18177208. LEARNED CONVERSION OF MEASUREMENT SOURCES WITH DIFFERENT UNITS simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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LEARNED CONVERSION OF MEASUREMENT SOURCES WITH DIFFERENT UNITS

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Marco Luca Sbodio of Castaheany (IE)

Joao H. Bettencourt-silva of Dublin (IE)

Thanh Lam Hoang of Maynooth (IE)

Natalia Mulligan of Dublin (IE)

Gabriele Picco of Dublin (IE)

[[:Category:Marcos Mart�nez Galindo of Dublin (IE)|Marcos Mart�nez Galindo of Dublin (IE)]][[Category:Marcos Mart�nez Galindo of Dublin (IE)]]

LEARNED CONVERSION OF MEASUREMENT SOURCES WITH DIFFERENT UNITS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18177208 titled 'LEARNED CONVERSION OF MEASUREMENT SOURCES WITH DIFFERENT UNITS

Simplified Explanation: The patent application describes a method for automatically converting measurements from different sources with varying units of measurement.

  • The method involves receiving multiple data streams from different sources, each containing measurement data.
  • A target data source is selected, and a generative model is pre-trained on its measurement data to estimate the true distribution in a selected unit of measurement.
  • A unit conversion neural network is trained for each non-target data source to convert the measurement data to the selected unit of measurement.
  • The measurement data from a non-target data source is converted using the trained neural network and combined with the target data source in a data lake.

Key Features and Innovation:

  • Automatic conversion of measurements from different sources with varying units.
  • Pre-training a generative model on target data to estimate the true distribution.
  • Training unit conversion neural networks for non-target data sources.
  • Combining converted data from different sources in a data lake.

Potential Applications: This technology can be applied in industries such as manufacturing, healthcare, finance, and environmental monitoring where data from different sources with varying units needs to be standardized and combined for analysis.

Problems Solved:

  • Eliminates the manual effort required to convert measurements from different sources.
  • Ensures consistency and accuracy in converting measurement data to a common unit.
  • Facilitates seamless integration and analysis of data from diverse sources.

Benefits:

  • Saves time and resources by automating the conversion process.
  • Improves data accuracy and consistency.
  • Enhances data analysis and decision-making capabilities.

Commercial Applications: Automated Measurement Conversion Technology for Data Integration and Analysis

Prior Art: Readers can explore prior research on measurement data conversion techniques, generative models, and neural networks in the field of data science and machine learning.

Frequently Updated Research: Stay updated on advancements in generative models, neural networks, and data integration techniques for measurement data conversion.

Questions about Measurement Data Conversion: 1. How does this technology improve data standardization and analysis in industries like healthcare and finance? 2. What are the potential challenges in implementing automated measurement data conversion systems in real-world applications?


Original Abstract Submitted

Aspects of the invention include techniques for automatically converting measurements from measurement sources having different units. A non-limiting example method includes receiving a plurality of data streams. Each data stream is received from a respective data source and includes measurement data. A target data source is selected from the respective data sources and a generative model is pre-trained on the measurement data of the target data stream to estimate a true distribution of the measurement data in a selected unit of measurement. A unit conversion neural network is trained for each non-target data source to convert the measurement data to the selected unit of measurement. The measurement data of a first non-target data source is converted to the selected unit of measurement using the respective trained unit conversion neural network and the converted measurement data is combined with the measurement data of the target data source in a data lake.

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