Learning Model Creation With Blockchain Data Usage Tracking
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Solution Overview
Problem
Existing technologies fail to accurately track and manage the usage aspect of data, particularly medical data, leading to unintended usage and disclosure of unnecessary information during learning model creation.
Innovation Solution
A learning model creation device and method that includes a learning request reception unit, data readout unit, and learning model creation unit, which designates data usage and creates models while preserving learning processes on a blockchain, enabling data usage tracking and preventing unintended disclosure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is provided to multiple learning models, then the utility of the data is improved, but the ability to track and manage data usage deteriorates
Solution Approach 1:
The patent introduces a data management server as an intermediary between data providers and learning model creation devices. This server maintains usage records and manages data access, enabling tracking of data usage across multiple learning models while preserving data utility. The intermediary coordinates data provision and records usage information without preventing multi-model utilization.
2Ease of operation
If data is provided to learning request sources, then the creation of learning models is facilitated, but unintended usage and disclosure of unnecessary information occurs
Solution Approach 1:
The patent implements a feedback mechanism where the data management server monitors and records data usage by learning request sources. This feedback loop enables tracking of which data is used for which learning models, allowing the system to prevent unintended usage and disclosure by providing visibility into data utilization patterns.
Solution Approach 2:
The data management server acts as an intermediary that controls data provision to learning request sources. It manages data access and records usage information, preventing direct access that could lead to unintended disclosure while still facilitating learning model creation through controlled data provision.
3Adaptability or versatility
If various types of information are included in data, then the number of learnable models increases, but the complexity of managing data usage increases
Solution Approach 1:
The patent segments data management into distinct functional components: the data management server handles usage tracking and access control, while learning request sources focus on model creation. This segmentation reduces complexity by distributing management responsibilities and allowing each component to specialize in specific tasks.
Data Source
AI summary
A processor receives a learning request and starts a process of creating a learning model. In a case where the process is started, the processor reads out medical data corresponding to a range designated in the learning request from a medical data management server. Then, the processor creates the learning model using the read out medical data and a learning algorithm designated in the learning request.