system

Abstract

An object of a system according to an embodiment is to predict the prevalence of a disease in a region and take early measures.SOLUTION: A system according to an embodiment includes a data collection unit, a data analysis unit, and a prediction result providing unit. The data collection part collects patient data of an SNS and a hospital. The data analysis unit analyzes the patient data of the SNS or the hospital collected by the data collection unit. The prediction result providing unit provides the result analyzed by the data analysis unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to predict local disease outbreaks and take early countermeasures.

[0005] The system according to the embodiment aims to predict the spread of disease in a region and take early measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, and a prediction result providing unit. The data collection unit collects patient data from social media and hospitals. The data analysis unit analyzes the patient data from social media and hospitals collected by the data collection unit. The prediction result providing unit provides the user with the results analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the spread of disease in a region and take early measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The epidemic prediction system according to an embodiment of the present invention uses a generative AI to predict the spread of colds, influenza, and other diseases in the area where a user lives, allowing the user to take early countermeasures. This system collects data from social media and the status of patients in hospitals, and the generative AI analyzes the data to predict epidemics. This allows the epidemic prediction system to grasp the spread of diseases in the area and efficiently take countermeasures, vaccinate, and secure medicines.

[0029] The epidemic prediction system according to the embodiment includes a data collection unit, a data analysis unit, and a prediction result provision unit. The data collection unit collects patient data from social media and hospitals. For example, the data collection unit collects posts about colds, influenza, and COVID-19 from social media platforms such as Twitter and Facebook. The data collection unit can also collect patient data such as hospital medical records, test results, and prescriptions. The data analysis unit analyzes the patient data from social media and hospitals collected by the data collection unit. For example, the data analysis unit may analyze the content of posts on social media using natural language processing technology to extract disease-related keywords and trends. The data analysis unit may also analyze hospital patient data using statistical analysis and machine learning algorithms to identify trends in the number of medical visits and symptoms. The prediction result provision unit provides the results analyzed by the data analysis unit to a user. For example, the prediction result provision unit may display the local disease epidemic situation and prediction results through a dedicated app or website. The prediction result provision unit may also provide the user with prediction results via email notifications or dashboard displays. As a result, the epidemic prediction system according to the embodiment can predict the spread of disease in a region and take early countermeasures.

[0030] The data collection unit can analyze images and videos included in SNS posts and detect signs of illness from visual information. For example, the data collection unit analyzes images included in SNS posts and detects signs of illness from visual information. For example, it analyzes images of people coughing or the congestion of hospital waiting rooms. The data collection unit also analyzes videos included in SNS posts and detects signs of illness from movements and facial expressions. For example, it analyzes videos of people coughing or videos of people complaining of feeling unwell. The data collection unit also uses image recognition technology to detect images of medicines and masks included in SNS posts and predict disease outbreaks. For example, it analyzes images of medicines lined up on pharmacy shelves. This makes it possible to detect signs of illness from visual information.

[0031] The data collection unit can cooperate with the hospital's electronic medical record system to collect medical data, prescriptions, and test results. The data collection unit, for example, cooperates with the hospital's electronic medical record system to collect medical data. For example, it collects patient medical records and diagnosis results. The data collection unit also cooperates with the hospital's electronic medical record system to collect prescription data. For example, it collects the type and amount of prescribed medication. The data collection unit also cooperates with the hospital's electronic medical record system to collect test result data. For example, it collects the results of blood tests and diagnostic imaging. This makes it possible to collect medical data, prescriptions, and test results.

[0032] The data collection unit collects health data from the wearable device and can detect signs of illness early. The data collection unit, for example, collects heart rate data from the wearable device and detects abnormal fluctuations. For example, it analyzes a sudden increase in heart rate. The data collection unit also collects body temperature data from the wearable device and detects signs of fever. For example, it analyzes an increase in body temperature. The data collection unit also collects sleep data from the wearable device and detects changes in sleep quality. For example, it analyzes a decrease or interruption in sleep time. In this way, health data from the wearable device can be collected and signs of illness can be detected early.

[0033] The data collection unit can collect local weather data and environmental data and analyze its relevance to disease outbreaks. The data collection unit, for example, collects local weather data and analyzes its relevance to disease outbreaks. For example, it analyzes changes in temperature and humidity. The data collection unit also collects local air quality data and analyzes its relevance to disease outbreaks. For example, it analyzes PM2.5 and pollen concentrations. The data collection unit also collects local environmental data and analyzes its relevance to disease outbreaks. For example, it analyzes the concentration of viruses and bacteria in the air. This makes it possible to collect local weather data and environmental data and analyze its relevance to disease outbreaks.

[0034] The data collection unit can analyze the content of SNS posts using natural language processing technology and automatically discover new keywords and trends related to diseases. For example, the data collection unit analyzes the content of SNS posts using natural language processing technology and automatically discovers new keywords related to diseases. For example, it extracts keywords related to new symptoms and treatments. The data collection unit also uses natural language processing technology to automatically discover trends related to diseases from the content of SNS posts. For example, it analyzes the epidemic status in a specific region. The data collection unit also analyzes the content of SNS posts and tracks new keywords and trends related to diseases over time. For example, it identifies the peak period of an epidemic. In this way, it is possible to analyze the content of SNS posts and automatically discover new keywords and trends related to diseases.

