System

Abstract

An object of the system according to the embodiment is to perform quick and effective prediction and measures for preventing occurrence of a new pandemic.SOLUTION: A system includes a variant prediction unit, a vaccine design unit, a data collection unit, and a preventive measure proposal unit. The variant prediction unit predicts a variant of a virus. The vaccine design unit designs a vaccine based on the structure information of the variant predicted by the variant prediction unit. The data collection unit collects and analyzes disease data collected from around the world in real time. A preventive measure proposal part proposes preventive measures in cooperation with an international health organization on the basis of the data collected and analyzed by the data collection part.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] Conventional technology has been unable to provide rapid and effective predictions and countermeasures to prevent the outbreak of new pandemics.

[0005] The system according to the embodiment aims to make rapid and effective predictions and take measures to prevent the outbreak of new pandemics. [Means for solving the problem]

[0006] The system according to the embodiment includes a variant prediction unit, a vaccine design unit, a data collection unit, and a preventive measures proposal unit. The variant prediction unit predicts viral variants. The vaccine design unit designs vaccines based on structural information about the variants predicted by the variant prediction unit. The data collection unit collects and analyzes disease data collected from around the world in real time. The preventive measures proposal unit works with international health organizations to propose preventive measures based on the data collected and analyzed by the data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can make quick and effective predictions and take measures to prevent the outbreak of new pandemics. [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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile 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 AI ​​system according to an embodiment of the present invention uses AlphaFold2 to predict virus mutations and rapidly design vaccines. This system collects and analyzes disease data in real time and works with international health organizations to propose preventive measures. This enables the AI ​​system to prevent new pandemics and minimize their social and economic impact.

[0029] The AI ​​system according to the embodiment includes a variant prediction unit, a vaccine design unit, a data collection unit, and a preventive measures proposal unit. The variant prediction unit predicts virus variants using AlphaFold2. For example, AlphaFold2 uses the virus's genetic information as input to predict its structure. The variant prediction unit can also evaluate the characteristics and infectivity of variants based on the predicted structure. The vaccine design unit designs vaccines based on the structural information of the variant predicted by the variant prediction unit. For example, the generation AI designs an antigen optimal for the predicted virus structure and develops a vaccine based on that antigen. The data collection unit collects and analyzes disease data collected from around the world in real time. For example, the generation AI analyzes data such as the number of infected people, the number of deaths, and the infection status by region to identify trends in infection spread and high-risk areas. The preventive measures proposal unit works with international health organizations to propose preventive measures based on the data collected and analyzed by the data collection unit. For example, it proposes measures such as strengthened testing, travel restrictions, and vaccination promotion for areas at high risk of infection spread. As a result, the AI ​​system according to the embodiment can prevent new pandemics and minimize their social and economic impact.

[0030] The variant prediction unit uses AlphaFold2 to analyze the evolutionary pattern of the virus and predict the possibility of future mutations. The variant prediction unit analyzes the evolutionary pattern based on, for example, the structural data of the virus predicted by AlphaFold2. For example, it compares this data with past mutation data and predicts what kind of mutations may occur in the future. This allows the evolutionary pattern of the virus to be analyzed and the possibility of future mutations to be predicted.

[0031] The mutant strain prediction unit can simulate the infectivity and possibility of immune escape based on the predicted mutant strain structure, and perform risk assessment. The mutant strain prediction unit simulates the infectivity and possibility of immune escape based on the structural data of mutant strains predicted by AlphaFold2, for example. For example, it analyzes the binding site of the spike protein and predicts changes in infectivity. This makes it possible to simulate the infectivity and possibility of immune escape of mutant strains and perform risk assessment.

[0032] The vaccine design department can design multiple antigen candidates based on structural information of predicted mutant strains and select the optimal antigen. The vaccine design department can, for example, use generative AI to design multiple antigen candidates for predicted mutant strains and select the optimal antigen. For example, it can design antigens for different parts of the spike protein. This allows multiple antigen candidates to be designed and the optimal antigen to be selected.

[0033] In the vaccine design process, the generative AI can refer to past vaccine data and learn effective design patterns. For example, the vaccine design unit designs a new vaccine based on the design patterns of successful vaccines in the past. This allows the generative AI to refer to past vaccine data and learn effective design patterns.

[0034] The vaccine design department can simultaneously design therapeutic drugs for predicted mutant strains using the generative AI. The vaccine design department can simultaneously design therapeutic drugs for predicted mutant strains using the generative AI. For example, it can design therapeutic drugs that act on specific parts of the virus. This allows it to simultaneously design therapeutic drugs for predicted mutant strains.

