LLM Flow Generation Service for Process Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current flow builder methods require a high level of proficiency and may generate incorrect or inaccurate process flows if they cannot understand or process user instructions, limiting the ability to create custom or complex process flows and reducing automation accuracy.
Innovation Solution
The use of a large language model (LLM) to decompose natural language inputs into elements and connectors, allowing the flow generation service to generate process flow metadata and create accurate process flows based on user instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional flow builder methods are used, then users can create process flows, but the system requires a high level of proficiency and may generate incorrect or inaccurate process flows
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between the user and the flow builder system. Users provide natural language descriptions of desired processes, and the system automatically translates these descriptions into structured process flow definitions, eliminating the need for users to manually configure complex flow builder parameters while improving accuracy through AI-based interpretation
Solution Approach 2:
The patent replaces the manual mechanical interaction with traditional flow builders (drag-and-drop interfaces, parameter configuration, node connection) with an automated linguistic system. The natural language input is processed by machine learning models that automatically generate the corresponding process flow structure, substituting manual operational mechanics with intelligent automation
2Reliability
If flow generation service cannot understand user instructions, then it may generate incorrect process flows, but improving understanding requires complex processing
Solution Approach 1:
The patent segments the instruction processing into distinct functional components: natural language input reception, semantic interpretation, process flow mapping, and validation. Each component handles a specific aspect of the transformation from language to automated process, reducing overall system complexity while improving reliability through specialized processing at each stage
Solution Approach 2:
The patent implements a universal natural language processing framework that can handle multiple types of user instructions and process flow configurations through a single integrated system. This multi-functional approach eliminates the need for separate processing pathways for different instruction types, reducing complexity while maintaining high accuracy across diverse scenarios
Data Source
AI summary
Methods, systems, apparatuses, devices, and computer program products are described. A flow generation service may receive a natural language input that indicates instructions for automating a task according to a first process flow. Using a large language model (LLM), the flow generation service may decompose the natural language input into a set of elements (e.g., logical actions) and connectors, where the LLM may be trained on first metadata corresponding to a second process flow that is created manually by a user. In addition, using the LLM, the flow generation service may generate second metadata corresponding to each of the set of elements based on decomposing the natural language input. The flow generation service may sequence and merge the set of elements to generate the first process flow. In some examples, the flow generation service may send, for display to a user interface of a user device, the first process flow.