Machine-Learning Command Verification for Software Product Guides
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Solution Overview
Problem
Manually verifying commands in software product guides is time-consuming and prone to human error, leading to potential installation errors and customer dissatisfaction due to typographical mistakes or misidentification of file names or user identifiers.
Innovation Solution
Implementing a machine-learning model (MLM) trained on textual phrases and process identifiers to automatically verify input commands in software product guides, generating command records that identify the correct processes for each command and storing them in a data structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual verification of commands is performed, then human review can identify errors, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces the manual mechanical verification process with an automated machine-learning model that processes commands programmatically. The MLM analyzes command syntax, identifies target processes, and verifies correctness automatically, eliminating human manual review while improving both speed and accuracy through consistent algorithmic evaluation.
Solution Approach 2:
The system enables self-service verification where the machine-learning model autonomously validates commands without requiring human intervention. The model independently assesses command correctness, identifies process mismatches, and flags errors, allowing the verification process to serve itself rather than relying on external human reviewers.
2Productivity
If automated machine-learning verification is implemented, then verification speed increases and human error is eliminated, but system complexity increases
Solution Approach 1:
The machine-learning model serves multiple functions within a single system: it verifies command syntax, identifies target processes, checks for typos in file names and user identifiers, and provides detailed error reporting. This multi-functionality consolidates what would otherwise require multiple separate verification tools into one unified MLM-based system.
Solution Approach 2:
The patent introduces a machine-learning model as an intermediary layer between command input and verification. The MLM acts as a mediator that translates raw commands into structured analysis, identifying process identifiers and validating syntax through learned patterns, thereby simplifying the overall verification architecture while maintaining high accuracy.
3Reliability
If comprehensive command verification is performed, then installation errors are reduced, but the verification process becomes more complex
Solution Approach 1:
The system performs preliminary verification of commands before installation execution. The machine-learning model pre-validates command syntax, checks process identifier matching, and identifies potential errors in advance, preventing installation errors before they occur rather than detecting them after execution.
Solution Approach 2:
The verification system provides detailed feedback about command correctness, including specific identification of syntax errors, process mismatches, and typographical issues. This feedback mechanism guides users in correcting errors and improves installation reliability by clearly communicating what needs to be fixed.
Data Source
AI summary
A document that includes a plurality of textual portions is accessed. At least some of the textual portions include an input command that is configured to be directed to a process. For each respective textual portion that includes an input command the respective textual portion is input to a machine-learning model (MLM) that has been trained with a plurality of textual phrases and process identifiers that identify corresponding processes, a process identifier that identifies a process to which the input command included in the respective textual portion is to be directed is received from the MLM, and a command record that identifies the input command is stored in a data structure.