System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting disease data in real time, a means for predicting a mutant strain of a virus based on the collected data, a means for quickly designing a customized vaccine based on the predicted mutant strain, a means for cooperating with an international health organization for implementing the designed vaccine and preventive measures, and a means for monitoring and evaluating the effect of the implemented preventive measures.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] In modern society, unknown epidemics and their rapid spread pose a major threat. Conventional manual methods for disease surveillance and vaccine development make it difficult to respond quickly when mutant strains emerge. If a pandemic were to break out, this would likely have a devastating impact on society and the economy. The present invention aims to solve this problem by providing a system that collects and analyzes disease data in real time, enabling the rapid design of vaccines and the implementation of preventive measures. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means. Specifically, it provides a system including a means for collecting disease data in real time, a means for predicting virus mutations based on the collected data, a means for rapidly designing customized vaccines based on the predicted mutations, a means for collaborating with international health organizations to implement the designed vaccines and preventive measures, and a means for monitoring and evaluating the effectiveness of the implemented preventive measures. Furthermore, the system includes a means for preprocessing the collected disease data to remove noise and impute missing values, thereby improving data quality. Furthermore, the system includes a means for predicting virus mutations using the AlphaFold algorithm, thereby achieving highly accurate predictions.

[0006] "Real-time" refers to data collection and processing occurring immediately with very low latency.

[0007] "Disease data" refers to data that includes various information about infectious diseases and illnesses, such as the number of infected people, the number of deaths, test results, and regional information.

[0008] "Collect" means to gather data or information, in this case to obtain information related to a disease.

[0009] A "virus" is a pathogen that causes infectious diseases, and in this case refers to a microscopic pathogen that contains RNA or DNA.

[0010] A "mutant strain" refers to a strain of a virus or bacteria that has different characteristics due to genetic mutations.

[0011] "Prediction" refers to estimating future events or conditions based on data and algorithms.

[0012] "Customized" means specially created or adapted to meet particular conditions or requirements.

[0013] "Vaccine" refers to a biological product for inducing immunity against a pathogen.

[0014] "Design" refers to the process of planning and realizing new systems and products.

[0015] "International health organization" refers to an intergovernmental organization whose purpose is to improve public health throughout the world.

[0016] "Collaboration" refers to activities in which multiple organizations or individuals work together to achieve a common goal.

[0017] "Preventive measures" refer to measures or actions taken to prevent the occurrence or spread of disease.

[0018] "Surveillance" refers to the close observation of a particular activity or event, in this case, the ongoing monitoring of disease outbreaks or the effectiveness of vaccines.

[0019] "Evaluation" refers to judging the value or effectiveness of something based on specific standards or measures.

[0020] "Preprocessing" refers to the initial stage of processing raw data to make it easier to analyze.

[0021] "Noise removal" refers to removing unnecessary information and errors contained in data.

[0022] "Missing value imputation" refers to inferring and filling in missing information in an incomplete data set.

[0023] "AlphaFold" refers to an algorithm for predicting the three-dimensional structure of proteins using artificial intelligence. [Brief explanation of the drawings]

[0024] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] 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.

[0026] First, the terms used in the following description will be explained.

[0027] 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, a 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), and an APU (Accelerated Processing Unit).

[0028] 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.

[0029] 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.

[0030] 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), Bluetooth (registered trademark), etc.

[0031] 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."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0036] 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.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0039] 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.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] The system for carrying out the present invention comprises a plurality of servers, terminals, and users. The specific configuration of this system and the processing of the programs will be described in detail below.

[0046] System Configuration

[0047] 1. Server:

[0048] Data Collection Server

[0049] Data analysis server

[0050] Prediction Server

[0051] Vaccine Design Server

[0052] Collaboration Server

[0053] Monitoring and evaluation server

[0054] 2. Terminal:

[0055] Public health officer terminal

[0056] Medical staff terminals

[0057] Researcher terminal

[0058] Biopharmaceutical company terminals

[0059] 3. User:

[0060] Public health officials

[0061] Healthcare workers

[0062] researcher

[0063] Biopharmaceutical company researchers

[0064] Program processing

[0065] The overall system program is designed so that each server and terminal work together. Here, we will explain the roles of each server, terminal, and user, along with specific examples.

[0066] Data collection

[0067] Server: The data collection server collects disease data in real time from national public health agencies, hospitals, research institutes, etc. This includes the ability to retrieve information using APIs and database connections and convert it into standardized data formats.

[0068] Example: A data collection server receives reports from international health organizations about outbreaks of new strains of influenza.

[0069] Users: Public health and healthcare workers use devices to manually or automatically enter case information and send it to a server.

[0070] Data preprocessing and analysis

[0071] Server: The data analysis server preprocesses the collected data, removing noise and completing missing values. Using the preprocessed data, it performs clustering using a machine learning algorithm to group similar cases.

[0072] Example: A data analysis server identifies areas where similar cases are occurring frequently and performs clustering.

[0073] Predicting mutant strains

[0074] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations. Based on the collected viral genetic data, it analyzes the structure of mutations that may emerge in the future.

[0075] Example: A prediction server calculates the probability of new mutant strains emerging and visualizes their structural changes.

[0076] Terminal: Researchers check the predicted data for mutant strains provided by the prediction server on their terminals and perform detailed analysis.

[0077] Vaccine Design

[0078] Server: The vaccine design server rapidly designs customized vaccines based on predicted variant information, including generating optimal vaccine components and manufacturing processes.

[0079] Example: A vaccine design server designs vaccine components that are effective against specific mutant strains and proposes their manufacturing process.

[0080] Terminal: Researchers at biopharmaceutical companies use the terminal to review the details of the designed vaccine and prepare for production at the laboratory level.

[0081] Implementing preventive measures and coordinating

[0082] Server: Based on the collected data and prediction results, the collaborative server will propose appropriate preventive measures to international health organizations and local governments, such as planning vaccination campaigns and proposing lockdown measures.

[0083] Example: A federated server provides international health organizations with detailed plans for launching vaccination campaigns in specific regions.

[0084] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccinations.

[0085] Monitoring and Evaluation

[0086] Server: The monitoring and evaluation server monitors the effectiveness of the implemented preventive measures and vaccines in real time. It analyzes the reduction in the number of infections after vaccination and reports of side effects to confirm the effectiveness of the vaccine.

[0087] Example: A monitoring and evaluation server analyzes data on the reduction in infection rates after vaccination to assess the success of the vaccine.

[0088] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into the database.

[0089] These processes provide a system for early detection of epidemics, rapid response, and prevention of pandemics.

[0090] The processing flow will be explained below.

[0091] Step 1:

[0092] Server: The data collection server collects disease data in real time from public health agencies, hospitals, research institutes, etc. in each country. It obtains information via APIs and database connections and converts it into a standardized data format.

[0093] Step 2:

[0094] Users: Public health and healthcare workers use devices to manually or automatically enter case information and send it to a data collection server.

[0095] Step 3:

[0096] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing the data, and preparing it for analysis.

[0097] Step 4:

[0098] Server: The data analysis server uses pre-processed data to perform clustering using machine learning algorithms, which allows for grouping of similar cases and identification of infection patterns.

[0099] Step 5:

[0100] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations based on the clustered data. It uses viral genetic data as input and analyzes the structure of mutations that may emerge in the future.

[0101] Step 6:

[0102] Terminal: Researchers receive the predicted data on variants provided by the prediction server and perform detailed analysis, including evaluation of the characteristics and infectivity of the variants.

[0103] Step 7:

[0104] Server: The vaccine design server will rapidly design customized vaccines based on predicted variant information, select vaccine components, design manufacturing processes, and simulate preventive effects.

[0105] Step 8:

[0106] Terminal: Researchers at biopharmaceutical companies receive design data from the vaccine design server and prepare for production at the laboratory level, including setting up production equipment and manufacturing prototypes.

[0107] Step 9:

[0108] Server: Based on the collected data and prediction results, the collaborative server will propose appropriate preventive measures to international health organizations and local governments, providing instructions on planning vaccination campaigns and implementing lockdown measures.

[0109] Step 10:

[0110] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccination implementation.

[0111] Step 11:

[0112] Server: The monitoring and evaluation server will monitor the effectiveness of the implemented preventive measures and vaccines in real time, collecting and analyzing reports of the reduction in the number of infections and side effects after vaccination.

[0113] Step 12:

[0114] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into a database, allowing the effectiveness of the entire system to be continuously monitored.

[0115] In this way, through each step, a system is provided that enables early detection of epidemics, rapid response, and prevention of pandemics.

[0116] Example 1

[0117] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0118] Conventional disease data collection and analysis systems often lack real-time capabilities, and noise and missing values ​​in the collected data often adversely affect the analysis results. Furthermore, they lack the speed to predict virus mutations and design vaccines, making it difficult to prevent the spread of infectious diseases. A system that can resolve these issues and enable more effective and rapid public health measures is needed.

[0119] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0120] In this invention, the server includes: means for collecting disease data in real time from public health organizations, hospitals, research institutions, etc.; means for preprocessing the collected data to remove noise and fill in missing values; means for analyzing the preprocessed data using a machine learning algorithm and clustering similar cases; means for predicting virus mutations using the AlphaFold algorithm; means for quickly designing customized vaccines based on the predicted mutations; means for providing plans for vaccination campaigns and lockdown measures to international health organizations and local governments through a linked server; and means for monitoring and evaluating the effectiveness of implemented preventive measures and vaccines in real time. This enables real-time data collection and analysis, rapid prediction of virus mutations and vaccine design, and implementation and evaluation of effective public health measures.

[0121] "Means for collecting disease data in real time" refers to methods for obtaining timely disease data from public health agencies, hospitals, research institutions, etc.

[0122] "Noise removal" is the process of cleaning data by removing unnecessary data and outliers from the data.

[0123] "Missing value imputation" is the process of filling in missing values ​​in a dataset with appropriate values ​​(e.g., averages or estimated values).

[0124] "Preprocessed data" refers to a dataset after noise removal and missing value imputation have been performed.

[0125] A "machine learning algorithm" is a computational method for learning from data and making predictions or classifications.

[0126] "Clustering" is a machine learning algorithm that divides data into multiple groups based on similarity.

[0127] The "AlphaFold algorithm" is a computational model for predicting the three-dimensional structure of proteins.

[0128] "Mutant prediction" is the process of predicting how pathogens such as viruses will mutate.

[0129] A "customized vaccine" is a vaccine that is optimized for a specific variant.

[0130] A "collaboration server" is a server that allows multiple institutions and devices to share information and work together to carry out tasks.

[0131] A "vaccination campaign" is an activity that encourages and administers vaccination to a wide range of people.

[0132] "Lockdown measures" are measures to close specific areas or facilities to prevent the spread of infectious diseases.

[0133] "Monitoring and evaluation measures" are methods for continuously observing and evaluating the effectiveness of implemented measures and vaccines.

[0134] MODE FOR CARRYING OUT THE INVENTION

[0135] The system for implementing the present invention comprises a plurality of servers, terminals, and users. The specific configuration of this system and the processing of the programs are described below.

[0136] System Configuration

[0137] server

[0138] 1. Data Collection Server

[0139] The data collection server is a server that collects disease data in real time from public health agencies, hospitals, and research institutes. Specifically, it has the function of retrieving information using APIs and database connections and converting it into a standardized data format.

[0140] 2. Data analysis server

[0141] The data analysis server preprocesses the collected data, removes noise, and fills in missing values. It also uses the preprocessed data to implement machine learning algorithms and cluster similar cases.

[0142] 3. Prediction Server

[0143] The prediction server uses the AlphaFold algorithm to predict virus mutations. Based on collected viral genetic data, it analyzes the structure of mutations that may emerge in the future.

[0144] 4. Vaccine Design Server

[0145] The Vaccine Design Server is a server for rapidly designing customized vaccines based on predicted variant information, including generating optimal vaccine components and manufacturing processes.

[0146] 5. Collaboration Server

[0147] The collaborative server will use the collected data and prediction results to propose appropriate preventive measures to international health organizations and local governments, such as planning vaccination campaigns and proposing lockdown measures.

[0148] 6. Monitoring and evaluation server

[0149] The monitoring and evaluation server will monitor the effectiveness of the implemented preventive measures and the vaccine in real time, analyzing the reduction in the number of infected people after vaccination and reports of side effects to confirm the effectiveness of the vaccine.

[0150] Terminal

[0151] 1. Public Health Officer Terminals

[0152] Public health personnel terminals are terminals that public health personnel use to input disease data and connect to a server, for example, with the ability to input case data through dedicated software or a web app.

[0153] 2. Medical device

[0154] The medical staff terminal is a terminal where medical staff input and check case data and send it to the data collection server.

[0155] 3. Researcher terminals

[0156] The researcher terminals are used by researchers to check data analysis results and mutant strain information and perform detailed analysis, for example, using dedicated analysis software (e.g., Biopython, PyMOL).

[0157] 4. Terminals for biopharmaceutical companies

[0158] The biopharmaceutical company terminal is a terminal where researchers at biopharmaceutical companies can check the details of the designed vaccine and prepare for the manufacturing process.

[0159] User

[0160] 1. Public health officials

[0161] Public health officials are responsible for collecting disease data and implementing and monitoring preventive measures. They input data using dedicated terminals.

[0162] 2. Healthcare workers

[0163] Medical professionals collect case data at hospitals and clinics, send it to a data collection server, and enter and check the case data.

[0164] 3. Researcher

[0165] Researchers will conduct detailed studies based on data analysis and predictions of mutant strains, and plan further experiments based on the results of the analysis.

[0166] 4. Researchers at biopharmaceutical companies

[0167] Researchers at biopharmaceutical companies will review the details of the designed vaccine, prepare for production at the laboratory level, and conduct efficacy and safety tests.

[0168] Examples of concrete examples and prompts

[0169] Example: A data collection server accesses the API of an international health organization to periodically retrieve reports of new influenza outbreaks.

[0170] Example: A prediction server uses the AlphaFold algorithm to predict structural changes in new mutant strains and provides the results to researchers' terminals.

[0171] Example prompt: "Please explain how the data collection server receives reports of new influenza outbreaks from international health organizations, and how the analysis server performs noise removal and missing value imputation."

[0172] In this way, the entire system works together to enable early detection of epidemics, rapid response, and pandemic prevention.

[0173] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0174] Step 1: Data collection

[0175] The server collects disease data in real time from public health agencies, hospitals, research institutes, etc. Specifically, it retrieves information using APIs and database connections and converts it into standardized data formats (e.g., JSON, CSV). This collected data is then sent to a data analysis server for analysis in the next step. For example, the server periodically requests the API of an international health organization to retrieve reports of new influenza outbreaks.

[0176] Input: Disease data from public health agencies, hospitals, and research institutes

[0177] Output: Standardized disease data

[0178] Step 2: Data Preprocessing

[0179] The server preprocesses the collected data, including noise removal and missing value imputation. Specifically, the data analysis server removes unnecessary data and outliers from the data and imputes missing data with appropriate values ​​(e.g., mean values ​​or estimated values). This preprocessed data is used in the next analysis step. For example, the data analysis server imputes missing values ​​with the mean values ​​calculated for the data rows in which they exist.

[0180] Input: Collected disease data

[0181] Output: Preprocessed disease data

[0182] Step 3: Data analysis

[0183] The server then applies a machine learning algorithm to the preprocessed data. Specifically, it uses a clustering algorithm (e.g., k-means) to group similar cases. The results of this analysis are then used in the next step of variant prediction. For example, the data analysis server analyzes cases that are occurring frequently in a specific region and generates groups using k-means clustering.

[0184] Input: Preprocessed disease data

[0185] Output: Clustered case data

[0186] Step 4: Predicting variants

[0187] The server uses the AlphaFold algorithm to predict virus mutations. Specifically, it receives viral genetic data as input and analyzes the protein structures of potential future mutations. The prediction results are used in the next step of vaccine design. For example, the prediction server receives the amino acid sequence of a specific part of the virus as input and uses AlphaFold to predict likely mutations.

[0188] Input: viral genetic data

[0189] Output: Predicted variant information

[0190] Step 5: Vaccine design

[0191] The server designs optimal vaccines based on predicted variant information. Specifically, it identifies epitopes that are effective against different parts of the virus and uses algorithms (e.g., RosettaDesign) to design peptide vaccines. The designed vaccine information is then used in the next step of implementing preventive measures. For example, the vaccine design server calculates the optimal epitope for a specific variant and proposes its production procedure.

[0192] Input: Predicted mutant information

[0193] Output: Designed vaccine information

[0194] Step 6: Implement preventative measures and collaborate

[0195] Based on the collected data and prediction results, the collaboration server will propose appropriate preventive measures to international health organizations and local governments. Specifically, it will generate plans for vaccination campaigns and lockdown measures and notify relevant organizations. For example, the collaboration server will develop a detailed schedule for public health campaigns and vaccinations to prevent the spread of a new strain of influenza in a specific area.

[0196] Input: Designed vaccine information, predicted results

[0197] Output: Suggested preventative measures

[0198] Step 7: Monitor and evaluate

[0199] The monitoring and evaluation server monitors the effectiveness of implemented preventive measures and vaccinations in real time. Specifically, it evaluates the effectiveness of vaccines by collecting and analyzing data on the number of infected people after vaccination and reports of side effects. For example, the monitoring and evaluation server analyzes data on the reduction in infection rates after vaccination to evaluate the success of the vaccine.

[0200] Input: Preventive measures taken, vaccination data

[0201] Output: Effect evaluation results

[0202] This processing step enables the entire system to work together to detect diseases early, respond quickly, and prevent pandemics.

[0203] (Application example 1)

[0204] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0205] Rapid detection and prediction of infectious diseases and the implementation of effective preventive measures are crucial for modern public health. However, current systems lack responsiveness and accuracy because they lack centralized collection of real-time disease data, prediction of mutant strains, design and implementation of vaccines, and monitoring of their effectiveness. Therefore, there is a need for a system that can monitor infectious disease risk at the individual user level in real time, quickly send alerts in high-risk areas, and provide timely vaccination information.

[0206] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0207] In this invention, the server includes means for collecting disease data in real time, means for predicting virus mutations based on the collected data, means for quickly designing customized vaccines based on the predicted mutations, means for coordinating with international health organizations to implement the designed vaccines and preventive measures, means for monitoring and evaluating the effectiveness of the implemented preventive measures, means for monitoring disease risk in real time and for issuing alerts for high-risk areas, and means for managing and notifying the timing of vaccinations. This enables rapid detection and prediction of infectious diseases, real-time risk monitoring, and the implementation and monitoring of appropriate preventive measures.

[0208] "Disease Data" refers to information that includes the occurrence, progression, and related information of a disease in an individual or population.

[0209] A "virus variant" refers to a subspecies of a virus with certain mutations or genetic changes.

[0210] A "customized vaccine" refers to a vaccine designed to have specific efficacy against predicted variants.

[0211] "international health organization" means an international organization established to prevent and control disease and provide health-related services;

[0212] "Real-time monitoring" refers to the immediate observation and analysis of ongoing situations and data.

[0213] A "high-risk area" refers to an area where the risk of certain diseases or health conditions is considered higher than in other areas.

[0214] "Alert notification" refers to a system that warns or notifies users when certain criteria are met.

[0215] "Managing vaccination timing" refers to adjusting schedules and providing notifications so that vaccinations are given at the appropriate time.

[0216] "Monitoring and evaluation" refers to the continuous observation and analysis of the effectiveness and feasibility of preventive measures implemented.

[0217] The embodiment of the present invention is a system in which various servers and terminals work in cooperation with each other. The program processing of this system will be explained below.

[0218] Hardware and software used

[0219] Hardware:

[0220] Smartphone (iOS / ANDROID (registered trademark))

[0221] software:

[0222] Mobile app development framework: React Native

[0223] Data Collection API: Firebase

[0224] Machine learning model: TENSORFLOW(R) Lite

[0225] Map API: Google(R) Maps API

[0226] Notification service: OneSignal

[0227] Program processing explanation

[0228] 1. Data Collection and Preprocessing

[0229] The server uses Firebase to collect disease data in real time, including location information and case report information. The data collection server preprocesses the collected data, removing noise and imputing missing values.

[0230] 2. Data analysis and mutant strain prediction

[0231] The server uses TensorFlow Lite to predict virus mutations based on collected data, and the AlphaFold algorithm to analyze the structure of potential future mutations.

[0232] 3. Risk monitoring and alert notifications

[0233] The device uses Google Maps API for real-time disease risk monitoring, visualizing high-risk areas based on the user's current location, and provides alerts via OneSignal if the user enters a high-risk area.

[0234] 4. Vaccine design and timing management

[0235] The server will quickly design a customized vaccine based on predicted variant information, manage vaccination information on the device, and notify users when to get vaccinated.

[0236] Specific examples and generated AI model prompts

[0237] Example 1: If a new strain of influenza is confirmed within 10km of a user's home, the app will display that information on a map and highlight the area as a risk. An alert will be displayed saying, "You have entered a high-risk area. Please take precautions."

[0238] Example 2: When it's time for vaccination, you'll be notified with the message, "It's almost time for your vaccination. The next vaccination site is your local hospital. Would you like to make an appointment?"

[0239] Generative AI model prompt:

[0240] "Please develop a smartphone app that will monitor the risk of infection within a 10km radius in real time and notify you if you approach a risk area."

[0241] "Design an application that manages users' vaccination information and notifies them of the best time to get vaccinated."

[0242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0243] Step 1:

[0244] The server uses Firebase to collect disease data. The input here is real-time data such as user location information and case report information. The server collects this data and converts it into a standardized data format. The output is a dataset in a format that can be preprocessed. Specific operations include collecting location information and obtaining real-time data through an API.

[0245] Step 2:

[0246] The server preprocesses the collected data, removing noise and imputing missing values. The input is the dataset collected in step 1. The server uses machine learning algorithms to cleanse the data and generate reliable data. The output is a high-quality dataset suitable for analysis. Specific operations include applying missing value imputation algorithms and noise filtering.

[0247] Step 3:

[0248] The server uses TensorFlow Lite to cluster disease risks and predict virus mutations based on the preprocessed data. The input is the dataset obtained in step 2. The server applies a machine learning model to analyze future risk areas and the likelihood of mutations. The output is detailed information on risk assessment results and mutation predictions. Specific operations include predictive modeling using a clustering algorithm and the AlphaFold algorithm.

[0249] Step 4:

[0250] The device uses the Google Maps API to display risk areas based on the user's current location on a map. The input is the risk assessment result obtained in step 3 and the current location information. The device visualizes the risk areas on a map and highlights dangerous areas. The output is the display of risk areas on the user interface. Specific operations include rendering the map and drawing polygons of the risk areas.

[0251] Step 5:

[0252] The device will send an alert notification via OneSignal if it enters a high-risk area. The input is the risk area information displayed in step 4 and the user's real-time location information. If the location information matches the risk area, the device will immediately send a notification to the user. The output is an alert message. Specific operations include real-time location comparison and notification triggering.

