AI Process Error Detection and Correction
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Solution Overview
Problem
Conventional process management techniques are resource-intensive and error-prone, relying on complex manual identification and response efforts by specialized personnel to address process errors, which can lead to significant detrimental effects such as lower margins and lost sales.
Innovation Solution
The implementation of artificial intelligence techniques to automatically identify and correct erroneous process actions by discovering process action variants, categorizing them using density-based clustering algorithms, and determining resolution actions, which are then performed automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification and response efforts are used to address process errors, then specialized personnel can detect and correct errors, but the process becomes resource-intensive and error-prone
Solution Approach 1:
The system enables self-service by automatically detecting and correcting process errors without requiring specialized personnel intervention. The AI model autonomously identifies erroneous process actions, categorizes them, determines resolution actions, and executes corrections, allowing the system to serve itself in error management tasks.
Solution Approach 2:
The patent replaces the mechanical manual process with an AI-based automated system. The AI model substitutes human specialists by processing process data, identifying errors through pattern recognition, and executing corrections automatically, thereby eliminating the need for manual mechanical error handling processes.
2Reliability
If manual processes are used for error identification and correction, then human expertise can be applied, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The system performs self-service error correction by automatically detecting errors, determining appropriate resolution actions, and executing corrections without human intervention. This autonomous operation maintains high reliability while dramatically improving productivity by eliminating resource-intensive manual processes.
Solution Approach 2:
The AI-based system enables continuous error detection and correction operations without the interruptions inherent in manual processes. The system can continuously monitor process data, identify errors in real-time, and execute corrections continuously, thereby maintaining uninterrupted productive operation while ensuring reliable error handling.
3Reliability
If specialized personnel are deployed for error management, then expert judgment can be utilized, but the process requires significant human resources and training
Solution Approach 1:
The system replaces the need for specialized personnel with an AI model that performs error detection and correction autonomously. The AI model captures expert judgment capabilities while eliminating the requirement for human specialists, thereby maintaining high decision accuracy while reducing human resource requirements to minimal levels for system oversight.
Solution Approach 2:
The AI model serves multiple functions that previously required different specialized personnel: it detects errors, categorizes them, determines resolution strategies, and executes corrections. This multi-functional capability consolidates what previously required multiple specialized roles into a single automated system, reducing overall human resource requirements while maintaining expert-level performance.
4Ease of operation
If conventional process management techniques are used, then established procedures can be followed, but the process becomes complex and difficult to manage at scale
Solution Approach 1:
The patent replaces complex manual process management mechanics with an AI-based automated system. The AI model simplifies operation by automatically handling error detection and correction without requiring personnel to navigate complex manual procedures, thereby improving ease of operation while managing system complexity through automation.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automatically identifying and correcting erroneous process actions using artificial intelligence techniques are provided herein. An example computer-implemented method includes discovering, during execution of a given process, one or more process action variants by processing data related to the given process using at least a first set of artificial intelligence techniques; categorizing at least a portion of the discovered process action variants into one or more groups based at least in part on historical process-related data and at least one density-based clustering algorithm; determining at least one resolution action in response to at least a portion of the one or more discovered process action variants by processing data pertaining to the one or more groups using at least a second set of artificial intelligence techniques; and performing the at least one determined resolution action.