Neural Network Intelligence Report Generation via Data Normalization
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Solution Overview
Problem
Traditional data analytics solutions face computational intensity issues, leading to high costs and inefficient processing, which hinders their ability to generate effective intelligence reports.
Innovation Solution
A processor-implemented method using neural network models that involves obtaining and normalizing datasets, applying statistical modeling techniques, and generating intelligence reports through clustering and analysis, which includes filtering missing or duplicate elements and images, and training on incoming datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analytics solutions process large datasets using computationally intensive processes, then they can generate intelligence reports, but the computational cost becomes huge and processing efficiency deteriorates
Solution Approach 1:
The patent segments the data processing workflow into distinct phases: data normalization, statistical modeling technique selection, clustering analysis, and neural network-based intelligence report generation. This segmentation allows each phase to be optimized independently, reducing overall computational complexity while maintaining accuracy.
Solution Approach 2:
The patent applies preliminary data normalization and filtering operations before main processing. By pre-processing the dataset to remove duplicates and normalize values, the system reduces the complexity of subsequent statistical modeling and clustering operations, thereby improving processing efficiency without sacrificing report accuracy.
2Reliability
If traditional data analytics solutions use computationally intensive processes, then they can analyze data, but the computational cost increases and system performance deteriorates
Solution Approach 1:
The patent changes key processing parameters by selecting statistical modeling techniques based on dataset characteristics rather than using fixed intensive algorithms. The system adapts parameters such as clustering methods and neural network configurations to match data properties, reducing computational cost while maintaining analysis reliability.
Solution Approach 2:
The system performs self-optimization by automatically selecting appropriate statistical modeling techniques and clustering algorithms based on the normalized dataset characteristics. This self-service approach eliminates the need for manual configuration of computationally intensive parameters, reducing energy consumption while maintaining reliable data analysis.
3Ease of manufacture
If the system processes large datasets without enhancing processing units, then it avoids hardware upgrades, but the system cannot give desired output
Solution Approach 1:
The patent achieves desired output quality without hardware enhancement by changing algorithmic parameters - selecting statistical modeling techniques and clustering methods optimized for the given dataset. This software-based parameter optimization allows the system to maintain high output quality while avoiding complex hardware upgrades, ensuring easy deployment.
Data Source
AI summary
Statistical modeling techniques based neural network models for generating intelligence reports is provided. The system obtains test dataset and training dataset, each of which include at least one of images and elements. Statistical modeling techniques are identified and selected based on the test dataset for normalizing the test dataset to obtain normalized dataset. The system further associates, using one or more clustering techniques a unique cluster head to at least one of (i) normalized elements set and (ii) normalized images set in the normalized dataset to obtain a labeled dataset. The labeled dataset is further analysed by integrated trained modeling techniques into neural network model(s) and intelligence reports are generated.