AI Integration Recommendations Using Organization-Specific Clustering

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Solution Overview

Problem

Current recommendation engines for integration platforms do not account for how different organizations utilize the same software applications differently, leading to irrelevant suggestions and lack of explainability, thereby undermining user confidence and value.

Innovation Solution

A recommendation engine using artificial intelligence that retrieves organization-specific data, applies clustering algorithms to identify similar organizations, and suggests pre-built integration processes, accompanied by explanations to enhance relevance and user trust.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a recommendation engine suggests integration processes based on software applications an organization is using, then the suggestion is related to the software application, but the suggestion may be irrelevant to the specific manner in which the organization utilizes the software application

Engineering Contradiction:

ImproveRelevance of integration process suggestions to organization-specific usage patternsVSAvoidLoss of organization-specific utilization context

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The patent segments the recommendation approach by creating separate processing paths: one for extracting software application usage data and another for extracting integration process data. This segmentation allows the system to analyze organization-specific usage patterns in detail and match them with relevant integration processes, thereby improving relevance while preserving organizational context.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies local quality by tailoring the recommendation engine to each organization's specific context. It extracts and analyzes organization-specific data including software application usage patterns, organizational structure, and integration needs. The recommendation engine then provides customized suggestions aligned with each organization's unique characteristics rather than generic recommendations.

Inventive Principle:

Principle #3Local quality

2Reliability

If a recommendation engine provides suggestions based on limited knowledge and generalized data, then the engine is simple to operate, but the suggestions do not inspire confidence or trust

Engineering Contradiction:

ImproveUser confidence and trust in recommendation suggestionsVSAvoidComplexity of data processing and analysis systems

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements feedback mechanisms where the recommendation engine continuously learns from organization-specific data and usage patterns. The system analyzes extracted data, generates recommendations, and refines its suggestions based on organizational feedback and actual usage outcomes. This feedback loop enhances reliability and user trust while managing system complexity through iterative improvement.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces an intermediary layer between raw data and recommendations: a data extraction and analysis module. This intermediary processes organization-specific data, extracts relevant features, and transforms them into meaningful patterns that the recommendation engine can use. This intermediary layer builds trust by providing transparent, data-driven recommendations while managing complexity through structured data processing.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If the recommendation engine suggests pre-built integration processes, then implementation time is reduced, but the processes may not align with actual organizational needs

Engineering Contradiction:

ImproveSpeed of integration process implementationVSAvoidAlignment of suggested processes with organizational needs

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent applies preliminary action by pre-building integration processes that are designed to be quickly deployed. However, before deployment, the system extracts and analyzes organization-specific data to ensure these pre-built processes align with actual organizational needs. This preliminary analysis ensures both quick implementation and proper alignment with organizational requirements.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent utilizes parameter changes by allowing the recommendation engine to adjust and customize pre-built integration processes based on extracted organizational parameters. The system modifies process parameters such as data sources, destinations, and transformation rules to match the specific context of each organization, thereby maintaining both implementation speed and alignment with organizational needs.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260004209A1Recommendation engine for suggesting integration processes using artificial intelligence

Publication Date: 2026.01.01 BOOMI LP

AI summary

A recommendation engine that utilizes artificial intelligence to suggest integration processes for an integration platform is disclosed. The recommendation engine may utilize clustering to identify and suggest pre-built integration processes that have been implemented by similarly situated organizations on an Integration Platform as a Service (iPaaS) platform. In addition, an explanation model may be used to generate an explanation for each suggested pre-built integration process. The suggestions and explanations, which may be automatically provided to a user, may be visually represented in a graphical user interface that allows a user to easily and immediately implement each suggested pre-built integration process on their own integration platform.