View source for 18753343. SYSTEM, METHOD, AND COMPUTER-ACCESSIBLE MEDIUM TO VERIFY DATA COMPLIANCE BY ITERATIVE LEARNING simplified abstract (Capital One Services, LLC)

=SYSTEM, METHOD, AND COMPUTER-ACCESSIBLE MEDIUM TO VERIFY DATA COMPLIANCE BY ITERATIVE LEARNING=


==Organization Name==

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


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Vincent Pham of Champaign IL (US)|Vincent Pham of Champaign IL (US)]][[Category:Vincent Pham 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:Fardin Abdi Taghi Abad of Champaign IL (US)|Fardin Abdi Taghi Abad of Champaign IL (US)]][[Category:Fardin Abdi Taghi Abad 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:Anh Troung of Champaign IL (US)|Anh Troung of Champaign IL (US)]][[Category:Anh Troung of Champaign IL (US)]]

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

==SYSTEM, METHOD, AND COMPUTER-ACCESSIBLE MEDIUM TO VERIFY DATA COMPLIANCE BY ITERATIVE LEARNING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18753343 titled 'SYSTEM, METHOD, AND COMPUTER-ACCESSIBLE MEDIUM TO VERIFY DATA COMPLIANCE BY ITERATIVE LEARNING

The abstract describes a system, method, and computer-accessible medium for establishing unique rule identifiers corresponding to sets of unknown time-variable rules, making data compliant, obtaining metadata, and generating estimated rules using machine learning algorithms.

*Simplified Explanation: The patent application outlines a method for identifying and generating rules based on compliant and non-compliant data against unknown time-variable rules.

*Key Features and Innovation:
- Establishing unique rule identifiers for unknown time-variable rules
- Making data compliant against these rules
- Obtaining metadata from compliant and non-compliant data
- Generating estimated rules using machine learning algorithms

*Potential Applications:
- Data compliance and regulation
- Quality control in manufacturing processes
- Fraud detection in financial transactions

*Problems Solved:
- Difficulty in identifying and adapting to unknown time-variable rules
- Ensuring data compliance and accuracy
- Automating rule generation based on data analysis

*Benefits:
- Improved data compliance and accuracy
- Enhanced efficiency in rule generation
- Better decision-making based on compliant data

*Commercial Applications:
"Rule Generation System for Data Compliance and Analysis": This technology can be utilized in industries such as finance, healthcare, and manufacturing to ensure data compliance, quality control, and fraud detection, leading to improved operational efficiency and decision-making.

*Prior Art:
Readers can explore prior research on machine learning algorithms for rule generation and data compliance in industries such as finance and healthcare.

*Frequently Updated Research:
Stay updated on advancements in machine learning algorithms for rule generation and data compliance to enhance the efficiency and accuracy of compliance processes.

Questions about Rule Generation System for Data Compliance and Analysis:
1. How does this technology improve data compliance and accuracy?
- This technology improves data compliance and accuracy by establishing unique rule identifiers and generating estimated rules based on compliant and non-compliant data.

2. What are the potential applications of this rule generation system?
- The potential applications include data compliance and regulation, quality control in manufacturing processes, and fraud detection in financial transactions.


==Original Abstract Submitted==

An exemplary system, method, and computer-accessible medium can include, for example, establishing a unique rule-identifier in one-to-one correspondence with at least one set of unknown time-variable rules against which data is to be made compliant, obtaining at least one set of data marked compliant against the one or more set of rules, obtaining meta-data from the compliant data, obtaining at least one set of data marked non-compliant against the set of unknown time-variable rules, extracting meta-data from the non-compliant data, joining the set of compliant and non-compliant metadata to generate a set of estimated rules corresponding to the rule-identifier based at least one of (i) the meta-data of the joined set and (ii) machine learning algorithms.



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[[Category:G06F16/11]]
[[Category:G06F18/24]]
[[Category:G06F21/62]]
[[Category:G06N5/025]]
[[Category:CPC_G06F16/285]]