AI Mindmap Generation in CRM for Multi-User Customization

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Solution Overview

Problem

Creating and maintaining mindmap charts for multiple users within a CRM system is time-consuming and inefficient, especially for larger teams, preventing widespread adoption.

Innovation Solution

A generative AI-augmented mindmap chart engine integrated with a CRM database that automates mindmap creation, customization, and updating through a discovery module, generative AI interface, retrieval-augmented generation, and updater module, using user and subject profiles to generate and update mindmaps efficiently.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If manual mindmap creation is used for multiple users, then customization accuracy is improved, but time consumption and productivity are worsened

Engineering Contradiction:

Improvemindmap customization accuracyVSAvoidmindmap generation speed

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system enables self-service through automated mindmap generation where the AI model creates mindmaps autonomously based on CRM data without requiring manual intervention from users or administrators, thus maintaining customization accuracy while dramatically improving generation speed

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of creating mindmaps with an automated AI-based system that uses machine learning models to generate mindmaps automatically from CRM database information, eliminating the time-consuming manual work while preserving customization through intelligent data processing

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If manual mindmap updates are performed, then accuracy is maintained, but time consumption increases significantly

Engineering Contradiction:

Improvemindmap accuracyVSAvoidupdate time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system implements continuous automated updates where the AI model continuously monitors CRM data changes and automatically updates mindmaps in real-time or near-real-time, ensuring accuracy is maintained without requiring periodic manual intervention that would consume significant time

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The patent incorporates feedback mechanisms where the system automatically detects changes in CRM data, processes these changes through the AI model, and updates the corresponding mindmaps, creating a closed-loop system that maintains accuracy through continuous automated feedback-driven updates

Inventive Principle:

Principle #23Feedback

3Productivity

If mindmap generation is automated, then productivity is improved, but system complexity increases

Engineering Contradiction:

Improvemindmap generation efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent applies universality by integrating the AI model within the existing CRM platform, allowing the same system to perform multiple functions including customer data management, mindmap generation, and updates, thereby improving productivity without proportionally increasing system complexity through multi-functional consolidation

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12505127B2Cloud-based multi-user mindmap generation engine and integrated customer relations management database platform

Publication Date: 2025.12.23 HULICK THOMAS

AI summary

A generative artificial-intelligence augmented mindmap chart engine and customer relations management database platform includes an customer relations management database module, and a mindmap generation engine including a discovery module, a generative artificial intelligence interface module configured to communicate with one or more generative artificial-intelligence models using one or more application programming interfaces, a retrieval-augmented generation database module, a mindmap database configured to store one or more mindmap charts and one or more metadata tags associated with the one or more mindmap charts, and an updater module configured to initiate a mindmap chart update sequence.