[0035] The Vaccine Design Department can develop hybrid vaccines that combine different technologies during the vaccine design process. For example, the Vaccine Design Department can develop hybrid vaccines that combine different technologies during the vaccine design process. For example, an mRNA vaccine can be combined with a viral vector vaccine. This allows for the development of hybrid vaccines that combine different technologies.

[0036] The data collection unit can use the generation AI to automatically detect outliers and missing data in the collected disease data, thereby improving data quality. The data collection unit, for example, uses the generation AI to automatically detect outliers and missing data in the collected disease data. For example, it identifies outliers and missing data and improves data quality. This makes it possible to automatically detect outliers and missing data in the collected disease data, thereby improving data quality.

[0037] During the data analysis process, the data collection unit allows the generation AI to refer to past pandemic data and learn patterns of infection spread. For example, the data collection unit allows the generation AI to refer to past pandemic data and learn patterns of infection spread. For example, the data collection unit predicts current trends in infection spread based on past infection spread data. This allows the generation AI to refer to past pandemic data and learn patterns of infection spread.

[0038] The data collection unit can simulate the spread of infection based on the data collected using the generation AI and predict the effectiveness of preventive measures. The data collection unit, for example, uses the generation AI to simulate the spread of infection based on the collected data. For example, it can simulate an infection spread scenario in a specific area and predict the effectiveness of preventive measures. This makes it possible to simulate the spread of infection based on the collected data and predict the effectiveness of preventive measures.

[0039] The data collection department can integrate different data sources in the data collection process to perform more comprehensive analysis. For example, the data collection department can collect social media data and traffic data to analyze the factors behind the spread of infection. This allows the data collection department to integrate different data sources and perform more comprehensive analysis.

[0040] The preventive measures proposal unit can use the generating AI to analyze data linked with international health organizations and develop an algorithm that proposes optimal preventive measures. The preventive measures proposal unit can, for example, use the generating AI to analyze data linked with international health organizations and develop an algorithm that proposes optimal preventive measures. For example, it can propose preventive measures for areas with a high risk of infection spreading. This makes it possible to develop an algorithm that analyzes data linked with international health organizations and proposes optimal preventive measures.

[0041] In the preventive measure suggestion unit, the generation AI refers to past preventive measure data during the preventive measure suggestion process, allowing it to learn effective proposal patterns. For example, the preventive measure suggestion unit allows the generation AI to refer to past preventive measure data and learn effective proposal patterns. For example, it proposes new preventive measures based on data on preventive measures that have been successful in the past. This makes it possible to refer to past preventive measure data and learn effective proposal patterns.

[0042] The preventive measures proposal unit can use the generation AI to simulate different scenarios when proposing preventive measures and select the optimal measures. The preventive measures proposal unit can, for example, use the generation AI to simulate different scenarios when proposing preventive measures and select the optimal measures. For example, it can compare an infection spread scenario with a containment scenario and propose the optimal preventive measures. This makes it possible to simulate different scenarios when proposing preventive measures and select the optimal measures.

[0043] The Prevention Measures Proposal Department can develop a data-sharing platform and promote real-time information exchange to strengthen cooperation with international health organizations. The Prevention Measures Proposal Department can develop a data-sharing platform and promote real-time information exchange to strengthen cooperation with international health organizations. For example, infection data from each country can be shared in real time. This can strengthen cooperation with international health organizations and promote real-time information exchange.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The variant prediction unit can take environmental factors and host immune responses into account when analyzing virus evolution patterns. For example, it can build models that incorporate the effects of climate change and urbanization to evaluate their impact on viral evolution. It can also analyze host immune response data to predict the impact of specific immune responses on viral evolution. This allows for more accurate variant prediction.

[0046] The data collection unit can build a predictive model of infection spread based on the data collected using generative AI and simulate the effectiveness of preventive measures. For example, it can simulate an infection spread scenario in a specific area and evaluate the effectiveness of preventive measures. It can also simulate different combinations of preventive measures and select the optimal one. This allows it to build a predictive model of infection spread based on the collected data and simulate the effectiveness of preventive measures.

[0047] The data collection unit can identify the factors behind the spread of infection and evaluate the effectiveness of preventive measures based on the data collected using generative AI. For example, it can analyze the factors behind the spread of infection in a specific area and evaluate the effectiveness of preventive measures. It can also simulate different combinations of preventive measures and select the optimal one. This makes it possible to identify the factors behind the spread of infection based on the collected data and evaluate the effectiveness of preventive measures.

