vector.

• The STAN model includes sensors, attention modules, a merge module, and a task-specific module.
• Sensors are configured to collect input signals.
• Attention modules calculate attention scores for feature vectors corresponding to the input signals.
• A merge module calculates attention values and generates a merged transformation vector based on the attention values and feature vectors.
• A task-specific module classifies the merged transformation vector.

Potential Applications

• This technology can be applied in various fields where sensor data needs to be processed and classified, such as robotics, autonomous vehicles, and industrial automation.
• It can be used for activity recognition, object detection, and scene understanding in computer vision applications.
• The STAN model can be utilized in healthcare for monitoring patient data and detecting anomalies or patterns.

Problems Solved

• The STAN model addresses the challenge of efficiently processing and classifying sensor data by incorporating attention mechanisms.
• It solves the problem of extracting relevant information from input signals by calculating attention scores for feature vectors.
• The merge module solves the problem of combining attention values and feature vectors to generate a merged transformation vector.

Benefits

• The STAN model improves the accuracy and efficiency of processing sensor data by utilizing attention mechanisms.
• It allows for better understanding and interpretation of input signals by calculating attention scores for feature vectors.
• The merge module enables the generation of a merged transformation vector that captures the most relevant information from the input signals.

Original Abstract Submitted

A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.