Neural Network Model for Automated Distribution System Setup
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Solution Overview
Problem
Current methods for establishing a distribution system with network effects and n-tiered incentives are complex, costly, and require specialized knowledge, making them difficult to implement and prone to human error, especially when trying to set up systems over the Internet or interactive networks.
Innovation Solution
A computerized method using artificial neural network models to automatically establish and manage distribution systems with network effects and n-tiered incentives, allowing users to set up a distribution system without specialized knowledge or equipment, by training neural networks on existing data sets to determine system parameters and generate algorithms for specific goals, enabling on-the-spot implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If current methods are used to establish a distribution system with network effects and n-tiered incentives, then the system can be implemented, but it requires specialized knowledge, multiple non-integrated systems, data centers, and significant time and resources
Solution Approach 1:
The patent combines multiple non-integrated systems and components into a single integrated system. The neural network model unifies previously separate functions including data center parameter establishment, goal algorithm generation, distribution system setup, and incentive management into one cohesive automated system, eliminating the need for multiple separate systems and reducing implementation complexity
Solution Approach 2:
The system enables users to automatically establish distribution systems without requiring specialized knowledge. The neural network model autonomously performs complex tasks including determining data center parameters, generating goal algorithms, and configuring n-tiered incentives, allowing users to benefit from expert-level system setup without needing to understand the underlying complexity
2Productivity
If current methods are used to establish a distribution system, then the system can be implemented, but it is costly and time-consuming
Solution Approach 1:
The neural network model is pre-trained on existing data sets to automatically determine optimal system parameters before actual implementation. This preliminary training phase enables the system to quickly generate appropriate configurations without requiring users to spend time on complex setup procedures, significantly reducing implementation time from what would traditionally be required
3Reliability
If current methods are used to establish a distribution system, then the system can be implemented, but it is prone to human error
Solution Approach 1:
The system applies the specific goal algorithm automatically for each transaction that occurs in the interactive network. This automated feedback loop ensures consistent application of the distribution logic without manual intervention, eliminating human error in transaction processing while maintaining high system reliability through algorithmic consistency
Data Source
AI summary
In one aspect, a computerized method of an artificial neural network model for implementing a distribution of automated interactions with network effects and n-tiered incentives via an interactive network comprising: training one or more artificial neural network models to automatically: establish a set of specified system-wide data center parameters, wherein the artificial neural network model is trained on a set of existing data sets and is used to automatically determine the specified system-wide data center parameters, receive an item added by a user added to the interactive network, wherein for each item added by the user adds to the system, the user assigns a specific goal, provide a set of data for each specific goals, generate a specified goal algorithm for each goal described by the user, automatically establish a distribution with a plurality of network effects and n-tiered incentives for each item the user adds, and for each transaction related to the item that occurs in the interactive network, apply the specific goal algorithm based on the user's goal.