Identifying and remediating gaps in artificial intelligence use cases using a generative artificial intelligence model

US12346820B1 · kind B1 · utility

Assignee

• CITIBANK, N.A. · US

Inventors

• Sofia Rahman · New York, US
• Christopher Tucker · Spanaway, US
• James Myers · New York, US
• Prashant Praveen · New York, US
• Shardul Malviya · London, GB
• Wayne Liao · London, GB
• Deepak Jain · Garland, US
• Samantha Cory · London, GB
• Mariusz Saternus
• Daniel Lewandowski
• Biraj Krushna Rath · London, GB
• Stuart Murray · London, GB
• Philip Davies · London, GB
• Payal Jain · London, GB
• Tariq Husayn Maonah · London, GB
• Vishal Mysore · Mississauga, CA
• Ramkumar Ayyadurai · Jersey City, US
• Chamindra Desilva · London, GB

Key dates

• Filing date: Sep 18, 2024
• Grant date: Jul 1, 2025
• Expiry date: Sep 18, 2044

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N5/045
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output, AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.