View source for 17966792. SYSTEM AND METHOD FOR A VISUAL ANALYTICS FRAMEWORK FOR SLICE-BASED MACHINE LEARN MODELS simplified abstract (Robert Bosch GmbH)

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


==Organization Name==

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


[[Category:Robert Bosch GmbH]]

==Inventor(s)==

[[:Category:Jorge Henrique Piazentin Ono of Sunnyvale CA (US)|Jorge Henrique Piazentin Ono of Sunnyvale CA (US)]][[Category:Jorge Henrique Piazentin Ono of Sunnyvale CA (US)]]

[[:Category:Xiaoyu Zhang of Davis CA (US)|Xiaoyu Zhang of Davis CA (US)]][[Category:Xiaoyu Zhang of Davis CA (US)]]

[[:Category:Huan Song of San Jose CA (US)|Huan Song of San Jose CA (US)]][[Category:Huan Song of San Jose CA (US)]]

[[:Category:Liang Gou of San Jose CA (US)|Liang Gou of San Jose CA (US)]][[Category:Liang Gou of San Jose CA (US)]]

[[:Category:Liu Ren of Saratoga CA (US)|Liu Ren of Saratoga CA (US)]][[Category: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 17966792 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 analyze different types of data (image, tabular, radar, sonar, sound) and output predictions. The method involves slicing the input data and common attributes to improve performance over multiple iterations.

* Machine-learning network method for analyzing diverse data types:
  * Receives input dataset with image, tabular, radar, sonar, or sound information
  * Sends data to machine-learning model for predictions
  * Slices data and common attributes for improved performance over iterations
  * Outputs interface with slice information and performance measurements

== Potential Applications ==
This technology could be applied in various fields such as image recognition, data analysis, radar systems, sonar systems, and sound processing.

== Problems Solved ==
* Efficient analysis of diverse data types
* Improved performance through iterative slicing and attribute identification

== Benefits ==
* Enhanced accuracy in predictions
* Better understanding of complex datasets
* Optimization of machine-learning models

== Potential Commercial Applications ==
Optimizing machine-learning models for image recognition systems, data analytics platforms, radar and sonar technologies, and sound processing applications.

== Possible Prior Art ==
One possible prior art could be the use of machine-learning models for data analysis and prediction tasks in various industries. Additionally, the concept of iterative slicing and attribute identification to improve performance may have been explored in previous research or patents.

=== Unanswered Questions ===

=== How does this method compare to traditional machine-learning approaches for analyzing diverse data types? ===
This article does not provide a direct comparison between this method and traditional machine-learning approaches. It would be interesting to see a study or analysis that evaluates the effectiveness and efficiency of this method compared to more conventional techniques.

=== What are the specific performance measurements used to evaluate the slices in each iteration? ===
The abstract mentions performance measurements of the slices in the first iteration and subsequent iterations, but it does not specify the exact metrics or criteria used for evaluation. Understanding the specific performance measurements would provide more insight into the effectiveness of the method.


==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.



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[[Category:G06K9/62]]