DIKWP-TRIZ Comprehensive Version 

(初学者版)

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)

DIKWP-TRIZ: A Detailed Framework for Innovation

Building on the foundations of DIKWP (Data, Information, Knowledge, Wisdom, Purpose) and integrating it with the principles of TRIZ (Theory of Inventive Problem Solving), this framework aims to bridge the gap between cognitive-semantic models and systematic problem-solving in the innovation space. Here's a step-by-step proposal of DIKWP-TRIZ with detailed processes and applications.

1. DIKWP Semantics Transformation

The DIKWP-TRIZ framework leverages the key strengths of both models:

• TRIZ focuses on inventive solutions by identifying contradictions and resolving them systematically through predefined inventive principles.

• DIKWP is a semantic framework that maps concepts across cognitive spaces, transforming Data, Information, Knowledge, Wisdom, and Purpose into each other.

By integrating both, the DIKWP-TRIZ method allows for inventive solutions that emerge not just from identifying technical contradictions but also from semantic contradictions and cognitive mismatches between different stages of the DIKWP model.

2. The Five DIKWP Contradictions (5x5 Networked Mode)

Instead of following hierarchical models, DIKWP-TRIZ recognizes the networked nature of cognitive transformation. In the 5x5 DIKWP matrix, contradictions and transformation opportunities can occur across any pair of elements, not limited to the traditional linear Data → Information → Knowledge model.

The possible interactions are:

• Data-Information

• Information-Knowledge

• Knowledge-Wisdom

• Wisdom-Purpose

• Data-Wisdom (direct transformations of low-level cognitive components into high-level cognitive purposes)

Each of these interactions represents an opportunity to apply TRIZ-like inventive solutions to semantic, technical, or conceptual contradictions.

3. Mutual Remedies through Semantic Transformations

A central innovation of DIKWP-TRIZ is its focus on solving semantic contradictions. By resolving inconsistencies, incompleteness, and imprecision in DIKWP content, the framework aligns inventive processes with cognitive patterns.

The mutual remedy principle works by transforming DIKWP elements within a 3-No framework (dealing with incomplete, inconsistent, and imprecise data). Here’s how it would look:

• Incomplete Data → Wisdom: When data is missing or incomplete, knowledge and wisdom are used to fill in the gaps.

• Inconsistent Information → Data: Inconsistent information is resolved by returning to data sources for verification or correction.

• Imprecise Knowledge → Information: Imprecise knowledge is corrected by validating against more precise information.

Invention in DIKWP-TRIZ occurs when semantic mismatches or cognitive gaps between these transformations are resolved using innovative techniques inspired by TRIZ.

4. Cross-Space Mapping for Invention Opportunities

Traditional TRIZ offers a set of 40 inventive principles, which can be adapted within the DIKWP cognitive spaces. Each space (Data, Information, Knowledge, Wisdom, Purpose) can generate or solve contradictions. Here’s how the DIKWP-TRIZ inventive principles might apply:

• Data Space: Focuses on sensory, measurable contradictions (e.g., insufficient data accuracy). Inventive principles might suggest refining data collection methods, validating inputs, or automating data verification.

• Information Space: Addresses contradictions related to semantic differences, diverse interpretations, or missing links between pieces of information. DIKWP-TRIZ can suggest information fusion methods or cognitive disambiguation techniques.

• Knowledge Space: Resolves contradictions in structured knowledge, such as conflicting rules or incomplete knowledge. Innovation may involve hypothesis generation, testing alternative knowledge models, or using machine learning to discover new patterns.

• Wisdom Space: Deals with ethical or value-based contradictions. DIKWP-TRIZ offers inventive solutions that align technical outcomes with human values and ethics, especially in the context of AI.

• Purpose Space: Resolves contradictions at the goal level, offering inventive solutions to realign organizational objectives or individual purposes.

5. Applying the DIKWP-TRIZ Matrix to Specific Examples

The following is a step-by-step breakdown of a typical DIKWP-TRIZ inventive process:

1. Define the Problem: Identify the input problem based on contradictions within the 3-No framework (e.g., incomplete knowledge leads to poor decision-making).

2. Identify Contradictions: Map the contradictions using the 5x5 DIKWP matrix (e.g., Knowledge → Wisdom contradiction where incorrect knowledge leads to unwise decisions).

3. Apply Inventive Principles: Choose a relevant inventive principle from TRIZ and adapt it for DIKWP. For example, the TRIZ principle of "Separation of Contradictions" can be applied to separate incomplete knowledge from the decision-making process by temporarily substituting wisdom-based heuristics.