[0048] The preventive measures proposal unit uses generative AI to simulate different scenarios when proposing preventive measures and select the optimal measures. For example, it compares an infection spread scenario with a containment scenario and proposes the optimal preventive measures. It also simulates different combinations of preventive measures and selects the optimal measures. This makes it possible to simulate different scenarios when proposing preventive measures and select the optimal measures.

[0049] The Prevention Measures Proposal Unit uses generative AI to analyze data from collaboration with international health organizations and develop algorithms that propose optimal prevention measures. For example, it can propose preventive measures for areas with a high risk of infection spreading. It can also simulate different combinations of preventive measures and select the optimal one. This makes it possible to analyze data from collaboration with international health organizations and develop algorithms that propose optimal prevention measures.

[0050] The data collection unit can simulate the spread of infection based on the data collected using generative AI and predict the effectiveness of preventive measures. For example, it can simulate an infection spread scenario in a specific area and predict the effectiveness of preventive measures. It can also simulate different combinations of preventive measures and select the optimal one. This makes it possible to simulate the spread of infection based on the collected data and predict the effectiveness of preventive measures.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The mutant strain prediction unit uses AlphaFold2 to predict virus mutant strains. AlphaFold2 takes the virus's genetic information as input and predicts its structure. The mutant strain prediction unit can also evaluate the characteristics and infectivity of mutant strains based on the predicted structure. Step 2: The vaccine design department designs a vaccine based on the structural information of the mutant strain predicted by the mutant strain prediction department. The generative AI designs an antigen that is optimal for the predicted virus structure and develops a vaccine based on that antigen. Step 3: The data collection unit collects and analyzes disease data from around the world in real time. The generation AI analyzes data such as the number of infected people, the number of deaths, and the infection situation by region to identify trends in the spread of infection and high-risk areas. Step 4: The Prevention Measures Proposal Division will work with international health organizations to propose preventive measures based on the data collected and analyzed by the Data Collection Division, such as strengthening testing, restricting movement, and promoting vaccinations in areas at high risk of infection.

[0053] (Example 2) The AI ​​system according to an embodiment of the present invention uses AlphaFold2 to predict virus mutations and rapidly design vaccines. This system collects and analyzes disease data in real time and works with international health organizations to propose preventive measures. This enables the AI ​​system to prevent new pandemics and minimize their social and economic impact.

[0054] The AI ​​system according to the embodiment includes a variant prediction unit, a vaccine design unit, a data collection unit, and a preventive measures proposal unit. The variant prediction unit predicts virus variants using AlphaFold2. For example, AlphaFold2 uses the virus's genetic information as input to predict its structure. The variant prediction unit can also evaluate the characteristics and infectivity of variants based on the predicted structure. The vaccine design unit designs vaccines based on the structural information of the variant predicted by the variant prediction unit. For example, the generation AI designs an antigen optimal for the predicted virus structure and develops a vaccine based on that antigen. The data collection unit collects and analyzes disease data collected from around the world in real time. For example, the generation AI analyzes data such as the number of infected people, the number of deaths, and the infection status by region to identify trends in infection spread and high-risk areas. The preventive measures proposal unit works with international health organizations to propose preventive measures based on the data collected and analyzed by the data collection unit. For example, it proposes measures such as strengthened testing, travel restrictions, and vaccination promotion for areas at high risk of infection spread. As a result, the AI ​​system according to the embodiment can prevent new pandemics and minimize their social and economic impact.

[0055] The variant prediction unit uses AlphaFold2 to analyze the evolutionary pattern of the virus and predict the possibility of future mutations. The variant prediction unit analyzes the evolutionary pattern based on, for example, the structural data of the virus predicted by AlphaFold2. For example, it compares this data with past mutation data and predicts what kind of mutations may occur in the future. This allows the evolutionary pattern of the virus to be analyzed and the possibility of future mutations to be predicted.

[0056] The mutant strain prediction unit can simulate the infectivity and possibility of immune escape based on the predicted mutant strain structure, and perform risk assessment. The mutant strain prediction unit simulates the infectivity and possibility of immune escape based on the structural data of mutant strains predicted by AlphaFold2, for example. For example, it analyzes the binding site of the spike protein and predicts changes in infectivity. This makes it possible to simulate the infectivity and possibility of immune escape of mutant strains and perform risk assessment.

