Document Classification Rule Update for Multi-User Storage
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Solution Overview
Problem
Conventional image forming apparatuses fail to accurately store documents in desired folders, leading to operational burdens when multiple users need to classify and store images, as the storage destination is often misaligned with user intentions, causing difficulties in document retrieval and reuse.
Innovation Solution
An image forming apparatus with a processor that updates user classification rules based on specific feature quantities, allowing users to set preferred storage destinations for documents with unique characteristics, and applies overall classification rules when user-specific rules are absent, ensuring documents are stored in folders intended by multiple users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the classification rule is updated based on the operation log of a single user, then the storage destination can be adjusted to match that user's preference, but documents with similar feature quantities stored by other users may be incorrectly placed in the same folder, reducing accessibility for multiple users
Solution Approach 1:
The classification rule is segmented into user-specific rules and overall rules. Each user has their own classification rule stored in association with their user ID, which is applied preferentially to documents they operate. If no user-specific rule exists, the overall classification rule is applied. This segmentation allows the system to adapt to individual user preferences while maintaining reliability for multi-user environments.
Solution Approach 2:
The system applies different classification qualities to different users. User-specific classification rules are applied when available (local quality for each user), while the overall classification rule serves as a default for users without specific rules. This local quality approach ensures that each user's documents are stored according to their specific preferences when possible, while maintaining a consistent overall behavior when individual preferences are not defined.
2Extent of automation
If the storage destination is automatically determined based on feature quantity, then the classification process is automated, but users cannot set their own preferred storage destinations, increasing operational burden when documents need to be stored in specific folders
Solution Approach 1:
The system performs preliminary action by automatically determining storage destinations based on feature quantities before user intervention is needed. When a user operates a document, the system first checks if a user-specific classification rule exists and applies it automatically. This preliminary automated classification reduces the operational burden on users while still allowing them to set their own preferences through the operation device.
Solution Approach 2:
The system provides self-service by automatically applying user-specific classification rules that were previously set by users through the operation device. Users can set their preferred storage destinations once, and the system automatically applies these rules to subsequent document operations, reducing the need for repeated manual intervention while maintaining user control over storage locations.
3Adaptability or versatility
If the classification rule is regenerated using operation logs from unknown folder images, then the system can learn from user operations, but the learned rules may reflect unusual or non-representative user behavior, reducing the reliability of future classifications
Solution Approach 1:
The operation log is segmented by user ID, and classification rules are generated and stored separately for each user. This segmentation allows the system to learn from each user's specific behavior patterns without being influenced by unusual operations from other users. Each user's classification rule is independently generated and stored in association with their user ID, ensuring that the learned rules reflect typical rather than unusual behavior.
Solution Approach 2:
The system extracts and stores user-specific classification rules separately from the overall classification rule. By taking out the user-specific rules from the general rule set and storing them in association with user IDs, the system can selectively apply only the relevant user-specific rules to each document operation, filtering out unusual or non-representative behavior from the learning process.
Data Source
AI summary
An image forming apparatus includes an operation device; a storage that stores classification determination information and a user classification rule; a memory that stores instructions; and a processor. The processor executes the instructions stored to: input image data on a document; acquire the input image data on the document; acquire, from the acquired image data, feature quantity; and update the user classification rule. The user classification rule is information in which a criterion for classifying a document having specific feature quantity is set for each user in association with the specific feature quantity. When the user uses the operation device to perform a specific classification operation on image data on a document having the acquired feature quantity, the processor executes the instructions to change a criterion of the user classification rule of the user associated with the acquired feature quantity to a criterion corresponding to the specific classification operation.