4. Generate Solutions: Develop innovative solutions by transforming elements across the DIKWP spaces. In this case, you could use data validation techniques from the Data space to verify knowledge, or apply heuristic decision-making from the Wisdom space to compensate for incomplete knowledge.

5. Evaluate and Optimize: Continuously evaluate the solution's effectiveness and optimize the mapping between DIKWP spaces.

6. Expanding Innovation Opportunities: A Focus on 3-No Problems

• Incomplete Inputs/Outputs: Solutions can be generated by transforming incomplete inputs (such as incomplete data sets or unstructured information) into structured knowledge using DIKWP-TRIZ inventive principles.

• Inconsistent DIKWP Content: Use DIKWP as a consistency-checking framework to flag contradictions early in the semantic processing chain.

• Imprecise Outputs: Imprecise outputs, such as vague wisdom or incomplete purposes, can be sharpened by tracing their semantic origins back through the DIKWP chain and applying inventive adjustments to improve clarity.

Example 1: Enhancing Large Language Models (LLM) with Incomplete Knowledge HandlingStep 1: Problem Identification (3-No Problem)

The issue arises with LLMs like GPT-4, where incomplete knowledge leads to imprecise outputs in certain contexts. For example, the LLM might generate technically correct answers, but lack depth or nuance when dealing with specialized knowledge domains. The challenge is to generate comprehensive, precise answers despite incomplete training data or knowledge.

Step 2: Map the Contradiction (5x5 DIKWP Network)

Here, we encounter a Knowledge-Wisdom contradiction. The LLM possesses Knowledge but struggles with applying it as Wisdom (i.e., it cannot generate high-level, decision-making insights from its incomplete knowledge base).

The transformation between Knowledge (K) and Wisdom (W) is crucial. Specifically, incomplete knowledge should be used to generate wise decisions by filling in the gaps with heuristic rules or approximations of wisdom.

Step 3: Apply DIKWP-TRIZ Inventive Principle

We choose an inventive principle such as “Separation of Contradictions” from TRIZ and adapt it to DIKWP. Here, we can separate the imprecise outputs of the LLM from its internal incomplete knowledge.

For instance, we introduce a wisdom-based heuristic layer on top of the LLM. This layer processes incomplete knowledge by referencing external sources (data), and using semantic understanding of past knowledge, applies heuristics for better decision-making. These heuristics will be based on Wisdom concepts, such as ethical considerations, values, and user preferences, which can supplement the missing knowledge.

Step 4: Generate Solution

In the final stage, the LLM integrates incomplete knowledge with a wisdom-driven layer that compensates for gaps through heuristic decision-making. The LLM becomes capable of identifying when its knowledge is incomplete and can employ external knowledge sources or semantic reasoning to provide a more complete answer.

This process showcases how DIKWP-TRIZ resolves contradictions within the knowledge-wisdom interaction by applying cognitive-level decision-making in real-time, improving the system’s ability to generate well-rounded answers even when knowledge is incomplete.

Example 2: Patentable Innovation for Medical Diagnosis using DIKWP-TRIZStep 1: Problem Identification (Inconsistent Knowledge-Information Transition)

In a medical diagnostic system, there may be inconsistent knowledge about patient data and medical history due to incomplete or missing records. These inconsistencies result in imprecise information used for diagnostic purposes.

Step 2: Map the Contradiction (5x5 DIKWP Network)

In this case, we encounter a Data-Knowledge contradiction. The medical diagnostic system’s data (patient records) contains inconsistencies or missing elements, but it is expected to generate knowledge-based conclusions (diagnosis). The contradiction arises because the data is incomplete, yet the system must generate knowledge from it.

Step 3: Apply DIKWP-TRIZ Inventive Principle

Using the mutual remedy principle from DIKWP-TRIZ, the system will attempt to validate inconsistent information through additional data collection, and cross-referencing external sources of knowledge to fill in the gaps.

The inventive principle applied here is "Use of External Resources" from TRIZ, adapted to the DIKWP system. The system automatically queries external medical databases, such as genetic markers or similar case studies, to provide additional contextual information. This allows the system to resolve the Data-Knowledge contradiction by enriching its data with external knowledge, thereby transforming incomplete patient records into actionable knowledge.

