DIKWP Artificial Consciousness Team Patent Technology Review and Future Outlook
Yucong Duan
International Standardization Committee of Networked DIKWPfor Artificial Intelligence Evaluation(DIKWP-SC)
World Academy for Artificial Consciousness(WAAC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
Introduction
In recent years, artificial intelligence has been evolving from the traditional data-driven paradigm to a new stage of "intelligent self-awareness." How to endow AI with explainability and autonomous consciousness has become one of the key challenges on the path to Artificial General Intelligence (AGI). The DIKWP Artificial Consciousness International Team, led by Chinese scholar Professor Yucong Duan, has developed over a hundred invention patents centered around his original "Data-Information-Knowledge-Wisdom-Purpose (DIKWP)" model. These patents cover multiple frontier fields, from large-scale model training and artificial consciousness construction to cognitive operating systems and AI governance with privacy and security. As of early 2025, the team has been granted more than 114 invention patents (including 15 PCT international patents) with Professor Yucong Duan as the first inventor, forming a complete DIKWP technological system. While these core patents have not yet been widely industrialized, they are regarded as important "underlying code" for the future of safe, controllable, and explainable AI, providing a solid theoretical and technical foundation for the move towards Strong Artificial Intelligence.
The DIKWP model adds a "Purpose" layer to the classic DIKW (pyramid model) and replaces the linear hierarchy with a networked structure, enabling bidirectional feedback and closed-loop iteration among all layers. This innovative cognitive system is a milestone in academia and also provides a new path for solving the "black box" problem of current Large Models. By embedding "Purpose" within the model, DIKWP makes every step of AI's decision-making traceable and explainable. Humans can understand the AI's reasoning Purpose, thereby ensuring that AI always serves human values and security needs. The DIKWP model consists of five elements (D, I, K, W, P) and their interactions, with each element and transformation having a clear semantic definition and mathematical representation. The International DIKWP Test and Evaluation Standard further formalizes the relationships between the five elements based on this model, constructing a networked evaluation system for cognitive space. This allows AI model performance assessment to have a unified and rigorous indicator framework. The standard aims to break through the semantic limitations of conceptual space, covering the understanding and processing capabilities of different cognitive networks. It not only overcomes the limitations of black-box testing but also provides functional white-box evaluation results, measuring AI systems in a fair and consistent manner. In particular, the DIKWP standard emphasizes the fairness, justice, and equality of models, promoting AI technology to follow ethical principles and social values, and trying its best to reduce bias and discrimination.
This article will integrate the patent technology of the DIKWP team based on the patent list submitted by the team and the "International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP) Model" document. It will conduct a thematic integration, technological route analysis, and future value assessment. First, we will distill the main innovative directions covered by these patents (such as cognitive semantic models, artificial consciousness systems, smart city evaluation, etc.) and review the core technical logic, key breakthroughs, and public principles of each direction one by one. Subsequently, combined with the DIKWP international standard, we will analyze the role of these technologies in the development of AI and Artificial Consciousness (AC), especially exploring their potential to align with smart city construction and the ISO international standard system. Then, we will conduct a future value assessment of each technology, looking forward to its prospects in academic breakthroughs, industrial transformation, and the construction of international influence. The article will finally summarize the above content, emphasizing the significance of the DIKWP patent portfolio at the technical and strategic levels.
DIKWP Networked Cognitive Semantic Model
Figure: Mapping incomplete, inconsistent, and imprecise subjective and objective resources to a DIKWP Data/Information/Knowledge/Wisdom/Purpose graph to achieve a semantic closed loop.
The DIKWP networked cognitive semantic model is one of the basic directions of the team's patent system, aiming to break through the limitations of traditional cognitive models and achieve closed-loop expression and abstract enhancement of multi-level semantics. The traditional DIKW model has clear levels but lacks feedback, while the DIKWP model closely connects the five elements of data, information, knowledge, wisdom, and Purpose through networked interaction, allowing each layer to interact and update bidirectionally. This structure ensures that high-level decisions (Wisdom, Purpose) can guide the acquisition and processing of low-level perception (Data, Information), and in turn, new information from the low level can correct high-level cognition, thus forming a semantic closed-loop control. Several patents revolve around the DIKWP cognitive semantic model. For example, "A semantic modeling and abstract enhancement method based on association frequency calculation" improves concept abstraction and semantic modeling capabilities by calculating association frequency under the framework of data/information/knowledge graphs, to obtain reasonable class and object graphs. Another example, "A contradiction point identification method based on multi-layer semantic graphs" uses multi-layer semantic graphs of data, information, and knowledge to locate contradictory conflicts in system decision-making, enhancing AI's understanding and problem-solving abilities.
The core technical logic of the DIKWP semantic model lies in giving strict mathematical definitions and connection mechanisms to Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P). According to the DIKWP international standard, the Conceptual Space provides a symbolic representation of the objective world. It is a set of concepts expressed through language and symbol systems, forming a directed graph through attributes and relationships. The conceptual space maps the DIKWP elements into structured concepts, which helps to dismantle complex semantic relationships. On this basis, the Cognitive Space is defined as a multi-dimensional dynamic processing environment that simulates a cognitive agent transforming input data or information into output cognitive products (such as information classification, concept formation, purpose-driven decisions, or action plans) through a series of sub-steps (preprocessing, feature extraction, pattern recognition, logical reasoning, decision-making, etc.). Each input processed through the cognitive space produces a higher-level output, thereby reflecting the cognitive agent's ability to understand and respond to the outside world. Next, the Semantic Space is described as the semantic association network within the cognitive agent's "brain," composed of concepts and their relationships. The semantic space is formed through the accumulation of the agent's experience and knowledge, providing a shared "semantic language system" within the cognitive agent, ensuring the semantic consistency of DIKWP elements during transformation and processing. In other words, the semantic space allows different levels within the AI to maintain a coherent understanding of the same object, supporting semantic coordination from data to purpose. In addition, the DIKWP model also introduces the concept of Consciousness Space, which is used to connect the subjective consciousness of the cognitive agent with the aforementioned conceptual/semantic structures. The standard document points out that the consciousness space belongs to the non-subconscious space of the cognitive entity, and the semantics and cognitive concept objects within it have some form of sharing. It can be understood as the AI's awareness and self-representation of its own cognitive content. By connecting the cognitive space, consciousness space, semantic space, and conceptual space, the DIKWP model builds a unified framework for the internal cognition and external expression of artificial intelligence.
