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

=SYSTEMS AND METHODS FOR A PROFILE-BASED MODEL SELECTOR=


==Organization Name==

[[:Category:Capital One Services, LLC|Capital One Services, LLC]]


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Jeremy Goodsitt of Champaign IL (US)|Jeremy Goodsitt of Champaign IL (US)]][[Category:Jeremy Goodsitt of Champaign IL (US)]]

[[:Category:Kenny Bean of Herndon VA (US)|Kenny Bean of Herndon VA (US)]][[Category:Kenny Bean of Herndon VA (US)]]

[[:Category:Austin Walters of Savoy IL (US)|Austin Walters of Savoy IL (US)]][[Category: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.



[[Category:G06F11/07]]
[[Category:G06F11/34]]
[[Category:CPC_G06F11/0766]]