Step 4: Generate Solution

The solution involves a semantic validation process where the system flags incomplete data and supplements it with external knowledge sources (e.g., global medical databases or clinical trials). The system then applies a decision-making model (wisdom) to generate a diagnosis based on the supplemented knowledge, improving the system’s diagnostic accuracy despite initial data gaps.

This patented invention could represent a novel AI-based diagnostic tool that leverages the DIKWP transformation principles to intelligently resolve incomplete and inconsistent patient data, creating a commercially viable product for healthcare providers.

Example 3: Optimizing Autonomous Vehicle Navigation Using DIKWP-TRIZStep 1: Problem Identification (Imprecise Data Impacting Safety Decisions)

Autonomous vehicles face challenges when dealing with imprecise sensor data caused by environmental factors like rain, fog, or obstacles. This leads to issues in making safety-critical navigation decisions, as the vehicle’s system might not fully comprehend the surroundings.

Step 2: Map the Contradiction (5x5 DIKWP Network)

This involves a Data-Wisdom contradiction. The sensor data (D) is unreliable or imprecise, but the system must make wise decisions (W) regarding the safest path forward. The challenge is to compensate for the lack of clarity in the data while maintaining safety and efficiency.

Step 3: Apply DIKWP-TRIZ Inventive Principle

The principle applied here is "Segmentation" from TRIZ, which suggests breaking down the problem into manageable parts. In the DIKWP context, we can break down the Data-Wisdom transformation into smaller units of analysis and apply Wisdom based on partial data.

For example, we could integrate multiple small independent decision-making modules that work with fragmented data. Each module processes a subset of the available sensor data and makes localized, context-aware decisions. This reduces the cognitive load on the system and allows it to aggregate partial data insights into a complete decision.

Step 4: Generate Solution

The vehicle’s navigation system can implement adaptive decision-making algorithms that operate even under incomplete sensor data. These algorithms segment the navigation environment into smaller areas, allowing each sensor to analyze the local environment with heuristic adjustments based on Wisdom (prior knowledge, rules about safety margins, or common driving patterns). The system makes real-time adjustments to its route, ensuring optimal safety even with incomplete or imprecise data.

This method, based on segmentation of the data and wisdom layers, provides a potential for patenting a unique adaptive navigation system for autonomous vehicles that ensures safety without requiring perfect data.

Example 4: Intelligent Energy Grid Management Using DIKWP-TRIZStep 1: Problem Identification (Inconsistent Information Leading to Energy Distribution Inefficiencies)

Smart energy grids often deal with inconsistent real-time data about energy demand and supply due to fluctuations in usage, unpredictable renewable energy inputs (solar, wind), and communication issues. These inconsistencies can lead to inefficient energy distribution and blackouts.

Step 2: Map the Contradiction (5x5 DIKWP Network)

This involves an Information-Knowledge contradiction. The energy grid possesses inconsistent information (I) about the real-time energy requirements and supply levels. However, it needs to convert this into consistent knowledge (K) to manage and distribute energy effectively.

Step 3: Apply DIKWP-TRIZ Inventive Principle

Using the "Local Quality" principle from TRIZ, adapted to the DIKWP model, we can create local energy distribution nodes that independently manage their specific areas based on available data. The Information-Knowledge transformation in each local node can handle inconsistencies and still provide localized optimization.

Instead of relying on central processing of all energy grid information, the system can apply local heuristics and knowledge rules to manage energy flow based on regional conditions. For instance, one part of the grid may receive more renewable energy inputs, while another may rely more on traditional power sources. Each region’s energy node can independently balance its local inputs and regional energy demands.

Step 4: Generate Solution

The solution is a decentralized, intelligent energy grid where each node applies local information to optimize energy flow. Inconsistent information about energy input (e.g., from wind turbines) is compensated by using local knowledge about expected energy demand patterns in that region. The system dynamically reallocates energy based on each node’s real-time data, ensuring efficient distribution even when global information is inconsistent.

This method can be patented as a smart, decentralized energy grid management system, where information inconsistencies are remedied by localized knowledge-based adjustments for efficient and sustainable energy distribution.

Conclusion:

The first 2 examples illustrate how DIKWP-TRIZ can be applied to resolve complex contradictions by combining inventive principles with semantic and cognitive transformations. This framework opens up numerous patenting opportunities across diverse fields like AI, healthcare, and decision-making systems, enabling transparent, adaptive, and efficient solutions for today’s most challenging problems.