[0057] The mutant strain prediction unit uses the emotion estimation function to analyze societal anxiety and concern about predicted mutant strains, and can formulate a risk communication strategy. The mutant strain prediction unit uses the emotion estimation function to analyze societal anxiety and concern about mutant strains predicted by AlphaFold2, for example. For example, it collects emotional data from social media and news articles and analyzes societal reactions. This makes it possible to analyze societal anxiety and concern about mutant strains and formulate a risk communication strategy.

[0058] The vaccine design department can design multiple antigen candidates based on structural information of predicted mutant strains and select the optimal antigen. The vaccine design department can, for example, use generative AI to design multiple antigen candidates for predicted mutant strains and select the optimal antigen. For example, it can design antigens for different parts of the spike protein. This allows multiple antigen candidates to be designed and the optimal antigen to be selected.

[0059] In the vaccine design process, the generative AI can refer to past vaccine data and learn effective design patterns. For example, the vaccine design unit designs a new vaccine based on the design patterns of successful vaccines in the past. This allows the generative AI to refer to past vaccine data and learn effective design patterns.

[0060] The vaccine design department can use the emotion estimation function to analyze societal expectations and concerns regarding vaccine design and formulate a communication strategy. The vaccine design department can, for example, use the emotion estimation function to analyze societal expectations and concerns regarding vaccine design. For example, it can collect emotion data from social media and news articles and analyze social reactions. This makes it possible to analyze societal expectations and concerns regarding vaccine design and formulate a communication strategy.

[0061] The vaccine design department can simultaneously design therapeutic drugs for predicted mutant strains using the generative AI. The vaccine design department can simultaneously design therapeutic drugs for predicted mutant strains using the generative AI. For example, it can design therapeutic drugs that act on specific parts of the virus. This allows it to simultaneously design therapeutic drugs for predicted mutant strains.

[0062] The Vaccine Design Department can develop hybrid vaccines that combine different technologies during the vaccine design process. For example, the Vaccine Design Department can develop hybrid vaccines that combine different technologies during the vaccine design process. For example, an mRNA vaccine can be combined with a viral vector vaccine. This allows for the development of hybrid vaccines that combine different technologies.

[0063] The vaccine design unit uses the emotion estimation function to analyze users' emotional reactions to vaccination and can propose measures to increase the vaccination rate. The vaccine design unit, for example, uses the emotion estimation function to analyze users' emotional reactions to vaccination. For example, it collects emotional data from social media and news articles and analyzes reactions to vaccination. This makes it possible to analyze users' emotional reactions to vaccination and propose measures to increase the vaccination rate.

[0064] The data collection unit can use the generation AI to automatically detect outliers and missing data in the collected disease data, thereby improving data quality. The data collection unit, for example, uses the generation AI to automatically detect outliers and missing data in the collected disease data. For example, it identifies outliers and missing data and improves data quality. This makes it possible to automatically detect outliers and missing data in the collected disease data, thereby improving data quality.

[0065] During the data analysis process, the data collection unit allows the generation AI to refer to past pandemic data and learn patterns of infection spread. For example, the data collection unit allows the generation AI to refer to past pandemic data and learn patterns of infection spread. For example, the data collection unit predicts current trends in infection spread based on past infection spread data. This allows the generation AI to refer to past pandemic data and learn patterns of infection spread.

[0066] The data collection unit can analyze social reactions to the data collected using the emotion estimation function and optimize the timing and method of data disclosure. The data collection unit, for example, uses the emotion estimation function to analyze social reactions to the collected data. For example, it collects emotion data from social media and news articles and optimizes the timing and method of data disclosure. This makes it possible to analyze social reactions to the collected data and optimize the timing and method of data disclosure.

[0067] The data collection unit can simulate the spread of infection based on the data collected using the generation AI and predict the effectiveness of preventive measures. The data collection unit, for example, uses the generation AI to simulate the spread of infection based on the collected data. For example, it can simulate an infection spread scenario in a specific area and predict the effectiveness of preventive measures. This makes it possible to simulate the spread of infection based on the collected data and predict the effectiveness of preventive measures.

[0068] The data collection department can integrate different data sources in the data collection process to perform more comprehensive analysis. For example, the data collection department can collect social media data and traffic data to analyze the factors behind the spread of infection. This allows the data collection department to integrate different data sources and perform more comprehensive analysis.

[0069] The data collection unit can use the emotion estimation function to analyze the user's emotional response to data collection and design incentives for data provision. The data collection unit, for example, uses the emotion estimation function to analyze the user's emotional response to data collection. For example, the data collection unit collects emotion data from social media and news articles and designs incentives for data provision. This makes it possible to analyze the user's emotional response to data collection and design incentives for data provision.

