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