Customizable AI Model Tuning for Secure Large-Scale Data Analysis
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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 patent implements a universal AI model platform that can perform multiple data analysis tasks across different organizations and domains. The system provides pre-configured AI models that can be selectively applied to various data types (structured, unstructured, semi-structured) and analysis needs (classification, clustering, prediction), eliminating the need to develop separate models for each organization while maintaining task-specific accuracy through parameter customization and selective model deployment
2Reliability
If large data sets are processed using traditional sequential methods, then data privacy is maintained, but processing time increases
Solution Approach 1:
The patent implements parallel processing that divides large data sets into multiple segments or batches that can be processed simultaneously across distributed computing resources. The system partitions the data horizontally or vertically, processes each segment independently using multiple AI model instances, and aggregates results, thereby reducing overall processing time while maintaining data privacy through distributed computation that prevents any single node from accessing the complete data set
Solution Approach 2:
The patent introduces an intermediary processing layer that mediates between raw data and AI model processing. This layer performs data anonymization, pseudonymization, or differential privacy transformations before data enters the AI processing pipeline, ensuring that even during parallel processing across multiple nodes, the original sensitive information remains protected while still enabling effective analysis
3Productivity
If AI models are customized for specific organizational needs, then task performance is improved, but ease of operation decreases
Solution Approach 1:
The patent implements dynamic model configuration that allows users to adjust model parameters, features, and hyperparameters in real-time based on their specific organizational needs and data characteristics. The system provides interactive interfaces where non-technical users can modify model behavior through intuitive controls, and the models dynamically adapt their processing logic, feature selection, and parameter settings without requiring retraining or complex configuration files, thereby maintaining high task performance while improving ease of operation
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.