Principles

40 inventive principles

https://www.triz-consulting.de/wp-content/uploads/2023/10/40InventivePrinciples_v1_lowRes.pdf. 

In contrast, Figure 2 presents the problem-solving process under the DIKWP-TRIZ methodology. Using the DIKWP theory, cognitive agents construct a DIKWP processing model for the 3-N problem. We have developed 25 transformation methods to handle 3-N problems by facilitating the conversion between DIKWP contents (as shown in the bottom left of Figure 2). This construction and mapping process is then translated into corresponding TRIZ invention principles. This approach enhances the applicability of TRIZ by mapping the essential problem-solving semantics to concepts, thereby extending the scope of TRIZ invention principles. Additionally, the generated general solutions incorporate DIKWP conceptual content, ensuring that the solutions are both credible and controllable. 

In the DIKWP model, Data semantics (DIKWP-Data) represent specific manifestations of shared semantics in cognition. Within the Conceptual Space, Data concepts embody specific facts or observations recognized by cognitive entities, confirmed through their alignment with the conscious (non-subconscious) space of these entities and their correspondence with the shared semantics of existing cognitive concepts. When processing Data concepts, cognitive systems identify and extract shared semantic elements, which serve as labels for these concepts, unifying them based on corresponding shared attributes. For example, when observing a group of sheep, despite variations in size, color, or gender, cognitive processing categorizes them under the concept of “sheep” by identifying shared or probabilistically related semantics. Similarly, in distinguishing between a silicone arm and a human arm, the cognitive process may rely on features like shape or color; however, a lack of functionality, such as rotation, would exclude the silicone arm from the concept of “arm” based on the semantic criterion of rotatability.

The distinction between conceptual and Semantic Spaces reflects different philosophical perspectives on technology. Conceptual Space processing corresponds to natural language communication, but the core function of communication lies in conveying semantics. In the Cognitive Space, effective comprehension of transmitted concepts hinges on semantic correspondence within the Semantic Space of the cognitive entity. However, since the Semantic Space is often subjective and cannot be fully shared through conceptual forms, it is referred to as subjective.

In the Semantic Space, the semantics of Data concepts are manifestations of a shared set of cognitive semantics. There is a shared or approximate semantic attribute set S, defined by a collection of feature semantics:

4.1. Mapping the 40 TRIZ Principles to the DIKWP Model

The research methodology will involve identifying which TRIZ principles are applicable to specific DIKWP transformations. In Table A1 in the Appendix A, we provide a comparison of TRIZ invention principles. Each DIKWP transformation can be guided by one or more TRIZ principles to optimize the cognitive process. To address this, we will explore possible solutions such as merging overlapping principles or creating sub-principles to improve clarity and distinction.

4.1.1. Data (D) → Data (D)

The transformative process explores the conversion of Data, as a set under the same semantics, into various interpretations within a cognitive subject, as well as how existing Knowledge guides the collection and interpretation of these Data. This bidirectional transformation process emphasizes the dynamic interaction among Data and aims to establish a continuous cognitive improvement cycle. The following are several TRIZ principles related to this transformation process, along with their application examples:

• Principle 1: Segmentation
  This principle involves dividing large datasets into smaller, more manageable segments to facilitate detailed analysis and obtain targeted insights. This strategy helps optimize resource utilization and enhances the efficiency of the analytical process [37].

• Principle 2: Extraction
  Extracting necessary subsets or key Data elements from large datasets to focus attention on core Information, avoiding distractions caused by irrelevant Data [38,39]. This method significantly improves both the speed and effectiveness of Data processing.

• Principle 5: Merging
  Integrating datasets from different sources to create a more complete and robust dataset. This not only enhances the overall accuracy of the Data but also increases its utility, providing a solid foundation for decision making [40].

• Principle 10: Preliminary Actions
  Preprocessing the Data, such as cleansing, standardization, or structural adjustment, prior to formal analysis to ensure the smoothness and effectiveness of subsequent Data operations. This step is essential in Data analysis, as it directly impacts the quality of the final results. For instance, in Data mining, preprocessing steps like Data cleaning, normalization, and integration are essential for reducing noise and inconsistencies, which are common in large datasets. Proper preprocessing improves the performance of Data analysis and mining algorithms, leading to more robust findings. Additionally, preprocessing also includes standardization, which ensures that all Data points are on the same scale, enhancing the accuracy of analytical models and decision-making processes [41].

• Principle 12: Equipotentiality
  We have extended the semantic content of this principle here, which is to reduce excessive content flow, complexity, and inconsistency through certain optimization strategies, such as layering or preset boundaries, in order to improve the coordination of the entire system. For example, in software development in Domain-Driven Design [42] mode, all sub-business domain models with the same level are divided into the same equipotential surface. In addition, a unified domain modeling language can be established to ensure that domain experts and developers have the same understanding of the same term.

• Principle 18: Mechanical vibration
  We have expanded the semantic content of this principle by adding a specific mechanism to randomize or denoise the data content, ensuring that the entire system operates at its optimal state. For example, in the protection algorithm for Differential Privacy [43], researchers use Differential Privacy technology to protect individual data without significantly affecting the statistical results and try to avoid individual data leakage as much as possible [44].

• Principle 26: Copying
  The main principle is to use a simple, inexpensive, and easy-to-manufacture replica instead of an expensive or complex original. This principle is reflected in the concept of metaverse [45], which models display targets through computer graphics technology to form data-driven content, allowing users to achieve immersive experiences through virtual reality- or augmented reality-related technologies.