[0253] Step 6:

[0254] The server designs a vaccine based on the predicted mutant strain data. The input is the mutant strain prediction data obtained in step 3. The server designs a customized vaccine that is adapted to the new mutant strain based on current vaccine information. The output is detailed information about the designed vaccine. Specifically, the vaccine components are optimized using bioinformatics algorithms.

[0255] Step 7:

[0256] The terminal manages the user's vaccination information and notifies the user of vaccination at the appropriate time. The input is the user's vaccination history and vaccine information provided by the server. The terminal automatically calculates the next vaccination date and sends a notification to the user when the time approaches. The output is a vaccination reminder message. Specific operations include managing historical data and generating a notification schedule.

[0257] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0258] The system for implementing this invention is composed of multiple servers, terminals, and users. Furthermore, by incorporating an emotion engine, the effectiveness of the system can be improved by utilizing user emotion data. The specific configuration of this system and the program processing are described in detail below.

[0259] System Configuration

[0260] 1. Server:

[0261] Data Collection Server

[0262] Data analysis server

[0263] Prediction Server

[0264] Vaccine Design Server

[0265] Collaboration Server

[0266] Monitoring and evaluation server

[0267] Emotion Engine Server

[0268] 2. Terminal:

[0269] Public health officer terminal

[0270] Medical staff terminals

[0271] Researcher terminal

[0272] Biopharmaceutical company terminals

[0273] General user terminal

[0274] 3. User:

[0275] Public health officials

[0276] Healthcare workers

[0277] researcher

[0278] Biopharmaceutical company researchers

[0279] General users

[0280] Program processing

[0281] The entire system is designed to work in cooperation with each server, terminal, and user. Here, we will explain the roles and specific examples of each server, terminal, and user.

[0282] Data collection

[0283] Server: The data collection server collects disease data in real time from public health agencies, hospitals, and research institutes. It obtains information through APIs and database connections and converts it into a standardized data format.

[0284] Example: A data collection server receives reports from international health organizations about outbreaks of new strains of influenza.

[0285] Users: Public health officials and medical workers use terminals to enter case information and send it to a data collection server.

[0286] Data preprocessing and analysis

[0287] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing data, etc. Using the preprocessed data, clustering is performed using machine learning algorithms.

[0288] Example: A data analysis server identifies areas where similar cases are occurring frequently and performs clustering.

[0289] Predicting mutant strains

[0290] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations. It analyzes the structure of potential mutations based on viral genetic data.

[0291] Example: A prediction server calculates the probability of a new mutant strain emerging and visualizes its structural changes.

[0292] Terminal: Researchers receive predicted data on mutant strains provided by the prediction server and perform detailed analysis.

[0293] Vaccine Design

[0294] Server: The vaccine design server will rapidly design customized vaccines based on predicted variant information, including selecting vaccine components and designing manufacturing processes.

[0295] Example: A vaccine design server designs vaccine components that are effective against specific mutant strains and proposes their manufacturing process.

[0296] Terminal: Researchers at biopharmaceutical companies use the terminal to review the details of the designed vaccine and prepare for production at the laboratory level.

[0297] Emotion data collection and analysis

[0298] Server: The emotion engine server collects and analyzes the user's emotion data from the device. It uses emotion recognition technology to evaluate the user's emotional state.

[0299] Example: An emotion engine server analyzes a user's emotional response to vaccination and detects emotions such as anxiety and fear.

[0300] Terminal: The general user inputs their emotional state at the time of vaccination into the terminal and sends it to the emotion engine server.

[0301] Implementing preventive measures and coordinating

[0302] Server: The collaborative server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results, and will also incorporate emotional data to design more effective preventive measures.

[0303] Example: A collaborative server creates a plan to launch a vaccination campaign in a specific region and adjusts communication strategies based on sentiment data.

[0304] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server and develop public health campaigns.

[0305] Monitoring and Evaluation

[0306] Server: The monitoring and evaluation server monitors the implemented preventive measures and vaccine effectiveness in real time. It analyzes the reduction in the number of infected people after vaccination and sentiment data to evaluate the effectiveness of the vaccine.

[0307] Example: A monitoring and evaluation server verifies the success of a vaccine by analyzing data on the reduction in infection rates after vaccination and user sentiment data.

[0308] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into the database.

[0309] This system will comprehensively address everything from early detection of epidemics to implementing preventative measures and users' emotional responses to vaccinations, and each step will help prevent the risk of a pandemic and minimize its social and economic impact.

[0310] The processing flow will be explained below.

[0311] Step 1:

[0312] Server: The data collection server collects disease data in real time from public health agencies, hospitals, research institutes, etc. in each country. It obtains the information via APIs and database connections and converts it into a standardized data format, thereby generating a centrally managed dataset.

[0313] Step 2:

[0314] Users: Public health officials and medical personnel use terminals to manually or automatically input case information and send it to the data collection server. The terminals standardize the format of the input data to prevent input errors and missing data.

[0315] Step 3:

[0316] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing data, etc. The preprocessed data is stored in a clean state, ensuring that subsequent analysis can proceed smoothly.

[0317] Step 4:

[0318] Server: The data analysis server uses preprocessed data to perform clustering using machine learning algorithms, which allows similar cases to be grouped together and infection patterns to be identified. Identified cluster information is updated in real time.

[0319] Step 5:

[0320] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations based on the clustered data. It uses viral genetic data as input to analyze the structure of mutations that may emerge in the future and stores the prediction results in a database.

[0321] Step 6:

[0322] Terminal: Researchers receive the predicted data on variants provided by the prediction server and perform detailed analysis, including evaluation of the characteristics and infectivity of the variants, and then send the results back to the server.

[0323] Step 7:

[0324] Server: The vaccine design server rapidly designs customized vaccines based on predicted variant information. It selects vaccine components, designs manufacturing processes, and simulates preventive effects. The designed vaccine prototypes are stored on the server.

[0325] Step 8:

[0326] Terminal: Biopharmaceutical company researchers receive design data from the vaccine design server and prepare for production at the laboratory level. This includes setting up production equipment and manufacturing prototypes. Initial test results of the prototypes are sent to the server.

[0327] Step 9:

[0328] Server: The emotion engine server collects and analyzes user emotion data from the device. It uses emotion recognition technology to evaluate the user's emotional state and shares that data with other servers.

[0329] Step 10:

[0330] Example: The emotion engine server analyzes the user's emotional response to vaccination and detects emotions such as anxiety and fear. Based on this, it determines the need for specific information and explanations.

[0331] Step 11:

[0332] Server: The collaborative server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results. It will also take into account emotional data and adjust the communication method of preventive measures according to the user's reaction.

[0333] Step 12:

[0334] Example: A collaborative server creates a plan for launching a vaccination campaign in a specific region and adjusts communication strategies based on emotion data, for example, providing more reassuring information in advance based on emotion data.

[0335] Step 13:

[0336] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccination implementation.

[0337] Step 14:

[0338] Server: The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It analyzes the reduction in the number of infected people after vaccination and emotional data to evaluate the effectiveness of the vaccine. The evaluation results are fed back and stored in a database.

[0339] Step 15:

[0340] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into a database, allowing the effectiveness of the entire system to be continuously monitored.

[0341] In this way, a system is provided that goes through each step to achieve early detection of epidemics, rapid response, and pandemic prevention. Evaluation and adjustment of preventive measures based on user emotion data enables more effective countermeasures.

[0342] Example 2

[0343] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0344] Conventional epidemic management systems often require manual collection and preprocessing of disease data, making rapid data analysis and prediction difficult. Predicting mutant strains and designing vaccines also takes time, making them ineffective in situations where rapid response is required. Furthermore, preventive measures implemented without considering user sentiment data are likely to create social anxiety and resistance, potentially reducing the overall effectiveness of preventive measures.

[0345] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0346] In this invention, the server includes means for collecting disease data in real time, means for preprocessing the collected data to remove noise and fill in missing values, means for clustering the preprocessed data using a machine learning algorithm, means for predicting virus mutations using the AlphaFold algorithm, means for quickly designing customized vaccines based on the predicted mutations, means for collecting and analyzing user emotion data, means for designing preventive measures based on the analyzed emotion data and coordinating with international health organizations or local governments, and means for monitoring and evaluating the effectiveness of the implemented preventive measures. This enables comprehensive response from early detection of epidemics to the implementation of preventive measures and users' emotional reactions to vaccinations.

[0347] "Disease data" refers to data collected from public health agencies and medical institutions, including disease occurrence status, case information, patient information, etc.

[0348] "Preprocessing" refers to the process of removing noise and missing values ​​from acquired data and standardizing it, which can improve the accuracy of data analysis and prediction.

[0349] "Noise reduction" refers to the process of removing unnecessary or erroneous data from a dataset, thereby improving the quality of the data.

[0350] "Missing value imputation" refers to the process of filling in missing values ​​in a dataset in an appropriate way, thereby maintaining the accuracy of data analysis and machine learning.

[0351] "Clustering" refers to a machine learning algorithm that classifies data points into groups based on their similarity, allowing you to discover patterns and anomalies in the data.

[0352] The "AlphaFold algorithm" is a machine learning algorithm for predicting virus and protein structures, which makes it possible to predict mutant strains.

[0353] A "mutant strain" refers to a strain of the virus that has changed due to genetic mutation. Mutant strains may differ in their infectivity and resistance to vaccines.

[0354] "Vaccine design" refers to the process of selecting vaccine components to combat a specific virus or its variants and planning the manufacturing process.

[0355] "Emotional Data" refers to data collected to assess a user's emotional state, which may include text data, multiple choice responses, biometrics, etc.

[0356] "Analysis" refers to the process of examining and analyzing collected data in detail using statistical and machine learning techniques, which allows useful information to be extracted from the data.

[0357] "Preventive measures" refer to specific measures or action plans taken to prevent the spread of a disease, including vaccinations, public campaigns, and quarantine measures.

[0358] "International health organizations" refer to organizations that primarily manage and coordinate international public health responses to global health issues.

[0359] "Local government" refers to an administrative unit that has the right to self-govern within a certain area, which allows specific health measures to be implemented in that area.

[0360] "Real-time" refers to near-instant data processing and response, enabling timely information gathering and response.

[0361] MODE FOR CARRYING OUT THE INVENTION

[0362] The system for implementing this invention is composed of multiple servers, terminals, and users. Each server, terminal, and user works in cooperation to comprehensively perform early detection of epidemics, prediction of mutant strains, vaccine design, implementation of preventive measures, and even analysis of user emotion data.

[0363] 1. Hardware and Software Used

[0364] server:

[0365] Data Collection Server

[0366] Data analysis server

[0367] Prediction Server

[0368] Vaccine Design Server

[0369] Emotion Engine Server

[0370] Collaboration Server

[0371] Monitoring and evaluation server

[0372] Software and Algorithms:

[0373] API (Application Programming Interface)

[0374] Database (e.g. SQL database)

[0375] Machine learning algorithms (e.g., K-means clustering)

[0376] AlphaFold algorithm

[0377] Emotion recognition technology (Natural Language Processing)

[0378] Device:

[0379] Public health officer terminal

[0380] Medical staff terminals

[0381] Researcher terminal

[0382] Biopharmaceutical company terminals

[0383] General user terminal

[0384] 2. Data Collection

[0385] The data collection server collects disease data in real time from public health agencies, hospitals, and research institutes, obtains the information through APIs and database connections, and converts it into a standardized data format.

[0386] Example: A data collection server retrieves influenza case data in JSON format from the Ministry of Health, Labor and Welfare's API and stores it in an internal SQL database.

[0387] Users, such as public health officials and medical professionals, enter case information using dedicated terminals, and the data is sent to a data collection server.

[0388] 3. Data Preprocessing and Analysis

[0389] The data analysis server preprocesses the collected data. This preprocessing includes noise removal, missing value completion, and data standardization. After preprocessing is complete, clustering is performed using a machine learning algorithm (e.g., K-means clustering).

[0390] Example: A data analysis server detects incomplete entries in collected case data and imputes them with average values, then standardizes the data and runs a clustering algorithm to group similar cases together.

[0391] 4. Prediction of mutant strains

[0392] The prediction server uses the AlphaFold algorithm to predict virus mutations based on the collected viral genetic data.

[0393] Example: The prediction server takes a new viral gene sequence and inputs it into the AlphaFold algorithm, generating predicted structural changes and visualizing the characteristics of the next likely mutant strain.

[0394] Researchers using the terminals, known as research terminals, will receive the predicted data and perform detailed analysis, including assessing the transmissibility of the variants and their resistance to existing vaccines.

[0395] Example prompt sentence:

[0396] When a new strain of influenza emerges, collect data from international health organizations, preprocess it on the data analysis server, predict the mutant strain using the prediction server, and design a new vaccine on the vaccine design server. Then, evaluate user emotion data on the emotion engine server, and based on the results, propose preventive measures on the collaboration server. Finally, monitor and evaluate their effectiveness on the monitoring and evaluation server.

[0397] 5. Vaccine Design

[0398] The vaccine design server will rapidly design a customized vaccine based on predicted variant information, including selecting vaccine components and designing the manufacturing process.

[0399] Example: A vaccine design server generates sequences for new vaccine components targeting specific antigens based on predicted variant data, and also creates instructions for the manufacturing process.

[0400] Researchers at biopharmaceutical companies using the terminal, a biopharmaceutical company terminal, will review the details of the designed vaccine and begin experiments to prepare for production.

[0401] 6. Emotional Data Collection and Analysis

[0402] The emotion engine server collects and analyzes the user's emotion data from the device, and evaluates the user's emotional state using emotion recognition technology.

[0403] Example: The emotion engine server analyzes data collected from the device and uses Natural Language Processing (NLP) algorithms to extract emotions from text data entered by the user.

[0404] A general user using a general user terminal inputs the emotional state at the time of vaccination and transmits the data to the emotion engine server.

[0405] 7. Implementation of preventive measures and coordination

[0406] The linked server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results, and will also take into account emotional data to design more effective preventive measures.

[0407] Example: The collaborative server analyzes infection and sentiment data by region and creates proposals for implementing vaccination campaigns in specific regions.

[0408] Local government officials, who are the users, implement preventive measures based on suggestions from the collaboration server and launch public health campaigns.

[0409] 8. Monitoring and Evaluation

[0410] The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It analyzes the decrease in the number of infected people after vaccination and emotional data to evaluate the effectiveness of the vaccine.

[0411] Example: The monitoring and evaluation server periodically updates and analyzes post-vaccination infection rate data and sentiment data. Statistical methods such as hypothesis testing are used to evaluate the effectiveness of the vaccine.

[0412] Local medical institutions and local government officials using the local medical institution terminals report feedback to the monitoring and evaluation server and enter the evaluation results into a database.

[0413] The system will comprehensively respond to users' emotional reactions to early detection of epidemics, implementation of preventive measures, and vaccination, aiming to prevent the risk of pandemics and minimize their social and economic impacts through each processing step.

[0414] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0415] Step 1: Data collection

[0416] The server, the data collection server, collects disease data in real time from public health agencies, hospitals, and research institutes. As input, it uses the information obtained through APIs and database connections and converts it into a standardized data format. As output, the standardized disease data is stored in a database.

[0417] Specific operation: The data collection server retrieves influenza case data in JSON format from the Ministry of Health, Labor and Welfare's API and stores it in an internal SQL database.

[0418] Step 2: Data preprocessing and analysis

[0419] The data analysis server preprocesses the standardized disease data obtained from the data collection server. Using the standardized data as input, it removes noise, fills in missing values, and standardizes the data. The preprocessed data is obtained as output, and clustering is performed using a machine learning algorithm based on this data.

[0420] What it does: The data analysis server detects incomplete entries in the collected case data and imputes them with average values, then standardizes the data and runs a clustering algorithm (e.g., K-means) to group similar cases.

[0421] Step 3: Predicting variants

[0422] The prediction server uses the AlphaFold algorithm to predict virus mutations based on preprocessed data provided by the data analysis server. It uses viral genetic data and clustered case data as input. The output is structural change data for the newly predicted mutations.

[0423] How it works: The prediction server takes a new viral gene sequence and inputs it into the AlphaFold algorithm, which generates predicted structural changes and visualizes the characteristics of the next likely mutant strain.

[0424] Step 4: Vaccine design

[0425] The vaccine design server rapidly designs customized vaccines based on the prediction data provided by the prediction server. It uses the predicted variant data as input. The output is the sequence data of the vaccine components and instructions for the manufacturing process.

[0426] How it works: The vaccine design server generates sequences of new vaccine components that target specific antigens based on predicted variant data, and simultaneously creates instructions for the manufacturing process.

[0427] Step 5: Collect and analyze emotion data

[0428] The emotion engine server collects and analyzes the user's emotional data from the device. As input, it uses the user's text data indicating their emotional state, analyzes it, and evaluates their emotional state. As output, it obtains the analyzed emotional data.

[0429] Specific operation: The emotion engine server analyzes data collected from the device and uses Natural Language Processing (NLP) algorithms to extract emotions from text data entered by the user.

[0430] Step 6: Implement preventative measures and collaborate

[0431] The collaboration server, which is a server, proposes appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results. It also takes into account emotional data to design more effective preventive measures. The analyzed disease data, prediction data, and emotional data are used as input. The preventive measures planning data is obtained as output.

[0432] Specific operation: The collaboration server analyzes infection data and sentiment data by region and creates proposals for implementing vaccination campaigns in specific regions.

[0433] Step 7: Monitor and evaluate

[0434] The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It uses post-vaccination infection rate data and user sentiment data as input and analyzes them. The output is evaluation data on vaccine effectiveness.

[0435] Specific operation: The monitoring and evaluation server periodically updates and analyzes post-vaccination infection rate data and sentiment data. It evaluates the effectiveness of the vaccine using statistical methods such as hypothesis testing.

[0436] This system will enable comprehensive response from early detection of epidemics to the implementation of preventive measures and users' emotional reactions to vaccinations. By going through each processing step, it will be possible to prevent the risk of a pandemic and minimize its social and economic impact.

[0437] (Application example 2)

[0438] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0439] Conventional disease prevention systems and autonomous vehicle safety and comfort management systems lack the ability to integrate public health measures and emergency responses based on passenger emotional states. These shortcomings make it difficult to implement rapid and efficient epidemic prevention measures or respond appropriately based on passenger emotional states, resulting in insufficient prevention effectiveness and safety.

[0440] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0441] In this invention, the server includes a means for collecting disease data in real time, a means for predicting virus mutations based on the collected data, and a means for quickly designing a customized vaccine based on the predicted mutations. It also includes a means for collecting passenger emotional data in real time, a means for analyzing passenger emotional states based on the collected emotional data, a means for implementing appropriate safety and comfort measures based on the analysis results, and a means for taking emergency action when an abnormality is detected. This enables early disease prevention and improved passenger safety and comfort.

[0442] "Means for collecting disease data in real time" refers to technologies for collecting disease-related data from public health agencies and medical institutions without time delay.

[0443] "Means for predicting virus mutations based on collected data" refers to technology that analyzes collected disease data and predicts future virus mutations based on the results.

[0444] "Means for rapidly designing customized vaccines based on predicted variants" refers to technology for rapidly designing vaccines optimized for predicted viral variants.

[0445] "Means for collaborating with international health organizations to implement designed vaccines and preventive measures" means techniques and protocols for collaborating with international health organizations to implement designed vaccines and preventive measures.

[0446] "Means for monitoring and evaluating the effectiveness of implemented preventive measures" refers to technology that monitors and evaluates the effectiveness of implemented preventive measures in real time.

[0447] "Means for collecting passenger emotional data in real time" refers to technology for collecting passenger emotional states in autonomous vehicles without time delay.

[0448] The "means for analyzing the emotional state of passengers based on collected emotional data" is a technique for analyzing collected emotional data and evaluating the emotional state of passengers.

[0449] "Means for implementing appropriate safety and comfort measures based on the analysis results" refers to technology that takes specific measures to improve safety and comfort based on the analysis results of passengers' emotional states.

[0450] "Means for taking emergency action when an abnormality is detected" refers to technology that quickly implements appropriate emergency measures when an abnormality is detected through analysis of passengers' emotional states and real-time data.

[0451] The system for implementing the present invention consists of various servers, terminals, and sensors that collect real-time disease data, predict virus mutations, design customized vaccines, and analyze passengers' emotional states to respond to emergencies.

[0452] System Configuration

[0453] server

[0454] Data collection server: collects disease data in real time.

[0455] Data analysis server: Preprocesses collected data, removes noise, and fills in missing values.

[0456] Prediction server: Predicts virus mutations using the AlphaFold algorithm.

[0457] Vaccine design server: Rapidly design customized vaccines based on predicted variant information.

[0458] Collaboration server: Collaborates with international health organizations on the implementation of preventive measures.

[0459] Monitoring and evaluation server: Monitors and evaluates the effectiveness of implemented preventive measures in real time.

[0460] Emotion Engine Server: Collects passengers' emotion data and analyzes it.

[0461] Terminal

[0462] Public health officer terminal: Enters disease data and sends it to the data collection server.

[0463] General user terminal: Emotional states are input via smartphones or in-vehicle displays and sent to the emotion engine server.

[0464] User

[0465] Public health officials: Enter disease data into a collection server.

[0466] General users: Input their emotional state when receiving a vaccination or riding in an autonomous vehicle.

[0467] Program processing

[0468] Hardware and Software

[0469] The system operates using the following hardware and software:

[0470] Facial recognition camera: Used to analyze passengers' facial expressions in real time.

[0471] Microphone: Used to analyze voice tones and recognize emotions.

[0472] Heart rate sensor: Used to measure heart rate and include it in emotion data.

[0473] OpenCV: A software library for performing face recognition processing.

[0474] Google Speech-to-Text API: Used to integrate speech recognition and text conversion.

[0475] Affectiva API: Software for analyzing emotional data as an emotion engine.

[0476] AlphaFold: A machine learning algorithm used to predict virus variants.

[0477] Specific examples of processing

[0478] Emotion data collection and analysis

[0479] Passenger emotional data is collected using facial recognition cameras, microphones, and heart rate sensors. The emotion engine server analyzes the passenger's facial expressions, voice tone, and heart rate data to evaluate the passenger's emotional state. If the result indicates that the passenger is feeling stressed or anxious, the system will take measures such as playing relaxing music. In addition, if an abnormality is detected, emergency response will be initiated. For example, if a passenger feels strong stress while on board, the data will be sent to the emotion engine server, and the analysis results will be returned immediately. Depending on the analysis results, the display in the vehicle will display "Playing relaxing music" and music will be played.

[0480] Prompt Sentence Examples

[0481] For example, a prompt to input the passenger's stress level into the emotion engine might look like this:

[0482] "Analyze passengers' stress levels based on facial expressions, voice tone, and heart rate data."

