World Conference of AI-Powered Innovation and TRIZ Methodology
Description
This book constitutes the proceedings of the 25th IFIP WG 5.4 International TRIZ Future Conference on AI-Powered Innovation and Inventive Design, TFC 2025, held in Paris, France, during November 5-7, 2025.
The 48 full papers included in this book were carefully reviewed and selected from 75 submissions. They were focused on topical section as below.
Part I: Neuro-Symbolic and AI-Assisted Contradictions; Generative Agents for Ideation and Design; Tech Mining, Forecasting and Cross-Domain Exploration; Modeling, Verification and Optimization of Technical Systems and Frameworks for Digital Transformation and Industry 5.0.
Part II Cognition, Causality and Systematic Prototyping; Innovation Governance and Standardization; Innovation Governance and Standardization; Data, Forecasting and Intelligent Services and User Experience and Interoperable Public Policies.
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Content
.- Neuro-Symbolic and AI-Assisted Contradictions.
.- Research on Technology Opportunity Discovery and Implementation Based on Multi-Layer Knowledge Graphs .
.- Systematic Cause Elimination in TRIZ Cause-Effect Models With Partially Defined Logical Operators.
.- Comparative Study on AI-Augmented Inventive Problem Solving With TRIZ, TRIZ-AI Hybrid, and Autonomous AI: An Inline Coating Measurement Case in Lithium-Ion Cell Production.
.- AI-Powered Identification of Main Parameters of Value (MPVs) for Trends of Engineering System Evolution (TESE): Benchmarking Language Models on Wound Care Medical Device Patents.
.- Generative Agents for Ideation and Design.
.- Enhancing Patent Drafting Through Contradiction Identification.
.- Discovery Omnia: Dynamic RAG for Enhanced Patent Analysis and Systematic Innovation.
.- Study on PBL (Problem/Project-Based Learning) Based on TRIZ Methodology.
.- Evaluating LLM Performance in TRIZ-Based System Forecasting: A Study Using the 9-Windows Tool.
.- Leveraging Problem Graph Extraction and Improvement Perspectives for AI-Assisted Invention.
.- Leveraging Large Language Models and TRIZ for Automated Patent Drafting and Innovation Generation.
.- Tech Mining, Forecasting and Cross-Domain Exploration.
.- Artificial Intelligence for Contradiction Solving in Lean Green Supply Chain Performance Context: A Comparative Case Study.
.- Contradiction Formulation Using Large Language Models and Generative AI.
.- Patents as Signals for Green Investment Opportunity Evaluation: A Full-Cycle AI-Powered TRIZ Framework.
.- Automated TRIZ Function Model Generation Using Large Language Models: An Ontology-Guided Framework for Engineering Problem Analysis.
.- TRIZ in Speech Analytics: Overcoming the AI Precision-Resource Efficiency Contradiction for Scalable Contact Center Innovation.
.- Modeling, Verification and Optimization of Technical Systems.
.- LLM-Based Functional Modeling of Technical Systems.
.- Measuring Transdisciplinarity in Startups With NLP and TRIZ Principles.
.- CPU-Efficient Verification of Science Problem-Solution Pairs: Design Rationale and Baselines.
.- Classification of Geometric Effects Based on the 40 Inventive Principles.
.- Frameworks for Digital Transformation and Industry 5.0.
.- A TRIZ and Socratic AI-Based Problem-Solving Framework.
.- Resilient Innovation Portfolio Management Approach: Exploring Alternative Strategies.
.- Investigating the Integration and Adaptation of ARIZ With Large Language Models.
.- Seeking Additional Mitigation Strategies for State Machine Cause-Effect Modeling.
