Dynamic Automation Procedure Editing via Knowledge Graph
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Solution Overview
Problem
Conventional software development approaches require significant skill and often necessitate restarting the entire process when changes are needed, as they lack efficient mechanisms to correct or update procedures mid-execution, leading to inefficiencies and increased complexity.
Innovation Solution
An approach that allows for retrospective examination and editing of facts within an automation run, enabling users to continue processing from the point of change without restarting, utilizing natural language to program behaviors and implement new logic or data dynamically during runtime, with a system architecture that includes a knowledge graph for storing and retrieving procedures and data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional software code is used to implement procedures, then the system can execute automated tasks, but the system cannot efficiently correct or update procedures mid-execution without restarting the entire process
Solution Approach 1:
The system transforms static, pre-defined software code into a dynamic execution model where procedures can be modified during runtime. The graphical procedure editor allows users to add, remove, or modify steps in a procedure while it is executing, and the system automatically adapts by restarting only from the point of modification rather than the beginning. This dynamic approach resolves the contradiction by enabling adaptability without the time penalty of full process restarts.
Solution Approach 2:
The system segments the procedure execution into discrete, independently manageable steps that can be individually modified. When a change is made to a procedure, the system identifies the specific step that was modified and restarts execution from that point onward, rather than restarting the entire procedure from step one. This segmentation allows partial re-execution, maintaining adaptability while minimizing time loss.
2Ease of operation
If natural language processing is used to program behaviors, then programming becomes more accessible to non-experts, but the system requires sophisticated processing to understand and execute human-like instructions
Solution Approach 1:
The system introduces a graphical procedure editor as an intermediary layer between natural language input and system execution. Users can define procedures using intuitive drag-and-drop graphical elements representing common operations (e.g., read file, write file, calculate), which the system then executes. This intermediary simplifies the interface for non-experts while the underlying NLP engine handles the complexity of understanding and translating these graphical representations into executable code, resolving the contradiction between ease of use and processing complexity.
3Reliability
If software development follows conventional approaches, then structured code can be implemented, but significant skill and training are required to correctly implement products
Solution Approach 1:
The system provides pre-built, validated procedure templates that users can copy and adapt for common tasks. Instead of requiring users to write complex code from scratch, the system offers ready-made procedures for frequent operations (e.g., data processing workflows, file operations, calculations) that have been pre-tested for correctness. Users can copy these templates and modify them through the graphical editor, ensuring reliable implementation while reducing the skill barrier.
Solution Approach 2:
The system performs preliminary validation and structuring of procedures through the graphical editor before execution. The editor automatically organizes user actions into a structured procedure format with proper sequencing, error handling, and variable management. This preliminary structuring ensures that even users without programming expertise can create reliable, well-structured software behavior by simply defining the desired steps graphically, while the system handles the complex structural requirements in advance.
Data Source
AI summary
Disclosed is an improved approach to implement an improved approach to retrospectively examiner and edit facts for an automation run. The user can then continue processing from the point of the fact change, rather than being required to restart the entire process from the very beginning.