System

Abstract

An object of the system according to the embodiment is to provide a preventive measure against future infectious diseases and a quick intervention plan.SOLUTION: A system according to an embodiment includes a scenario generation unit, an intervention evaluation unit, and a policy suggestion unit. The scenario generation part predicts an unknown pathogen pattern. The intervention evaluation unit evaluates a countermeasure based on the scenario generated by the scenario generation unit. The policy proposal unit proposes a policy on the basis of the result evaluated by the intervention evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] The current technology lacks preventative measures and rapid intervention plans for future infectious diseases, leaving room for improvement.

[0005] The system of the embodiment aims to provide preventative measures and rapid intervention plans for future infectious diseases. [Means for solving the problem]

[0006] The system according to the embodiment includes a scenario generation unit, an intervention evaluation unit, and a policy proposal unit. The scenario generation unit predicts unknown pathogen patterns. The intervention evaluation unit evaluates countermeasures based on the scenario generated by the scenario generation unit. The policy proposal unit proposes policies based on the results of the evaluation by the intervention evaluation unit. [Effects of the Invention]

[0007] Systems according to embodiments can provide preventative measures and rapid intervention plans for future infectious diseases. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The infectious disease prevention system according to an embodiment of the present invention is a system that uses generative AI to predict unknown pathogen patterns, construct new infection scenarios, quickly evaluate and propose countermeasures, and support policymaking. As a result, the infectious disease prevention system can realize preventive measures and rapid intervention plans for future infectious diseases, improving crisis prediction capabilities and reducing public health risks.

[0029] An infectious disease prevention system according to an embodiment includes a scenario generation unit, an intervention evaluation unit, and a policy proposal unit. The scenario generation unit predicts unknown pathogen patterns. For example, the generation AI analyzes past infection data and environmental data to predict future infectious disease outbreak patterns. The generation AI also simulates how a new virus will spread and constructs an infection scenario based on the results. The generation AI, for example, receives prompts including past infection data and environmental data as input and generates a scenario based on the prompts. The intervention evaluation unit evaluates countermeasures based on the scenario generated by the scenario generation unit. For example, the generation AI simulates the extent to which a lockdown in a specific area will suppress the spread of infection and proposes optimal countermeasures based on the results. The generation AI, for example, receives prompts including an evaluation of countermeasures based on the scenario as input and evaluates and proposes countermeasures based on the prompts. The policy proposal unit proposes policies based on the results evaluated by the intervention evaluation unit. For example, the generation AI simulates optimal policies to prevent the spread of infection and proposes them to policymakers based on the results. The generation AI, for example, receives prompts including policy proposals based on simulation data as input and proposes policies based on the prompts. As a result, the infectious disease prevention system according to the embodiment can predict unknown pathogen patterns and quickly evaluate and propose countermeasures to prevent the spread of infectious diseases.

[0030] The scenario generation unit can analyze past infection data and environmental data to predict future patterns of infectious disease outbreaks. For example, the generation AI analyzes past infection data and environmental data to predict future patterns of infectious disease outbreaks. For example, the generation AI analyzes past data on the number of infected people, infection routes, and regional data to predict future patterns of infectious disease outbreaks. The generation AI also analyzes environmental data such as temperature, humidity, and population density to predict future patterns of infectious disease outbreaks. Furthermore, the generation AI uses statistical models and simulation models to predict future patterns of infectious disease outbreaks. This makes it possible to predict future patterns of infectious disease outbreaks by analyzing past data.

[0031] The intervention evaluation unit can generate scenarios that take into account different climatic conditions and seasonal variations based on the scenario predicted by the generation AI. For example, the intervention evaluation unit generates scenarios that take into account different climatic conditions and seasonal variations based on the pathogen patterns predicted by the generation AI. For example, the generation AI compares influenza epidemic patterns in winter with dengue fever epidemic patterns in summer and proposes preventive measures appropriate for each season. The generation AI also predicts infection spread patterns in specific areas by taking into account the climatic characteristics of the region. Furthermore, the generation AI generates scenarios that take into account seasonal infection spread patterns and seasonal pathogens. In this way, by generating scenarios that take into account climatic conditions and seasonal variations, more realistic countermeasures can be proposed.

[0032] The intervention evaluation unit can simulate the usage of different medical resources based on the scenario predicted by the generation AI and propose optimal resource allocation. The intervention evaluation unit, for example, simulates the usage of different medical resources based on the scenario predicted by the generation AI and proposes optimal resource allocation. For example, the generation AI performs a simulation to optimize the number of hospital beds and the allocation of medical staff, and proposes optimal resource allocation based on the results. The generation AI also simulates the placement and usage of medical equipment and proposes optimal resource allocation. Furthermore, the generation AI proposes optimal resource allocation based on the utilization rate and demand forecast of medical resources. This makes it possible to reduce the burden on medical facilities by proposing optimal allocation of medical resources.

[0033] The intervention evaluation unit can simulate infection spread patterns in areas with different socioeconomic backgrounds based on the scenario predicted by the generation AI. For example, the intervention evaluation unit simulates infection spread patterns in areas with different socioeconomic backgrounds based on the scenario predicted by the generation AI. For example, the generation AI analyzes the difference in the rate of infection spread between urban and rural areas and proposes countermeasures appropriate for each area. The generation AI also simulates infection spread patterns taking into account income level, education level, and the economic situation of the region. Furthermore, the generation AI simulates infection spread patterns in specific administrative districts and communities and proposes countermeasures appropriate for each region. This makes it possible to propose countermeasures appropriate for each region through simulations that take socioeconomic background into account.

[0034] The intervention assessment unit can assess the risk of animal-to-human infection based on the scenario predicted by the generative AI and propose animal protection measures. The intervention assessment unit can, for example, assess the risk of animal-to-human infection based on the scenario predicted by the generative AI and propose animal protection measures. For example, the generative AI assesses the risk of contact between wild animal habitats and human settlements and proposes animal protection measures based on the results. The generative AI also assesses the risk of infection for livestock and pets and proposes appropriate isolation measures and vaccinations. Furthermore, the generative AI analyzes the health status and infection routes of animals and proposes strengthening the surveillance system. This makes it possible to assess the risk of animal-to-human infection and propose appropriate animal protection measures.

[0035] The policy proposal unit can simulate collaboration scenarios between different medical institutions based on the countermeasures proposed by the generation AI and propose the optimal collaboration method. For example, the policy proposal unit can simulate collaboration scenarios between different medical institutions based on the countermeasures proposed by the generation AI and propose the optimal collaboration method. For example, the generation AI can perform a simulation to optimize patient transfer plans between hospitals and propose the optimal collaboration method based on the results. The generation AI can also simulate information sharing methods between medical institutions and propose the optimal collaboration procedure. Furthermore, the generation AI can simulate the frequency and scope of collaboration between medical institutions and propose the optimal collaboration method. This enables the efficient use of medical resources by optimizing collaboration between medical institutions.

