AI Data Processing System for Enterprise Outlier Detection
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Solution Overview
Problem
Current data processing systems in enterprise applications, particularly in complex supply chain management and blockchain-based systems, face inefficiencies due to limited parameter processing, oversight of dependent parameters, and challenges with real-time alterations, leading to inefficient functioning and security vulnerabilities.
Innovation Solution
An AI-based data processing system that identifies outliers and recommends actions by utilizing an AI engine with machine learning algorithms, neural networks, and blockchain integration to automate approval flows, determine execution paths, and ensure secure transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data processing systems are used in enterprise applications, then the system structure is simple and easy to implement, but the system cannot handle complex parameters and dependent variables efficiently, leading to oversight of critical factors
Solution Approach 1:
The patent segments the data processing system into multiple specialized components: AI engine for intelligent analysis, outlier detection module for anomaly identification, dependency tracking module for relationship mapping, and blockchain module for secure execution. Each component handles specific aspects of complex parameter processing, enabling the system to manage enterprise-level complexity while maintaining modularity and manageability.
Solution Approach 2:
The patent introduces a new dimension of processing by integrating AI-based outlier detection and dependency analysis alongside traditional data processing. This multi-dimensional approach allows the system to simultaneously handle routine operations and complex parameter relationships, transforming the processing architecture from flat to hierarchical with multiple operational layers.
2Adaptability or versatility
If real-time alterations are implemented in enterprise applications, then the system responds dynamically to changes, but glitches and errors may arise that the system cannot detect
Solution Approach 1:
The patent implements continuous feedback mechanisms where the AI engine constantly monitors data alterations and their impacts. When real-time changes occur, the system immediately detects outliers, analyzes dependency violations, and provides corrective recommendations, creating a closed-loop control system that maintains reliability during dynamic operations.
Solution Approach 2:
The patent performs preliminary validation and outlier detection before executing real-time alterations. By pre-identifying potential glitches and dependency conflicts, the system prevents errors from propagating, ensuring that only validated changes are implemented while maintaining real-time responsiveness.
3Reliability
If decentralized systems with blockchain are used, then security and transparency are improved, but the system becomes resource-consuming and costly due to frequent blockchain interactions
Solution Approach 1:
The patent applies partial blockchain integration by using blockchain technology only for critical security functions and immutable record-keeping, rather than for all data processing operations. The AI engine and outlier detection operate in-memory or in traditional databases, resorting to blockchain only when security verification or permanent audit trails are required, thus reducing resource consumption while maintaining security.
Solution Approach 2:
The patent introduces an intermediary layer (the AI engine and processing server) between the enterprise application and the blockchain network. This intermediary handles most processing locally and only interacts with blockchain when necessary, reducing the frequency and cost of blockchain transactions while maintaining security through selective verification.
4Measurement precision
If comprehensive parameter processing is implemented to consider all dependent variables, then the accuracy and completeness of data analysis is improved, but the computing efficiency and processing speed decrease
Solution Approach 1:
The patent performs preliminary identification and categorization of parameters and their dependencies before full analysis. The system pre-maps dependency relationships and identifies critical parameters that require intensive analysis, allowing it to focus computational resources on high-impact areas while maintaining comprehensive coverage of all parameters.
Solution Approach 2:
The patent applies different processing intensities to different parameters based on their importance and complexity. Critical parameters with high impact on outcomes receive full AI-based analysis and outlier detection, while less critical parameters use streamlined processing, optimizing the balance between accuracy and speed across the entire parameter set.
Data Source
AI summary
The present invention provides an artificial intelligence-based data processing system and method for enterprise application. The data processing system and method are configured to receive an input data for executing a task at a server, identify and fetch one or more outliers from a data network based on the task to be executed, process the one or more outliers by at least one outlier data model trained on a historical outlier dataset to identify one or more glitches in execution of the task and in response to the recommended action, determine by at least one path identifier data model, at least one path for execution of an action.