Robert bosch gmbh (20240135159). SYSTEM AND METHOD FOR A VISUAL ANALYTICS FRAMEWORK FOR SLICE-BASED MACHINE LEARN MODELS simplified abstract

SYSTEM AND METHOD FOR A VISUAL ANALYTICS FRAMEWORK FOR SLICE-BASED MACHINE LEARN MODELS

Organization Name

robert bosch gmbh

Inventor(s)

Jorge Henrique Piazentin Ono of Sunnyvale CA (US)

Xiaoyu Zhang of Davis CA (US)

Huan Song of San Jose CA (US)

Liang Gou of San Jose CA (US)

Liu Ren of Saratoga CA (US)

SYSTEM AND METHOD FOR A VISUAL ANALYTICS FRAMEWORK FOR SLICE-BASED MACHINE LEARN MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240135159 titled 'SYSTEM AND METHOD FOR A VISUAL ANALYTICS FRAMEWORK FOR SLICE-BASED MACHINE LEARN MODELS

Simplified Explanation

The computer-implemented method described in the abstract involves using a machine-learning network to process input datasets containing various types of information and output predictions. The method involves identifying slices of the input dataset and the machine learning model, outputting an interface with information and performance measurements of these slices, and identifying subsequent slices in subsequent iterations.

• Machine-learning network processing input datasets:

 - The method involves receiving input datasets containing image, tabular, radar, sonar, or sound information.
 - These datasets are sent to a machine-learning model to generate predictions associated with the input data.

• Slice identification and performance measurement:

 - Slices are identified in the input dataset and machine learning model in the first iteration, each containing common attributes and input data.
 - An interface is outputted with information and performance measurements of these slices in the first iteration and subsequent iterations.
 - Subsequent slices are identified in subsequent iterations, with performance measurements related to the predictions.

Potential Applications

The technology described in this patent application could be applied in various fields such as image recognition, data analysis, radar and sonar signal processing, and sound classification.

Problems Solved

This technology addresses the challenge of efficiently processing and analyzing diverse types of data using a machine-learning network. It also helps in identifying common attributes and performance measurements of different slices of data.

Benefits

The benefits of this technology include improved prediction accuracy, enhanced data processing efficiency, and better understanding of the relationships between different types of information in the input datasets.

Potential Commercial Applications

Potential commercial applications of this technology include image recognition systems, data analytics platforms, radar and sonar signal processing tools, and sound classification software.

Possible Prior Art

One possible prior art for this technology could be the use of machine-learning networks for processing and analyzing different types of data in various industries. Additionally, there may be existing methods for identifying common attributes and performance measurements of data slices in machine learning models.

How does this technology compare to existing methods for processing diverse types of data in machine learning models?

This technology offers a more structured approach to identifying and analyzing slices of data in machine learning models, potentially leading to more accurate predictions and insights.

What are the potential limitations or challenges of implementing this technology in real-world applications?

One potential limitation could be the complexity of managing and analyzing multiple slices of data in large datasets, which may require significant computational resources and expertise.

Original Abstract Submitted

a computer-implemented method for a machine-learning network includes receiving an input dataset, wherein the input dataset is indicative of image information, tabular information, radar information, sonar information, or sound information, sending the input dataset to the machine-learning model to output predictions associated with the input data, identifying one or more slices associated with the input dataset and the machine learning model in a first iteration, wherein each of the one or more slices include input data from the input dataset and common attributes associated with each slice, outputting an interface that includes information associated with the one or more slices and performance measurements of the one or more slices of the first iteration and subsequent iterations identifying subsequent slices, wherein the performance measurements relate to the predictions associated with the first iteration and subsequent iterations.