[0035] The data collection unit can anonymize hospital patient data, enabling detailed data analysis while protecting privacy. The data collection unit, for example, anonymizes hospital patient data and performs detailed data analysis while protecting privacy. For example, it deletes personal information and analyzes symptoms and diagnosis results. The data collection unit also uses anonymization technology to securely collect and analyze hospital patient data. For example, it encrypts and pseudonyms the data. The data collection unit also anonymizes hospital patient data and analyzes disease prevalence by region. For example, it analyzes the number of medical consultations and symptom trends in a specific region. This allows hospital patient data to be anonymized, enabling detailed data analysis while protecting privacy.

[0036] The data collection unit can analyze the number of comments and retweets on SNS posts to evaluate the level of interest in and the degree of spread of the disease. The data collection unit, for example, analyzes the number of comments on SNS posts to evaluate the level of interest in the disease. For example, the number of comments on posts about a specific disease is tallied. The data collection unit also analyzes the number of retweets on SNS posts to evaluate the level of spread of the disease. For example, the number of retweets on posts about a specific disease is tallied. The data collection unit also analyzes the number of comments and retweets to track the level of interest in and the degree of spread of the disease over time. For example, it analyzes changes in the number of comments and retweets during peak periods of an epidemic. This makes it possible to analyze the number of comments and retweets on SNS posts to evaluate the level of interest in and the degree of spread of the disease.

[0037] The data collection unit can collect pharmacy sales data in addition to hospital patient data and analyze the relationship between drug demand and disease epidemics. The data collection unit, for example, collects pharmacy sales data in addition to hospital patient data and analyzes the relationship between drug demand and disease epidemics. For example, it compares the sales volume of a specific drug with the disease epidemic status. The data collection unit also analyzes pharmacy sales data to understand changes in drug demand due to disease epidemics. For example, it analyzes the sales volume of cold medicines and influenza medicines over time. The data collection unit also integrates hospital patient data and pharmacy sales data to analyze the relationship between disease epidemics and drug demand over time. For example, it analyzes changes in drug sales volume during peak periods of epidemics. In this way, it is possible to collect pharmacy sales data in addition to hospital patient data and analyze the relationship between drug demand and disease epidemics.

[0038] The data analysis unit uses the generation AI to perform an integrated analysis of social media postings and hospital patient data, enabling more accurate epidemic predictions. The data analysis unit, for example, uses the generation AI to perform an integrated analysis of social media postings and hospital patient data to perform epidemic predictions. For example, it integrates and analyzes keywords in postings and the number of medical consultations. The data analysis unit also integrates social media postings and hospital patient data and analyzes them over time to accurately predict the peak period of the epidemic. For example, it analyzes changes in posting frequency and the number of medical consultations. The data analysis unit also uses the generation AI to integrate social media postings and hospital patient data to perform epidemic predictions by region. For example, it compares postings and the number of medical consultations in a specific region. This allows for an integrated analysis of social media postings and hospital patient data to perform more accurate epidemic predictions.

[0039] The data analysis unit uses time series analysis to learn disease epidemic patterns from past data and predict future epidemics. The data analysis unit, for example, uses time series analysis to learn disease epidemic patterns from past data and predict future epidemics. For example, it builds a prediction model based on past epidemic data. The data analysis unit also analyzes past data in time series to predict peak and convergence times of epidemics. For example, it analyzes the number of past medical consultations and the frequency of social media posts. The data analysis unit also uses time series analysis to learn disease epidemic patterns from past data and predict future epidemics for each region. For example, it makes predictions based on past epidemic data in a specific region. In this way, it is possible to learn disease epidemic patterns from past data and predict future epidemics.

[0040] The data analysis unit can predict the user's health condition and propose individual countermeasures in addition to predicting the prevalence of diseases by region. For example, the data analysis unit can predict the user's health condition and propose individual countermeasures in addition to predicting the prevalence of diseases by region. For example, it can propose preventive measures based on the user's health data. The data analysis unit can also analyze the health condition of each individual user and propose individual countermeasures based on the prevalence prediction. For example, it can propose how to deal with the onset of specific symptoms. The data analysis unit can also integrate the prevalence prediction by region with the health condition of each individual user and propose individual countermeasures. For example, it can propose preventive measures based on the user's area of ​​residence and health data. This makes it possible to predict the health condition of each individual user and propose individual countermeasures in addition to predicting the prevalence of diseases by region.

[0041] The data analysis unit can share the results of the epidemic prediction with local medical institutions and government agencies, thereby strengthening countermeasures throughout the region. The data analysis unit, for example, shares the results of the epidemic prediction with local medical institutions and strengthens countermeasures. For example, the allocation of medical resources is adjusted based on the prediction results. The data analysis unit also shares the results of the epidemic prediction with government agencies and strengthens countermeasures throughout the region. For example, infection prevention measures are implemented based on the prediction results. The data analysis unit also shares the results of the epidemic prediction with local medical institutions and government agencies in real time, thereby taking swift countermeasures. For example, emergency responses are implemented based on the prediction results. In this way, the results of the epidemic prediction can be shared with local medical institutions and government agencies, thereby strengthening countermeasures throughout the region.

[0042] The data analysis unit uses generative AI to analyze collected data in real time and detect signs of an epidemic early. The data analysis unit, for example, uses generative AI to analyze collected data in real time and detect signs of an epidemic early. For example, it analyzes social media posts and medical data in real time. The data analysis unit also performs real-time analysis and builds a system that detects signs of an epidemic early. For example, it monitors data fluctuations in real time. The data analysis unit also uses generative AI to analyze collected data in real time and detect signs of an epidemic by region early. For example, it analyzes data fluctuations in a specific region in real time. This makes it possible to analyze collected data in real time and detect signs of an epidemic early.

