Project Name: "Smart Medical Care" Active Medical Intelligent Service SystemProject
Abstract: This project aims to build an "active AI" digital health service system for intelligent medical/health care scenarios, and realize the paradigm transition from passive Q&A to active medical care. The system is based on the DIKWP five-layer artificial consciousness model proposed by Professor Yucong Duan as the core framework, which organically integrates data, information, knowledge, wisdom and intention. With the help of the semantic mathematics engine, the system can perform semantic understanding and reasoning on multi-source health data, and realize self-awareness and purpose-driven decision-making feedback. The digital agent can be used as a digital health companion or AI consultation assistant, with the ability to actively perceive the user's health status, cognitively assess potential risks, intent-driven personalized intervention, and closed-loop feedback self-optimization. The project innovatively emphasizes the active service characteristics of artificial consciousness AI, which is obviously different from traditional passive response AI, and is expected to significantly improve the intelligence, initiative and reliability of medical escorts.

1. Project background and social pain points

China is facing severe challenges such as aging and the high incidence of chronic diseases, and the medical and health care fields urgently need intelligent means to provide continuous and efficient services. However, most of the existing medical AI tools are passive consultation or monitoring tools, which have the following pain points:

Limitations of passive services: Traditional medical consultation dialogue systems are often limited to pattern matching or superficial understanding, lacking a grasp of the deep semantics and intentions of patients' discourse, and it is difficult to detect hidden symptoms, contradictory information, or patients' real concerns in time. As a result, in the face of complex scenarios such as chronic disease management and elderly care, it is difficult for AI to provide timely warning and personalized response, and cannot meet the needs of proactive medical services.

Cognitive silos: At present, medical data is fragmented on different devices and platforms, and it is difficult to integrate information to form overall health cognition, and traditional AI cannot transform multi-source data into meaningful knowledge and decision-making wisdom. Data such as patient sign monitoring, electronic medical records, lifestyle, etc., are underutilized to proactively assess health risks.

Shortage of human resources: In the face of a large population of chronic diseases and disabled elderly people, it is difficult for medical staff to provide one-on-one continuous care in a timely manner. In the passive response mode, the patient often waits until the patient has obvious symptoms to intervene, missing the window for early intervention. How to use "active AI" to achieve early detection and early intervention of key groups has become a social necessity.

The above pain points show that traditional intelligent systems that rely only on passive response are no longer enough to support the complex needs in the scenario of "intelligent medical care and health care". We need to introduce a new paradigm of artificial consciousness and active intelligence, so that AI has human-like self-perception and purpose-driven capabilities, and truly participates in the closed loop of medical services.

2. System architecture design (DIKWP active intelligence architecture)

Figure 1: The system architecture of this project adopts the DIKWP five-layer artificial consciousness model, and the functions and intelligent behaviors of each layer show the purpose. The red dotted line represents the feedback and regulation of the upper level to the lower level, realizing the cognitive closed loop.

This system adopts the original DIKWP five-layer artificial consciousness cognitive structure construction architecture of Professor Yucong Duan. The functional modules and corresponding AI capabilities at each level are as follows:

Data layer: It is responsible for sensing and collecting raw data, including physiological signs of wearable devices, medical instrument test results, patient voice/text descriptions and other raw information. The data layer preprocesses discrete signals to provide the underlying input to the upper layer. For example, the heart rate, blood pressure, blood sugar, and daily activity records of the elderly are continuously collected.

Information Layer: Transforms raw data into meaningful units of information, extracting symptoms, events, and context. For example, symptom information such as "headache for three days" and "nausea and vomiting" can be identified from patient conversations. Through multimodal recognition and entity extraction, the information layer forms a structured information graph (triples, etc.), which correspondingly answers questions such as "What/When/Where", i.e., "What happened". At the information layer, the system can combine common sense to complete the incomplete information to capture the patient's implicit purpose.