[0036] The policy proposal department can simulate different vaccination strategies based on the countermeasures proposed by the generation AI and propose the optimal vaccination plan. For example, the policy proposal department can simulate different vaccination strategies based on the countermeasures proposed by the generation AI and propose the optimal vaccination plan. For example, the generation AI performs a simulation to select priority vaccination groups and optimize the vaccination schedule, and proposes the optimal vaccination plan based on the results. The generation AI also simulates the vaccination location, vaccination method, and vaccination timing and proposes the optimal vaccination plan. Furthermore, the generation AI simulates the priority and vaccination schedule of those to be vaccinated and proposes the optimal vaccination plan. In this way, by simulating vaccination strategies and proposing the optimal vaccination plan, it is possible to make effective use of vaccines.

[0037] The policy proposal department can simulate operating scenarios for different educational institutions based on the response measures proposed by the generation AI and propose optimal educational management measures. For example, the policy proposal department can simulate operating scenarios for different educational institutions based on the response measures proposed by the generation AI and propose optimal educational management measures. For example, the generation AI can conduct a simulation to optimize the balance between online and face-to-face classes and propose optimal educational management measures based on the results. The generation AI can also simulate class formats and operating procedures and propose optimal educational management measures. Furthermore, the generation AI can simulate infection control measures implemented by educational institutions and propose optimal educational management measures. In this way, by simulating operating scenarios for educational institutions and proposing optimal educational management measures, it is possible to prevent the spread of infection while maintaining the quality of education.

[0038] The policy proposal department can evaluate the impact of different legal regulations based on the simulation data provided by the generation AI and propose optimal legal responses. For example, the policy proposal department can evaluate the impact of different legal regulations based on the simulation data provided by the generation AI and propose optimal legal responses. For example, the generation AI can simulate the effects of new legal regulations to prevent the spread of infection and propose optimal legal responses based on the results. The generation AI can also evaluate the impact of existing legal regulations and propose necessary legal amendments or the introduction of new regulations. Furthermore, the generation AI can simulate the introduction of penalties for legal regulations or proposals for legal amendments and propose optimal legal responses. In this way, the effectiveness of legal responses can be maximized by evaluating the impact of legal regulations and proposing optimal legal responses.

[0039] The policy proposal department can evaluate the impact of different economic policies based on the simulation data provided by the generation AI and propose optimal economic measures. For example, the policy proposal department can evaluate the impact of different economic policies based on the simulation data provided by the generation AI and propose optimal economic measures. For example, the generation AI can simulate policies to balance infection prevention with economic activity, and propose optimal economic measures based on the results. The generation AI can also evaluate the impact of fiscal policy, monetary policy, and employment measures, and propose optimal economic measures. Furthermore, the generation AI can simulate the effects of economic support measures and tax incentives, and propose optimal economic measures. In this way, the impact of economic policies can be evaluated and optimal economic measures proposed, thereby stabilizing the economy.

[0040] The policy proposal department can simulate different international cooperation scenarios based on the simulation data provided by the generation AI and propose optimal international cooperation measures. The policy proposal department, for example, can simulate different international cooperation scenarios based on the simulation data provided by the generation AI and propose optimal international cooperation measures. For example, the generation AI can simulate international collaboration methods for infectious disease control and propose optimal international cooperation measures based on the results. The generation AI can also simulate international support measures and methods for implementing joint research and propose optimal international cooperation measures. Furthermore, the generation AI can simulate international collaboration methods and the scope of cooperation and propose optimal international cooperation measures. In this way, by simulating international cooperation scenarios and proposing optimal international cooperation measures, it is possible to strengthen international collaboration in infectious disease control.

[0041] The policy proposal department can evaluate the impact of different health insurance systems based on the simulation data provided by the generation AI and propose optimal health insurance policies. For example, the policy proposal department can evaluate the impact of different health insurance systems based on the simulation data provided by the generation AI and propose optimal health insurance policies. For example, the generation AI can evaluate the effectiveness of a health insurance system specialized in infectious disease countermeasures and propose optimal health insurance policies based on the results. The generation AI can also evaluate the impact of public and private health insurance and propose optimal health insurance policies. Furthermore, the generation AI can simulate the setting of insurance premiums and the expansion of benefit scope and propose optimal health insurance policies. This makes it possible to maximize the effectiveness of the health insurance system by evaluating the impact of the health insurance system and proposing optimal health insurance policies.

[0042] The scenario generation unit can use the generation AI to generate scenarios that take into account different climate conditions and seasonal variations. For example, the scenario generation unit generates scenarios that take into account different climate conditions and seasonal variations based on the pathogen patterns predicted by the generation AI. For example, the generation AI compares the influenza epidemic pattern in winter with the dengue fever epidemic pattern in summer and proposes preventive measures appropriate for each season. The generation AI also takes into account the climate characteristics of the region and predicts the infection spread pattern in a specific region. Furthermore, the generation AI generates scenarios that take into account seasonal infection spread patterns and seasonal pathogens. In this way, by generating scenarios that take into account climate conditions and seasonal variations, more realistic countermeasures can be proposed.

[0043] The scenario generation unit can simulate infection spread patterns in areas with different socioeconomic backgrounds based on the scenario predicted by the generation AI. For example, the scenario generation unit simulates infection spread patterns in areas with different socioeconomic backgrounds based on the scenario predicted by the generation AI. For example, the generation AI analyzes the difference in the rate of infection spread between urban and rural areas and proposes countermeasures appropriate for each area. The generation AI also simulates infection spread patterns taking into account income levels, education levels, and the economic situation of the region. Furthermore, the generation AI simulates infection spread patterns in specific administrative districts and communities and proposes countermeasures appropriate for each region. This makes it possible to propose countermeasures appropriate for each region through simulations that take socioeconomic background into account.

[0044] The scenario generation unit can evaluate the risk of infection from animals to humans based on the scenario predicted by the generation AI and propose animal protection measures. The scenario generation unit, for example, evaluates the risk of infection from animals to humans based on the scenario predicted by the generation AI and proposes animal protection measures. For example, the generation AI evaluates the risk of contact between wild animal habitats and human settlements and proposes animal protection measures based on the results. The generation AI also evaluates the infection risk of livestock and pets and proposes appropriate isolation measures and vaccinations. Furthermore, the generation AI analyzes the health status and infection routes of animals and proposes strengthening the surveillance system. This makes it possible to evaluate the risk of infection from animals to humans and propose appropriate animal protection measures.

[0045] The intervention evaluation unit can simulate collaboration scenarios between different medical institutions based on the countermeasures proposed by the generation AI and propose the optimal collaboration method. The intervention evaluation unit, for example, simulates collaboration scenarios between different medical institutions based on the countermeasures proposed by the generation AI and proposes the optimal collaboration method. For example, the generation AI performs a simulation to optimize patient transfer plans between hospitals and proposes the optimal collaboration method based on the results. The generation AI also simulates information sharing methods between medical institutions and proposes the optimal collaboration procedure. Furthermore, the generation AI simulates the frequency and scope of collaboration between medical institutions and proposes the optimal collaboration method. This enables the efficient use of medical resources by optimizing collaboration between medical institutions.

