Image-Based Operation Recommendation Model for Printers
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Solution Overview
Problem
Existing methods for estimating user operations in electronic devices, such as printers, do not adequately consider image information, leading to inefficiencies and increased user burden, as they fail to account for the varying preferences and requirements based on different types of image data.
Innovation Solution
An information processing device that learns the relationship between image information and user operations using a machine learning model, allowing it to recommend optimal operations based on the input image information, thereby reducing user input requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If general operation estimation methods are used without considering image information, then the system complexity is reduced, but the estimation accuracy and user satisfaction deteriorate
Solution Approach 1:
The system performs preliminary machine learning training to build a model that captures the relationship between image information and user operations. This pre-computed model enables accurate real-time recommendations without adding complex processing during actual operation, thus maintaining low system complexity while improving estimation accuracy.
Solution Approach 2:
The patent introduces an intermediate learnt model that mediates between raw image information and operation recommendations. This model acts as a bridge, processing image data offline to create a compact representation that can be quickly queried during operation, avoiding the need for complex real-time image analysis while maintaining high accuracy.
2Measurement precision
If image information is incorporated into operation estimation, then the operation recommendation accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs image information processing and model training in advance, before actual operation estimation is needed. The learnt model stores pre-processed relationships between image characteristics and user operations, enabling fast retrieval during real-time use without requiring complex computational resources at operation time.
3Adaptability or versatility
If a learnt model based on machine learning is used, then the adaptability to different image types and user preferences is improved, but the device complexity and learning resources required increase
Solution Approach 1:
The patent creates a universal learnt model that can handle multiple types of image information and various user operations through a single unified framework. This model learns general patterns from training data that apply across different image types and operations, eliminating the need for separate specialized models for each case and thus reducing overall system complexity.
Data Source
AI summary
The information processing apparatus includes a reception section, a processor, and a storage section. The storage section stores a learnt model obtained by mechanically learning the relationship between image information and operation information based on a data set in which the image information and the operation information indicating a user operation are associated with each other. The reception section receives image information as an input. The processor performs a process of displaying a recommended operation associated with the image information received as an input based on a learnt model.