Learning-Based Parameter Suggestion for Image Transmission
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Solution Overview
Problem
As the number of past transmission destinations increases, it becomes time-consuming and effort-intensive for users to find a desired transmission destination when processing images, as the history of parameters displayed grows, making it difficult to efficiently specify parameters for new image processing tasks.
Innovation Solution
An information processing apparatus that includes a receiving unit for specifying images and parameters, an execution unit for processing images based on determined parameters, and an output unit that utilizes learning results to suggest parameters related to the features of new images, reducing the effort required in specifying transmission destinations by leveraging the relationship between past processing parameters and image features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a history of past transmission destinations is displayed to help users easily select destinations, then the ease of operation is improved, but the device complexity increases as the number of displayed destinations grows
Solution Approach 1:
The patent segments the transmission destination selection process into two parts: (1) display of recent/history destinations for quick selection, and (2) automatic learning and suggestion of destinations based on image features. This segmentation allows the system to provide both manual selection capability and automated assistance without overwhelming the user with all possible destinations at once.
Solution Approach 2:
The system implements self-service by automatically learning from past transmission operations and generating destination suggestions based on image feature analysis. The learning unit automatically updates the destination suggestion database without requiring user intervention, and the suggestion presentation unit automatically presents relevant destinations to the user, reducing the burden of manual destination management.
2Adaptability or versatility
If all past transmission destinations are displayed as history, then the adaptability is improved, but the loss of time increases as users spend more time searching through numerous destinations
Solution Approach 1:
The system performs preliminary action by pre-analyzing image features and pre-generating destination suggestions before the user needs to select a destination. The learning unit continuously learns from past operations and prepares suggestion data in advance, so when a user needs to transmit an image, relevant destinations are already prepared and presented, eliminating the need to search through all past destinations.
Solution Approach 2:
The system changes the parameter of destination selection from a static list-based approach to a dynamic feature-based approach. Instead of displaying all destinations regardless of relevance, the system transforms the selection process by analyzing image features (such as document type, size, color) and dynamically presenting destinations that match the current image characteristics, thereby reducing search time while maintaining adaptability.
3Productivity
If the system learns and stores relationships between image features and transmission destinations, then the productivity is improved through automated suggestions, but the device complexity increases due to the learning unit and additional processing
Solution Approach 1:
The patent applies universality by designing the learning unit to handle multiple types of image features (document type, size, color, etc.) and multiple destination attributes using a single unified learning framework. The suggestion presentation unit also serves multiple functions by presenting destinations in various formats (list, thumbnail, categorized groups) based on user needs. This multi-functionality reduces the need for separate specialized components for each feature type.
Solution Approach 2:
The patent introduces an intermediary layer (the suggestion presentation unit) that mediates between the complex learning unit and the user interface. This intermediary translates the learned relationships into user-friendly destination suggestions, shielding the user from the underlying complexity of the learning algorithms and data structures while still providing advanced functionality.
Data Source
AI summary
An information processing apparatus includes a receiving unit that receives an operation for specifying an image to be subjected to processing and an operation for determining a parameter to be used in the processing, an execution unit that performs processing based on the determined parameter on the specified image, and an output unit that outputs, when the receiving unit receives an operation for specifying a new image, a parameter that has a particular relationship with a feature of the new image on a basis of a learning result obtained by a learning unit that learns a relationship between a parameter used in the processing that has been performed before by the execution unit and a feature of an image that has undergone the processing.