[0070] The preventive measures proposal unit can use the generating AI to analyze data linked with international health organizations and develop an algorithm that proposes optimal preventive measures. The preventive measures proposal unit can, for example, use the generating AI to analyze data linked with international health organizations and develop an algorithm that proposes optimal preventive measures. For example, it can propose preventive measures for areas with a high risk of infection spreading. This makes it possible to develop an algorithm that analyzes data linked with international health organizations and proposes optimal preventive measures.

[0071] In the preventive measure suggestion unit, the generation AI refers to past preventive measure data during the preventive measure suggestion process, allowing it to learn effective proposal patterns. For example, the preventive measure suggestion unit allows the generation AI to refer to past preventive measure data and learn effective proposal patterns. For example, it proposes new preventive measures based on data on preventive measures that have been successful in the past. This makes it possible to refer to past preventive measure data and learn effective proposal patterns.

[0072] The preventive measure suggestion unit can use the emotion estimation function to analyze social reactions to the proposed preventive measures and formulate acceptable suggestions. The preventive measure suggestion unit can, for example, use the emotion estimation function to analyze social reactions to the proposed preventive measures. For example, it can collect emotion data from social media and news articles and evaluate the acceptability of the preventive measures. This makes it possible to analyze social reactions to the proposed preventive measures and formulate acceptable suggestions.

[0073] The preventive measures proposal unit can use the generation AI to simulate different scenarios when proposing preventive measures and select the optimal measures. The preventive measures proposal unit can, for example, use the generation AI to simulate different scenarios when proposing preventive measures and select the optimal measures. For example, it can compare an infection spread scenario with a containment scenario and propose the optimal preventive measures. This makes it possible to simulate different scenarios when proposing preventive measures and select the optimal measures.

[0074] The Prevention Measures Proposal Department can develop a data-sharing platform and promote real-time information exchange to strengthen cooperation with international health organizations. The Prevention Measures Proposal Department can develop a data-sharing platform and promote real-time information exchange to strengthen cooperation with international health organizations. For example, infection data from each country can be shared in real time. This can strengthen cooperation with international health organizations and promote real-time information exchange.

[0075] The preventive measure proposal unit can use the emotion estimation function to analyze the emotional reactions of medical professionals to the proposed preventive measures and propose measures that will increase the feasibility of the measures being implemented in the field. The preventive measure proposal unit can, for example, use the emotion estimation function to analyze the emotional reactions of medical professionals to the proposed preventive measures. For example, it can collect data from questionnaires and interviews and calculate emotion scores. This makes it possible to analyze the emotional reactions of medical professionals to the proposed preventive measures and propose measures that will increase the feasibility of the measures being implemented in the field.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The variant prediction unit can take environmental factors and host immune responses into account when analyzing virus evolution patterns. For example, it can build models that incorporate the effects of climate change and urbanization to evaluate their impact on viral evolution. It can also analyze host immune response data to predict the impact of specific immune responses on viral evolution. This allows for more accurate variant prediction.

[0078] The Vaccine Design Department can use the emotion estimation function to monitor social reactions to vaccinations in real time and evaluate the effectiveness of vaccination campaigns. For example, it collects emotion data from social media and news articles and analyzes changes in emotion before and after the start of a vaccination campaign. It also compares emotion data by region and proposes measures to increase vaccination rates in specific areas. This allows it to grasp social reactions to vaccinations in real time and implement effective vaccination campaigns.

[0079] The data collection unit can build a predictive model of infection spread based on the data collected using generative AI and simulate the effectiveness of preventive measures. For example, it can simulate an infection spread scenario in a specific area and evaluate the effectiveness of preventive measures. It can also simulate different combinations of preventive measures and select the optimal one. This allows it to build a predictive model of infection spread based on the collected data and simulate the effectiveness of preventive measures.

[0080] The preventive measures proposal unit uses the emotion estimation function to analyze the emotional reactions of medical professionals to proposed preventive measures and propose measures to increase their feasibility in the field. For example, it collects questionnaire and interview data and calculates an emotion score. It also evaluates the acceptability of preventive measures based on the emotional data of medical professionals and proposes measures to increase their feasibility. This makes it possible to analyze the emotional reactions of medical professionals to proposed preventive measures and propose measures to increase their feasibility in the field.

