Mapping the DIKWP Model to the 40 TRIZ Principles

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)

Mapping the DIKWP Model to the 40 TRIZ Principles

Introduction

The integration of the DIKWP model—Data, Information, Knowledge, Wisdom, and Purpose—with the 40 Principles of TRIZ (Theory of Inventive Problem Solving) provides a comprehensive framework for systematic innovation, problem-solving, and cognitive processing. This bidirectional mapping aims to:

• Enhance Understanding: By relating DIKWP components to TRIZ principles, we can better understand how cognitive processes align with inventive problem-solving strategies.

• Facilitate Application: This mapping helps practitioners apply TRIZ principles within the DIKWP framework in both human cognition and artificial intelligence systems.

• Promote Innovation: Combining these models encourages creative thinking and systematic innovation.

Overview of DIKWP Model

1. Data (D): Raw facts and observations collected from the environment.

2. Information (I): Processed data given meaning through relational connections.

3. Knowledge (K): Organized information understood and applicable in context.

4. Wisdom (W): Insight gained from knowledge and experience, enabling sound judgments.

5. Purpose (P): Goals or objectives guiding the use and interpretation of wisdom.

Overview of TRIZ Principles

TRIZ comprises 40 inventive principles used to solve complex problems by overcoming contradictions and promoting innovation. These principles are strategies derived from analyzing patterns of invention in global patent literature.

Bidirectional Mapping Between DIKWP and TRIZ Principles1. Data (D) and TRIZ Principles

Data represents raw, unprocessed facts. TRIZ principles can be applied to improve data collection, management, and initial processing.

Relevant TRIZ Principles:

• Principle 1: Segmentation

• Mapping: Break down complex data into smaller, manageable parts for detailed analysis.

• Application: Segment data sets to identify patterns or anomalies.

• Principle 5: Merging

• Mapping: Combine similar data sources to enhance completeness.

• Application: Merge data from different sensors to improve accuracy.

• Principle 10: Preliminary Action

• Mapping: Prepare data in advance to simplify processing.

• Application: Clean and format data before analysis.

• Principle 19: Periodic Action

• Mapping: Collect data at regular intervals for consistency.

• Application: Schedule data collection to monitor trends.

• Principle 20: Continuity of Useful Action

• Mapping: Ensure continuous data flow for real-time analysis.

• Application: Implement streaming data systems.

• Principle 28: Mechanics Substitution

• Mapping: Replace manual data collection with automated methods.

• Application: Use sensors and IoT devices for data gathering.

From Data to TRIZ:

• Data Challenges: Handling large volumes of data, data quality issues.

• Applying TRIZ: Use Principle 35 (Parameter Changes) to adjust data collection parameters for optimal results.

2. Information (I) and TRIZ Principles

Information emerges when data is processed and gains meaning through context and relationships.

Relevant TRIZ Principles:

• Principle 2: Taking Out

• Mapping: Extract valuable information from raw data.

• Application: Use data mining techniques to find relevant information.

• Principle 3: Local Quality

• Mapping: Focus on specific data areas to extract detailed information.

• Application: Zoom into subsets of data for in-depth analysis.

• Principle 9: Preliminary Anti-Action

• Mapping: Identify and remove irrelevant information early.

• Application: Filter out noise in data preprocessing.

• Principle 13: The Other Way Round

• Mapping: Analyze data from different perspectives to uncover hidden information.

• Application: Use reverse analytics to find causality.

• Principle 17: Another Dimension

• Mapping: Represent information in multiple dimensions for better understanding.

• Application: Utilize 3D graphs or multidimensional scaling.

• Principle 18: Mechanical Vibration

• Mapping: Introduce controlled perturbations to test information robustness.

• Application: Stress-test data models to ensure reliability.

• Principle 35: Parameter Changes

• Mapping: Adjust information parameters to reveal different insights.

• Application: Change thresholds in data analysis.

From Information to TRIZ:

• Information Challenges: Information overload, relevancy.

• Applying TRIZ: Use Principle 11 (Beforehand Cushioning) to anticipate information needs and prepare accordingly.

3. Knowledge (K) and TRIZ Principles

Knowledge is organized information that is understood and can be applied in context.

Relevant TRIZ Principles:

• Principle 6: Universality

• Mapping: Develop knowledge that can be applied universally.

• Application: Create models or theories applicable across domains.

• Principle 12: Equipotentiality

• Mapping: Ensure knowledge elements are balanced and equally accessible.

