Generating credit building recommendations through machine learning analysis of user activity-based feedback
US11900451B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 15, 2020 |
| Grant date | Feb 13, 2024 |
| Priority date | — |
| Expiry date | Sep 15, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q40/06
- WIPO fieldIT methods for management
- WIPO sectorElectrical engineering
Abstract
A real-time activity recommendation system receives an input from a user device regarding a targeted financial goal, such as a target credit score. Using machine learning models to evaluate patterns of user activity that contribute positively towards the goal, and to evaluate the limitations and opportunities of the user's financial circumstances and profile, the recommendation system makes an assessment in real time to determine user actions that can be taken to improve credit health based on a user's profile and activity data. A user-specific recommendation regarding an activity that should be performed to reach the goal is generated and transmitted to the user. User and third party activity is later monitored as the user's financial status changes over time, and the recommendations are updated accordingly.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.