[0483] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0484] Step 1:

[0485] Data collection

[0486] The server collects real-time emotional data from passengers through facial recognition cameras, microphones, and heart rate sensors. The collected data includes facial expression data, voice data, and heart rate data. These data are sent directly from each sensor to the data collection server.

[0487] Input: Facial expression data, voice data, heart rate data

[0488] Output: Unified emotion dataset (facial expressions, vocal tone, heart rate)

[0489] Step 2:

[0490] Data Preprocessing

[0491] The server receives the collected data and performs noise removal and missing value completion. For example, it preprocesses facial recognition data to prevent closed eyes and open mouths from affecting emotion recognition. Similarly, it removes background noise from audio data to obtain clear audio data.

[0492] Input: Unified sentiment dataset

[0493] Output: Preprocessed emotion dataset

[0494] Step 3:

[0495] Emotional Data Analysis

[0496] The server then uses the pre-processed emotion dataset to perform analysis using the emotion engine server, which uses OpenCV to recognize emotions from facial expressions, Google Speech-to-Text API to convert voice data to text, and Affectiva API to analyze emotions. Heart rate data is also analyzed in the same way to assess the passenger's emotional state.

[0497] Input: Preprocessed emotion dataset

[0498] Output: Passenger's emotional state (e.g., stress level, anxiety level)

[0499] Step 4:

[0500] Implementing countermeasures based on analysis results

[0501] The server then instructs the system to take appropriate countermeasures based on the analysis results. For example, if a passenger is in a high stress state, the server can instruct the in-car sound system to play relaxing music. If an abnormality is detected, the server can activate the emergency call system.

[0502] Input: Passenger emotional state (e.g., stress level, anxiety level)

[0503] Output: Execution instructions (e.g., music playback, emergency call)

[0504] Step 5:

[0505] Feedback and Learning

[0506] The server collects feedback from passengers and uses it to improve the system. Specifically, it receives feedback data from passenger terminals, stores it in a database, and reflects it in future analyses and countermeasures.

[0507] Input: Passenger feedback data

[0508] Output: Feedback data stored in a database, and a dataset for system improvement

[0509] 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.

[0510] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0511] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0512] [Second embodiment]

[0513] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0514] 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.

[0515] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0516] 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.

[0517] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0518] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0519] 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. 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.

[0520] 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.

[0521] 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 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.

[0522] 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.

[0523] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0524] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0525] The system for carrying out the present invention comprises a plurality of servers, terminals, and users. The specific configuration of this system and the processing of the programs will be described in detail below.

[0526] System Configuration

[0527] 1. Server:

[0528] Data Collection Server

[0529] Data analysis server

[0530] Prediction Server

[0531] Vaccine Design Server

[0532] Collaboration Server

[0533] Monitoring and evaluation server

[0534] 2. Terminal:

[0535] Public health officer terminal

[0536] Medical staff terminals

[0537] Researcher terminal

[0538] Biopharmaceutical company terminals

[0539] 3. User:

[0540] Public health officials

[0541] Healthcare workers

[0542] researcher

[0543] Biopharmaceutical company researchers

[0544] Program processing

[0545] The overall system program is designed so that each server and terminal work together. Here, we will explain the roles of each server, terminal, and user, along with specific examples.

[0546] Data collection

[0547] Server: The data collection server collects disease data in real time from national public health agencies, hospitals, research institutes, etc. This includes the ability to retrieve information using APIs and database connections and convert it into standardized data formats.

[0548] Example: A data collection server receives reports from international health organizations about outbreaks of new strains of influenza.

[0549] Users: Public health and healthcare workers use devices to manually or automatically enter case information and send it to a server.

[0550] Data preprocessing and analysis

[0551] Server: The data analysis server preprocesses the collected data, removing noise and completing missing values. Using the preprocessed data, it performs clustering using a machine learning algorithm to group similar cases.

[0552] Example: A data analysis server identifies areas where similar cases are occurring frequently and performs clustering.

[0553] Predicting mutant strains

[0554] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations. Based on the collected viral genetic data, it analyzes the structure of mutations that may emerge in the future.

[0555] Example: A prediction server calculates the probability of new mutant strains emerging and visualizes their structural changes.

[0556] Terminal: Researchers check the predicted data for mutant strains provided by the prediction server on their terminals and perform detailed analysis.

[0557] Vaccine Design

[0558] Server: The vaccine design server rapidly designs customized vaccines based on predicted variant information, including generating optimal vaccine components and manufacturing processes.

[0559] Example: A vaccine design server designs vaccine components that are effective against specific mutant strains and proposes their manufacturing process.

[0560] Terminal: Researchers at biopharmaceutical companies use the terminal to review the details of the designed vaccine and prepare for production at the laboratory level.

[0561] Implementing preventive measures and coordinating

[0562] Server: Based on the collected data and prediction results, the collaborative server will propose appropriate preventive measures to international health organizations and local governments, such as planning vaccination campaigns and proposing lockdown measures.

[0563] Example: A federated server provides international health organizations with detailed plans for launching vaccination campaigns in specific regions.

[0564] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccinations.

[0565] Monitoring and Evaluation

[0566] Server: The monitoring and evaluation server monitors the effectiveness of the implemented preventive measures and vaccines in real time. It analyzes the reduction in the number of infections after vaccination and reports of side effects to confirm the effectiveness of the vaccine.

[0567] Example: A monitoring and evaluation server analyzes data on the reduction in infection rates after vaccination to assess the success of the vaccine.

[0568] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into the database.

[0569] These processes provide a system for early detection of epidemics, rapid response, and prevention of pandemics.

[0570] The processing flow will be explained below.

[0571] Step 1:

[0572] Server: The data collection server collects disease data in real time from public health agencies, hospitals, research institutes, etc. in each country. It obtains information via APIs and database connections and converts it into a standardized data format.

[0573] Step 2:

[0574] Users: Public health and healthcare workers use devices to manually or automatically enter case information and send it to a data collection server.

[0575] Step 3:

[0576] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing the data, and preparing it for analysis.

[0577] Step 4:

[0578] Server: The data analysis server uses pre-processed data to perform clustering using machine learning algorithms, which allows for grouping of similar cases and identification of infection patterns.

[0579] Step 5:

[0580] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations based on the clustered data. It uses viral genetic data as input and analyzes the structure of mutations that may emerge in the future.

[0581] Step 6:

[0582] Terminal: Researchers receive the predicted data on variants provided by the prediction server and perform detailed analysis, including evaluation of the characteristics and infectivity of the variants.

[0583] Step 7:

[0584] Server: The vaccine design server will rapidly design customized vaccines based on predicted variant information, select vaccine components, design manufacturing processes, and simulate preventive effects.

[0585] Step 8:

[0586] Terminal: Researchers at biopharmaceutical companies receive design data from the vaccine design server and prepare for production at the laboratory level, including setting up production equipment and manufacturing prototypes.

[0587] Step 9:

[0588] Server: Based on the collected data and prediction results, the collaborative server will propose appropriate preventive measures to international health organizations and local governments, providing instructions on planning vaccination campaigns and implementing lockdown measures.

[0589] Step 10:

[0590] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccination implementation.

[0591] Step 11:

[0592] Server: The monitoring and evaluation server will monitor the effectiveness of the implemented preventive measures and vaccines in real time, collecting and analyzing reports of the reduction in the number of infections and side effects after vaccination.

[0593] Step 12:

[0594] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into a database, allowing the effectiveness of the entire system to be continuously monitored.

[0595] In this way, through each step, a system is provided that enables early detection of epidemics, rapid response, and prevention of pandemics.

[0596] Example 1

[0597] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0598] Conventional disease data collection and analysis systems often lack real-time capabilities, and noise and missing values ​​in the collected data often adversely affect the analysis results. Furthermore, they lack the speed to predict virus mutations and design vaccines, making it difficult to prevent the spread of infectious diseases. A system that can resolve these issues and enable more effective and rapid public health measures is needed.

[0599] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0600] In this invention, the server includes: means for collecting disease data in real time from public health organizations, hospitals, research institutions, etc.; means for preprocessing the collected data to remove noise and fill in missing values; means for analyzing the preprocessed data using a machine learning algorithm and clustering similar cases; means for predicting virus mutations using the AlphaFold algorithm; means for quickly designing customized vaccines based on the predicted mutations; means for providing plans for vaccination campaigns and lockdown measures to international health organizations and local governments through a linked server; and means for monitoring and evaluating the effectiveness of implemented preventive measures and vaccines in real time. This enables real-time data collection and analysis, rapid prediction of virus mutations and vaccine design, and implementation and evaluation of effective public health measures.

[0601] "Means for collecting disease data in real time" refers to methods for obtaining timely disease data from public health agencies, hospitals, research institutions, etc.

[0602] "Noise removal" is the process of cleaning data by removing unnecessary data and outliers from the data.

[0603] "Missing value imputation" is the process of filling in missing values ​​in a dataset with appropriate values ​​(e.g., averages or estimated values).

[0604] "Preprocessed data" refers to a dataset after noise removal and missing value imputation have been performed.

[0605] A "machine learning algorithm" is a computational method for learning from data and making predictions or classifications.

[0606] "Clustering" is a machine learning algorithm that divides data into multiple groups based on similarity.

[0607] The "AlphaFold algorithm" is a computational model for predicting the three-dimensional structure of proteins.

[0608] "Mutant prediction" is the process of predicting how pathogens such as viruses will mutate.

[0609] A "customized vaccine" is a vaccine that is optimized for a specific variant.

[0610] A "collaboration server" is a server that allows multiple institutions and devices to share information and work together to carry out tasks.

[0611] A "vaccination campaign" is an activity that encourages and administers vaccination to a wide range of people.

[0612] "Lockdown measures" are measures to close specific areas or facilities to prevent the spread of infectious diseases.

[0613] "Monitoring and evaluation measures" are methods for continuously observing and evaluating the effectiveness of implemented measures and vaccines.

[0614] MODE FOR CARRYING OUT THE INVENTION

[0615] The system for implementing the present invention comprises a plurality of servers, terminals, and users. The specific configuration of this system and the processing of the programs are described below.

[0616] System Configuration

[0617] server

[0618] 1. Data Collection Server

[0619] The data collection server is a server that collects disease data in real time from public health agencies, hospitals, and research institutes. Specifically, it has the function of retrieving information using APIs and database connections and converting it into a standardized data format.

[0620] 2. Data analysis server

[0621] The data analysis server preprocesses the collected data, removes noise, and fills in missing values. It also uses the preprocessed data to implement machine learning algorithms and cluster similar cases.

[0622] 3. Prediction Server

[0623] The prediction server uses the AlphaFold algorithm to predict virus mutations. Based on collected viral genetic data, it analyzes the structure of mutations that may emerge in the future.

[0624] 4. Vaccine Design Server

[0625] The Vaccine Design Server is a server for rapidly designing customized vaccines based on predicted variant information, including generating optimal vaccine components and manufacturing processes.

[0626] 5. Collaboration Server

[0627] The collaborative server will use the collected data and prediction results to propose appropriate preventive measures to international health organizations and local governments, such as planning vaccination campaigns and proposing lockdown measures.

[0628] 6. Monitoring and evaluation server

[0629] The monitoring and evaluation server will monitor the effectiveness of the implemented preventive measures and the vaccine in real time, analyzing the reduction in the number of infected people after vaccination and reports of side effects to confirm the effectiveness of the vaccine.

[0630] Terminal

[0631] 1. Public Health Officer Terminals

[0632] Public health personnel terminals are terminals that public health personnel use to input disease data and connect to a server, for example, with the ability to input case data through dedicated software or a web app.

[0633] 2. Medical device

[0634] The medical staff terminal is a terminal where medical staff input and check case data and send it to the data collection server.

[0635] 3. Researcher terminals

[0636] The researcher terminals are used by researchers to check data analysis results and mutant strain information and perform detailed analysis, for example, using dedicated analysis software (e.g., Biopython, PyMOL).

[0637] 4. Terminals for biopharmaceutical companies

[0638] The biopharmaceutical company terminal is a terminal where researchers at biopharmaceutical companies can check the details of the designed vaccine and prepare for the manufacturing process.

[0639] User

[0640] 1. Public health officials

[0641] Public health officials are responsible for collecting disease data and implementing and monitoring preventive measures. They input data using dedicated terminals.

[0642] 2. Healthcare workers

[0643] Medical professionals collect case data at hospitals and clinics, send it to a data collection server, and enter and check the case data.

[0644] 3. Researcher

[0645] Researchers will conduct detailed studies based on data analysis and predictions of mutant strains, and plan further experiments based on the results of the analysis.

[0646] 4. Researchers at biopharmaceutical companies

[0647] Researchers at biopharmaceutical companies will review the details of the designed vaccine, prepare for production at the laboratory level, and conduct efficacy and safety tests.

[0648] Examples of concrete examples and prompts

[0649] Example: A data collection server accesses the API of an international health organization to periodically retrieve reports of new influenza outbreaks.

[0650] Example: A prediction server uses the AlphaFold algorithm to predict structural changes in new mutant strains and provides the results to researchers' terminals.

[0651] Example prompt: "Please explain how the data collection server receives reports of new influenza outbreaks from international health organizations, and how the analysis server performs noise removal and missing value imputation."

[0652] In this way, the entire system works together to enable early detection of epidemics, rapid response, and pandemic prevention.

[0653] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0654] Step 1: Data collection

[0655] The server collects disease data in real time from public health agencies, hospitals, research institutes, etc. Specifically, it retrieves information using APIs and database connections and converts it into standardized data formats (e.g., JSON, CSV). This collected data is then sent to a data analysis server for analysis in the next step. For example, the server periodically requests the API of an international health organization to retrieve reports of new influenza outbreaks.

[0656] Input: Disease data from public health agencies, hospitals, and research institutes

[0657] Output: Standardized disease data

[0658] Step 2: Data Preprocessing

[0659] The server preprocesses the collected data, including noise removal and missing value imputation. Specifically, the data analysis server removes unnecessary data and outliers from the data and imputes missing data with appropriate values ​​(e.g., mean values ​​or estimated values). This preprocessed data is used in the next analysis step. For example, the data analysis server imputes missing values ​​with the mean values ​​calculated for the data rows in which they exist.

[0660] Input: Collected disease data

[0661] Output: Preprocessed disease data

[0662] Step 3: Data analysis

[0663] The server then applies a machine learning algorithm to the preprocessed data. Specifically, it uses a clustering algorithm (e.g., k-means) to group similar cases. The results of this analysis are then used in the next step of variant prediction. For example, the data analysis server analyzes cases that are occurring frequently in a specific region and generates groups using k-means clustering.

[0664] Input: Preprocessed disease data

[0665] Output: Clustered case data

[0666] Step 4: Predicting variants

[0667] The server uses the AlphaFold algorithm to predict virus mutations. Specifically, it receives viral genetic data as input and analyzes the protein structures of potential future mutations. The prediction results are used in the next step of vaccine design. For example, the prediction server receives the amino acid sequence of a specific part of the virus as input and uses AlphaFold to predict likely mutations.

[0668] Input: viral genetic data

[0669] Output: Predicted variant information

[0670] Step 5: Vaccine design

[0671] The server designs optimal vaccines based on predicted variant information. Specifically, it identifies epitopes that are effective against different parts of the virus and uses algorithms (e.g., RosettaDesign) to design peptide vaccines. The designed vaccine information is then used in the next step of implementing preventive measures. For example, the vaccine design server calculates the optimal epitope for a specific variant and proposes its production procedure.

[0672] Input: Predicted mutant information

[0673] Output: Designed vaccine information

[0674] Step 6: Implement preventative measures and collaborate

[0675] Based on the collected data and prediction results, the collaboration server will propose appropriate preventive measures to international health organizations and local governments. Specifically, it will generate plans for vaccination campaigns and lockdown measures and notify relevant organizations. For example, the collaboration server will develop a detailed schedule for public health campaigns and vaccinations to prevent the spread of a new strain of influenza in a specific area.

[0676] Input: Designed vaccine information, predicted results

[0677] Output: Suggested preventative measures

[0678] Step 7: Monitor and evaluate

[0679] The monitoring and evaluation server monitors the effectiveness of implemented preventive measures and vaccinations in real time. Specifically, it evaluates the effectiveness of vaccines by collecting and analyzing data on the number of infected people after vaccination and reports of side effects. For example, the monitoring and evaluation server analyzes data on the reduction in infection rates after vaccination to evaluate the success of the vaccine.

[0680] Input: Preventive measures taken, vaccination data

[0681] Output: Effect evaluation results

[0682] This processing step enables the entire system to work together to detect diseases early, respond quickly, and prevent pandemics.

[0683] (Application example 1)

[0684] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0685] Rapid detection and prediction of infectious diseases and the implementation of effective preventive measures are crucial for modern public health. However, current systems lack responsiveness and accuracy because they lack centralized collection of real-time disease data, prediction of mutant strains, design and implementation of vaccines, and monitoring of their effectiveness. Therefore, there is a need for a system that can monitor infectious disease risk at the individual user level in real time, quickly send alerts in high-risk areas, and provide timely vaccination information.

[0686] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0687] In this invention, the server includes means for collecting disease data in real time, means for predicting virus mutations based on the collected data, means for quickly designing customized vaccines based on the predicted mutations, means for coordinating with international health organizations to implement the designed vaccines and preventive measures, means for monitoring and evaluating the effectiveness of the implemented preventive measures, means for monitoring disease risk in real time and for issuing alerts for high-risk areas, and means for managing and notifying the timing of vaccinations. This enables rapid detection and prediction of infectious diseases, real-time risk monitoring, and the implementation and monitoring of appropriate preventive measures.

[0688] "Disease Data" refers to information that includes the occurrence, progression, and related information of a disease in an individual or population.

[0689] A "virus variant" refers to a subspecies of a virus with certain mutations or genetic changes.

[0690] A "customized vaccine" refers to a vaccine designed to have specific efficacy against predicted variants.

[0691] "international health organization" means an international organization established to prevent and control disease and provide health-related services;

[0692] "Real-time monitoring" refers to the immediate observation and analysis of ongoing situations and data.

[0693] A "high-risk area" refers to an area where the risk of certain diseases or health conditions is considered higher than in other areas.

[0694] "Alert notification" refers to a system that warns or notifies users when certain criteria are met.

[0695] "Managing vaccination timing" refers to adjusting schedules and providing notifications so that vaccinations are given at the appropriate time.

[0696] "Monitoring and evaluation" refers to the continuous observation and analysis of the effectiveness and feasibility of preventive measures implemented.

[0697] The embodiment of the present invention is a system in which various servers and terminals work in cooperation with each other. The program processing of this system will be explained below.

[0698] Hardware and software used

[0699] Hardware:

[0700] Smartphone (iOS / Android)

[0701] software:

[0702] Mobile app development framework: React Native

[0703] Data Collection API: Firebase

[0704] Machine learning model: TensorFlow Lite

[0705] Map API: Google Maps API

[0706] Notification service: OneSignal

[0707] Program processing explanation

[0708] 1. Data Collection and Preprocessing

[0709] The server uses Firebase to collect disease data in real time, including location information and case report information. The data collection server preprocesses the collected data, removing noise and imputing missing values.

[0710] 2. Data analysis and mutant strain prediction

[0711] The server uses TensorFlow Lite to predict virus mutations based on collected data, and the AlphaFold algorithm to analyze the structure of potential future mutations.

[0712] 3. Risk monitoring and alert notifications

[0713] The device uses Google Maps API for real-time disease risk monitoring, visualizing high-risk areas based on the user's current location, and provides alerts via OneSignal if the user enters a high-risk area.

[0714] 4. Vaccine design and timing management

[0715] The server will quickly design a customized vaccine based on predicted variant information, manage vaccination information on the device, and notify users when to get vaccinated.

[0716] Specific examples and generated AI model prompts

[0717] Example 1: If a new strain of influenza is confirmed within 10km of a user's home, the app will display that information on a map and highlight the area as a risk. An alert will be displayed saying, "You have entered a high-risk area. Please take precautions."

[0718] Example 2: When it's time for vaccination, you'll be notified with the message, "It's almost time for your vaccination. The next vaccination site is your local hospital. Would you like to make an appointment?"

[0719] Generative AI model prompt:

[0720] "Please develop a smartphone app that will monitor the risk of infection within a 10km radius in real time and notify you if you approach a risk area."

[0721] "Design an application that manages users' vaccination information and notifies them of the best time to get vaccinated."

[0722] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0723] Step 1:

[0724] The server uses Firebase to collect disease data. The input here is real-time data such as user location information and case report information. The server collects this data and converts it into a standardized data format. The output is a dataset in a format that can be preprocessed. Specific operations include collecting location information and obtaining real-time data through an API.

[0725] Step 2:

[0726] The server preprocesses the collected data, removing noise and imputing missing values. The input is the dataset collected in step 1. The server uses machine learning algorithms to cleanse the data and generate reliable data. The output is a high-quality dataset suitable for analysis. Specific operations include applying missing value imputation algorithms and noise filtering.

[0727] Step 3:

[0728] The server uses TensorFlow Lite to cluster disease risks and predict virus mutations based on the preprocessed data. The input is the dataset obtained in step 2. The server applies a machine learning model to analyze future risk areas and the likelihood of mutations. The output is detailed information on risk assessment results and mutation predictions. Specific operations include predictive modeling using a clustering algorithm and the AlphaFold algorithm.

[0729] Step 4:

[0730] The device uses the Google Maps API to display risk areas based on the user's current location on a map. The input is the risk assessment result obtained in step 3 and the current location information. The device visualizes the risk areas on a map and highlights dangerous areas. The output is the display of risk areas on the user interface. Specific operations include rendering the map and drawing polygons of the risk areas.

[0731] Step 5:

[0732] The device will send an alert notification via OneSignal if it enters a high-risk area. The input is the risk area information displayed in step 4 and the user's real-time location information. If the location information matches the risk area, the device will immediately send a notification to the user. The output is an alert message. Specific operations include real-time location comparison and notification triggering.

[0733] Step 6:

[0734] The server designs a vaccine based on the predicted mutant strain data. The input is the mutant strain prediction data obtained in step 3. The server designs a customized vaccine that is adapted to the new mutant strain based on current vaccine information. The output is detailed information about the designed vaccine. Specifically, the vaccine components are optimized using bioinformatics algorithms.

[0735] Step 7:

[0736] The terminal manages the user's vaccination information and notifies the user of vaccination at the appropriate time. The input is the user's vaccination history and vaccine information provided by the server. The terminal automatically calculates the next vaccination date and sends a notification to the user when the time approaches. The output is a vaccination reminder message. Specific operations include managing historical data and generating a notification schedule.

[0737] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0738] The system for implementing this invention is composed of multiple servers, terminals, and users. Furthermore, by incorporating an emotion engine, the effectiveness of the system can be improved by utilizing user emotion data. The specific configuration of this system and the program processing are described in detail below.