• Principle 30: Flexible Shells and Thin Films
  We have expanded the semantics of this principle by isolating and processing specific data content from other data content under certain conditions, in order to prevent the environment in which specific content is located by directly accessing that content. For example, in encryption application scenarios, the application of Homomorphic Encryption [46] technology is extremely important, as it can be used and searched without affecting its confidentiality.

4.1.2. Data (D) → Information (I)

The transformation of Data into Information involves the cognitive subject distinguishing Data under different semantic contexts to obtain the corresponding Information semantics. This process requires not only technical support but also a deep understanding of the Knowledge embedded within the Data. Below are several TRIZ principles and their specific applications related to this transformation process:

• Principle 3: Local Quality
  Focus on analyzing specific Data subsets to generate detailed Information. This approach can reveal insights that might be overlooked from a global perspective, providing in-depth understanding at a local level. For example, in large-scale market research Data, examining the behavior patterns of a particular consumer group can uncover unique preferences and needs, thereby informing product customization. For example, a study on large-scale software engineering Data demonstrated the benefits of splitting Data into smaller, more homogeneous subsets, resulting in improved model performance and more accurate predictions [47].

• Principle 5: Merging
  Integrate Data points from various sources to form a coherent and valuable Information system, facilitating a comprehensive understanding of the subject matter. For instance, combining user behavior Data, geographical Information, and social media feedback can help companies build a more comprehensive user profile, thereby enhancing the effectiveness of marketing strategies [48].

• Principle 7: Nested Doll
  Cognitive subjects are able to nest multiple data fields together, progressively forming contrasting content and constructing different semantic expressions. For example, a company’s sales report can reflect the content that users want to understand and compare at once through multiple layers of nesting.

• Principle 9: Preliminary Anti-Action
  The principle of preliminary anti-action in TRIZ, which involves preprocessing steps like filtering out noise or irrelevant Data, is crucial to enhancing the clarity and quality of Information. For instance, techniques such as noise reduction in various applications, like hyperspectral imaging, improve the analysis of key signals by eliminating non-white noise prior to formal analysis [49]. Additionally, noise filtering algorithms can significantly improve Data quality, especially in scenarios involving imbalanced Data or chaotic signals, thereby enhancing the subsequent Data analysis [50]. These preprocessing steps are essential for ensuring the authenticity and reliability of Information, reducing potential biases during analysis, and improving the overall quality of results [51].

• Principle 17: Another Dimension
  By utilizing multidimensional representations of data, such as visualization tools or multi-layer models, content with the same semantics can be presented and differentiated through another dimension, revealing deeper insights and providing rich and detailed understanding. For example, using 3D charts to display trend changes in time series data, or using heat maps to illustrate the strength of correlations between variables, can help analysts more intuitively grasp data characteristics [52].

• Principle 28: Mechanics Substitution
  Automate Data processing tasks using algorithms and tools to efficiently transform large amounts of Data into meaningful Information. With the advancement of machine learning and AI technologies, such automated methods have become an indispensable part of modern Data analysis, significantly enhancing the speed and accuracy of Information extraction [53].

• Principle 35: Parameter Changes
  By altering the measurement standards, formats, or parameters of Data, new Information semantics can be revealed. In financial market analysis, applying different smoothing parameters to historical trading Data can distinguish between short-term and long-term market fluctuations. For example, using moving averages with different periods (such as 5-day and 200-day moving averages) can identify short-term market volatility and long-term trends, thereby providing a basis for investment decisions [54].

4.1.3. Data (D) → Knowledge (K)

The cognitive subject systematically analyzes Data semantics by identifying their similarities and differences, thereby organizing them into structured Knowledge and forming a logically coherent Knowledge system. Through this process, the cognitive subject not only categorizes and integrates disparate Data points but also establishes a comprehensive and interconnected framework of Knowledge. This structured organization facilitates a deeper understanding of the relationships and underlying patterns within the Data, ultimately contributing to the development of a cohesive and consistent Knowledge system that can support complex decision-making and reasoning processes. The following are several TRIZ principles related to this transformation process, along with their application examples:

• Principle 6: Universality
  Extract general principles or models from specific Data points and extend localized insights to broader contexts. For example, in collaborative environments such as cross-functional software development teams, applying universal principles from one industry can enhance team Knowledge sharing and efficiency across different sectors [55].

• Principle 24: Intermediary
  Utilize intermediate models, such as simulations or analytical frameworks, as bridges between raw Data and refined Knowledge to facilitate the understanding and interpretation of complex datasets. For example, in financial risk management, intermediary models like stochastic differential equations are used to model economic scenarios and bridge the gap between complex raw Data and actionable insights [56].

4.1.4. Data (D) → Wisdom (W)

The cognitive subject integrates ethical and value judgments to generate goal-aligned, wise decisions through the semantic analysis of Data. This process requires not only a profound understanding of the Data itself but also the integration of experience and intuition to achieve a comprehensive cognition that transcends specific contexts. The following are several TRIZ principles closely related to this transformation process, along with their specific applications:

• Principle 40: Composite Materials
  We extend the semantics of materials to transform them into Data, while composites are expanded into multiple channels or methods serving as Data sources. Correspondingly, in Information technology, this process involves integrating various Data sources and analytical models to generate comprehensive, high-quality decisions guided by Wisdom. This approach is exemplified in applications such as financial risk management [57] and the formulation of optimized traffic management strategies [58], where diverse Data inputs and sophisticated analysis converge to produce holistic, informed decisions.

