18146823. SYSTEMS AND METHODS FOR A PROFILE-BASED MODEL SELECTOR simplified abstract (Capital One Services, LLC)

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SYSTEMS AND METHODS FOR A PROFILE-BASED MODEL SELECTOR

Organization Name

Capital One Services, LLC

Inventor(s)

Jeremy Goodsitt of Champaign IL (US)

Kenny Bean of Herndon VA (US)

Austin Walters of Savoy IL (US)

SYSTEMS AND METHODS FOR A PROFILE-BASED MODEL SELECTOR - A simplified explanation of the abstract

This abstract first appeared for US patent application 18146823 titled 'SYSTEMS AND METHODS FOR A PROFILE-BASED MODEL SELECTOR

Simplified Explanation: The patent application describes systems and methods for a profile-based model selector that determines the most suitable model for processing a given dataset based on similarity metrics between data profiles.

  • The system receives an input dataset and corresponding data profile, then calculates similarity metrics with respect to a set of data profiles.
  • It selects the model associated with the data profile that has the highest similarity metric with the input data profile.
  • If the performance of the selected model meets a threshold, the system verifies the placement of a separating hyperplane to categorize data profiles into different domains.

Key Features and Innovation:

  • Profile-based model selector for dataset processing.
  • Utilizes similarity metrics to determine the most suitable model.
  • Verifies separating hyperplane placement based on model performance.

Potential Applications:

  • Data analysis and classification.
  • Machine learning model selection.
  • Pattern recognition systems.

Problems Solved:

  • Efficient model selection for processing datasets.
  • Improved accuracy in data analysis.
  • Automated profiling and model verification.

Benefits:

  • Enhanced data processing efficiency.
  • Increased accuracy in model selection.
  • Automation of data profiling tasks.

Commercial Applications: The technology can be applied in various industries such as finance, healthcare, and marketing for optimizing data analysis processes and improving decision-making based on accurate model selection.

Questions about Profile-Based Model Selector: 1. How does the system determine the similarity metric between data profiles? 2. What are the potential challenges in implementing this technology in real-world applications?

Frequently Updated Research: Stay updated on advancements in machine learning algorithms and data profiling techniques to enhance the performance of the profile-based model selector.


Original Abstract Submitted

Systems and methods for a profile-based model selector are described. In some aspects, the system receives an input dataset and a corresponding input data profile and determines a similarity metric for the input data profile with respect to each of a plurality of data profiles. Based on the similarity metric for the input data profile being highest with respect to a first data profile, the system processes the input dataset using a first model associated with the first data profile. Based on determining that performance of the first model when applied to the input dataset is above a threshold, the system verifies a separating hyperplane is placed such that the first data profile and the input data profile are included in a first profile domain and a second data profile is included in a second profile domain.

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