Neural Network Intermediary Layer for Real-Time Enterprise Data Integration
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Solution Overview
Problem
Current enterprise application integration methods are costly, complex, and fail to provide real-time decision support due to the lack of a unifying structure, leading to isolated data silos and inefficient information sharing within organizations.
Innovation Solution
A system utilizing distributed intelligence processors with neural agents and an Executive Control Module that enables real-time data integration and decision support without physical data movement, using plug-ins to connect data and transaction sources, and providing actionable intelligence through a Heads Up Display without altering existing applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If physical data integration and custom screen modifications are implemented, then data sharing capability is improved, but system complexity and cost increase
Solution Approach 1:
The patent introduces a neural network as an intermediary layer that sits between existing enterprise applications and users. This neural network layer provides data integration and analysis capabilities without requiring modifications to the underlying applications or physical data movement, thus improving data sharing while maintaining system simplicity
Solution Approach 2:
The patent creates virtual copies of data through neural network processing and presentation layers. Instead of physically integrating data across systems, the neural network creates intelligent copies and representations of data that can be accessed and analyzed without altering the source systems or requiring complex integration infrastructure
2Loss of time
If real-time data integration is implemented, then decision support speed is improved, but implementation cost and complexity increase
Solution Approach 1:
The neural network is trained in advance on historical data and patterns, so that when real-time queries are made, the network can immediately provide intelligent insights without requiring complex real-time data aggregation or processing infrastructure. The heavy lifting of pattern recognition is done beforehand during training phases
Solution Approach 2:
The neural network acts as a real-time intermediary that processes queries and provides instant intelligent responses without requiring complex real-time integration between multiple enterprise systems. It achieves real-time performance by maintaining trained models that can independently analyze data patterns
3Loss of information
If existing applications are modified for integration, then data accessibility is improved, but compatibility and maintenance difficulty increase
Solution Approach 1:
The neural network serves as an intermediary layer between users and existing applications, providing data accessibility and intelligent analysis without requiring any modifications to the underlying applications. This preserves application compatibility while improving data accessibility through the neural network's ability to interface with multiple data sources
Solution Approach 2:
The patent segments the data access functionality from the application layer by introducing a separate neural network layer. This allows data accessibility to be improved independently of the existing applications, maintaining their original functionality and compatibility while adding intelligent access capabilities through the neural network segment
Data Source
AI summary
A system and integration infrastructure to provide a distributed matrix or neural network of connected real-time decision support modules designed to perform business intelligence evaluations in real time. The system and integration infrastructure provide a network of intelligence superimposed upon any company's existing IT data centers, and cloud computing connections. The system is highly customizable to the unique business model deployed by the client company within the best practices of the client company's industry. Whether or not the client company has integrated their diverse enterprise systems, the elements of the matrix are annealed to the various data sources, transaction logs and client software installations currently deployed. These matrix elements or neurons are designed to house critical operational data, determined by the operational model of the client company to be of critical importance. When combined with monitor neurons, they automatically assess the gap between the desired state of a critical element and the current condition in real time. Trigger conditions are pre-established, but modified by an executive controller in real-time, and the system is pre programmed to automatically respond in a prescribed manner to critical conditions having been met even when these conditions come from otherwise stove-piped enterprise applications.