Intelligent Rule Configuration for Collaboration Systems

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Solution Overview

Problem

Coordination and synchronization of central rules with local rules in a collaboration system are resource intensive and error prone, often resulting in mismatched rules between collaboration partners and the central system.

Innovation Solution

An automated system uses a generative large language model to generate transaction rules recommendations by comparing local rejection information from collaboration partners with collaboration system master data, identifying correlated rejection categories, and analyzing them against predetermined trend rules to provide insights and recommendations for configuring transaction rules in the central collaboration system.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual coordination and synchronization of central rules with local rules is performed, then rule consistency can be maintained, but resource consumption increases and errors occur

Engineering Contradiction:

Improverule consistencyVSAvoidresource consumption

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system automatically generates transaction rule recommendations by analyzing local rejection information and comparing it with collaboration system master data. The AI model self-configures rules without requiring manual intervention, allowing the system to serve itself in maintaining rule consistency while reducing resource consumption

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical coordination processes with an automated AI-based system. The generative large language model and automated analysis mechanisms substitute human operators, eliminating the need for manual rule synchronization while maintaining or improving rule consistency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual coordination and synchronization of central rules with local rules is performed, then rule consistency can be maintained, but errors increase

Engineering Contradiction:

Improverule consistencyVSAvoiderrors

Core Design Contradiction:

ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The automated system performs self-analysis of local rejection information and self-generates appropriate transaction rules by comparing them against collaboration system master data. This self-service mechanism eliminates human errors in rule configuration while maintaining consistency between central and local rules

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously analyzes local rejection information as feedback and uses it to automatically adjust and configure transaction rules. This closed-loop feedback mechanism ensures that rules remain consistent with actual document processing outcomes, reducing errors while maintaining reliability

Inventive Principle:

Principle #23Feedback

3Loss of energy

If automated rule configuration is implemented using AI models, then resource consumption is reduced, but system complexity increases

Engineering Contradiction:

Improveresource consumptionVSAvoidsystem complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The patent introduces an AI-based intermediary layer (generative large language model) that sits between the local document processing system and the collaboration system. This intermediary automatically analyzes rejection information and generates rule recommendations, reducing the need for complex manual coordination mechanisms while consuming fewer resources

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If automated analysis of local rejection information is performed, then document processing speed increases, but data processing requirements increase

Engineering Contradiction:

Improvedocument processing speedVSAvoiddata processing requirements

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The system extracts only the necessary rejection information from local document processing operations and compares it with collaboration system master data. By taking out only the relevant data elements needed for rule configuration, the system accelerates document processing while minimizing unnecessary data processing requirements

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250190893A1Intelligent rule configuration in a collaboration system

Publication Date: 2025.06.12 SAP SE

AI summary

The present disclosure involves systems, software, and computer implemented methods for intelligent rule configuration in a collaboration system. One example method includes generating master data that describes central document rejection types for central rejections of documents. Local rejection information that includes local rejection categories and corresponding document fields is extracted for a collaboration partner. The local rejection information is compared to the master data to identify correlated local rejection categories that are correlated to a corresponding central document rejection category by prompting a generative large language model. Correlated local rejection categories are analyzed against predetermined trend rules. In response to determining that a correlated local rejection category satisfies a predetermined trend rule, a transaction rule is identified for the central collaboration system and a rejection insight is provided to the collaboration partner along with a recommendation to configure the transaction rule in the central collaboration system.