[0043] The data analysis unit can perform epidemic predictions for specific age groups and occupations in addition to region-specific epidemic predictions. For example, the data analysis unit performs epidemic predictions for specific age groups in addition to region-specific epidemic predictions. For example, it analyzes the epidemic status among children and the elderly. Furthermore, the data analysis unit performs epidemic predictions for specific occupations in addition to region-specific epidemic predictions. For example, it analyzes the epidemic status among medical professionals and teachers. Furthermore, the data analysis unit integrates the epidemic predictions for each region with the epidemic predictions for specific age groups and occupations to perform detailed predictions. For example, it analyzes the epidemic status for each age group and occupation in a specific region. This allows it to perform epidemic predictions for specific age groups and occupations in addition to region-specific epidemic predictions.

[0044] The data analysis unit can share the epidemic prediction results with local medical institutions and pharmacies to support the optimal allocation of medical resources. The data analysis unit, for example, shares the epidemic prediction results with local medical institutions to support the optimal allocation of medical resources. For example, it adjusts the allocation of medical staff based on the prediction results. The data analysis unit also shares the epidemic prediction results with pharmacies to optimize drug inventory management. For example, it adjusts the amount of drugs ordered based on the prediction results. The data analysis unit also shares the epidemic prediction results with local medical institutions and pharmacies in real time to support a rapid response. For example, it takes an emergency response based on the prediction results. In this way, the epidemic prediction results are shared with local medical institutions and pharmacies to support the optimal allocation of medical resources.

[0045] The data analysis unit can share the epidemic prediction results with local schools and businesses to promote the implementation of infection prevention measures. The data analysis unit, for example, shares the epidemic prediction results with local schools to promote the implementation of infection prevention measures. For example, based on the prediction results, it may consider closing schools or moving classes online. The data analysis unit also shares the epidemic prediction results with local businesses to promote the implementation of infection prevention measures. For example, it may recommend remote work or staggered work hours based on the prediction results. The data analysis unit also shares the epidemic prediction results with local schools and businesses in real time to support a rapid response. For example, it may take emergency action based on the prediction results. In this way, the epidemic prediction results can be shared with local schools and businesses to promote the implementation of infection prevention measures.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection unit can collect local transportation data and analyze the relationship between disease outbreaks and people's movement patterns. For example, it can collect public transportation usage data and analyze the infection risk at specific times or along specific routes. The data collection unit can also collect vehicle movement data and analyze the relationship between the frequency of visits to specific areas and disease outbreaks. Furthermore, the data collection unit can collect pedestrian movement data and analyze the relationship between the degree of people gathering in specific areas and disease outbreaks. This makes it possible to collect local transportation data and analyze the relationship between disease outbreaks and people's movement patterns.

[0048] The data collection unit can collect regional economic data and analyze the relationship between disease outbreaks and fluctuations in economic activity. For example, it can collect store sales data and analyze fluctuations in sales during specific disease outbreaks. The data collection unit can also collect unemployment rates and job information and analyze the impact of disease outbreaks on the labor market. Furthermore, the data collection unit can collect tourism industry data and analyze the impact of disease outbreaks on the number of tourists and accommodation facility occupancy rates. This makes it possible to collect regional economic data and analyze the relationship between disease outbreaks and fluctuations in economic activity.

[0049] The data collection unit can collect local educational data and analyze the correlation between disease outbreaks and school attendance rates and academic performance. For example, it can collect school attendance data and analyze fluctuations in absenteeism rates during specific disease outbreak periods. The data collection unit can also collect online learning usage data and analyze the impact of disease outbreaks on the spread of online learning. Furthermore, the data collection unit can collect academic performance data and analyze the impact of disease outbreaks on students' academic performance. This makes it possible to collect local educational data and analyze the correlation between disease outbreaks and school attendance rates and academic performance.

[0050] The data collection unit can collect regional medical resource data and analyze the relationship between disease epidemics and medical resource utilization. For example, it can collect hospital bed occupancy rate data and analyze bed utilization during periods of particular disease epidemics. The data collection unit can also collect medical staff work data and analyze the impact of disease epidemics on medical staff work status. Furthermore, the data collection unit can collect pharmaceutical inventory data and analyze the impact of disease epidemics on pharmaceutical demand. This makes it possible to collect regional medical resource data and analyze the relationship between disease epidemics and medical resource utilization.

[0051] The data collection unit can collect local social activity data and analyze the relationship between disease outbreaks and fluctuations in social activity. For example, it can collect event hosting data and analyze the status of event cancellations or postponements during periods of specific disease outbreaks. The data collection unit can also collect public facility usage data and analyze the impact of disease outbreaks on the usage of public facilities. Furthermore, the data collection unit can collect local volunteer activity data and analyze the impact of disease outbreaks on the number of participants in volunteer activities and the content of activities. This makes it possible to collect local social activity data and analyze the relationship between disease outbreaks and fluctuations in social activity.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The data collection unit collects patient data from social media and hospitals. For example, the data collection unit collects posts about colds, flu, and COVID-19 from social media such as Twitter and Facebook. The data collection unit can also collect patient data such as hospital medical records, test results, and prescriptions. Step 2: The data analysis unit analyzes the social media and hospital patient data collected by the data collection unit. For example, the data analysis unit uses natural language processing technology to analyze the content of social media posts and extract disease-related keywords and trends. The data analysis unit can also analyze hospital patient data using statistical analysis and machine learning algorithms to understand trends in the number of medical consultations and symptoms. Step 3: The prediction result provider provides the results analyzed by the data analyzer to the user. For example, the prediction result provider displays the local disease epidemic situation and prediction results through a dedicated app or website. The prediction result provider can also provide prediction results to the user via email notifications or dashboard displays.