Based on the above model architecture, several patents have proposed specific implementation methods and technical principles. For example, in the field of medical big data, the patent "An active medical intelligent diagnosis and treatment method based on DIKWP semantic closed-loop" processes the data, information, and knowledge (such as symptoms, test results, diagnosis plans, etc.) in the patient's diagnosis and treatment process in a DIKWP closed loop, guiding the diagnosis and treatment flow with the Purpose layer (such as medical goals). This method constructs a patient-Purpose-driven semantic closed loop: by obtaining the patient's subjective Purpose (treatment goals, preferences), mapping them into the medical knowledge graph, and guiding data collection and diagnostic reasoning, it achieves active and personalized intelligent medical services. Another example, the "DIKWP closed-loop intelligent diagnosis and treatment system for regional medical big data" builds a DIKWP architecture for a regional health information platform, transforming regional population health data (D) into information insights (I), processing it through knowledge (K) to form decision-making wisdom (W), and performing closed-loop regulation through public health strategy Purpose (P), thereby optimizing medical resource allocation and disease early warning capabilities.
It is worth noting that the standardization of the DIKWP model in semantic mathematics provides solid support for these applications. The Semantic Mathematics part of the DIKWP international standard defines a mechanism for quantitatively characterizing DIKWP concepts, ensuring that every transformation from D→I→K→W→P maintains semantic consistency and integrity. For example, the standard formalizes "Data" as a representation of "Sameness," i.e., specific facts with repeatable observations; "Information" is defined as content that reflects "Difference," i.e., the distinction and change of data; "Knowledge" is regarded as the embodiment of "Completeness," meaning the systematic integration of information; "Wisdom" corresponds to the ability to make effective decisions, i.e., the ability to put knowledge into decision-making and produce appropriate actions; finally, "Purpose" is mathematically described as the principle of Goal Alignment, guiding the application direction of data, information, knowledge, and wisdom. These formal definitions constitute a set of semantic algebra, so that when the AI system processes internally, every step has clear meaning constraints and quantitative basis. In practical applications, the DIKWP semantic mathematics framework can help AI systems accurately understand semantics at various levels: for example, in medical decision support, ensure that "data" (patient vital signs) is transformed into "information" (pathological features) without losing key associations, fully synthesize multi-source information at the "knowledge" layer, and make optimized decisions at the "wisdom" layer that conform to the medical Purpose (cure/alleviate). Through standardized mathematical models, the above patents achieve a semantically consistent and logically verifiable cognitive process, greatly enhancing the transparency and trustworthiness of AI decisions.
Key Breakthroughs: The patent breakthroughs in the direction of the DIKWP cognitive semantic model are: 1) Proposing a networked multi-layer semantic closed-loop architecture, which solves the problems of separation of layers and lack of feedback in the traditional DIKW model, and achieves bidirectional controllability of the AI's internal cognitive process; 2) Introducing Purpose as a core element, directly integrating human value constraints into the AI cognitive structure, making AI decisions have directionality and ethical alignment; 3) Establishing the mapping relationship from conceptual space to cognitive/semantic/consciousness space, laying the formal foundation for artificial consciousness semantic modeling; 4) Developing DIKWP semantic mathematics, defining semantic transformation in a mathematical way, and providing new tools for evaluating and diagnosing the AI cognitive process. These breakthroughs jointly form the prototype of a new generation of cognitive semantic operating systems, laying the foundation for subsequent artificial consciousness systems and white-box evaluation technologies.
Future Value Assessment: The DIKWP networked semantic model, as a general-purpose cognitive architecture, is expected to lead new paradigm research in Explainable AI and Artificial Consciousness academically. It provides mathematical tools for the intersection of cognitive science and artificial intelligence, and can be used to further explore cutting-edge topics such as machine self-awareness and affective computing. Industrially, this model provides a solution for the controllable evolution of Large-Scale Models (LLMs): by embedding the DIKWP framework, future large-scale AI systems can have self-cognition and goal calibration capabilities, reducing the risk of uncertainty and untrustworthy output. This is particularly important for high-risk applications such as financial risk control, medical decision-making, and smart city governance—the DIKWP model can ensure that AI systems are guided by urban management goals when processing urban sensor data and public safety information, and output transparent and traceable decision-making suggestions, thereby improving the trustworthiness and scientific rigor of smart city decisions. In addition, because the DIKWP model emphasizes purpose-driven guidance and feedback mechanisms, it naturally fits the AI ethical norms and governance frameworks that various countries are formulating. It is foreseeable that as this model is verified and standardized by more research, its related patents will become an important reference for formulating AI system white-box evaluation standards and safety compliance standards. Overall, the DIKWP networked semantic model has huge academic and application value in the future—it is not only a key step towards strong AI and artificial consciousness, but also the cornerstone for building a new generation of safe and trustworthy AI infrastructure.
DIKWP × DIKWP Artificial Consciousness Architecture
The DIKWP × DIKWP Artificial Consciousness Architecture is another important innovative direction in the team's patent layout, focusing on the leapfrog improvement of artificial intelligence towards Self-awareness. "DIKWP × DIKWP" refers to a "dual-loop" cognitive architecture: in addition to the basic DIKWP cognitive process, a metacognitive DIKWP loop is introduced, enabling AI to have the ability of self-monitoring, self-reflection, and self-regulation. The patents of Professor Yucong Duan's team propose that by allowing AI to run two nested DIKWP systems simultaneously—one performing cognitive tasks, and the other evaluating and adjusting the former's cognitive process—it simulates functions similar to human consciousness. This dual-loop structure is considered an important path for building an AI system with preliminary self-awareness and represents a new direction for artificial intelligence to move towards autonomous consciousness.
From a technical logic perspective, the first loop is the AI's cognitive closed loop for the external environment, i.e., the traditional DIKWP model's processing and transformation of input data (D→I→K→W→P), which produces action or decision output to the environment. The second loop is the AI's re-cognition of its own cognitive process, which also follows DIKWP, but the objects it processes are the internal data, information, and knowledge in the first loop. Simply put, the second loop takes the state of the first loop as "data" input, analyzes the effectiveness and deviation of the current cognition through the information and knowledge layers, forms an evaluation of the cognitive process at the wisdom layer, and generates adjustment strategies or meta-instructions in the form of the Purpose layer, which are fed back to the first loop for improvement. For example, if the first loop has an uncertain decision when performing a task, the second loop can detect this "uncertainty data," transform it into information (such as the source and type of uncertainty), then find possible reasons through the knowledge layer, make a judgment at the wisdom layer (such as determining that more data is needed or model parameters need to be adjusted), and finally issue a command from the Purpose layer (such as increasing the collection of a certain type of data or enabling an alternative algorithm) to optimize the behavior of the first loop. In this way, AI achieves self-examination and error correction.