• Application: Organize knowledge bases for easy retrieval.

• Principle 14: Spheroidality

• Mapping: Structure knowledge in non-linear, interconnected ways.

• Application: Use mind maps or semantic networks.

• Principle 15: Dynamics

• Mapping: Allow knowledge structures to adapt over time.

• Application: Update knowledge bases with new information continuously.

• Principle 24: Intermediary

• Mapping: Use intermediary knowledge to bridge gaps.

• Application: Implement middleware for knowledge integration.

• Principle 26: Copying

• Mapping: Replicate knowledge structures for redundancy.

• Application: Backup knowledge repositories.

• Principle 30: Flexible Shells and Thin Films

• Mapping: Protect knowledge with adaptable frameworks.

• Application: Use modular architectures.

From Knowledge to TRIZ:

• Knowledge Challenges: Knowledge silos, obsolescence.

• Applying TRIZ: Use Principle 23 (Feedback) to incorporate new insights and validate knowledge.

4. Wisdom (W) and TRIZ Principles

Wisdom is the ability to make sound judgments based on knowledge, considering ethical, social, and contextual factors.

Relevant TRIZ Principles:

• Principle 16: Partial or Excessive Actions

• Mapping: Apply wisdom to decide when less or more action is appropriate.

• Application: Exercise judgment in decision-making processes.

• Principle 19: Periodic Action

• Mapping: Review decisions periodically to ensure they remain wise.

• Application: Implement regular audits or reflections.

• Principle 21: Skipping

• Mapping: Use wisdom to know when to bypass unnecessary steps.

• Application: Streamline processes intelligently.

• Principle 22: "Blessing in Disguise" or "Turn Lemons into Lemonade"

• Mapping: Transform challenges into opportunities through wise insight.

• Application: Reframe problems to find innovative solutions.

• Principle 23: Feedback

• Mapping: Use feedback loops to refine wisdom.

• Application: Learn from outcomes to improve future judgments.

• Principle 34: Discarding and Recovering

• Mapping: Wisely decide what knowledge or practices to discard or retain.

• Application: Practice continuous improvement.

• Principle 35: Parameter Changes

• Mapping: Adjust decision parameters based on changing contexts.

• Application: Adapt strategies as circumstances evolve.

From Wisdom to TRIZ:

• Wisdom Challenges: Ethical dilemmas, contextual complexity.

• Applying TRIZ: Use Principle 32 (Color Changes) to shift perspectives and gain deeper wisdom.

5. Purpose (P) and TRIZ Principles

Purpose provides direction and goals that guide all cognitive processes.

Relevant TRIZ Principles:

• Principle 4: Asymmetry

• Mapping: Define purposes that are not uniform, allowing for specialized goals.

• Application: Tailor objectives to specific needs or contexts.

• Principle 7: "Nested Doll"

• Mapping: Align purposes at different levels, from overarching to specific.

• Application: Set strategic, tactical, and operational goals.

• Principle 25: Self-Service

• Mapping: Design systems that autonomously align actions with purposes.

• Application: Implement self-regulating processes.

• Principle 31: Porous Materials

• Mapping: Allow purposes to be permeable to new ideas and influences.

• Application: Encourage open innovation.

• Principle 33: Homogeneity

• Mapping: Ensure all components work towards the same purpose.

• Application: Foster organizational alignment.

• Principle 34: Discarding and Recovering

• Mapping: Reevaluate and adjust purposes as needed.

• Application: Pivot strategies when necessary.

• Principle 35: Parameter Changes

• Mapping: Modify goals in response to changing environments.

• Application: Update KPIs and objectives accordingly.

From Purpose to TRIZ:

• Purpose Challenges: Misalignment, rigidity.

• Applying TRIZ: Use Principle 15 (Dynamics) to keep purposes flexible and adaptable.

6. Integrative Principles Across DIKWP

Some TRIZ principles are applicable across multiple DIKWP components due to their broad nature.

Principle 35: Parameter Changes

• Application in DIKWP:

• Data: Adjust data collection parameters.

• Information: Modify analysis thresholds.

• Knowledge: Update frameworks.

• Wisdom: Adapt decision criteria.

• Purpose: Revise goals.

Principle 9: Preliminary Anti-Action

• Application in DIKWP:

• Data: Filter out irrelevant data early.

• Information: Anticipate and prevent misinformation.

• Knowledge: Identify and address knowledge gaps.

