Machine Learning Method for Food Sensory Data Privacy
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Solution Overview
Problem
Current systems for sake brewing analysis do not consider the public or private nature of sensory results, limiting their sharing and utilization across organizations, and there is a lack of a systematic approach to manage sensory results as public information in the food industry.
Innovation Solution
A machine learning method that involves a computing device to acquire and store analysis results and sensory results, associate them with public setting information, create a learned model for predicting sensory results, and output private results as public or private, taking into account the privacy settings of the analysis and sensory results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensory results are shared across organizations, then accuracy and efficiency of research and development is improved, but information security and privacy control deteriorate
Solution Approach 1:
The patent segments sensory data into public and private categories, allowing selective sharing. Public sensory results can be freely used for model training while private results remain protected, resolving the contradiction between sharing benefits and security risks through granular data classification and access control.
Solution Approach 2:
The patent introduces a system management company as an intermediary that operates a server to manage sensory result storage and distribution. This intermediary handles the complexity of access control and data sharing policies, enabling organizations to benefit from shared data without directly managing the security risks themselves.
2Adaptability or versatility
If a system manages sensory results as public information, then data sharing and utilization improve, but system complexity increases
Solution Approach 1:
The patent creates a universal platform managed by a system management company that serves multiple organizations simultaneously. The server system provides multi-functional capabilities including data storage, access control, model training, and result distribution, reducing individual organization complexity while enabling broad data sharing through a single unified system.
Solution Approach 2:
The system enables organizations to independently manage their own sensory result publication settings and access permissions through automated processes. The machine learning model automatically handles data retrieval, training, and prediction based on predefined access rules, reducing manual intervention and system management overhead.
Data Source
AI summary
A second device performs receiving an analysis result of analyzing a food sample by an analysis instrument, examining the analysis result to obtain an examination result, acquiring first information regarding whether the analysis result is made public or private and second information regarding whether the examination result is made public or private, storing the first information and the analysis result with the first information and the analysis result associated with each other and storing the second information and the examination result with the second information and the examination result associated with each other, and performing machine learning for predicting an examination result using, as training data, at least either the analysis result or the examination result, the analysis result or the examination result being set public.