[0054] (Example 2) The epidemic prediction system according to an embodiment of the present invention uses a generative AI to predict the spread of colds, influenza, and other diseases in the area where a user lives, allowing the user to take early countermeasures. This system collects data from social media and the status of patients in hospitals, and the generative AI analyzes the data to predict epidemics. This allows the epidemic prediction system to grasp the spread of diseases in the area and efficiently take countermeasures, vaccinate, and secure medicines.

[0055] The epidemic prediction system according to the embodiment includes a data collection unit, a data analysis unit, and a prediction result provision unit. The data collection unit collects patient data from social media and hospitals. For example, the data collection unit collects posts about colds, influenza, and COVID-19 from social media platforms such as Twitter and Facebook. The data collection unit can also collect patient data such as hospital medical records, test results, and prescriptions. The data analysis unit analyzes the patient data from social media and hospitals collected by the data collection unit. For example, the data analysis unit may analyze the content of posts on social media using natural language processing technology to extract disease-related keywords and trends. The data analysis unit may also analyze hospital patient data using statistical analysis and machine learning algorithms to identify trends in the number of medical visits and symptoms. The prediction result provision unit provides the results analyzed by the data analysis unit to a user. For example, the prediction result provision unit may display the local disease epidemic situation and prediction results through a dedicated app or website. The prediction result provision unit may also provide the user with prediction results via email notifications or dashboard displays. As a result, the epidemic prediction system according to the embodiment can predict the spread of disease in a region and take early countermeasures.

[0056] The data collection unit can analyze images and videos included in SNS posts and detect signs of illness from visual information. For example, the data collection unit analyzes images included in SNS posts and detects signs of illness from visual information. For example, it analyzes images of people coughing or the congestion of hospital waiting rooms. The data collection unit also analyzes videos included in SNS posts and detects signs of illness from movements and facial expressions. For example, it analyzes videos of people coughing or videos of people complaining of feeling unwell. The data collection unit also uses image recognition technology to detect images of medicines and masks included in SNS posts and predict disease outbreaks. For example, it analyzes images of medicines lined up on pharmacy shelves. This makes it possible to detect signs of illness from visual information.

[0057] The data collection unit can cooperate with the hospital's electronic medical record system to collect medical data, prescriptions, and test results. The data collection unit, for example, cooperates with the hospital's electronic medical record system to collect medical data. For example, it collects patient medical records and diagnosis results. The data collection unit also cooperates with the hospital's electronic medical record system to collect prescription data. For example, it collects the type and amount of prescribed medication. The data collection unit also cooperates with the hospital's electronic medical record system to collect test result data. For example, it collects the results of blood tests and diagnostic imaging. This makes it possible to collect medical data, prescriptions, and test results.

[0058] The data collection unit uses the emotion estimation function to analyze the emotions of SNS posters and understand the degree of anxiety and fear regarding the disease outbreak. The data collection unit, for example, analyzes the emotions of SNS posters and understands the degree of anxiety and fear regarding the disease outbreak. For example, it calculates an emotion score for the content of the post. The data collection unit also uses the emotion estimation function to analyze changes in the emotions of SNS posters and understand their emotional reactions to the disease outbreak. For example, it analyzes time series data of posts. The data collection unit also analyzes the emotions of SNS posters and understands emotional reactions by region. For example, it compares the degree of anxiety and fear in specific regions. In this way, it is possible to analyze the emotions of SNS posters and understand the degree of anxiety and fear regarding the disease outbreak.

[0059] The data collection unit collects health data from the wearable device and can detect signs of illness early. The data collection unit, for example, collects heart rate data from the wearable device and detects abnormal fluctuations. For example, it analyzes a sudden increase in heart rate. The data collection unit also collects body temperature data from the wearable device and detects signs of fever. For example, it analyzes an increase in body temperature. The data collection unit also collects sleep data from the wearable device and detects changes in sleep quality. For example, it analyzes a decrease or interruption in sleep time. In this way, health data from the wearable device can be collected and signs of illness can be detected early.

[0060] The data collection unit can collect local weather data and environmental data and analyze its relevance to disease outbreaks. The data collection unit, for example, collects local weather data and analyzes its relevance to disease outbreaks. For example, it analyzes changes in temperature and humidity. The data collection unit also collects local air quality data and analyzes its relevance to disease outbreaks. For example, it analyzes PM2.5 and pollen concentrations. The data collection unit also collects local environmental data and analyzes its relevance to disease outbreaks. For example, it analyzes the concentration of viruses and bacteria in the air. This makes it possible to collect local weather data and environmental data and analyze its relevance to disease outbreaks.