[0046] The intervention evaluation unit can simulate different vaccination strategies based on the countermeasures proposed by the generation AI and propose an optimal vaccination plan. The intervention evaluation unit, for example, simulates different vaccination strategies based on the countermeasures proposed by the generation AI and proposes an optimal vaccination plan. For example, the generation AI performs a simulation to select priority vaccination groups and optimize the vaccination schedule, and proposes an optimal vaccination plan based on the results. The generation AI also simulates the vaccination location, vaccination method, and vaccination timing and proposes an optimal vaccination plan. Furthermore, the generation AI simulates the priority of vaccination recipients and the vaccination schedule and proposes an optimal vaccination plan. In this way, by simulating vaccination strategies and proposing an optimal vaccination plan, effective use of vaccines is possible.

[0047] The intervention evaluation unit can simulate different scenarios for restricting the use of transportation means based on the countermeasures proposed by the generation AI and propose optimal traffic restriction measures. The intervention evaluation unit, for example, simulates different scenarios for restricting the use of transportation means based on the countermeasures proposed by the generation AI and proposes optimal traffic restriction measures. For example, the generation AI performs a simulation to evaluate restrictions on the operation of public transportation and their impact, and proposes optimal traffic restriction measures based on the results. The generation AI also performs a simulation to evaluate restrictions on the use of personal vehicles and their impact, and proposes optimal traffic restriction measures based on the results. Furthermore, the generation AI simulates the scope and duration of restrictions on the use of transportation means and proposes optimal traffic restriction measures. In this way, by simulating scenarios for restricting the use of transportation means and proposing optimal traffic restriction measures, the spread of infection can be prevented.

[0048] The intervention evaluation unit can simulate operating scenarios for different educational institutions based on the response measures proposed by the generation AI and propose optimal educational management measures. The intervention evaluation unit, for example, simulates operating scenarios for different educational institutions based on the response measures proposed by the generation AI and proposes optimal educational management measures. For example, the generation AI performs a simulation to optimize the balance between online and face-to-face classes and proposes optimal educational management measures based on the results. The generation AI also simulates class formats and operating procedures and proposes optimal educational management measures. Furthermore, the generation AI simulates how educational institutions will implement infection control measures and propose optimal educational management measures. In this way, by simulating operating scenarios for educational institutions and proposing optimal educational management measures, it is possible to prevent the spread of infection while maintaining the quality of education.

[0049] The policy proposal department can evaluate the impact of different legal regulations based on the simulation data provided by the generation AI and propose optimal legal responses. For example, the policy proposal department can evaluate the impact of different legal regulations based on the simulation data provided by the generation AI and propose optimal legal responses. For example, the generation AI can simulate the effects of new legal regulations to prevent the spread of infection and propose optimal legal responses based on the results. The generation AI can also evaluate the impact of existing legal regulations and propose necessary legal amendments or the introduction of new regulations. Furthermore, the generation AI can simulate the introduction of penalties for legal regulations or proposals for legal amendments and propose optimal legal responses. In this way, the effectiveness of legal responses can be maximized by evaluating the impact of legal regulations and proposing optimal legal responses.

[0050] The policy proposal department can simulate different international cooperation scenarios based on the simulation data provided by the generation AI and propose optimal international cooperation measures. The policy proposal department, for example, can simulate different international cooperation scenarios based on the simulation data provided by the generation AI and propose optimal international cooperation measures. For example, the generation AI can simulate international collaboration methods for infectious disease control and propose optimal international cooperation measures based on the results. The generation AI can also simulate international support measures and methods for implementing joint research and propose optimal international cooperation measures. Furthermore, the generation AI can simulate international collaboration methods and the scope of cooperation and propose optimal international cooperation measures. In this way, by simulating international cooperation scenarios and proposing optimal international cooperation measures, it is possible to strengthen international collaboration in infectious disease control.

[0051] The policy proposal department can evaluate the impact of different health insurance systems based on the simulation data provided by the generation AI and propose optimal health insurance policies. For example, the policy proposal department can evaluate the impact of different health insurance systems based on the simulation data provided by the generation AI and propose optimal health insurance policies. For example, the generation AI can evaluate the effectiveness of a health insurance system specialized in infectious disease countermeasures and propose optimal health insurance policies based on the results. The generation AI can also evaluate the impact of public and private health insurance and propose optimal health insurance policies. Furthermore, the generation AI can simulate the setting of insurance premiums and the expansion of benefit scope and propose optimal health insurance policies. This makes it possible to maximize the effectiveness of the health insurance system by evaluating the impact of the health insurance system and proposing optimal health insurance policies.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The infectious disease prevention system can further include a data collection unit. The data collection unit monitors the outbreak of infectious diseases in real time and collects data. For example, the data collection unit automatically collects reported data from hospitals and clinics to grasp the outbreak of infectious diseases in real time. The data collection unit also analyzes information from social media and news sites to quickly grasp the spread of infectious diseases. Furthermore, the data collection unit collects weather data and population movement data to evaluate the risk of infectious disease outbreaks. This allows the outbreak of infectious diseases to be grasped in real time and enables rapid response.

[0054] The infectious disease prevention system may further include a prevention education unit. The prevention education unit provides educational content related to infectious disease prevention. For example, the prevention education unit provides videos and articles about infectious disease prevention methods and countermeasures. The prevention education unit also holds infectious disease prevention workshops and seminars for schools and companies. The prevention education unit also provides quizzes and games related to infectious disease prevention, providing content that is fun and educational. This can help spread knowledge about infectious disease prevention and raise awareness of prevention.

[0055] The infectious disease prevention system can further include a community liaison department. The community liaison department works with the local community to promote infectious disease prevention activities. For example, the community liaison department works with local health centers and local governments to implement infectious disease prevention campaigns. The community liaison department also works with local volunteer groups to carry out infectious disease prevention awareness activities. Furthermore, the community liaison department provides information on infectious disease prevention to local residents and raises awareness of prevention throughout the community. This makes it possible to work with the local community to promote infectious disease prevention activities and strengthen infectious disease control measures throughout the community.

[0056] The infectious disease prevention system can further include a risk assessment unit. The risk assessment unit assesses the risk of infectious disease outbreaks and proposes countermeasures according to the risk level. For example, the risk assessment unit analyzes past infection data and environmental data to assess the risk of infection in a specific region or period. The risk assessment unit also predicts fluctuations in infection risk based on weather data and population movement data. Furthermore, the risk assessment unit proposes preventive measures and countermeasures according to the risk level, supporting rapid response. In this way, the spread of infectious diseases can be prevented by assessing infection risk and proposing appropriate countermeasures.

