Dynamic EUC Change Control via Similarity Index
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Solution Overview
Problem
End-User Computing (EUC) applications in enterprise environments face challenges in ensuring proper use and mitigating risks associated with input, output, and calculation perils, necessitating intelligent and dynamic control of changes and control rules.
Innovation Solution
A system that calculates a similarity index for each data entry field in EUC applications based on linked functions and formulas, using machine learning to categorize fields and dynamically update control rules in real-time, applying actions such as approval, denial, or alerts based on historical patterns and reviewer comments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static control rules are applied to EUC applications, then implementation simplicity is maintained, but the system cannot adapt to dynamic changes and evolving risk patterns
Solution Approach 1:
The patent implements dynamic control rules that automatically adjust based on real-time analysis of change patterns, historical data, and risk assessments. The control system transitions from static pre-defined rules to dynamic rules that evolve with usage patterns, enabling adaptability to changing conditions while managing complexity through automated learning mechanisms
Solution Approach 2:
The control system performs self-learning and self-adjustment by automatically analyzing historical approval/denial patterns, user behavior, and change characteristics. The system autonomously updates control rules without requiring manual reconfiguration, allowing adaptability while keeping operational complexity manageable through automated self-optimization
2Reliability
If comprehensive controls are applied to all data entry field changes, then reliability is improved, but productivity decreases due to excessive approvals and denials
Solution Approach 1:
The patent applies differentiated control strategies to different data entry fields based on their characteristics, sensitivity, and impact. High-risk fields receive stricter controls while low-risk fields have streamlined or automated approval processes. This localized approach maintains reliability for critical changes while preserving productivity for routine modifications
Solution Approach 2:
The system applies controls selectively rather than universally - using partial action by implementing rigorous controls only where necessary based on risk assessment, and using automated or simplified controls for low-risk scenarios. This prevents excessive manual intervention while maintaining adequate oversight where needed
3Measurement precision
If manual review of all changes is performed, then measurement precision of change appropriateness is improved, but loss of time increases significantly
Solution Approach 1:
The system implements automated feedback loops that analyze historical approval/denial patterns, user credentials, and change characteristics to pre-assess change appropriateness. This automated preliminary assessment provides accurate risk evaluation for most changes, requiring manual review only for exceptional cases, thereby maintaining measurement precision while dramatically reducing time loss
4Adaptability or versatility
If dynamic real-time control decisions are implemented, then adaptability to specific change contexts is improved, but device complexity increases
Solution Approach 1:
The real-time control system performs self-learning by automatically analyzing patterns in historical data, user behavior, and change characteristics. The system autonomously develops contextual understanding and adjusts control decisions based on learned patterns, providing context-specific adaptability while managing complexity through automated learning rather than manual rule configuration
Data Source
AI summary
Embodiments of the invention are directed to intelligently and dynamically controlling both changes made within EUC applications and the control rules associated with such changes. A similarity index is calculated/assigned for each data entry field (i.e., cell/intersection) and the controls implemented when a changes to data in the entry fields occurs is based on the similarity index. In other embodiments, a change to data entry fields dynamically prompts analysis of the change based on historical approval and/or denial patterns specific to the EUC application, the data entry field(s) and/or the user of the application. In response to the analysis the control rules may be dynamically updated, and applied to the current change. In other embodiments, inputs, such as reviewer's comments, are the basis for determining a need to update existing controls or add new controls associated with data entry field(s) and the conditions associated therewith are determined and applied.