Graph-Based ML for Printer Usage Pattern Analysis
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Solution Overview
Problem
Current methods lack an effective way to analyze data from printers and customer data to recommend sales targets and product upgrades for small to medium-sized businesses, such as printer upgrades or other necessary products, using a graph-based machine learning system.
Innovation Solution
A method and system utilizing a graph-based machine learning computational system that collects and classifies usage data from multifunctional printers, compares it with other printers, identifies usage patterns, and recommends products or services through a graphical user interface, incorporating customer data like business size and industry to determine suitable upgrade needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis methods are used for printer usage data, then implementation is simple, but the ability to identify usage patterns and generate accurate sales recommendations is insufficient
Solution Approach 1:
The patent replaces traditional mechanical data analysis methods with graph-based machine learning computational systems. The neural network processes printer usage data through graph structures, enabling automated pattern recognition and sales recommendation generation without manual analysis, thereby improving accuracy while managing complexity through algorithmic automation.
Solution Approach 2:
The patent introduces graph-based machine learning models as intermediaries between raw printer usage data and sales recommendations. These models act as mediators that transform complex usage patterns into actionable insights, bridging the gap between data collection and sales target identification while maintaining system manageability.
2Measurement precision
If comprehensive usage data from multiple printers is collected and analyzed, then sales recommendation accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary classification of usage data into categories before detailed analysis. By pre-organizing data from multiple printers into structured formats and identifying basic patterns early in the process, the system reduces the computational burden of subsequent graph-based machine learning operations, thereby decreasing overall processing time while maintaining recommendation accuracy.
Solution Approach 2:
The patent segments the analysis process into distinct stages: data collection, classification into usage categories, pattern determination through comparison, and recommendation generation. This segmentation allows parallel processing of different printer data streams and enables the system to handle comprehensive multi-printer data efficiently without excessive processing time.
3Adaptability or versatility
If graph-based machine learning is implemented to analyze printer data, then product recommendation capability improves, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent implements a universal graph-based machine learning framework that can analyze various types of printer usage data (printing, scanning, copying, faxing) and generate recommendations for different product categories. This multi-functional approach allows the same computational system to handle diverse data types and recommendation scenarios, improving versatility while reducing the need for multiple specialized systems.
Solution Approach 2:
The patent enables the system to automatically determine usage patterns and generate sales recommendations without requiring manual configuration or intervention. The graph-based machine learning model self-adjusts to different data patterns and automatically identifies sales targets, reducing implementation complexity by eliminating the need for manual system setup and ongoing manual analysis.
Data Source
AI summary
A method, a non-transitory computer readable medium, and a system are disclosed for recommending products based on usage of one or more multifunctional printers. The method includes collecting usage data from a multifunctional printer; classifying the usage data from the multifunctional printer into one or more categories; comparing the usage data from the multifunctional printer in each of the one or more categories to usage data from one or more other multifunctional printers; determining one or more usage patterns for the multifunctional printer from the comparison of the usage data between the multifunctional printer in each of the one or more categories to the usage data from one or more other multifunctional printers; identifying one or more products or services based on the one or more usage patterns for the multifunctional printer; and recommending one or more products or services for the multifunctional printer on a graphical user interface (GUI).