RPA Schema Mapping Using Empty-Target Semantic Matching
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Solution Overview
Problem
Current robotic process automation (RPA) technologies face challenges in understanding and mapping schemas between documents or web pages from a single source instance, requiring substantial human and computing resources and often failing to accurately determine semantic relationships.
Innovation Solution
The implementation of target-based schema identification and semantic mapping, where an RPA designer application determines labels and types of an empty target, performs semantic matching using a semantic matching model, and displays matched labels with confidence scores, enabling efficient mapping between sources and targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple source instances are analyzed to learn the schema, then the accuracy of semantic relationships improves, but the human and computing resources required increase substantially
Solution Approach 1:
Instead of analyzing multiple source instances to infer the target schema (traditional approach), the patent inverts the approach by using the target schema to identify and match fields in source documents. The RPA robot determines the target schema first, then uses it to guide the matching process, reducing the need for extensive source instance analysis and associated resource consumption.
Solution Approach 2:
The patent performs preliminary determination of the target schema before conducting field matching operations. By pre-establishing the target schema structure and field relationships, the system prepares the matching framework in advance, which reduces the computational resources needed during the actual matching process and eliminates the need for multiple source instance analyses.
2Quantity of substance
If a single source instance is used for schema understanding, then the human and computing resources are reduced, but the ability to understand and map fields accurately deteriorates
Solution Approach 1:
The patent reverses the traditional mapping direction by using target schema information to guide source field identification, rather than inferring target schema from source instances. This inversion allows accurate field mapping even when working with a single source instance, as the target schema provides the reference framework for accurate matching.
3Adaptability or versatility
If traditional schema matching methods are used, then compatibility with existing systems is maintained, but the productivity and automation capability are limited
Solution Approach 1:
The patent implements self-service automation where the RPA robot autonomously determines target schemas, performs semantic field matching, and creates mappings without requiring extensive human configuration or multiple source instance analyses. The system serves itself by using the target schema as the primary reference, eliminating the need for manual schema inference and significantly improving automation capability and productivity.
Data Source
AI summary
Target-based schema identification and semantic mapping for robotic process automation (RPA) are disclosed. When looking at a source, such as a document, a web form, a user interface of a software application, a data file, etc., it is often difficult for software to determine which fields are labels and which are values associated with those labels. Since values have not yet been entered for various labels (e.g., first name, company, customer number, etc.), these labels are easier to detect than when the target also includes various values associated with the labels. A selection of an empty target may be received and target-based schema identification may be performed on the empty target, determining labels and a type of the target. Semantic matching may then be performed between a source and the target. These features may be performed at design time or runtime.