Network Data Feature Extraction With Validation Feedback
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Solution Overview
Problem
Current data feature extraction methods in communication networks are inefficient, as they do not consider the usefulness of extracted features, leading to poor network operation performance, especially in scenarios like 3D hologram technology where high-volume data processing poses challenges.
Innovation Solution
A system comprising a feature extraction router and a feature validation center, which collaboratively extract and validate data features. The feature validation center determines whether the extracted feature belongs to a predefined feature set, generates feedback for re-extraction if necessary, and triggers feature-enabled network operations based on valid data features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional feature extraction methods are used without considering usefulness, then extraction speed is maintained, but network operation performance deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the feature validation center evaluates extracted features and provides feedback to the feature extraction router. This feedback loop enables the system to learn from validation results and improve feature extraction accuracy over time, directly addressing the issue of poor network operation performance caused by useless features.
Solution Approach 2:
The patent introduces a feature validation center as an intermediary component between the feature extraction router and the network operations. This mediator validates features before they are used in network operations, ensuring that only useful features contribute to network performance without significantly increasing overall system complexity.
2Adaptability or versatility
If high-volume data from 3D hologram technology is processed, then service capability is enhanced, but network performance and scalability deteriorate
Solution Approach 1:
The patent extracts and processes only the most relevant features from high-volume 3D hologram data rather than processing all raw data. By taking out only the essential features needed for network operations, the system maintains service capability for 3D hologram technology while improving network performance and scalability.
Solution Approach 2:
The patent segments the data processing task into two parts: feature extraction from raw data, and subsequent processing of only the extracted features. This segmentation reduces the computational burden on the network by processing a smaller, more manageable set of features rather than the complete high-volume raw data.
3Productivity
If lossy compression is applied to reduce data size, then data transmission efficiency is improved, but data analytics quality deteriorates
Solution Approach 1:
The patent performs feature extraction as a preliminary action before compression. By identifying and extracting the most important features first, the system ensures that subsequent lossy compression operations preserve the most critical information for data analytics, thereby maintaining analytics quality while improving transmission efficiency.
Data Source
AI summary
There is provided systems and methods for data feature extraction based on feedback from a network. The system may be used generally to enable feature-specific network operations, including one or more of access control, data compression and selective encryption. Further, embodiments provide methods and systems for data compression based on feature similarity and may include privacy protection.