Yucong Duan

International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

1. Introduction

2. Overview of Traditional TRIZ Principles

3. Overview of DIKWP-TRIZ Principles

4. Comparative Analysis

• 4.1 Data-Level Principles

• 4.2 Information-Level Principles

• 4.3 Knowledge-Level Principles

• 4.4 Wisdom-Level Principles

• 4.5 Purpose-Level Principles

5. Advantages of DIKWP-TRIZ Over Traditional TRIZ

6. Case Study: Application in Technology Innovation

7. Conclusion

8. References

The Theory of Inventive Problem Solving, known as TRIZ, is a well-established methodology that provides a systematic approach to understanding and solving complex problems through 40 inventive principles. While TRIZ has been instrumental in driving innovation across various industries, the rapidly evolving technological landscape and the increasing importance of data and information management necessitate an enhanced framework.

DIKWP-TRIZ is an evolved version of traditional TRIZ, integrating the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model into its core. This integration aims to address the limitations of traditional TRIZ by incorporating cognitive processes and aligning with modern technological and organizational needs.

This report provides a detailed analysis and comparison between the principles of DIKWP-TRIZ and traditional TRIZ, highlighting the enhancements and innovations introduced in the DIKWP-TRIZ framework.

Traditional TRIZ is based on the analysis of patterns of problems and solutions in engineering, distilled into 40 inventive principles. These principles serve as tools to overcome contradictions and foster innovation. Some of the key principles include:

1. Segmentation: Dividing a system into independent parts.

2. Taking Out: Isolating a problematic part or property.

3. Local Quality: Changing the structure from uniform to non-uniform.

4. Asymmetry: Altering symmetry to improve functionality.

5. Merging: Combining similar objects or functions.

6. Universality: Making a part serve multiple functions.

7. Nested Doll: Placing one object inside another.

8. Anti-Weight: Using balancing methods to counteract weight.

9. Preliminary Anti-Action: Counteracting a harmful effect before it occurs.

10. Preliminary Action: Preparing systems in advance for potential issues.

11. Cushion in Advance: Preemptively arranging safeguards.

12. Equipotentiality: Eliminating differences in potential energy.

13. The Other Way Round: Inverting actions or processes.

14. Spheroidality: Using curves or spheres instead of straight lines.

15. Dynamics: Allowing systems to adapt or change.

16. Partial or Excessive Action: Using more or less of an action than usual.

17. Another Dimension: Moving into a new dimension or plane.

18. Mechanical Vibration: Using vibrations to achieve desired effects.

19. Periodic Action: Using periodic or pulsating actions.

20. Continuity of Useful Action: Ensuring actions occur without interruption.

21. Skipping: Omitting certain steps or processes.

22. "Blessing in Disguise" or "Turn Lemons into Lemonade": Turning harmful factors into benefits.

23. Feedback: Using feedback loops to improve systems.

24. Intermediary: Introducing an intermediary element.

25. Self-Service: Systems servicing themselves.

26. Copying: Replacing an object with a visual or simplified copy.

27. Cheap Short-Lived Objects: Using inexpensive, disposable objects.

28. Mechanics Substitution: Replacing mechanical means with other methods.

29. Pneumatics and Hydraulics: Using fluid-based methods.

30. Flexible Shells and Thin Films: Using flexible materials.

31. Porous Materials: Incorporating porosity.

32. Color Changes: Changing colors to improve visibility or detection.

33. Homogeneity: Making interacting elements of the same material.

34. Discarding and Recovering: Eliminating or regenerating parts of a system.

35. Parameter Changes: Changing physical parameters.

36. Phase Transitions: Using changes in states of matter.

37. Thermal Expansion: Utilizing expansion due to temperature changes.

38. Strong Oxidants: Introducing oxygen into reactions.

39. Inert Atmosphere: Using neutral environments.

40. Composite Materials: Combining materials with different properties.

DIKWP-TRIZ builds upon traditional TRIZ by integrating the DIKWP cognitive model. It introduces new principles and refines existing ones to address modern challenges, particularly in data management, information processing, knowledge organization, wisdom application, and purposeful innovation.

