Printer Selection Using Learned Model for User Intent
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Solution Overview
Problem
Conventional network print systems struggle to accurately select the most suitable printer for user intentions due to limitations in printer attribute compatibility and user preference, often resulting in suboptimal printer selection.
Innovation Solution
A print system and method that utilizes a learned model based on previous printing data to infer the most suitable printer for new print jobs, incorporating AI technology to connect multiple printers, a server, and a terminal via a network, allowing for user-specific printer selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional printer selection methods are used (displaying default printer and last used printer), then the system is simple to operate, but the printer selection accuracy does not match user intentions
Solution Approach 1:
The patent introduces an intermediary component (printer selection recommendation unit) that analyzes user intentions from input data and matches them with appropriate printers. This intermediary layer bridges the gap between simple display and accurate selection, using learned models to infer user preferences without requiring complex user interaction.
Solution Approach 2:
The patent replaces manual printer selection (mechanical user action) with an automated inference system using learned models. The system substitutes the mechanical process of user browsing and selection with an intelligent system that automatically infers and recommends appropriate printers based on input data analysis.
2Adaptability or versatility
If printer selection is based on predetermined printer functions, then the selection process is straightforward, but the applicability and user satisfaction are limited
Solution Approach 1:
The patent implements dynamic printer selection by using learned models that adapt to user intentions and input data characteristics. Instead of static predetermined function matching, the system dynamically adjusts printer recommendations based on real-time analysis of input data, user behavior patterns, and printer capabilities.
Solution Approach 2:
The patent changes the selection parameters from fixed printer functions to dynamic user intention attributes. The learned model analyzes various parameters in the input data and transforms them into meaningful selection criteria, allowing the system to adapt to different printing scenarios and user preferences flexibly.
3Ease of operation
If the system displays only default and last used printers, then the display is simple, but the user must manually change printer names which is troublesome
Solution Approach 1:
The patent performs preliminary action by pre-analyzing user intentions and pre-selecting appropriate printers before the user actually needs to print. The learned model infers user preferences in advance and prepares printer recommendations, so when printing is needed, the system already has the optimal printer ready, eliminating manual selection time.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically understand user intentions and select appropriate printers without requiring manual user input or intervention. The learned model serves itself by continuously learning from user behavior patterns and automatically making informed printer selection decisions.
Data Source
AI summary
A printer system connects a plurality of printers and a terminal via a network. First, the system inputs data for a new print from the terminal. Next, based on the inputted data, the system uses a learned model that has learned to select one printer among the plurality of printers based on data that was used in previous printing by the plurality of printers, to infer a printer suited to the new print from the plurality of printers. As a result of the inference, the system conveys the obtained printer to the terminal.