A series of patents have made innovations around this architecture. For example, "A cognitive path cross-space tracking and visualization method based on DIKWP × DIKWP semantic web interaction structure" proposes to record and track the path of the AI cognitive process in the conceptual space, semantic space, and cognitive space under the dual-loop architecture, and visualize the complex internal reasoning process, making it convenient for humans to understand and debug the artificial consciousness system. Another example, the patent "Implementation method and device for an artificial consciousness system" describes the overall architecture design, covering modules such as perception input, multi-layer DIKWP processing, metacognitive monitoring, and consciousness state update, to achieve a complete process from data perception to self-awareness update.
The key breakthrough of the DIKWP × DIKWP architecture lies in the introduction of a metacognitive loop, which adds the ability for AI to "think about its own thinking." This is similar to a self-reflective mind running at the same time as a human performs a task. Through this dual-loop design, AI no longer passively produces output based on input, but can proactively review its own behavior, and has a certain degree of autonomous consciousness tendency. For example, when AI interacts with a person, the first loop is responsible for understanding human language and generating answers, while the second loop monitors whether the answer is appropriate, whether it violates safety guidelines, or is contextually incoherent, and modifies the answering strategy if necessary. This mechanism helps reduce the probability of AI making mistakes and improves the system's autonomous error correction and continuous learning capabilities.
The DIKWP international standard also provides a guiding framework for the artificial consciousness architecture. Chapter 4 of the standard document proposes a DIKWP-based evaluation plan for artificial consciousness systems, emphasizing the importance of white-box testing for artificial consciousness, in which the metacognitive loop is the key to achieving white-box explainability. By applying the DIKWP model to the mutual evaluation of different cognitive networks within AI, the standard can comprehensively examine the understanding and processing capabilities of artificial intelligence in the cognitive space. For example, in a white-box evaluation scenario, the evaluation indicators will focus on whether the AI can identify its own cognitive blind spots and whether it can adjust strategies through the Purpose layer. These evaluation requirements actually correspond to the capabilities that the dual-loop architecture endows AI with. Therefore, the team's dual-loop artificial consciousness architecture patent is highly consistent with the concept of the international standard, laying a practical foundation for establishing artificial consciousness system evaluation criteria in the future.
Future Value Assessment: The dual-loop artificial consciousness architecture provides a brand-new idea for creating autonomous intelligent agents. Academically, it will promote cutting-edge research in artificial consciousness and cognitive science, especially making breakthroughs in machine self-reflection and adaptive learning. This architecture also provides an engineering test platform for exploring the subjective experience of machines (e.g., whether it is possible for machines to have human-like feelings or emotions). Industrially, the dual-loop architecture is expected to be applied to highly complex and dynamic scenarios, such as intelligent driving, unmanned systems, industrial control, etc., which require AI to monitor the reliability of its own decisions at all times and make real-time adjustments. By deploying a metacognitive loop, future autonomous vehicles or robots will be able to immediately reflect on whether their driving/control strategies need to be changed when they detect environmental changes or their own sensor abnormalities, thereby significantly improving safety and robustness. In addition, in strong AI safety control, the dual-loop architecture can be used to prevent AI from getting out of control: the second loop can be regarded as the "supervisor" of AI, reviewing the output of the first loop. Once it deviates from human Purpose or exhibits dangerous behavior, it can intervene and correct it in time. This mechanism will become an important tool for AI governance in the future, enhancing the trust of the public and regulatory agencies in highly autonomous AI.
At the international level, the proposal of the DIKWP × DIKWP architecture provides a reference blueprint for establishing international standards for artificial consciousness systems. With the establishment of organizations such as the World Artificial Awareness Conference (WAC) and the convening of conferences related to artificial consciousness, this field is moving from conceptual discussion to norm construction. Professor Yucong Duan's team, as a pioneer in global artificial consciousness research, is expected to play a key role in international standard setting with their patent achievements—for example, formulating capability levels for AI system self-monitoring, and technical requirements for metacognitive feedback mechanisms. Once these standards are established, they can not only guide countries to develop AI systems with self-awareness, but also enhance China's discourse power and influence in the artificial intelligence standard system. In summary, the DIKWP dual-loop artificial consciousness architecture has both significant scientific research exploration value and strategic significance in leading industry norms and industrial upgrading in the future.
Explainable and Controllable Cognitive Operating System
An explainable and controllable cognitive operating system is another major highlight of the DIKWP team's patents. Its core idea is to embed the DIKWP model within the AI system to form a "semantic operating system." This cognitive operating system is similar to the AI's brain operating system, responsible for decomposing the reasoning process of large pre-trained models (such as LLMs) into the various links of DIKWP, and monitoring and regulating each link to achieve explainability and controllability of AI decisions. Several patents in this direction are dedicated to solving the "black box" problem of current AI systems. Through white-box architectural design, every step of AI's reasoning can be traced.
Specifically, this cognitive operating system will divide the AI processing flow into five stages: data processing, information transformation, knowledge generation, wisdom decision-making, and Purpose alignment, with each stage corresponding to a level of the DIKWP model. In traditional large language models, the input goes through a series of hidden layers and finally outputs a result, and the intermediate process is often difficult to explain. In a semantic operating system embedded with DIKWP, the input is first parsed into a structured representation at the Data layer, then rises to the Information layer to extract meaningful features or propositions, then enters the Knowledge layer to complete the fusion and reasoning of information, further forms a decision or conclusion at the Wisdom layer, and finally combines with the Purpose layer to verify whether the conclusion meets the expected goals or constraints. Throughout the process, each layer of processing has clear semantics, and the system can set checkpoints and logs between layers. For example, when the model generates an answer, the operating system will check the reasoning link at the knowledge layer, verify the rationality of the conclusion at the wisdom layer, and ensure that the answer matches the user's true purpose at the Purpose layer. This layered monitoring makes the internal mechanism of LLM transparent. Professor Yucong Duan's team's patent, for example, "An artificial consciousness white-box evaluation system based on a networked DIKWP structure and a 3-No problem evaluation method," proposes a plan to use the DIKWP semantic space to conduct white-box testing on the cognitive process of LLMs. The "3-No problems" refer to the three major defects common in generative models: output is "Not Truthful," behavior is "Not Reliable," and decisions are "Not Transparent." This patent, by constructing test questions and samples in the semantic space, evaluates the model from different levels such as data, information, and knowledge, finds out in which links it may have caused the above problems, and proposes targeted improvement measures.