[0739] System Configuration

[0740] 1. Server:

[0741] Data Collection Server

[0742] Data analysis server

[0743] Prediction Server

[0744] Vaccine Design Server

[0745] Collaboration Server

[0746] Monitoring and evaluation server

[0747] Emotion Engine Server

[0748] 2. Terminal:

[0749] Public health officer terminal

[0750] Medical staff terminals

[0751] Researcher terminal

[0752] Biopharmaceutical company terminals

[0753] General user terminal

[0754] 3. User:

[0755] Public health officials

[0756] Healthcare workers

[0757] researcher

[0758] Biopharmaceutical company researchers

[0759] General users

[0760] Program processing

[0761] The entire system is designed to work in cooperation with each server, terminal, and user. Here, we will explain the roles and specific examples of each server, terminal, and user.

[0762] Data collection

[0763] Server: The data collection server collects disease data in real time from public health agencies, hospitals, and research institutes. It obtains information through APIs and database connections and converts it into a standardized data format.

[0764] Example: A data collection server receives reports from international health organizations about outbreaks of new strains of influenza.

[0765] Users: Public health officials and medical workers use terminals to enter case information and send it to a data collection server.

[0766] Data preprocessing and analysis

[0767] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing data, etc. Using the preprocessed data, clustering is performed using machine learning algorithms.

[0768] Example: A data analysis server identifies areas where similar cases are occurring frequently and performs clustering.

[0769] Predicting mutant strains

[0770] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations. It analyzes the structure of potential mutations based on viral genetic data.

[0771] Example: A prediction server calculates the probability of a new mutant strain emerging and visualizes its structural changes.

[0772] Terminal: Researchers receive predicted data on mutant strains provided by the prediction server and perform detailed analysis.

[0773] Vaccine Design

[0774] Server: The vaccine design server will rapidly design customized vaccines based on predicted variant information, including selecting vaccine components and designing manufacturing processes.

[0775] Example: A vaccine design server designs vaccine components that are effective against specific mutant strains and proposes their manufacturing process.

[0776] Terminal: Researchers at biopharmaceutical companies use the terminal to review the details of the designed vaccine and prepare for production at the laboratory level.

[0777] Emotion data collection and analysis

[0778] Server: The emotion engine server collects and analyzes the user's emotion data from the device. It uses emotion recognition technology to evaluate the user's emotional state.

[0779] Example: An emotion engine server analyzes a user's emotional response to vaccination and detects emotions such as anxiety and fear.

[0780] Terminal: The general user inputs their emotional state at the time of vaccination into the terminal and sends it to the emotion engine server.

[0781] Implementing preventive measures and coordinating

[0782] Server: The collaborative server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results, and will also incorporate emotional data to design more effective preventive measures.

[0783] Example: A collaborative server creates a plan to launch a vaccination campaign in a specific region and adjusts communication strategies based on sentiment data.

[0784] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server and develop public health campaigns.

[0785] Monitoring and Evaluation

[0786] Server: The monitoring and evaluation server monitors the implemented preventive measures and vaccine effectiveness in real time. It analyzes the reduction in the number of infected people after vaccination and sentiment data to evaluate the effectiveness of the vaccine.

[0787] Example: A monitoring and evaluation server verifies the success of a vaccine by analyzing data on the reduction in infection rates after vaccination and user sentiment data.

[0788] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into the database.

[0789] This system will comprehensively address everything from early detection of epidemics to implementing preventative measures and users' emotional responses to vaccinations, and each step will help prevent the risk of a pandemic and minimize its social and economic impact.

[0790] The processing flow will be explained below.

[0791] Step 1:

[0792] Server: The data collection server collects disease data in real time from public health agencies, hospitals, research institutes, etc. in each country. It obtains the information via APIs and database connections and converts it into a standardized data format, thereby generating a centrally managed dataset.

[0793] Step 2:

[0794] Users: Public health officials and medical personnel use terminals to manually or automatically input case information and send it to the data collection server. The terminals standardize the format of the input data to prevent input errors and missing data.

[0795] Step 3:

[0796] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing data, etc. The preprocessed data is stored in a clean state, ensuring that subsequent analysis can proceed smoothly.

[0797] Step 4:

[0798] Server: The data analysis server uses preprocessed data to perform clustering using machine learning algorithms, which allows similar cases to be grouped together and infection patterns to be identified. Identified cluster information is updated in real time.

[0799] Step 5:

[0800] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations based on the clustered data. It uses viral genetic data as input to analyze the structure of mutations that may emerge in the future and stores the prediction results in a database.

[0801] Step 6:

[0802] Terminal: Researchers receive the predicted data on variants provided by the prediction server and perform detailed analysis, including evaluation of the characteristics and infectivity of the variants, and then send the results back to the server.

[0803] Step 7:

[0804] Server: The vaccine design server rapidly designs customized vaccines based on predicted variant information. It selects vaccine components, designs manufacturing processes, and simulates preventive effects. The designed vaccine prototypes are stored on the server.

[0805] Step 8:

[0806] Terminal: Biopharmaceutical company researchers receive design data from the vaccine design server and prepare for production at the laboratory level. This includes setting up production equipment and manufacturing prototypes. Initial test results of the prototypes are sent to the server.

[0807] Step 9:

[0808] Server: The emotion engine server collects and analyzes user emotion data from the device. It uses emotion recognition technology to evaluate the user's emotional state and shares that data with other servers.

[0809] Step 10:

[0810] Example: The emotion engine server analyzes the user's emotional response to vaccination and detects emotions such as anxiety and fear. Based on this, it determines the need for specific information and explanations.

[0811] Step 11:

[0812] Server: The collaborative server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results. It will also take into account emotional data and adjust the communication method of preventive measures according to the user's reaction.

[0813] Step 12:

[0814] Example: A collaborative server creates a plan for launching a vaccination campaign in a specific region and adjusts communication strategies based on emotion data, for example, providing more reassuring information in advance based on emotion data.

[0815] Step 13:

[0816] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccination implementation.

[0817] Step 14:

[0818] Server: The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It analyzes the reduction in the number of infected people after vaccination and emotional data to evaluate the effectiveness of the vaccine. The evaluation results are fed back and stored in a database.

[0819] Step 15:

[0820] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into a database, allowing the effectiveness of the entire system to be continuously monitored.

[0821] In this way, a system is provided that goes through each step to achieve early detection of epidemics, rapid response, and pandemic prevention. Evaluation and adjustment of preventive measures based on user emotion data enables more effective countermeasures.

[0822] Example 2

[0823] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0824] Conventional epidemic management systems often require manual collection and preprocessing of disease data, making rapid data analysis and prediction difficult. Predicting mutant strains and designing vaccines also takes time, making them ineffective in situations where rapid response is required. Furthermore, preventive measures implemented without considering user sentiment data are likely to create social anxiety and resistance, potentially reducing the overall effectiveness of preventive measures.

[0825] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0826] In this invention, the server includes means for collecting disease data in real time, means for preprocessing the collected data to remove noise and fill in missing values, means for clustering the preprocessed data using a machine learning algorithm, means for predicting virus mutations using the AlphaFold algorithm, means for quickly designing customized vaccines based on the predicted mutations, means for collecting and analyzing user emotion data, means for designing preventive measures based on the analyzed emotion data and coordinating with international health organizations or local governments, and means for monitoring and evaluating the effectiveness of the implemented preventive measures. This enables comprehensive response from early detection of epidemics to the implementation of preventive measures and users' emotional reactions to vaccinations.

[0827] "Disease data" refers to data collected from public health agencies and medical institutions, including disease occurrence status, case information, patient information, etc.

[0828] "Preprocessing" refers to the process of removing noise and missing values ​​from acquired data and standardizing it, which can improve the accuracy of data analysis and prediction.

[0829] "Noise reduction" refers to the process of removing unnecessary or erroneous data from a dataset, thereby improving the quality of the data.

[0830] "Missing value imputation" refers to the process of filling in missing values ​​in a dataset in an appropriate way, thereby maintaining the accuracy of data analysis and machine learning.

[0831] "Clustering" refers to a machine learning algorithm that classifies data points into groups based on their similarity, allowing you to discover patterns and anomalies in the data.

[0832] The "AlphaFold algorithm" is a machine learning algorithm for predicting virus and protein structures, which makes it possible to predict mutant strains.

[0833] A "mutant strain" refers to a strain of the virus that has changed due to genetic mutation. Mutant strains may differ in their infectivity and resistance to vaccines.

[0834] "Vaccine design" refers to the process of selecting vaccine components to combat a specific virus or its variants and planning the manufacturing process.

[0835] "Emotional Data" refers to data collected to assess a user's emotional state, which may include text data, multiple choice responses, biometrics, etc.

[0836] "Analysis" refers to the process of examining and analyzing collected data in detail using statistical and machine learning techniques, which allows useful information to be extracted from the data.

[0837] "Preventive measures" refer to specific measures or action plans taken to prevent the spread of a disease, including vaccinations, public campaigns, and quarantine measures.

[0838] "International health organizations" refer to organizations that primarily manage and coordinate international public health responses to global health issues.

[0839] "Local government" refers to an administrative unit that has the right to self-govern within a certain area, which allows specific health measures to be implemented in that area.

[0840] "Real-time" refers to near-instant data processing and response, enabling timely information gathering and response.

[0841] MODE FOR CARRYING OUT THE INVENTION

[0842] The system for implementing this invention is composed of multiple servers, terminals, and users. Each server, terminal, and user works in cooperation to comprehensively perform early detection of epidemics, prediction of mutant strains, vaccine design, implementation of preventive measures, and even analysis of user emotion data.

[0843] 1. Hardware and Software Used

[0844] server:

[0845] Data Collection Server

[0846] Data analysis server

[0847] Prediction Server

[0848] Vaccine Design Server

[0849] Emotion Engine Server

[0850] Collaboration Server

[0851] Monitoring and evaluation server

[0852] Software and Algorithms:

[0853] API (Application Programming Interface)

[0854] Database (e.g. SQL database)

[0855] Machine learning algorithms (e.g., K-means clustering)

[0856] AlphaFold algorithm

[0857] Emotion recognition technology (Natural Language Processing)

[0858] Device:

[0859] Public health officer terminal

[0860] Medical staff terminals

[0861] Researcher terminal

[0862] Biopharmaceutical company terminals

[0863] General user terminal

[0864] 2. Data Collection

[0865] The data collection server collects disease data in real time from public health agencies, hospitals, and research institutes, obtains the information through APIs and database connections, and converts it into a standardized data format.

[0866] Example: A data collection server retrieves influenza case data in JSON format from the Ministry of Health, Labor and Welfare's API and stores it in an internal SQL database.

[0867] Users, such as public health officials and medical professionals, enter case information using dedicated terminals, and the data is sent to a data collection server.

[0868] 3. Data Preprocessing and Analysis

[0869] The data analysis server preprocesses the collected data. This preprocessing includes noise removal, missing value completion, and data standardization. After preprocessing is complete, clustering is performed using a machine learning algorithm (e.g., K-means clustering).

[0870] Example: A data analysis server detects incomplete entries in collected case data and imputes them with average values, then standardizes the data and runs a clustering algorithm to group similar cases together.

[0871] 4. Prediction of mutant strains

[0872] The prediction server uses the AlphaFold algorithm to predict virus mutations based on the collected viral genetic data.

[0873] Example: The prediction server takes a new viral gene sequence and inputs it into the AlphaFold algorithm, generating predicted structural changes and visualizing the characteristics of the next likely mutant strain.

[0874] Researchers using the terminals, known as research terminals, will receive the predicted data and perform detailed analysis, including assessing the transmissibility of the variants and their resistance to existing vaccines.

[0875] Example prompt sentence:

[0876] When a new strain of influenza emerges, collect data from international health organizations, preprocess it on the data analysis server, predict the mutant strain using the prediction server, and design a new vaccine on the vaccine design server. Then, evaluate user emotion data on the emotion engine server, and based on the results, propose preventive measures on the collaboration server. Finally, monitor and evaluate their effectiveness on the monitoring and evaluation server.

[0877] 5. Vaccine Design

[0878] The vaccine design server will rapidly design a customized vaccine based on predicted variant information, including selecting vaccine components and designing the manufacturing process.

[0879] Example: A vaccine design server generates sequences for new vaccine components targeting specific antigens based on predicted variant data, and also creates instructions for the manufacturing process.

[0880] Researchers at biopharmaceutical companies using the terminal, a biopharmaceutical company terminal, will review the details of the designed vaccine and begin experiments to prepare for production.

[0881] 6. Emotional Data Collection and Analysis

[0882] The emotion engine server collects and analyzes the user's emotion data from the device, and evaluates the user's emotional state using emotion recognition technology.

[0883] Example: The emotion engine server analyzes data collected from the device and uses Natural Language Processing (NLP) algorithms to extract emotions from text data entered by the user.

[0884] A general user using a general user terminal inputs the emotional state at the time of vaccination and transmits the data to the emotion engine server.

[0885] 7. Implementation of preventive measures and coordination

[0886] The linked server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results, and will also take into account emotional data to design more effective preventive measures.

[0887] Example: The collaborative server analyzes infection and sentiment data by region and creates proposals for implementing vaccination campaigns in specific regions.

[0888] Local government officials, who are the users, implement preventive measures based on suggestions from the collaboration server and launch public health campaigns.

[0889] 8. Monitoring and Evaluation

[0890] The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It analyzes the decrease in the number of infected people after vaccination and emotional data to evaluate the effectiveness of the vaccine.

[0891] Example: The monitoring and evaluation server periodically updates and analyzes post-vaccination infection rate data and sentiment data. Statistical methods such as hypothesis testing are used to evaluate the effectiveness of the vaccine.

[0892] Local medical institutions and local government officials using the local medical institution terminals report feedback to the monitoring and evaluation server and enter the evaluation results into a database.

[0893] The system will comprehensively respond to users' emotional reactions to early detection of epidemics, implementation of preventive measures, and vaccination, aiming to prevent the risk of pandemics and minimize their social and economic impacts through each processing step.

[0894] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0895] Step 1: Data collection

[0896] The server, the data collection server, collects disease data in real time from public health agencies, hospitals, and research institutes. As input, it uses the information obtained through APIs and database connections and converts it into a standardized data format. As output, the standardized disease data is stored in a database.

[0897] Specific operation: The data collection server retrieves influenza case data in JSON format from the Ministry of Health, Labor and Welfare's API and stores it in an internal SQL database.

[0898] Step 2: Data preprocessing and analysis

[0899] The data analysis server preprocesses the standardized disease data obtained from the data collection server. Using the standardized data as input, it removes noise, fills in missing values, and standardizes the data. The preprocessed data is obtained as output, and clustering is performed using a machine learning algorithm based on this data.

[0900] What it does: The data analysis server detects incomplete entries in the collected case data and imputes them with average values, then standardizes the data and runs a clustering algorithm (e.g., K-means) to group similar cases.

[0901] Step 3: Predicting variants

[0902] The prediction server uses the AlphaFold algorithm to predict virus mutations based on preprocessed data provided by the data analysis server. It uses viral genetic data and clustered case data as input. The output is structural change data for the newly predicted mutations.

[0903] How it works: The prediction server takes a new viral gene sequence and inputs it into the AlphaFold algorithm, which generates predicted structural changes and visualizes the characteristics of the next likely mutant strain.

[0904] Step 4: Vaccine design

[0905] The vaccine design server rapidly designs customized vaccines based on the prediction data provided by the prediction server. It uses the predicted variant data as input. The output is the sequence data of the vaccine components and instructions for the manufacturing process.

[0906] How it works: The vaccine design server generates sequences of new vaccine components that target specific antigens based on predicted variant data, and simultaneously creates instructions for the manufacturing process.

[0907] Step 5: Collect and analyze emotion data

[0908] The emotion engine server collects and analyzes the user's emotional data from the device. As input, it uses the user's text data indicating their emotional state, analyzes it, and evaluates their emotional state. As output, it obtains the analyzed emotional data.

[0909] Specific operation: The emotion engine server analyzes data collected from the device and uses Natural Language Processing (NLP) algorithms to extract emotions from text data entered by the user.

[0910] Step 6: Implement preventative measures and collaborate

[0911] The collaboration server, which is a server, proposes appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results. It also takes into account emotional data to design more effective preventive measures. The analyzed disease data, prediction data, and emotional data are used as input. The preventive measures planning data is obtained as output.

[0912] Specific operation: The collaboration server analyzes infection data and sentiment data by region and creates proposals for implementing vaccination campaigns in specific regions.

[0913] Step 7: Monitor and evaluate

[0914] The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It uses post-vaccination infection rate data and user sentiment data as input and analyzes them. The output is evaluation data on vaccine effectiveness.

[0915] Specific operation: The monitoring and evaluation server periodically updates and analyzes post-vaccination infection rate data and sentiment data. It evaluates the effectiveness of the vaccine using statistical methods such as hypothesis testing.

[0916] This system will enable comprehensive response from early detection of epidemics to the implementation of preventive measures and users' emotional reactions to vaccinations. By going through each processing step, it will be possible to prevent the risk of a pandemic and minimize its social and economic impact.

[0917] (Application example 2)

[0918] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0919] Conventional disease prevention systems and autonomous vehicle safety and comfort management systems lack the ability to integrate public health measures and emergency responses based on passenger emotional states. These shortcomings make it difficult to implement rapid and efficient epidemic prevention measures or respond appropriately based on passenger emotional states, resulting in insufficient prevention effectiveness and safety.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0921] In this invention, the server includes a means for collecting disease data in real time, a means for predicting virus mutations based on the collected data, and a means for quickly designing a customized vaccine based on the predicted mutations. It also includes a means for collecting passenger emotional data in real time, a means for analyzing passenger emotional states based on the collected emotional data, a means for implementing appropriate safety and comfort measures based on the analysis results, and a means for taking emergency action when an abnormality is detected. This enables early disease prevention and improved passenger safety and comfort.

[0922] "Means for collecting disease data in real time" refers to technologies for collecting disease-related data from public health agencies and medical institutions without time delay.

[0923] "Means for predicting virus mutations based on collected data" refers to technology that analyzes collected disease data and predicts future virus mutations based on the results.

[0924] "Means for rapidly designing customized vaccines based on predicted variants" refers to technology for rapidly designing vaccines optimized for predicted viral variants.

[0925] "Means for collaborating with international health organizations to implement designed vaccines and preventive measures" means techniques and protocols for collaborating with international health organizations to implement designed vaccines and preventive measures.

[0926] "Means for monitoring and evaluating the effectiveness of implemented preventive measures" refers to technology that monitors and evaluates the effectiveness of implemented preventive measures in real time.

[0927] "Means for collecting passenger emotional data in real time" refers to technology for collecting passenger emotional states in autonomous vehicles without time delay.

[0928] The "means for analyzing the emotional state of passengers based on collected emotional data" is a technique for analyzing collected emotional data and evaluating the emotional state of passengers.

[0929] "Means for implementing appropriate safety and comfort measures based on the analysis results" refers to technology that takes specific measures to improve safety and comfort based on the analysis results of passengers' emotional states.

[0930] "Means for taking emergency action when an abnormality is detected" refers to technology that quickly implements appropriate emergency measures when an abnormality is detected through analysis of passengers' emotional states and real-time data.

[0931] The system for implementing the present invention consists of various servers, terminals, and sensors that collect real-time disease data, predict virus mutations, design customized vaccines, and analyze passengers' emotional states to respond to emergencies.

[0932] System Configuration

[0933] server

[0934] Data collection server: collects disease data in real time.

[0935] Data analysis server: Preprocesses collected data, removes noise, and fills in missing values.

[0936] Prediction server: Predicts virus mutations using the AlphaFold algorithm.

[0937] Vaccine design server: Rapidly design customized vaccines based on predicted variant information.

[0938] Collaboration server: Collaborates with international health organizations on the implementation of preventive measures.

[0939] Monitoring and evaluation server: Monitors and evaluates the effectiveness of implemented preventive measures in real time.

[0940] Emotion Engine Server: Collects passengers' emotion data and analyzes it.

[0941] Terminal

[0942] Public health officer terminal: Enters disease data and sends it to the data collection server.

[0943] General user terminal: Emotional states are input via smartphones or in-vehicle displays and sent to the emotion engine server.

[0944] User

[0945] Public health officials: Enter disease data into a collection server.

[0946] General users: Input their emotional state when receiving a vaccination or riding in an autonomous vehicle.

[0947] Program processing

[0948] Hardware and Software

[0949] The system operates using the following hardware and software:

[0950] Facial recognition camera: Used to analyze passengers' facial expressions in real time.

[0951] Microphone: Used to analyze voice tones and recognize emotions.

[0952] Heart rate sensor: Used to measure heart rate and include it in emotion data.

[0953] OpenCV: A software library for performing face recognition processing.

[0954] Google Speech-to-Text API: Used to integrate speech recognition and text conversion.

[0955] Affectiva API: Software for analyzing emotional data as an emotion engine.

[0956] AlphaFold: A machine learning algorithm used to predict virus variants.

[0957] Specific examples of processing

[0958] Emotion data collection and analysis

[0959] Passenger emotional data is collected using facial recognition cameras, microphones, and heart rate sensors. The emotion engine server analyzes the passenger's facial expressions, voice tone, and heart rate data to evaluate the passenger's emotional state. If the result indicates that the passenger is feeling stressed or anxious, the system will take measures such as playing relaxing music. In addition, if an abnormality is detected, emergency response will be initiated. For example, if a passenger feels strong stress while on board, the data will be sent to the emotion engine server, and the analysis results will be returned immediately. Depending on the analysis results, the display in the vehicle will display "Playing relaxing music" and music will be played.

[0960] Prompt Sentence Examples

[0961] For example, a prompt to input the passenger's stress level into the emotion engine might look like this:

[0962] "Analyze passengers' stress levels based on facial expressions, voice tone, and heart rate data."

[0963] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0964] Step 1:

[0965] Data collection

[0966] The server collects real-time emotional data from passengers through facial recognition cameras, microphones, and heart rate sensors. The collected data includes facial expression data, voice data, and heart rate data. These data are sent directly from each sensor to the data collection server.

[0967] Input: Facial expression data, voice data, heart rate data

[0968] Output: Unified emotion dataset (facial expressions, vocal tone, heart rate)

[0969] Step 2:

[0970] Data Preprocessing

[0971] The server receives the collected data and performs noise removal and missing value completion. For example, it preprocesses facial recognition data to prevent closed eyes and open mouths from affecting emotion recognition. Similarly, it removes background noise from audio data to obtain clear audio data.

[0972] Input: Unified sentiment dataset

[0973] Output: Preprocessed emotion dataset

[0974] Step 3:

[0975] Emotional Data Analysis

[0976] The server then uses the pre-processed emotion dataset to perform analysis using the emotion engine server, which uses OpenCV to recognize emotions from facial expressions, Google Speech-to-Text API to convert voice data to text, and Affectiva API to analyze emotions. Heart rate data is also analyzed in the same way to assess the passenger's emotional state.

