18476076. METHOD FOR TRAINING A MACHINE LEARNING MODEL simplified abstract (Robert Bosch GmbH)

METHOD FOR TRAINING A MACHINE LEARNING MODEL

Organization Name

Robert Bosch GmbH

Inventor(s)

Joerg Wagner of Renningen (DE)

Nils Oliver Ferguson of Weil Der Stadt-Merklingen (DE)

Stephan Scheiderer of Leonberg (DE)

Yu Yao of Herzogenrath (DE)

Avinash Kumar of Bangalore (IN)

Barbara Rakitsch of Stuttgart (DE)

Eitan Kosman of Haifa (IL)

Gonca Guersun of Stuttgart (DE)

Michael Herman of Sindelfingen (DE)

METHOD FOR TRAINING A MACHINE LEARNING MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 18476076 titled 'METHOD FOR TRAINING A MACHINE LEARNING MODEL

Simplified Explanation

The abstract describes a method for training a machine learning model using sensor data from training sequences with prespecified events.

• Determining training sequences of sensor data with prespecified events
• Calculating temporal distances between sensor data and event time points
• Training the machine learning model based on the temporal distances

Potential Applications

This technology could be applied in various fields such as:

• Predictive maintenance in industrial settings
• Anomaly detection in healthcare monitoring systems
• Behavior analysis in security systems

Problems Solved

This technology helps in:

• Improving accuracy in event prediction
• Enhancing the efficiency of machine learning models
• Providing insights into the relationship between sensor data and events

Benefits

The benefits of this technology include:

• Increased reliability in event detection
• Better utilization of sensor data
• Enhanced decision-making based on predictive analytics

Potential Commercial Applications

The technology could be commercially applied in:

• Smart manufacturing for predictive maintenance
• Healthcare for patient monitoring and early detection of abnormalities
• Security systems for behavior analysis and threat detection

Possible Prior Art

One possible prior art could be the use of time series analysis in machine learning models for event prediction. Another could be the application of sensor data in training sequences for anomaly detection.

What are the specific prespecified events mentioned in the abstract?

The abstract does not specify the nature of the prespecified events that are used in the training sequences.

How are the temporal distances between sensor data and event time points calculated?

The abstract does not detail the specific method or algorithm used to calculate the temporal distances between sensor data and event time points.

Original Abstract Submitted

A method for training a machine learning model. The method includes: determining a plurality of training sequences of training-input data elements, wherein for each training sequence each training-input data element contains sensor data for a time point from a time period assigned to the training sequence in which a prespecified event takes place at least once at one or more respective event time points; determining, for each training-input data element, the temporal distance between the time point for which the training-input data element contains sensor data and one of the one or more respective event time points; and training the machine learning model depending on the determined temporal distances.