AI Control Device User-Specific Model Generation
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Solution Overview
Problem
Obtaining sufficient training data to build a trained model is burdensome for users, and it is difficult to improve the accuracy of output from a trained model.
Innovation Solution
An AI control device and server device system that identifies individual users and generates trained models based on user-specific input data, allowing for discretionary event detection by learning characteristics of the input data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users generate trained models using conventional methods, then the models can detect abnormalities, but the burden on users for obtaining sufficient training data increases and accuracy improvement becomes difficult
Solution Approach 1:
The system segments the trained model generation process into two parts: (1) server-side processing that automatically collects and processes input data from multiple sources, and (2) user-side execution that simply applies the pre-generated model. This segmentation removes the burden of data collection and model training from users while maintaining high accuracy through sophisticated server-side processing.
Solution Approach 2:
The server acts as an intermediary between raw input data and the trained model. It automatically collects input data from various sources, processes this data, and generates the trained model without requiring direct user involvement in these complex processes. Users only need to interact with the final model through the AI control device.
2Reliability
If users collect sufficient training data manually, then model accuracy can be improved, but the time and effort required increases significantly
Solution Approach 1:
The server performs preliminary actions by automatically collecting, processing, and generating trained models from input data before users need them. This preliminary processing eliminates the time users would otherwise spend on data collection and model training, while ensuring high-quality models are ready for immediate use.
Solution Approach 2:
The system enables self-service by allowing the server to automatically generate trained models using collected input data without requiring user intervention. The server independently performs data collection, processing, and model generation, significantly reducing the time and effort users would need to invest while maintaining model quality.
Data Source
AI summary
An AI control device, which identifies individual users from a plurality of users to receive input data, and is connectable to a server device that generates a trained model based on input data for each user, includes a control unit, and a communication unit connected to the server device. The control unit acquires input data, associates acquired input data and identifying information used to identify the user of the AI control device, and sends the data and information to the server device via the communication unit. The control unit uses the sent acquired input data to execute a trained model that is generated separately from trained models of other users by the server device, and that learns characteristics of acquired input data and detects input data having the same characteristics from unknown input data.