1. Principle D1: Data Segmentation and Aggregation

• Breaking down complex data sets or aggregating small data for analysis.

2. Principle D2: Data Quality Enhancement

• Implementing processes to improve data accuracy and reliability.

3. Principle D3: Automated Data Processing

• Utilizing automation and AI for efficient data handling.

1. Principle I1: Contextualization

• Providing context to data to create meaningful information.

2. Principle I2: Information Visualization

• Using visual tools to represent information clearly.

3. Principle I3: Information Filtering

• Implementing systems to filter irrelevant information.

1. Principle K1: Knowledge Sharing Platforms

• Creating systems for easy knowledge exchange.

2. Principle K2: Continuous Learning

• Encouraging ongoing education and knowledge updates.

3. Principle K3: Knowledge Standardization

• Developing standards for organizing and categorizing knowledge.

1. Principle W1: Ethical Frameworks

• Incorporating ethics into decision-making processes.

2. Principle W2: Scenario Planning

• Using foresight techniques to anticipate future challenges.

3. Principle W3: Reflective Practices

• Encouraging reflection on past decisions to improve wisdom.

1. Principle P1: Goal Alignment

• Ensuring all activities support the overall purpose.

2. Principle P2: Adaptive Strategy

• Allowing purposes to evolve in response to new insights.

3. Principle P3: Stakeholder Engagement

• Involving stakeholders in defining and refining purposes.

Traditional TRIZ vs. DIKWP-TRIZ

• Segmentation (Principle 1) in traditional TRIZ focuses on dividing systems into parts for better manageability.

• Data Segmentation and Aggregation (Principle D1) in DIKWP-TRIZ specifically addresses handling data sets, emphasizing both division and combination for meaningful analysis.

Enhancements:

• DIKWP-TRIZ extends segmentation to data contexts, considering the challenges of big data and data integration.

• It introduces principles focused on data quality and automation (Principles D2 and D3), which are not explicitly covered in traditional TRIZ.

Traditional TRIZ vs. DIKWP-TRIZ

• Local Quality (Principle 3) and Another Dimension (Principle 17) in traditional TRIZ can be related to transforming information representation.

• Information Visualization (Principle I2) in DIKWP-TRIZ directly addresses the need to represent information clearly using modern tools.

Enhancements:

• DIKWP-TRIZ introduces principles that focus on contextualizing data (Principle I1) and filtering information (Principle I3), reflecting the modern challenge of information overload.

Traditional TRIZ vs. DIKWP-TRIZ

• Merging (Principle 5) and Universality (Principle 6) in traditional TRIZ encourage combining systems and multifunctionality.

• Knowledge Sharing Platforms (Principle K1) in DIKWP-TRIZ emphasizes creating systems for knowledge exchange, a modern necessity for organizations.

Enhancements:

• DIKWP-TRIZ places a greater emphasis on continuous learning (Principle K2) and standardization (Principle K3), addressing the rapid pace of knowledge obsolescence.

Traditional TRIZ vs. DIKWP-TRIZ

• Feedback (Principle 23) in traditional TRIZ involves using feedback loops to improve systems.

• Reflective Practices (Principle W3) in DIKWP-TRIZ builds on this by encouraging reflection to improve wisdom.

Enhancements:

• DIKWP-TRIZ introduces ethical considerations (Principle W1) and scenario planning (Principle W2), which are not explicitly covered in traditional TRIZ but are critical in modern decision-making.

Traditional TRIZ vs. DIKWP-TRIZ

• Nested Doll (Principle 7) involves placing one object inside another, which can metaphorically relate to aligning purposes at different levels.

• Goal Alignment (Principle P1) in DIKWP-TRIZ explicitly focuses on ensuring activities support overall objectives.

Enhancements:

• DIKWP-TRIZ introduces adaptive strategies (Principle P2) and stakeholder engagement (Principle P3), acknowledging the importance of adaptability and inclusivity in defining purposes.

1. Incorporation of Cognitive Processes:

• DIKWP-TRIZ integrates cognitive elements, addressing data management, information flow, and wisdom application.

• Traditional TRIZ focuses more on physical and technical contradictions without explicit consideration of cognitive aspects.