4.1.5. Data (D) → Purpose (P)

The cognitive subject or process identifies and analyzes common semantics among Data to clarify objectives and purposes, thereby guiding the formulation of future strategies. This ensures that the output of goal setting remains consistent within the scope of the identified semantics. By maintaining alignment with the semantic framework, the cognitive subject can develop coherent and Purpose-driven strategies that are well grounded in the underlying Data patterns, thus enhancing the effectiveness and precision of decision-making processes.

• Principle 4: Asymmetry
  We can prioritize subsequent objectives based on the relevance of Data collection and analysis activities to the intended Purpose, ensuring that resources are concentrated in high-impact areas. This approach allows for a strategic allocation of efforts, maximizing the effectiveness of decision making and optimizing outcomes by focusing on areas that most significantly contribute to achieving the desired goals [59].

• Principle 11: In-advance cushioning
  Stakeholders utilize historical traffic and log Data from the same period in previous years to proactively prepare Data systems and infrastructure, supporting anticipated goal-driven demands and enhancing flexibility and readiness. For instance, in e-commerce platforms, to accommodate the surge in traffic during upcoming shopping seasons, stakeholders may upgrade server capacity and optimize database architecture in advance. These proactive measures ensure that the system can handle high concurrent access loads, thereby maintaining performance and reliability under increased demand [60,61].

• Principle 15: Dynamism
  Maintain a flexible Data strategy that can evolve with changing purposes or goals, ensuring its ongoing relevance and effectiveness. For example, in a rapidly changing market environment, regularly evaluating and adjusting Data analysis models to reflect the latest market demands and trends enables the organization to base its decisions on the most up-to-date Information. For instance, the concept of asymmetric resource allocation is also applicable in situations involving emergency medical services. During mass casualty incidents, allocating resources based on prioritization, such as through column generation models, maximizes lifesaving capacity [62].

• Principle 29: Pneumatics and Hydraulics
  We have expanded the semantics of this principle to include the concept of increasing the degrees of freedom of the target. Adjusting the degrees of freedom of the system through external data-driven means to achieve the desired goals or intentions. For example, in the field of software engineering, the goal of commonly used Inversion of Control during the development process is to delegate key dependencies to specific containers for management and control through external means, reducing coupling [63].

4.1.6. Information (I) → Data (D)

In the Cognitive Space, Information is transformed into Data through semantic differentiation and contextual processing, thereby concretizing semantic content into a Data form. The semantic distinctions within the Information serve as the basis for generating specific Data, enabling a more precise representation of abstract concepts in a structured and quantifiable manner.

• Principle 10: Preliminary Action
  Based on the differentiated content of Information semantics, specific datasets that may be required in the future can be generated in advance. For example, by collecting meteorological Information, agricultural Data models can be developed beforehand, such as generating Data on the projected impact of rainfall and temperature variations on crop yields [64]. This proactive approach provides robust Data support for agricultural decision making, enabling stakeholders to make informed choices based on predictive insights.

• Principle 22: Blessing in Disguise
  Negative or anomalous elements within Information can be transformed into specific Data points for further analysis and decision making. For instance, by identifying anomalies in business operations, such as production delays, specific operational Data can be generated or retrieved to optimize production processes. This targeted approach enables a deeper understanding of underlying issues and supports the development of more effective strategies for process improvement and risk mitigation [65].

4.1.7. Information (I) → Information (I)

This phase discusses the process of refining and reorganizing Information to enhance its quality and utility. This process involves not only optimizing the Information itself but also improving its structure and presentation to facilitate better understanding and effective communication. The following are several TRIZ principles related to this transformation process and their specific applications:

• Principle 13: The Other Way Round
  In the transformation of Information to Information, principle 13 is mainly reflected in the reorganization of information flow or structure to promote a better understanding of complex concepts and more effective communication. For example, in the previous studies and improvement of business information flow [66,67], stakeholders were able to better understand and use corresponding workflows, which promoted business development and improved efficiency.

• Principle 17: Another Dimension
  The cognitive subject can employ various forms of representation, such as charts and diagrams, to transform one type of differentiation into another, providing multiple perspectives and deeper insights into the Information. For example, in Data visualization, representing Information across different dimensions, such as through multidimensional scaling and graph visualizations, can help users better understand high-dimensional Data. A study on visualizing dimension coverage in exploratory analysis demonstrated that representing Data visually with multiple perspectives helps analysts form new questions and gain deeper insights into datasets [68]. In market research, combining bar charts and pie charts can enhance the analysis by offering different viewpoints on the same Data. For instance, visualizing research topics using a three-dimensional strategic diagram enabled researchers to gain a more nuanced understanding of interdisciplinary research areas and emerging trends [69]. This multi-faceted approach enhances the ability to analyze complex Data and facilitates more informed and strategic decision making.

4.1.8. Information (I) → Knowledge (K)

This process represents the cognitive subject’s transformation of structured Information semantics into organized Knowledge. It highlights the importance of transitioning from Information to Knowledge and the application of this Knowledge in new informational domains. This transformation is a fundamental aspect of constructing Knowledge semantics. Below are several TRIZ principles related to this transformation process and their specific applications:

• Principle 15: Dynamism
  The Information represents different semantics, and as information updates, the complete semantics corresponding to its knowledge represent our gradually clearer understanding of the world. Therefore, the transformation of information into knowledge represents the continuous expansion of the integrity of knowledge boundaries. And the dynamism of Principle 15 corresponds well to this point. For example, many studies on the prediction and characteristics of protein molecules are constantly expanding human understanding of the field of biomolecules [70,71]. In order to record such dynamics, researchers have constructed corresponding databases to record these dynamic changes [72].