[0977] Input: Preprocessed emotion dataset

[0978] Output: Passenger's emotional state (e.g., stress level, anxiety level)

[0979] Step 4:

[0980] Implementing countermeasures based on analysis results

[0981] The server then instructs the system to take appropriate countermeasures based on the analysis results. For example, if a passenger is in a high stress state, the server can instruct the in-car sound system to play relaxing music. If an abnormality is detected, the server can activate the emergency call system.

[0982] Input: Passenger emotional state (e.g., stress level, anxiety level)

[0983] Output: Execution instructions (e.g., music playback, emergency call)

[0984] Step 5:

[0985] Feedback and Learning

[0986] The server collects feedback from passengers and uses it to improve the system. Specifically, it receives feedback data from passenger terminals, stores it in a database, and reflects it in future analyses and countermeasures.

[0987] Input: Passenger feedback data

[0988] Output: Feedback data stored in a database, and a dataset for system improvement

[0989] 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.

[0990] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0991] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0992] [Third embodiment]

[0993] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0994] 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.

[0995] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0996] 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.

[0997] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0998] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0999] 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. 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.

[1000] 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.

[1001] 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 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.

[1002] 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.

[1003] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1004] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1005] The system for carrying out the present invention comprises a plurality of servers, terminals, and users. The specific configuration of this system and the processing of the programs will be described in detail below.

[1006] System Configuration

[1007] 1. Server:

[1008] Data Collection Server

[1009] Data analysis server

[1010] Prediction Server

[1011] Vaccine Design Server

[1012] Collaboration Server

[1013] Monitoring and evaluation server

[1014] 2. Terminal:

[1015] Public health officer terminal

[1016] Medical staff terminals

[1017] Researcher terminal

[1018] Biopharmaceutical company terminals

[1019] 3. User:

[1020] Public health officials

[1021] Healthcare workers

[1022] researcher

[1023] Biopharmaceutical company researchers

[1024] Program processing

[1025] The overall system program is designed so that each server and terminal work together. Here, we will explain the roles of each server, terminal, and user, along with specific examples.

[1026] Data collection

[1027] Server: The data collection server collects disease data in real time from national public health agencies, hospitals, research institutes, etc. This includes the ability to retrieve information using APIs and database connections and convert it into standardized data formats.

[1028] Example: A data collection server receives reports from international health organizations about outbreaks of new strains of influenza.

[1029] Users: Public health and healthcare workers use devices to manually or automatically enter case information and send it to a server.

[1030] Data preprocessing and analysis

[1031] Server: The data analysis server preprocesses the collected data, removing noise and completing missing values. Using the preprocessed data, it performs clustering using a machine learning algorithm to group similar cases.

[1032] Example: A data analysis server identifies areas where similar cases are occurring frequently and performs clustering.

[1033] Predicting mutant strains

[1034] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations. Based on the collected viral genetic data, it analyzes the structure of mutations that may emerge in the future.

[1035] Example: A prediction server calculates the probability of new mutant strains emerging and visualizes their structural changes.

[1036] Terminal: Researchers check the predicted data for mutant strains provided by the prediction server on their terminals and perform detailed analysis.

[1037] Vaccine Design

[1038] Server: The vaccine design server rapidly designs customized vaccines based on predicted variant information, including generating optimal vaccine components and manufacturing processes.

[1039] Example: A vaccine design server designs vaccine components that are effective against specific mutant strains and proposes their manufacturing process.

[1040] Terminal: Researchers at biopharmaceutical companies use the terminal to review the details of the designed vaccine and prepare for production at the laboratory level.

[1041] Implementing preventive measures and coordinating

[1042] Server: Based on the collected data and prediction results, the collaborative server will propose appropriate preventive measures to international health organizations and local governments, such as planning vaccination campaigns and proposing lockdown measures.

[1043] Example: A federated server provides international health organizations with detailed plans for launching vaccination campaigns in specific regions.

[1044] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccinations.

[1045] Monitoring and Evaluation

[1046] Server: The monitoring and evaluation server monitors the effectiveness of the implemented preventive measures and vaccines in real time. It analyzes the reduction in the number of infections after vaccination and reports of side effects to confirm the effectiveness of the vaccine.

[1047] Example: A monitoring and evaluation server analyzes data on the reduction in infection rates after vaccination to assess the success of the vaccine.

[1048] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into the database.

[1049] These processes provide a system for early detection of epidemics, rapid response, and prevention of pandemics.

[1050] The processing flow will be explained below.

[1051] Step 1:

[1052] Server: The data collection server collects disease data in real time from public health agencies, hospitals, research institutes, etc. in each country. It obtains information via APIs and database connections and converts it into a standardized data format.

[1053] Step 2:

[1054] Users: Public health and healthcare workers use devices to manually or automatically enter case information and send it to a data collection server.

[1055] Step 3:

[1056] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing the data, and preparing it for analysis.

[1057] Step 4:

[1058] Server: The data analysis server uses pre-processed data to perform clustering using machine learning algorithms, which allows for grouping of similar cases and identification of infection patterns.

[1059] Step 5:

[1060] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations based on the clustered data. It uses viral genetic data as input and analyzes the structure of mutations that may emerge in the future.

[1061] Step 6:

[1062] Terminal: Researchers receive the predicted data on variants provided by the prediction server and perform detailed analysis, including evaluation of the characteristics and infectivity of the variants.

[1063] Step 7:

[1064] Server: The vaccine design server will rapidly design customized vaccines based on predicted variant information, select vaccine components, design manufacturing processes, and simulate preventive effects.

[1065] Step 8:

[1066] Terminal: Researchers at biopharmaceutical companies receive design data from the vaccine design server and prepare for production at the laboratory level, including setting up production equipment and manufacturing prototypes.

[1067] Step 9:

[1068] Server: Based on the collected data and prediction results, the collaborative server will propose appropriate preventive measures to international health organizations and local governments, providing instructions on planning vaccination campaigns and implementing lockdown measures.

[1069] Step 10:

[1070] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccination implementation.

[1071] Step 11:

[1072] Server: The monitoring and evaluation server will monitor the effectiveness of the implemented preventive measures and vaccines in real time, collecting and analyzing reports of the reduction in the number of infections and side effects after vaccination.

[1073] Step 12:

[1074] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into a database, allowing the effectiveness of the entire system to be continuously monitored.

[1075] In this way, through each step, a system is provided that enables early detection of epidemics, rapid response, and prevention of pandemics.

[1076] Example 1

[1077] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1078] Conventional disease data collection and analysis systems often lack real-time capabilities, and noise and missing values ​​in the collected data often adversely affect the analysis results. Furthermore, they lack the speed to predict virus mutations and design vaccines, making it difficult to prevent the spread of infectious diseases. A system that can resolve these issues and enable more effective and rapid public health measures is needed.

[1079] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1080] In this invention, the server includes: means for collecting disease data in real time from public health organizations, hospitals, research institutions, etc.; means for preprocessing the collected data to remove noise and fill in missing values; means for analyzing the preprocessed data using a machine learning algorithm and clustering similar cases; means for predicting virus mutations using the AlphaFold algorithm; means for quickly designing customized vaccines based on the predicted mutations; means for providing plans for vaccination campaigns and lockdown measures to international health organizations and local governments through a linked server; and means for monitoring and evaluating the effectiveness of implemented preventive measures and vaccines in real time. This enables real-time data collection and analysis, rapid prediction of virus mutations and vaccine design, and implementation and evaluation of effective public health measures.

[1081] "Means for collecting disease data in real time" refers to methods for obtaining timely disease data from public health agencies, hospitals, research institutions, etc.

[1082] "Noise removal" is the process of cleaning data by removing unnecessary data and outliers from the data.

[1083] "Missing value imputation" is the process of filling in missing values ​​in a dataset with appropriate values ​​(e.g., averages or estimated values).

[1084] "Preprocessed data" refers to a dataset after noise removal and missing value imputation have been performed.

[1085] A "machine learning algorithm" is a computational method for learning from data and making predictions or classifications.

[1086] "Clustering" is a machine learning algorithm that divides data into multiple groups based on similarity.

[1087] The "AlphaFold algorithm" is a computational model for predicting the three-dimensional structure of proteins.

[1088] "Mutant prediction" is the process of predicting how pathogens such as viruses will mutate.

[1089] A "customized vaccine" is a vaccine that is optimized for a specific variant.

[1090] A "collaboration server" is a server that allows multiple institutions and devices to share information and work together to carry out tasks.

[1091] A "vaccination campaign" is an activity that encourages and administers vaccination to a wide range of people.

[1092] "Lockdown measures" are measures to close specific areas or facilities to prevent the spread of infectious diseases.

[1093] "Monitoring and evaluation measures" are methods for continuously observing and evaluating the effectiveness of implemented measures and vaccines.

[1094] MODE FOR CARRYING OUT THE INVENTION

[1095] The system for implementing the present invention comprises a plurality of servers, terminals, and users. The specific configuration of this system and the processing of the programs are described below.

[1096] System Configuration

[1097] server

[1098] 1. Data Collection Server

[1099] The data collection server is a server that collects disease data in real time from public health agencies, hospitals, and research institutes. Specifically, it has the function of retrieving information using APIs and database connections and converting it into a standardized data format.

[1100] 2. Data analysis server

[1101] The data analysis server preprocesses the collected data, removes noise, and fills in missing values. It also uses the preprocessed data to implement machine learning algorithms and cluster similar cases.

[1102] 3. Prediction Server

[1103] The prediction server uses the AlphaFold algorithm to predict virus mutations. Based on collected viral genetic data, it analyzes the structure of mutations that may emerge in the future.

[1104] 4. Vaccine Design Server

[1105] The Vaccine Design Server is a server for rapidly designing customized vaccines based on predicted variant information, including generating optimal vaccine components and manufacturing processes.

[1106] 5. Collaboration Server

[1107] The collaborative server will use the collected data and prediction results to propose appropriate preventive measures to international health organizations and local governments, such as planning vaccination campaigns and proposing lockdown measures.

[1108] 6. Monitoring and evaluation server

[1109] The monitoring and evaluation server will monitor the effectiveness of the implemented preventive measures and the vaccine in real time, analyzing the reduction in the number of infected people after vaccination and reports of side effects to confirm the effectiveness of the vaccine.

[1110] Terminal

[1111] 1. Public Health Officer Terminals

[1112] Public health personnel terminals are terminals that public health personnel use to input disease data and connect to a server, for example, with the ability to input case data through dedicated software or a web app.

[1113] 2. Medical device

[1114] The medical staff terminal is a terminal where medical staff input and check case data and send it to the data collection server.

[1115] 3. Researcher terminals

[1116] The researcher terminals are used by researchers to check data analysis results and mutant strain information and perform detailed analysis, for example, using dedicated analysis software (e.g., Biopython, PyMOL).

[1117] 4. Terminals for biopharmaceutical companies

[1118] The biopharmaceutical company terminal is a terminal where researchers at biopharmaceutical companies can check the details of the designed vaccine and prepare for the manufacturing process.

[1119] User

[1120] 1. Public health officials

[1121] Public health officials are responsible for collecting disease data and implementing and monitoring preventive measures. They input data using dedicated terminals.

[1122] 2. Healthcare workers

[1123] Medical professionals collect case data at hospitals and clinics, send it to a data collection server, and enter and check the case data.

[1124] 3. Researcher

[1125] Researchers will conduct detailed studies based on data analysis and predictions of mutant strains, and plan further experiments based on the results of the analysis.

[1126] 4. Researchers at biopharmaceutical companies

[1127] Researchers at biopharmaceutical companies will review the details of the designed vaccine, prepare for production at the laboratory level, and conduct efficacy and safety tests.

[1128] Examples of concrete examples and prompts

[1129] Example: A data collection server accesses the API of an international health organization to periodically retrieve reports of new influenza outbreaks.

[1130] Example: A prediction server uses the AlphaFold algorithm to predict structural changes in new mutant strains and provides the results to researchers' terminals.

[1131] Example prompt: "Please explain how the data collection server receives reports of new influenza outbreaks from international health organizations, and how the analysis server performs noise removal and missing value imputation."

[1132] In this way, the entire system works together to enable early detection of epidemics, rapid response, and pandemic prevention.

[1133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1134] Step 1: Data collection

[1135] The server collects disease data in real time from public health agencies, hospitals, research institutes, etc. Specifically, it retrieves information using APIs and database connections and converts it into standardized data formats (e.g., JSON, CSV). This collected data is then sent to a data analysis server for analysis in the next step. For example, the server periodically requests the API of an international health organization to retrieve reports of new influenza outbreaks.

[1136] Input: Disease data from public health agencies, hospitals, and research institutes

[1137] Output: Standardized disease data

[1138] Step 2: Data Preprocessing

[1139] The server preprocesses the collected data, including noise removal and missing value imputation. Specifically, the data analysis server removes unnecessary data and outliers from the data and imputes missing data with appropriate values ​​(e.g., mean values ​​or estimated values). This preprocessed data is used in the next analysis step. For example, the data analysis server imputes missing values ​​with the mean values ​​calculated for the data rows in which they exist.

[1140] Input: Collected disease data

[1141] Output: Preprocessed disease data

[1142] Step 3: Data analysis

[1143] The server then applies a machine learning algorithm to the preprocessed data. Specifically, it uses a clustering algorithm (e.g., k-means) to group similar cases. The results of this analysis are then used in the next step of variant prediction. For example, the data analysis server analyzes cases that are occurring frequently in a specific region and generates groups using k-means clustering.

[1144] Input: Preprocessed disease data

[1145] Output: Clustered case data

[1146] Step 4: Predicting variants

[1147] The server uses the AlphaFold algorithm to predict virus mutations. Specifically, it receives viral genetic data as input and analyzes the protein structures of potential future mutations. The prediction results are used in the next step of vaccine design. For example, the prediction server receives the amino acid sequence of a specific part of the virus as input and uses AlphaFold to predict likely mutations.

[1148] Input: viral genetic data

[1149] Output: Predicted variant information

[1150] Step 5: Vaccine design

[1151] The server designs optimal vaccines based on predicted variant information. Specifically, it identifies epitopes that are effective against different parts of the virus and uses algorithms (e.g., RosettaDesign) to design peptide vaccines. The designed vaccine information is then used in the next step of implementing preventive measures. For example, the vaccine design server calculates the optimal epitope for a specific variant and proposes its production procedure.

[1152] Input: Predicted mutant information

[1153] Output: Designed vaccine information

[1154] Step 6: Implement preventative measures and collaborate

[1155] Based on the collected data and prediction results, the collaboration server will propose appropriate preventive measures to international health organizations and local governments. Specifically, it will generate plans for vaccination campaigns and lockdown measures and notify relevant organizations. For example, the collaboration server will develop a detailed schedule for public health campaigns and vaccinations to prevent the spread of a new strain of influenza in a specific area.

[1156] Input: Designed vaccine information, predicted results

[1157] Output: Suggested preventative measures

[1158] Step 7: Monitor and evaluate

[1159] The monitoring and evaluation server monitors the effectiveness of implemented preventive measures and vaccinations in real time. Specifically, it evaluates the effectiveness of vaccines by collecting and analyzing data on the number of infected people after vaccination and reports of side effects. For example, the monitoring and evaluation server analyzes data on the reduction in infection rates after vaccination to evaluate the success of the vaccine.

[1160] Input: Preventive measures taken, vaccination data

[1161] Output: Effect evaluation results

[1162] This processing step enables the entire system to work together to detect diseases early, respond quickly, and prevent pandemics.

[1163] (Application example 1)

[1164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1165] Rapid detection and prediction of infectious diseases and the implementation of effective preventive measures are crucial for modern public health. However, current systems lack responsiveness and accuracy because they lack centralized collection of real-time disease data, prediction of mutant strains, design and implementation of vaccines, and monitoring of their effectiveness. Therefore, there is a need for a system that can monitor infectious disease risk at the individual user level in real time, quickly send alerts in high-risk areas, and provide timely vaccination information.

[1166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1167] In this invention, the server includes means for collecting disease data in real time, means for predicting virus mutations based on the collected data, means for quickly designing customized vaccines based on the predicted mutations, means for coordinating with international health organizations to implement the designed vaccines and preventive measures, means for monitoring and evaluating the effectiveness of the implemented preventive measures, means for monitoring disease risk in real time and for issuing alerts for high-risk areas, and means for managing and notifying the timing of vaccinations. This enables rapid detection and prediction of infectious diseases, real-time risk monitoring, and the implementation and monitoring of appropriate preventive measures.

[1168] "Disease Data" refers to information that includes the occurrence, progression, and related information of a disease in an individual or population.

[1169] A "virus variant" refers to a subspecies of a virus with certain mutations or genetic changes.

[1170] A "customized vaccine" refers to a vaccine designed to have specific efficacy against predicted variants.

[1171] "international health organization" means an international organization established to prevent and control disease and provide health-related services;

[1172] "Real-time monitoring" refers to the immediate observation and analysis of ongoing situations and data.

[1173] A "high-risk area" refers to an area where the risk of certain diseases or health conditions is considered higher than in other areas.

[1174] "Alert notification" refers to a system that warns or notifies users when certain criteria are met.

[1175] "Managing vaccination timing" refers to adjusting schedules and providing notifications so that vaccinations are given at the appropriate time.

[1176] "Monitoring and evaluation" refers to the continuous observation and analysis of the effectiveness and feasibility of preventive measures implemented.

[1177] The embodiment of the present invention is a system in which various servers and terminals work in cooperation with each other. The program processing of this system will be explained below.

[1178] Hardware and software used

[1179] Hardware:

[1180] Smartphone (iOS / Android)

[1181] software:

[1182] Mobile app development framework: React Native

[1183] Data Collection API: Firebase

[1184] Machine learning model: TensorFlow Lite

[1185] Map API: Google Maps API

[1186] Notification service: OneSignal

[1187] Program processing explanation

[1188] 1. Data Collection and Preprocessing

[1189] The server uses Firebase to collect disease data in real time, including location information and case report information. The data collection server preprocesses the collected data, removing noise and imputing missing values.

[1190] 2. Data analysis and mutant strain prediction

[1191] The server uses TensorFlow Lite to predict virus mutations based on collected data, and the AlphaFold algorithm to analyze the structure of potential future mutations.

[1192] 3. Risk monitoring and alert notifications

[1193] The device uses Google Maps API for real-time disease risk monitoring, visualizing high-risk areas based on the user's current location, and provides alerts via OneSignal if the user enters a high-risk area.

[1194] 4. Vaccine design and timing management

[1195] The server will quickly design a customized vaccine based on predicted variant information, manage vaccination information on the device, and notify users when to get vaccinated.

[1196] Specific examples and generated AI model prompts

[1197] Example 1: If a new strain of influenza is confirmed within 10km of a user's home, the app will display that information on a map and highlight the area as a risk. An alert will be displayed saying, "You have entered a high-risk area. Please take precautions."

[1198] Example 2: When it's time for vaccination, you'll be notified with the message, "It's almost time for your vaccination. The next vaccination site is your local hospital. Would you like to make an appointment?"

[1199] Generative AI model prompt:

[1200] "Please develop a smartphone app that will monitor the risk of infection within a 10km radius in real time and notify you if you approach a risk area."

[1201] "Design an application that manages users' vaccination information and notifies them of the best time to get vaccinated."

[1202] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1203] Step 1:

[1204] The server uses Firebase to collect disease data. The input here is real-time data such as user location information and case report information. The server collects this data and converts it into a standardized data format. The output is a dataset in a format that can be preprocessed. Specific operations include collecting location information and obtaining real-time data through an API.

[1205] Step 2:

[1206] The server preprocesses the collected data, removing noise and imputing missing values. The input is the dataset collected in step 1. The server uses machine learning algorithms to cleanse the data and generate reliable data. The output is a high-quality dataset suitable for analysis. Specific operations include applying missing value imputation algorithms and noise filtering.

[1207] Step 3:

[1208] The server uses TensorFlow Lite to cluster disease risks and predict virus mutations based on the preprocessed data. The input is the dataset obtained in step 2. The server applies a machine learning model to analyze future risk areas and the likelihood of mutations. The output is detailed information on risk assessment results and mutation predictions. Specific operations include predictive modeling using a clustering algorithm and the AlphaFold algorithm.

[1209] Step 4:

[1210] The device uses the Google Maps API to display risk areas based on the user's current location on a map. The input is the risk assessment result obtained in step 3 and the current location information. The device visualizes the risk areas on a map and highlights dangerous areas. The output is the display of risk areas on the user interface. Specific operations include rendering the map and drawing polygons of the risk areas.

[1211] Step 5:

[1212] The device will send an alert notification via OneSignal if it enters a high-risk area. The input is the risk area information displayed in step 4 and the user's real-time location information. If the location information matches the risk area, the device will immediately send a notification to the user. The output is an alert message. Specific operations include real-time location comparison and notification triggering.

[1213] Step 6:

[1214] The server designs a vaccine based on the predicted mutant strain data. The input is the mutant strain prediction data obtained in step 3. The server designs a customized vaccine that is adapted to the new mutant strain based on current vaccine information. The output is detailed information about the designed vaccine. Specifically, the vaccine components are optimized using bioinformatics algorithms.

[1215] Step 7:

[1216] The terminal manages the user's vaccination information and notifies the user of vaccination at the appropriate time. The input is the user's vaccination history and vaccine information provided by the server. The terminal automatically calculates the next vaccination date and sends a notification to the user when the time approaches. The output is a vaccination reminder message. Specific operations include managing historical data and generating a notification schedule.

[1217] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1218] The system for implementing this invention is composed of multiple servers, terminals, and users. Furthermore, by incorporating an emotion engine, the effectiveness of the system can be improved by utilizing user emotion data. The specific configuration of this system and the program processing are described in detail below.

[1219] System Configuration

[1220] 1. Server:

[1221] Data Collection Server

[1222] Data analysis server

[1223] Prediction Server

[1224] Vaccine Design Server

[1225] Collaboration Server

[1226] Monitoring and evaluation server

[1227] Emotion Engine Server

[1228] 2. Terminal:

[1229] Public health officer terminal

[1230] Medical staff terminals

[1231] Researcher terminal

[1232] Biopharmaceutical company terminals

[1233] General user terminal

[1234] 3. User:

[1235] Public health officials

[1236] Healthcare workers

[1237] researcher

[1238] Biopharmaceutical company researchers

[1239] General users

[1240] Program processing

[1241] The entire system is designed to work in cooperation with each server, terminal, and user. Here, we will explain the roles and specific examples of each server, terminal, and user.

[1242] Data collection

[1243] Server: The data collection server collects disease data in real time from public health agencies, hospitals, and research institutes. It obtains information through APIs and database connections and converts it into a standardized data format.

[1244] Example: A data collection server receives reports from international health organizations about outbreaks of new strains of influenza.

