17946926. COMPUTER-BASED SYSTEMS CONFIGURED FOR AUTOMATICALLY UPDATING A DATABASE BASED ON AN INITIATION OF A DYNAMIC MACHINE-LEARNING VERIFICATION AND METHODS OF USE THEREOF simplified abstract (Capital One Services, LLC)
Contents
- 1 COMPUTER-BASED SYSTEMS CONFIGURED FOR AUTOMATICALLY UPDATING A DATABASE BASED ON AN INITIATION OF A DYNAMIC MACHINE-LEARNING VERIFICATION AND METHODS OF USE THEREOF
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 COMPUTER-BASED SYSTEMS CONFIGURED FOR AUTOMATICALLY UPDATING A DATABASE BASED ON AN INITIATION OF A DYNAMIC MACHINE-LEARNING VERIFICATION AND METHODS OF USE THEREOF - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
COMPUTER-BASED SYSTEMS CONFIGURED FOR AUTOMATICALLY UPDATING A DATABASE BASED ON AN INITIATION OF A DYNAMIC MACHINE-LEARNING VERIFICATION AND METHODS OF USE THEREOF
Organization Name
Inventor(s)
Shabnam Kousha of Washington DC (US)
Joshua Edwards of Philadelphia PA (US)
Lin Ni Lisa Cheng of Great Neck NY (US)
Tyler Maiman of Melville NY (US)
Asher Smith-rose of Midlothian VA (US)
COMPUTER-BASED SYSTEMS CONFIGURED FOR AUTOMATICALLY UPDATING A DATABASE BASED ON AN INITIATION OF A DYNAMIC MACHINE-LEARNING VERIFICATION AND METHODS OF USE THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 17946926 titled 'COMPUTER-BASED SYSTEMS CONFIGURED FOR AUTOMATICALLY UPDATING A DATABASE BASED ON AN INITIATION OF A DYNAMIC MACHINE-LEARNING VERIFICATION AND METHODS OF USE THEREOF
Simplified Explanation
The present disclosure describes a method that involves receiving input data from an external data aggregator, utilizing a machine learning algorithm to generate a database of known queries, updating the database with subsequent input data, performing cross-reference analysis to determine the presence of a data record, predicting triggers associated with the data record, and initiating verification of the data record.
- Receiving input data from external data aggregator
- Utilizing machine learning algorithm to generate a database of known queries
- Updating the database with subsequent input data
- Performing cross-reference analysis to determine presence of data record
- Predicting triggers associated with data record
- Initiating verification of data record
Potential Applications
This technology could be applied in the fields of data security, fraud detection, and personalized user experiences.
Problems Solved
This technology helps in efficiently managing and verifying large amounts of data, improving data accuracy, and enhancing user security.
Benefits
The benefits of this technology include increased data accuracy, improved fraud detection capabilities, and enhanced user trust and security.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of secure data verification systems for financial institutions.
Possible Prior Art
One possible prior art could be the use of machine learning algorithms for data analysis and verification in various industries.
Unanswered Questions
1. How does the machine learning algorithm handle new, unknown queries? 2. What are the potential limitations of this technology in terms of scalability and accuracy?
Original Abstract Submitted
In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving input data from at least one external data aggregator; utilizing a trained machine learning algorithm to generate a database of known queries; receiving subsequent input data from the at least one external aggregator; automatically updating the database of known queries associated with the plurality of users; utilizing the trained machine learning algorithm to perform a cross-reference analysis to determine a presence of a data record within the database of known queries; dynamically removing the data record from the database of known queries; utilizing the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record; and instructing a computing device to initiate a verification of the presence of the at least one data record.
