Information Processing Icons Generated From Operation Features
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Solution Overview
Problem
Existing information processing systems face challenges in predicting the content of processing from icons prepared in advance, making it difficult for users to understand the operations associated with them.
Innovation Solution
An information processing system that extracts features defining processing, generates a character string to output an icon, and uses a trained model to acquire icons, allowing for better prediction of processing content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If icons are prepared in advance for interface operations, then the interface structure is simple and easy to implement, but the content of processing cannot be predicted from the icon
Solution Approach 1:
The patent introduces a trained model (AI intermediary) between the icon generation request and the final icon output. The model takes feature definitions of processing operations as input and generates icons that visually represent the processing content, thereby bridging the gap between simple icon implementation and informative icon representation.
Solution Approach 2:
The patent changes the parameters of icon generation from selecting from pre-prepared icons to generating icons based on feature parameters of processing operations. By using the trained model with feature inputs (such as operation type, object, action), the system dynamically generates icons whose visual characteristics correspond to the processing content, making the processing predictable from the icon.
2Device complexity
If a limited number of icons are prepared in advance, then the device complexity is reduced, but it becomes difficult to predict the content of processing from the icon
Solution Approach 1:
The system enables self-service icon generation where the trained model automatically creates appropriate icons based on processing operation features without requiring manual icon preparation or selection. This eliminates the need for maintaining a large library of pre-prepared icons while still providing informative, content-specific icons for each processing operation.
Solution Approach 2:
The patent replaces the mechanical system of manually preparing and managing icon libraries with an automated AI-based generation system. The trained model substitutes the manual process of icon selection and management, dynamically generating icons that match processing operations based on their feature definitions, thereby reducing device complexity while improving information retention.
3Ease of operation
If icons are selected from pre-prepared options, then the ease of operation is improved, but the user cannot easily predict the content of processing
Solution Approach 1:
The system performs preliminary action by pre-defining the feature parameters of processing operations (such as operation type, target object, action verb). These feature definitions are prepared in advance and used as inputs to the trained model, which then generates icons that visually encode the processing content. This preliminary preparation of feature information enables both ease of operation and processing predictability.
Solution Approach 2:
The trained model acts as an intermediary that translates processing operation features into visual icon representations. This intermediary process ensures that icons generated are directly related to the processing content they represent, making the processing predictable from the icon while maintaining ease of operation through automated icon provision.
Data Source
AI summary
An information processing system includes one or plural processors configured to extract one or plural features defining processing designated by a user, generate a character string having content of providing an instruction to output an icon representing the extracted one or the plural features, and acquire one or plural icons by providing the generated character string to a trained model.