17808143. COMPOSING A MACHINE LEARNING MODEL FOR COMPLEX DATA SOURCES simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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COMPOSING A MACHINE LEARNING MODEL FOR COMPLEX DATA SOURCES

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Ana Paula Appel of São Paulo (BR)

Renato Luiz De Freitas Cunha of São Paulo (BR)

PAULO Rodrigo Cavalin of Rio De Janeiro (BR)

Bruno Silva of São Paulo (BR)

COMPOSING A MACHINE LEARNING MODEL FOR COMPLEX DATA SOURCES - A simplified explanation of the abstract

This abstract first appeared for US patent application 17808143 titled 'COMPOSING A MACHINE LEARNING MODEL FOR COMPLEX DATA SOURCES

Simplified Explanation

The patent application describes a method for creating a machine learning model for complex data sources. Here are the key points:

  • The computer receives data and metadata related to a machine learning task from a user.
  • It determines the task context and problem domain.
  • The computer identifies the specific machine learning task.
  • It evaluates pre-compiled models to find a match with the problem domain.
  • At least two pre-compiled models are selected.
  • The computer generates multiple combinations of these models.
  • The combinations are executed with the data and metadata.
  • The results of the executed combinations are displayed to the user.
  • The computer checks if the user finds the level of error in the results acceptable.

Potential Applications

This technology has potential applications in various fields, including:

  • Healthcare: Creating machine learning models to analyze medical data and assist in diagnosis.
  • Finance: Developing models to predict market trends and make investment decisions.
  • Manufacturing: Optimizing production processes by analyzing complex data from sensors and machines.
  • Natural Language Processing: Building models to understand and generate human-like text.

Problems Solved

This technology addresses the following problems:

  • Complex data sources: It enables the creation of machine learning models for data that is difficult to analyze using traditional methods.
  • Model selection: It automates the process of selecting and combining pre-compiled models, saving time and effort.
  • Error evaluation: It allows users to determine if the level of error in the results is acceptable, ensuring the reliability of the models.

Benefits

The use of this technology offers several benefits:

  • Improved accuracy: By combining multiple models, the accuracy of predictions and analysis can be enhanced.
  • Time and resource savings: The automated model selection process saves time and effort compared to manual selection.
  • User feedback: The ability to evaluate the acceptability of error levels based on user response ensures user satisfaction and trust in the models.


Original Abstract Submitted

In an approach to composing a machine learning model for complex data sources, a computer receives data and associated metadata corresponding to a machine learning task from a user. A computer determines a task context and a problem domain. A computer identifies the machine learning task. A computer evaluates a match between the problem domain and one or more pre-compiled models. A computer selects at least two of the one or more pre-compiled models. A computer generates one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models. A computer executes the multimodal model combinations with the data and associated metadata. A computer displays the results of the executed one or more multimodal model combinations to the user. A computer determines whether a level of error associated with the results is acceptable to the user based on a response from the user.

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