Overview

This book constitutes the proceedings of the 24th IFIP WG 5.4 International TRIZ Future Conference on AI-Powered Innovation and Inventive Design, TFC 2024, held in Cluj-Napoca, Romania, during November 6–8, 2024. The 42 full papers presented were carefully reviewed and selected from 72 submissions. They were organized in the following topical sections:  Part I - AI-Driven TRIZ and Innovation Part II - Sustainable and Industrial Design with TRIZ; Digital Transformation, Industry 4.0, and Predictive Analytics; Interdisciplinary and Cognitive Approaches in TRIZ; Customer Experience and Service Innovation with TRIZ.

Full Product Details

Author:   Denis Cavallucci ,  Stelian Brad ,  Pavel Livotov
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
ISBN:  

9783031759215

ISBN 10:   3031759214
Pages:   248
Publication Date:   30 October 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

.- AI-Driven TRIZ and Innovation. .- LLM-based Extraction of Contradictions from Patents. .- AI based search engine to deploy a TRIZ pointer to chemical effects. .- Integrating Generative AI with TRIZ for Evolutionary Product Design. . Harnessing Generative AI for Sustainable Innovation: A Comparative Study of Prompting Techniques and Integration with Nature-Inspired Principles. .- Neuro-Symbolic AI-Driven Inventive Design of a Benzoic Acid Extraction Installation from Styrax Resin. .- Enhancing TRIZ Contradiction Resolution with AI-driven Contradiction Navigator (AICON). .- Research on disruptive technology prediction methods based on BERT model and graph theory analysis. .- Exploring Cross-Domain Technological Opportunities through Customized Training of the Bert Model. .- Resource Mining Method of Idealization Driven Product Innovation Process of AI-Aided. .- Use of AI in the TRIZ innovation process: a TESE-based forecast. .- Evaluating the effectiveness of generative AI in TRIZ: A comparative case study. .- On opportunities and challenges of large language models and GPT for problem solving and TRIZ education. .- Challenges in Inventive Design Problem Solving with Generative AI: Interactive Problem Definition, Multi-Directional Prompting, and Concept Development. .- The Evolving Landscape of TRIZ: A Generative AI-Powered Perspective.

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