18061703. BIASING MACHINE LEARNING MODEL OUTPUTS simplified abstract (Capital One Services, LLC)
Contents
BIASING MACHINE LEARNING MODEL OUTPUTS
Organization Name
Inventor(s)
Galen Rafferty of Mahomet IL (US)
Samuel Sharpe of Cambridge MA (US)
Brian Barr of Schenectady NY (US)
Jeremy Goodsitt of Champaign IL (US)
Austin Walters of Savoy IL (US)
BIASING MACHINE LEARNING MODEL OUTPUTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18061703 titled 'BIASING MACHINE LEARNING MODEL OUTPUTS
The abstract of the patent application describes a device that utilizes machine learning models to determine an output for a user based on data indicating reparations issued to the user by entities. The machine learning model is trained to consider the probability of the user obtaining reparations when determining the output.
- The device obtains data indicating reparations issued to a user by entities.
- Utilizes at least one machine learning model to determine an output for the user.
- The machine learning model is trained to consider the probability of the user obtaining reparations when determining the output.
- Transmits information based on the output to a user device.
Potential Applications: - This technology could be applied in financial institutions to assess the likelihood of users receiving reparations. - It could be used in legal settings to predict outcomes of reparations cases. - Companies could implement this to tailor services or products based on the likelihood of users receiving reparations.
Problems Solved: - Provides a more accurate prediction of outcomes for users receiving reparations. - Helps in decision-making processes based on the probability of reparations being issued.
Benefits: - Enhances efficiency in determining outcomes for users. - Improves user experience by providing tailored information based on the output.
Commercial Applications: Title: Predictive Reparations Outcome Analysis Tool for Financial Institutions This technology could revolutionize how financial institutions assess and predict reparations outcomes, leading to more informed decision-making processes and improved customer satisfaction.
Prior Art: There is no known prior art related to this specific technology.
Frequently Updated Research: Currently, there are no frequently updated research sources relevant to this technology.
Questions about Predictive Reparations Outcome Analysis Tool for Financial Institutions: 1. How does this technology differentiate from traditional methods of predicting reparations outcomes? 2. What are the potential limitations of using machine learning models to determine reparations outcomes?
Original Abstract Submitted
In some implementations, a device may obtain data indicating reparations issued to a user by one or more entities. The device may determine, using at least one machine learning model, an output in connection with the user. The at least one machine learning model may be trained to determine the output based on the data, and the at least one machine learning model may be trained to determine the output with a bias based on a probability, indicated by the data, of the user obtaining reparations for the output being erroneous. The device may transmit, to a user device, information based on the output.
