RPA Trigger Event Detection via NLP Message Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current RPA platforms face challenges in reliably triggering bot scripts due to the difficulty in defining trigger events that capture specific requests amidst varying message types, formats, and content.
Innovation Solution
The solution involves using natural language processing (NLP) and machine-learning techniques to classify and cluster incoming communication messages, identifying semantic similarities to define trigger events automatically, thereby relieving users from manually defining these events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to define trigger events for RPA bot scripts, then users can control bot execution, but the system cannot reliably capture specific requests amidst varying message types, formats, and content
Solution Approach 1:
The system performs self-service by automatically classifying incoming messages and identifying trigger events using NLP and machine learning, eliminating the need for manual trigger event definition. The system serves itself by autonomously determining when to initiate bot scripts based on semantic analysis of message content, sender, and context.
Solution Approach 2:
The patent replaces manual mechanical methods of defining trigger events with automated electronic processing using NLP and machine learning algorithms. The system substitutes human analysis with computational models that can process varying message types, formats, and content to reliably identify trigger events.
2Measurement precision
If NLP and machine-learning techniques are used to automatically classify messages and identify trigger events, then trigger event detection accuracy improves, but processing complexity and computational resources increase
Solution Approach 1:
The system segments the complex task of trigger event detection into distinct processing stages: message classification using NLP, clustering analysis of similar messages, workflow dataset extraction, and trigger event identification. This segmentation allows each component to specialize in one aspect, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary classification and clustering of messages before final trigger event identification. By pre-processing messages through NLP classification and grouping similar messages together, the system reduces the complexity of the final detection step and improves accuracy through progressive refinement.
3Reliability
If manual trigger event definition is used, then system simplicity is maintained, but false positives and missed triggers occur frequently
Solution Approach 1:
The system continuously processes incoming messages through NLP classification and clustering operations, maintaining constant analysis rather than periodic or manual checking. This continuous automated processing improves reliability by ensuring no trigger events are missed while reducing the time burden on users compared to manual definition approaches.
Data Source
AI summary
An embodiment includes classifying an incoming communication message as having an instruction classification based on a natural language processing (NLP) analysis of the incoming communication message. The embodiment extracts, responsive to classifying the incoming communication message as having the instruction classification, user operations as a workflow dataset. The embodiment clusters the workflow dataset into a task cluster with other similar workflow datasets representative of previously-extracted user operations. The embodiment designates the instruction classification as a trigger event based at least in part on the NLP analysis of the incoming communication message. The embodiment generates a robotic process automation (RPA) bot script that comprises program instructions executable by a processor to cause the processor to perform operations that automate the user operations in response to the trigger event.