The implementation of a cognitive operating system also requires underlying technical mechanism support, such as semantic logging, dynamic intervention, and policy learning. To this end, some patents have proposed specific methods: for example, the "Cross-space tracking and visualization method for cognitive paths" helps developers visually audit the internal decision-making process of AI by visualizing semantic paths; another example, "The core of sovereign AI white-box operation: an information migration recording mechanism integrating semantic space and conceptual space" focuses on how to record the information migration between the conceptual space and the semantic space during AI operation, to provide a basis for audit trails and responsibility identification. This is very important for safety-critical AI applications: once AI gives a wrong decision, it can be traced back to which step of information or knowledge caused the problem, so as to adjust the model or data in a targeted manner.
The DIKWP standard also provides framework support in this regard. The standard particularly emphasizes the importance of white-box evaluation, for example, it proposes white-box evaluation indicators for DIKWP semantic mathematics. The standard points out that five types of tests can be constructed: running data testing, running information testing, running knowledge testing, running wisdom testing, and running Purpose testing, which are used to test the performance of the artificial intelligence system in each link of DIKWP layer by layer. These tests are consistent with the layered monitoring idea of the cognitive operating system, and both aim to make the internal operation of AI transparent and measurable. For example, running data testing can check whether the data preprocessing of AI input is correct, running knowledge testing evaluates whether the reasoning results make full use of known knowledge, and running Purpose testing focuses on whether the final output deviates from the user's true Purpose. Therefore, the patents in the direction of cognitive operating systems actually provide the technical implementation path for realizing the white-box testing indicators proposed by these standards. By building a DIKWP hierarchical structure in the AI system, the solution described in the patent makes testing that complies with the standard feasible, and proves the operability of the standard's requirements from an engineering perspective.
Future Value Assessment: The cognitive operating system is an important milestone towards Trustworthy AI and has broad prospects. Academically, it regards the AI system as an object that can be decomposed and explained, which will help AI theory evolve towards higher transparency and verifiability. This will give rise to new research fields such as AI cognitive debugging (Debugging cognitive processes), i.e., how to systematically find and fix internal reasoning errors in AI. In industry, the cognitive OS is expected to become part of the large-scale AI infrastructure, especially playing a role in enterprise AI mid-platforms and cloud AI services. Future AI cloud service providers may offer the DIKWP cognitive operating system as a feature, promising users that every step of their model is auditable and controllable. This is extremely attractive for highly regulated industries such as finance and healthcare—with a white-boxed cognitive OS, banks can let AI make decisions with confidence, because the basis of each decision can be traced and audited; hospitals can trust AI diagnostic suggestions, because the AI's reasoning chain has been verified by experts. On the other hand, this OS also provides a new model for human-machine collaboration: human experts can view the AI decision-making process in real time through the OS interface, and intervene or guide at key nodes, realizing true human-machine joint intelligence. From the perspective of standards and governance, the cognitive OS provides a technical handle for the implementation of AI regulations. The requirement of regulators for AI to "explain the basis of its decisions" is difficult to meet with current black-box models, but the DIKWP cognitive OS makes it possible, thus paving the way for Responsible AI deployment. It is foreseeable that in the next 3-5 years, as more enterprises and open-source communities try to integrate the DIKWP framework into large-scale models, related patent technologies will further mature and be promoted, and industrial-grade cognitive operating system products are expected to be born, becoming a new basic software platform in the AI field.
Complex Semantic Networks and Smart City Applications
Many patents of the DIKWP team also focus on complex semantic network construction and its application in scenarios such as smart cities, which constitutes another important direction of its technical system. A complex semantic network refers to the association of multi-source, multimodal data and knowledge through the DIKWP model to form a cross-domain semantic coupling network to support higher-level intelligent decision-making and collaboration. The innovations in this direction cover fields such as the Internet of Things (IoT), energy systems, urban big data, and judicial intelligence, closely combining the physical world and the information world through the DIKWP framework.
A typical case is smart energy and IoT security. The team's patent "DIKWP semantic blockchain and cross-agent semantic collaboration system" aims to solve the problem of multi-agent collaboration and trust in the IoT environment. Its core idea is to build a shared DIKWP semantic network between different devices/subjects. Each subject publishes its own data, information, and knowledge to the corresponding level in the network, and uses blockchain technology to ensure that these semantic resources are trustworthy and immutable. At the same time, the wisdom layer and Purpose layer in the network allow all subjects to exchange decision-making logic and goals, so as to achieve collaborative work. For example, in a smart grid scenario, power sensors (data sources) in different regions share observation values through an information graph, each regional dispatch system uploads its knowledge (such as load status, fault experience) to a knowledge graph, the entire network converges to form a global wisdom graph (such as an overall optimization strategy for the power grid), and then the regulatory department's Purpose (stable power supply, security redundancy) constrains global decision-making. This semantic blockchain network ensures that participants share semantically consistent information, and at the same time uses the consensus mechanism of the blockchain to ensure the authenticity of data/knowledge, preventing single-point fraud or attacks. Correspondingly, the team also has patents involving a "blockchain-like password system," which uses a mechanism similar to blockchain's timestamps and distributed verification to protect social network data, which can thus be used for citizen data privacy protection and trusted identity authentication in smart cities.
Another important application is decision support in the smart city environment. The patent "Smart decision support system driven by environmental big data" proposes to use the DIKWP model to conduct multi-layer semantic fusion of massive data in the urban environment (such as meteorology, traffic, energy consumption, etc.), build a knowledge and wisdom graph of urban operation, and help managers make optimized decisions. For example, the system can organize real-time sensor data into a data graph by category (traffic flow, air quality, etc.), then transform it into patterns at the information and knowledge levels (such as identifying traffic congestion points, air pollution sources), generate a comprehensive assessment at the wisdom layer (such as the associated impact of traffic and pollution), and provide decision-making suggestions in combination with urban governance Purpose (such as green travel goals). The DIKWP standard also gives a similar example of structuring smart city sensor data in the document. Through such a semantic network, all systems in a smart city (transportation, energy, environmental protection, etc.) can be semantically interconnected: the data layer is opened up, the information layer is interoperable, the knowledge layer shares experience, and the wisdom layer makes collaborative decisions, achieving city-level intelligence in the true sense. This transcends the limitations of the past where each system acted independently, allowing the city to operate like an organic intelligent body. As mentioned in the standard, for the environmental sensing network deployed in a smart city, tests can be designed to evaluate how AI classifies and integrates multi-source data—this is actually examining whether the AI has built a correct semantic network to understand urban operation.