2. Alignment with Modern Challenges:

• DIKWP-TRIZ addresses contemporary issues such as big data, information overload, rapid technological change, and ethical considerations.

• Traditional TRIZ principles were developed in a different era and may not fully capture modern complexities.

3. Enhanced Ethical and Purposeful Focus:

• DIKWP-TRIZ includes principles that ensure ethical decision-making and purposeful innovation.

• Traditional TRIZ lacks explicit principles addressing ethics and purpose alignment.

4. Dynamic and Flexible Framework:

• DIKWP-TRIZ allows for continuous evolution of principles to adapt to new challenges.

• Traditional TRIZ principles are static and may not provide the flexibility required in fast-paced environments.

5. Holistic Approach:

• DIKWP-TRIZ considers the full spectrum of cognitive processes, from data to purpose.

• Traditional TRIZ primarily focuses on inventive problem-solving at the system or component level.

Scenario: A technology company is developing a new data analytics platform.

Application of Traditional TRIZ:

• Segmentation (Principle 1): Break down the platform into modules.

• Merging (Principle 5): Combine existing tools to create the platform.

• Dynamics (Principle 15): Allow the platform to adapt to different data sources.

• Feedback (Principle 23): Implement user feedback mechanisms.

Limitations:

• Traditional TRIZ does not explicitly address data quality, information overload, or ethical considerations in data analytics.

Application of DIKWP-TRIZ:

Data-Level Innovation:

• Principle D2 (Data Quality Enhancement): Implement data cleansing and validation processes.

• Principle D3 (Automated Data Processing): Use AI for real-time data analysis.

Information-Level Innovation:

• Principle I2 (Information Visualization): Create interactive dashboards for users.

• Principle I3 (Information Filtering): Provide customizable filters to manage information overload.

Knowledge-Level Innovation:

• Principle K1 (Knowledge Sharing Platforms): Integrate collaborative tools for sharing insights.

• Principle K2 (Continuous Learning): Offer training modules for users to stay updated.

Wisdom-Level Innovation:

• Principle W1 (Ethical Frameworks): Ensure compliance with data privacy laws.

• Principle W2 (Scenario Planning): Use predictive analytics to anticipate future trends.

Purpose-Level Innovation:

• Principle P1 (Goal Alignment): Align the platform's development with the company's mission to empower users.

• Principle P3 (Stakeholder Engagement): Involve clients in the development process to ensure the platform meets their needs.

Advantages of DIKWP-TRIZ Application:

• Addresses modern challenges such as data quality and ethical considerations.

• Enhances user experience by focusing on information visualization and filtering.

• Promotes continuous improvement through knowledge sharing and learning.

• Ensures alignment with organizational goals and stakeholder needs.

DIKWP-TRIZ enhances the traditional TRIZ framework by integrating the DIKWP cognitive model, addressing modern challenges, and providing a holistic approach to innovation. The key differences between the principles of DIKWP-TRIZ and traditional TRIZ include:

• Focus on Cognitive Processes: DIKWP-TRIZ incorporates data management, information processing, and wisdom application.

• Ethical Considerations: DIKWP-TRIZ includes principles that ensure ethical decision-making.

• Adaptability: DIKWP-TRIZ allows for the evolution of principles in response to new technologies and challenges.

• Purpose Alignment: DIKWP-TRIZ emphasizes aligning actions with overarching goals.

By comparing the principles, it's evident that DIKWP-TRIZ provides a more comprehensive framework suitable for today's complex and rapidly changing environment. It addresses the limitations of traditional TRIZ and offers enhanced strategies for systematic innovation.

• Altshuller, G. (1984). Creativity as an Exact Science. Gordon and Breach Science Publishers.

• Ackoff, R. L. (1989). From Data to Wisdom. Journal of Applied Systems Analysis, 16, 3–9.

• Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company. Oxford University Press.

• Senge, P. M. (1990). The Fifth Discipline. Doubleday/Currency.

• Additional resources on DIKWP model and TRIZ principles.

Note: This report provides a detailed analysis and comparison between the principles of DIKWP-TRIZ and traditional TRIZ. It highlights how DIKWP-TRIZ enhances the traditional framework by integrating cognitive processes and addressing modern innovation challenges.