[1245] Users: Public health officials and medical workers use terminals to enter case information and send it to a data collection server.

[1246] Data preprocessing and analysis

[1247] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing data, etc. Using the preprocessed data, clustering is performed using machine learning algorithms.

[1248] Example: A data analysis server identifies areas where similar cases are occurring frequently and performs clustering.

[1249] Predicting mutant strains

[1250] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations. It analyzes the structure of potential mutations based on viral genetic data.

[1251] Example: A prediction server calculates the probability of a new mutant strain emerging and visualizes its structural changes.

[1252] Terminal: Researchers receive predicted data on mutant strains provided by the prediction server and perform detailed analysis.

[1253] Vaccine Design

[1254] Server: The vaccine design server will rapidly design customized vaccines based on predicted variant information, including selecting vaccine components and designing manufacturing processes.

[1255] Example: A vaccine design server designs vaccine components that are effective against specific mutant strains and proposes their manufacturing process.

[1256] Terminal: Researchers at biopharmaceutical companies use the terminal to review the details of the designed vaccine and prepare for production at the laboratory level.

[1257] Emotion data collection and analysis

[1258] Server: The emotion engine server collects and analyzes the user's emotion data from the device. It uses emotion recognition technology to evaluate the user's emotional state.

[1259] Example: An emotion engine server analyzes a user's emotional response to vaccination and detects emotions such as anxiety and fear.

[1260] Terminal: The general user inputs their emotional state at the time of vaccination into the terminal and sends it to the emotion engine server.

[1261] Implementing preventive measures and coordinating

[1262] Server: The collaborative server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results, and will also incorporate emotional data to design more effective preventive measures.

[1263] Example: A collaborative server creates a plan to launch a vaccination campaign in a specific region and adjusts communication strategies based on sentiment data.

[1264] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server and develop public health campaigns.

[1265] Monitoring and Evaluation

[1266] Server: The monitoring and evaluation server monitors the implemented preventive measures and vaccine effectiveness in real time. It analyzes the reduction in the number of infected people after vaccination and sentiment data to evaluate the effectiveness of the vaccine.

[1267] Example: A monitoring and evaluation server verifies the success of a vaccine by analyzing data on the reduction in infection rates after vaccination and user sentiment data.

[1268] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into the database.

[1269] This system will comprehensively address everything from early detection of epidemics to implementing preventative measures and users' emotional responses to vaccinations, and each step will help prevent the risk of a pandemic and minimize its social and economic impact.

[1270] The processing flow will be explained below.

[1271] Step 1:

[1272] Server: The data collection server collects disease data in real time from public health agencies, hospitals, research institutes, etc. in each country. It obtains the information via APIs and database connections and converts it into a standardized data format, thereby generating a centrally managed dataset.

[1273] Step 2:

[1274] Users: Public health officials and medical personnel use terminals to manually or automatically input case information and send it to the data collection server. The terminals standardize the format of the input data to prevent input errors and missing data.

[1275] Step 3:

[1276] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing data, etc. The preprocessed data is stored in a clean state, ensuring that subsequent analysis can proceed smoothly.

[1277] Step 4:

[1278] Server: The data analysis server uses preprocessed data to perform clustering using machine learning algorithms, which allows similar cases to be grouped together and infection patterns to be identified. Identified cluster information is updated in real time.

[1279] Step 5:

[1280] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations based on the clustered data. It uses viral genetic data as input to analyze the structure of mutations that may emerge in the future and stores the prediction results in a database.

[1281] Step 6:

[1282] Terminal: Researchers receive the predicted data on variants provided by the prediction server and perform detailed analysis, including evaluation of the characteristics and infectivity of the variants, and then send the results back to the server.

[1283] Step 7:

[1284] Server: The vaccine design server rapidly designs customized vaccines based on predicted variant information. It selects vaccine components, designs manufacturing processes, and simulates preventive effects. The designed vaccine prototypes are stored on the server.

[1285] Step 8:

[1286] Terminal: Biopharmaceutical company researchers receive design data from the vaccine design server and prepare for production at the laboratory level. This includes setting up production equipment and manufacturing prototypes. Initial test results of the prototypes are sent to the server.

[1287] Step 9:

[1288] Server: The emotion engine server collects and analyzes user emotion data from the device. It uses emotion recognition technology to evaluate the user's emotional state and shares that data with other servers.

[1289] Step 10:

[1290] Example: The emotion engine server analyzes the user's emotional response to vaccination and detects emotions such as anxiety and fear. Based on this, it determines the need for specific information and explanations.

[1291] Step 11:

[1292] Server: The collaborative server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results. It will also take into account emotional data and adjust the communication method of preventive measures according to the user's reaction.

[1293] Step 12:

[1294] Example: A collaborative server creates a plan for launching a vaccination campaign in a specific region and adjusts communication strategies based on emotion data, for example, providing more reassuring information in advance based on emotion data.

[1295] Step 13:

[1296] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccination implementation.

[1297] Step 14:

[1298] Server: The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It analyzes the reduction in the number of infected people after vaccination and emotional data to evaluate the effectiveness of the vaccine. The evaluation results are fed back and stored in a database.

[1299] Step 15:

[1300] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into a database, allowing the effectiveness of the entire system to be continuously monitored.

[1301] In this way, a system is provided that goes through each step to achieve early detection of epidemics, rapid response, and pandemic prevention. Evaluation and adjustment of preventive measures based on user emotion data enables more effective countermeasures.

[1302] Example 2

[1303] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1304] Conventional epidemic management systems often require manual collection and preprocessing of disease data, making rapid data analysis and prediction difficult. Predicting mutant strains and designing vaccines also takes time, making them ineffective in situations where rapid response is required. Furthermore, preventive measures implemented without considering user sentiment data are likely to create social anxiety and resistance, potentially reducing the overall effectiveness of preventive measures.

[1305] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1306] In this invention, the server includes means for collecting disease data in real time, means for preprocessing the collected data to remove noise and fill in missing values, means for clustering the preprocessed data using a machine learning algorithm, means for predicting virus mutations using the AlphaFold algorithm, means for quickly designing customized vaccines based on the predicted mutations, means for collecting and analyzing user emotion data, means for designing preventive measures based on the analyzed emotion data and coordinating with international health organizations or local governments, and means for monitoring and evaluating the effectiveness of the implemented preventive measures. This enables comprehensive response from early detection of epidemics to the implementation of preventive measures and users' emotional reactions to vaccinations.

[1307] "Disease data" refers to data collected from public health agencies and medical institutions, including disease occurrence status, case information, patient information, etc.

[1308] "Preprocessing" refers to the process of removing noise and missing values ​​from acquired data and standardizing it, which can improve the accuracy of data analysis and prediction.

[1309] "Noise reduction" refers to the process of removing unnecessary or erroneous data from a dataset, thereby improving the quality of the data.

[1310] "Missing value imputation" refers to the process of filling in missing values ​​in a dataset in an appropriate way, thereby maintaining the accuracy of data analysis and machine learning.

[1311] "Clustering" refers to a machine learning algorithm that classifies data points into groups based on their similarity, allowing you to discover patterns and anomalies in the data.

[1312] The "AlphaFold algorithm" is a machine learning algorithm for predicting virus and protein structures, which makes it possible to predict mutant strains.

[1313] A "mutant strain" refers to a strain of the virus that has changed due to genetic mutation. Mutant strains may differ in their infectivity and resistance to vaccines.

[1314] "Vaccine design" refers to the process of selecting vaccine components to combat a specific virus or its variants and planning the manufacturing process.

[1315] "Emotional Data" refers to data collected to assess a user's emotional state, which may include text data, multiple choice responses, biometrics, etc.

[1316] "Analysis" refers to the process of examining and analyzing collected data in detail using statistical and machine learning techniques, which allows useful information to be extracted from the data.

[1317] "Preventive measures" refer to specific measures or action plans taken to prevent the spread of a disease, including vaccinations, public campaigns, and quarantine measures.

[1318] "International health organizations" refer to organizations that primarily manage and coordinate international public health responses to global health issues.

[1319] "Local government" refers to an administrative unit that has the right to self-govern within a certain area, which allows specific health measures to be implemented in that area.

[1320] "Real-time" refers to near-instant data processing and response, enabling timely information gathering and response.

[1321] MODE FOR CARRYING OUT THE INVENTION

[1322] The system for implementing this invention is composed of multiple servers, terminals, and users. Each server, terminal, and user works in cooperation to comprehensively perform early detection of epidemics, prediction of mutant strains, vaccine design, implementation of preventive measures, and even analysis of user emotion data.

[1323] 1. Hardware and Software Used

[1324] server:

[1325] Data Collection Server

[1326] Data analysis server

[1327] Prediction Server

[1328] Vaccine Design Server

[1329] Emotion Engine Server

[1330] Collaboration Server

[1331] Monitoring and evaluation server

[1332] Software and Algorithms:

[1333] API (Application Programming Interface)

[1334] Database (e.g. SQL database)

[1335] Machine learning algorithms (e.g., K-means clustering)

[1336] AlphaFold algorithm

[1337] Emotion recognition technology (Natural Language Processing)

[1338] Device:

[1339] Public health officer terminal

[1340] Medical staff terminals

[1341] Researcher terminal

[1342] Biopharmaceutical company terminals

[1343] General user terminal

[1344] 2. Data Collection

[1345] The data collection server collects disease data in real time from public health agencies, hospitals, and research institutes, obtains the information through APIs and database connections, and converts it into a standardized data format.

[1346] Example: A data collection server retrieves influenza case data in JSON format from the Ministry of Health, Labor and Welfare's API and stores it in an internal SQL database.

[1347] Users, such as public health officials and medical professionals, enter case information using dedicated terminals, and the data is sent to a data collection server.

[1348] 3. Data Preprocessing and Analysis

[1349] The data analysis server preprocesses the collected data. This preprocessing includes noise removal, missing value completion, and data standardization. After preprocessing is complete, clustering is performed using a machine learning algorithm (e.g., K-means clustering).

[1350] Example: A data analysis server detects incomplete entries in collected case data and imputes them with average values, then standardizes the data and runs a clustering algorithm to group similar cases together.

[1351] 4. Prediction of mutant strains

[1352] The prediction server uses the AlphaFold algorithm to predict virus mutations based on the collected viral genetic data.

[1353] Example: The prediction server takes a new viral gene sequence and inputs it into the AlphaFold algorithm, generating predicted structural changes and visualizing the characteristics of the next likely mutant strain.

[1354] Researchers using the terminals, known as research terminals, will receive the predicted data and perform detailed analysis, including assessing the transmissibility of the variants and their resistance to existing vaccines.

[1355] Example prompt sentence:

[1356] When a new strain of influenza emerges, collect data from international health organizations, preprocess it on the data analysis server, predict the mutant strain using the prediction server, and design a new vaccine on the vaccine design server. Then, evaluate user emotion data on the emotion engine server, and based on the results, propose preventive measures on the collaboration server. Finally, monitor and evaluate their effectiveness on the monitoring and evaluation server.

[1357] 5. Vaccine Design

[1358] The vaccine design server will rapidly design a customized vaccine based on predicted variant information, including selecting vaccine components and designing the manufacturing process.

[1359] Example: A vaccine design server generates sequences for new vaccine components targeting specific antigens based on predicted variant data, and also creates instructions for the manufacturing process.

[1360] Researchers at biopharmaceutical companies using the terminal, a biopharmaceutical company terminal, will review the details of the designed vaccine and begin experiments to prepare for production.

[1361] 6. Emotional Data Collection and Analysis

[1362] The emotion engine server collects and analyzes the user's emotion data from the device, and evaluates the user's emotional state using emotion recognition technology.

[1363] Example: The emotion engine server analyzes data collected from the device and uses Natural Language Processing (NLP) algorithms to extract emotions from text data entered by the user.

[1364] A general user using a general user terminal inputs the emotional state at the time of vaccination and transmits the data to the emotion engine server.

[1365] 7. Implementation of preventive measures and coordination

[1366] The linked server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results, and will also take into account emotional data to design more effective preventive measures.

[1367] Example: The collaborative server analyzes infection and sentiment data by region and creates proposals for implementing vaccination campaigns in specific regions.

[1368] Local government officials, who are the users, implement preventive measures based on suggestions from the collaboration server and launch public health campaigns.

[1369] 8. Monitoring and Evaluation

[1370] The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It analyzes the decrease in the number of infected people after vaccination and emotional data to evaluate the effectiveness of the vaccine.

[1371] Example: The monitoring and evaluation server periodically updates and analyzes post-vaccination infection rate data and sentiment data. Statistical methods such as hypothesis testing are used to evaluate the effectiveness of the vaccine.

[1372] Local medical institutions and local government officials using the local medical institution terminals report feedback to the monitoring and evaluation server and enter the evaluation results into a database.

[1373] The system will comprehensively respond to users' emotional reactions to early detection of epidemics, implementation of preventive measures, and vaccination, aiming to prevent the risk of pandemics and minimize their social and economic impacts through each processing step.

[1374] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1375] Step 1: Data collection

[1376] The server, the data collection server, collects disease data in real time from public health agencies, hospitals, and research institutes. As input, it uses the information obtained through APIs and database connections and converts it into a standardized data format. As output, the standardized disease data is stored in a database.

[1377] Specific operation: The data collection server retrieves influenza case data in JSON format from the Ministry of Health, Labor and Welfare's API and stores it in an internal SQL database.

[1378] Step 2: Data preprocessing and analysis

[1379] The data analysis server preprocesses the standardized disease data obtained from the data collection server. Using the standardized data as input, it removes noise, fills in missing values, and standardizes the data. The preprocessed data is obtained as output, and clustering is performed using a machine learning algorithm based on this data.

[1380] What it does: The data analysis server detects incomplete entries in the collected case data and imputes them with average values, then standardizes the data and runs a clustering algorithm (e.g., K-means) to group similar cases.

[1381] Step 3: Predicting variants

[1382] The prediction server uses the AlphaFold algorithm to predict virus mutations based on preprocessed data provided by the data analysis server. It uses viral genetic data and clustered case data as input. The output is structural change data for the newly predicted mutations.

[1383] How it works: The prediction server takes a new viral gene sequence and inputs it into the AlphaFold algorithm, which generates predicted structural changes and visualizes the characteristics of the next likely mutant strain.

[1384] Step 4: Vaccine design

[1385] The vaccine design server rapidly designs customized vaccines based on the prediction data provided by the prediction server. It uses the predicted variant data as input. The output is the sequence data of the vaccine components and instructions for the manufacturing process.

[1386] How it works: The vaccine design server generates sequences of new vaccine components that target specific antigens based on predicted variant data, and simultaneously creates instructions for the manufacturing process.

[1387] Step 5: Collect and analyze emotion data

[1388] The emotion engine server collects and analyzes the user's emotional data from the device. As input, it uses the user's text data indicating their emotional state, analyzes it, and evaluates their emotional state. As output, it obtains the analyzed emotional data.

[1389] Specific operation: The emotion engine server analyzes data collected from the device and uses Natural Language Processing (NLP) algorithms to extract emotions from text data entered by the user.

[1390] Step 6: Implement preventative measures and collaborate

[1391] The collaboration server, which is a server, proposes appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results. It also takes into account emotional data to design more effective preventive measures. The analyzed disease data, prediction data, and emotional data are used as input. The preventive measures planning data is obtained as output.

[1392] Specific operation: The collaboration server analyzes infection data and sentiment data by region and creates proposals for implementing vaccination campaigns in specific regions.

[1393] Step 7: Monitor and evaluate

[1394] The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It uses post-vaccination infection rate data and user sentiment data as input and analyzes them. The output is evaluation data on vaccine effectiveness.

[1395] Specific operation: The monitoring and evaluation server periodically updates and analyzes post-vaccination infection rate data and sentiment data. It evaluates the effectiveness of the vaccine using statistical methods such as hypothesis testing.

[1396] This system will enable comprehensive response from early detection of epidemics to the implementation of preventive measures and users' emotional reactions to vaccinations. By going through each processing step, it will be possible to prevent the risk of a pandemic and minimize its social and economic impact.

[1397] (Application example 2)

[1398] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1399] Conventional disease prevention systems and autonomous vehicle safety and comfort management systems lack the ability to integrate public health measures and emergency responses based on passenger emotional states. These shortcomings make it difficult to implement rapid and efficient epidemic prevention measures or respond appropriately based on passenger emotional states, resulting in insufficient prevention effectiveness and safety.

[1400] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1401] In this invention, the server includes a means for collecting disease data in real time, a means for predicting virus mutations based on the collected data, and a means for quickly designing a customized vaccine based on the predicted mutations. It also includes a means for collecting passenger emotional data in real time, a means for analyzing passenger emotional states based on the collected emotional data, a means for implementing appropriate safety and comfort measures based on the analysis results, and a means for taking emergency action when an abnormality is detected. This enables early disease prevention and improved passenger safety and comfort.

[1402] "Means for collecting disease data in real time" refers to technologies for collecting disease-related data from public health agencies and medical institutions without time delay.

[1403] "Means for predicting virus mutations based on collected data" refers to technology that analyzes collected disease data and predicts future virus mutations based on the results.

[1404] "Means for rapidly designing customized vaccines based on predicted variants" refers to technology for rapidly designing vaccines optimized for predicted viral variants.

[1405] "Means for collaborating with international health organizations to implement designed vaccines and preventive measures" means techniques and protocols for collaborating with international health organizations to implement designed vaccines and preventive measures.

[1406] "Means for monitoring and evaluating the effectiveness of implemented preventive measures" refers to technology that monitors and evaluates the effectiveness of implemented preventive measures in real time.

[1407] "Means for collecting passenger emotional data in real time" refers to technology for collecting passenger emotional states in autonomous vehicles without time delay.

[1408] The "means for analyzing the emotional state of passengers based on collected emotional data" is a technique for analyzing collected emotional data and evaluating the emotional state of passengers.

[1409] "Means for implementing appropriate safety and comfort measures based on the analysis results" refers to technology that takes specific measures to improve safety and comfort based on the analysis results of passengers' emotional states.

[1410] "Means for taking emergency action when an abnormality is detected" refers to technology that quickly implements appropriate emergency measures when an abnormality is detected through analysis of passengers' emotional states and real-time data.

[1411] The system for implementing the present invention consists of various servers, terminals, and sensors that collect real-time disease data, predict virus mutations, design customized vaccines, and analyze passengers' emotional states to respond to emergencies.

[1412] System Configuration

[1413] server

[1414] Data collection server: collects disease data in real time.

[1415] Data analysis server: Preprocesses collected data, removes noise, and fills in missing values.

[1416] Prediction server: Predicts virus mutations using the AlphaFold algorithm.

[1417] Vaccine design server: Rapidly design customized vaccines based on predicted variant information.

[1418] Collaboration server: Collaborates with international health organizations on the implementation of preventive measures.

[1419] Monitoring and evaluation server: Monitors and evaluates the effectiveness of implemented preventive measures in real time.

[1420] Emotion Engine Server: Collects passengers' emotion data and analyzes it.

[1421] Terminal

[1422] Public health officer terminal: Enters disease data and sends it to the data collection server.

[1423] General user terminal: Emotional states are input via smartphones or in-vehicle displays and sent to the emotion engine server.

[1424] User

[1425] Public health officials: Enter disease data into a collection server.

[1426] General users: Input their emotional state when receiving a vaccination or riding in an autonomous vehicle.

[1427] Program processing

[1428] Hardware and Software

[1429] The system operates using the following hardware and software:

[1430] Facial recognition camera: Used to analyze passengers' facial expressions in real time.

[1431] Microphone: Used to analyze voice tones and recognize emotions.

[1432] Heart rate sensor: Used to measure heart rate and include it in emotion data.

[1433] OpenCV: A software library for performing face recognition processing.

[1434] Google Speech-to-Text API: Used to integrate speech recognition and text conversion.

[1435] Affectiva API: Software for analyzing emotional data as an emotion engine.

[1436] AlphaFold: A machine learning algorithm used to predict virus variants.

[1437] Specific examples of processing

[1438] Emotion data collection and analysis

[1439] Passenger emotional data is collected using facial recognition cameras, microphones, and heart rate sensors. The emotion engine server analyzes the passenger's facial expressions, voice tone, and heart rate data to evaluate the passenger's emotional state. If the result indicates that the passenger is feeling stressed or anxious, the system will take measures such as playing relaxing music. In addition, if an abnormality is detected, emergency response will be initiated. For example, if a passenger feels strong stress while on board, the data will be sent to the emotion engine server, and the analysis results will be returned immediately. Depending on the analysis results, the display in the vehicle will display "Playing relaxing music" and music will be played.

[1440] Prompt Sentence Examples

[1441] For example, a prompt to input the passenger's stress level into the emotion engine might look like this:

[1442] "Analyze passengers' stress levels based on facial expressions, voice tone, and heart rate data."

[1443] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1444] Step 1:

[1445] Data collection

[1446] The server collects real-time emotional data from passengers through facial recognition cameras, microphones, and heart rate sensors. The collected data includes facial expression data, voice data, and heart rate data. These data are sent directly from each sensor to the data collection server.

[1447] Input: Facial expression data, voice data, heart rate data

[1448] Output: Unified emotion dataset (facial expressions, vocal tone, heart rate)

[1449] Step 2:

[1450] Data Preprocessing

[1451] The server receives the collected data and performs noise removal and missing value completion. For example, it preprocesses facial recognition data to prevent closed eyes and open mouths from affecting emotion recognition. Similarly, it removes background noise from audio data to obtain clear audio data.

[1452] Input: Unified sentiment dataset

[1453] Output: Preprocessed emotion dataset

[1454] Step 3:

[1455] Emotional Data Analysis

[1456] The server then uses the pre-processed emotion dataset to perform analysis using the emotion engine server, which uses OpenCV to recognize emotions from facial expressions, Google Speech-to-Text API to convert voice data to text, and Affectiva API to analyze emotions. Heart rate data is also analyzed in the same way to assess the passenger's emotional state.

[1457] Input: Preprocessed emotion dataset

[1458] Output: Passenger's emotional state (e.g., stress level, anxiety level)

[1459] Step 4:

[1460] Implementing countermeasures based on analysis results

[1461] The server then instructs the system to take appropriate countermeasures based on the analysis results. For example, if a passenger is in a high stress state, the server can instruct the in-car sound system to play relaxing music. If an abnormality is detected, the server can activate the emergency call system.

[1462] Input: Passenger emotional state (e.g., stress level, anxiety level)

[1463] Output: Execution instructions (e.g., music playback, emergency call)

[1464] Step 5:

[1465] Feedback and Learning

[1466] The server collects feedback from passengers and uses it to improve the system. Specifically, it receives feedback data from passenger terminals, stores it in a database, and reflects it in future analyses and countermeasures.

[1467] Input: Passenger feedback data

[1468] Output: Feedback data stored in a database, and a dataset for system improvement

[1469] 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.