Two additional examples demonstrate how DIKWP-TRIZ can be used to resolve data, information, and knowledge inconsistencies in various fields. By applying inventive principles from TRIZ and integrating them with DIKWP-based semantic transformations, this framework offers novel, patentable solutions for a wide range of modern challenges—from autonomous navigation to intelligent energy grid management.

Building a tool of DIKWP-TRIZ

Building a DIKWP-TRIZ tool step-by-step involves integrating the conceptual framework of DIKWP with the systematic problem-solving principles of TRIZ, creating a powerful approach for tackling contradictions and generating innovative solutions in various domains. Below is a detailed step-by-step plan for building this tool:Step 1: Define the Core Structure of DIKWP-TRIZ

1.1 Understand DIKWP Model:

• Data: The raw facts or observations.

• Information: Processed data, revealing relationships.

• Knowledge: Structured information forming patterns or systems.

• Wisdom: Ethical or strategic application of knowledge.

• Purpose: The overarching goal driving the system.

Each element interacts with others in a networked, non-hierarchical fashion, creating dynamic relationships between them. The 5x5 DIKWP network (DI, DK, DP, etc.) defines the interaction among these components.

1.2 Understand TRIZ (Theory of Inventive Problem Solving):TRIZ identifies inventive principles to resolve contradictions in technical or system-level challenges. These principles (like Segmentation, Local Quality, and Dynamization) are used to solve problems innovatively.

Step 2: Map DIKWP to TRIZ

2.1 Map Contradictions in DIKWP Terms:

• Identify contradictions in the relationships between Data, Information, Knowledge, Wisdom, and Purpose. For example:

• Contradiction in Data-Wisdom: Raw data may be insufficient for wise decisions, creating a gap.

• Contradiction in Information-Knowledge: Information might be inconsistent or incomplete, preventing reliable knowledge.

2.2 Apply TRIZ Principles:

• Use the 40 TRIZ principles to resolve the contradictions identified in the DIKWP structure. For instance:

• Segmentation for breaking down complex systems (Data → Wisdom).

• Local Quality for adapting solutions to local conditions (Information → Knowledge).

• Dynamization for increasing flexibility in transformations (Purpose → Data).

Step 3: Develop Core Functions of the DIKWP-TRIZ Tool

3.1 Input Module (Problem Definition):

• Users provide input data in terms of a problem scenario, defining the system in terms of DIKWP relationships. For instance, "Inconsistent Information leading to incorrect Knowledge formation."

• Input Format: Select one or more DIKWP elements involved in the contradiction (e.g., Information → Knowledge).

• Output Format: Define the desired output (e.g., accurate Knowledge or complete Data).

3.2 Contradiction Resolution Engine:

• The tool applies the mapped TRIZ principles based on the contradictions between the chosen DIKWP elements.

• The tool suggests inventive solutions based on how TRIZ principles can resolve the specific contradiction (e.g., Segmentation, Dynamization).

3.3 Visualization and Workflow Management:

• Graphical Interface: The tool visualizes the 5x5 DIKWP interactions, showing the selected contradiction.

• Step-by-Step Solution Path: Each TRIZ solution is broken down into actionable steps. For example, "Segment Data for localized Wisdom application" would show each sub-step graphically.

Step 4: Build the Problem-Solving Engine

4.1 Integrate TRIZ Solution Library:

• A database of TRIZ principles linked to each DIKWP element. For example:

• Segmentation applies to Data-Wisdom contradictions.

• Local Quality applies to Information-Knowledge contradictions.

4.2 Incorporate AI for Suggestive Reasoning:

• AI-driven analysis can assist in identifying the most relevant TRIZ principle for a given DIKWP contradiction. Machine learning models trained on historical problem-solving patterns can be incorporated to suggest the best principle for resolving contradictions.

Step 5: Prototype Development

5.1 User Interface:

• Simple UI for Inputs: Users can input a problem description, selecting the involved DIKWP elements.

• Step-by-Step Outputs: The tool will display possible TRIZ principles and their detailed application to the specific DIKWP scenario.

5.2 Integration with Existing Solutions:

• The tool should offer compatibility with other problem-solving frameworks (like Lean Six Sigma) or existing AI solutions (like GPT-based systems).

Step 6: Test the Tool with Real-World Problems

6.1 Create Test Cases:

• Use real-world case studies in fields such as autonomous systems, energy management, or large language models (LLMs).

• Example: Test the tool’s ability to solve imprecise sensor data in autonomous vehicles (as described in the earlier example).

6.2 Evaluate the Performance:

• Check how the tool provides effective and innovative solutions using DIKWP-TRIZ principles.

