Customizable AI Model Tuning for Accurate Private Data Categorization
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Solution Overview
Problem
Developing organization-specific artificial intelligence models for diverse data analysis tasks is challenging, requiring software engineers and data scientists, and there are concerns over data privacy and efficiency in processing large data sets.
Innovation Solution
A customizable AI infrastructure enables parallel processing of data science algorithms, allows users to select and adjust AI models, and employs vectorization and blockchain techniques for data protection, with federated learning to safeguard sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If organization-specific AI models are developed for diverse data analysis tasks, then analysis accuracy is improved, but development complexity and resource requirements increase
Solution Approach 1:
The system segments the AI model development process into modular components: data preprocessing modules, model selection modules, training modules, and deployment modules. Each module can be independently configured and optimized, reducing overall system complexity while maintaining analysis accuracy for diverse data types.
Solution Approach 2:
The patent implements a universal AI model framework that can handle multiple data types (structured, unstructured, semi-structured) and various analysis tasks through a single platform. The system provides pre-configured templates and reusable components that can be adapted to different organizational needs without requiring complete model redevelopment.
2Measurement precision
If AI models are trained on large data sets, then model accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary data preprocessing and feature extraction before model training, including data cleaning, normalization, and feature selection. This preliminary action reduces the complexity of the training process and enables faster convergence while maintaining accuracy on large data sets.
Solution Approach 2:
The patent implements distributed computing architecture where data is replicated across multiple nodes for parallel processing. Training data is copied and distributed to various computing nodes that process portions of the data set simultaneously, significantly reducing overall processing time while maintaining model accuracy.
3Measurement precision
If sensitive data is shared for AI training, then model performance is improved, but data privacy security deteriorates
Solution Approach 1:
The system introduces multiple intermediary layers between raw sensitive data and the AI training process. These include data anonymization intermediaries that remove personally identifiable information, encryption intermediaries that protect data during transmission and storage, and federated learning intermediaries that enable distributed training without centralizing sensitive data.
Solution Approach 2:
The patent transforms sensitive data into different parameter representations that preserve the statistical properties needed for training while removing identifiable information. Techniques include differential privacy parameter addition, data perturbation, and transformation to feature spaces that maintain model performance while protecting individual privacy.
Data Source
AI summary
A system for customizing an artificial intelligence model to process a data set. The system includes an electronic processor that is configured to receive a data set, train a plurality of artificial intelligence models using the received data set, and determine, for each the plurality of artificial intelligence models, an accuracy. The electronic processor is further configured to receive a selection of an artificial intelligence model, receive one or more adjustments to one or more features or feature weights of the selected artificial intelligence model, and execute the selected artificial intelligence model with the one or more adjustments to categorize records in a data set different from the received data set.