Enhancing problem-solving and data protection through the integration of function-oriented search and ChatGPT

Won-Shik Shin , Youngjoon Choi , Yong-Won Song

International Journal of Systematic Innovation ›› 2025, Vol. 9 ›› Issue (6) : 17 -26.

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International Journal of Systematic Innovation ›› 2025, Vol. 9 ›› Issue (6) :17 -26. DOI: 10.6977/IJoSI.202512_9(6).0002
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Enhancing problem-solving and data protection through the integration of function-oriented search and ChatGPT
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Abstract

As a large language model, ChatGPT’s ability to learn from big data and respond to diverse user queries makes it a powerful tool for research and development. Despite the potential benefits of using ChatGPT, there are risks concerning users’ data protection. To address this issue, this study proposes utilizing Function-Oriented Search (FOS), a methodology based on Theory of Inventive Problem Solving (TRIZ). FOS provides an innovative approach to problem-solving by functionally defining a problem and generating solutions from areas where the function can be optimally performed. Thus, this study argues that applying FOS when using ChatGPT can ensure accurate results while mitigating the exposure of sensitive information. Although implementing FOS requires specialized training and sufficient hands-on experience to identify and conceptualize problem focus areas, ChatGPT can serve as an efficient tool for developers adopting this methodology. For both experts and novices in FOS, ChatGPT enables users to conduct efficient and comprehensive problem explorations and devise solutions. By demonstrating the application of FOS in practical cases, the study’s findings support the potential benefits of ChatGPT as a dynamic collaborator in problem-solving. The findings also indicate that FOS can guide the use of ChatGPT to generate suitable solutions while maintaining the protection of personal or corporate information. Overall, this study contributes to the emerging field of artificial intelligence by illustrating the possible synergy between TRIZ-based FOS and ChatGPT, a large language model.

Keywords

ChatGPT / Data Protection / Function-Oriented Search / Large Language Model / Prompt Engineering / Theory of Inventive Problem Solving / TRIZ-Informed Prompt Engineering

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Won-Shik Shin, Youngjoon Choi, Yong-Won Song. Enhancing problem-solving and data protection through the integration of function-oriented search and ChatGPT. International Journal of Systematic Innovation, 2025, 9(6): 17-26 DOI:10.6977/IJoSI.202512_9(6).0002

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Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2025S1A5B5A16007035).

References

[1]

Aljanabi, M., Ghazi, M., Ali, A., & Abed, S.A. (2023). ChatGPT: Open possibilities. Iraqi Journal For Computer Science and Mathematics, 4(1), 62-64. https://doi.org/10.52866/20ijcsm.2023.01.01.0018

[2]

Altshuller, G.S. (1984). Creativity as an Exact Science: The Theory of the Solution of Inventive Problems. Gordon and Breach Science Publishers, New York.

[3]

Altshuller, G.S. (1999). The Innovation Algorithm: TRIZ, Systematic Innovation and Technical Creativity. Technical Innovation Center, Inc., Somayampalayam.

[4]

Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Holy, B.W., et al. (2023). A Multitask, Multilingual, Multimodal Evaluation of Chatgpt on Reasoning, Hallucination, and Interactivity; [arXiv Preprint].

[5]

Borji, A. (2023). A Categorical Archive of Chatgpt Failures. [arXiv Preprint].

[6]

Cameron, G. (2010). Trizics. Available from: https://www.com/trizics.com

[7]

Choi, S., Kang, D., Lim, J., & Kim, K. (2012). A fact-oriented ontological approach to SAO-based function modeling of patents for implementing function-based technology database. Expert Systems with Application, 39(10), 9129-9140. https://doi.org/10.1016/j.eswa.2012.02.041

[8]

Colton, S., Mántaras, R., De., & Stock, O. (2009). Computational creativity: Coming of age. AI Magazine, 30(3), 11-14. https://doi.org/10.1609/aimag.v30i3.2257

[9]

Cropley, D.H., Medeiros, K.E., & Damadzic, A.(2022). The intersection of human and artificial creativity. In: Creative Provocations: Speculations on the Future of Creativity, Technology and Learning. Springer International Publishing, Cham, p19-34.

[10]

Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kumar Kar, A., et al. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

[11]

Haines-Gadd, L. (2016). TRIZ for Dummies. John Wiley and Sons Inc., United States.

[12]

Ilevbare, I.M., Probert, D., & Phaal, R. (2013). A review of TRIZ, and its benefits and challenges in practice. Technovation, 33(2-3), 30-37. https://doi.org/10.1016/j.technovation.2012.11.003

[13]

Litvin, S. (2005). New TRIZ-based tool-function-oriented search (FOS). The TRIZ Journal.

[14]

Orloff, M.A. (2017). ABC-TRIZ. Springer International Publishing, Berlin.

[15]

Oviedo-Trespalacios, O., Peden, A., Science, T.C., Costantini, A., Haghani, M., Rod, JE., et al. (2023). The risks of using chatgpt to obtain common safety-related information and advice. Safety Science, 167, 106244. https://doi.org/10.1016/j.ssci.2023.106244

[16]

Shaharuzaman, M.A., Sapuan, S.M., Mansor, M.R., & Zuhri, M.Y.M. (2020). Conceptual design of natural fiber composites as a side-door impact beam using hybrid approach. Journal of Renewable Materials, 8(5), 549-563. https://doi.org/10.32604/jrm.2020.08769

[17]

Sinha, R., Roy, A., Kumar, N., & Mondal, H. (2023). Applicability of ChatGPT in assisting to solve higher order problems in pathology. Cureus, 15(2), e35237. https://doi.org/10.7759/cureus.35237

[18]

Tafferner, Z., Illés, B., Krammer, O., & Géczy, A. (2023). Can chatGPT help in electronics research and development? A case study with applied sensors. Sensors (Basel), 23(10), 4879. https://doi.org/10.3390/s23104879

[19]

Wach, K., Duong, C.D., Ejdys, J., Kazlauskaitė R., Korzynski, P., Mazurek, G., et al. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7-24. https://doi.org/10.15678/EBER.2023.110201

[20]

Wang, J., Zhang, Z., Feng, L., Lin, K.Y., & Liu, P. (2023). Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ. Technological Forecasting and Social Change, 191(122481), 122481. https://doi.org/10.1016/j.techfore.2023.122481

[21]

White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., et al. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with Chatgpt. In: PLoP ‘23: Proceedings of the 30th Conference on Pattern Languages of Program.

[22]

Wingström, R., Hautala, J., & Lundman, R. (2022). Redefining creativity in the Era of AI perspectives of computer scientists and new media artists. Creativity Research Journal, 36, 1-17. https://doi.org/10.1080/10400419.2022.2107850

[23]

Zhang, D., Wu, X., Liu, P., Qin, H., & Sciences, W.Z. (2023). Identification of product innovation path incorporating the FOS and BERTopic model from the perspective of invalid patents. Applied Sciences, 13(13), 7987. https://doi.org/10.3390/app13137987

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