View source for 18402937. SYSTEM, METHOD, AND COMPUTER-ACCESSIBLE MEDIUM FOR EVALUATING MULTI-DIMENSIONAL SYNTHETIC DATA USING INTEGRATED VARIANTS ANALYSIS simplified abstract (Capital One Services, LLC)

=SYSTEM, METHOD, AND COMPUTER-ACCESSIBLE MEDIUM FOR EVALUATING MULTI-DIMENSIONAL SYNTHETIC DATA USING INTEGRATED VARIANTS ANALYSIS=


==Organization Name==

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


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Mark Watson of Sedona AZ (US)|Mark Watson of Sedona AZ (US)]][[Category:Mark Watson of Sedona AZ (US)]]

[[:Category:Fardin Abdi Taghi Abad of Seattle WA (US)|Fardin Abdi Taghi Abad of Seattle WA (US)]][[Category:Fardin Abdi Taghi Abad of Seattle WA (US)]]

[[:Category:Anh Truong of Champaign IL (US)|Anh Truong of Champaign IL (US)]][[Category:Anh Truong of Champaign IL (US)]]

[[:Category:Kenneth Taylor of Champaign IL (US)|Kenneth Taylor of Champaign IL (US)]][[Category:Kenneth Taylor of Champaign IL (US)]]

[[:Category:Reza Farivar of Champaign IL (US)|Reza Farivar of Champaign IL (US)]][[Category:Reza Farivar of Champaign IL (US)]]

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

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

[[:Category:Vincent Pham of Champaign IL (US)|Vincent Pham of Champaign IL (US)]][[Category:Vincent Pham of Champaign IL (US)]]

==SYSTEM, METHOD, AND COMPUTER-ACCESSIBLE MEDIUM FOR EVALUATING MULTI-DIMENSIONAL SYNTHETIC DATA USING INTEGRATED VARIANTS ANALYSIS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18402937 titled 'SYSTEM, METHOD, AND COMPUTER-ACCESSIBLE MEDIUM FOR EVALUATING MULTI-DIMENSIONAL SYNTHETIC DATA USING INTEGRATED VARIANTS ANALYSIS

==Simplified Explanation==

The patent application abstract describes a system and method for training models using both original and synthetic datasets, and evaluating the synthetic datasets based on the training results.

* Receiving original dataset(s) and synthetic dataset(s)
* Training a first model with original dataset(s) and a second model with synthetic dataset(s)
* Evaluating synthetic dataset(s) by comparing results from the two models

== Potential Applications ==
This technology could be applied in various fields such as healthcare, finance, and marketing for improving data analysis and model training processes.

== Problems Solved ==
This technology helps address the challenge of limited or biased datasets by generating synthetic data to enhance model training and evaluation.

== Benefits ==
- Improved model performance through the use of synthetic data
- Enhanced accuracy and reliability of predictions
- Increased efficiency in training and evaluating models

== Potential Commercial Applications ==
"Enhancing Data Analysis and Model Training with Synthetic Datasets"

== Possible Prior Art ==
One possible prior art in this field is the use of data augmentation techniques in machine learning to increase the diversity and size of training datasets.

== Unanswered Questions ==

=== How does the system handle privacy and security concerns related to synthetic data generation? ===
The article does not address the specific measures or protocols in place to ensure the protection of sensitive information when using synthetic datasets.

=== What are the computational requirements for training models with both original and synthetic datasets? ===
The article does not provide information on the computational resources or infrastructure needed to implement this system effectively.


==Original Abstract Submitted==

An exemplary system, method, and computer-accessible medium can include, for example, receiving an original dataset(s), receiving a synthetic dataset(s), training a model(s) using the original dataset(s) and the synthetic dataset(s), and evaluating the synthetic dataset(s) based on the training of the model(s). The model(s) can include a first model and a second model, and the first model can be trained using the original dataset(s) and the second model can be trained using the synthetic dataset(s). The synthetic dataset(s) can be evaluated by comparing first results from the training of the first model to second results from the training of the second model.



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