World Conference of AI-Powered Innovation and TRIZ Methodology

.- 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.