• Principle 24: Intermediary
  This principle in the conversion refers to the use of frameworks or models to transform information into knowledge, serving as a bridge between raw information and structured understanding. For example, in the field of education, developing a curriculum as a framework to guide student learning can help integrate dispersed learning materials into a systematic knowledge system [73].

4.1.9. Information (I) → Wisdom (W)

This process involves integrating Information with ethical considerations, social values, and other factors to transform it into wise decisions within complex contexts. Wisdom is not merely a pathway for the realization of Information; it also requires the consideration of long-term objectives and multiple value judgments. This comprehensive approach ensures that decision making is guided by both practical insights and a deeper alignment with ethical and societal principles.

• Principle 23: Feedback
  Through feedback expressed through different semantic contents, we can assess the strengths and weaknesses of individuals or parts under similar conditions, and adjust or screen corresponding strategies or solutions accordingly. For example, in the scenario of autonomous driving, when facing multiple optimization objectives, researchers obtain the latest input information through feedback and choose the best driving strategy in a timely manner [74,75].

• Principle 32: Color Changes
  We extend the semantics of color to the realm of presentation, such as modifying the format or framing of Information to enhance the effectiveness of Wisdom-based communication. This approach, which may include storytelling or contextualization, makes the Information more engaging and impactful, thereby conveying it more effectively to the audience. For example, in marketing campaigns, communicating a product’s core values through a brand narrative, rather than simply listing its features, can create a deeper resonance with consumers [76].

4.1.10. Information (I) → Purpose (P)

Through the analysis and comprehension of Information semantics, specific goals and action purposes are generated based on the semantic differentiation of the Information. The transformation process from Information to Purpose involves extracting key semantic distinctions from the Information and formulating goal-oriented action plans. This process ensures that critical insights are translated into actionable strategies, aligning cognitive understanding with purposeful behavior.

• Principle 16: Partial or Excessive Actions
  Adjust the level of effort in Information processing according to the specific requirements of the objective, ensuring that the Information is neither excessive nor insufficient. This means determining the scope and depth of Information collection and analysis based on the importance and required detail of the objective. For example, in software project management, if the goal is to evaluate the cost-effectiveness of a project, efforts should be focused on collecting Data directly related to costs and benefits, avoiding the collection of excessive unrelated Data to improve the efficiency and relevance of Information processing [77].

• Principle 32: Color Changes
  We extend the semantics of color to the realm of presentation. Modify the presentation of Information according to the objective to enhance the clarity and impact of Information transmission. This principle emphasizes the importance of presentation format, suggesting that Information should be expressed or visually represented in a way that better aligns with the needs of the target audience. For example, in corporate annual reports, to better convey the company’s performance and future outlook to shareholders, key financial Data can be presented using charts, timelines, and other visual tools to make the Information more intuitive and understandable [78,79,80].

4.1.11. Knowledge (K) → Data (D)

This process refers to the cognitive subject’s use of the completeness of Knowledge semantics to reverse-engineer or refine expressions, thereby generating Data points that support the Knowledge system. The transformation from Knowledge to Data typically involves extracting specific facts, metrics, and verification Data from structured Knowledge. This approach enables the precise representation and empirical validation of abstract Knowledge constructs, facilitating the alignment of theoretical frameworks with tangible, Data-driven evidence.

• Principle 8: Anti-weight
  We have expanded the semantic content of this principle here, which is to achieve a solution to the problem through certain compensation methods. Therefore, in the conversion of knowledge to data, the idea of transforming complete content into consistent semantic content is often used in distributed processing. For example, in a distributed file system, to reduce the burden on a single storage system, complete content can be stored separately on each node through specific policies, which allows each node to undertake a small portion of the storage task, reducing the burden on individual nodes [81].

• Principle 9: Preliminary Anti-Action
  Using the completeness of knowledge semantics to filter out identical semantic content that does not belong to the knowledge system, in other words, the knowledge system pre-screens irrelevant data to ensure that only valuable data are collected and analyzed. This method not only improves data processing efficiency but also prevents irrelevant data from affecting the decision-making process. For example, before training an AI model, we need to preprocess the dataset in order to use our existing knowledge system to filter out data that do not match the actual situation.

• Principle 25: Self-service
  Apply existing Knowledge systems to autonomously guide Data collection and processing, reducing the need for external intervention. For instance, in the design of intelligent sensor networks, pre-set algorithms can be used to automatically identify important Data streams and prioritize them, thereby optimizing resource allocation and enhancing the system’s adaptive capabilities [82,83].

• Principle 27: Cheap Short-living Objects
  Data represent the same semantic content, and the conversion of knowledge to data through this principle represents the transformation from complex and complete to simple and lightweight. For example, in streaming media services, in order to reduce video content transmission time or bandwidth, service providers often use efficient video compression algorithms [84] to compress videos into smaller data formats while preserving semantic integrity as much as possible.

4.1.12. Knowledge (K) → Information (I)

By analyzing Knowledge nodes and their relationships, these elements are concretized into semantic content specific to a given context, thereby generating concrete Information. The transformation from Knowledge to Information typically involves simplifying and contextualizing complex Knowledge structures to meet the requirements of specific applications. This process may include extracting domain-specific Information from a Knowledge base or converting theoretical Knowledge into practical guidelines, ensuring that abstract concepts are translated into actionable insights tailored to particular scenarios.