[0061] The data collection unit can use the emotion estimation function to monitor patients' emotions in hospital waiting rooms in real time and collect emotional responses to disease outbreaks. The data collection unit, for example, monitors patients' emotions in hospital waiting rooms in real time and collects emotional responses to disease outbreaks. For example, it analyzes facial expressions and voice. The data collection unit also uses the emotion estimation function to analyze changes in patients' emotions in hospital waiting rooms. For example, it calculates an emotion score during waiting time. The data collection unit also analyzes patients' emotions in hospital waiting rooms and understands emotional responses by region. For example, it compares the degree of anxiety and fear in specific regions. In this way, it is possible to monitor patients' emotions in hospital waiting rooms in real time and collect emotional responses to disease outbreaks.

[0062] The data collection unit can analyze the content of SNS posts using natural language processing technology and automatically discover new keywords and trends related to diseases. For example, the data collection unit analyzes the content of SNS posts using natural language processing technology and automatically discovers new keywords related to diseases. For example, it extracts keywords related to new symptoms and treatments. The data collection unit also uses natural language processing technology to automatically discover trends related to diseases from the content of SNS posts. For example, it analyzes the epidemic status in a specific region. The data collection unit also analyzes the content of SNS posts and tracks new keywords and trends related to diseases over time. For example, it identifies the peak period of an epidemic. In this way, it is possible to analyze the content of SNS posts and automatically discover new keywords and trends related to diseases.

[0063] The data collection unit can anonymize hospital patient data, enabling detailed data analysis while protecting privacy. The data collection unit, for example, anonymizes hospital patient data and performs detailed data analysis while protecting privacy. For example, it deletes personal information and analyzes symptoms and diagnosis results. The data collection unit also uses anonymization technology to securely collect and analyze hospital patient data. For example, it encrypts and pseudonyms the data. The data collection unit also anonymizes hospital patient data and analyzes disease prevalence by region. For example, it analyzes the number of medical consultations and symptom trends in a specific region. This allows hospital patient data to be anonymized, enabling detailed data analysis while protecting privacy.

[0064] The data collection unit can use the emotion estimation function to track changes in the emotions of SNS posters over time and analyze their emotional reactions to the disease outbreak. For example, the data collection unit uses the emotion estimation function to track changes in the emotions of SNS posters over time and analyze their emotional reactions to the disease outbreak. For example, the emotion score of the content of posts is analyzed over time. The data collection unit also analyzes changes in the emotions of SNS posters to understand their emotional reactions to the disease outbreak. For example, it analyzes changes in emotions during the peak of the outbreak. The data collection unit also uses the emotion estimation function to analyze changes in the emotions of SNS posters by region. For example, it compares the levels of anxiety and fear in specific regions over time. This makes it possible to track changes in the emotions of SNS posters over time and analyze their emotional reactions to the disease outbreak.

[0065] The data collection unit can analyze the number of comments and retweets on SNS posts to evaluate the level of interest in and the degree of spread of the disease. The data collection unit, for example, analyzes the number of comments on SNS posts to evaluate the level of interest in the disease. For example, the number of comments on posts about a specific disease is tallied. The data collection unit also analyzes the number of retweets on SNS posts to evaluate the level of spread of the disease. For example, the number of retweets on posts about a specific disease is tallied. The data collection unit also analyzes the number of comments and retweets to track the level of interest in and the degree of spread of the disease over time. For example, it analyzes changes in the number of comments and retweets during peak periods of an epidemic. This makes it possible to analyze the number of comments and retweets on SNS posts to evaluate the level of interest in and the degree of spread of the disease.

[0066] The data collection unit can collect pharmacy sales data in addition to hospital patient data and analyze the relationship between drug demand and disease epidemics. The data collection unit, for example, collects pharmacy sales data in addition to hospital patient data and analyzes the relationship between drug demand and disease epidemics. For example, it compares the sales volume of a specific drug with the disease epidemic status. The data collection unit also analyzes pharmacy sales data to understand changes in drug demand due to disease epidemics. For example, it analyzes the sales volume of cold medicines and influenza medicines over time. The data collection unit also integrates hospital patient data and pharmacy sales data to analyze the relationship between disease epidemics and drug demand over time. For example, it analyzes changes in drug sales volume during peak periods of epidemics. In this way, it is possible to collect pharmacy sales data in addition to hospital patient data and analyze the relationship between drug demand and disease epidemics.

[0067] The data collection unit can use the emotion estimation function to analyze the emotions of patients recorded in the hospital's medical records and collect emotional reactions to the disease outbreak. The data collection unit, for example, uses the emotion estimation function to analyze the emotions of patients recorded in the hospital's medical records and collect emotional reactions to the disease outbreak. For example, the data collection unit analyzes text data of the medical records. The data collection unit also analyzes changes in the emotions of patients recorded in the hospital's medical records to understand their emotional reactions to the disease outbreak. For example, the data collection unit analyzes time-series data of the medical records. The data collection unit also uses the emotion estimation function to analyze the emotions of patients recorded in the hospital's medical records by region. For example, the degree of anxiety and fear in specific regions is compared. In this way, the emotions of patients recorded in the hospital's medical records can be analyzed and emotional reactions to the disease outbreak can be collected.

[0068] The data analysis unit uses the generation AI to perform an integrated analysis of social media postings and hospital patient data, enabling more accurate epidemic predictions. The data analysis unit, for example, uses the generation AI to perform an integrated analysis of social media postings and hospital patient data to perform epidemic predictions. For example, it integrates and analyzes keywords in postings and the number of medical consultations. The data analysis unit also integrates social media postings and hospital patient data and analyzes them over time to accurately predict the peak period of the epidemic. For example, it analyzes changes in posting frequency and the number of medical consultations. The data analysis unit also uses the generation AI to integrate social media postings and hospital patient data to perform epidemic predictions by region. For example, it compares postings and the number of medical consultations in a specific region. This allows for an integrated analysis of social media postings and hospital patient data to perform more accurate epidemic predictions.

