Semantic AI Data Mapping for Automated RPA Transfer
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Solution Overview
Problem
Current robotic process automation (RPA) technologies require manual selection of target graphical elements for data transfer, lacking full automation and intuitive data transfer capabilities, making the process time-consuming.
Innovation Solution
Employing AI/ML models for semantic matching between source and target elements, enabling automatic data transfer by mapping labels and values without manual intervention, and utilizing computer vision, OCR, and NLP to identify and copy data across different formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual selection of target graphical elements is used for data transfer, then the RPA workflow can be created with basic functionality, but the process becomes time-consuming and lacks full automation
Solution Approach 1:
The system performs self-service by automatically identifying and mapping data elements between source and target without requiring manual developer intervention. The AI/ML models autonomously analyze the source document structure, identify relevant data fields, and map them to corresponding target fields, enabling the system to serve itself in the data transfer process.
Solution Approach 2:
The patent replaces the mechanical manual process of selecting graphical elements with an intelligent system based on AI/ML models. Instead of requiring developers to manually click and select target elements on the screen, the system uses semantic AI to automatically understand and map data between different formats and interfaces, substituting manual mechanical operations with intelligent automation.
2Ease of manufacture
If manual creation of RPA workflows is performed, then the developer has full control over the process, but the complexity of workflow creation increases
Solution Approach 1:
The patent introduces an intermediary layer of AI/ML models that act as a mediator between the developer's intent and the RPA workflow creation process. The semantic AI models serve as intermediaries that automatically interpret source documents, identify data elements, and generate appropriate mapping rules, simplifying the workflow creation process while maintaining developer control through review and adjustment capabilities.
3Productivity
If semantic AI/ML models are used for automatic data transfer, then the productivity and efficiency improve, but the system requires advanced AI/ML integration
Solution Approach 1:
The patent implements a universal semantic AI framework that can handle multiple document formats, data types, and target systems through a single integrated system. The AI/ML models are designed to be multi-functional, capable of performing optical character recognition, semantic understanding, entity extraction, and data mapping across various source and target configurations, reducing the need for separate specialized systems.
Data Source
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AI summary
Automatic data transfer between a source and a target using semantic artificial intelligence (AI) for robotic process automation (RPA) is disclosed. A user may be provided with the option of selecting a source and a target and indicating through an intuitive user interface that he or she would like to copy data from the source to the destination, regardless of format. This may be done at design time or at run time. For instance, the source and/or target may be a web page, a graphical user interface (GUI) of an application, an image, a file explorer, a spreadsheet, a relational database, a flat file source, any other suitable format, or any combination thereof. The source and the target may have different formats. The source, target, or both may not necessarily be visible to the user.