DIKWP-TRIZ: Enpower AI/AC Innovation 

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. Executive Summary

2. Introduction

• 2.1 Background

• 2.2 Purpose of DIKWP-TRIZ

3. Foundations of DIKWP-TRIZ

• 3.1 The DIKWP Model

• 3.2 Limitations of Traditional TRIZ

4. The DIKWP-TRIZ Framework

• 4.1 Overview of DIKWP-TRIZ

• 4.2 Enhanced Principles

• 4.3 Mapping DIKWP Elements to Innovation Strategies

5. Integration of DIKWP and TRIZ

• 5.1 Data-Level Innovation

• 5.2 Information-Level Innovation

• 5.3 Knowledge-Level Innovation

• 5.4 Wisdom-Level Innovation

• 5.5 Purpose-Level Innovation

6. Advantages of DIKWP-TRIZ

• 6.1 Holistic Approach

• 6.2 Enhanced Problem-Solving

• 6.3 Alignment with Modern Needs

7. Application of DIKWP-TRIZ

• 7.1 Case Study: AI in Healthcare

• 7.2 Implementation Guidelines

8. Comparison with Traditional TRIZ

• 8.1 Strengths and Improvements

• 8.2 Addressing Traditional Limitations

9. Conclusion

10. References

DIKWP-TRIZ is an enhanced framework for systematic innovation that integrates the DIKWP model—Data, Information, Knowledge, Wisdom, and Purpose—with the principles of TRIZ (Theory of Inventive Problem Solving). This new framework aims to address the limitations of traditional TRIZ by incorporating cognitive processes and aligning with modern technological and organizational needs.

By mapping the DIKWP elements to innovation strategies and expanding upon the original 40 TRIZ principles, DIKWP-TRIZ offers a holistic and adaptable approach to problem-solving. It emphasizes the importance of data management, information processing, knowledge organization, wisdom application, and purposeful direction in driving innovation.

This report provides a comprehensive overview of DIKWP-TRIZ, detailing its foundations, enhanced principles, advantages, and practical applications. A case study in AI-driven healthcare demonstrates the framework's applicability, and a comparison with traditional TRIZ highlights its improvements.

TRIZ (Teoriya Resheniya Izobretatelskikh Zadach), or the Theory of Inventive Problem Solving, is a methodology developed by Genrich Altshuller in the 1940s. It provides a systematic approach to understanding and solving complex problems by analyzing patterns of invention and innovation in global patent literature.

DIKWP is a cognitive model representing the progression from Data to Information, Knowledge, Wisdom, and finally, Purpose. It reflects how raw data is transformed into meaningful insights and actions, emphasizing the interconnectedness of cognitive processes.

As industries evolve and the complexity of problems increases, traditional TRIZ principles may not fully address modern challenges, especially those involving data-driven technologies and human-centric considerations. DIKWP-TRIZ aims to:

• Enhance traditional TRIZ by integrating cognitive dimensions.

• Address limitations in handling data, information flow, and purposeful innovation.

• Provide a comprehensive framework for systematic problem-solving that aligns with contemporary needs.

The DIKWP model outlines a hierarchical yet networked relationship among cognitive elements:

1. Data (D): Raw, unprocessed facts without context.

2. Information (I): Processed data with context and meaning.

3. Knowledge (K): Organized information that is understood and can be applied.

4. Wisdom (W): Deep understanding that enables sound judgment and decision-making.

5. Purpose (P): The overarching goals or objectives guiding actions.

These elements interact dynamically, allowing for transformations in any direction, reflecting the complex nature of human cognition and organizational processes.

Traditional TRIZ, while powerful, has limitations:

• Static Principles: The original 40 principles may not fully capture the dynamic nature of modern problems.

• Lack of Cognitive Integration: TRIZ does not explicitly consider cognitive processes like data handling and wisdom application.

• Technological Gaps: Rapid technological advancements require adaptable frameworks.

DIKWP-TRIZ enhances traditional TRIZ by:

• Integrating DIKWP elements with TRIZ principles.

• Expanding and refining principles to address modern challenges.

• Emphasizing cognitive processes in innovation and problem-solving.

DIKWP-TRIZ introduces new principles and refines existing ones to align with the DIKWP model. Key enhancements include:

• Data Management Principles: Focusing on data quality, accessibility, and analysis.

• Information Flow Principles: Emphasizing the transformation of data into actionable information.

• Knowledge Integration Principles: Promoting organizational learning and knowledge sharing.

• Wisdom Application Principles: Guiding ethical and strategic decision-making.

• Purpose Alignment Principles: Ensuring that all actions contribute to overarching goals.

Each DIKWP element is associated with specific innovation strategies:

• Data: Techniques for efficient data collection, storage, and preprocessing.

• Information: Methods for contextualizing data and extracting insights.