Knowledge: Integrate the medical knowledge base and patient-personalized knowledge. Based on the extracted information, the knowledge layer uses medical knowledge graph and rule reasoning to carry out diagnostic inference and implicit relationship mining. For example, associating "headache" and "nausea" to possible conditions (e.g., migraine). The knowledge layer answers "why is this happening", providing causal connections and logical reasoning support. Formally, it can be expressed as K = Infer(Info, KnowledgeBase). The system has a built-in large-scale and updatable medical knowledge graph, covering disease-symptom, drug-mechanism of action and other knowledge to support accurate reasoning.

Wisdom layer: responsible for decision optimization and behavior planning. After obtaining a variety of diagnostic conclusions or treatment plans, the Wisdom layer comprehensively considers multiple objective factors (such as risk/benefit, patient preference, urgency, etc.) to make trade-offs and select the optimal solution. The Wisdom layer acts as the doctor's decision-making process, answering the question of "what to do". For example, for patients with chronic diseases, the Wisdom layer will weigh the pros and cons of medication adjustments, or decide when to sound an alarm based on the risk of falls in older adults. The Wisdom layer also has the ability to adaptively adjust: if it is found that the information is not enough to make a decision, it can request more data from the information layer through a feedback mechanism (e.g., ask the information layer to "ask the patient if the patient has blurred vision" to assist in diagnosis). This W→I (Wisdom Layer) mechanism ensures that the system does not stay in the blind spot.

Purpose/Intent: This is the highest layer added to the DIKWP model. The purpose layer clarifies the ultimate goal and motivation of the system service, which is the driving force of the entire intelligent behavior. In the medical escort scenario, it is necessary not only to understand the patient's purpose, but also to let AI have its own "service purpose", such as "helping patients alleviate pain" or "continuously monitoring and ensuring patient safety". The purpose layer translates these goals into guidance constraints for the lower layers, for example, for the purpose of "preventing complications", the data layer is adjusted to focus only on relevant signs, and the Wisdom layer prefers conservative solutions. At the same time, the Purpose layer receives feedback from each layer to evaluate the overall goal achievement, and if necessary, it can initiate the self-regulation of the metacognitive loop (i.e., the "dual cycle" architecture to achieve self-monitoring and reflection). The purpose layer ensures that AI behavior is always centered on the stated medical service goals, and has embedded ethical and safety constraints to make AI decisions in line with human values.

Through the above five-layer architecture, the system has formed a "active perception-semantic understanding-knowledge reasoning-wisdom decision-purpose-driven". Closed-loop intelligence. The mesh bidirectional feedback is embodied in the system as follows: the high-level can correct and guide the low-level processing (such as the Purpose layer guides data collection, the Wisdom layer corrects the information interpretation), and the low-level change can also gradually affect the decision-making. This makes AI human-like self-aware and adaptable: every step of the decision-making process can be traced and explained, and humans can see the processing logic of the system at all levels. Especially in the medical field, this explainable cognitive architecture helps build trust in AI among doctors and patients.

The system architecture also supports modular deployment and expansion. The functional modules of each layer can be independently upgraded or replaced as needed, for example, the introduction of new sensor data sources only needs to expand the data layer interface, and the upgrading of the medical knowledge base can directly enhance the reasoning ability of the knowledge layer. This modular design allows us to deliver DIKWP cognitive capabilities in the form of "XaaS": i.e., embedding a layer or entire functionality of the system as a service across different healthcare platforms via standard APIs. For example, the hospital's electronic medical record system can call the "Purpose Engine" service to analyze the purpose of doctor-patient communication, and the home care robot can connect to our "Active Semantic Communication" module to realize conversational companionship. This DIKWPaaS concept ensures that the system can be flexibly adapted to a variety of application scenarios in the fields of "smart healthcare" and "smart health care".