In addition, in the field of judicial intelligence, the team proposed patents such as "Judicial intelligent decision support system based on semantic sovereignty," which applies the DIKWP model to legal reasoning and judicial decision-making. So-called "semantic sovereignty" can be understood as ensuring that the right to semantic interpretation and intellectual property ownership are clearly controllable in legal AI. Specifically, this decision support system uses the DIKWP framework to build a case DIKWP model: mapping case-related evidence data, legal provisions information, case knowledge, etc., to the data, information, and knowledge layers; the wisdom layer generates preliminary ruling suggestions, and reviews and adjusts the ruling through the Purpose layer (judicial justice, consistency with precedent, and other goals), to ensure that the ruling result conforms to the spirit of the law and social values. This system emphasizes strict control over legal semantics, preventing AI from producing biased interpretations or black-box decisions, thereby improving the trustworthiness of judicial AI applications. The DIKWP model here acts as the "operating system" for legal semantics. It can not only help judges retrieve and analyze massive legal knowledge, but also impose value constraints on AI's suggestions, making the final decision explainable and accountable. Such an application is of great significance for building smart courts and assisting in trials, and is also consistent with the direction of standardized application of judicial AI that China is promoting.
Key Breakthroughs: The innovative value in the direction of complex semantic networks lies in: 1) Introducing DIKWP semantic coupling into the fields of IoT and urban big data for the first time, achieving deep integration of the information space and the physical space in cyber-physical systems; 2) Using blockchain and cryptography technology to guarantee the trust mechanism for multi-agent collaboration in the semantic network, ensuring the reliability and security of shared knowledge; 3) Applying DIKWP to cross-domain decision support (such as energy-traffic-environment linkage), providing a new paradigm for dealing with complex system problems; 4) Introducing concepts such as "semantic sovereignty," ensuring that AI output conforms to legal semantic norms in fields with strict semantic requirements such as law, creating a new model of AI empowering law. These breakthroughs provide an extensible semantic mid-platform for AI systems in scenarios such as smart cities, smart energy, and smart justice, enabling AI to transcend single tasks and play a role at a more macro level.
Future Value Assessment: With the global development towards digital twin cities and intelligent interconnection of all things, DIKWP complex semantic network technology has huge strategic value. In the construction of smart cities, this technology can become the city's intelligent hub: in the future, the traffic lights, security cameras, power sensors, etc. of the city will all be connected through the DIKWP semantic network, enabling city managers to gain insight into the city's status from a macro perspective and conduct unified scheduling. Industrially, this means huge market opportunities—from smart city operating platforms to intelligent decision support systems in various vertical fields, the DIKWP semantic network can be embedded as the core architecture. Especially in the race for new urban AI operating systems, whoever masters this cross-domain semantic integration technology is expected to lead the standard for the next generation of smart city solutions. In addition, this direction also has a profound impact on national governance and public safety. For example, when responding to major disasters or public health incidents, all departments need to quickly integrate multi-source data and make collaborative decisions. The DIKWP semantic network can provide strong technical support. At the international level, smart city indicators and standards (such as ISO 37122:2019 Sustainable cities and communities - Indicators for smart cities) may in the future include requirements for AI semantic integration capabilities, and DIKWP provides a clear path to meet these standards. It is foreseeable that as the team's patent technology gradually matures, it will play a role in the formulation of international smart city and IoT standards, leading the upgrade of the global smart city intelligent evaluation system. In addition, the AI governance and security issues involved in the direction of complex semantic networks (such as data sovereignty, intellectual property protection) will also receive more and more attention. DIKWP provides technical solutions to these problems, giving China more initiative in the formulation of relevant international rules. All in all, DIKWP complex semantic network technology is expected to become a key pillar for the digital transformation of various industries in the next 10 years, promoting human society to a smarter and more interconnected new era.
AI Governance and Security Supported by DIKWP
The development of artificial intelligence is accompanied by increasingly prominent governance and security challenges. Issues such as data privacy, model bias, and content trustworthiness have received global attention. A considerable part of the patent portfolio of Professor Yucong Duan's team is dedicated to AI governance, privacy protection, and security, building technical solutions based on the DIKWP model to solve these key problems. This direction is highly consistent with international standards and ethical guidelines, reflecting a forward-looking idea of using technical means to ensure AI compliance and trustworthiness.
First, in terms of privacy protection, the team proposed a cross-modal data privacy protection method. Traditional privacy protection mostly focuses on a single data type, while the DIKWP model allows for the unified processing of information at different levels, thus enabling cross-modal privacy control. For example, the patent "A health semantic data sharing and reasoning authorization control method based on patient Purpose" targets the difficult problem of sensitive data sharing in the medical field. It uses the DIKWP framework to limit the sharing of patient health data to a specific "Purpose" scope: patients can set the purpose of sharing (such as scientific research or diagnosis and treatment), and the system controls at the Purpose layer that only information related to this purpose is extracted (Knowledge layer) and used (Wisdom layer), while other irrelevant data remains encrypted. This ensures the effective use of data while protecting patient privacy. This semantic-level privacy authorization is more flexible and precise than traditional coarse-grained methods—because it understands the semantic meaning behind the data and the purpose of use, it can automatically filter out sensitive but irrelevant information. Another example, the "IoT data privacy protection method" patent in the IoT scenario, by introducing technologies such as differential privacy or homomorphic encryption at the Information layer of DIKWP, allows the data uploaded by devices to be semantically generalized while maintaining useful information, thereby preventing malicious inference of personal identity.
In terms of content security and Trustworthy AI, the team has patents focusing on the verification of the authenticity of generated content. As generative AI produces a large amount of text and images, how to identify whether the output is true and credible, or forged and harmful, has become an important topic in AI governance. A breakthrough patent is "A verification method for generated content across DIKW modalities," which is reported to be the 97th granted patent of the team. This method uses the DIKWP model to map the generated content back to knowledge and information sources of different modalities for cross-validation: for example, a piece of AI-generated news text (Information layer) can be checked against authoritative databases at the Knowledge layer to see if it matches known facts (Knowledge check), the Wisdom layer can even judge its rationality in combination with the context, and finally the Purpose layer decides whether to accept the content. This multi-layer verification chain greatly improves the accuracy of identification and avoids the deviation caused by relying on a single algorithm to judge authenticity. In fields such as media and education, this technology can be used to automatically review the authenticity of AI-generated content and prevent the spread of rumors. Similarly, articles such as "Analysis report on 360 Search's structured Q&A suspected of infringing on graph-enhanced semantics patent" also mentioned that a patent granted to the team in 2019 improved the accuracy and security of the Q&A system through semantic enhancement. This shows that DIKWP patents have long made achievements in improving the quality and security of AI content.