• Knowledge: Frameworks for organizing information and fostering expertise.

• Wisdom: Approaches for applying knowledge judiciously.

• Purpose: Strategies for defining and aligning objectives.

Challenges:

• Handling vast amounts of data.

• Ensuring data accuracy and relevance.

Enhanced Principles:

• Principle D1: Data Segmentation and Aggregation

• Break down complex data sets or aggregate small data for meaningful analysis.

• Principle D2: Data Quality Enhancement

• Implement processes to improve data accuracy and reliability.

• Principle D3: Automated Data Processing

• Use automation and AI for efficient data handling.

Challenges:

• Converting data into actionable information.

• Managing information overload.

Enhanced Principles:

• Principle I1: Contextualization

• Provide context to data to create meaningful information.

• Principle I2: Information Visualization

• Use visual tools to represent information clearly.

• Principle I3: Information Filtering

• Implement systems to filter irrelevant information.

Challenges:

• Knowledge silos within organizations.

• Rapid obsolescence of knowledge.

Enhanced Principles:

• Principle K1: Knowledge Sharing Platforms

• Create systems for easy knowledge exchange.

• Principle K2: Continuous Learning

• Encourage ongoing education and knowledge updates.

• Principle K3: Knowledge Standardization

• Develop standards for organizing and categorizing knowledge.

Challenges:

• Applying knowledge ethically and effectively.

• Navigating complex decision-making environments.

Enhanced Principles:

• Principle W1: Ethical Frameworks

• Incorporate ethics into decision-making processes.

• Principle W2: Scenario Planning

• Use foresight techniques to anticipate future challenges.

• Principle W3: Reflective Practices

• Encourage reflection on past decisions to improve wisdom.

Challenges:

• Aligning actions with organizational goals.

• Adapting to changing objectives.

Enhanced Principles:

• Principle P1: Goal Alignment

• Ensure all activities support the overall purpose.

• Principle P2: Adaptive Strategy

• Allow purposes to evolve in response to new insights.

• Principle P3: Stakeholder Engagement

• Involve stakeholders in defining and refining purposes.

• Integrates Cognitive Processes: Addresses all stages from data to purpose.

• Networked Transformations: Reflects the dynamic interactions between elements.

• Customized Principles: Tailored to modern challenges and technologies.

• Flexibility: Allows for adaptation in various contexts and industries.

• Data-Driven Focus: Emphasizes the importance of data in innovation.

• Ethical Considerations: Incorporates wisdom and ethics into the framework.

• Purposeful Direction: Ensures that innovation efforts are goal-oriented.

Scenario: Developing an AI system for patient diagnostics.

Data-Level Innovation:

• Implement Principle D3 (Automated Data Processing): Use AI to collect and process patient data efficiently.

Information-Level Innovation:

• Apply Principle I2 (Information Visualization): Use dashboards to present patient information to clinicians.

Knowledge-Level Innovation:

• Utilize Principle K1 (Knowledge Sharing Platforms): Create a knowledge base of diagnostic cases accessible to all medical staff.

Wisdom-Level Innovation:

• Implement Principle W1 (Ethical Frameworks): Ensure AI recommendations adhere to medical ethics and patient confidentiality.

Purpose-Level Innovation:

• Use Principle P1 (Goal Alignment): Align the AI system's development with the hospital's mission to improve patient outcomes.

• Assessment: Evaluate current processes at each DIKWP level.

• Customization: Select and tailor principles relevant to specific challenges.

• Integration: Ensure seamless interaction between DIKWP elements.

• Evaluation: Monitor outcomes and adjust strategies accordingly.

• Inclusion of Cognitive Elements: DIKWP-TRIZ incorporates data management, knowledge sharing, and wisdom application, which are less emphasized in traditional TRIZ.

• Modern Relevance: Addresses contemporary challenges like big data, AI, and rapid technological change.

• Ethical Focus: Integrates ethical considerations into innovation strategies.

• Dynamic Adaptability: DIKWP-TRIZ allows for continuous evolution of principles, unlike the static nature of traditional TRIZ.

• Holistic Perspective: Considers the full spectrum of cognitive processes, providing a more comprehensive framework.

DIKWP-TRIZ represents a significant advancement over traditional TRIZ by integrating cognitive processes and addressing modern innovation challenges. Its emphasis on data management, ethical considerations, and purposeful direction makes it a robust framework for organizations seeking to enhance their problem-solving capabilities.

By adopting DIKWP-TRIZ, organizations can benefit from a holistic approach that not only solves immediate problems but also fosters a culture of continuous learning and adaptation.

• 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.

Note: This report introduces DIKWP-TRIZ as an enhanced framework for systematic innovation. It builds upon the foundations of the DIKWP model and traditional TRIZ principles, offering a modern approach to problem-solving that aligns with current technological and organizational needs.