Augmented Event Code Verification for Data Processing Accuracy
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Solution Overview
Problem
Current data processing systems face challenges in accurately determining the trustworthiness and accuracy of event codes from various external sources, leading to inaccurate determinations and resource wastage, as they often process unverified or untrustworthy data codes without proper verification, resulting in inefficient and unreliable outcomes.
Innovation Solution
The implementation of a computer-implemented method that utilizes augmented event codes, which are identified and processed to determine a predicted risk-aware complexity determination by assigning code-wise risk scores and complexity designations, allowing for more accurate and trustworthy determinations regardless of the source data's accuracy and trustworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data systems process event codes from various external sources without verification, then processing speed and productivity are maintained, but the accuracy and trustworthiness of determinations deteriorate
Solution Approach 1:
The system performs preliminary verification of event codes against trusted data sources before processing. By conducting this verification action in advance, the system ensures that only accurate and trustworthy event codes are processed, thereby maintaining determination accuracy while still enabling efficient processing of verified codes.
Solution Approach 2:
The system introduces an intermediary verification layer that mediates between external event code sources and the main processing system. This intermediary layer validates event codes against trusted sources, acting as a filter that maintains processing efficiency by blocking inaccurate codes before they reach the processing pipeline.
2Productivity
If data systems process untrustworthy event codes, then processing throughput is maintained, but the reliability of determinations deteriorates
Solution Approach 1:
The system implements feedback mechanisms that continuously verify event codes against trusted sources and update the verification status. This feedback loop ensures that only reliable event codes are processed, maintaining determination reliability while allowing the system to process verified codes at high throughput.
Solution Approach 2:
By performing trust verification as a preliminary action before processing, the system ensures that only reliable event codes enter the processing pipeline. This preliminary verification maintains processing throughput for verified codes while preventing unreliable codes from compromising determination reliability.
3Measurement precision
If manual verification of event codes is implemented, then determination accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The system replaces manual verification mechanisms with automated computer-based verification. By substituting mechanical manual checking with automated electronic verification against trusted data sources, the system achieves high verification accuracy while minimizing processing time and resource consumption.
Solution Approach 2:
The system changes the verification parameter from manual human judgment to automated algorithmic comparison against trusted sources. This parameter change enables high-accuracy verification to be performed rapidly and consistently, reducing both processing time and resource requirements compared to manual methods.
4Measurement precision
If comprehensive verification of all event codes is performed, then determination accuracy is improved, but device complexity and processing overhead increase
Solution Approach 1:
The system applies local quality verification by checking event codes against trusted sources only when necessary and for specific code types. This selective verification approach maintains determination accuracy for critical codes while reducing overall system complexity and processing overhead by not verifying all codes uniformly.
Solution Approach 2:
The system implements partial verification by focusing verification resources on the most critical and high-risk event codes rather than uniformly verifying all codes. This partial action approach maintains sufficient determination accuracy for important determinations while reducing device complexity and processing overhead.
Data Source
AI summary
Embodiments of the present disclosure provide for improved determination of complexities associated with event data objects. Such complexity determinations may be identified based at least in part on complexity score(s) generated from any number of augmented event codes. Embodiments identify augmented event codes from any of a myriad of sources, including various service encodings and participant history profiles for particular event entities. The augmented event codes may be processed in a myriad of ways, utilizing the entirety and/or portions thereof, to further enhance the accuracy of the event complexity determined therefrom. Based at least in part on the accurately generated complexity determination(s), one or more prediction-based actions may be initiated that correspond to the particular complexity determination(s), for example such that only the correct prediction-based action is initiated based at least in part on such a determined event complexity.