[0057] The infectious disease prevention system can further include a data visualization unit. The data visualization unit visually displays the collected data, making it easier to understand. For example, the data visualization unit may display the outbreak status of an infectious disease on a map, allowing the spread of the infection to be grasped at a glance. The data visualization unit may also display the trends in the number of infected people and the number of deaths in graphs, visually showing the trend of the infectious disease. Furthermore, the data visualization unit may visually display the effects of preventive measures and evaluate their effectiveness. In this way, by visually displaying the data, the status of the infectious disease can be quickly grasped and appropriate responses can be taken.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The scenario generation unit predicts unknown pathogen patterns. For example, the generation AI analyzes past infection data and environmental data to predict future infectious disease outbreak patterns. The generation AI also simulates how a new virus spreads and builds an infection scenario based on the results. For example, the generation AI receives prompts containing past infection data and environmental data as input and generates a scenario based on those prompts. Step 2: The intervention evaluation component evaluates countermeasures based on the scenario generated by the scenario generation component. For example, the generation AI simulates the extent to which a lockdown in a specific area will curb the spread of infection and proposes optimal countermeasures based on the results. For example, the generation AI receives prompts as input, including an evaluation of countermeasures based on the scenario, and evaluates and proposes countermeasures based on the prompts. Step 3: The policy proposal component proposes policies based on the results evaluated by the intervention evaluation component. For example, the generation AI simulates optimal policies to prevent the spread of infection and makes proposals to policymakers based on the results. For example, the generation AI receives prompts containing policy proposals based on simulation data as input and proposes policies based on those prompts.

[0060] (Example 2) The infectious disease prevention system according to an embodiment of the present invention is a system that uses generative AI to predict unknown pathogen patterns, construct new infection scenarios, quickly evaluate and propose countermeasures, and support policymaking. As a result, the infectious disease prevention system can realize preventive measures and rapid intervention plans for future infectious diseases, improving crisis prediction capabilities and reducing public health risks.

[0061] An infectious disease prevention system according to an embodiment includes a scenario generation unit, an intervention evaluation unit, and a policy proposal unit. The scenario generation unit predicts unknown pathogen patterns. For example, the generation AI analyzes past infection data and environmental data to predict future infectious disease outbreak patterns. The generation AI also simulates how a new virus will spread and constructs an infection scenario based on the results. The generation AI, for example, receives prompts including past infection data and environmental data as input and generates a scenario based on the prompts. The intervention evaluation unit evaluates countermeasures based on the scenario generated by the scenario generation unit. For example, the generation AI simulates the extent to which a lockdown in a specific area will suppress the spread of infection and proposes optimal countermeasures based on the results. The generation AI, for example, receives prompts including an evaluation of countermeasures based on the scenario as input and evaluates and proposes countermeasures based on the prompts. The policy proposal unit proposes policies based on the results evaluated by the intervention evaluation unit. For example, the generation AI simulates optimal policies to prevent the spread of infection and proposes them to policymakers based on the results. The generation AI, for example, receives prompts including policy proposals based on simulation data as input and proposes policies based on the prompts. As a result, the infectious disease prevention system according to the embodiment can predict unknown pathogen patterns and quickly evaluate and propose countermeasures to prevent the spread of infectious diseases.

[0062] The scenario generation unit can analyze past infection data and environmental data to predict future patterns of infectious disease outbreaks. For example, the generation AI analyzes past infection data and environmental data to predict future patterns of infectious disease outbreaks. For example, the generation AI analyzes past data on the number of infected people, infection routes, and regional data to predict future patterns of infectious disease outbreaks. The generation AI also analyzes environmental data such as temperature, humidity, and population density to predict future patterns of infectious disease outbreaks. Furthermore, the generation AI uses statistical models and simulation models to predict future patterns of infectious disease outbreaks. This makes it possible to predict future patterns of infectious disease outbreaks by analyzing past data.

[0063] The intervention evaluation unit can generate scenarios that take into account different climatic conditions and seasonal variations based on the scenario predicted by the generation AI. For example, the intervention evaluation unit generates scenarios that take into account different climatic conditions and seasonal variations based on the pathogen patterns predicted by the generation AI. For example, the generation AI compares influenza epidemic patterns in winter with dengue fever epidemic patterns in summer and proposes preventive measures appropriate for each season. The generation AI also predicts infection spread patterns in specific areas by taking into account the climatic characteristics of the region. Furthermore, the generation AI generates scenarios that take into account seasonal infection spread patterns and seasonal pathogens. In this way, by generating scenarios that take into account climatic conditions and seasonal variations, more realistic countermeasures can be proposed.

[0064] The intervention evaluation unit can simulate the usage of different medical resources based on the scenario predicted by the generation AI and propose optimal resource allocation. The intervention evaluation unit, for example, simulates the usage of different medical resources based on the scenario predicted by the generation AI and proposes optimal resource allocation. For example, the generation AI performs a simulation to optimize the number of hospital beds and the allocation of medical staff, and proposes optimal resource allocation based on the results. The generation AI also simulates the placement and usage of medical equipment and proposes optimal resource allocation. Furthermore, the generation AI proposes optimal resource allocation based on the utilization rate and demand forecast of medical resources. This makes it possible to reduce the burden on medical facilities by proposing optimal allocation of medical resources.

[0065] The intervention evaluation unit can simulate infection spread patterns in areas with different socioeconomic backgrounds based on the scenario predicted by the generation AI. For example, the intervention evaluation unit simulates infection spread patterns in areas with different socioeconomic backgrounds based on the scenario predicted by the generation AI. For example, the generation AI analyzes the difference in the rate of infection spread between urban and rural areas and proposes countermeasures appropriate for each area. The generation AI also simulates infection spread patterns taking into account income level, education level, and the economic situation of the region. Furthermore, the generation AI simulates infection spread patterns in specific administrative districts and communities and proposes countermeasures appropriate for each region. This makes it possible to propose countermeasures appropriate for each region through simulations that take socioeconomic background into account.

[0066] The intervention assessment unit can assess the risk of animal-to-human infection based on the scenario predicted by the generative AI and propose animal protection measures. The intervention assessment unit can, for example, assess the risk of animal-to-human infection based on the scenario predicted by the generative AI and propose animal protection measures. For example, the generative AI assesses the risk of contact between wild animal habitats and human settlements and proposes animal protection measures based on the results. The generative AI also assesses the risk of infection for livestock and pets and proposes appropriate isolation measures and vaccinations. Furthermore, the generative AI analyzes the health status and infection routes of animals and proposes strengthening the surveillance system. This makes it possible to assess the risk of animal-to-human infection and propose appropriate animal protection measures.

[0067] The intervention evaluation unit can use the emotion estimation function to analyze the emotional reactions of medical workers to infection scenarios and propose stress reduction measures. The intervention evaluation unit, for example, uses the emotion estimation function to analyze the emotional reactions of medical workers to infection scenarios and propose stress reduction measures. For example, the emotion estimation function monitors the stress levels of medical workers in real time and proposes appropriate rest and support. The emotion estimation function also proposes counseling or the introduction of a leave system based on the emotional data of medical workers. Furthermore, the emotion estimation function analyzes the emotional scores of medical workers and proposes stress reduction programs. In this way, the burden on medical workers can be reduced by analyzing the emotional reactions of medical workers and proposing stress reduction measures.