[0069] The data analysis unit uses time series analysis to learn disease epidemic patterns from past data and predict future epidemics. The data analysis unit, for example, uses time series analysis to learn disease epidemic patterns from past data and predict future epidemics. For example, it builds a prediction model based on past epidemic data. The data analysis unit also analyzes past data in time series to predict peak and convergence times of epidemics. For example, it analyzes the number of past medical consultations and the frequency of social media posts. The data analysis unit also uses time series analysis to learn disease epidemic patterns from past data and predict future epidemics for each region. For example, it makes predictions based on past epidemic data in a specific region. In this way, it is possible to learn disease epidemic patterns from past data and predict future epidemics.

[0070] The data analysis unit uses the emotion estimation function to analyze changes in the emotions of SNS posters and evaluate the impact of their emotional responses on trend predictions. The data analysis unit, for example, uses the emotion estimation function to analyze changes in the emotions of SNS posters and evaluate the impact on trend predictions. For example, it analyzes the emotion score of the content of posts. The data analysis unit also analyzes changes in the emotions of SNS posters over time and evaluates the impact on trend predictions. For example, it analyzes changes in emotions during peak periods of trends. The data analysis unit also uses the emotion estimation function to analyze changes in the emotions of SNS posters by region and evaluate the impact on trend predictions. For example, it compares the levels of anxiety and fear in specific regions. This makes it possible to analyze changes in the emotions of SNS posters and evaluate the impact of their emotional responses on trend predictions.

[0071] The data analysis unit can predict the user's health condition and propose individual countermeasures in addition to predicting the prevalence of diseases by region. For example, the data analysis unit can predict the user's health condition and propose individual countermeasures in addition to predicting the prevalence of diseases by region. For example, it can propose preventive measures based on the user's health data. The data analysis unit can also analyze the health condition of each individual user and propose individual countermeasures based on the prevalence prediction. For example, it can propose how to deal with the onset of specific symptoms. The data analysis unit can also integrate the prevalence prediction by region with the health condition of each individual user and propose individual countermeasures. For example, it can propose preventive measures based on the user's area of ​​residence and health data. This makes it possible to predict the health condition of each individual user and propose individual countermeasures in addition to predicting the prevalence of diseases by region.

[0072] The data analysis unit can share the results of the epidemic prediction with local medical institutions and government agencies, thereby strengthening countermeasures throughout the region. The data analysis unit, for example, shares the results of the epidemic prediction with local medical institutions and strengthens countermeasures. For example, the allocation of medical resources is adjusted based on the prediction results. The data analysis unit also shares the results of the epidemic prediction with government agencies and strengthens countermeasures throughout the region. For example, infection prevention measures are implemented based on the prediction results. The data analysis unit also shares the results of the epidemic prediction with local medical institutions and government agencies in real time, thereby taking swift countermeasures. For example, emergency responses are implemented based on the prediction results. In this way, the results of the epidemic prediction can be shared with local medical institutions and government agencies, thereby strengthening countermeasures throughout the region.

[0073] The data analysis unit can use the emotion estimation function to collect users' emotional reactions to the trend prediction results and improve the reliability of the prediction results. The data analysis unit, for example, uses the emotion estimation function to collect users' emotional reactions to the trend prediction results and improve the reliability of the prediction results. For example, it analyzes emotion scores for the prediction results. The data analysis unit also analyzes users' emotional reactions and evaluates the reliability of the trend prediction results. For example, it preferentially adopts prediction results with a high number of positive reactions. The data analysis unit also uses the emotion estimation function to analyze users' emotional reactions to the trend prediction results in time series and improve the reliability of the prediction results. For example, it analyzes changes in emotions in response to the prediction results. This allows users' emotional reactions to the trend prediction results to be collected and the reliability of the prediction results to be improved.

[0074] The data analysis unit uses generative AI to analyze collected data in real time and detect signs of an epidemic early. The data analysis unit, for example, uses generative AI to analyze collected data in real time and detect signs of an epidemic early. For example, it analyzes social media posts and medical data in real time. The data analysis unit also performs real-time analysis and builds a system that detects signs of an epidemic early. For example, it monitors data fluctuations in real time. The data analysis unit also uses generative AI to analyze collected data in real time and detect signs of an epidemic by region early. For example, it analyzes data fluctuations in a specific region in real time. This makes it possible to analyze collected data in real time and detect signs of an epidemic early.

[0075] The data analysis unit can perform epidemic predictions for specific age groups and occupations in addition to region-specific epidemic predictions. For example, the data analysis unit performs epidemic predictions for specific age groups in addition to region-specific epidemic predictions. For example, it analyzes the epidemic status among children and the elderly. Furthermore, the data analysis unit performs epidemic predictions for specific occupations in addition to region-specific epidemic predictions. For example, it analyzes the epidemic status among medical professionals and teachers. Furthermore, the data analysis unit integrates the epidemic predictions for each region with the epidemic predictions for specific age groups and occupations to perform detailed predictions. For example, it analyzes the epidemic status for each age group and occupation in a specific region. This allows it to perform epidemic predictions for specific age groups and occupations in addition to region-specific epidemic predictions.