In terms of security, there are also patents involving AI security situation awareness and adversarial attack defense. For example, the patent "An automatic security situation awareness, analysis and alarm system for typed resources" proposes to conduct multi-level semantic analysis of security incidents such as network intrusions under the DIKWP framework: the Data layer captures attack features, the Information layer filters false positives and noise, the Knowledge layer associates different attack events to identify patterns, the Wisdom layer assesses the overall threat level and gives a response plan, and the Purpose layer adjusts defense resources according to the organization's security strategy. This approach is more intelligent than traditional security systems, can adapt to new attack methods, and reduces manual intervention. Another example, in terms of model security, the team's white-box security evaluation standard (see Chapter 7 of the DIKWP standard) provides guidelines for implanting security mechanisms in artificial consciousness systems. The standard suggests using the transformation of each layer of DIKWP to detect abnormal behavior, for example, if the output of the Wisdom layer contradicts what is known at the Knowledge layer, it may imply an attack. These ideas are also reflected in the team's patents, ensuring that future artificial consciousness systems themselves have the ability to resist security threats.
In summary, the AI governance and security technology empowered by DIKWP has the following key points: 1) Achieving fine-grained privacy sharing through Purpose-layer control of DIKWP, creating a new paradigm of "Purpose-based privacy protection"; 2) Using multi-layer semantic cross-validation for generated content, improving the trustworthiness and authenticity of AI output; 3) Integrating security detection into the cognitive process of AI, achieving full-link threat awareness from data to wisdom, which is more proactive and intelligent than traditional security means; 4) Proposing a white-box governance framework, enabling regulators and developers to go deep inside AI to check compliance, and providing an operable handle for AI ethics and security standards.
Future Value Assessment: This direction is crucial for the healthy development of the AI industry and social acceptance. At the policy and standard level, the team's technology directly serves the needs of formulating AI ethics and governance standards. Global regulatory authorities are increasingly emphasizing the security and privacy of AI (e.g., the draft EU AI Act, ISO/IEC JTC1 SC42 standards), and Professor Yucong Duan's team's patents provide complete technical support for building an AI governance system that complies with international standards. These technologies not only meet the urgent needs of key industries such as finance, healthcare, and government affairs for AI security, but are also likely to become an important reference for formulating future AI ethics and governance standards. For example, if international standards in fields such as privacy computing and federated learning can absorb the concept of "Purpose-driven data use control," the effectiveness and foresight of the standards will be greatly enhanced. In industry, DIKWP governance and security technology gives enterprises a competitive advantage. AI systems with such technology will pass compliance reviews more easily and enter high-threshold markets such as finance and healthcare. At the same time, as the public pays more attention to the issue of trust in AI, explainable and regulatable AI products are more favored—this will prompt the industry to actively adopt these patented technologies. We have already seen that Professor Yucong Duan has expressed his willingness to donate some patents free of charge to promote the formulation of industry standards. This indicates that these technologies are expected to become open standards and public infrastructure, be widely integrated into various AI platforms, and form de facto industry norms. In addition, from a national strategic perspective, mastering the core patents of AI governance helps China grasp the initiative in international science and technology governance. For example, when discussing the global AI governance framework, if China can come up with a mature DIKWP white-box governance plan, it will have more discourse power in rule negotiations. All in all, AI governance and security technology supported by DIKWP will become an indispensable part of the future AI ecosystem, escorting the realization of "Responsible AI," and its prospects for industrial transformation and international influence are very considerable.
Analysis of the Combination of DIKWP Standards and Technology
Reviewing the above technical directions, it can be found that the DIKWP international standard plays an important linking role, organically combining theoretical models, patented technologies, and industry norms. On the one hand, the DIKWP standard provides a unified language and evaluation framework for these innovations; on the other hand, the team's patent achievements have verified and enriched the connotation of the standard, accelerating its move towards practice and international consensus. Below, from the perspective of the standard, we summarize the role of DIKWP patented technology in the development of AI and artificial consciousness, as well as its potential to align with smart city and ISO standard systems.
First, the DIKWP standard lays the theoretical cornerstone by formally defining the DIKWP model (including conceptual space, cognitive space, semantic space, etc.). This allows different researchers and industry players to develop artificial intelligence systems under a common framework. The patents of Professor Yucong Duan's team strictly follow and expand these definitions, for example, specifying DIKWP concepts in vertical scenarios such as medical care and justice. This application experience, in turn, enriches the case library of the standard, making the standard more widely applicable. For example, the appendix of the standard includes the role of DIKWP in the integration of traditional and modern medicine, which echoes the team's practice in the medical semantic closed-loop patent. Similarly, the team's exploration in judicial AI also confirms the explanatory power of the "four-space framework" proposed by the standard in the cultural and legal fields. It can be said that patent practice makes the standard more down-to-earth, and standard theory makes the patent more general, which is a benign interaction between the two.
Second, in terms of white-box evaluation, the DIKWP standard has formulated a series of indicators and processes for evaluating artificial consciousness systems. These include cognitive-level comprehension tests, creativity's 3-Not problem evaluation, security vulnerability detection, debate reasoning competence, etc., almost covering all the advanced capabilities of AI systems. The patent directions of Professor Yucong Duan's team correspond to these evaluation dimensions one by one: the cognitive semantic model direction improves the comprehensiveness of understanding and reasoning, and can cope with the cognitive understanding test proposed by the standard; the dual-loop artificial consciousness architecture and cognitive operating system directly meet the standard's requirements for white-box explainability, making evaluations such as subjective feeling and creativity have an executable platform; complex semantic networks and AI governance technology ensure the realization of security and ethical indicators. In creativity evaluation, the standard introduces the TRIZ method to detect AI's innovation ability, and the team's patent "A TRIZ innovation plan generation method for DIKWP levels" is precisely the integration of TRIZ and DIKWP to improve AI's problem-solving and invention innovation ability. This shows that the patented technology is highly consistent with the standard's requirements. For example, in the creative evaluation in Chapter 6 of the standard, it is proposed to use DIKWP-TRIZ combination to solve the "three-no" problem of AI innovation (no novelty, no meaning, no value), and the team's corresponding patent provides an algorithm implementation, enabling AI to mine contradictions and generate innovative solutions based on the DIKWP model. This close combination ensures that when the standard is implemented in the future, there will be mature technical solutions available, so that it will not be vague.
