View source for 18145567. SYSTEMS AND METHODS FOR TRAINING A MACHINE LEARNING MODEL TO CONFIRM RESULTS OF EVENT DETECTION simplified abstract (Capital One Services, LLC)

=SYSTEMS AND METHODS FOR TRAINING A MACHINE LEARNING MODEL TO CONFIRM RESULTS OF EVENT DETECTION=


==Organization Name==

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


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Renee Gill of New York NY (US)|Renee Gill of New York NY (US)]][[Category:Renee Gill of New York NY (US)]]

[[:Category:Joshua Edwards of Philadelphia PA (US)|Joshua Edwards of Philadelphia PA (US)]][[Category:Joshua Edwards of Philadelphia PA (US)]]

[[:Category:Kathryn Tikoian of South Orange NJ (US)|Kathryn Tikoian of South Orange NJ (US)]][[Category:Kathryn Tikoian of South Orange NJ (US)]]

==SYSTEMS AND METHODS FOR TRAINING A MACHINE LEARNING MODEL TO CONFIRM RESULTS OF EVENT DETECTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18145567 titled 'SYSTEMS AND METHODS FOR TRAINING A MACHINE LEARNING MODEL TO CONFIRM RESULTS OF EVENT DETECTION

==Simplified Explanation==
A computing system can identify a feature in a dataset that helps distinguish between data more likely to represent the target population. By analyzing the values of this feature, the system can determine which samples are more or less likely to belong to the target population.

==Key Features and Innovation==
* The computing system identifies a feature in a dataset to distinguish between samples representing the target population.
* It analyzes the values of this feature to determine the likelihood of samples belonging to the target population.
* The system generates a training dataset based on the values of the feature to improve accuracy.

==Potential Applications==
This technology can be applied in various fields such as market research, demographic analysis, and predictive modeling.

==Problems Solved==
This technology helps in accurately identifying samples that are representative of the target population, leading to more precise analysis and decision-making.

==Benefits==
* Improved accuracy in identifying samples representing the target population.
* Enhanced predictive modeling and analysis.
* Efficient generation of training datasets for machine learning algorithms.

==Commercial Applications==
* Market research companies can use this technology to improve the accuracy of their demographic analysis.
* Predictive modeling companies can benefit from more precise data representation for better forecasting.

==Prior Art==
Readers can explore prior research in the fields of machine learning, data analysis, and predictive modeling to understand the evolution of similar technologies.

==Frequently Updated Research==
Stay updated on advancements in machine learning algorithms, data analysis techniques, and predictive modeling methodologies to enhance the application of this technology.

==Questions about the Technology==
===How does this technology improve the accuracy of identifying samples representing the target population?===
This technology utilizes a specific feature in the dataset to distinguish between samples more likely to belong to the target population, leading to improved accuracy in data analysis.

===What are the potential commercial applications of this technology beyond market research?===
Apart from market research, this technology can be applied in fields such as healthcare, finance, and social sciences for precise data analysis and predictive modeling.


==Original Abstract Submitted==

In some aspects, a computing system may identify a feature that can be used to distinguish between data that is more likely to be representative of the target population. A computing system may identify a feature in a dataset where a first value of the feature is associated with a higher likelihood that a corresponding sample is not a member of the target population. Due to the differences between samples that have the first value and samples that have the second value, the computing system may determine that samples with the first value are less likely to be members of the target population or samples with the second value are more likely to be members of the target population. The computing system may determine that a training dataset should be generated using samples that have the second value.



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[[Category:H04L41/0631]]
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[[Category:CPC_H04L63/1416]]