Neural Network Data Engine for Accurate Entity Scoring
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Solution Overview
Problem
Current computation engines face inaccuracies in determining scores for entities, leading to unreliable measures, which can impact associations and decisions related to those entities.
Innovation Solution
A data engine system utilizing a neural network configuration with a training module, working module, and incremental training module to process and normalize data, detect outliers, and generate accurate assessment scores by configuring the neural network with processed data, thereby improving score determination accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computation engines are used to determine scores for entities, then the system is simpler and easier to operate, but the measurement precision and reliability of the scores are insufficient
Solution Approach 1:
The patent replaces traditional computation engines with a neural network-based data engine. The neural network configuration includes multiple layers (input layer, hidden layers, output layer) that process entity data through learned patterns and relationships, substituting conventional algorithmic approaches with a machine learning system that achieves superior measurement precision in score determination
Solution Approach 2:
The patent transforms the computation engine by changing its fundamental parameters and structure. Instead of using traditional scoring algorithms with fixed rules, the system employs a neural network with configurable layers, nodes, and weights that can be trained and adjusted. This parameter transformation enables the system to adapt to different scoring scenarios and achieve higher accuracy
2Reliability
If traditional computation engines are used, then the device complexity is lower, but the reliability of entity assessments deteriorates
Solution Approach 1:
The patent replaces traditional computation engines with a neural network-based data engine. The neural network configuration includes multiple layers (input layer, hidden layers, output layer) that process entity data through learned patterns and relationships, substituting conventional algorithmic approaches with a machine learning system that achieves superior measurement precision in score determination
Solution Approach 2:
The neural network system incorporates feedback mechanisms through its training process, where the system learns from training data and adjusts its internal parameters (weights and biases) to minimize errors. This feedback loop continuously improves the reliability of entity assessments by learning from past performance and adapting to new patterns in the data
Data Source
AI summary
Various systems, mediums, and methods may involve data engines configured to generate results associated with one or more entities based on neural network configurations. An exemplary system includes a data engine with a training module, a working module, an incremental training module, and a neural network. The data engine may process data associated with the one or more entities and transfer the processed data to an input layer of the neural network. Further, outputs from the input layer may be transferred to a hidden layer of the neural network. Yet further, outputs from the hidden layer may be transferred to an output layer of the neural network. As such, one or more results may be generated from an output layer of the neural network. The one or more results may include an assessment score of the one or more entities.