• Compare the results against traditional problem-solving methods to show the effectiveness of this model.

Step 7: Launch and Continuous Improvement

7.1 Open Beta Testing:

• Release the tool for open beta testing with selected organizations or industries that deal with complex contradictions (e.g., AI companies, automotive, energy grids).

7.2 Collect Feedback and Refine:

• Gather user feedback on how intuitive the tool is and how effective the suggested solutions are.

• Refine the TRIZ principles applied to DIKWP interactions based on the complexity of different scenarios.

7.3 Incorporate New Inventive Principles:

• Continuously update the tool with new TRIZ principles or adaptations for specific industries.

By following this structured step-by-step approach, the DIKWP-TRIZ tool will be able to deliver actionable, inventive solutions to complex problems. It provides a flexible framework for both technical systems (autonomous driving, smart grids) and cognitive AI systems, making it a valuable resource for innovation in various fields.

Comparing DIKWP-TRIZ with Traditional TRIZ 

When comparing DIKWP-TRIZ to traditional TRIZ, the two approaches share some core methodologies around inventive problem-solving but differ significantly in their conceptual structures, applicability, and the way they handle data, knowledge, and contradictions. Below is a detailed comparison between DIKWP-TRIZ and traditional TRIZ across various dimensions:

1. Conceptual Framework

• Traditional TRIZ:

• TRIZ (Theory of Inventive Problem Solving), developed by Genrich Altshuller, is based on analyzing patterns of inventions and identifying universal principles for solving technical contradictions. It focuses on technical and physical systems, utilizing a hierarchical set of 40 inventive principles to resolve conflicts between elements (e.g., function vs. efficiency).

• TRIZ primarily operates at the technical level, addressing contradictions in engineering and product development (e.g., increasing strength while reducing weight).

• DIKWP-TRIZ:

• DIKWP-TRIZ is a specialized extension based on the DIKWP model, incorporating Data, Information, Knowledge, Wisdom, and Purpose as core cognitive elements. It introduces a networked rather than hierarchical structure, emphasizing dynamic interactions between these elements, and targets a broader range of cognitive, ethical, and purpose-driven challenges.

• DIKWP-TRIZ moves beyond technical and physical systems to include cognitive systems, artificial intelligence, and semantic spaces, where contradictions are resolved by leveraging the non-hierarchical transformation among DIKWP components. For instance, contradictions between incomplete or inconsistent knowledge and inaccurate data can be addressed through semantic transformations across DIKWP content.

2. Handling Contradictions

• Traditional TRIZ:

• TRIZ defines contradictions primarily in terms of conflicting physical properties or system requirements (e.g., a product that must be both strong and lightweight). The method involves resolving these contradictions through inventive principles such as Separation in Time or Segmentation.

• Contradictions are typically resolved in a binary manner: addressing either one property without compromising the other, or finding ways to satisfy both simultaneously.

• DIKWP-TRIZ:

• In DIKWP-TRIZ, contradictions are not limited to physical properties but can involve cognitive, semantic, or knowledge-based issues, such as conflicting wisdom or incomplete information. It emphasizes resolving contradictions within the DIKWP network by transforming or complementing incomplete/inconsistent data, information, or knowledge.

• DIKWP-TRIZ allows for more complex contradictions (such as ethical conflicts or incomplete knowledge) to be handled within the dynamic DIKWP space, making it suitable for high-level problems in AI or systems that require purpose-driven resolutions.

3. Invention Process and Creativity

• Traditional TRIZ:

• Traditional TRIZ uses a systematic and algorithmic approach to innovation, identifying patterns in global inventions and applying them to new problems. The invention process in TRIZ is deterministic and driven by empirical knowledge from existing technical solutions.

• TRIZ relies on known contradictions and leverages the established 40 inventive principles to propose systematic solutions. For example, "Segmentation" may suggest dividing a system into smaller parts to solve a technical contradiction.

• DIKWP-TRIZ:

• DIKWP-TRIZ applies semantic transformations and interactions between Data, Information, Knowledge, Wisdom, and Purpose to generate solutions. It encourages more adaptive and purpose-driven innovation, focusing on mapping between conceptual and semantic spaces. The system allows for emergent creativity, where contradictions within one component (e.g., incomplete data) can be transformed and complemented by another (e.g., wisdom or knowledge).