• Principle 3: Local Quality
  The key nodes and relationships within the Knowledge structure are concretized into Information content, aimed at addressing specific problems or application contexts. This process typically involves extracting critical components from the body of Knowledge and transforming them into actionable Information semantics that can be directly applied. By converting essential Knowledge elements into practical Information, this approach ensures that theoretical insights are effectively utilized in real-world scenarios. For example, in the field of software engineering, Domain-Driven Design [42] is one of the manifestations of local quality. Software development teams start from the business and abstract business knowledge into domain models to guide development. Through in-depth understanding and refinement of the domain, they can more accurately identify which parts need special attention and optimization in the business knowledge by reflecting different semantics. In addition, each subdomain under the DDD design can be independently designed and implemented. This modular design approach helps to concentrate resources and efforts on optimizing key modules, thereby improving the overall quality of the software system.

• Principle 13: The Other Way Around
  Generating specific Information content based on the inverse logical relationships within Knowledge is particularly useful in handling anomalies and reverse reasoning scenarios. This process involves reverse-analyzing causal relationships within the Knowledge base to produce Information that can explain or predict specific phenomena. For example, leveraging maintenance Knowledge to perform reverse reasoning can generate diagnostic Information about equipment failures, such as potential causes and corresponding solutions for a particular type of malfunction. This enables technical personnel to quickly identify the root cause of an issue, providing effective support in troubleshooting and decision making during equipment failures [85].

4.1.13. Knowledge (K) → Knowledge (K)

The transformation process emphasizes the targeted nature of Information processing, ensuring that the acquisition, analysis, and presentation of Information all serve specific objectives. The following are several TRIZ principles related to this transformation process and their specific applications:

• Principle 22: Blessing in Disguise
  Utilize challenges or limitations in knowledge as opportunities to develop deeper understanding or new methods. This approach emphasizes the potential for breakthroughs in solving problems and obstacles, advancing knowledge through critical thinking and innovation. For example, in technological innovation, traditional file systems cannot meet business needs, and companies developed their own file systems [81,86]. Therefore, by exploring alternative solutions or developing new methods, they promote the progress of related technologies and expand people’s understanding of file systems.

• Principle 34: Discarding and Recovering
  Update Knowledge by eliminating outdated concepts and incorporating new findings, maintaining a robust and up-to-date Knowledge base. This method underscores the importance of Knowledge renewal, retaining core Knowledge while continuously discarding obsolete parts and adding the latest research outcomes. For example, in medical education, with the release of new clinical research results, teaching content should be promptly updated by removing disproven theories and including the latest medical practices [87].

4.1.14. Knowledge (K) → Wisdom (W)

The cognitive subject integrates Knowledge with multiple factors, such as ethics and social values, to generate wise decisions that align with long-term goals and complex contexts. The transformation from Knowledge to Wisdom requires careful consideration of the practical applicability of Knowledge semantics and diverse value orientations. This process ensures that decision making not only leverages factual Knowledge but also aligns with broader ethical and societal principles, enabling the formulation of nuanced and contextually appropriate strategies.

• Principle 15: Dynamism
  Allow Knowledge and Wisdom to co-evolve over time, adapting to new Information and changing contexts to maintain their relevance and effectiveness. This means that Knowledge and Wisdom should not be static but should continually update and develop in a changing environment. For instance, in medical research, as new discoveries emerge, existing treatment methods need to be constantly adjusted to align with the latest scientific understanding [88].

• Principle 40: Composite Materials
  We extend the semantics of materials to analyze and transform Knowledge content. Combine diverse sources of Knowledge to develop a more nuanced and comprehensive form of Wisdom, integrating multiple perspectives and experiences. This approach highlights the importance of interdisciplinary Knowledge integration and cross-domain learning, where the fusion of Knowledge from different fields creates richer Wisdom. For example, in public policy making, integrating Knowledge from social sciences, economics, and ethics can lead to more comprehensive and sustainable policy measures [89,90].

4.1.15. Knowledge (K) → Purpose (P)

The transformation from Knowledge to Purpose emphasizes the dynamic interaction between Knowledge and objectives, ensuring that the accumulation and application of Knowledge better serve the strategic needs of organizations or individuals. The following are several TRIZ principles related to this transformation process and their specific applications:

• Principle 25: Self-service
  We can leverage the completeness of knowledge semantics to construct goals that can be autonomously adjusted or redefined, allowing goal setting to exhibit flexibility and adaptability based on new insights. For example, Computer Vision-based object recognition algorithms [91,92] can find corresponding targets and recognize them based on the knowledge learned during training.

• Principle 31: Porous Materials
  We will expand and extend the porous semantics. Therefore, this invention principle can be defined as maintaining the openness of objectives to the influx of new Knowledge, allowing goals to dynamically adjust and optimize as understanding evolves. This method emphasizes the plasticity and adaptability of objectives, ensuring that goals are continually updated as new Knowledge is acquired. For instance, in corporate strategic planning, regularly reviewing changes in the external environment and internal capabilities enables timely adjustments to the company’s development direction to respond to market changes [93].

• Principle 35: Parameter Changes
  By utilizing the semantic integrity of knowledge, parameters are adjusted to remain within the current knowledge system and improve the overall goal of the system, resulting in outputs that meet expectations [94].