[0068] The policy proposal unit can simulate collaboration scenarios between different medical institutions based on the countermeasures proposed by the generation AI and propose the optimal collaboration method. For example, the policy proposal unit can simulate collaboration scenarios between different medical institutions based on the countermeasures proposed by the generation AI and propose the optimal collaboration method. For example, the generation AI can perform a simulation to optimize patient transfer plans between hospitals and propose the optimal collaboration method based on the results. The generation AI can also simulate information sharing methods between medical institutions and propose the optimal collaboration procedure. Furthermore, the generation AI can simulate the frequency and scope of collaboration between medical institutions and propose the optimal collaboration method. This enables the efficient use of medical resources by optimizing collaboration between medical institutions.

[0069] The policy proposal unit can use the emotion estimation function to evaluate the emotional impact of proposed countermeasures on the public and propose acceptable countermeasures. The policy proposal unit, for example, uses the emotion estimation function to evaluate the emotional impact of proposed countermeasures on the public and propose acceptable countermeasures. For example, the emotion estimation function evaluates the psychological impact of a lockdown and provides appropriate information. The emotion estimation function also suggests communication methods and policy flexibility based on public emotion data. Furthermore, the emotion estimation function analyzes the public's emotion scores and proposes acceptable countermeasures. As a result, by evaluating the emotional impact on the public and proposing acceptable countermeasures, the feasibility of policies is improved.

[0070] The policy proposal department can simulate different vaccination strategies based on the countermeasures proposed by the generation AI and propose the optimal vaccination plan. For example, the policy proposal department can simulate different vaccination strategies based on the countermeasures proposed by the generation AI and propose the optimal vaccination plan. For example, the generation AI performs a simulation to select priority vaccination groups and optimize the vaccination schedule, and proposes the optimal vaccination plan based on the results. The generation AI also simulates the vaccination location, vaccination method, and vaccination timing and proposes the optimal vaccination plan. Furthermore, the generation AI simulates the priority and vaccination schedule of those to be vaccinated and proposes the optimal vaccination plan. In this way, by simulating vaccination strategies and proposing the optimal vaccination plan, it is possible to make effective use of vaccines.

[0071] The policy proposal department can simulate operating scenarios for different educational institutions based on the response measures proposed by the generation AI and propose optimal educational management measures. For example, the policy proposal department can simulate operating scenarios for different educational institutions based on the response measures proposed by the generation AI and propose optimal educational management measures. For example, the generation AI can conduct a simulation to optimize the balance between online and face-to-face classes and propose optimal educational management measures based on the results. The generation AI can also simulate class formats and operating procedures and propose optimal educational management measures. Furthermore, the generation AI can simulate infection control measures implemented by educational institutions and propose optimal educational management measures. In this way, by simulating operating scenarios for educational institutions and proposing optimal educational management measures, it is possible to prevent the spread of infection while maintaining the quality of education.

[0072] The policy proposal unit can use the emotion estimation function to analyze the policymaker's emotional reaction to proposed countermeasures and make proposals to improve the acceptability of the policy. The policy proposal unit, for example, uses the emotion estimation function to analyze the policymaker's emotional reaction to proposed countermeasures and make proposals to improve the acceptability of the policy. For example, the emotion estimation function proposes policy amendments that are more acceptable based on the policymaker's emotional data. The emotion estimation function also analyzes the policymaker's emotion score and suggests policy flexibility and communication methods. Furthermore, the emotion estimation function monitors the policymaker's emotional reactions in real time and provides appropriate feedback. As a result, by analyzing the policymaker's emotional reactions and making proposals to improve the acceptability of the policy, the feasibility of the policy is improved.

[0073] The policy proposal department can evaluate the impact of different legal regulations based on the simulation data provided by the generation AI and propose optimal legal responses. For example, the policy proposal department can evaluate the impact of different legal regulations based on the simulation data provided by the generation AI and propose optimal legal responses. For example, the generation AI can simulate the effects of new legal regulations to prevent the spread of infection and propose optimal legal responses based on the results. The generation AI can also evaluate the impact of existing legal regulations and propose necessary legal amendments or the introduction of new regulations. Furthermore, the generation AI can simulate the introduction of penalties for legal regulations or proposals for legal amendments and propose optimal legal responses. In this way, the effectiveness of legal responses can be maximized by evaluating the impact of legal regulations and proposing optimal legal responses.

[0074] The policy proposal unit can use the emotion estimation function to evaluate the emotional impact of policy proposals on the public and propose policies that are easy to accept. For example, the policy proposal unit uses the emotion estimation function to evaluate the emotional impact of policy proposals on the public and propose policies that are easy to accept. For example, the emotion estimation function modifies the content of policy proposals based on public emotion data. The emotion estimation function also analyzes public emotion scores and proposes policy flexibility and communication methods. Furthermore, the emotion estimation function monitors the public's emotional reactions in real time and provides appropriate feedback. This improves the feasibility of policies by evaluating the emotional impact on the public and proposing policies that are easy to accept.

[0075] The policy proposal department can evaluate the impact of different economic policies based on the simulation data provided by the generation AI and propose optimal economic measures. For example, the policy proposal department can evaluate the impact of different economic policies based on the simulation data provided by the generation AI and propose optimal economic measures. For example, the generation AI can simulate policies to balance infection prevention with economic activity, and propose optimal economic measures based on the results. The generation AI can also evaluate the impact of fiscal policy, monetary policy, and employment measures, and propose optimal economic measures. Furthermore, the generation AI can simulate the effects of economic support measures and tax incentives, and propose optimal economic measures. In this way, the impact of economic policies can be evaluated and optimal economic measures proposed, thereby stabilizing the economy.

[0076] The policy proposal department can simulate different international cooperation scenarios based on the simulation data provided by the generation AI and propose optimal international cooperation measures. The policy proposal department, for example, can simulate different international cooperation scenarios based on the simulation data provided by the generation AI and propose optimal international cooperation measures. For example, the generation AI can simulate international collaboration methods for infectious disease control and propose optimal international cooperation measures based on the results. The generation AI can also simulate international support measures and methods for implementing joint research and propose optimal international cooperation measures. Furthermore, the generation AI can simulate international collaboration methods and the scope of cooperation and propose optimal international cooperation measures. In this way, by simulating international cooperation scenarios and proposing optimal international cooperation measures, it is possible to strengthen international collaboration in infectious disease control.

