Print Setting Recommendation via User Activity and Job Data Classification
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Solution Overview
Problem
Current print systems lack an intelligent mechanism to automatically manage print settings based on print job content data, enterprise policies, and user printing patterns, often resulting in incorrect print intents and material waste.
Innovation Solution
A system integrating machine-learning models to retrieve print jobs, analyze user activity, classify print job data, and recommend optimal print settings, which are then applied to the image processing device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If default print settings are enforced by enterprise administrators, then cost saving policies are implemented, but print job accuracy deteriorates and material waste increases
Solution Approach 1:
The system enables print settings to be automatically determined through machine learning analysis of print job content, user behavior patterns, and enterprise policies. The intelligent system serves itself by making autonomous decisions about optimal print settings without requiring manual user configuration or strict administrator enforcement, thereby achieving both cost savings and print job accuracy simultaneously
Solution Approach 2:
The system dynamically adjusts print parameters (such as color mode, paper type, quality settings) based on real-time analysis of print job content characteristics, user preferences, and enterprise policies. This parameter optimization enables the system to achieve cost savings by selecting appropriate settings while maintaining print job accuracy through content-aware adjustments
2Reliability
If manual print setting configuration is required for each print job, then print job accuracy can be ensured, but user operation complexity increases and time is lost
Solution Approach 1:
The intelligent system automatically analyzes print job content and determines optimal print settings without requiring user intervention. The system serves itself by autonomously configuring print parameters based on content analysis, user behavior patterns, and enterprise policies, thereby ensuring print job accuracy while eliminating manual configuration complexity
Solution Approach 2:
The system incorporates feedback mechanisms that learn from user corrections and print job outcomes. When users manually adjust print settings or correct automatic recommendations, the system learns from this feedback to improve future automatic settings, thereby maintaining accuracy while reducing operational complexity over time
3Ease of operation
If last used or default print settings are applied automatically, then ease of operation is maintained, but print intent accuracy deteriorates
Solution Approach 1:
The system automatically analyzes each print job's content characteristics and autonomously determines the most appropriate print settings. Through self-service content analysis, the system generates accurate print intent recommendations without requiring users to manually configure settings or rely on generic defaults, thereby achieving both ease of operation and print intent accuracy
Solution Approach 2:
The system dynamically changes print parameters based on real-time content analysis rather than relying on static default settings. By adjusting parameters such as color mode, paper type, and quality settings according to the specific characteristics of each print job, the system maintains ease of automatic application while ensuring print intent accuracy
4Loss of energy
If enterprise default print settings are enforced, then cost saving policies are implemented, but user printing flexibility is reduced
Solution Approach 1:
The system dynamically optimizes print parameters by analyzing both enterprise cost-saving policies and user-specific printing patterns. Through content-aware parameter adjustment, the system achieves cost savings while adapting to different user needs and print job requirements, thereby maintaining both cost efficiency and printing flexibility
Solution Approach 2:
The system applies different print settings strategies to different users and print job types while adhering to enterprise policies. By customizing settings locally for each user's behavior patterns and specific print job characteristics, the system achieves cost savings through policy compliance while maintaining user flexibility through personalized recommendations
Data Source
AI summary
A system provides features for retrieving a print job. The print job includes print data, a source application and source job data type. The system determines if the print job requires a user activity analysis based on the source application and the source job data type. If user activity analysis is required, request a user-activity machine-learning model to provide user activity analysis data. The system establishes a relation between the print job and the user activity analysis data to generate input information to a job-data classification machine-learning model. The job-data classification machine-learning model provides print job data classification details as input information to a print setting recommendation learning model. The print setting recommendation machine-learning model processes the received print job data classification details and outputs a print setting recommendation. The system applies the print setting recommendation to the print job and processes the print job on the image processing device.