Charge Plan Determination Using Aggregated User Data
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Solution Overview
Problem
Existing charge plans for print apparatus consumables do not consider the usage patterns of other users, leading to suboptimal pricing for individual users, as they are based solely on individual user data without accounting for broader usage trends.
Innovation Solution
An information processing apparatus that determines a charge plan by combining individual user data with aggregated usage information from other users, using machine learning to select a plan that balances pricing and printable sheets based on usage patterns, and displays this plan to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If charge plans are determined based solely on individual user data, then the system is simple to operate, but the charge plan suitability is insufficient
Solution Approach 1:
The patent combines individual user data with aggregated usage data from multiple users to determine charge plans. The determination section integrates first use information (individual user) with second use information (other users) and selected charge plan information to propose suitable charge plans, thereby improving charge plan suitability while maintaining system simplicity through automated processing.
2Device complexity
If charge plans are determined using only individual user usage information, then the determination process is straightforward, but the proposed charge plans may not be optimal for the user
Solution Approach 1:
The system incorporates feedback from selected charge plans of other users into the determination process. The determination section uses second use information and selected charge plan information as feedback to improve the accuracy of proposed charge plans for the first user, enhancing charge plan optimality without significantly increasing process complexity through automated machine learning models.
3Adaptability or versatility
If machine learning models are used to determine charge plans based on aggregated user data, then charge plan suitability is improved, but the system complexity increases
Solution Approach 1:
The machine learning model operates autonomously to determine charge plans without requiring manual intervention. The determination section automatically processes first use information, second use information, and selected charge plan information through the trained model to propose optimized charge plans, reducing the perceived complexity for users while maintaining high adaptability and personalization.
Data Source
AI summary
A service providing server providing a delivery service delivering ink includes an obtaining section configured to obtain first use information associated with use of the delivery service by a first user, a determination section configured to determine a corresponding charge plan which is one of charge plans of the delivery service corresponding to the first use information obtained by the obtaining section based on the first use information obtained by the obtaining section, second use information associated with use of the delivery service by a second user, and a selected charge plan which is one of the charge plans of the delivery service selected by the second user, and a display controller configured to display corresponding charge plan information indicating the corresponding charge plan determined by the determination section in a terminal device of the first user.