3. Technology highlights and core innovations

This project integrates the cutting-edge theories of artificial intelligence with the needs of medical applications, and has the following technical highlights and innovations:

(1) Active artificial consciousness architecture: It is the first to introduce the artificial consciousness model into medical AI, and replace the traditional shallow perception model with the DIKWP five-layer network cognitive structure. This architecture gives the system self-awareness and purpose-driven capabilities, enabling AI to upgrade from a passive tool to an active service subject. The closed-loop design of two-way feedback ensures that when the system encounters incomplete or contradictory information, the high-level purpose can guide the low-level to supplement the data or adjust the understanding, rather than stagnating. Compared with the traditional black-box model, the decision-making at each level of the system is transparent and traceable, which meets the white-box evaluation standards of artificial consciousness, and can comprehensively evaluate the ability of AI at all cognitive levels.

(2) Semantic mathematics and explainable AI: The system integrates a semantic mathematics engine to formally define the representation and reasoning of semantic knowledge. Each step of semantic evolution is supported by strict mathematics to avoid relying on empirical rules, so as to reduce the illusion and bias of large models. Using semantic mathematics, we build a semantic firewall for LLM and other components: the generation process is decomposed into the semantic space of each layer of DIKWP for verification, so as to ensure that the output content is consistent with medical knowledge and purpose, and improve the accuracy and credibility of the results. The evolution process of the whole system is well-documented, and has good interpretability and verifiability.

(3) Purpose Engine and Active Semantic Communication: The Purpose Engine module is introduced, and the relevant patent achievements of Professor Duan are integrated to achieve a deep understanding of human Purpose and the dynamic generation of AI behavior Purpose. For example, through the patented technology of "Intelligent Reminder Mechanism for Matching Scenes, Events, Characters and Purpose" (Patent No. ZL201911277319.8), our system can automatically adjust reminder and intervention strategies for different user situations, so as to be intimate and not overly intrusive. At the same time, the system has the ability of active semantic communication, which can not only passively answer patients' questions, but also actively initiate dialogue and exchange when abnormalities are detected. For example, when the elderly live alone and have abnormal physiological indicators, the system will actively ask about subjective feelings or prompt measures to be taken. This interaction process relies on the active semantic generation technology proposed by Professor Duan's team, and related patents such as "Purpose-driven multimodal DIKW content transmission method" (patent number: ZL202110867169.7) support the integration of the purpose of the transmitter, receiver and system in the communication process, and dynamically balance the information content and interaction strategy. Through active semantic communication, AI can mimic empathetic healthcare professionals to create more natural and effective communication with users.

(4) Knowledge fusion and adaptive decision-making: The system has built-in large-scale medical knowledge graph and multi-source health data fusion technology (related patents such as "DIKW model construction method and device for purpose computing and reasoning", patent number: ZL202110430285.2), to realize data- Information-knowledge integration. Through the fusion of knowledge and data, the system can rely on knowledge completion to understand in the case of sparse or noisy data, and update the knowledge base through online learning when new situations arise. At the decision-making level, the system uses a multi-objective optimization algorithm to balance the safety and effectiveness of medical decision-making. For example, for chronic disease management, the Wisdom layer integrates the value evaluation model in the patent "Multi-dimensional Value-oriented Object-Oriented Numerical Calculation Method for Purpose" (patent number: ZL201911251907.4) to weigh the patient's quality of life and treatment risks, and give a personalized plan. The core of this intelligent decision-making not only considers medical standards, but also integrates patient wishes and multi-dimensional value judgments, so that interventions are more in line with individual needs.

(5) Modular XaaS platform: We have built a modular architecture to enable DIKWP artificial awareness capabilities to be output as a service on demand. The functions of each layer are encapsulated through standard interfaces, which can be deployed in the cloud to form a "cognitive middle platform". For example, "semantic understanding service" and "purpose decision service" can be provided as medical AI-as-a-service to hospital information systems, nursing home monitoring platforms, rehabilitation apps, etc. This XaaS model can help quickly integrate into the existing industry ecosystem and expand the influence of technology. The patent portfolio of Professor Yucong Duan's team (99 domestic and foreign invention patents and 15 PCT international patents have been authorized) provides a solid underlying technical support and intellectual property moat for the system, enabling us to safely empower the medical industry with the latest artificial awareness AI capabilities in a modular output way.

