AI User Manual Generator for Software Operations
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Solution Overview
Problem
The current process of creating software user manuals is time-consuming and inefficient, requiring manual description and organization of software functions, often necessitating separate production of textual and video versions, which can lead to duplicate work and reduced quality.
Innovation Solution
An AI-driven method using deep learning to automatically generate and organize user manuals by training neural networks with software training data, identifying operations and user interfaces, determining task order, and calculating level scores for intelligent manual assembly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual description and organization of software functions is used, then user manual quality can be maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The system enables self-service by allowing the neural network to automatically generate and organize user manual content without requiring manual intervention for each section. The model processes software documentation automatically, extracting operations, windows, and task relationships to assemble the complete user manual, thereby eliminating time-consuming manual writing while maintaining quality through AI-based organization and structure.
2Adaptability or versatility
If separate production of textual and video versions is implemented, then comprehensive documentation coverage is achieved, but duplicate work and reduced quality occur
Solution Approach 1:
The neural network model serves multiple functions simultaneously - it generates textual descriptions, identifies operations, determines task sequences, and organizes content structure all within a single unified process. This multi-functional approach eliminates the need for separate production pipelines for different documentation formats, preventing duplicate work while maintaining comprehensive coverage through a single comprehensive generation process.
3Productivity
If AI-driven automatic generation is used, then time and effort required are reduced, but complexity of the generation system increases
Solution Approach 1:
The system performs preliminary actions by pre-processing software documentation to extract operations, windows, and task relationships before generating the final user manual. The neural network is trained in advance on documentation patterns, allowing it to automatically organize content into proper sequences and structures without requiring complex real-time decision-making during generation, thus reducing overall system complexity while maintaining high productivity.
Data Source
AI summary
Aspects of the present disclosure relate to automatically generating a user manual using a technique that includes training a first model with a first set of training data. The technique further includes generating, by the first model, a set of operations and a set of windows, where the set of operations and the set of windows are functions of the program. The technique further includes, generating a plurality of tasks, where a first task comprises a first operation being performed on a first window. The technique further includes determining an order of the plurality of tasks and calculating a level score for the first operation of the first window. The technique further includes assembling the user manual having the plurality of tasks in the determined order.