4.1.16. Wisdom (W) → Data (D)

The process involves interpreting Data through Wisdom to gain profound insights, while also discussing how Wisdom can guide Data collection and interpretation. It requires not only a deep understanding of the Data itself but also the integration of experience and intuition to form a comprehensive cognition that transcends specific contexts. The following are several TRIZ principles closely related to this transformation process, along with their specific applications:

• Principle 6: Universality
  Wisdom is the information content with values, which is used in this principle to standardize or define universal expressions or contents of the same semantics, and can also integrate multiple independent data points into a coherent understanding beyond individual cases. For example, in the media field, in order to reach a wider audience of people of different ages, educational backgrounds, etc., corresponding adjustments need to be made to the expression methods and channels of the displayed data [95].

• Principle 24: Intermediary
  The cognitive subject can employ Wisdom-driven models or frameworks to analyze Data, offering more refined and contextually relevant insights. For example, in social science research, psychological theoretical models can be applied to interpret survey results. By leveraging the value-driven processes inherent in Wisdom, these models facilitate a deeper exploration of the motivations behind individual behaviors and the social factors that influence them. This approach allows for a more comprehensive and nuanced understanding of complex human dynamics, enhancing the ability to generate meaningful and actionable conclusions [96].

• Principle 25: Self-service
  Wisdom can guide Data collection and interpretation, enabling autonomous operations by the cognitive subject and reducing reliance on external guidance. This involves incorporating intelligent decision-making mechanisms into Data processing. For instance, in intelligent traffic management systems, historical traffic flow patterns and emergency response experiences can be utilized to automatically adjust traffic signal control strategies, thereby optimizing traffic flow. This approach enhances system responsiveness and efficiency by enabling adaptive, self-regulating decision-making processes [97].

• Principle 35: Parameter Changes
  The cognitive subject can adjust Data parameters based on insights derived from Wisdom to ensure that Data collection and analysis methods support deeper understanding. For example, in intelligent transportation management systems, historical traffic flow patterns and emergency response experience can be utilized to automatically adjust input parameters for traffic signal control, thereby optimizing traffic flow. This method improves the system’s responsiveness and efficiency by implementing an adaptive and self-regulating decision-making process [97].

4.1.17. Wisdom (W) → Information (I)

Transforming the semantic content of wise decisions into concrete Information expressions involves converting complex decision-making logic, ethical judgments, and other nuanced considerations into contextualized semantic Information that is accessible and easy to communicate. This conversion from Wisdom to Information ensures that sophisticated insights are articulated in a clear and comprehensible manner, facilitating effective dissemination and application across various contexts.

• Principle 16: Partial or Excessive Actions
  The cognitive subject can leverage Wisdom to determine appropriate Information processing methods, ensuring efficient resource allocation. This means that in handling Information, it is crucial to apply measures that correspond to the level of semantic differentiation. For instance, in corporate strategy development, Wisdom can guide which Information requires in-depth exploration based on market trend analysis and which content only needs a high-level overview, thus preventing resource waste or Information overload [98]. This approach optimizes the decision-making process by aligning the depth of Information analysis with strategic priorities and resource availability.

• Principle 22: Blessing in Disguise
  Leveraging Wisdom to identify the deeper meanings or hidden value within Information semantics can transform potential problems into opportunities for insight. For example, when faced with negative customer feedback, a wise analysis might uncover new ways to improve products or services, turning challenges into a catalyst for innovation and enhancement. This proactive approach not only addresses immediate concerns but also contributes to long-term growth and development by converting setbacks into strategic advancements [99].

4.1.18. Wisdom (W) → Knowledge (K)

• Principle 2: Taking Out
  The cognitive subject can extract the core goals and strategies from purposes to form independent Knowledge modules. These modules, containing comprehensive semantics, can be used to guide future actions. In corporate management, for instance, the core strategic purposes for company growth can be structured into Knowledge modules, such as market expansion strategies or brand development strategies, to support strategic implementation across various departments. This approach ensures that each department aligns its actions with the overarching strategic vision, facilitating coherent and coordinated organizational growth [116].

Transforming the principles, strategies, and values inherent in wise decisions into a systematically applicable Knowledge framework involves structuring and systematizing the abstract concepts found within Wisdom-based decision making. This process of converting Wisdom into Knowledge creates reusable Knowledge modules, enabling the consistent application of these insights across different scenarios and contexts. It ensures that the sophisticated understanding embedded in Wisdom is translated into a coherent and operational Knowledge system that can be effectively utilized and adapted for future decision-making processes.

• Principle 3: Local Quality
  The cognitive subject transforms high-quality strategies derived from wise decision making in a specific domain into specialized Knowledge systems. For example, in healthcare management, Wisdom-based medical decisions—such as strategies for responding to pandemics—are translated into structured Knowledge systems for managing specific diseases. This may include Knowledge of the allocation of medical resources and patient management tailored to infectious diseases, thereby enabling a more systematic and effective approach to healthcare administration and crisis response [100].

• Principle 23: Feedback
  Utilize experiential feedback to refine both Knowledge and Wisdom, enhancing understanding and decision-making abilities through the lessons learned from both successes and failures. This process emphasizes learning and reflection in practice, where continuous trial and error lead to the development of a more mature Knowledge system and wise judgment. For instance, researchers have drawn inspiration from the theory of evolution in nature, utilizing feedback and self-regulation in the process of biological evolution to abstract these theories into evolutionary algorithms for multi-objective optimization [101,102], achieving great success.

