Database Record Prioritization for Rule-Heavy Post-Processing
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Solution Overview
Problem
Existing systems face inefficiencies and delays in processing database records, particularly in environments with complex rules and regulations, leading to resource wastage and delayed payments.
Innovation Solution
A records management and processing system that employs a rules engine, tag definition and assignment module, workflow engine, and AI engine to selectively process records based on predefined conditions and tags, applying machine learning to identify and address inefficiencies and redundancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all database records are processed through complex rules and regulations, then compliance and accuracy are ensured, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent segments the database records into different categories based on their characteristics and risk levels. Low-risk records are processed through expedited pathways with reduced rule checking, while high-risk records undergo comprehensive validation. This segmentation allows the system to maintain high compliance accuracy for critical records while reducing processing time for routine records.
Solution Approach 2:
The system performs preliminary classification and risk assessment of records before they enter the main processing pipeline. By pre-identifying low-risk records that are likely to comply with regulations, the system can apply simplified processing rules upfront, avoiding the need to subject all records to the full complexity of regulatory checks, thus reducing overall processing time while maintaining compliance.
2Manufacturing precision
If comprehensive rule checking is applied to all records, then processing accuracy is maintained, but resource overhead and computational cost increase
Solution Approach 1:
The patent applies local quality by tailoring the level of rule checking to the specific characteristics of each record. Instead of uniformly applying comprehensive rules to all records, the system dynamically adjusts the intensity of validation based on record type, source reliability, and risk indicators. This allows high processing accuracy for critical records while reducing computational resource consumption for routine records.
Solution Approach 2:
The system implements partial action by applying only the necessary subset of rules to each record based on its classification. For low-risk records, a partial set of essential rules is applied rather than the complete rule set, reducing computational overhead. For high-risk or ambiguous records, the full rule checking is performed to ensure accuracy, optimizing the balance between resource consumption and processing quality.
3Reliability
If manual review processes are used for complex records, then processing thoroughness is improved, but processing speed and productivity decrease
Solution Approach 1:
The patent implements self-service by enabling the system to automatically handle the majority of records through intelligent rule-based processing and machine learning models. Only records that genuinely require human expertise are routed to manual review. This allows the system to maintain thorough processing for complex records while dramatically increasing overall productivity by automating routine assessments.
Solution Approach 2:
The system introduces an intermediary layer of automated analysis between complete automation and full manual review. This intermediary uses machine learning models and heuristic algorithms to pre-assess records, flagging only those with genuine complexity or ambiguity for manual review. This intermediary layer maintains processing thoroughness for complex records while improving overall productivity by filtering out records that can be handled automatically.
Data Source
AI summary
Embodiments of the present disclosure are directed to methods and systems for the processing of database records. Processing database records can comprise maintaining a records in a database. A subset of records from the plurality of records can be identified for further process, e.g., based on a value stored in a field of each record. Each record of the identified subset of records can be scored based on a plurality of factors related to each record of the identified subset of records. The identified subset of records can then be prioritized into an ordered list of records based on the score for each record of the identified subset of records and one or more workflows can be executed on the identified subset of records based on the ordered list of records.