DSL Validator for Faster, More Accurate RPA PDD Development
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Solution Overview
Problem
Creating accurate and up-to-date process definition documentation (PDD) for robotic process automation (RPA) is a tedious task, involving manual efforts and siloed communication that leads to delays and inefficiencies.
Innovation Solution
A method using a domain-specific language (DSL) validator that tokenizes PDD documents with part-of-speech grammar, extracts automation unit pairs, constructs a tree-like mapping, and defines a vocabulary interface to streamline the PDD process, enabling faster and more accurate RPA development.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual process documentation methods are used with sequential email communications, then stakeholders can gather all requirements and context, but the process experiences undesired delays and inefficiencies
Solution Approach 1:
The system performs preliminary actions by automatically generating process definition documentation from existing source documents before manual review is needed. The automated extraction and structuring of process steps, decisions, and artifacts occurs in advance, reducing the time stakeholders need to spend on documentation while maintaining accuracy through systematic validation.
Solution Approach 2:
The patent introduces an intermediary automated documentation system that mediates between source documents and final PDD output. This intermediary uses natural language processing and validation rules to bridge the gap between raw source material and structured documentation, eliminating the need for sequential manual email communications while preserving information accuracy.
2Loss of information
If consultants manually document observations and communicate via multiple emails, then process details can be captured, but communication becomes siloed and disconnected making collaboration inefficient
Solution Approach 1:
The system merges previously separate communication channels and documentation processes into a single integrated automated workflow. By combining information extraction, validation, and documentation generation into one unified system, it eliminates siloed email communications while maintaining complete process information through systematic data collection and validation.
Solution Approach 2:
The patent creates a universal documentation system that performs multiple functions: extracting information from various source formats, validating process logic, generating structured documentation, and enabling collaboration. This multi-functional system replaces multiple separate manual processes, improving both information completeness and collaboration efficiency simultaneously.
3Reliability
If sequential email communications are used to gather requirements, then all stakeholder input can be collected, but the process experiences undesired delays
Solution Approach 1:
The patent replaces the mechanical system of sequential email communications with an automated information processing system. The new system uses computational methods to extract, validate, and structure requirements from source documents simultaneously rather than sequentially, maintaining requirements accuracy through validation rules while dramatically increasing documentation throughput.
Solution Approach 2:
The system enables continuous automated processing of documentation requirements rather than intermittent sequential email exchanges. The automated extraction and validation processes run continuously on source documents, maintaining information accuracy through systematic validation while achieving continuous documentation production without the delays of manual back-and-forth communications.
Data Source
AI summary
A method includes preparing key actions in a process design document (PDD) from historical PDD documents by tokenizing the PDD with part-of-speech (POS) grammar against key verbs and phrases, extracting automation unit pairs from the historical process documents to map the automation unit pairs with the key verbs and phrases, constructing a set of automation tasks against one or more of the key verbs and phrases, coupling different process models with domain verbs for the phrases to mark one or more of the automation unit pairs as actions against one or more of the key verbs, constructing a tree-like mapping where each key action points to different actions in a target specific automation language, and defining a vocabulary interface of a domain specific language (DSL) by using the key actions and phrases.