Third, in terms of smart cities and ISO standard systems, DIKWP technology shows great potential. ISO standards related to smart cities (such as ISO 37122:2019 Sustainable cities and communities - Indicators for smart cities) currently focus more on the statistics of data indicators. However, with the in-depth application of AI in urban management, future standards may need to cover the assessment of the intelligence level of urban AI systems. The DIKWP model happens to provide such a framework: one can imagine that a smart city can be regarded as a macroscopic DIKWP system, where the city's sensor network provides Data, the government information system generates Information, the expert knowledge base precipitates Knowledge, the auxiliary decision-making system forms Wisdom, and the government's strategic goals represent Purpose. Through standardized evaluation methods, the capabilities of this "city AI system" at all levels can be measured, such as data collection coverage, information fusion degree, knowledge completeness, wisdom decision-making accuracy, and the consistency of Purpose and decision-making. The complex semantic network patents of Professor Yucong Duan's team have already built city-level DIKWP applications (such as environmental decision support) in practice, providing a prototype for the smart city AI evaluation system. If an international standard for a Smart City AI Maturity Model is formulated in the future, DIKWP is undoubtedly one of the ideal blueprints. In addition, in the directions of Safe City, Digital Government, and other ISO standards, the white-box explainability and ethical alignment characteristics of DIKWP can meet the standard's requirements for transparency and fairness. For example, draft standards such as ISO/IEC 24029 (AI trustworthy process assessment) emphasize process transparency, and the DIKWP cognitive OS provides specific implementation means. Overall, the combination of DIKWP technology with smart cities and ISO standards will help to define and evaluate intelligent systems across scales: whether it is a microscopic single AI model or a macroscopic urban intelligent agent, it can be measured with a unified indicator system. This unity is a good medicine to break through the current dilemma of fragmented AI standards.
Finally, from a strategic height, the DIKWP standard and patents jointly build China's discourse system in the field of international artificial intelligence standards. Currently, the global AI standard landscape has not yet been finalized, and China already has a certain influence in some fields (such as facial recognition, smart cities). By promoting DIKWP to become an internationally accepted evaluation framework, China has the opportunity to seize the standard high ground in the emerging fields of artificial consciousness and strong artificial intelligence. The efforts of Professor Yucong Duan's team are precisely in this direction: they not only develop patented technologies, but also take the lead in establishing the Networked DIKWP Artificial Intelligence Evaluation International Standardization Committee (DIKWP-SC), as well as organizations such as the World Artificial Awareness Conference (WAC). The establishment of these organizations and the release of a series of draft standards show that Chinese experts are actively shaping the rules of the game, not just following. It is particularly worth mentioning that Professor Yucong Duan has expressed his willingness to open up some patents free of charge to promote standard formulation. This means that DIKWP core technology is expected to enter the public domain as a basic specification and be adopted globally. From the perspective of promoting the common development of technology and humanity, this open attitude will increase the probability of the standard being accepted, and also demonstrate China's responsibility as a major AI country.
In summary, the DIKWP standard and the team's patents have formed a perfect combination of theory and practice, standard and implementation. It embodies a paradigm of "standards lead innovation, and innovation feeds back standards." Under this paradigm, smart cities and the ISO standard system will all benefit from it: standards have technical handles, technology has promotion channels, industry has compliance basis, and regulation has evaluation tools. It is foreseeable that in the near future, as the DIKWP standard may rise to become a formal international standard such as ISO/IEC (currently, related series of draft standards have been released on platforms such as ResearchGate), the patented technology of Professor Yucong Duan's team will be adopted globally as a best practice. This is not only the team's own success, but also a major contribution of China to the global development of AI.
Future Outlook
Looking to the future from the node of 2025, the technical landscape constructed by the DIKWP Artificial Consciousness Team presents a grand blueprint for leading the future development of AI. Academically, their philosophy of integrating data, knowledge, wisdom, and Purpose provides a brand-new path for cracking the ultimate problem of artificial intelligence—machine consciousness. It is conceivable that with the in-depth research and open sharing of these patents, more scientific research forces around the world will be invested in the exploration of the DIKWP model, for example, verifying whether AI can truly produce phenomena similar to self-awareness through the dual-loop architecture, and whether DIKWP semantic mathematics can be expanded into a new cognitive computing theory. New interdisciplinary subjects may be born from this, such as "Semantic Mathematics and Cognitive Science," "Artificial Consciousness Engineering," etc., promoting another leap in basic AI theory. In the field of education, this series of achievements will also feed back into talent cultivation: in future AI textbooks, the DIKWP model and semantic mathematics may become standard chapters, and the students trained will have a more comprehensive understanding of the entire process of AI from data to wisdom.
Industrially, the application prospects of DIKWP technology are equally exciting. First, we may see the emergence of a new generation of artificial intelligence basic platforms. For example, a general-purpose AI platform integrating the DIKWP cognitive operating system, on which enterprises can quickly customize and develop explainable, controllable AI applications with a certain degree of autonomy. This will subvert the current industrial pattern dominated by "black-box models" and bring a comprehensive upgrade in quality and security to AI software. Second, artificial consciousness products are expected to emerge. From intelligent assistants to robot butlers, and then to virtual digital humans, these future products, if built with the DIKWP dual-loop architecture and semantic network, will show an unprecedented level of intelligence: not only can they communicate with people, but they can also self-reflect and continuously evolve. For example, a future home service robot may, while helping its owner with housework, learn the owner's preferences and habits through the Purpose layer, and continuously adjust the way it provides services, truly achieving "reading facial expressions and understanding intentions." Once such products appear, they will trigger a new round of industrial revolution, and their market size will be limitless. Third, in key industry empowerment, DIKWP technology will play the role of "AI's central nervous system." For example, in the medical field, it is expected to create a clinical decision support system with a cognitive closed loop. Doctors and AI will collaborate in diagnosis and treatment, reducing the misdiagnosis rate to an extremely low level; in the financial field, intelligent investment advisors will be able to explain the basis of each investment decision, and risk control will be more precise; in the field of urban governance, the urban AI hub will make every decision of municipal management traceable and transparent. These applications will greatly improve the efficiency and trust of various industries, bringing huge economic and social benefits.
In terms of international competition and cooperation, the DIKWP technology system creates an opportunity for China to achieve "corner overtaking" in the global AI field. Currently, the United States and the European Union have advantages in AI underlying technology and rules, but on the new track of artificial consciousness and explainable AI, the DIKWP system provides a window of opportunity for China to lead. If we can accelerate the industrial transformation and standardization of these patents, and promote this framework through international cooperation, we will have the opportunity to make it a global universal paradigm, and then occupy a dominant position in the discourse system of the next stage of AI. It is gratifying that Professor Yucong Duan's team is already actively building an international cooperation network, attracting researchers from all over the world to participate in the discussion of DIKWP standards through platforms such as the World Artificial Awareness Conference (WCAC). This indicates that in the next few years, a series of international alliances and transnational projects will be launched around DIKWP to jointly tackle the difficult problems of artificial consciousness. China should continue to support such civil and academic diplomacy, and shape an international image of "Responsible AI" through technology export. At the same time, it is necessary to be vigilant and respond in a timely manner to the competition from similar frameworks that may rise rapidly in other countries, and maintain the leading and open nature of the DIKWP system, so as to promote cooperation with openness, and promote standards with cooperation.