[1470] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1471] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1472] [Fourth embodiment]

[1473] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1474] 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.

[1475] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1476] 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.

[1477] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1478] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1479] 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. 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.

[1480] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1481] 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.

[1482] 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 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.

[1483] 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.

[1484] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1485] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1486] The system for carrying out the present invention comprises a plurality of servers, terminals, and users. The specific configuration of this system and the processing of the programs will be described in detail below.

[1487] System Configuration

[1488] 1. Server:

[1489] Data Collection Server

[1490] Data analysis server

[1491] Prediction Server

[1492] Vaccine Design Server

[1493] Collaboration Server

[1494] Monitoring and evaluation server

[1495] 2. Terminal:

[1496] Public health officer terminal

[1497] Medical staff terminals

[1498] Researcher terminal

[1499] Biopharmaceutical company terminals

[1500] 3. User:

[1501] Public health officials

[1502] Healthcare workers

[1503] researcher

[1504] Biopharmaceutical company researchers

[1505] Program processing

[1506] The overall system program is designed so that each server and terminal work together. Here, we will explain the roles of each server, terminal, and user, along with specific examples.

[1507] Data collection

[1508] Server: The data collection server collects disease data in real time from national public health agencies, hospitals, research institutes, etc. This includes the ability to retrieve information using APIs and database connections and convert it into standardized data formats.

[1509] Example: A data collection server receives reports from international health organizations about outbreaks of new strains of influenza.

[1510] Users: Public health and healthcare workers use devices to manually or automatically enter case information and send it to a server.

[1511] Data preprocessing and analysis

[1512] Server: The data analysis server preprocesses the collected data, removing noise and completing missing values. Using the preprocessed data, it performs clustering using a machine learning algorithm to group similar cases.

[1513] Example: A data analysis server identifies areas where similar cases are occurring frequently and performs clustering.

[1514] Predicting mutant strains

[1515] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations. Based on the collected viral genetic data, it analyzes the structure of mutations that may emerge in the future.

[1516] Example: A prediction server calculates the probability of new mutant strains emerging and visualizes their structural changes.

[1517] Terminal: Researchers check the predicted data for mutant strains provided by the prediction server on their terminals and perform detailed analysis.

[1518] Vaccine Design

[1519] Server: The vaccine design server rapidly designs customized vaccines based on predicted variant information, including generating optimal vaccine components and manufacturing processes.

[1520] Example: A vaccine design server designs vaccine components that are effective against specific mutant strains and proposes their manufacturing process.

[1521] Terminal: Researchers at biopharmaceutical companies use the terminal to review the details of the designed vaccine and prepare for production at the laboratory level.

[1522] Implementing preventive measures and coordinating

[1523] Server: Based on the collected data and prediction results, the collaborative server will propose appropriate preventive measures to international health organizations and local governments, such as planning vaccination campaigns and proposing lockdown measures.

[1524] Example: A federated server provides international health organizations with detailed plans for launching vaccination campaigns in specific regions.

[1525] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccinations.

[1526] Monitoring and Evaluation

[1527] Server: The monitoring and evaluation server monitors the effectiveness of the implemented preventive measures and vaccines in real time. It analyzes the reduction in the number of infections after vaccination and reports of side effects to confirm the effectiveness of the vaccine.

[1528] Example: A monitoring and evaluation server analyzes data on the reduction in infection rates after vaccination to assess the success of the vaccine.

[1529] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into the database.

[1530] These processes provide a system for early detection of epidemics, rapid response, and prevention of pandemics.

[1531] The processing flow will be explained below.

[1532] Step 1:

[1533] Server: The data collection server collects disease data in real time from public health agencies, hospitals, research institutes, etc. in each country. It obtains information via APIs and database connections and converts it into a standardized data format.

[1534] Step 2:

[1535] Users: Public health and healthcare workers use devices to manually or automatically enter case information and send it to a data collection server.

[1536] Step 3:

[1537] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing the data, and preparing it for analysis.

[1538] Step 4:

[1539] Server: The data analysis server uses pre-processed data to perform clustering using machine learning algorithms, which allows for grouping of similar cases and identification of infection patterns.

[1540] Step 5:

[1541] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations based on the clustered data. It uses viral genetic data as input and analyzes the structure of mutations that may emerge in the future.

[1542] Step 6:

[1543] Terminal: Researchers receive the predicted data on variants provided by the prediction server and perform detailed analysis, including evaluation of the characteristics and infectivity of the variants.

[1544] Step 7:

[1545] Server: The vaccine design server will rapidly design customized vaccines based on predicted variant information, select vaccine components, design manufacturing processes, and simulate preventive effects.

[1546] Step 8:

[1547] Terminal: Researchers at biopharmaceutical companies receive design data from the vaccine design server and prepare for production at the laboratory level, including setting up production equipment and manufacturing prototypes.

[1548] Step 9:

[1549] Server: Based on the collected data and prediction results, the collaborative server will propose appropriate preventive measures to international health organizations and local governments, providing instructions on planning vaccination campaigns and implementing lockdown measures.

[1550] Step 10:

[1551] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccination implementation.

[1552] Step 11:

[1553] Server: The monitoring and evaluation server will monitor the effectiveness of the implemented preventive measures and vaccines in real time, collecting and analyzing reports of the reduction in the number of infections and side effects after vaccination.

[1554] Step 12:

[1555] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into a database, allowing the effectiveness of the entire system to be continuously monitored.

[1556] In this way, through each step, a system is provided that enables early detection of epidemics, rapid response, and prevention of pandemics.

[1557] Example 1

[1558] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1559] Conventional disease data collection and analysis systems often lack real-time capabilities, and noise and missing values ​​in the collected data often adversely affect the analysis results. Furthermore, they lack the speed to predict virus mutations and design vaccines, making it difficult to prevent the spread of infectious diseases. A system that can resolve these issues and enable more effective and rapid public health measures is needed.

[1560] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1561] In this invention, the server includes: means for collecting disease data in real time from public health organizations, hospitals, research institutions, etc.; means for preprocessing the collected data to remove noise and fill in missing values; means for analyzing the preprocessed data using a machine learning algorithm and clustering similar cases; means for predicting virus mutations using the AlphaFold algorithm; means for quickly designing customized vaccines based on the predicted mutations; means for providing plans for vaccination campaigns and lockdown measures to international health organizations and local governments through a linked server; and means for monitoring and evaluating the effectiveness of implemented preventive measures and vaccines in real time. This enables real-time data collection and analysis, rapid prediction of virus mutations and vaccine design, and implementation and evaluation of effective public health measures.

[1562] "Means for collecting disease data in real time" refers to methods for obtaining timely disease data from public health agencies, hospitals, research institutions, etc.

[1563] "Noise removal" is the process of cleaning data by removing unnecessary data and outliers from the data.

[1564] "Missing value imputation" is the process of filling in missing values ​​in a dataset with appropriate values ​​(e.g., averages or estimated values).

[1565] "Preprocessed data" refers to a dataset after noise removal and missing value imputation have been performed.

[1566] A "machine learning algorithm" is a computational method for learning from data and making predictions or classifications.

[1567] "Clustering" is a machine learning algorithm that divides data into multiple groups based on similarity.

[1568] The "AlphaFold algorithm" is a computational model for predicting the three-dimensional structure of proteins.

[1569] "Mutant prediction" is the process of predicting how pathogens such as viruses will mutate.

[1570] A "customized vaccine" is a vaccine that is optimized for a specific variant.

[1571] A "collaboration server" is a server that allows multiple institutions and devices to share information and work together to carry out tasks.

[1572] A "vaccination campaign" is an activity that encourages and administers vaccination to a wide range of people.

[1573] "Lockdown measures" are measures to close specific areas or facilities to prevent the spread of infectious diseases.

[1574] "Monitoring and evaluation measures" are methods for continuously observing and evaluating the effectiveness of implemented measures and vaccines.

[1575] MODE FOR CARRYING OUT THE INVENTION

[1576] The system for implementing the present invention comprises a plurality of servers, terminals, and users. The specific configuration of this system and the processing of the programs are described below.

[1577] System Configuration

[1578] server

[1579] 1. Data Collection Server

[1580] The data collection server is a server that collects disease data in real time from public health agencies, hospitals, and research institutes. Specifically, it has the function of retrieving information using APIs and database connections and converting it into a standardized data format.

[1581] 2. Data analysis server

[1582] The data analysis server preprocesses the collected data, removes noise, and fills in missing values. It also uses the preprocessed data to implement machine learning algorithms and cluster similar cases.

[1583] 3. Prediction Server

[1584] The prediction server uses the AlphaFold algorithm to predict virus mutations. Based on collected viral genetic data, it analyzes the structure of mutations that may emerge in the future.

[1585] 4. Vaccine Design Server

[1586] The Vaccine Design Server is a server for rapidly designing customized vaccines based on predicted variant information, including generating optimal vaccine components and manufacturing processes.

[1587] 5. Collaboration Server

[1588] The collaborative server will use the collected data and prediction results to propose appropriate preventive measures to international health organizations and local governments, such as planning vaccination campaigns and proposing lockdown measures.

[1589] 6. Monitoring and evaluation server

[1590] The monitoring and evaluation server will monitor the effectiveness of the implemented preventive measures and the vaccine in real time, analyzing the reduction in the number of infected people after vaccination and reports of side effects to confirm the effectiveness of the vaccine.

[1591] Terminal

[1592] 1. Public Health Officer Terminals

[1593] Public health personnel terminals are terminals that public health personnel use to input disease data and connect to a server, for example, with the ability to input case data through dedicated software or a web app.

[1594] 2. Medical device

[1595] The medical staff terminal is a terminal where medical staff input and check case data and send it to the data collection server.

[1596] 3. Researcher terminals

[1597] The researcher terminals are used by researchers to check data analysis results and mutant strain information and perform detailed analysis, for example, using dedicated analysis software (e.g., Biopython, PyMOL).

[1598] 4. Terminals for biopharmaceutical companies

[1599] The biopharmaceutical company terminal is a terminal where researchers at biopharmaceutical companies can check the details of the designed vaccine and prepare for the manufacturing process.

[1600] User

[1601] 1. Public health officials

[1602] Public health officials are responsible for collecting disease data and implementing and monitoring preventive measures. They input data using dedicated terminals.

[1603] 2. Healthcare workers

[1604] Medical professionals collect case data at hospitals and clinics, send it to a data collection server, and enter and check the case data.

[1605] 3. Researcher

[1606] Researchers will conduct detailed studies based on data analysis and predictions of mutant strains, and plan further experiments based on the results of the analysis.

[1607] 4. Researchers at biopharmaceutical companies

[1608] Researchers at biopharmaceutical companies will review the details of the designed vaccine, prepare for production at the laboratory level, and conduct efficacy and safety tests.

[1609] Examples of concrete examples and prompts

[1610] Example: A data collection server accesses the API of an international health organization to periodically retrieve reports of new influenza outbreaks.

[1611] Example: A prediction server uses the AlphaFold algorithm to predict structural changes in new mutant strains and provides the results to researchers' terminals.

[1612] Example prompt: "Please explain how the data collection server receives reports of new influenza outbreaks from international health organizations, and how the analysis server performs noise removal and missing value imputation."

[1613] In this way, the entire system works together to enable early detection of epidemics, rapid response, and pandemic prevention.

[1614] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1615] Step 1: Data collection

[1616] The server collects disease data in real time from public health agencies, hospitals, research institutes, etc. Specifically, it retrieves information using APIs and database connections and converts it into standardized data formats (e.g., JSON, CSV). This collected data is then sent to a data analysis server for analysis in the next step. For example, the server periodically requests the API of an international health organization to retrieve reports of new influenza outbreaks.

[1617] Input: Disease data from public health agencies, hospitals, and research institutes

[1618] Output: Standardized disease data

[1619] Step 2: Data Preprocessing

[1620] The server preprocesses the collected data, including noise removal and missing value imputation. Specifically, the data analysis server removes unnecessary data and outliers from the data and imputes missing data with appropriate values ​​(e.g., mean values ​​or estimated values). This preprocessed data is used in the next analysis step. For example, the data analysis server imputes missing values ​​with the mean values ​​calculated for the data rows in which they exist.

[1621] Input: Collected disease data

[1622] Output: Preprocessed disease data

[1623] Step 3: Data analysis

[1624] The server then applies a machine learning algorithm to the preprocessed data. Specifically, it uses a clustering algorithm (e.g., k-means) to group similar cases. The results of this analysis are then used in the next step of variant prediction. For example, the data analysis server analyzes cases that are occurring frequently in a specific region and generates groups using k-means clustering.

[1625] Input: Preprocessed disease data

[1626] Output: Clustered case data

[1627] Step 4: Predicting variants

[1628] The server uses the AlphaFold algorithm to predict virus mutations. Specifically, it receives viral genetic data as input and analyzes the protein structures of potential future mutations. The prediction results are used in the next step of vaccine design. For example, the prediction server receives the amino acid sequence of a specific part of the virus as input and uses AlphaFold to predict likely mutations.

[1629] Input: viral genetic data

[1630] Output: Predicted variant information

[1631] Step 5: Vaccine design

[1632] The server designs optimal vaccines based on predicted variant information. Specifically, it identifies epitopes that are effective against different parts of the virus and uses algorithms (e.g., RosettaDesign) to design peptide vaccines. The designed vaccine information is then used in the next step of implementing preventive measures. For example, the vaccine design server calculates the optimal epitope for a specific variant and proposes its production procedure.

[1633] Input: Predicted mutant information

[1634] Output: Designed vaccine information

[1635] Step 6: Implement preventative measures and collaborate

[1636] Based on the collected data and prediction results, the collaboration server will propose appropriate preventive measures to international health organizations and local governments. Specifically, it will generate plans for vaccination campaigns and lockdown measures and notify relevant organizations. For example, the collaboration server will develop a detailed schedule for public health campaigns and vaccinations to prevent the spread of a new strain of influenza in a specific area.

[1637] Input: Designed vaccine information, predicted results

[1638] Output: Suggested preventative measures

[1639] Step 7: Monitor and evaluate

[1640] The monitoring and evaluation server monitors the effectiveness of implemented preventive measures and vaccinations in real time. Specifically, it evaluates the effectiveness of vaccines by collecting and analyzing data on the number of infected people after vaccination and reports of side effects. For example, the monitoring and evaluation server analyzes data on the reduction in infection rates after vaccination to evaluate the success of the vaccine.

[1641] Input: Preventive measures taken, vaccination data

[1642] Output: Effect evaluation results

[1643] This processing step enables the entire system to work together to detect diseases early, respond quickly, and prevent pandemics.

[1644] (Application example 1)

[1645] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1646] Rapid detection and prediction of infectious diseases and the implementation of effective preventive measures are crucial for modern public health. However, current systems lack responsiveness and accuracy because they lack centralized collection of real-time disease data, prediction of mutant strains, design and implementation of vaccines, and monitoring of their effectiveness. Therefore, there is a need for a system that can monitor infectious disease risk at the individual user level in real time, quickly send alerts in high-risk areas, and provide timely vaccination information.

[1647] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1648] In this invention, the server includes means for collecting disease data in real time, means for predicting virus mutations based on the collected data, means for quickly designing customized vaccines based on the predicted mutations, means for coordinating with international health organizations to implement the designed vaccines and preventive measures, means for monitoring and evaluating the effectiveness of the implemented preventive measures, means for monitoring disease risk in real time and for issuing alerts for high-risk areas, and means for managing and notifying the timing of vaccinations. This enables rapid detection and prediction of infectious diseases, real-time risk monitoring, and the implementation and monitoring of appropriate preventive measures.

[1649] "Disease Data" refers to information that includes the occurrence, progression, and related information of a disease in an individual or population.

[1650] A "virus variant" refers to a subspecies of a virus with certain mutations or genetic changes.

[1651] A "customized vaccine" refers to a vaccine designed to have specific efficacy against predicted variants.

[1652] "international health organization" means an international organization established to prevent and control disease and provide health-related services;

[1653] "Real-time monitoring" refers to the immediate observation and analysis of ongoing situations and data.

[1654] A "high-risk area" refers to an area where the risk of certain diseases or health conditions is considered higher than in other areas.

[1655] "Alert notification" refers to a system that warns or notifies users when certain criteria are met.

[1656] "Managing vaccination timing" refers to adjusting schedules and providing notifications so that vaccinations are given at the appropriate time.

[1657] "Monitoring and evaluation" refers to the continuous observation and analysis of the effectiveness and feasibility of preventive measures implemented.

[1658] The embodiment of the present invention is a system in which various servers and terminals work in cooperation with each other. The program processing of this system will be explained below.

[1659] Hardware and software used

[1660] Hardware:

[1661] Smartphone (iOS / Android)

[1662] software:

[1663] Mobile app development framework: React Native

[1664] Data Collection API: Firebase

[1665] Machine learning model: TensorFlow Lite

[1666] Map API: Google Maps API

[1667] Notification service: OneSignal

[1668] Program processing explanation

[1669] 1. Data Collection and Preprocessing

[1670] The server uses Firebase to collect disease data in real time, including location information and case report information. The data collection server preprocesses the collected data, removing noise and imputing missing values.

[1671] 2. Data analysis and mutant strain prediction

[1672] The server uses TensorFlow Lite to predict virus mutations based on collected data, and the AlphaFold algorithm to analyze the structure of potential future mutations.

[1673] 3. Risk monitoring and alert notifications

[1674] The device uses Google Maps API for real-time disease risk monitoring, visualizing high-risk areas based on the user's current location, and provides alerts via OneSignal if the user enters a high-risk area.

[1675] 4. Vaccine design and timing management

[1676] The server will quickly design a customized vaccine based on predicted variant information, manage vaccination information on the device, and notify users when to get vaccinated.

[1677] Specific examples and generated AI model prompts

[1678] Example 1: If a new strain of influenza is confirmed within 10km of a user's home, the app will display that information on a map and highlight the area as a risk. An alert will be displayed saying, "You have entered a high-risk area. Please take precautions."

[1679] Example 2: When it's time for vaccination, you'll be notified with the message, "It's almost time for your vaccination. The next vaccination site is your local hospital. Would you like to make an appointment?"

[1680] Generative AI model prompt:

[1681] "Please develop a smartphone app that will monitor the risk of infection within a 10km radius in real time and notify you if you approach a risk area."

[1682] "Design an application that manages users' vaccination information and notifies them of the best time to get vaccinated."

[1683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1684] Step 1:

[1685] The server uses Firebase to collect disease data. The input here is real-time data such as user location information and case report information. The server collects this data and converts it into a standardized data format. The output is a dataset in a format that can be preprocessed. Specific operations include collecting location information and obtaining real-time data through an API.

[1686] Step 2:

[1687] The server preprocesses the collected data, removing noise and imputing missing values. The input is the dataset collected in step 1. The server uses machine learning algorithms to cleanse the data and generate reliable data. The output is a high-quality dataset suitable for analysis. Specific operations include applying missing value imputation algorithms and noise filtering.

[1688] Step 3:

[1689] The server uses TensorFlow Lite to cluster disease risks and predict virus mutations based on the preprocessed data. The input is the dataset obtained in step 2. The server applies a machine learning model to analyze future risk areas and the likelihood of mutations. The output is detailed information on risk assessment results and mutation predictions. Specific operations include predictive modeling using a clustering algorithm and the AlphaFold algorithm.

[1690] Step 4:

[1691] The device uses the Google Maps API to display risk areas based on the user's current location on a map. The input is the risk assessment result obtained in step 3 and the current location information. The device visualizes the risk areas on a map and highlights dangerous areas. The output is the display of risk areas on the user interface. Specific operations include rendering the map and drawing polygons of the risk areas.

[1692] Step 5:

[1693] The device will send an alert notification via OneSignal if it enters a high-risk area. The input is the risk area information displayed in step 4 and the user's real-time location information. If the location information matches the risk area, the device will immediately send a notification to the user. The output is an alert message. Specific operations include real-time location comparison and notification triggering.

[1694] Step 6:

[1695] The server designs a vaccine based on the predicted mutant strain data. The input is the mutant strain prediction data obtained in step 3. The server designs a customized vaccine that is adapted to the new mutant strain based on current vaccine information. The output is detailed information about the designed vaccine. Specifically, the vaccine components are optimized using bioinformatics algorithms.

[1696] Step 7:

[1697] The terminal manages the user's vaccination information and notifies the user of vaccination at the appropriate time. The input is the user's vaccination history and vaccine information provided by the server. The terminal automatically calculates the next vaccination date and sends a notification to the user when the time approaches. The output is a vaccination reminder message. Specific operations include managing historical data and generating a notification schedule.

[1698] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1699] The system for implementing this invention is composed of multiple servers, terminals, and users. Furthermore, by incorporating an emotion engine, the effectiveness of the system can be improved by utilizing user emotion data. The specific configuration of this system and the program processing are described in detail below.

[1700] System Configuration

[1701] 1. Server:

[1702] Data Collection Server

[1703] Data analysis server

[1704] Prediction Server

[1705] Vaccine Design Server

[1706] Collaboration Server

[1707] Monitoring and evaluation server

[1708] Emotion Engine Server

[1709] 2. Terminal:

[1710] Public health officer terminal

[1711] Medical staff terminals

[1712] Researcher terminal

[1713] Biopharmaceutical company terminals

[1714] General user terminal

[1715] 3. User:

[1716] Public health officials

[1717] Healthcare workers

[1718] researcher

[1719] Biopharmaceutical company researchers

[1720] General users

[1721] Program processing

[1722] The entire system is designed to work in cooperation with each server, terminal, and user. Here, we will explain the roles and specific examples of each server, terminal, and user.

[1723] Data collection

[1724] Server: The data collection server collects disease data in real time from public health agencies, hospitals, and research institutes. It obtains information through APIs and database connections and converts it into a standardized data format.

[1725] Example: A data collection server receives reports from international health organizations about outbreaks of new strains of influenza.

[1726] Users: Public health officials and medical workers use terminals to enter case information and send it to a data collection server.

[1727] Data preprocessing and analysis

[1728] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing data, etc. Using the preprocessed data, clustering is performed using machine learning algorithms.

[1729] Example: A data analysis server identifies areas where similar cases are occurring frequently and performs clustering.

[1730] Predicting mutant strains

[1731] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations. It analyzes the structure of potential mutations based on viral genetic data.

[1732] Example: A prediction server calculates the probability of a new mutant strain emerging and visualizes its structural changes.

[1733] Terminal: Researchers receive predicted data on mutant strains provided by the prediction server and perform detailed analysis.

[1734] Vaccine Design

[1735] Server: The vaccine design server will rapidly design customized vaccines based on predicted variant information, including selecting vaccine components and designing manufacturing processes.

[1736] Example: A vaccine design server designs vaccine components that are effective against specific mutant strains and proposes their manufacturing process.

[1737] Terminal: Researchers at biopharmaceutical companies use the terminal to review the details of the designed vaccine and prepare for production at the laboratory level.

