View source for 18149352. MACHINE LEARNING SYSTEMS AND METHODS simplified abstract (Truist Bank)

=MACHINE LEARNING SYSTEMS AND METHODS=


==Organization Name==

[[:Category:Truist Bank|Truist Bank]]


[[Category:Truist Bank]]

==Inventor(s)==

[[:Category:Barath Jayaraman of Fort Mill SC (US)|Barath Jayaraman of Fort Mill SC (US)]][[Category:Barath Jayaraman of Fort Mill SC (US)]]

==MACHINE LEARNING SYSTEMS AND METHODS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18149352 titled 'MACHINE LEARNING SYSTEMS AND METHODS

The abstract describes systems and methods for training and deploying a machine learning model that correlates numerical levels to stored quantities and analyzes user data and input relative to these levels.

* Training and deploying a machine learning model
* Tuning parameters of input data to correlate numerical levels to stored quantities
* Accessing user data to determine stored user quantities
* Processing user inputs associated with numerical levels
* Applying the machine learning model to user data and inputs
* Generating an output with analysis of user quantities and inputs
* Displaying the output via a user interface on a user device

Potential Applications:
- Personalized recommendations based on user data and inputs
- Predictive analytics for user behavior
- Adaptive user interfaces based on user quantities and inputs

Problems Solved:
- Improving user experience by analyzing user data and inputs
- Enhancing decision-making processes with machine learning insights

Benefits:
- Increased user engagement and satisfaction
- More accurate predictions and recommendations
- Streamlined user interactions with intelligent systems

Commercial Applications:
Title: "Enhanced User Experience through Machine Learning Analysis"
This technology can be applied in various industries such as e-commerce, social media, and healthcare to provide personalized services and improve user interactions. The market implications include increased customer retention, higher conversion rates, and improved user satisfaction.

Questions about the technology:
1. How does this technology ensure user data privacy and security?
2. What are the potential limitations of the machine learning model in analyzing user quantities and inputs?


==Original Abstract Submitted==

Systems and methods train and deploy a machine learning model, the training including tuning parameters of the input data to correlate ascertained numerical levels to ascertained stored quantities. Further, systems and methods access user data of user register(s) of a user to determine a user quantity stored in the user register(s), and process user input(s) associated with a numerical level. The deployed machine learning model is applied to the accessed user data and the user input(s), the applying generating an output comprising analysis of the user quantity and the user input(s) relative to the ascertained numerical levels of multiple users of the plurality of users, the multiple users having an associated numerical level determined to be similar to the numerical level of the user input(s). The generated output comprising the analysis is displayed via a user interface of a user device.



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[[Category:G06Q40/06]]
[[Category:CPC_G06N3/08]]