[0081] The data collection unit can identify the factors behind the spread of infection and evaluate the effectiveness of preventive measures based on the data collected using generative AI. For example, it can analyze the factors behind the spread of infection in a specific area and evaluate the effectiveness of preventive measures. It can also simulate different combinations of preventive measures and select the optimal one. This makes it possible to identify the factors behind the spread of infection based on the collected data and evaluate the effectiveness of preventive measures.

[0082] The Vaccine Design Department uses the emotion estimation function to analyze users' emotional reactions to vaccinations and propose measures to increase vaccination rates. For example, it collects emotional data from social media and news articles and analyzes reactions to vaccinations. It also compares emotional data by region and proposes measures to increase vaccination rates in specific regions. This allows it to analyze users' emotional reactions to vaccinations and propose measures to increase vaccination rates.

[0083] The preventive measures proposal unit uses generative AI to simulate different scenarios when proposing preventive measures and select the optimal measures. For example, it compares an infection spread scenario with a containment scenario and proposes the optimal preventive measures. It also simulates different combinations of preventive measures and selects the optimal measures. This makes it possible to simulate different scenarios when proposing preventive measures and select the optimal measures.

[0084] The data collection unit can use the emotion estimation function to analyze social reactions to collected data and optimize the timing and method of data release. For example, it collects emotion data from social media and news articles and optimizes the timing and method of data release. It can also compare emotion data by region and optimize the timing and method of data release in specific regions. This allows it to analyze social reactions to collected data and optimize the timing and method of data release.

[0085] The Prevention Measures Proposal Unit uses generative AI to analyze data from collaboration with international health organizations and develop algorithms that propose optimal prevention measures. For example, it can propose preventive measures for areas with a high risk of infection spreading. It can also simulate different combinations of preventive measures and select the optimal one. This makes it possible to analyze data from collaboration with international health organizations and develop algorithms that propose optimal prevention measures.

[0086] The data collection unit can simulate the spread of infection based on the data collected using generative AI and predict the effectiveness of preventive measures. For example, it can simulate an infection spread scenario in a specific area and predict the effectiveness of preventive measures. It can also simulate different combinations of preventive measures and select the optimal one. This makes it possible to simulate the spread of infection based on the collected data and predict the effectiveness of preventive measures.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The mutant strain prediction unit uses AlphaFold2 to predict virus mutant strains. AlphaFold2 takes the virus's genetic information as input and predicts its structure. The mutant strain prediction unit can also evaluate the characteristics and infectivity of mutant strains based on the predicted structure. Step 2: The vaccine design department designs a vaccine based on the structural information of the mutant strain predicted by the mutant strain prediction department. The generative AI designs an antigen that is optimal for the predicted virus structure and develops a vaccine based on that antigen. Step 3: The data collection unit collects and analyzes disease data from around the world in real time. The generation AI analyzes data such as the number of infected people, the number of deaths, and the infection situation by region to identify trends in the spread of infection and high-risk areas. Step 4: The Prevention Measures Proposal Division will work with international health organizations to propose preventive measures based on the data collected and analyzed by the Data Collection Division, such as strengthening testing, restricting movement, and promoting vaccinations in areas at high risk of infection.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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 mutant strain prediction unit that predicts a mutant strain of the virus; a vaccine design unit that designs a vaccine based on structural information of the mutant strain predicted by the mutant strain prediction unit; The Data Collection Department collects and analyzes disease data from around the world in real time, and a preventive measures proposal unit that proposes preventive measures in cooperation with international health organizations based on the data collected and analyzed by the data collection unit. A system characterized by:

2. The mutant strain prediction unit Analyzing the evolutionary patterns of the virus and predicting its potential future mutations 2. The system of claim 1.

3. The vaccine design department Based on the predicted structural information of the mutant strain, a plurality of antigen candidates are designed and an optimal antigen is selected.

2. The system of claim 1.

4. The data collection unit Using generative AI to automatically detect outliers and missing data in the collected disease data, improving data quality.

2. The system of claim 1.

5. The preventive measure suggestion unit Using generative AI, we will analyze data from collaboration with international health organizations and develop algorithms that suggest optimal preventive measures.

2. The system of claim 1.

6. The mutant strain prediction unit Analyze public fears and concerns about the predicted variants and develop a risk communication strategy.

2. The system of claim 1.

7. The vaccine design department Analyze public expectations and concerns regarding vaccine design and develop a communication strategy 2. The system of claim 1.

8. The data collection unit Analyze social reactions to the collected data and optimize the timing and method of releasing the data.

2. The system of claim 1.

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