International Journal of Systematic Innovation

COREX - Contradiction Oriented Exploration: A Dual-Track Methodology based on OTSM-TRIZ and Six-Box Scheme

2025-11-13T19:25:35+08:00

This study introduces COREX - Contradiction Oriented Exploration, a dual-track methodology designed to solve complex design problems involving both technical systems and human behavior. This approach combines two powerful tools: OTSM-TRIZ, which focuses on identifying and resolving system-level contradictions, and the Six-Box Scheme, which provides a user-centered and process-based framework for creative problem solving. By linking contradiction analysis with recursive exploration and real-world testing, this approach helps teams move from unclear user needs to structured innovations. The method was applied in an R&D setting focused on adaptive seat design. Participants followed a procedure that included problem modeling, contradiction identification, and inventive solution development. Results showed that COREX helped teams address design trade-offs more effectively than when using either method alone. The feedback cycles allowed for continuous improvement and system refinement. It offers practical value for design teams working in emerging socio-technical domains by supporting both analytical thinking and creative ideation in an integrated process.

Enhancing Efficacy in Problem Solving and Data Protection through the Integration of Function-Oriented Search and ChatGPT

2025-10-30T13:04:48+08:00

As a Large Language Models, ChatGPT's ability to learn from big data and respond to diverse user queries makes it a powerful tool for R&D. Despite the potential benefits of using ChatGPT, there exist the vulnerabilities for users' protection. As a countermeasure of this issue, this study proposes to utilize Function-Oriented Search (FOS), a particular methodology based on TRIZ (Theory of Inventive Problem Solving). FOS projects an innovative approach to problem solving by abstracting the essence of a problem by defining it functionally and generating solutions from different areas where the function can best be performed. When using ChatGPT, thus, this study argues that FOS would ensure proper results while mitigating the exposure of sensitive information. Although it requires specialized training and sufficient hands-on experiences to implement FOS in order to select and conceptualize problem focus areas, this study suggests that ChatGPT can be an efficient tool for developers who adopt FOS. Not only for experts on FOS, but also for those who are not familiar with FOS, the use of ChatGPT enables to conduct efficient and comprehensive problem explorations and devise solutions. By demonstrating the application of FOS in practical cases, this study findings supported the potential benefits of ChatGPT as a dynamic collaborator in problem solving. The findings also showed that FOS can generate suitable solutions of using ChatGPT while maintaining the protection of personal or corporate information. This study would contribute to the emerging field of AI by confirming the possible synergy with TRIZ's FOS and ChatGPT, a Large Language Model.

HIN-MELM-AE AND DePori-BASED AUTOMATIC TEXT SUMMARIZATION FOR MULTI-TEXT DOCUMENTS AND MULTI-LINGUAL SUMMARIES VIA ENSEMBLE LEARNING

2025-11-23T03:01:51+08:00

Automatic Text Summarization (ATS) emerged from the need to manage the growing volume of textual information. ATS is a process of creating a short and accurate summary of a longer text document.The prevailing studies didn’t perform ATS for multi-document and multi-lingual summaries.This paper presents an improved ensemble learning-based automatic text summarization with slang filtering using HIN-MELM-AE and Dehghani Poor and rich optimization algorithm (DePori) techniques.Initially, the text document is taken and then pre-processed. Afterward, the slang identification and filtering are done on the pre-processed text by using DePori. Next, the slang-filtered text is transformed by InS-FCM-based clustering, LDA-based topic modeling, TF-IDF analysis, and frequent term selection. From the transformed data, the POS tagging is performed by utilizing SemSim-HMM. Then, the significant entity is extracted from the transformed data and POS-tagged text. After that, the SBERT is employed to perform entity vectorization. Finally, the ATS is done by the ensemble models, which include HIN-MELM-AE, AE, VAE, and SBERT. Next, the cosine similarity evaluation is done from the output of ensemble models. Next, the voting-based fusion, re-ranking, and optimal sentence selection are performed. At last, the summarized text is obtained.The results proved that the proposed model achieved a high accuracy of 98.72%, thus outperforming conventional methods.