[1738] Emotion data collection and analysis

[1739] Server: The emotion engine server collects and analyzes the user's emotion data from the device. It uses emotion recognition technology to evaluate the user's emotional state.

[1740] Example: An emotion engine server analyzes a user's emotional response to vaccination and detects emotions such as anxiety and fear.

[1741] Terminal: The general user inputs their emotional state at the time of vaccination into the terminal and sends it to the emotion engine server.

[1742] Implementing preventive measures and coordinating

[1743] Server: The collaborative server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results, and will also incorporate emotional data to design more effective preventive measures.

[1744] Example: A collaborative server creates a plan to launch a vaccination campaign in a specific region and adjusts communication strategies based on sentiment data.

[1745] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server and develop public health campaigns.

[1746] Monitoring and Evaluation

[1747] Server: The monitoring and evaluation server monitors the implemented preventive measures and vaccine effectiveness in real time. It analyzes the reduction in the number of infected people after vaccination and sentiment data to evaluate the effectiveness of the vaccine.

[1748] Example: A monitoring and evaluation server verifies the success of a vaccine by analyzing data on the reduction in infection rates after vaccination and user sentiment data.

[1749] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into the database.

[1750] This system will comprehensively address everything from early detection of epidemics to implementing preventative measures and users' emotional responses to vaccinations, and each step will help prevent the risk of a pandemic and minimize its social and economic impact.

[1751] The processing flow will be explained below.

[1752] Step 1:

[1753] Server: The data collection server collects disease data in real time from public health agencies, hospitals, research institutes, etc. in each country. It obtains the information via APIs and database connections and converts it into a standardized data format, thereby generating a centrally managed dataset.

[1754] Step 2:

[1755] Users: Public health officials and medical personnel use terminals to manually or automatically input case information and send it to the data collection server. The terminals standardize the format of the input data to prevent input errors and missing data.

[1756] Step 3:

[1757] Server: The data analysis server preprocesses the collected data, removing noise, filling in missing values, standardizing data, etc. The preprocessed data is stored in a clean state, ensuring that subsequent analysis can proceed smoothly.

[1758] Step 4:

[1759] Server: The data analysis server uses preprocessed data to perform clustering using machine learning algorithms, which allows similar cases to be grouped together and infection patterns to be identified. Identified cluster information is updated in real time.

[1760] Step 5:

[1761] Server: The prediction server uses the AlphaFold algorithm to predict virus mutations based on the clustered data. It uses viral genetic data as input to analyze the structure of mutations that may emerge in the future and stores the prediction results in a database.

[1762] Step 6:

[1763] Terminal: Researchers receive the predicted data on variants provided by the prediction server and perform detailed analysis, including evaluation of the characteristics and infectivity of the variants, and then send the results back to the server.

[1764] Step 7:

[1765] Server: The vaccine design server rapidly designs customized vaccines based on predicted variant information. It selects vaccine components, designs manufacturing processes, and simulates preventive effects. The designed vaccine prototypes are stored on the server.

[1766] Step 8:

[1767] Terminal: Biopharmaceutical company researchers receive design data from the vaccine design server and prepare for production at the laboratory level. This includes setting up production equipment and manufacturing prototypes. Initial test results of the prototypes are sent to the server.

[1768] Step 9:

[1769] Server: The emotion engine server collects and analyzes user emotion data from the device. It uses emotion recognition technology to evaluate the user's emotional state and shares that data with other servers.

[1770] Step 10:

[1771] Example: The emotion engine server analyzes the user's emotional response to vaccination and detects emotions such as anxiety and fear. Based on this, it determines the need for specific information and explanations.

[1772] Step 11:

[1773] Server: The collaborative server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results. It will also take into account emotional data and adjust the communication method of preventive measures according to the user's reaction.

[1774] Step 12:

[1775] Example: A collaborative server creates a plan for launching a vaccination campaign in a specific region and adjusts communication strategies based on emotion data, for example, providing more reassuring information in advance based on emotion data.

[1776] Step 13:

[1777] Users: International health organizations and local government officials implement preventive measures suggested by the collaboration server, launch public health campaigns, and oversee vaccination implementation.

[1778] Step 14:

[1779] Server: The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It analyzes the reduction in the number of infected people after vaccination and emotional data to evaluate the effectiveness of the vaccine. The evaluation results are fed back and stored in a database.

[1780] Step 15:

[1781] Terminal: Local medical institutions report feedback to the monitoring and evaluation server and enter the evaluation results into a database, allowing the effectiveness of the entire system to be continuously monitored.

[1782] In this way, a system is provided that goes through each step to achieve early detection of epidemics, rapid response, and pandemic prevention. Evaluation and adjustment of preventive measures based on user emotion data enables more effective countermeasures.

[1783] Example 2

[1784] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1785] Conventional epidemic management systems often require manual collection and preprocessing of disease data, making rapid data analysis and prediction difficult. Predicting mutant strains and designing vaccines also takes time, making them ineffective in situations where rapid response is required. Furthermore, preventive measures implemented without considering user sentiment data are likely to create social anxiety and resistance, potentially reducing the overall effectiveness of preventive measures.

[1786] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1787] In this invention, the server includes means for collecting disease data in real time, means for preprocessing the collected data to remove noise and fill in missing values, means for clustering the preprocessed data using a machine learning algorithm, means for predicting virus mutations using the AlphaFold algorithm, means for quickly designing customized vaccines based on the predicted mutations, means for collecting and analyzing user emotion data, means for designing preventive measures based on the analyzed emotion data and coordinating with international health organizations or local governments, and means for monitoring and evaluating the effectiveness of the implemented preventive measures. This enables comprehensive response from early detection of epidemics to the implementation of preventive measures and users' emotional reactions to vaccinations.

[1788] "Disease data" refers to data collected from public health agencies and medical institutions, including disease occurrence status, case information, patient information, etc.

[1789] "Preprocessing" refers to the process of removing noise and missing values ​​from acquired data and standardizing it, which can improve the accuracy of data analysis and prediction.

[1790] "Noise reduction" refers to the process of removing unnecessary or erroneous data from a dataset, thereby improving the quality of the data.

[1791] "Missing value imputation" refers to the process of filling in missing values ​​in a dataset in an appropriate way, thereby maintaining the accuracy of data analysis and machine learning.

[1792] "Clustering" refers to a machine learning algorithm that classifies data points into groups based on their similarity, allowing you to discover patterns and anomalies in the data.

[1793] The "AlphaFold algorithm" is a machine learning algorithm for predicting virus and protein structures, which makes it possible to predict mutant strains.

[1794] A "mutant strain" refers to a strain of the virus that has changed due to genetic mutation. Mutant strains may differ in their infectivity and resistance to vaccines.

[1795] "Vaccine design" refers to the process of selecting vaccine components to combat a specific virus or its variants and planning the manufacturing process.

[1796] "Emotional Data" refers to data collected to assess a user's emotional state, which may include text data, multiple choice responses, biometrics, etc.

[1797] "Analysis" refers to the process of examining and analyzing collected data in detail using statistical and machine learning techniques, which allows useful information to be extracted from the data.

[1798] "Preventive measures" refer to specific measures or action plans taken to prevent the spread of a disease, including vaccinations, public campaigns, and quarantine measures.

[1799] "International health organizations" refer to organizations that primarily manage and coordinate international public health responses to global health issues.

[1800] "Local government" refers to an administrative unit that has the right to self-govern within a certain area, which allows specific health measures to be implemented in that area.

[1801] "Real-time" refers to near-instant data processing and response, enabling timely information gathering and response.

[1802] MODE FOR CARRYING OUT THE INVENTION

[1803] The system for implementing this invention is composed of multiple servers, terminals, and users. Each server, terminal, and user works in cooperation to comprehensively perform early detection of epidemics, prediction of mutant strains, vaccine design, implementation of preventive measures, and even analysis of user emotion data.

[1804] 1. Hardware and Software Used

[1805] server:

[1806] Data Collection Server

[1807] Data analysis server

[1808] Prediction Server

[1809] Vaccine Design Server

[1810] Emotion Engine Server

[1811] Collaboration Server

[1812] Monitoring and evaluation server

[1813] Software and Algorithms:

[1814] API (Application Programming Interface)

[1815] Database (e.g. SQL database)

[1816] Machine learning algorithms (e.g., K-means clustering)

[1817] AlphaFold algorithm

[1818] Emotion recognition technology (Natural Language Processing)

[1819] Device:

[1820] Public health officer terminal

[1821] Medical staff terminals

[1822] Researcher terminal

[1823] Biopharmaceutical company terminals

[1824] General user terminal

[1825] 2. Data Collection

[1826] The data collection server collects disease data in real time from public health agencies, hospitals, and research institutes, obtains the information through APIs and database connections, and converts it into a standardized data format.

[1827] Example: A data collection server retrieves influenza case data in JSON format from the Ministry of Health, Labor and Welfare's API and stores it in an internal SQL database.

[1828] Users, such as public health officials and medical professionals, enter case information using dedicated terminals, and the data is sent to a data collection server.

[1829] 3. Data Preprocessing and Analysis

[1830] The data analysis server preprocesses the collected data. This preprocessing includes noise removal, missing value completion, and data standardization. After preprocessing is complete, clustering is performed using a machine learning algorithm (e.g., K-means clustering).

[1831] Example: A data analysis server detects incomplete entries in collected case data and imputes them with average values, then standardizes the data and runs a clustering algorithm to group similar cases together.

[1832] 4. Prediction of mutant strains

[1833] The prediction server uses the AlphaFold algorithm to predict virus mutations based on the collected viral genetic data.

[1834] Example: The prediction server takes a new viral gene sequence and inputs it into the AlphaFold algorithm, generating predicted structural changes and visualizing the characteristics of the next likely mutant strain.

[1835] Researchers using the terminals, known as research terminals, will receive the predicted data and perform detailed analysis, including assessing the transmissibility of the variants and their resistance to existing vaccines.

[1836] Example prompt sentence:

[1837] When a new strain of influenza emerges, collect data from international health organizations, preprocess it on the data analysis server, predict the mutant strain using the prediction server, and design a new vaccine on the vaccine design server. Then, evaluate user emotion data on the emotion engine server, and based on the results, propose preventive measures on the collaboration server. Finally, monitor and evaluate their effectiveness on the monitoring and evaluation server.

[1838] 5. Vaccine Design

[1839] The vaccine design server will rapidly design a customized vaccine based on predicted variant information, including selecting vaccine components and designing the manufacturing process.

[1840] Example: A vaccine design server generates sequences for new vaccine components targeting specific antigens based on predicted variant data, and also creates instructions for the manufacturing process.

[1841] Researchers at biopharmaceutical companies using the terminal, a biopharmaceutical company terminal, will review the details of the designed vaccine and begin experiments to prepare for production.

[1842] 6. Emotional Data Collection and Analysis

[1843] The emotion engine server collects and analyzes the user's emotion data from the device, and evaluates the user's emotional state using emotion recognition technology.

[1844] Example: The emotion engine server analyzes data collected from the device and uses Natural Language Processing (NLP) algorithms to extract emotions from text data entered by the user.

[1845] A general user using a general user terminal inputs the emotional state at the time of vaccination and transmits the data to the emotion engine server.

[1846] 7. Implementation of preventive measures and coordination

[1847] The linked server will propose appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results, and will also take into account emotional data to design more effective preventive measures.

[1848] Example: The collaborative server analyzes infection and sentiment data by region and creates proposals for implementing vaccination campaigns in specific regions.

[1849] Local government officials, who are the users, implement preventive measures based on suggestions from the collaboration server and launch public health campaigns.

[1850] 8. Monitoring and Evaluation

[1851] The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It analyzes the decrease in the number of infected people after vaccination and emotional data to evaluate the effectiveness of the vaccine.

[1852] Example: The monitoring and evaluation server periodically updates and analyzes post-vaccination infection rate data and sentiment data. Statistical methods such as hypothesis testing are used to evaluate the effectiveness of the vaccine.

[1853] Local medical institutions and local government officials using the local medical institution terminals report feedback to the monitoring and evaluation server and enter the evaluation results into a database.

[1854] The system will comprehensively respond to users' emotional reactions to early detection of epidemics, implementation of preventive measures, and vaccination, aiming to prevent the risk of pandemics and minimize their social and economic impacts through each processing step.

[1855] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1856] Step 1: Data collection

[1857] The server, the data collection server, collects disease data in real time from public health agencies, hospitals, and research institutes. As input, it uses the information obtained through APIs and database connections and converts it into a standardized data format. As output, the standardized disease data is stored in a database.

[1858] Specific operation: The data collection server retrieves influenza case data in JSON format from the Ministry of Health, Labor and Welfare's API and stores it in an internal SQL database.

[1859] Step 2: Data preprocessing and analysis

[1860] The data analysis server preprocesses the standardized disease data obtained from the data collection server. Using the standardized data as input, it removes noise, fills in missing values, and standardizes the data. The preprocessed data is obtained as output, and clustering is performed using a machine learning algorithm based on this data.

[1861] What it does: The data analysis server detects incomplete entries in the collected case data and imputes them with average values, then standardizes the data and runs a clustering algorithm (e.g., K-means) to group similar cases.

[1862] Step 3: Predicting variants

[1863] The prediction server uses the AlphaFold algorithm to predict virus mutations based on preprocessed data provided by the data analysis server. It uses viral genetic data and clustered case data as input. The output is structural change data for the newly predicted mutations.

[1864] How it works: The prediction server takes a new viral gene sequence and inputs it into the AlphaFold algorithm, which generates predicted structural changes and visualizes the characteristics of the next likely mutant strain.

[1865] Step 4: Vaccine design

[1866] The vaccine design server rapidly designs customized vaccines based on the prediction data provided by the prediction server. It uses the predicted variant data as input. The output is the sequence data of the vaccine components and instructions for the manufacturing process.

[1867] How it works: The vaccine design server generates sequences of new vaccine components that target specific antigens based on predicted variant data, and simultaneously creates instructions for the manufacturing process.

[1868] Step 5: Collect and analyze emotion data

[1869] The emotion engine server collects and analyzes the user's emotional data from the device. As input, it uses the user's text data indicating their emotional state, analyzes it, and evaluates their emotional state. As output, it obtains the analyzed emotional data.

[1870] Specific operation: The emotion engine server analyzes data collected from the device and uses Natural Language Processing (NLP) algorithms to extract emotions from text data entered by the user.

[1871] Step 6: Implement preventative measures and collaborate

[1872] The collaboration server, which is a server, proposes appropriate preventive measures to international health organizations and local governments based on the collected data and prediction results. It also takes into account emotional data to design more effective preventive measures. The analyzed disease data, prediction data, and emotional data are used as input. The preventive measures planning data is obtained as output.

[1873] Specific operation: The collaboration server analyzes infection data and sentiment data by region and creates proposals for implementing vaccination campaigns in specific regions.

[1874] Step 7: Monitor and evaluate

[1875] The monitoring and evaluation server monitors the implemented preventive measures and the effectiveness of the vaccine in real time. It uses post-vaccination infection rate data and user sentiment data as input and analyzes them. The output is evaluation data on vaccine effectiveness.

[1876] Specific operation: The monitoring and evaluation server periodically updates and analyzes post-vaccination infection rate data and sentiment data. It evaluates the effectiveness of the vaccine using statistical methods such as hypothesis testing.

[1877] This system will enable comprehensive response from early detection of epidemics to the implementation of preventive measures and users' emotional reactions to vaccinations. By going through each processing step, it will be possible to prevent the risk of a pandemic and minimize its social and economic impact.

[1878] (Application example 2)

[1879] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1880] Conventional disease prevention systems and autonomous vehicle safety and comfort management systems lack the ability to integrate public health measures and emergency responses based on passenger emotional states. These shortcomings make it difficult to implement rapid and efficient epidemic prevention measures or respond appropriately based on passenger emotional states, resulting in insufficient prevention effectiveness and safety.

[1881] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1882] In this invention, the server includes a means for collecting disease data in real time, a means for predicting virus mutations based on the collected data, and a means for quickly designing a customized vaccine based on the predicted mutations. It also includes a means for collecting passenger emotional data in real time, a means for analyzing passenger emotional states based on the collected emotional data, a means for implementing appropriate safety and comfort measures based on the analysis results, and a means for taking emergency action when an abnormality is detected. This enables early disease prevention and improved passenger safety and comfort.

[1883] "Means for collecting disease data in real time" refers to technologies for collecting disease-related data from public health agencies and medical institutions without time delay.

[1884] "Means for predicting virus mutations based on collected data" refers to technology that analyzes collected disease data and predicts future virus mutations based on the results.

[1885] "Means for rapidly designing customized vaccines based on predicted variants" refers to technology for rapidly designing vaccines optimized for predicted viral variants.

[1886] "Means for collaborating with international health organizations to implement designed vaccines and preventive measures" means techniques and protocols for collaborating with international health organizations to implement designed vaccines and preventive measures.

[1887] "Means for monitoring and evaluating the effectiveness of implemented preventive measures" refers to technology that monitors and evaluates the effectiveness of implemented preventive measures in real time.

[1888] "Means for collecting passenger emotional data in real time" refers to technology for collecting passenger emotional states in autonomous vehicles without time delay.

[1889] The "means for analyzing the emotional state of passengers based on collected emotional data" is a technique for analyzing collected emotional data and evaluating the emotional state of passengers.

[1890] "Means for implementing appropriate safety and comfort measures based on the analysis results" refers to technology that takes specific measures to improve safety and comfort based on the analysis results of passengers' emotional states.

[1891] "Means for taking emergency action when an abnormality is detected" refers to technology that quickly implements appropriate emergency measures when an abnormality is detected through analysis of passengers' emotional states and real-time data.

[1892] The system for implementing the present invention consists of various servers, terminals, and sensors that collect real-time disease data, predict virus mutations, design customized vaccines, and analyze passengers' emotional states to respond to emergencies.

[1893] System Configuration

[1894] server

[1895] Data collection server: collects disease data in real time.

[1896] Data analysis server: Preprocesses collected data, removes noise, and fills in missing values.

[1897] Prediction server: Predicts virus mutations using the AlphaFold algorithm.

[1898] Vaccine design server: Rapidly design customized vaccines based on predicted variant information.

[1899] Collaboration server: Collaborates with international health organizations on the implementation of preventive measures.

[1900] Monitoring and evaluation server: Monitors and evaluates the effectiveness of implemented preventive measures in real time.

[1901] Emotion Engine Server: Collects passengers' emotion data and analyzes it.

[1902] Terminal

[1903] Public health officer terminal: Enters disease data and sends it to the data collection server.

[1904] General user terminal: Emotional states are input via smartphones or in-vehicle displays and sent to the emotion engine server.

[1905] User

[1906] Public health officials: Enter disease data into a collection server.

[1907] General users: Input their emotional state when receiving a vaccination or riding in an autonomous vehicle.

[1908] Program processing

[1909] Hardware and Software

[1910] The system operates using the following hardware and software:

[1911] Facial recognition camera: Used to analyze passengers' facial expressions in real time.

[1912] Microphone: Used to analyze voice tones and recognize emotions.

[1913] Heart rate sensor: Used to measure heart rate and include it in emotion data.

[1914] OpenCV: A software library for performing face recognition processing.

[1915] Google Speech-to-Text API: Used to integrate speech recognition and text conversion.

[1916] Affectiva API: Software for analyzing emotional data as an emotion engine.

[1917] AlphaFold: A machine learning algorithm used to predict virus variants.

[1918] Specific examples of processing

[1919] Emotion data collection and analysis

[1920] Passenger emotional data is collected using facial recognition cameras, microphones, and heart rate sensors. The emotion engine server analyzes the passenger's facial expressions, voice tone, and heart rate data to evaluate the passenger's emotional state. If the result indicates that the passenger is feeling stressed or anxious, the system will take measures such as playing relaxing music. In addition, if an abnormality is detected, emergency response will be initiated. For example, if a passenger feels strong stress while on board, the data will be sent to the emotion engine server, and the analysis results will be returned immediately. Depending on the analysis results, the display in the vehicle will display "Playing relaxing music" and music will be played.

[1921] Prompt Sentence Examples

[1922] For example, a prompt to input the passenger's stress level into the emotion engine might look like this:

[1923] "Analyze passengers' stress levels based on facial expressions, voice tone, and heart rate data."

[1924] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1925] Step 1:

[1926] Data collection

[1927] The server collects real-time emotional data from passengers through facial recognition cameras, microphones, and heart rate sensors. The collected data includes facial expression data, voice data, and heart rate data. These data are sent directly from each sensor to the data collection server.

[1928] Input: Facial expression data, voice data, heart rate data

[1929] Output: Unified emotion dataset (facial expressions, vocal tone, heart rate)

[1930] Step 2:

[1931] Data Preprocessing

[1932] The server receives the collected data and performs noise removal and missing value completion. For example, it preprocesses facial recognition data to prevent closed eyes and open mouths from affecting emotion recognition. Similarly, it removes background noise from audio data to obtain clear audio data.

[1933] Input: Unified sentiment dataset

[1934] Output: Preprocessed emotion dataset

[1935] Step 3:

[1936] Emotional Data Analysis

[1937] The server then uses the pre-processed emotion dataset to perform analysis using the emotion engine server, which uses OpenCV to recognize emotions from facial expressions, Google Speech-to-Text API to convert voice data to text, and Affectiva API to analyze emotions. Heart rate data is also analyzed in the same way to assess the passenger's emotional state.

[1938] Input: Preprocessed emotion dataset

[1939] Output: Passenger's emotional state (e.g., stress level, anxiety level)

[1940] Step 4:

[1941] Implementing countermeasures based on analysis results

[1942] The server then instructs the system to take appropriate countermeasures based on the analysis results. For example, if a passenger is in a high stress state, the server can instruct the in-car sound system to play relaxing music. If an abnormality is detected, the server can activate the emergency call system.

[1943] Input: Passenger emotional state (e.g., stress level, anxiety level)

[1944] Output: Execution instructions (e.g., music playback, emergency call)

[1945] Step 5:

[1946] Feedback and Learning

[1947] The server collects feedback from passengers and uses it to improve the system. Specifically, it receives feedback data from passenger terminals, stores it in a database, and reflects it in future analyses and countermeasures.

[1948] Input: Passenger feedback data

[1949] Output: Feedback data stored in a database, and a dataset for system improvement

[1950] 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.

[1951] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1952] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1953] 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.

[1954] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind...

Claims

1. a means of collecting disease data in real time; A means of predicting virus mutations based on collected data; and a means to rapidly design customized vaccines based on predicted variants; means of coordinating with international health organizations to implement designed vaccines and preventative measures; means of monitoring and evaluating the effectiveness of the preventive measures implemented; A system including:

2. The system of claim 1 , further comprising means for preprocessing the collected disease data to remove noise and impute missing values.

3. The system of claim 1 , further comprising means for predicting viral mutations using the AlphaFold algorithm.

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