International business machines corporation (20240296322). LEARNED CONVERSION OF MEASUREMENT SOURCES WITH DIFFERENT UNITS simplified abstract

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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 20240296322 titled 'LEARNED CONVERSION OF MEASUREMENT SOURCES WITH DIFFERENT UNITS

Simplified Explanation: The patent application describes techniques for automatically converting measurements from different units using generative models and neural networks.

  • **Key Features and Innovation:**
   - Receiving data streams from different sources with measurement data.
   - Pre-training a generative model on the target data source to estimate the true distribution of the measurement data.
   - Training unit conversion neural networks for non-target data sources to convert measurements to the selected unit.
   - Combining converted measurement data with the target data source in a data lake.
  • **Potential Applications:**
   - Data integration and standardization in diverse measurement systems.
   - Automation of unit conversion processes in data analysis.
   - Enhancing accuracy and consistency in measurement data processing.
  • **Problems Solved:**
   - Addressing the challenge of converting measurements from different units.
   - Improving efficiency in handling diverse measurement data sources.
   - Facilitating seamless data analysis and comparison across different sources.
  • **Benefits:**
   - Streamlining data processing workflows.
   - Enhancing data accuracy and reliability.
   - Enabling better decision-making based on standardized measurement data.
  • **Commercial Applications:**
   - Title: Automated Measurement Unit Conversion System
   - Potential commercial uses: Data analytics platforms, IoT devices, scientific research tools
   - Market implications: Increased efficiency in data analysis, improved data quality, potential cost savings for businesses
  • **Prior Art:**
   - Further research can be conducted in the field of machine learning for unit conversion in data processing.
   - Existing techniques in data integration and standardization may provide insights into similar approaches.
  • **Frequently Updated Research:**
   - Stay updated on advancements in machine learning models for data conversion.
   - Monitor developments in data processing tools and techniques for improved measurement data handling.

Questions about Automated Measurement Unit Conversion System: 1. How does the use of generative models improve measurement data conversion accuracy? 2. What are the potential challenges in implementing neural networks for unit conversion in diverse data sources?


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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