Neural Network Vector Database for Privacy-Compliant Data Extraction
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Solution Overview
Problem
Conventional systems lack a mechanism to extract information from ancillary data in training datasets, which are often lost due to privacy laws, computational constraints, or other factors, rendering them inaccessible for further analysis.
Innovation Solution
A method and system for classifying historic data derived from neural networks by creating a vector database from the first set of layers up to an intermediate layer of a neural network, retraining the second set of layers based on a new classification objective, and processing the vector database to generate object or attribute classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If training data is stored and retained for future analysis, then information availability is improved, but data privacy compliance and storage costs worsen
Solution Approach 1:
The patent extracts only the essential vector representations from the original training data that are needed for future analysis, separating them from the complete original datasets. This extraction allows retrieval of useful information while discarding or anonymizing the rest, thus complying with data privacy requirements.
Solution Approach 2:
Instead of storing the original training data, the system creates vector database copies that contain only the necessary extracted features and representations. These vector copies serve as sufficient substitutes for future analysis tasks without containing the sensitive original data.
2Adaptability or versatility
If complete training datasets are retained for retraining, then model adaptability is improved, but computational resources and storage requirements worsen
Solution Approach 1:
The system extracts only the essential vector representations from the original training data that capture the key features needed for model retraining. This extraction reduces the data volume from complete datasets to compact vector forms while preserving the necessary information for adapting models to new tasks.
Solution Approach 2:
The patent transforms the original training data into a different parameter representation (vectors) that is more compact and efficient for storage and processing. This parameter transformation maintains the essential information needed for model retraining while significantly reducing storage requirements.
3Loss of information
If vector database is processed by retrained layers, then information extraction capability is improved, but system complexity worsens
Solution Approach 1:
The system segments the neural network into fixed initial layers and retrainable subsequent layers. The vector database is processed by only the retrained layers, which are separate and modular components. This segmentation allows information extraction without requiring the entire original system, reducing operational complexity.
Solution Approach 2:
The system performs preliminary extraction of vector representations from the training data before the actual analysis task. These pre-extracted vectors are stored in the vector database and can be directly processed by retrained layers, eliminating the need to reprocess the complete original datasets and simplifying the analysis workflow.
Data Source
AI summary
A method for classifying historic data derived from neural networks is disclosed. The method includes creating a vector database based on vector data derived from a first set of layers up to an intermediate layer of a neural network. The vector data corresponds to a plurality of objects and a plurality of attributes processed by the neural network based on a first set of training data used to train the neural network for a first classification objective. The method includes retraining a second set of layers in the neural network excluding the first set of layers and the intermediate layer based on a second classification objective and a second set of training data. The method includes processing the vector database by the retrained second set of layers. The method includes generating at least one of an object classification or an attribute classification based on the processing.