Dynamic Data Control Limits for Real-Time Deviation Detection

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Solution Overview

Problem

Existing data analysis systems face challenges in accurately setting and maintaining data control limits, leading to false positives and false negatives due to stale or inaccurate limits.

Innovation Solution

A computing platform that dynamically configures data control limits by receiving historical data, processing it for input into a control limit prediction model, and adjusting existing control limits based on predicted limits, while also receiving real-time data and sending breach notifications to user devices for feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If control limits are set statically based on historical data, then the system is simple to implement, but the control limits become stale and inaccurate leading to false positives and negatives

Engineering Contradiction:

Improveaccuracy of data control limitsVSAvoidcomplexity of control limit configuration

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic control limits by continuously updating the prediction model with new historical data and real-time feedback. The control limits are no longer static but dynamically adjust based on changing data distributions and user feedback, resolving the contradiction between reliability and complexity through automated adaptation.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback loops where user feedback on false positives and negatives is collected and used to retrain the prediction model. This feedback mechanism continuously improves the accuracy of control limits without requiring manual reconfiguration, addressing the reliability-complexity tradeoff.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If control limits are manually adjusted to improve accuracy, then detection precision improves, but the process becomes time-consuming and labor-intensive

Engineering Contradiction:

Improveprecision of data breach detectionVSAvoidtime required for control limit configuration

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically adjusting control limits through machine learning models that continuously learn from historical data and user feedback. This eliminates the need for manual adjustment while maintaining high detection precision, resolving the contradiction between precision and time investment.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The prediction model performs preliminary processing of historical data to pre-establish control limits before real-time data analysis. This preliminary action reduces the time needed for manual configuration while ensuring accurate detection thresholds are in place.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If the system processes large volumes of historical data to improve model accuracy, then the prediction accuracy improves, but the data processing complexity and computational resources increase

Engineering Contradiction:

Improveaccuracy of predicted control limitsVSAvoidcomplexity of data processing pipeline

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the data processing pipeline into distinct stages: data collection, data cleaning and formatting, model training, and real-time prediction. This segmentation manages complexity by breaking down the large-scale processing into manageable modules that can be optimized independently.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts processing parameters such as data sampling rates, model complexity levels, and computational resource allocation based on the volume and complexity of historical data available. This allows the system to maintain high accuracy while adapting computational complexity to data volume.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250181472A1Dynamically Providing Real-Time Configuration of Data Research Control Limits

Publication Date: 2025.06.05 BANK OF AMERICA CORP

AI summary

Aspects of the disclosure relate to computing hardware and software for dynamic control limit configuration. A computing platform may receive and format historical data. The computing platform may input the formatted data into a control limit prediction model, which may output predicted control limits by weighting, using an ensemble model, outputs from a plurality of other models to produce predicted control limits. The computing platform may adjust existing control limits based on predicted control limits to create actual control limits. The computing platform may receive real time data, and may identify, using the actual control limits and the real time data, a deviation score for the real time data. The computing platform may compare the deviation score to the actual control limits. Based on detecting that the deviation score breaches the actual control limits, the computing platform may send an indication of the breach.