Prioritized Data Object Processing Under Time Constraints
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Solution Overview
Problem
Conventional investigative processes in industries are inefficient and inaccurate due to their reliance on manual methods and first-in, first-out approaches, leading to high false positive rates and inefficient use of resources, especially when operating under time constraints.
Innovation Solution
Implementing a prioritization system that uses deterministic and probabilistic rules, combined with machine learning models, to categorize processing objects into high and low priority subsets, allowing for automated processing and resource optimization under time constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional FIFO processes are used to investigate processing objects, then all items are investigated in order of arrival, but this leads to high false positive rates and inefficient use of limited resources
Solution Approach 1:
The system performs preliminary automated analysis of processing objects before human investigation to identify and prioritize high-risk items. This preliminary action filters the dataset to present only the most suspicious items to investigators, improving both accuracy and resource efficiency.
Solution Approach 2:
The system changes the prioritization parameter from simple FIFO ordering to a risk-based scoring system that evaluates multiple attributes of processing objects. This parameter transformation enables investigators to focus on items with highest risk scores, reducing false positives and improving resource allocation.
2Measurement precision
If manual investigation processes are used, then personnel can conduct thorough investigations, but this leads to slow processing speed and inability to handle high volumes within time constraints
Solution Approach 1:
The investigation process is segmented into two distinct stages: automated preliminary analysis and manual detailed investigation. This segmentation allows the system to handle high volumes through automation while preserving thorough human analysis for prioritized items, achieving both speed and precision.
Solution Approach 2:
An automated analysis system acts as an intermediary between the large volume of processing objects and human investigators. This intermediary performs initial filtering and scoring, enabling human investigators to work at a manageable pace while maintaining thoroughness.
3Productivity
If high-level filtering is applied to reduce the number of items for investigation, then resource usage is reduced, but this results in high false positive rates and loss of potentially important items
Solution Approach 1:
The system transforms simple filtering into multi-parameter risk scoring, evaluating multiple attributes simultaneously to assign priority scores. This approach reduces false positives by considering combinations of risk factors rather than relying on single-criteria filtering.
Solution Approach 2:
The system incorporates feedback loops where investigation outcomes are used to refine and update the risk scoring model. This continuous learning mechanism improves the accuracy of prioritization over time, reducing false positives while maintaining resource efficiency.
Data Source
AI summary
Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score. An investigatory subset is then composed of the high priority subset and objects from the low priority subset added until a termination time according to a data processing model and the probabilistic ordering.