The above-mentioned technological innovations complement each other to build an intelligent medical system with artificial consciousness as the core and active service as the feature. With the support of core patents, this project is expected to lead a new path of "active medicine" and inject explainable, controllable, and human-centered innovation into medical AI.

4. Application scenarios and service value

This project focuses on the concept of "active medicine", which can be widely used in a variety of scenarios in the field of intelligent medical care and wisdom health care. The system will be of unique value in the following representative scenarios:

Chronic disease management assistant: For patients with chronic diseases such as hypertension and diabetes, the system acts as a personal health management assistant. Through 24-hour monitoring of patients' physiological indicators and living habits, combined with the analysis of medical knowledge at the knowledge level, abnormal indicators or medication compliance problems are actively found. For example, when diabetic patients have high blood sugar fluctuations and insufficient exercise, the system will push intervention suggestions (diet adjustment, exercise reminders) in time, and even contact online doctors for remote consultation. Compared with manual follow-up, the AI assistant can accompany the patient continuously and personally, reducing the incidence of complications. For medical institutions, this kind of proactive management can significantly reduce the hospitalization rate caused by the exacerbation of chronic diseases and save medical insurance expenses.

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Smart Companion for the Elderly: For the elderly who live alone at home or in nursing homes, the system can continue to protect their physical and mental health as a "digital health companion". The data layer is connected to wearable devices and smart home sensors to monitor the heart rate, sleep, and falls of the elderly in real time. The information layer integrates voice assistants to actively care for the emotions and needs of the elderly. If an abnormality is detected (such as leaving bed at night, abnormal heart rate), the system immediately confirms the situation through voice interaction with the elderly, and decides the follow-up action (reminding to take medication, notifying family members, or calling medical care) according to the preset safety purpose of the Purpose layer. In daily life, AI escorts will also chat regularly to relieve emotions and provide personalized health advice (diet, rehabilitation exercises, etc.), so that they can truly be both doctors and friends. This will greatly improve the coverage and quality of elderly care services, reduce the pressure on children and caregivers, and allow the elderly to receive continuous care and psychological support.

Sub-health and psychological intervention: For sub-health groups such as urban white-collar workers, the system can be deployed as a personal health consultant app. The Purpose engine is used to identify potential health demands of users, such as weight loss and anxiety relief, and proactively push targeted suggestions and greetings. For example, when a user stays up late for a long time, the system will intelligently generate a "caring conversation" to remind them to take a break and provide a soothing training video. This proactive, caring service helps to correct unhealthy lifestyles and intervene before the disease occurs (reflecting the idea of "curing the disease" before it occurs). For users with mild psychological problems, AI can identify negative emotional changes through semantic analysis, actively initiate heart-to-heart talks or recommend counseling resources, and provide support and care at an early stage.

Clinical diagnosis and treatment assistance: This system can also be integrated into the hospital's outpatient system as a doctor's "AI consultation assistant". In the process of doctor consultation, the data layer automatically enters the patient's description and examination data, the knowledge layer immediately calls on medical knowledge to provide doctors with differential diagnosis suggestions, and the Wisdom layer optimizes the treatment plan according to the patient's wishes and diagnosis and treatment goals. In particular, the purpose layer of the system can gain insight into the potential purpose in doctor-patient communication, such as the hidden concerns of patients, and remind doctors to respond in a timely manner. This multi-agent collaborative consultation and decision support will improve the accuracy and efficiency of diagnosis, help young doctors make up for lack of experience, and reduce misdiagnosis and missed diagnosis.

Rehabilitation and Nursing Platform: In rehabilitation centers or community nursing, the system can be used as an intelligent rehabilitation coach. By perceiving the patient's rehabilitation training data, the difficulty of the rehabilitation plan was actively adjusted, and verbal encouragement and corrective action guidance were given. For patients who require long-term care, the AI assistant can monitor changes in their condition, identify risks (bedsores, infections, etc.) in advance, and notify caregivers to prevent treatment. This intelligent service improves the real-time and targeted care and reduces the risk of emergencies.

