User-Specific Engineering Workflows From Role-Based Action Profiling

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Solution Overview

Problem

Conventional systems in multi-user engineering environments fail to classify user actions by user role and scope of operations, leading to non-personalized recommendations and high manual effort in training machine learning models.

Innovation Solution

A method and system that generate user-specific engineering programs by creating user profiles based on unsupervised learning algorithms analyzing user actions, and training AI models to generate customized workflows for each user profile.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If conventional machine learning models are used to generate user action recommendations, then automation is improved, but personalization is worsened because the models fail to classify user actions by role and scope of operations

Engineering Contradiction:

Improveautomation in generating user action recommendationsVSAvoidpersonalization of recommendations for different user roles

Core Design Contradiction:

Extent of automationVSAdaptability or versatility

Solution Approach 1:

The patent segments user actions into different categories based on user roles (e.g., coder, tester, commissioning engineer, architect) and scope of operations. This segmentation allows the system to provide personalized recommendations tailored to each user's specific role and context, resolving the contradiction between automation and personalization by enabling role-specific automated recommendations.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements local quality by generating different types of recommendations for different user roles and contexts. Instead of a one-size-fits-all approach, the system provides customized recommendations that are locally optimized for each user's specific needs, responsibilities, and current engineering tasks, thereby achieving both automation and personalization.

Inventive Principle:

Principle #3Local quality

2Measurement precision

If a machine learning model is trained using a gigantic number of user actions, then accuracy is improved, but time consumption and complexity are worsened

Engineering Contradiction:

Improveaccuracy of user action recommendationsVSAvoidtime required to gather and train on user actions

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent applies preliminary action by pre-classifying and organizing user actions into role-based categories and scope of operations before training the machine learning model. This preliminary organization of data into structured segments allows the model to be trained more efficiently on categorized data, reducing the time and computational resources needed while maintaining or improving recommendation accuracy.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If user profiles are generated manually for personalization, then personalization is improved, but device complexity and administrative effort are worsened

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidsystem administration complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements self-service by automatically generating user profiles and classifying user actions without requiring manual system administration. The system autonomously performs data collection, classification by user role and scope of operations, and profile generation, thereby achieving personalization while eliminating the complexity and effort associated with manual profile management.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical process of manual profile creation and management with an automated computational system. Machine learning algorithms and automated classification processes substitute for manual administrative tasks, reducing system complexity and administrative burden while maintaining personalization capabilities.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250124380A1Method and system for generating user specific engineering programs in a multi-user engineering environment

Publication Date: 2025.04.17 SIEMENS AG

AI summary

A method and system for generating engineering programs in a multi-user engineering environment is provided. The method includes generating a plurality of user profiles for the plurality of users, based on an analysis of a plurality of user actions performed by the plurality of users. The method further includes training an artificial intelligence model to generate a workflow which is specific to each user profile of the plurality of user profiles, based on the analysis of the plurality of user actions of the plurality of users. The method further includes generating a workflow which is specific to the user based on the determined user profile and an application of the generated artificial intelligence model on the detected at least one user action performed by the user in the multi-user engineering environment.