4.1.19. Wisdom (W) → Wisdom (W)

This process emphasizes the dynamic and adaptive nature of Wisdom, ensuring that it can effectively guide decision making in an ever-changing environment. The following are several TRIZ principles related to this transformation process and their specific applications:

• Principle 15: Dynamism
  Enable Wisdom to adapt and evolve with changing circumstances, ensuring its continued relevance and effectiveness in guiding decisions. This means that Wisdom is not a static collection of Knowledge but requires adjustment in response to changes in the external environment. For example, in business management, leaders should continuously adjust their management philosophies and strategies in response to market changes and competitive dynamics to maintain the company’s competitiveness. Through dynamic adjustment, Wisdom can better address future uncertainties. Research has shown that businesses that adapt their strategies in response to competitive intensity and market dynamism tend to achieve better performance outcomes [103]. Similarly, a study on business model adaptation highlights how proactive adjustment to environmental threats can significantly improve business performance [104].

• Principle 34: Discarding and Recovering
  Abandon outdated or ineffective Wisdom to make room for new insights, ensuring that Wisdom remains a dynamic and flexible cognitive asset. This means being willing to let go of perspectives or methods that are no longer applicable while accumulating new Knowledge. For example, in the field of software engineering, with the development of new technologies and methodologies, old theories and practices may become outdated, requiring timely updating of professional knowledge and abandoning outdated concepts and methods [105].

4.1.20. Wisdom (W) → Purpose (P)

In the Cognitive Space, strategic goals and action guidelines from Wisdom-based decision making can be concretized into target purposes, providing a clear direction for action. The transformation from Wisdom to Purpose involves extracting specific goals and actionable pathways from the broader strategic framework. This process ensures that abstract strategic objectives are translated into well-defined purposes and execution plans, thereby guiding decision-making processes with precise and goal-oriented directives.

• Principle 10: Preliminary Action
  This principle is reflected in the transformation from wisdom to Purpose by setting goals using valuable information content. For example, investment banks and asset management companies will establish market monitoring systems to adopt corresponding hedging strategies before financial market fluctuations occur, in order to avoid potential losses caused by future price fluctuations [106].

• Principle 15: Dynamism
  Cognitive subjects need to adjust or redefine their goals based on new values in order to maintain their relevance and effectiveness in a constantly changing environment. This principle emphasizes the dynamic adjustment of goals and suggests updating the original goals when new knowledge or experience arises to better align with the current context. For example, in the medical field, medical institutions need to adjust treatment goals in a timely manner based on factors such as the patient’s age, economic level, and other conditions to avoid unexpected situations [107].

• Principle 20: Continuity of Useful Action
  The cognitive subject transforms wisdom into corresponding intentions through appropriate strategies under limited conditions, enabling it to output reliable goals or effects. For example, for the auto drive system [74], in the context of limited input content and computing power, the Purpose output of the autonomous drive system needs to be aligned under human values [108], so that the decisions of the autonomous drive system output can always conform to human values.

• Principle 33: Homogeneity
  The important concept of this invention principle is to transform multiple similar intelligences into consistent knowledge expressions, which can form a synergy and increase efficiency without causing additional side effects. For example, using the Semantic Web [109], adopting unified classification standards and annotation methods to form a consistent knowledge network facilitates the association and inference of knowledge.

• Principle 39: Inert Atmosphere
  We extend the semantics of this principle to improve target output efficiency by reducing environmental interference, while also facilitating analysis of the resulting phenomena. This principle embodies the transformation from wisdom to Purpose in the DIKWP model. For example, in the field of computer science, containerization is a lightweight and portable way to configure computing environments. It packages applications and their required libraries, dependencies, and configuration files together, as the simplicity of the environment makes it easy to deploy on various hosts, with consistent performance and reduced testing time costs [110].

4.1.21. Purpose (P) → Data (D)

The cognitive subject transforms Purpose content into specific Data collection requirements and Data formats through goal-oriented analysis. This process of converting Purpose into Data involves translating the semantic content of expected objectives into concrete Data formats and collection plans. By aligning the intended goals with precise Data specifications, this transformation ensures that Data collection efforts are systematically guided to support the achievement of targeted outcomes.

• Principle 10: Preliminary Action
  Future Data collection plans and requirements can be proactively generated based on the content of purposes to ensure that the Data will meet the analytical needs of anticipated goals. In market analysis, for example, new Data collection strategies can be developed in advance based on market expansion purposes, such as gathering Information on emerging market trends and competitor activities. This approach ensures that Data acquisition is strategically aligned with the expected objectives, facilitating more effective analysis and informed decision making [111].

• Principle 19: Periodic Action
  The cognitive subject collects specific data based on periodic similar purposes for analysis or decision making in other aspects. In the context of the Internet of Things, sensors in the system collect status data of monitored devices every once in a while, which is used for the working status of each device [112].

• Principle 21: Skipping
  When facing problems caused by slow efficiency, in order to achieve rapid response, it is often necessary to preset corresponding strategies based on known experience. For example, in personalized recommendation systems, the system can directly recommend content of interest to users based on their preferences, without the need to collect a large amount of user behavior data for analysis first [113].

• Principle 23: Feedback
  Based on the feedback mechanisms of the intended goals, supplementary and optimized Data can be generated to create datasets that more accurately align with the objectives. In user experience optimization, for instance, new Data collection requirements can be derived from user feedback to capture additional user behavior Information. This helps refine product design according to the intent of product improvements, ensuring that the Data collected support targeted enhancements and deliver a better user experience [114].

