Microsoft technology licensing, llc. (20240118967). FAILURE RECOVERY RECOMMENDATIONS FOR CLI COMMANDS simplified abstract
Contents
- 1 FAILURE RECOVERY RECOMMENDATIONS FOR CLI COMMANDS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FAILURE RECOVERY RECOMMENDATIONS FOR CLI COMMANDS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
FAILURE RECOVERY RECOMMENDATIONS FOR CLI COMMANDS
Organization Name
microsoft technology licensing, llc.
Inventor(s)
CHRISTOPHER O'toole of REDMOND WA (US)
ROSHANAK Zilouchian Moghaddam of KIRKLAND WA (US)
FAILURE RECOVERY RECOMMENDATIONS FOR CLI COMMANDS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240118967 titled 'FAILURE RECOVERY RECOMMENDATIONS FOR CLI COMMANDS
Simplified Explanation
The failure recommendation system for a command line interface (CLI) uses machine learning to predict the most likely command to correct an unsuccessful or failed attempt to perform an intended operation using the CLI. The system is based on a conditional probability model trained on failure-success pairs of commands from CLI telemetry data to learn the most likely command to remediate a failure. The model predicts the most likely command based on a failure type and the failed command, identified through a failure type classifier.
- The system uses machine learning to predict the most likely command to correct a failed operation in a CLI.
- It is based on a conditional probability model trained on failure-success pairs of commands from CLI telemetry data.
- The model predicts the most likely command based on a failure type and the failed command, identified through a failure type classifier.
Potential Applications
The technology can be applied in various industries where command line interfaces are used, such as software development, system administration, and network management.
Problems Solved
1. Helps users quickly recover from failed operations in a CLI environment. 2. Improves efficiency and productivity by providing accurate recommendations for remediation.
Benefits
1. Reduces downtime by quickly identifying and suggesting the correct command to fix a failure. 2. Enhances user experience by offering personalized recommendations based on past telemetry data.
Potential Commercial Applications
Optimizing system administration tasks, improving software development workflows, enhancing network management processes.
Possible Prior Art
One possible prior art could be a similar system that uses rule-based algorithms instead of machine learning to recommend commands for failed operations in a CLI environment.
Unanswered Questions
How does the system handle edge cases or rare failure scenarios?
The article does not provide information on how the system deals with uncommon failure types or scenarios that may not have sufficient training data.
What is the computational overhead of running the failure recommendation system in real-time?
The article does not mention the computational resources required to run the system and whether it can provide recommendations in real-time without significant delays.
Original Abstract Submitted
a failure recommendation system for a command line interface (cli) uses machine learning to predict the most likely command to correct an unsuccessful or failed attempt to perform an intended operation using the cli. the failure recommendation system is based on a conditional probability model trained on failure-success pairs of commands from cli telemetry data to learn the most likely command to remediate a failure. the conditional probability model predicts the most likely command based on a failure type and the failed command. the failure type is identified through a failure type classifier and is used to select the most likely command to remediate a failure from the different events that may lead to a failure.