[0077] The policy proposal department can evaluate the impact of different health insurance systems based on the simulation data provided by the generation AI and propose optimal health insurance policies. For example, the policy proposal department can evaluate the impact of different health insurance systems based on the simulation data provided by the generation AI and propose optimal health insurance policies. For example, the generation AI can evaluate the effectiveness of a health insurance system specialized in infectious disease countermeasures and propose optimal health insurance policies based on the results. The generation AI can also evaluate the impact of public and private health insurance and propose optimal health insurance policies. Furthermore, the generation AI can simulate the setting of insurance premiums and the expansion of benefit scope and propose optimal health insurance policies. This makes it possible to maximize the effectiveness of the health insurance system by evaluating the impact of the health insurance system and proposing optimal health insurance policies.

[0078] The policy proposal unit can use the emotion estimation function to analyze the emotional reactions of medical professionals to policy proposals and make proposals to improve the feasibility of the policy. The policy proposal unit, for example, uses the emotion estimation function to analyze the emotional reactions of medical professionals to policy proposals and make proposals to improve the feasibility of the policy. For example, the emotion estimation function proposes acceptable policy amendments based on the emotional data of medical professionals. The emotion estimation function also analyzes the emotional scores of medical professionals and suggests policy flexibility and communication methods. Furthermore, the emotion estimation function monitors the emotional reactions of medical professionals in real time and provides appropriate feedback. As a result, analyzing the emotional reactions of medical professionals and making proposals to improve the feasibility of the policy will help ensure smooth policy implementation.

[0079] The scenario generation unit can use the generation AI to generate scenarios that take into account different climate conditions and seasonal variations. For example, the scenario generation unit generates scenarios that take into account different climate conditions and seasonal variations based on the pathogen patterns predicted by the generation AI. For example, the generation AI compares the influenza epidemic pattern in winter with the dengue fever epidemic pattern in summer and proposes preventive measures appropriate for each season. The generation AI also takes into account the climate characteristics of the region and predicts the infection spread pattern in a specific region. Furthermore, the generation AI generates scenarios that take into account seasonal infection spread patterns and seasonal pathogens. In this way, by generating scenarios that take into account climate conditions and seasonal variations, more realistic countermeasures can be proposed.

[0080] The scenario generation unit can use the emotion estimation function to evaluate the psychological impact of an infection scenario on the public and propose a scenario for reducing the psychological burden. The scenario generation unit, for example, uses the emotion estimation function to evaluate the psychological impact of an infection scenario on the public and propose a scenario for reducing the psychological burden. For example, the emotion estimation function simulates a method of providing information to alleviate fears about the spread of infection and proposes a scenario for reducing the psychological burden based on the results. The emotion estimation function also evaluates the stress level and psychological burden based on the public's emotion data and proposes appropriate information and support. Furthermore, the emotion estimation function analyzes the public's emotion score and proposes a scenario for reducing the psychological burden. In this way, by evaluating the psychological impact on the public and proposing a scenario for reducing the psychological burden, the acceptability of infectious disease control measures is improved.

[0081] The scenario generation unit can simulate infection spread patterns in areas with different socioeconomic backgrounds based on the scenario predicted by the generation AI. For example, the scenario generation unit simulates infection spread patterns in areas with different socioeconomic backgrounds based on the scenario predicted by the generation AI. For example, the generation AI analyzes the difference in the rate of infection spread between urban and rural areas and proposes countermeasures appropriate for each area. The generation AI also simulates infection spread patterns taking into account income levels, education levels, and the economic situation of the region. Furthermore, the generation AI simulates infection spread patterns in specific administrative districts and communities and proposes countermeasures appropriate for each region. This makes it possible to propose countermeasures appropriate for each region through simulations that take socioeconomic background into account.

[0082] The scenario generation unit can evaluate the risk of infection from animals to humans based on the scenario predicted by the generation AI and propose animal protection measures. The scenario generation unit, for example, evaluates the risk of infection from animals to humans based on the scenario predicted by the generation AI and proposes animal protection measures. For example, the generation AI evaluates the risk of contact between wild animal habitats and human settlements and proposes animal protection measures based on the results. The generation AI also evaluates the infection risk of livestock and pets and proposes appropriate isolation measures and vaccinations. Furthermore, the generation AI analyzes the health status and infection routes of animals and proposes strengthening the surveillance system. This makes it possible to evaluate the risk of infection from animals to humans and propose appropriate animal protection measures.

[0083] The scenario generation unit can use the emotion estimation function to analyze the emotional reactions of medical workers to an infection scenario and propose stress reduction measures. The scenario generation unit, for example, uses the emotion estimation function to analyze the emotional reactions of medical workers to an infection scenario and propose stress reduction measures. For example, the emotion estimation function monitors the stress levels of medical workers in real time and proposes appropriate rest and support. The emotion estimation function also proposes counseling or the introduction of a leave system based on the emotional data of medical workers. Furthermore, the emotion estimation function analyzes the emotional scores of medical workers and proposes a stress reduction program. In this way, the burden on medical workers can be reduced by analyzing the emotional reactions of medical workers and proposing stress reduction measures.

[0084] The intervention evaluation unit can simulate collaboration scenarios between different medical institutions based on the countermeasures proposed by the generation AI and propose the optimal collaboration method. The intervention evaluation unit, for example, simulates collaboration scenarios between different medical institutions based on the countermeasures proposed by the generation AI and proposes the optimal collaboration method. For example, the generation AI performs a simulation to optimize patient transfer plans between hospitals and proposes the optimal collaboration method based on the results. The generation AI also simulates information sharing methods between medical institutions and proposes the optimal collaboration procedure. Furthermore, the generation AI simulates the frequency and scope of collaboration between medical institutions and proposes the optimal collaboration method. This enables the efficient use of medical resources by optimizing collaboration between medical institutions.

[0085] The intervention evaluation unit can use the emotion estimation function to evaluate the emotional impact of proposed countermeasures on the public and propose acceptable countermeasures. The intervention evaluation unit, for example, uses the emotion estimation function to evaluate the emotional impact of proposed countermeasures on the public and propose acceptable countermeasures. For example, the emotion estimation function evaluates the psychological impact of a lockdown and provides appropriate information. The emotion estimation function also suggests communication methods and policy flexibility based on public emotion data. Furthermore, the emotion estimation function analyzes the public's emotion scores and proposes acceptable countermeasures. As a result, by evaluating the emotional impact on the public and proposing acceptable countermeasures, the feasibility of policies is improved.

[0086] The intervention evaluation unit can simulate different vaccination strategies based on the countermeasures proposed by the generation AI and propose an optimal vaccination plan. The intervention evaluation unit, for example, simulates different vaccination strategies based on the countermeasures proposed by the generation AI and proposes an optimal vaccination plan. For example, the generation AI performs a simulation to select priority vaccination groups and optimize the vaccination schedule, and proposes an optimal vaccination plan based on the results. The generation AI also simulates the vaccination location, vaccination method, and vaccination timing and proposes an optimal vaccination plan. Furthermore, the generation AI simulates the priority of vaccination recipients and the vaccination schedule and proposes an optimal vaccination plan. In this way, by simulating vaccination strategies and proposing an optimal vaccination plan, effective use of vaccines is possible.

