Application Data Object Management for Validation Accuracy
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Solution Overview
Problem
Managing application data across multiple applications for an applicant is complex, inefficient, and prone to errors due to variations in data across different applications, making it challenging to track and validate changes accurately.
Innovation Solution
A system and method that utilize a processor to receive, store, and validate application data, generating data objects for each application, and transmitting validated data to institutional systems while discarding unvalidated data, with event monitoring and snapshot processing to manage cumulative and elementary changes, ensuring data integrity and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If application data is stored and managed manually across multiple applications, then data can be tracked, but the process becomes complex and inefficient
Solution Approach 1:
The patent creates data objects that are copies of application data stored in a structured database. These data objects can be generated, stored, and managed separately from the original database records, enabling efficient tracking and validation without directly manipulating the source data. The system generates initial data objects when applications are created and updates them when data changes occur.
Solution Approach 2:
The patent segments the data management process into distinct components: data objects representing individual applications, validation mechanisms that check data integrity, and event monitoring systems that track changes. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while improving reliability.
2Reliability
If data validation is performed manually, then data integrity can be ensured, but submission timeliness is delayed
Solution Approach 1:
The patent performs data validation in advance before application submission. Validation rules are defined and applied to data objects prior to submission, identifying any issues that need to be resolved. This preliminary validation ensures data integrity is checked before the submission deadline, preventing last-minute delays.
Solution Approach 2:
The patent implements a feedback mechanism where validation results are communicated back to users. When validation issues are detected, the system provides feedback about specific problems and allows users to correct them. This iterative feedback loop ensures data integrity while maintaining submission timeliness by addressing issues promptly.
3Reliability
If all application data is validated and submitted, then completeness is achieved, but computational resources are wasted on invalid data
Solution Approach 1:
The patent applies validation selectively rather than uniformly to all data. Validation rules are applied based on data type, application context, and priority levels. This partial validation approach ensures that critical data is thoroughly validated while less critical data receives minimal validation, reducing computational overhead while maintaining data completeness for essential information.
Solution Approach 2:
The patent changes validation parameters dynamically based on the state of the application and data object. Validation strictness can be adjusted according to the application type, data sensitivity, and submission stage. This parameter adjustment allows the system to be more lenient with non-critical data and more strict with essential data, optimizing resource usage while ensuring completeness where needed.
4Productivity
If data objects are generated and stored for each application, then data management is streamlined, but storage requirements increase
Solution Approach 1:
The patent creates data objects that serve multiple functions: they store application data, track validation status, monitor events, and enable submission. Rather than creating separate systems for each function, the data object acts as a universal container that handles all these tasks, improving management efficiency without proportionally increasing storage requirements.
Solution Approach 2:
The patent implements a nested structure where data objects contain references to the original database records rather than duplicating all data. The data object nests validation information, event logs, and submission status within a compact structure that links back to the source data. This nesting approach streamlines management while minimizing storage overhead by avoiding complete data duplication.
Data Source
AI summary
Systems and methods are provided for managing program application data. An example system includes a storage device; a communication component; and a processor in communication with the one or more storage devices and a plurality of computing devices via the communication component. The processor is operable to receive, from a computing device on behalf of an applicant, initial program application data for the applicant; and store the initial program application data on the one or more storage devices. The processor is further operable to receive an initialization indicator that at least one program application of one or more program applications is to be created; generate an initial program application data object for the at least one program application; store the initial program application data object on the one or more storage devices; and designate the initial program application data object as the reference program application data object for the at least one program application.