RPA Sequence Extraction for Process Discovery and State Transitions
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Solution Overview
Problem
Existing robotic process automation (RPA) technologies face challenges in discovering, capturing, and prioritizing critical processes and state transitions due to time, resource, and computing intensity, often leading to manual intervention that may expose sensitive information.
Innovation Solution
The method involves using sequence or pattern extraction to identify and prioritize processes for automation, reducing manual input by designing or redesigning workflows, and generating process documentation for RPA, while employing techniques like clustering actions, statistical modeling, and de-noising to extract meaningful sequences from log files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual discovery or design is used to identify processes for RPA, then process understanding can be achieved, but it is time, resource, and computing intensive and may expose sensitive information
Solution Approach 1:
The system performs self-service by automatically discovering and documenting processes through sequence extraction from existing logs and records, eliminating the need for manual human intervention that would expose sensitive information while consuming time and resources
Solution Approach 2:
The patent replaces the manual mechanical process of process discovery with an automated computational system that extracts sequences from logs and records, substituting human analysts with algorithmic processing to reduce both time consumption and information exposure risks
2Productivity
If existing sequence extraction configurations are used, then some sequences can be extracted, but critical tasks may be missed, misinterpreted, or overlapped
Solution Approach 1:
The system incorporates feedback mechanisms where extracted sequences are validated against multiple data sources including logs, records, and files, and where the extraction process iteratively refines results to eliminate misinterpretations and overlaps while maintaining extraction efficiency
Solution Approach 2:
The patent segments the sequence extraction process into distinct stages and components, allowing critical tasks to be identified and validated separately, which improves both the efficiency of extraction and the precision of critical task identification by reducing overlaps and misinterpretations
3Reliability
If comprehensive process discovery is performed manually, then all processes can be identified, but the cost and complexity increase significantly
Solution Approach 1:
The system achieves universality by designing a multi-functional automated discovery platform that can handle multiple data sources (logs, records, files) and perform various extraction and validation functions through a single integrated system, reducing overall complexity while maintaining comprehensive process identification
Solution Approach 2:
The patent introduces intermediary components that mediate between raw data sources and the final process identification results, using sequence extraction as an intermediate step to bridge comprehensive data analysis with reliable process identification, thereby reducing system complexity
Data Source
AI summary
A sequencing model may be utilized for captured steps of a workflow of an application. The sequencing model may determine processes and state transitions for the captured steps. A variance, a judgment action, or a rule-based action may be identified based on the sequencing model and changes of a log file of actions in relation to the captured steps. A hierarchy may be generated based on the identified variance, the judgment action, or the rule-based action to automatically generate an automated workflow for robotic process automation (RPA) by a robot.