ML Card Parameter Bundling for Flexible Program Configuration
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Solution Overview
Problem
Conventional systems for generating and deploying payment cards lack flexibility and efficiency due to rigid configurations, leading to increased processing delays and complexity, which restricts the usability and reach of card programs.
Innovation Solution
A card parameter bundling system utilizes machine-learning to automatically generate card management programs by determining predetermined card parameter configurations, generating card usage scores, and selecting optimal combinations for bundled sets of parameter configurations, providing recommendations for efficient and flexible card management program generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional systems use rigid configurations for card programs, then system complexity is reduced, but flexibility and adaptability are lost
Solution Approach 1:
The patent implements dynamic parameter configurations that allow card programs to be flexibly adjusted based on user segments and use cases. The system enables runtime modification of card parameters without requiring complete system redesign, transforming the rigid static configuration into a dynamic adaptable structure that maintains low complexity while achieving high flexibility.
Solution Approach 2:
The patent utilizes parameter changes by allowing different card programs to be defined through varying parameter sets rather than structural modifications. This enables the system to maintain a consistent core architecture while achieving adaptability through parameter variation, resolving the contradiction between simplicity and flexibility.
2Loss of time
If conventional systems restrict the number of new card programs, then processing delays are reduced, but usability and reach are limited
Solution Approach 1:
The patent applies preliminary action by pre-defining parameter templates and configurations that can be rapidly deployed. This allows the system to quickly generate new card programs without extensive processing delays, as the foundational parameter structures are prepared in advance and can be customized efficiently for different use cases.
Solution Approach 2:
By enabling parameter changes through template-based configuration, the system can rapidly create multiple card programs with different parameters without requiring complex processing for each new program. This resolves the contradiction by allowing unlimited card program creation while maintaining efficient processing speeds.
3Adaptability or versatility
If conventional systems integrate multiple systems and devices, then card program capabilities are enhanced, but integration complexity and time delays increase
Solution Approach 1:
The patent implements universality by creating a unified parameter configuration system that can accommodate multiple card programs and use cases through a single integrated framework. This universal approach allows different card programs to share common infrastructure while maintaining unique parameter configurations, reducing integration complexity while preserving enhanced capabilities.
Solution Approach 2:
The patent applies segmentation by dividing the card program configuration into independent parameter modules that can be selectively combined. This allows the system to integrate multiple systems and devices by organizing their functionalities as discrete parameter segments that can be assembled without creating proportional integration complexity.
Data Source
AI summary
This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize machine-learning to automatically generate card management programs with varying configurations of card parameters. For example, the disclosed system determines predetermined card parameter configurations from different card parameter categories for generating a card management program. In particular, the disclosed system utilizes a machine-learning model to generate card usage scores for various combinations of the predetermined card parameter configurations. The disclosed system utilizes the card usage scores generated by the machine-learning model to generate a bundled set of parameter configurations including a combination of a subset of the predetermined card parameter configurations. The disclosed system also provides the bundled set of parameter configurations as a recommendation for generating the card management program.