Finally, regarding the ultimate goal of artificial intelligence, AGI (Artificial General Intelligence), DIKWP gives an exciting path. It suggests to us that AGI must not only have powerful computing power and large-scale model support, but also have a human-like cognitive structure, knowledge iteration mechanism, and value guidance. DIKWP is precisely preparing for this in terms of architecture and mathematics. It is conceivable that when stronger pre-trained models and richer knowledge bases are integrated with the DIKWP system in the future, the AI born will be able to break through the current narrow task scope and become a truly generalist-type intelligent agent. It will be able to transfer learning across different fields, integrate knowledge like a human, and because it is purpose-driven and has a feedback closed loop, it will have self-evolution capabilities, avoiding falling into meaningless consumption of computing power. Once this prototype of AGI appears, humanity will enter a new era of artificial intelligence. In that era, AI will no longer be just a tool, but more like our partner and even a form of digital life. The design of DIKWP, which advocates letting AI follow human Purpose and values, will ensure that these "new lives" grow in a direction beneficial to mankind. This is perhaps the more profound significance that the DIKWP model brings to us: it provides us with a paradigm for how to shape AI, so that before humans create intelligence more powerful than themselves, they have already implanted in it the genes of ethics and controllable mechanisms.
Summary
In summary, the patent portfolio of the DIKWP Artificial Consciousness Team revolves around directions such as cognitive semantic models, artificial consciousness architecture, cognitive operating systems, complex semantic networks, and AI governance and security, forming an innovative ecosystem that is both breaking through in each area and mutually supportive. These patents, with the DIKWP model as their common foundation, theoretically achieve a semantic closed loop from data to Purpose, and practically overcome the core AI difficulties such as explainability, autonomy, and security. With the help of the bridging role of the DIKWP international standard, the team's technical achievements are aligned with the global AI evaluation system, especially showing great potential in smart city and AI ethics standards. It can be said that the DIKWP system points out a broad path for artificial intelligence to move from "usable" to "controllable" and "trustworthy": letting AI have both wisdom and talent, as well as a heart of benevolence and the ability of self-reflection.
Of course, any cutting-edge exploration faces unknown challenges. The DIKWP model and artificial consciousness research are still in their early stages, and there is still a lot of work to be done in verification and performance optimization in actual large-scale systems. For example, the efficiency of the dual-loop architecture on super-large models, the robustness of the DIKWP semantic network in an open environment, and the effectiveness of the semantic mathematics model in extreme cases (such as brand new concepts), all need further research and improvement. But there is no doubt that the set of ideas and technical frameworks pioneered by Professor Yucong Duan's team provides valuable wealth and tools for successors. Just like the spirit embodied by their selfless opening of patents and promotion of standards, the future of artificial intelligence requires the collaboration and sharing of all mankind. The story of DIKWP confirms a philosophy: Standards lead innovation, and mission drives technology. When we have the correct vision and norms, the building blocks of technology can be built up layer by layer, and will eventually construct the grand edifice leading to AGI.
The era of humans and machine wisdom dancing together has arrived. Standing at this historical juncture, the exploration of the DIKWP Artificial Consciousness Team provides us with an anchor to grasp the future. Looking ahead, a general artificial intelligence integrating the DIKWP cognitive architecture will appear before us in a knowable, controllable, and trustworthy form, serving society. And looking back at the present, the realization of all this is inseparable from the foundation stones laid by those visionaries. The series of patents and standards of the DIKWP team are precisely such shining pearls among the foundation stones. We have reason to believe that in the near future, these pearls will converge into a bright galaxy guiding the development of AI, illuminating humanity's new voyage to a wisdom civilization.
References:
·Duan, Yucong, et al. International Test and Evaluation Standards for Artificial Intelligence based on Networked Data-Information-Knowledge-Wisdom-Purpose (DIKWP) Model, 2025, etc.
·Phoenix New Media Region. "Professor Yucong Duan: DIKWP Artificial Consciousness Model Leads the Future of AI, 114 Patents Await Industrial Landing." China Rong Media Industry Net, 2025, etc.
·Sina Finance. "Perspective on Human-Machine Integration: Exploration of Multi-domain Applications of the DIKWP Model." Sci-Tech China, 2025, etc.
·Zhihu Column. "Business Value Example Evaluation of Professor Yucong Duan's Authorized Invention Patents." 2024, etc.
·ResearchGate. "List of 117 Authorized Patents of Professor Yucong Duan's DIKWP Artificial Consciousness International Team." 2025, etc.
Citation Sources:
·Professor Yucong Duan: DIKWP Artificial Consciousness Model Leads the Future of AI, 114 Patents Await Industrial Landing_Phoenix New Media Region_Phoenix New Media, https://i.ifeng.com/c/8i7jv0YL0ic
·Perspective on Human-Machine Integration: Exploration of Multi-domain Applications of the DIKWP Model_Sina Finance_Sina.com, https://finance.sina.com.cn/jjxw/2025-01-21/doc-ineftfic8111559.shtml
·DIKWP Model and Proactive AI "Self": Construction Logic, Technical Implementation, and Human-Machine... - Zhihu Column, https://zhuanlan.zhihu.com/p/1928490993335903386
·Business Value Example Evaluation of Professor Yucong Duan's Authorized Invention Patents - Zhihu Column, https://zhuanlan.zhihu.com/p/1888912700140155659
·(PDF) List of 117 Authorized Patents of Professor Yucong Duan's DIKWP Artificial Consciousness International Team, https://www.researchgate.net/publication/389389952_DIKWPrengongyishiguojituanduiduanyucong117jianyishouquanzhuanliliebiao
·Professor Yucong Duan's DIKWP Artificial Consciousness International Team, http://www.yucongduan.org/
·Chio-Hero International (00381.HK) Chief Scientist's 241 Invention Patents Commercially Valued at Over 355 Million USD, https://www.moomoo.com/hans/news/post/46107803
·Analysis report on 360 Search's structured Q&A suspected of infringing on graph-enhanced semantics patent - Zhihu Column, https://zhuanlan.zhihu.com/p/1933136538574758710
·DIKWP Related Authorized Invention Patent List and Comprehensive Valuation Analysis Report - Yucong Duan's Blog, https://wap.sciencenet.cn/blog-3429562-1479850.html?mobile=1
