Automated RPA Workflow Wizard for CRM Data Mapping

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current robotic process automation (RPA) configurations for customer relationship management (CRM) components are time and resource intensive, requiring intelligence to handle incorrect information and understanding of CRM objects for workflow creation, especially when matching information fields from source applications to CRM fields.

Innovation Solution

A method and apparatus using a wizard component or module to automatically create RPA workflows through sequencing, matching, field matching, and error correction, enabling the automatic configuration of RPA workflows for adding, fetching, changing, updating, or modifying information or data in CRM components or tools.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If current RPA configurations use manual code or drag-and-drop technology to create workflows, then the workflow can be created with understanding of CRM objects, but the process becomes time and resource intensive

Engineering Contradiction:
Improveworkflow accuracyVSAvoiddevelopment time
Core Design Contradiction:
Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically generating RPA workflows through AI/ML algorithms that analyze source application data and CRM field mappings without requiring manual programming or drag-and-drop configuration. The automated workflow generation engine creates executable workflows autonomously, eliminating the need for developers to manually understand CRM object structures and relationships.

Inventive Principle:
Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes (coding and drag-and-drop configuration) with automated AI/ML-based workflow generation. The system uses machine learning models to automatically map fields, sequence operations, and generate executable workflows, substituting human cognitive and manual operations with automated intelligent systems.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If current RPA configurations require understanding of CRM objects for workflow creation, then the workflow can be properly configured, but the resource requirements increase

Engineering Contradiction:
Improveworkflow configuration accuracyVSAvoidresource requirements
Core Design Contradiction:
ReliabilityVSQuantity of substance

Solution Approach 1:

The system performs self-service by automatically generating RPA workflows through AI/ML algorithms that analyze source application data and CRM field mappings without requiring manual programming or drag-and-drop configuration. The automated workflow generation engine creates executable workflows autonomously, eliminating the need for developers to manually understand CRM object structures and relationships.

Inventive Principle:
Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes (coding and drag-and-drop configuration) with automated AI/ML-based workflow generation. The system uses machine learning models to automatically map fields, sequence operations, and generate executable workflows, substituting human cognitive and manual operations with automated intelligent systems.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If manual field matching is used between source applications and CRM fields, then the matching can be customized, but the process becomes time intensive

Engineering Contradiction:
Improvefield matching flexibilityVSAvoidmatching time
Core Design Contradiction:
Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processes (coding and drag-and-drop configuration) with automated AI/ML-based workflow generation. The system uses machine learning models to automatically map fields, sequence operations, and generate executable workflows, substituting human cognitive and manual operations with automated intelligent systems.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the parameters of field matching by using AI/ML algorithms to automatically determine mapping relationships based on data analysis, pattern recognition, and learned relationships from training data. This transforms the matching process from manual parameter specification to automated parameter optimization.

Inventive Principle:
Principle #35Parameter changes

4Reliability

If intelligence is added to accept incorrect information in RPA, then the system becomes more robust, but the complexity increases

Engineering Contradiction:
Improveerror handling capabilityVSAvoidsystem complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical processes (coding and drag-and-drop configuration) with automated AI/ML-based workflow generation. The system uses machine learning models to automatically map fields, sequence operations, and generate executable workflows, substituting human cognitive and manual operations with automated intelligent systems.

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11334828B2Automated data mapping wizard for robotic process automation (RPA) or enterprise systems
Publication Date: 2022.05.17 UIPATH INC
  • US11334828B2 patent drawing
  • US11334828B2 patent drawing
  • US11334828B2 patent drawing

AI summary

A computing device may execute a wizard component for a customer resource management (CRM) component. The wizard component may generate a match result between a data model and fields in the CRM component. The wizard component may generate a robotic process automation (RPA) workflow based on the match result. The RPA workflow may add or update data of the fields of the CRM component based on a RPA activity component.