[0087] The intervention evaluation unit can simulate different scenarios for restricting the use of transportation means based on the countermeasures proposed by the generation AI and propose optimal traffic restriction measures. The intervention evaluation unit, for example, simulates different scenarios for restricting the use of transportation means based on the countermeasures proposed by the generation AI and proposes optimal traffic restriction measures. For example, the generation AI performs a simulation to evaluate restrictions on the operation of public transportation and their impact, and proposes optimal traffic restriction measures based on the results. The generation AI also performs a simulation to evaluate restrictions on the use of personal vehicles and their impact, and proposes optimal traffic restriction measures based on the results. Furthermore, the generation AI simulates the scope and duration of restrictions on the use of transportation means and proposes optimal traffic restriction measures. In this way, by simulating scenarios for restricting the use of transportation means and proposing optimal traffic restriction measures, the spread of infection can be prevented.

[0088] The intervention evaluation unit can simulate operating scenarios for different educational institutions based on the response measures proposed by the generation AI and propose optimal educational management measures. The intervention evaluation unit, for example, simulates operating scenarios for different educational institutions based on the response measures proposed by the generation AI and proposes optimal educational management measures. For example, the generation AI performs a simulation to optimize the balance between online and face-to-face classes and proposes optimal educational management measures based on the results. The generation AI also simulates class formats and operating procedures and proposes optimal educational management measures. Furthermore, the generation AI simulates how educational institutions will implement infection control measures and propose optimal educational management measures. In this way, by simulating operating scenarios for educational institutions and proposing optimal educational management measures, it is possible to prevent the spread of infection while maintaining the quality of education.

[0089] The intervention evaluation unit can use the emotion estimation function to analyze the policymaker's emotional response to proposed countermeasures and make proposals to improve the acceptability of the policy. The intervention evaluation unit, for example, uses the emotion estimation function to analyze the policymaker's emotional response to proposed countermeasures and make proposals to improve the acceptability of the policy. For example, the emotion estimation function proposes policy amendments to make the policy more acceptable based on the policymaker's emotional data. The emotion estimation function also analyzes the policymaker's emotion score and suggests policy flexibility and communication methods. Furthermore, the emotion estimation function monitors the policymaker's emotional response in real time and provides appropriate feedback. As a result, by analyzing the policymaker's emotional response and making proposals to improve the acceptability of the policy, the feasibility of the policy is improved.

[0090] The policy proposal department can evaluate the impact of different legal regulations based on the simulation data provided by the generation AI and propose optimal legal responses. For example, the policy proposal department can evaluate the impact of different legal regulations based on the simulation data provided by the generation AI and propose optimal legal responses. For example, the generation AI can simulate the effects of new legal regulations to prevent the spread of infection and propose optimal legal responses based on the results. The generation AI can also evaluate the impact of existing legal regulations and propose necessary legal amendments or the introduction of new regulations. Furthermore, the generation AI can simulate the introduction of penalties for legal regulations or proposals for legal amendments and propose optimal legal responses. In this way, the effectiveness of legal responses can be maximized by evaluating the impact of legal regulations and proposing optimal legal responses.

[0091] The policy proposal unit can use the emotion estimation function to evaluate the emotional impact of policy proposals on the public and propose policies that are easy to accept. For example, the policy proposal unit uses the emotion estimation function to evaluate the emotional impact of policy proposals on the public and propose policies that are easy to accept. For example, the emotion estimation function modifies the content of policy proposals based on public emotion data. The emotion estimation function also analyzes public emotion scores and proposes policy flexibility and communication methods. Furthermore, the emotion estimation function monitors the public's emotional reactions in real time and provides appropriate feedback. This improves the feasibility of policies by evaluating the emotional impact on the public and proposing policies that are easy to accept.

[0092] The policy proposal department can simulate different international cooperation scenarios based on the simulation data provided by the generation AI and propose optimal international cooperation measures. The policy proposal department, for example, can simulate different international cooperation scenarios based on the simulation data provided by the generation AI and propose optimal international cooperation measures. For example, the generation AI can simulate international collaboration methods for infectious disease control and propose optimal international cooperation measures based on the results. The generation AI can also simulate international support measures and methods for implementing joint research and propose optimal international cooperation measures. Furthermore, the generation AI can simulate international collaboration methods and the scope of cooperation and propose optimal international cooperation measures. In this way, by simulating international cooperation scenarios and proposing optimal international cooperation measures, it is possible to strengthen international collaboration in infectious disease control.

[0093] The policy proposal department can evaluate the impact of different health insurance systems based on the simulation data provided by the generation AI and propose optimal health insurance policies. For example, the policy proposal department can evaluate the impact of different health insurance systems based on the simulation data provided by the generation AI and propose optimal health insurance policies. For example, the generation AI can evaluate the effectiveness of a health insurance system specialized in infectious disease countermeasures and propose optimal health insurance policies based on the results. The generation AI can also evaluate the impact of public and private health insurance and propose optimal health insurance policies. Furthermore, the generation AI can simulate the setting of insurance premiums and the expansion of benefit scope and propose optimal health insurance policies. This makes it possible to maximize the effectiveness of the health insurance system by evaluating the impact of the health insurance system and proposing optimal health insurance policies.

[0094] The policy proposal unit can use the emotion estimation function to analyze the emotional reactions of medical professionals to policy proposals and make proposals to improve the feasibility of the policy. The policy proposal unit, for example, uses the emotion estimation function to analyze the emotional reactions of medical professionals to policy proposals and make proposals to improve the feasibility of the policy. For example, the emotion estimation function proposes acceptable policy amendments based on the emotional data of medical professionals. The emotion estimation function also analyzes the emotional scores of medical professionals and suggests policy flexibility and communication methods. Furthermore, the emotion estimation function monitors the emotional reactions of medical professionals in real time and provides appropriate feedback. As a result, analyzing the emotional reactions of medical professionals and making proposals to improve the feasibility of the policy will help ensure smooth policy implementation.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The infectious disease prevention system can further include a data collection unit. The data collection unit monitors the outbreak of infectious diseases in real time and collects data. For example, the data collection unit automatically collects reported data from hospitals and clinics to grasp the outbreak of infectious diseases in real time. The data collection unit also analyzes information from social media and news sites to quickly grasp the spread of infectious diseases. Furthermore, the data collection unit collects weather data and population movement data to evaluate the risk of infectious disease outbreaks. This allows the outbreak of infectious diseases to be grasped in real time and enables rapid response.

[0097] The infectious disease prevention system may further include a prevention education unit. The prevention education unit provides educational content related to infectious disease prevention. For example, the prevention education unit provides videos and articles about infectious disease prevention methods and countermeasures. The prevention education unit also holds infectious disease prevention workshops and seminars for schools and companies. The prevention education unit also provides quizzes and games related to infectious disease prevention, providing content that is fun and educational. This can help spread knowledge about infectious disease prevention and raise awareness of prevention.