4.1.22. Purpose (P) → Information (I)

The cognitive subject transforms the content of purposes into specific expressions with distinct semantics through decomposition and contextualization. In other words, the conversion from Purpose to Information typically involves translating goals and strategies into semantic Information that is clear, comprehensible, and easy to disseminate. This process ensures that complex purposes are articulated in a way that facilitates understanding and effective communication across different contexts and audiences.

• Principle 6: Universality
  Purposes can be transformed into various reusable standardized Information templates, reflecting semantic differences through distinct, specific purposes. These templates can be adapted to suit different contexts and objectives, allowing for the effective communication of nuanced Information semantics across diverse scenarios [115]. This structured approach enhances the clarity and consistency of Information dissemination while ensuring alignment with varying goals and situational requirements.

4.1.23. Purpose (P) → Knowledge (K)

This transformation involves converting the purposes and action plans embedded within purposes into a structured Knowledge framework and logical system, thereby forming a comprehensive Knowledge base aligned with the intended objectives. The process of transforming Purpose into Knowledge requires concretizing the abstract aspects of the goals into actionable strategies and execution pathways. This ensures that the underlying purposes are systematically integrated into a cohesive and applicable Knowledge system that can be effectively utilized for decision making and implementation.

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• Principle 14: Circularity
  The transformation of this principle between purposes is reflected in transforming the linear structure that emphasizes the relationship between purposes into a circular structure, where all purposes are executed in a cyclic order under certain conditions. For example, in the field of software engineering, CI (Continuous Integration) and CD (Continuous Delivery/Continuous Deployment) belong to the concept of DevOps [121], which refers to the transformation of manual intervention in a series of processes such as code building, testing, deployment, and infrastructure configuration in the traditional development process into automation. Using CI/CD, code can be automatically tested, delivered, and deployed after being modified by developers. Appropriate CI/CD pipelines can minimize computer downtime and enable faster code release [122].

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• Principle 15: Dynamism
  In risk management, transforming dynamic risk assessment purposes into a real-time, adaptive risk management Knowledge system is crucial. For instance, a large financial institution must navigate an ever-changing market environment and policy risks, such as macroeconomic fluctuations and international market uncertainties. Traditional static risk management strategies are no longer sufficient to meet the demands of rapidly evolving market conditions. It is essential to translate risk management purposes into a dynamic, continuously updated Knowledge framework that can effectively respond to emerging risks and challenges [19,117].

4.1.24. Purpose (P) → Wisdom (W)

This process integrates the content of purposes with multiple factors such as ethics and social values to form wise decisions that align with long-term objectives. The transformation from Purpose to Wisdom requires a comprehensive evaluation of the feasibility, ethical considerations, and impact on the overall system. It ensures that decisions are sustainable and balanced across multiple dimensions, supporting the long-term strategic vision while maintaining ethical integrity and systemic harmony.

• Principle 36: Phase Transitions
  The transformation from Purpose to wisdom is reflected in the principle that the cognitive subject forms wisdom beyond generalization through its subjective Purpose, that is, it can output value content beyond the general scope through limited resources under specific conditions. For example, when artificial neural networks were first proposed, they were just a simple computational model based on the concept of bionics. After decades of development, models based on artificial neural networks have been applied in various fields and have achieved excellent application results [118].

• Principle 40: Composite Materials
  We have expanded the semantics of composite materials, transforming them into multidimensional objectives. The cognitive subject comprehensively considers the multidimensional goals embedded within the intentions—such as economic benefits and social responsibility—to generate wise decisions that balance the interests of all stakeholders. This holistic approach ensures that decision making is aligned with diverse objectives, facilitating sustainable and ethically sound outcomes. For example, when generating content for generative AI models, it is necessary to consider economic benefits, social responsibility, and compliance with human ethics and values, in order to avoid ethical and moral issues as much as possible [119]. However, fully considering the above content can construct different paths and solutions, promoting innovation in research related to generative AI.

4.1.25. Purpose (P) → Purpose (P)

This process emphasizes the dynamic adjustment and continuous optimization of purposes, ensuring their adaptability to the constantly changing internal and external environments. The following are several TRIZ principles related to this transformation process and their specific applications:

• Principle 1: Segmentation
  When the cognitive subject is processing purposes, if the purposes are complex or difficult to process at once, they can use principle 1 to transform the purposes into multiple sub-purposes for processing. For example, in software engineering, designers often need to divide software into various sub-functions or sub-modules, with each sub-function or sub-module completing a small part of the overall Purpose while maintaining the same Purpose for the whole [120].

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• Principle 37: Thermal Expansion
  We have expanded this invention principle to include the concept of elasticity, which means that cognitive agents can construct more solutions by improving target elasticity. This principle can be verified through the transformation of wisdom into Purpose. In the cloud computing scenario, users can adjust the resources required for cloud computing by modifying static parameters or load levels according to their own business needs, while providers need to build an elastic cloud computing environment to meet users’ personalized needs [123].

• Principle 38: Strong Oxidants
  The semantic content of Principle 38 has been expanded to include the use of various means to accelerate the process of achieving a certain goal. This principle is reflected in the transformation between intentions. In the field of computer hardware, specific algorithms are accelerated through dedicated processors or instruction sets to achieve fast output results and increase system throughput [124].