Through the application of the above scenarios, the project will create significant social and economic value: for individuals, it provides round-the-clock digital health protection, enhancing the accessibility and personalized experience of medical services; For hospitals and elderly care institutions, the efficiency and quality of services have been improved, and the health of more people has been guaranteed in the face of manpower shortage; For the entire medical system, promote the transformation from "treating the disease" to "treating the disease before it happens" to reduce the waste of medical expenditure. It is foreseeable that the "active AI + medical" model will play a revolutionary role in the fields of chronic disease management and home care, and contribute scientific and technological strength to the Healthy China strategy.

5. Feasibility and implementation path

Although there is no complete prototype yet, the project has good feasibility in terms of technology and implementation, and a clear implementation path has been formulated:

Phase 1: Core technology research and development (0-6 months). Relying on the existing research results of Prof. Yucong Duan and his team, we will give priority to the development of core modules of the system: (a) Semantic Parsing and Purpose Engine – Based on the existing semantic mathematical model, train the Purpose recognition and generation module in the medical field to achieve in-depth understanding of patient questions and descriptions and the planning of the system response to Purpose; (b) Medical Knowledge Graph and Reasoning – Construct a knowledge graph covering common diseases, symptoms, and drugs, and develop reasoning algorithms to combine knowledge base and LLM to achieve interpretable diagnosis and decision support; (c) Multi-modal data access – Integrate the interfaces of common medical IoT devices and build data collection and standardized processing pipelines. The team will use simulation data and small-scale user trials to continuously iterate the algorithm to ensure that the functions of each layer are coordinated and stable. Relying on the patented algorithm library and open-source model of Professor Duan's team, we are confident that we will be able to build the smallest available version of the DIKWP framework in the short term.

Phase 2: Prototype System Integration & Testing (6-12 months). After the core modules are basically ready, the full-stack integration is carried out to develop the user interaction interface and back-end service architecture. The front-end is planned to use a combination of mobile App and Web to provide functional interfaces such as chat consultation and health dashboards; The back-end adopts the cloud microservice architecture, and the five-layer modules are containerized and deployed, drawing on the idea of "DIKWP cognitive middle platform" to ensure that the modules are loosely coupled and extensible. We plan to work with affiliated hospitals and elderly care facilities to select dozens of patients with chronic diseases and elderly people living alone to carry out closed testing. During the test, the real medical staff verified and corrected the system suggestions, and evaluated the working effect of the system at all levels of data, information, knowledge, Wisdom and Purpose through the artificial awareness white-box evaluation index, and found out the deficiencies and improved. Particular attention is paid to the security and ethical verification of the system to ensure that there are no serious misjudgments or improper interventions. At this stage, we will also continue to optimize the human-computer interaction experience based on the test feedback, so that the elderly and other user groups feel friendly and easy to use.

Phase 3: Scenario development and demonstration application (12-24 months). After verifying the basic functions, gradually expand the application scenarios and implementation paths. First of all, in terms of intelligent medical care and health care, it cooperated with a large elderly care service company to connect the system to its home care platform to provide digital escort demonstration services for hundreds of elderly people. At the same time, in terms of intelligent medical care, we have cooperated with hospitals to deploy outpatient AI assistants to assist in the consultation and triage of common departments. We will establish a remote monitoring and operation and maintenance mechanism, collect system behavior data in real time for analysis, and continuously upgrade and iterate algorithms online (such as fine-tuning the knowledge graph according to the characteristics of people in different regions). In terms of business model, it adopts the form of modular authorization or SaaS subscription, allowing partners to purchase specific functional module services on demand, such as chronic disease management sub-modules, psychological counseling sub-modules, etc., so as to flexibly implement different scenarios.

