18514561. AUTOMATIC SUGGESTION OF VARIATION PARAMETERS & PRE-PACKAGED SYNTHETIC DATASETS simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)

AUTOMATIC SUGGESTION OF VARIATION PARAMETERS & PRE-PACKAGED SYNTHETIC DATASETS

Organization Name

MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor(s)

Kamran Zargahi of Bellevue WA (US)

AUTOMATIC SUGGESTION OF VARIATION PARAMETERS & PRE-PACKAGED SYNTHETIC DATASETS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18514561 titled 'AUTOMATIC SUGGESTION OF VARIATION PARAMETERS & PRE-PACKAGED SYNTHETIC DATASETS

Simplified Explanation

Various techniques are described for automatically suggesting variation parameters used to generate a tailored synthetic dataset to train a particular machine learning model. A seeding taxonomy associates a plurality of machine learning scenarios with corresponding subsets of variation parameters. A selected machine learning scenario is used to retrieve a corresponding subset of variation parameters associated with the selected machine learning scenario by the seeding taxonomy. The seeding taxonomy may be adaptable using a feedback loop that tracks selected variation parameters and updates the seeding taxonomy. The suggested variation parameters are presented as suggestions to assist users to identify and select relevant variation parameters faster and more efficiently. Further embodiments relate to pre-packaging synthetic datasets for common or anticipated machine learning scenarios. A user interface may present available packages of synthetic data for a selected industry sector and/or scenario, and a selected package may be made available for download.

• Techniques for automatically suggesting variation parameters for synthetic dataset generation
• Seeding taxonomy associates machine learning scenarios with variation parameters
• Feedback loop for updating and adapting the seeding taxonomy
• Pre-packaging synthetic datasets for common machine learning scenarios
• User interface for selecting and downloading synthetic data packages

Potential Applications

This technology can be applied in various industries where machine learning models are used for training and prediction tasks. It can be particularly useful in sectors such as healthcare, finance, marketing, and manufacturing.

Problems Solved

- Streamlining the process of generating synthetic datasets for machine learning models - Assisting users in identifying relevant variation parameters efficiently - Providing pre-packaged datasets for common machine learning scenarios

Benefits

- Faster and more efficient dataset generation for machine learning training - Improved accuracy and performance of machine learning models - Simplified selection and customization of synthetic datasets

Potential Commercial Applications

- Data analytics companies - Machine learning software developers - Research institutions - Financial institutions

Possible Prior Art

One possible prior art could be the use of automated data generation techniques for machine learning applications. Another could be the development of pre-packaged datasets for specific industries or use cases.

How does this technology handle privacy and security concerns related to synthetic data generation?

This article does not address the specific methods or protocols in place to ensure the privacy and security of the synthetic datasets generated.

Are there any limitations or constraints in the types of machine learning models that can be trained using the suggested variation parameters?

The article does not mention any restrictions or limitations on the compatibility of the suggested variation parameters with different types of machine learning models.

Original Abstract Submitted

Various techniques are described for automatically suggesting variation parameters used to generate a tailored synthetic dataset to train a particular machine learning model. A seeding taxonomy associates a plurality of machine learning scenarios with corresponding subsets of variation parameters. A selected machine learning scenario is used to retrieve a corresponding subset of variation parameters associated with the selected machine learning scenario by the seeding taxonomy. The seeding taxonomy may be adaptable using a feedback loop that tracks selected variation parameters and updates the seeding taxonomy. The suggested variation parameters are presented as suggestions to assist users to identify and select relevant variation parameters faster and more efficiently. Further embodiments relate to pre-packaging synthetic datasets for common or anticipated machine learning scenarios. A user interface may present available packages of synthetic data for a selected industry sector and/or scenario, and a selected package may be made available for download.