• Instead of just using physical principles, DIKWP-TRIZ also incorporates cognitive reasoning and ethical considerations. For instance, in AI ethics, DIKWP-TRIZ might handle the contradiction between algorithmic efficiency and moral fairness by exploring interactions between the wisdom and purpose elements.

4. Flexibility and Applicability

• Traditional TRIZ:

• Traditional TRIZ is highly effective for physical and engineering systems, particularly in product design and mechanical engineering. Its applicability is primarily in technical domains like automotive, aerospace, and industrial product design, where system constraints and contradictions can be clearly identified.

• The structured, principle-based approach works well for well-defined engineering problems but is limited in handling high-level cognitive, social, or ethical dilemmas.

• DIKWP-TRIZ:

• DIKWP-TRIZ extends the application to more abstract, conceptual, and cognitive domains, including fields like artificial intelligence, autonomous systems, ethics, and even social decision-making. It is particularly suitable for scenarios where contradictions involve incomplete, inconsistent, or imprecise data and where knowledge gaps exist.

• Its networked nature allows for solving problems that involve multiple layers of uncertainty, typical in AI development (e.g., natural language processing), or decision-making systems that need to balance ethical considerations with technical performance.

5. Resolution of Incomplete and Inconsistent Information

• Traditional TRIZ:

• TRIZ does not explicitly focus on the incompleteness or inconsistency of input information or knowledge. The assumption is that sufficient technical data exists to analyze the contradictions, and the principles apply based on known parameters.

• Its focus is on optimization rather than addressing the uncertainty in the problem space.

• DIKWP-TRIZ:

• DIKWP-TRIZ integrates solutions for the 3-No problem by Prof. Yucong Duan (i.e., incomplete, inconsistent, and imprecise DIKWP input/output). It resolves contradictions where knowledge or data may be missing, inconsistent, or unreliable by leveraging the mutual transformation between DIKWP elements (e.g., using data to validate knowledge, using wisdom to correct inconsistent information).

• This makes DIKWP-TRIZ more adept at handling problems with uncertainty or incomplete information, which is critical for developing artificial consciousness systems or AI models where data might be noisy or imprecise.

6. Applications in AI and Cognitive Systems

• Traditional TRIZ:

• Although TRIZ can be applied to technical problems in AI (such as optimizing algorithms or resolving computational inefficiencies), its framework is not inherently designed for cognitive systems or AI ethics.

• It deals with problem-solving in a more mechanical or physical sense, making it harder to apply to semantic-level challenges in AI, such as natural language understanding or ethical AI development.

• DIKWP-TRIZ:

• DIKWP-TRIZ is designed to address high-level AI problems, especially in systems requiring artificial consciousness or ethical reasoning. It can handle contradictions between algorithmic complexity and human values, leveraging wisdom and purpose to ensure AI systems align with ethical standards.

• It is also useful for enhancing LLM models like GPT-4, where contradictions between imprecise language input and output (knowledge) can be handled by integrating purpose-driven transformations within the DIKWP network.

Summary Table: Traditional TRIZ vs. DIKWP-TRIZ

DIKWP-TRIZ builds on traditional TRIZ principles but extends them to handle more complex, abstract, and semantic-level contradictions. By leveraging the networked structure of DIKWP and addressing the 3-No problem, DIKWP-TRIZ can solve challenges that traditional TRIZ might not, particularly in the domains of AI, ethics, and advanced cognitive systems.

Here’s a detailed table comparing Traditional TRIZ and DIKWP-TRIZ, highlighting the differences across key aspects:

Example Comparison

This table provides a clearer understanding of how DIKWP-TRIZ extends the capabilities of traditional TRIZ by addressing more abstract, cognitive, and ethical contradictions, making it suitable for advanced AI systems and artificial consciousness.

Conclusion: DIKWP-TRIZ as a Systematic Innovation Tool

DIKWP-TRIZ is a highly structured and scalable approach for driving innovation. It systematically analyzes contradictions across networked cognitive spaces and provides inventive solutions based on the interaction between Data, Information, Knowledge, Wisdom, and Purpose. This combined framework integrates the cognitive power of DIKWP with the problem-solving methodology of TRIZ, offering unique opportunities to invent new solutions, particularly in fields such as Artificial Intelligence, consciousness systems, and semantic transformation models.

By adopting the DIKWP-TRIZ approach, organizations and researchers can explore new avenues for creating patents, systems, and solutions that are both technologically advanced and philosophically grounded. This fusion ensures that innovation remains aligned with both technical objectives and cognitive coherence, paving the way for the next generation of AI inventions.