LLM-Assisted Workflow Graph Generation for Task Mining
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Solution Overview
Problem
Conventional task mining is time-consuming and requires significant manual intervention, such as manually recording and merging workflows, which hinders efficient workflow analysis and optimization.
Innovation Solution
Utilizing large language models (LLMs) to automatically generate and modify structured definitions of workflows based on natural language descriptions and API mappings, reducing the need for manual recording and merging processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional task mining is performed by manually recording user interactions, then workflow data can be captured, but the process becomes time-consuming and requires significant manual intervention
Solution Approach 1:
The patent replaces manual mechanical recording operations with an automated system that uses machine learning models to capture and analyze workflow data. The system automatically records user interactions, screens, and contextual information without requiring manual intervention, thereby substituting the mechanical process of manual recording with an automated computational approach.
Solution Approach 2:
The system enables self-service by allowing the workflow capture process to occur automatically without human intervention. The automated recording system captures data, the machine learning model processes and analyzes it, and workflows are generated autonomously, making the entire process self-sufficient and eliminating the need for manual task mining operations.
2Productivity
If conventional task mining requires manual merging of workflow records, then workflow consolidation can be achieved, but the process becomes complex and time-consuming
Solution Approach 1:
The patent replaces manual workflow merging operations with automated machine learning-based consolidation. The system uses trained models to automatically merge and de-duplicate workflow records, eliminating the need for manual merging operations and reducing the complexity associated with manual intervention in workflow consolidation.
Solution Approach 2:
The machine learning model acts as an intermediary between raw workflow data and consolidated workflow processes. It automatically processes, analyzes, and merges workflow records, serving as a mediator that handles the complexity of workflow consolidation without requiring direct manual intervention.
3Reliability
If manual workflow recording is performed for every variation, then complete workflow coverage can be achieved, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically capturing and recording all workflow variations as they occur in real-time without requiring manual intervention for each variation. The automated recording system proactively collects data across multiple workflow scenarios, ensuring comprehensive coverage before analysis begins.
Solution Approach 2:
The machine learning model uses feedback from analyzed workflow data to automatically identify and capture additional workflow variations. As the system processes recorded data, it learns from patterns and automatically adjusts to ensure complete workflow coverage, using the feedback loop to maintain reliability while reducing manual recording requirements.
Data Source
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AI summary
Systems and methods for generating a structured definition of a graph of a workflow are provided. 1) a natural language description of a workflow and 2) a description of one or more APIs (application programming interfaces) for performing the workflow are received. A structured definition of a graph of the workflow is generated using a language model based on the natural language description of the workflow and the description of the one or more APIs. One or more nodes in the structured definition of the graph of the workflow is mapped to at least one of the one or more APIs. The structured definition of the graph of the workflow is output.