Phase 4: Standardization and scale up (more than 24 months). After achieving initial results, we began to promote the formulation and large-scale implementation of industry standards. On the one hand, under the guidance of the state and industry associations, summarize the project experience, participate in the formulation of active medical AI interface standards, artificial awareness evaluation standards, etc., and lead the standardized development. On the other hand, seek industrial investment or incubate start-up companies to accelerate productization. Relying on the technical barriers provided by the team's patent pool, we will expand cooperation with medical device manufacturers and health care institutions, embed the system into their products and platforms, and form an ecological alliance. For example, we have cooperated with wearable device companies to develop smart watches equipped with our AI services, and docked with Internet hospital platforms to provide 24-hour AI follow-up services. With the maturity of medical data federated learning and privacy-preserving computing technologies, we will also integrate relevant achievements (such as purpose-driven crowd differential privacy protection and other technologies) to ensure data security and compliance under large-scale applications.

To sum up, the implementation path of this project is clear: first build core capabilities, then verify them in a small area, then expand applications in multiple fields, and finally promote the implementation of industry standards and large-scale applications. In the process of technology and application advancement, the continuous guidance and rich patent technology reserves of Professor Yucong Duan's team will escort the project, so that our "active AI" medical service system can steadily move from the laboratory to industrialization. As Professor Duan said, the DIKWP model and its patented technology are regarded as the key underlying code for the future of AI security, controllability and explainability. We firmly believe that relying on these cutting-edge technologies, this project has the ability to launch feasible demonstration applications in the near future and contribute tangible results to the national smart healthcare strategy.

6. Team structure

The project team is an interdisciplinary university innovation team established under the guidance of Professor Yucong Duan, whose members include master's and doctoral students in the fields of computer science, medicine, biomedical engineering, etc., and have the comprehensive capabilities required to implement this project:

Supervisor: Prof. Yucong Duan (Project General Advisor) – An international leader in the field of artificial consciousness and the founder of the DIKWP artificial consciousness model. Responsible for the top-level design of the project, control the correct implementation of the theory of artificial consciousness, and provide patent technical support.

Team Leader: XX Zhang (Ph.D. candidate, Artificial Intelligence) – Responsible for overall coordination, architecture design and technical research, proficient in knowledge graph and dialogue system, mainly engaged in semantic mathematics engine development under the guidance of Professor Duan.

Algorithm R&D Group: Li XX (Master, NLP), Wang XX (Ph.D., Machine Learning), etc. – Responsible for the R&D and optimization of Purpose recognition, semantic parsing and decision-making algorithms. Mr. Li has participated in the DIKWP model related projects of Professor Duan's team and has rich experience in semantic reasoning. Mr. Wang is good at multimodal learning and will lead the development of physiological signal processing and anomaly detection models.

Medical Consultant & Data Group: XX Liu (Master's degree, medical informatics, with clinical background) – responsible for the construction of medical knowledge graph, medical data annotation and docking with hospitals. A number of other undergraduates assisted with data collection and system testing. The addition of medical consultants ensures that AI decision-making is in line with clinical common sense, and the output content is professionally verified.

Software Engineering Group: Zhao XX (Master's degree, software engineering direction), etc. – responsible for system front-end and back-end development and integration deployment. It includes the development of user applications (App/Web interface), back-end services and database optimization to ensure the ease of use and reliability of the system. Mr. Zhao has participated in the development of medical information systems and will coordinate the team to implement the product-level software architecture.

Operation & Cooperation Group: XX Qian (Master's degree, project management direction) – Responsible for external liaison, scenario demand research and application promotion plan formulation. The members of the team will continue to collect feedback from medical staff and users, adjust the focus of project development, and strive for cooperation opportunities with enterprises and hospitals.

