World Conference of AI-Powered Innovation and Inventive Design (eBook, PDF)

24th IFIP WG 5.4 International TRIZ Future Conference, TFC 2024, Cluj-Napoca, Romania, November 6-8, 2024, Proceedings, Part I
Redaktion: Cavallucci, Denis; Livotov, Pavel; Brad, Stelian

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 248
• Erscheinungstermin: 28. Oktober 2024
• Englisch
• ISBN-13: 9783031759192
• Artikelnr.: 72243981

Inhaltsangabe

.- 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.