AI Engineering Documentation Using Templates and Zero-Trust Data
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Solution Overview
Problem
Current digital engineering documentation processes are inefficient, error-prone, and costly due to manual handling, leading to redundant physical tests, increased archiving costs, and security risks, particularly in industries like aircraft certification where thorough documentation is essential for certification.
Innovation Solution
Implementing artificial intelligence (AI) and machine learning (ML) to assist in generating and updating documentation, using zero-trust access control to protect sensitive data, and integrating a digital documentation system within a digital engineering ecosystem to enhance efficiency, accuracy, and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual documentation processes are used, then flexibility and human judgment are maintained, but efficiency is low, errors increase, and costs rise
Solution Approach 1:
The documentation system automatically generates and updates documentation by retrieving data from digital engineering tools and populating templates without manual intervention. The system serves itself by autonomously extracting relevant information, formatting it according to templates, and maintaining documentation consistency throughout the product lifecycle.
Solution Approach 2:
Manual mechanical documentation processes are replaced with an automated digital system that uses software to retrieve data from engineering tools, process information through templates, and generate documentation. This substitution eliminates manual errors while maintaining flexibility through configurable templates and digital data sources.
2Reliability
If physical tests are repeated due to insufficient data sharing, then certification thoroughness is maintained, but time and costs increase
Solution Approach 1:
The patent merges documentation generation with the digital engineering workflow by integrating with existing digital engineering tools. This combination allows certification data to be automatically captured and shared across the ecosystem, eliminating redundant physical tests while maintaining certification thoroughness through comprehensive data utilization.
Solution Approach 2:
The system implements feedback loops where documentation is continuously updated based on data from digital engineering tools and previous test results. This feedback mechanism ensures that certification authorities have access to complete information, preventing redundant tests by leveraging existing data while maintaining rigorous certification standards.
3Device complexity
If manual documentation handling is used, then simplicity of system architecture is maintained, but security risks and archiving costs increase
Solution Approach 1:
The patent introduces a documentation system as an intermediary layer between digital engineering tools and certification authorities. This intermediary automatically manages data retrieval, processing, and security protocols, reducing direct security exposure while maintaining architectural simplicity through a standardized interface that handles complex security requirements.
4Extent of automation
If AI and ML are integrated into the documentation system, then automation and efficiency are improved, but system complexity and data security requirements increase
Solution Approach 1:
The patent implements a universal documentation system that serves multiple functions: data retrieval from various digital engineering tools, automatic template population, documentation generation, and security management. This multi-functional approach consolidates complexity into a single platform while achieving high automation, rather than adding separate systems for each function.
Data Source
AI summary
A digital documentation system for preparation of engineering documents utilizing one or more artificial intelligence (AI) algorithms is provided. The system includes a user interface for selecting and populating templates with data, and one or more AI algorithms for creating and recommending templates, and preparing documents based on the recommended templates. The system uses natural language processing and semantic analysis algorithms to understand the content of the templates, documents, and associated engineering data, and to generate and recommend relevant templates to the user based on user prompts. The system also uses machine learning and predictive modeling and decision-tree algorithms to assist with the preparation of documents, by generating suggestions for data fields and values based on the user's previous inputs and the overall context of the document and available engineering data, including model data and metadata from digital models accessed in a zero-trust framework.