The whole team has a reasonable structure and a clear division of labor, and under the guidance of Professor Duan, it has been condensed into a joint force of "production, education and research". We will maintain close cooperation in the implementation: the algorithm group and the medical team communicate in two-way every day to ensure that the technology meets the medical needs, the software group and the algorithm group are closely connected to ensure the effective implementation of the model, and the management team coordinates and promotes milestones. We are also fortunate to have the support of the Institute of Artificial Consciousness at Hainan University to share its computing resources and patent licensing. This model of "student team + professor guidance" not only trains newcomers, but also provides a reliable talent guarantee for the success of the project.

7. Future planning and industrialization prospects

Looking forward to the future, the "Smart Medical Care" active medical intelligent service system has broad upgrade space and industrialization prospects:

1. Technology upgrade direction: continue to integrate the latest AI technology achievements and continuously enhance the intelligence level and adaptability of the system. On the one hand, we plan to incorporate a larger-scale medical large model (LLM) as a sub-module and domesticate it through the DIKWP framework to improve the naturalness of the system in terms of human-machine dialogue and text generation, while ensuring that it does not deviate from the bottom line of medical knowledge and ethics. On the other hand, we should strengthen cross-modal capabilities, incorporate cutting-edge technologies such as brain-computer interface and biosensing into the data layer, and explore a new model of active medical treatment of "human-machine symbiosis". For example, the DIKWP model of brain region mapping proposed by Professor Duan's team was introduced to integrate neurofeedback into AI decision-making, which is closer to the intuitive judgment of human doctors.

2. Application ecological expansion: The core concept and architecture of the system can be extended to more medical and health care subdivisions to form a modular product matrix. In the future, we will launch customized solutions such as a subsystem for active intervention in Alzheimer's disease (combining memory training and real-time monitoring), and an active health monitoring system for community epidemic prevention and control to meet the needs of different scenarios. At the same time, we will attract third parties to develop innovative applications based on our XaaS capabilities through open APIs and developer platforms, and build a thriving active medical AI ecosystem. For example, a third party can call our Purpose analysis service to develop a mental health robot; Or use our knowledge graph service to build an integrated diagnosis and treatment application of traditional Chinese and Western medicine.

3. Business model and cooperation: In terms of industrialization strategy, we will adopt the model of B2B2C combination. On the one hand, it has in-depth cooperation with large medical groups and pension enterprises to output the system as an overall solution; On the other hand, we work with hardware vendors to embed our AI services into their end products (e.g., smart hearing aids, health care robots) and make a profit in the form of licence licensing or SaaS subscriptions. According to the analysis of the Phoenix report, once the DIKWP cognitive middle platform is combined with the enterprise business, it is expected to bring tens of millions or even hundreds of millions of dollars in contract value every year. Our patent portfolio is systematic and innovative, and has a strong voice in negotiations with potential customers. At the same time, we are also actively seeking government support and scientific research transformation funds, and strive to include some core technologies in the national next-generation AI demonstration application project to accelerate market recognition.

4. Social benefits and brand positioning: We will build "Huiyi Yicare" into a leading active medical AI brand in China, and establish a benchmark image for the positive application of artificial awareness technology. Through the accumulation of successful cases, it will gradually influence the public's and industry's perception of AI - let everyone see that AI can not only answer questions, but also actively care for people. This will help alleviate concerns about the ethical risks of AI and pave the way for AI to penetrate into the healthcare sector. On a global scale, our exploration is also forward-looking: explainable and controllable active AI is becoming the vane of international AI safety and ethics research. The results of this project are expected to be exported overseas, participate in international competition, and win the right to speak in technology for China in the era of artificial general intelligence (AGI).

In summary, with the maturity of technology and the expansion of applications, the "Smart Medical Care" project has the potential to grow into an active intelligent hub of the "medical metaverse", connecting individuals and medical resources, and creating a digital health guardian that will never go offline. We believe that with the support of leaders and experts at all levels, and with the deep accumulation of Professor Yucong Duan's team in the field of artificial consciousness, this project will definitely stand out in the 3rd National Artificial Intelligence Application Scenario Competition in 2025, and then create a new situation for the industrialization of Wisdom medical care in China, and achieve a double harvest of social benefits and commercial value.