# DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness
## 1 Introduction  
Since the twentieth century, Theory of Inventive Problem Solving (TRIZ) [1, 2] has emerged as a systematic methodology for innovation, gradually attracting widespread attention and being applied across various invention and innovation scenarios. TRIZ has expanded into a comprehensive set of methodologies [3, 4, 5] and practical experiences [6, 7], and has also achieved good results in some other fields such as education [8, 9], finance [10, 11], biomimetics [12, 13], etc. However, due to the diversity and complexity of its methodologies, individuals with a limited understanding of TRIZ and cross-domain innovation often find it difficult to master, potentially overlooking certain details and processes [14]. Moreover, the TRIZ method, in its pursuit of innovation, may sometimes neglect the human Purpose and value orientation inherent in the innovation process [15]. To address these issues, we have synthesized the core ideas and principles of the DIKWP theory [16] and proposed the DIKWP-TRIZ method. This approach aims to transform the innovation methodology of TRIZ into Data innovation, Information innovation, Knowledge innovation, Wisdom innovation, and Purpose innovation within the DIKWP-TRIZ framework. By reducing the essence of invention and innovation pathways to these five innovation directions, we place maximum emphasis on human Purpose and value orientation in the innovation process. This strategy reduces the cognitive burden on users of the TRIZ innovation method, allowing them to focus more on innovations in Data, Information, Knowledge, Wisdom, and Purpose, as well as effectively handling problems characterized by incompleteness, inconsistency, and imprecision (the 3-N problems). The main contributions of this paper are as follows:  
1. Integration of DIKWP Model with TRIZ Methodology for Artificial Consciousness Innovation: This paper introduces the DIKWP-TRIZ framework, which integrationprovides a structured approach for applying the TRIZ principle to cognitive processes, particularly to address the incompleteness issues that exist in the concept to Cognitive Space, such as the incompleteness of solutions caused by searching for solutions without understanding the optimization parameters. By bridging the gap between cognitive modeling and inventive problem solving, this approach offers a novel methodology that enhances innovation capabilities in complex, value-driven contexts. 2. Comprehensive Mapping of TRIZ Principles to Cognitive Transformations in DIKWP: The research systematically maps TRIZ principles to various cognitive transformations within the DIKWP model, such as the transition from Data to Knowledge or from Wisdom to Purpose. This mapping clarifies the applicability of each TRIZ principle in cognitive processes among the principles. By proposing a refined set of principles and contextual guidelines, the paper improves the precision and efficiency of TRIZ applications in complex cognitive scenarios, thereby facilitating more effective problem solving and innovation. 3. Development of a Methodological Framework for Reducing Redundancies and Enhancing Consistency in TRIZ-DIKWP Applications: This study addressed potential inconsistencies in the application of TRIZ principles within the DIKWP framework by extending the TRIZ invention principle in Semantic Space and using Cognitive Space coverage integrity analysis. The proposed methodological framework includes strategies for integrating overlapping principles and offers decision-making tools to guide practitioners in selecting the most appropriate principles for specific cognitive processes. This structured approach ensures a coherent and systematic application of TRIZ principles, enhancing the overall robustness and effectiveness of the DIKWP-TRIZ methodology. This paper is organized as follows. Section 2 presents related work, introducing the current applications and limitations of TRIZ and the DIKWP theory. Section 3 provides relevant definitions in research methods.