Command Prediction Using Multi-User History Grouping
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Solution Overview
Problem
Existing command prediction systems in computer systems rely solely on a user's past commands, which may not accurately predict future commands due to limitations in data scope and accuracy, especially for users with similar roles or system groupings.
Innovation Solution
The system collects and analyzes command history data from multiple users based on system grouping, user role, and time of command execution, using user profiles to determine command prediction preferences and calculate probabilities for suggested commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If command prediction is based solely on a single user's past commands, then the system is simple to implement, but prediction accuracy is limited
Solution Approach 1:
The patent merges command history data from multiple users into a unified prediction model. The system collects commands from different users, groups them by classification data (such as user role, system grouping, time of execution), and uses this aggregated data to improve prediction accuracy for any given user, thereby resolving the contradiction between simplicity and accuracy.
Solution Approach 2:
The system creates a universal command prediction model that serves multiple users simultaneously. By classifying users into groups with similar command patterns and using shared historical data from these groups, the system achieves accurate predictions across different users without requiring separate models for each individual, thus maintaining system simplicity while improving accuracy.
2Measurement precision
If command history data from multiple users is collected and analyzed, then prediction accuracy improves, but data privacy concerns increase
Solution Approach 1:
The patent extracts and removes personally identifiable information from command history data before analysis. The system processes only anonymized command sequences and classification categories, separating the useful predictive patterns from sensitive user identities, thus enabling multi-user data utilization while protecting privacy.
Solution Approach 2:
The system introduces classification data (such as user roles, system groupings, and time periods) as an intermediary layer between raw user commands and the prediction model. This intermediary abstraction allows the system to leverage patterns from multiple users without directly exposing or processing sensitive personal information, thereby balancing accuracy improvement with privacy protection.
Data Source
AI summary
A method, system, and computer program product for intelligent command prediction are provided. The method includes determining a command prediction preference associated with a user from user profile data, and selecting one or more command history repositories responsive to the command prediction preference. The one or more command history repositories include command history data collected from a plurality of users and classification data associated with the plurality of users. The method also includes calculating command probabilities for commands in the command history data of the selected one or more command history repositories as a function of the classification data associated with the plurality of users in relation to the user. The method additionally includes presenting a next suggested command as a command from the command history data of the selected one or more command history repositories with a highest calculated command probability.