• Wisdom: Avoid potential pitfalls in decisions.

• Purpose: Preempt conflicts with objectives.

7. Mapping TRIZ Principles to DIKWP ComponentsPrinciple 1: Segmentation

• Data: Breaking down complex data sets.

• Knowledge: Dividing knowledge domains for specialization.

Principle 2: Taking Out

• Information: Extracting valuable insights.

• Wisdom: Focusing on critical factors in decision-making.

Principle 3: Local Quality

• Information: Analyzing specific data subsets.

• Knowledge: Developing specialized expertise.

Principle 4: Asymmetry

• Purpose: Setting non-uniform goals.

• Wisdom: Recognizing uneven impacts of decisions.

Principle 5: Merging

• Data: Combining data sources.

• Knowledge: Integrating theories.

8. Example Case Study

Scenario: Developing an AI system for healthcare diagnostics.

Data (D):

• TRIZ Application: Use Principle 28 (Mechanics Substitution) to automate data collection via sensors.

Information (I):

• TRIZ Application: Apply Principle 13 (The Other Way Round) to analyze patient data from different perspectives.

Knowledge (K):

• TRIZ Application: Utilize Principle 6 (Universality) to create diagnostic models applicable to various conditions.

Wisdom (W):

• TRIZ Application: Implement Principle 23 (Feedback) to learn from diagnostic outcomes and improve accuracy.

Purpose (P):

• TRIZ Application: Use Principle 7 ("Nested Doll") to align patient care objectives from individual to systemic levels.

Conclusion

This bidirectional and detailed mapping between the DIKWP model and the 40 TRIZ principles demonstrates how cognitive processes and inventive problem-solving strategies can be integrated. By aligning each DIKWP component with relevant TRIZ principles, we can:

• Enhance Cognitive Processes: Improve data handling, information processing, knowledge organization, wisdom application, and purpose alignment.

• Promote Systematic Innovation: Apply TRIZ principles methodically within the DIKWP framework to solve complex problems.

• Facilitate AI Development: Guide the creation of intelligent systems that mimic human cognitive processes and innovation strategies.

Appendix:

Mapping the DIKWP Model to the 40 TRIZ Principles in Table Format

Introduction

To provide a clear and organized view of the bidirectional mapping between the DIKWP model (Data, Information, Knowledge, Wisdom, Purpose) and the 40 TRIZ Principles, the following tables display the relationships, applications, and examples in a structured format. This will facilitate understanding and practical application of the integrated framework for systematic innovation and problem-solving.

1. Data (D) and TRIZ Principles

Data represents raw, unprocessed facts. TRIZ principles can enhance data collection, management, and initial processing.

Table 1: Mapping Data (D) to TRIZ Principles

2. Information (I) and TRIZ Principles

Information arises when data is processed and gains meaning through context and relationships.

Table 2: Mapping Information (I) to TRIZ Principles

3. Knowledge (K) and TRIZ Principles

Knowledge is organized information that is understood and applicable in context.

Table 3: Mapping Knowledge (K) to TRIZ Principles

4. Wisdom (W) and TRIZ Principles

Wisdom involves applying knowledge with insight, considering ethical, social, and contextual factors.

Table 4: Mapping Wisdom (W) to TRIZ Principles

5. Purpose (P) and TRIZ Principles

Purpose defines goals or objectives guiding all cognitive processes.

Table 5: Mapping Purpose (P) to TRIZ Principles

6. Integrative Principles Across DIKWP

Some TRIZ principles apply broadly across multiple DIKWP components.

Table 6: Integrative TRIZ Principles Applied to DIKWP

7. Example Case Study Table

Scenario: Developing an AI system for healthcare diagnostics.

Table 7: Applying DIKWP and TRIZ Principles in Healthcare AI

8. Mapping TRIZ Principles to DIKWP ComponentsTable 8: TRIZ Principles Mapped to Multiple DIKWP Components

9. Summary of Bidirectional MappingTable 9: Summary Mapping Between DIKWP and TRIZ Principles

Conclusion

The tables above provide a detailed and organized mapping between the DIKWP model and the 40 TRIZ principles. By visualizing the relationships in table format, we can more easily:

• Understand the Interconnections: See how each TRIZ principle aligns with components of the DIKWP model.

• Apply Systematically: Use the tables as a reference to apply TRIZ principles within the DIKWP framework effectively.

• Facilitate Innovation: Encourage creative problem-solving by integrating cognitive processes with inventive strategies.