[0076] The data analysis unit uses the emotion estimation function to analyze the user's emotional response to the trend prediction result based on the collected data, thereby improving the accuracy of the prediction. For example, the data analysis unit uses the emotion estimation function to analyze the user's emotional response to the trend prediction result based on the collected data, thereby improving the accuracy of the prediction. For example, the emotion score for the prediction result is analyzed. The data analysis unit also analyzes the user's emotional response and evaluates the accuracy of the trend prediction result. For example, the data analysis unit preferentially adopts prediction results with a high number of positive responses. The data analysis unit also uses the emotion estimation function to analyze the user's emotional response to the trend prediction result based on the collected data in time series, thereby improving the accuracy of the prediction. For example, the emotion estimation function analyzes changes in emotion in response to the prediction result. This analyzes the user's emotional response to the trend prediction result based on the collected data, thereby improving the accuracy of the prediction.

[0077] The data analysis unit can share the epidemic prediction results with local medical institutions and pharmacies to support the optimal allocation of medical resources. The data analysis unit, for example, shares the epidemic prediction results with local medical institutions to support the optimal allocation of medical resources. For example, it adjusts the allocation of medical staff based on the prediction results. The data analysis unit also shares the epidemic prediction results with pharmacies to optimize drug inventory management. For example, it adjusts the amount of drugs ordered based on the prediction results. The data analysis unit also shares the epidemic prediction results with local medical institutions and pharmacies in real time to support a rapid response. For example, it takes an emergency response based on the prediction results. In this way, the epidemic prediction results are shared with local medical institutions and pharmacies to support the optimal allocation of medical resources.

[0078] The data analysis unit can share the epidemic prediction results with local schools and businesses to promote the implementation of infection prevention measures. The data analysis unit, for example, shares the epidemic prediction results with local schools to promote the implementation of infection prevention measures. For example, based on the prediction results, it may consider closing schools or moving classes online. The data analysis unit also shares the epidemic prediction results with local businesses to promote the implementation of infection prevention measures. For example, it may recommend remote work or staggered work hours based on the prediction results. The data analysis unit also shares the epidemic prediction results with local schools and businesses in real time to support a rapid response. For example, it may take emergency action based on the prediction results. In this way, the epidemic prediction results can be shared with local schools and businesses to promote the implementation of infection prevention measures.

[0079] The data analysis unit can use the emotion estimation function to collect the emotional reactions of local residents to the epidemic prediction results and evaluate the effectiveness of preventive measures. The data analysis unit, for example, uses the emotion estimation function to collect the emotional reactions of local residents to the epidemic prediction results and evaluate the effectiveness of preventive measures. For example, it analyzes the emotion scores for the prediction results. The data analysis unit also analyzes the emotional reactions of local residents and evaluates the effectiveness of preventive measures. For example, it prioritizes the adoption of preventive measures that have a high number of positive reactions. The data analysis unit also uses the emotion estimation function to analyze the emotional reactions of local residents to the epidemic prediction results in time series and evaluate the effectiveness of preventive measures. For example, it analyzes changes in emotions toward preventive measures. In this way, it collects the emotional reactions of local residents to the epidemic prediction results and evaluates the effectiveness of preventive measures.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The data collection unit can collect local transportation data and analyze the relationship between disease outbreaks and people's movement patterns. For example, it can collect public transportation usage data and analyze the infection risk at specific times or along specific routes. The data collection unit can also collect vehicle movement data and analyze the relationship between the frequency of visits to specific areas and disease outbreaks. Furthermore, the data collection unit can collect pedestrian movement data and analyze the relationship between the degree of people gathering in specific areas and disease outbreaks. This makes it possible to collect local transportation data and analyze the relationship between disease outbreaks and people's movement patterns.

[0082] The data collection unit can collect regional economic data and analyze the relationship between disease outbreaks and fluctuations in economic activity. For example, it can collect store sales data and analyze fluctuations in sales during specific disease outbreaks. The data collection unit can also collect unemployment rates and job information and analyze the impact of disease outbreaks on the labor market. Furthermore, the data collection unit can collect tourism industry data and analyze the impact of disease outbreaks on the number of tourists and accommodation facility occupancy rates. This makes it possible to collect regional economic data and analyze the relationship between disease outbreaks and fluctuations in economic activity.

[0083] The data collection unit can collect local educational data and analyze the correlation between disease outbreaks and school attendance rates and academic performance. For example, it can collect school attendance data and analyze fluctuations in absenteeism rates during specific disease outbreak periods. The data collection unit can also collect online learning usage data and analyze the impact of disease outbreaks on the spread of online learning. Furthermore, the data collection unit can collect academic performance data and analyze the impact of disease outbreaks on students' academic performance. This makes it possible to collect local educational data and analyze the correlation between disease outbreaks and school attendance rates and academic performance.

[0084] The data collection unit can collect regional medical resource data and analyze the relationship between disease epidemics and medical resource utilization. For example, it can collect hospital bed occupancy rate data and analyze bed utilization during periods of particular disease epidemics. The data collection unit can also collect medical staff work data and analyze the impact of disease epidemics on medical staff work status. Furthermore, the data collection unit can collect pharmaceutical inventory data and analyze the impact of disease epidemics on pharmaceutical demand. This makes it possible to collect regional medical resource data and analyze the relationship between disease epidemics and medical resource utilization.

