20240012832. MACHINE LEARNING-BASED DATA SET PROFILING AND VISUALIZATION simplified abstract (AT&T Intellectual Property I, L.P.)

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MACHINE LEARNING-BASED DATA SET PROFILING AND VISUALIZATION

Organization Name

AT&T Intellectual Property I, L.P.

Inventor(s)

Joseph Soryal of Glendale NY (US)

MACHINE LEARNING-BASED DATA SET PROFILING AND VISUALIZATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240012832 titled 'MACHINE LEARNING-BASED DATA SET PROFILING AND VISUALIZATION

Simplified Explanation

The abstract of this patent application describes a processing system that obtains a data object from a dataset and creates a profile for the data object. The profile includes properties of the data object and relationships between the data object and other data objects in the dataset. The processing system then selects a visual component for the data object based on its properties and relationships, labels the visual component according to the data object, and presents the visual object with the labeled component.

  • The processing system obtains a data object from a dataset.
  • The system creates a profile for the data object, defining its properties and relationships with other data objects.
  • A visual component is selected for the data object based on its properties and relationships.
  • The selected visual component is labeled according to the data object.
  • The visual object with the labeled component is presented.

Potential applications of this technology:

  • Data visualization: The system can be used to create visual representations of complex datasets, making it easier for users to understand and analyze the data.
  • User interface design: The technology can be applied to design user interfaces that dynamically adapt to the properties and relationships of data objects, providing a more intuitive and personalized user experience.
  • Data analysis: The system can assist in analyzing large datasets by visually representing the properties and relationships of data objects, enabling users to identify patterns and insights more easily.

Problems solved by this technology:

  • Complex data representation: The technology simplifies the representation of complex datasets by visually organizing and labeling data objects based on their properties and relationships.
  • User comprehension: By presenting data objects with labeled visual components, the system helps users understand the data more effectively, especially when dealing with large and intricate datasets.
  • Personalization: The technology allows for the customization of visual components based on the properties and relationships of data objects, providing a personalized and tailored experience for users.

Benefits of this technology:

  • Improved data understanding: The visual representation and labeling of data objects enhance users' comprehension and interpretation of the data.
  • Efficient data analysis: The system facilitates the analysis of large datasets by visually highlighting the properties and relationships of data objects.
  • Enhanced user experience: The personalized and adaptive visual components improve the user interface, making it more intuitive and user-friendly.


Original Abstract Submitted

a processing system may obtain at least a first data object of a data set and obtain a profile of the at least the first data object, where the profile defines at least one property of the first data object and at least one relationship between the at least the first data object and at least a second data object of the data set. the processing system may then select at least a first component of a visual object for the at least the first data object based upon the at least one property and the at least one relationship, label the at least the first component of the visual object in accordance with the at least the first data object, in response to the selecting, and present the visual object with the at least the first component labeled in accordance with the at least the first data object.

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