Learning Device for Automatic Image Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for selecting images to be printed from a large collection are cumbersome due to the need for manual parameter setting, which requires significant user effort and time, especially when different types of images and user preferences are involved.
Innovation Solution
A learning device that performs machine learning on image data associated with instruction history information to automatically recommend images for printing, using a neural network to determine the probability of an image being printed based on user preferences and settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter setting is used to select images for printing, then the selection accuracy can be improved, but the user burden and time consumption increase significantly
Solution Approach 1:
The system automatically performs image selection by analyzing user printing history and behavior patterns, eliminating the need for manual parameter setting. The learning device self-adjusts selection criteria based on accumulated data, allowing the system to serve itself rather than requiring continuous user intervention for parameter configuration.
Solution Approach 2:
The system pre-establishes selection criteria by analyzing user printing history before actual image selection is needed. By performing preliminary learning on user behavior patterns and preferences, the system prepares selection models in advance, so that when image selection is required, the process is rapid and accurate without requiring real-time user input.
2Adaptability or versatility
If comprehensive parameter setting is performed to account for different image types and user preferences, then the personalization and appropriateness of recommendations improve, but the complexity of the system increases
Solution Approach 1:
The system dynamically adjusts selection parameters based on learned user preferences and image characteristics rather than requiring fixed manual configuration. By changing parameters automatically through machine learning on printing history data, the system achieves high adaptability while keeping the user interface simple.
Solution Approach 2:
The learning device acts as an intermediary between the user and the image selection process. It translates complex user preferences and image attributes into automated selection decisions, mediating between the need for comprehensive personalization and the desire for simple operation.
3Ease of operation
If automated image selection is implemented without machine learning, then the user burden is reduced, but the accuracy and appropriateness of recommendations deteriorate
Solution Approach 1:
The system incorporates feedback loops where printing history and user behavior data are continuously analyzed to improve selection accuracy. The learning device uses feedback from actual printing decisions to refine its models, enabling automated selection that becomes increasingly accurate over time while maintaining ease of operation.
Solution Approach 2:
The patent replaces manual mechanical parameter adjustment with automated machine learning systems. Instead of users manually configuring selection criteria, the system uses computational algorithms to automatically determine optimal selection parameters based on data analysis, substituting mechanical user actions with intelligent automation.
Data Source
AI summary
A learning device includes an acquiring unit and a learning unit. The acquiring unit acquires an image and instruction history information that indicates whether or not an instruction for printing was given to the image. The learning unit performs machine learning on conditions of a recommended image to be recommended for printing, based on a data set in which the image is associated with the instruction history information.