User-Based Application Grouping on Table-Top Displays
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Solution Overview
Problem
Existing table-top computing environments struggle to effectively manage applications for multiple users, as they primarily focus on single-user systems and lack the capability to group applications based on user-specific usage patterns in multi-user environments.
Innovation Solution
A method for user-based application grouping in a multi-user environment, which involves collecting geometric information of application windows, such as coordinates and rotation angles, and using a clustering algorithm like K-Nearest Neighbor (K-NN) to classify applications for each user, allowing for the management of system resources and display of application histories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a table-top computing environment is designed for single-user systems, then the system complexity is low and ease of operation is high, but the adaptability to multi-user environments is poor and application management capability is insufficient
Solution Approach 1:
The patent segments the application management system by introducing user identification mechanisms that divide applications into user-specific groups. Each user's applications are managed separately through clustering algorithms that group applications based on geometric information and user interaction patterns, enabling multi-user adaptability while maintaining manageable complexity through modular user-profile-based organization.
Solution Approach 2:
The patent adds a new dimension to application management by incorporating spatial geometric information (coordinates, rotation angles) of application windows as clustering features. This dimensional expansion enables the system to distinguish between different users' application arrangements and preferences, enhancing multi-user adaptability without significantly increasing overall system complexity.
2Ease of operation
If application windows are arranged freely for each user in multi-user environment, then user customization and ease of operation are improved, but the difficulty of detecting and measuring user-specific usage patterns increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors and collects geometric information (coordinates, rotation angles) of application windows and user interaction patterns. This feedback is processed through clustering algorithms that analyze usage patterns and automatically adjust application groupings, making it easier to detect and respond to user-specific behaviors while maintaining high customization capability.
Solution Approach 2:
The patent changes the parameters used for detecting user patterns by focusing on specific geometric features (coordinates, rotation angles) of application windows rather than attempting to analyze all possible user behaviors. This parameter specialization reduces the complexity of detection while maintaining accurate user-specific pattern recognition and customization capability.
3Measurement precision
If clustering algorithms are used to classify applications for each user, then application grouping accuracy is improved and user-specific resource management is enhanced, but the computational processing time and energy consumption increase
Solution Approach 1:
The patent extracts only the essential geometric features (coordinates, rotation angles) of application windows as input data for clustering algorithms, rather than processing complete application metadata or full user interaction histories. This extraction of critical features maintains high grouping accuracy while significantly reducing computational energy consumption by minimizing the data volume requiring complex processing.
4Reliability
If geometric information of all application windows is collected and processed, then the reliability of user-based classification is improved, but the loss of time for data collection and processing increases
Solution Approach 1:
The patent applies partial action by collecting and processing only the most relevant geometric information (coordinates, rotation angles) of application windows rather than comprehensive application data. This selective data collection maintains sufficient reliability for user-based classification while reducing processing time by avoiding unnecessary data gathering and computation of less critical parameters.
Data Source
AI summary
A method for user-based application grouping in a table-top multi-user environment where a plurality of users execute applications individually comprises collecting geometric information of each window of a plurality of applications displayed on a table-top display—each window of the plurality of applications has different geometric information according to the position at which each user uses the table-top display; and processing geometric information of each window of the plurality of applications through a clustering algorithm processing unit by using the collected geometric information as a criterion for classifying the plurality of applications and grouping the processed geometric information for each user. Through grouping of applications currently executed in a table-top environment for each user, a user-specific special function such as a user-specific application history may be provided.