[0098] The infectious disease prevention system can further include a community liaison department. The community liaison department works with the local community to promote infectious disease prevention activities. For example, the community liaison department works with local health centers and local governments to implement infectious disease prevention campaigns. The community liaison department also works with local volunteer groups to carry out infectious disease prevention awareness activities. Furthermore, the community liaison department provides information on infectious disease prevention to local residents and raises awareness of prevention throughout the community. This makes it possible to work with the local community to promote infectious disease prevention activities and strengthen infectious disease control measures throughout the community.

[0099] The infectious disease prevention system can further include a risk assessment unit. The risk assessment unit assesses the risk of infectious disease outbreaks and proposes countermeasures according to the risk level. For example, the risk assessment unit analyzes past infection data and environmental data to assess the risk of infection in a specific region or period. The risk assessment unit also predicts fluctuations in infection risk based on weather data and population movement data. Furthermore, the risk assessment unit proposes preventive measures and countermeasures according to the risk level, supporting rapid response. In this way, the spread of infectious diseases can be prevented by assessing infection risk and proposing appropriate countermeasures.

[0100] The infectious disease prevention system can further include a data visualization unit. The data visualization unit visually displays the collected data, making it easier to understand. For example, the data visualization unit may display the outbreak status of an infectious disease on a map, allowing the spread of the infection to be grasped at a glance. The data visualization unit may also display the trends in the number of infected people and the number of deaths in graphs, visually showing the trend of the infectious disease. Furthermore, the data visualization unit may visually display the effects of preventive measures and evaluate their effectiveness. In this way, by visually displaying the data, the status of the infectious disease can be quickly grasped and appropriate responses can be taken.

[0101] The intervention evaluation unit can use the emotion estimation function to analyze the public's emotional reactions to infection scenarios and propose communication strategies. For example, the emotion estimation function evaluates the public's level of anxiety and fear and proposes appropriate methods of providing information. The emotion estimation function also proposes reassuring messages and support based on the public's emotional data. Furthermore, the emotion estimation function analyzes the public's emotion scores and proposes effective communication strategies. This allows for the analysis of the public's emotional reactions and the proposal of appropriate communication strategies, thereby improving the acceptance of infectious disease control measures.

[0102] The policy proposal unit can use the emotion estimation function to evaluate the emotional impact of proposed policies on medical workers and propose policies that are easy to accept. For example, the emotion estimation function evaluates the stress and anxiety levels of medical workers and proposes appropriate support measures. The emotion estimation function also suggests the flexibility and feasibility of policies based on the emotional data of medical workers. Furthermore, the emotion estimation function analyzes the emotional scores of medical workers and proposes amendments to policies that are easy to accept. In this way, by evaluating the emotional impact on medical workers and proposing policies that are easy to accept, the feasibility of policies is improved.

[0103] The scenario generation unit can use the emotion estimation function to evaluate the psychological impact of infection scenarios on the public and propose scenarios to reduce the psychological burden. For example, the emotion estimation function simulates ways to provide information to alleviate fears about the spread of infection and, based on the results, proposes scenarios to reduce the psychological burden. The emotion estimation function also evaluates stress levels and psychological burden based on the public's emotional data and proposes appropriate information and support. Furthermore, the emotion estimation function analyzes the public's emotion scores and proposes scenarios to reduce the psychological burden. As a result, by evaluating the psychological impact on the public and proposing scenarios to reduce the psychological burden, the acceptability of infectious disease control measures can be improved.

[0104] The intervention evaluation unit can use the emotion estimation function to analyze the emotional reactions of medical workers to infection scenarios and propose stress reduction measures. For example, the emotion estimation function can monitor the stress levels of medical workers in real time and suggest appropriate rest and support. The emotion estimation function can also suggest counseling or the introduction of a leave system based on the emotional data of medical workers. Furthermore, the emotion estimation function can analyze the emotional scores of medical workers and propose stress reduction programs. In this way, by analyzing the emotional reactions of medical workers and proposing stress reduction measures, the burden on medical workers can be reduced.

[0105] The policy proposal department can use the emotion estimation function to evaluate the emotional impact of proposed countermeasures on the public and propose countermeasures that are easy to accept. For example, the emotion estimation function evaluates the psychological impact of a lockdown and provides appropriate information. The emotion estimation function also suggests communication methods and policy flexibility based on public emotion data. Furthermore, the emotion estimation function analyzes the public's emotion scores and proposes countermeasures that are easy to accept. As a result, by evaluating the emotional impact on the public and proposing countermeasures that are easy to accept, the feasibility of policies is improved.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The scenario generation unit predicts unknown pathogen patterns. For example, the generation AI analyzes past infection data and environmental data to predict future infectious disease outbreak patterns. The generation AI also simulates how a new virus spreads and builds an infection scenario based on the results. For example, the generation AI receives prompts containing past infection data and environmental data as input and generates a scenario based on those prompts. Step 2: The intervention evaluation component evaluates countermeasures based on the scenario generated by the scenario generation component. For example, the generation AI simulates the extent to which a lockdown in a specific area will curb the spread of infection and proposes optimal countermeasures based on the results. For example, the generation AI receives prompts as input, including an evaluation of countermeasures based on the scenario, and evaluates and proposes countermeasures based on the prompts. Step 3: The policy proposal component proposes policies based on the results evaluated by the intervention evaluation component. For example, the generation AI simulates optimal policies to prevent the spread of infection and makes proposals to policymakers based on the results. For example, the generation AI receives prompts containing policy proposals based on simulation data as input and proposes policies based on those prompts.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0152] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a scenario generation unit that predicts unknown pathogen patterns; an intervention evaluation unit that evaluates countermeasures based on the scenario generated by the scenario generation unit; a policy proposal unit that proposes a policy based on the results of the evaluation by the intervention evaluation unit. A system characterized by:

2. The scenario generation unit Analyzing past infection data and environmental data to predict future patterns of infectious disease outbreaks 2. The system of claim 1.

3. The intervention evaluation unit Based on the scenarios predicted by the generative AI, the spread of infection in areas with different socioeconomic backgrounds is simulated.

2. The system of claim 1.

4. The policy proposal department Based on the countermeasures proposed by the generative AI, the system simulates collaboration scenarios between different medical institutions and proposes the optimal collaboration method.

2. The system of claim 1.

5. The scenario generation unit Using emotion estimation capabilities, we evaluate the psychological impact of infection scenarios on the public and propose scenarios to reduce the psychological burden.

2. The system of claim 1.

6. The intervention evaluation unit Using emotion estimation capabilities, we analyze the emotional responses of healthcare workers to infection scenarios and suggest stress reduction measures.

2. The system of claim 1.

7. The policy proposal department Using an emotion estimation function, the emotional impact of the proposed countermeasures on the public is evaluated, and the acceptable countermeasures are proposed.

2. The system of claim 1.

8. The policy proposal department Based on the simulation data provided by the AI, the impact of different legal regulations will be evaluated and optimal legal countermeasures will be proposed.

2. The system of claim 1.

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