[0085] The data collection unit can collect local social activity data and analyze the relationship between disease outbreaks and fluctuations in social activity. For example, it can collect event hosting data and analyze the status of event cancellations or postponements during periods of specific disease outbreaks. The data collection unit can also collect public facility usage data and analyze the impact of disease outbreaks on the usage of public facilities. Furthermore, the data collection unit can collect local volunteer activity data and analyze the impact of disease outbreaks on the number of participants in volunteer activities and the content of activities. This makes it possible to collect local social activity data and analyze the relationship between disease outbreaks and fluctuations in social activity.

[0086] The data collection unit can use the emotion estimation function to analyze the emotions of SNS posters and understand the emotional reactions to the disease outbreak by region. For example, the degree of anxiety and fear in a specific region can be compared. The data collection unit can also use the emotion estimation function to analyze changes in the emotions of SNS posters over time and understand the emotional reactions to the disease outbreak. Furthermore, the data collection unit can also use the emotion estimation function to analyze the emotions of SNS posters and evaluate the impact of emotional reactions to the disease outbreak on the effectiveness of preventive measures. This makes it possible to analyze the emotions of SNS posters and understand the emotional reactions to the disease outbreak by region.

[0087] The data collection unit can use the emotion estimation function to monitor the emotions of patients in hospital waiting rooms in real time and collect emotional responses to the disease outbreak. For example, by analyzing facial expressions and voice. The data collection unit can also use the emotion estimation function to analyze changes in the emotions of patients in hospital waiting rooms. Furthermore, the data collection unit can analyze the emotions of patients in hospital waiting rooms and understand emotional responses by region. This makes it possible to monitor the emotions of patients in hospital waiting rooms in real time and collect emotional responses to the disease outbreak.

[0088] The data collection unit can use the emotion estimation function to track changes in the emotions of SNS posters over time and analyze their emotional reactions to the disease outbreak. For example, the emotion score of the content of posts can be analyzed over time. The data collection unit can also analyze changes in the emotions of SNS posters to understand their emotional reactions to the disease outbreak. Furthermore, the data collection unit can use the emotion estimation function to analyze changes in the emotions of SNS posters by region and compare the levels of anxiety and fear in specific regions over time. This makes it possible to track changes in the emotions of SNS posters over time and analyze their emotional reactions to the disease outbreak.

[0089] The data collection unit can use the emotion estimation function to analyze the emotions of patients recorded in the hospital's medical records and collect emotional responses to the disease outbreak. For example, the data collection unit analyzes text data of the medical records. The data collection unit can also analyze changes in the emotions of patients recorded in the hospital's medical records and understand their emotional responses to the disease outbreak. Furthermore, the data collection unit can use the emotion estimation function to analyze the emotions of patients recorded in the hospital's medical records by region and compare the levels of anxiety and fear in specific regions. This makes it possible to analyze the emotions of patients recorded in the hospital's medical records and collect emotional responses to the disease outbreak.

[0090] The data collection unit can use the emotion estimation function to collect users' emotional reactions to the trend prediction results and improve the reliability of the prediction results. For example, the data collection unit analyzes the emotion scores for the prediction results. The data collection unit can also analyze users' emotional reactions to the trend prediction results and evaluate the reliability of the trend prediction results. Furthermore, the data collection unit can use the emotion estimation function to analyze users' emotional reactions to the trend prediction results in time series and improve the reliability of the prediction results. In this way, the data collection unit can collect users' emotional reactions to the trend prediction results and improve the reliability of the prediction results.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The data collection unit collects patient data from social media and hospitals. For example, the data collection unit collects posts about colds, flu, and COVID-19 from social media such as Twitter and Facebook. The data collection unit can also collect patient data such as hospital medical records, test results, and prescriptions. Step 2: The data analysis unit analyzes the social media and hospital patient data collected by the data collection unit. For example, the data analysis unit uses natural language processing technology to analyze the content of social media posts and extract disease-related keywords and trends. The data analysis unit can also analyze hospital patient data using statistical analysis and machine learning algorithms to understand trends in the number of medical consultations and symptoms. Step 3: The prediction result provider provides the results analyzed by the data analyzer to the user. For example, the prediction result provider displays the local disease epidemic situation and prediction results through a dedicated app or website. The prediction result provider can also provide prediction results to the user via email notifications or dashboard displays.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] 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.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] 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.

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

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0114] The 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.

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

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

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

[0118] Fig. 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.

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

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0123] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the 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.

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

[0129] The 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.

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

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

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

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] 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.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects patient data from social media and hospitals; a data analysis unit that analyzes the patient data of the SNS and the hospital collected by the data collection unit; a prediction result providing unit that provides the user with the results analyzed by the data analysis unit. A system characterized by:

2. The data collection unit Analyzes images and videos contained in posts on the social networking site and detects signs of illness from visual information.

2. The system of claim 1.

3. The data collection unit Link with the hospital's electronic medical record system to collect medical data, prescriptions, and test results 2. The system of claim 1.

4. The data collection unit Analyzing the emotions of the SNS posters to understand the level of anxiety and fear regarding the spread of the disease 2. The system of claim 1.

5. The data collection unit Collecting health data from wearable devices to detect early signs of illness 2. The system of claim 1.

6. The data collection unit Collecting local meteorological and environmental data and analyzing its relationship to disease outbreaks 2. The system of claim 1.

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